structure stringlengths 50 3.96k | text stringlengths 49 3.87k | image imagewidth (px) 288 1.28k | download_url stringlengths 33 34 | instance_name stringlengths 18 19 | date stringclasses 53
values | additional_info stringlengths 785 2.42k | date_scrapped stringlengths 28 28 | file_filters stringlengths 546 45.5k | compilation_info stringclasses 27
values | rendering_filters stringlengths 168 184 | assets listlengths 0 0 | category stringclasses 1
value | uuid stringlengths 38 38 | length stringlengths 2 4 | difficulty stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
\begin{algorithmic}[1]
\State Input: Two multi-sets of $n$ points $R,B$ in $Q_d$.
\State Output: A matching from $R$ to $B$.
\State$\triangleright$ The set B is shared across all threads
\Procedure{WeightedMatch}{$R,B$}
\For {$r \in R$}\Comment{All for loop statements run in parallel}
\State $b\gets\mathrm{BreadthFirst... | \begin{algorithmic}
[1]
\State Input: Two multi-sets of $n$ points $R,B$ in $Q_d$.
\State Output: A matching from $R$ to $B$.
\State$\triangleright$ The set B is shared across all threads
\Procedure{WeightedMatch}{$R,B$}
\For {$r \in R$}\Comment{All for loop statements run in parallel}
\State $b\gets\mathrm{BreadthFirs... | "https://arxiv.org/src/2401.11562" | "2401.11562.tar.gz" | "2024-01-21" | {
"title": "enhancing selectivity using wasserstein distance based reweighing",
"id": "2401.11562",
"abstract": "given two labeled data-sets $\\mathcal{s}$ and $\\mathcal{t}$, we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of the neural net... | "2024-03-15T07:03:20.799704" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bib": {
"toxicity_score": 0.00719407,
"severe_toxicity_score": 0.0010347366,
"identity_attack_score": 0.0014706649,
"insult_score": 0.0070948736,
"profanit... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.60778608434285,
"hash": "07079f3e0f9f9f3f",
"most_frequent_color_ratio": 93.60778608434285
}
} | [] | "algorithm" | "6198b44e-7ef3-4092-abff-15603e4a2143" | 681 | easy | |
\begin{algorithmic}[1]
\Require current node $u$, candidate node $v$, Walker $W$, {\sf HuGE} parameter $\mu$
\Ensure walker state updates
{\flushleft{{\bf sendStateQuery($u$, $v$, $W$)}}} %//{submit the walker-to-vertex query messages and process the state queries}
\State{$P(u,v) = Z\left(\frac{1}{deg(u)-Cm(u, v)}\cdot... | \begin{algorithmic}
[1]
\Require current node $u$, candidate node $v$, Walker $W$, {\sf HuGE} parameter $\mu$
\Ensure walker state updates
{\flushleft{{\bf sendStateQuery($u$, $v$, $W$)}}} %//{submit the walker-to-vertex query messages and process the state queries}
\State{$P(u,v) = Z\left(\frac{1}{deg(u)-Cm(u, v)}\cdo... | "https://arxiv.org/src/2303.15702" | "2303.15702.tar.gz" | "2024-02-25" | {
"title": "distributed graph embedding with information-oriented random walks",
"id": "2303.15702",
"abstract": "graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. the increasing availability of billion-edge graphs underscores the importance of lea... | "2024-03-15T03:43:03.810720" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Figures/SG_Pw2v_DSGL_pSGNScc_1.eps": {
"toxicity_score": 0.0061573703,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0013041744,
"insult_score": 0.00640136... | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.99172862862423,
"hash": "01170f1f1f1f3f1f",
"most_frequent_color_ratio": 91.99172862862423
}
} | [] | "algorithm" | "495ba26c-9d13-4916-8708-fb41c8065401" | 1121 | medium | |
\begin{algorithmic}[1]
\Procedure{ModifiedGD}{$\nabla F, \hat{L}_{i, 0}, \delta_{(i, \cdot)}, R, \theta_{i, 0}, \kappa_{(i,\cdot)}, T^*, \ell, \gamma$}
\State $ \Delta \leftarrow 0 $ \Comment{Measure of predicted decrease of objective}
\For{$j = 0, 1, ..., T^*-1$}
\State $\alpha_{i,... | \begin{algorithmic}
[1]
\Procedure{ModifiedGD}{$\nabla F, \hat{L}_{i, 0}, \delta_{(i, \cdot)}, R, \theta_{i, 0}, \kappa_{(i,\cdot)}, T^*, \ell, \gamma$}
\State $ \Delta \leftarrow 0 $ \Comment{Measure of predicted decrease of objective}
\For{$j = 0, 1, ..., T^*-1$}
\State $\alpha_{i,j} \leftarrow \delta_{(i,\cdot)} (2|... | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in dat... | "2024-03-15T05:23:50.845023" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"table/novel-step-size-param-table.tex": {
"toxicity_score": 0.011309455,
"severe_toxicity_score": 0.0012969971,
"identity_attack_score": 0.0034592997,
"insult_score": 0.0074558... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.98582783127969,
"hash": "038788939f93879f",
"most_frequent_color_ratio": 92.98582783127969
}
} | [] | "algorithm" | "d5824813-2875-4568-bb05-f16712f12f47" | 1447 | hard | |
\begin{algorithmic}[1]
\State \textbf{Input:} Stock data for two assets $S_1$ and $S_2$, buy threshold, sell threshold
\State \textbf{Output:} Trade signals for pairs trading
\State
\Procedure{Compute Hedge Ratio}{data1, data2}
\State model $\gets$ perform OLS regression (data1, data2)
\State \Return model.params$[1]$
... | \begin{algorithmic}
[1]
\State \textbf{Input:} Stock data for two assets $S_1$ and $S_2$, buy threshold, sell threshold
\State \textbf{Output:} Trade signals for pairs trading
\State
\Procedure{Compute Hedge Ratio}{data1, data2}
\State model $\gets$ perform OLS regression (data1, data2)
\State \Return model.params$[1]$... | "https://arxiv.org/src/2401.14761" | "2401.14761.tar.gz" | "2024-01-26" | {
"title": "esg driven pairs algorithm for sustainable trading: analysis from the indian market",
"id": "2401.14761",
"abstract": "this paper proposes an algorithmic trading framework integrating environmental, social, and governance (esg) ratings with a pairs trading strategy. it addresses the demand for... | "2024-03-15T05:30:05.430403" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"bib.bib": {
"toxicity_score": 0.012063419,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0023401144,
"insult_score": 0.008519882,
"profanity... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 87.3581322268664,
"hash": "078f8f8f9f9f813f",
"most_frequent_color_ratio": 87.3581322268664
}
} | [] | "algorithm" | "d4f074dd-00b8-4ebd-9eff-3126db854c98" | 1105 | medium | |
\begin{algorithm}
GPB(N) Algorithm
\end{algorithm}
| \begin{algorithm}
GPB(N) Algorithm
\end{algorithm} | "https://arxiv.org/src/2402.08051" | "2402.08051.tar.gz" | "2024-02-12" | {
"title": "on bayesian filtering for markov regime switching models",
"id": "2402.08051",
"abstract": "this paper presents a framework for empirical analysis of dynamic macroeconomic models using bayesian filtering, with a specific focus on the state-space formulation of dynamic stochastic general equilibr... | "2024-03-15T04:21:14.605813" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"paper_draft_8_arxiv.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.52194925493355,
"hash": "00ff7f3f3f3fff00",
"most_frequent_color_ratio": 88.52194925493355
}
} | [] | "algorithm" | "75bee723-8648-4b9d-82cd-341a3bad5259" | 50 | easy | |
\begin{algorithmic}[1]
\State Initialize an empty list $l$
\For{\textbf{each} $i$ \textbf{in} $producers$}
\For{$t$ \textbf{in} $[500, 900)$}
\If{$\forall x \in \{p_{it},p_{i(t+1)},\dots,p_{i1000}\}(x < \epsilon)$}
\State $l$\textbf{.push}(True)
\State \textbf{break}
\EndIf
\E... | \begin{algorithmic}
[1]
\State Initialize an empty list $l$
\For{\textbf{each} $i$ \textbf{in} $producers$}
\For{$t$ \textbf{in} $[500, 900)$}
\If{$\forall x \in \{p_{it},p_{i(t+1)},\dots,p_{i1000}\}(x < \epsilon)$}
\State $l$\textbf{.push}(True)
\State \textbf{break}
\EndIf
\EndFor
\State $l$\textbf{.push}(False)
\End... | "https://arxiv.org/src/2401.07070" | "2401.07070.tar.gz" | "2024-01-13" | {
"title": "a dynamic agent based model of the real economy with monopolistic competition, perfect product differentiation, heterogeneous agents, increasing returns to scale and trade in disequilibrium",
"id": "2401.07070",
"abstract": "we have used agent-based modeling as our numerical method to artifi... | "2024-03-15T06:13:08.276479" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sn-article.tex": {
"toxicity_score": 0.0046494426,
"severe_toxicity_score": 0.00053167343,
"identity_attack_score": 0.001415168,
"insult_score": 0.006011867,
"p... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.80746395250212,
"hash": "078ecf9f9f0f0f2f",
"most_frequent_color_ratio": 92.80746395250212
}
} | [] | "algorithm" | "596d1a2a-5955-45c6-abc2-8a6ffce88a00" | 481 | easy | |
\begin{algorithm}[!ht]
\caption{Discrete empirical interpolation method (DEIM)}\label{alg:DEIM}
\begin{algorithmic}[1]
\State \textbf{Input:} matrix $M \in \mathbb{R}^{n\times m}$ with orthonormal columns
\State \textbf{Output:} index set $I$ if cardinality $m$
\State $I = \{\mathsf{argmax}\ |M(:,1)|\}$
\For $k = 2,\do... | \begin{algorithm}
[!ht]
\caption{Discrete empirical interpolation method (DEIM)}\begin{algorithmic}
[1]
\State \textbf{Input:} matrix $M \in \mathbb{R}^{n\times m}$ with orthonormal columns
\State \textbf{Output:} index set $I$ if cardinality $m$
\State $I = \{\mathsf{argmax}\ |M(:,1)|\}$
\For $k = 2,\dots,m$
\State $c... | "https://arxiv.org/src/2211.11338" | "2211.11338.tar.gz" | "2024-02-25" | {
"title": "approximation in the extended functional tensor train format",
"id": "2211.11338",
"abstract": "this work proposes the extended functional tensor train (eftt) format for compressing and working with multivariate functions on tensor product domains. our compression algorithm combines tensorized c... | "2024-03-15T03:21:44.181934" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Figures/RankImpactError.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.75809517457564,
"hash": "01030f3f3f8f9f3f",
"most_frequent_color_ratio": 91.75809517457564
}
} | [] | "algorithm" | "080b212f-f820-4ed0-803b-0cd7ba5e82d4" | 467 | easy | |
\begin{algorithm}
\caption{Multi-Period Transition Framework}\label{alg:MPTF}
\begin{algorithmic}[H]
\Require $P_0, \mathcal{T}, Y_{-L:0}, \mathbf{X}_{-L:0}, V_0, \mathbf{T}, C_0$, OPT, Forecaster, MarketObserver
\Ensure $Y_0^T\mathcal{T} + C_0 \leq V_0$
\State $t \gets 0$
\State $P_t \gets P_0$
\State $C_t \gets C_0... | \begin{algorithm}
\caption{Multi-Period Transition Framework}\begin{algorithmic}
[H]
\Require $P_0, \mathcal{T}, Y_{-L:0}, \mathbf{X}_{-L:0}, V_0, \mathbf{T}, C_0$, OPT, Forecaster, MarketObserver
\Ensure $Y_0^T\mathcal{T} + C_0 \leq V_0$
\State $t \gets 0$
\State $P_t \gets P_0$
\State $C_t \gets C_0$
\State $Y \gets ... | "https://arxiv.org/src/2401.13126" | "2401.13126.tar.gz" | "2024-01-24" | {
"title": "optimizing transition strategies for small to medium sized portfolios",
"id": "2401.13126",
"abstract": "this work discusses the benefits of constrained portfolio turnover strategies for small to medium-sized portfolios. we propose a dynamic multi-period model that aims to minimize transaction c... | "2024-03-15T05:38:12.261498" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"README.md": {
"toxicity_score": 0.007382561,
"severe_toxicity_score": 0.00096321106,
"identity_attack_score": 0.001933138,
"insult_score": 0.0070853736,
"profan... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.81160905916647,
"hash": "1d7f3f0000803f3f",
"most_frequent_color_ratio": 92.81160905916647
}
} | [] | "algorithm" | "9d6c0f2d-70af-4e25-9cee-9bec2c661226" | 1119 | medium | |
\begin{algorithm}
\caption{getNextStage(candidates, k)}\label{alg:te2rules_stagek}
\begin{algorithmic}
\State $newCandidates \gets []$
\\
\For{$r_1 \gets candidates$}
\For{$r_2 \gets candidates$}
\State $nodes_1 = r_1.sourceNodes$
\State $nodes_2 = r_2.sourceNodes$
\If{$|nodes_1 \ca... | \begin{algorithm}
\caption{getNextStage(candidates, k)}\begin{algorithmic}
\State $newCandidates \gets []$
\\
\For{$r_1 \gets candidates$}
\For{$r_2 \gets candidates$}
\State $nodes_1 = r_1.sourceNodes$
\State $nodes_2 = r_2.sourceNodes$
\If{$|nodes_1 \cap nodes_2| = k - 2$}
\State $r \gets r_1 \& r_2$
\State $r.source... | "https://arxiv.org/src/2206.14359" | "2206.14359.tar.gz" | "2024-01-23" | {
"title": "te2rules: explaining tree ensembles using rules",
"id": "2206.14359",
"abstract": "tree ensemble (te) models, such as gradient boosted trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. this paper ... | "2024-03-15T09:04:28.850184" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"checklist.tex": {
"toxicity_score": 0.007288316,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0020811295,
"insult_score": 0.00654387,
"pro... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.70896211887897,
"hash": "001f0f8f8f8f3f1f",
"most_frequent_color_ratio": 94.70896211887897
}
} | [] | "algorithm" | "4838572e-3197-4b9d-96d2-c4263ddd219d" | 496 | easy | |
\begin{algorithmic}[1]
\State Simulate $N$ random variables from $\text{Uniform}(0,1)$; Denote $\tilde{U}=(\tilde{U}_{(1)},...,\tilde{U}_{(N)})$ as the vector of $N$ simulated Uniformly distributed random variables
\State Denote $\tilde{Y}^{*}_{ij}=(\tilde{Y}^{*}_{ij,(1)},...,\tilde{Y}^{*}_{ij,(N)})$ as the vec... | \begin{algorithmic}
[1]
\State Simulate $N$ random variables from $\text{Uniform}(0,1)$; Denote $\tilde{U}=(\tilde{U}_{(1)},...,\tilde{U}_{(N)})$ as the vector of $N$ simulated Uniformly distributed random variables
\State Denote $\tilde{Y}^{*}_{ij}=(\tilde{Y}^{*}_{ij,(1)},...,\tilde{Y}^{*}_{ij,(N)})$ as the vector of ... | "https://arxiv.org/src/2206.08541" | "2206.08541.tar.gz" | "2024-02-19" | {
"title": "ensemble distributional forecasting for insurance loss reserving",
"id": "2206.08541",
"abstract": "loss reserving generally focuses on identifying a single model that can generate superior predictive performance. however, different loss reserving models specialise in capturing different aspects... | "2024-03-15T03:14:29.396980" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"libraries.bib": {
"toxicity_score": 0.015707577,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0058826595,
"insult_score": 0.008975885,
"pro... | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.19083617058111,
"hash": "03131f037f1f0303",
"most_frequent_color_ratio": 92.19083617058111
}
} | [] | "algorithm" | "1d289ead-1c69-4045-8011-bd2568d3d304" | 1151 | medium | |
\begin{algorithmic}
\vspace{1mm}
\State{// \texttt{Iterate over all the haloes in catalogue}}
\For{ halo in catalogue }
\vspace{3mm}
\State{// \texttt{Compute probability of central}}
\State{$p_\text{cen} \leftarrow \text{model.}N_\text{cen}( $ halo.mass $... | \begin{algorithmic}
\vspace{1mm}
\State{// \texttt{Iterate over all the haloes in catalogue}}
\For{ halo in catalogue }
\vspace{3mm}
\State{// \texttt{Compute probability of central}}
\State{$p_\text{cen} \leftarrow \text{model.}N_\text{cen}( $ halo.mass $ )$ }
\vspace{3mm}
\State{// \texttt{Define a binomial random va... | "https://arxiv.org/src/2002.07179" | "2002.07179.tar.gz" | "2024-02-14" | {
"title": "scampy -- a sub-halo clustering & abundance matching based python interface for painting galaxies on the dark matter halo/sub-halo hierarchy",
"id": "2002.07179",
"abstract": "we present a computational framework for \"painting\" galaxies on top of the dark matter halo/sub-halo hierarchy obtai... | "2024-03-15T04:34:46.153353" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"tab/python_modules_tab.tex": {
"toxicity_score": 0.013634177,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.002830336,
"insult_score": 0.007474876,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.39439892012867,
"hash": "0d8f831fbf878300",
"most_frequent_color_ratio": 91.39439892012867
}
} | [] | "algorithm" | "845af096-d4f2-4f54-96bd-5d99fe1e642a" | 861 | medium | |
\begin{algorithmic}[1]
\Require $n \geq 0 \vee x \neq 0$
\Ensure $y = x^n$
\State $y \Leftarrow 1$
\If{$n < 0$}\label{algln2}
\State $X \Leftarrow 1 / x$
\State $N \Leftarrow -n$
\Else
\State $X \Leftarrow x$
\State $N \Leftarrow n$
\EndIf
\While{$N \neq 0$}
\If{$N$ is even}
... | \begin{algorithmic}
[1]
\Require $n \geq 0 \vee x \neq 0$
\Ensure $y = x^n$
\State $y \Leftarrow 1$
\If{$n < 0$} \State $X \Leftarrow 1 / x$
\State $N \Leftarrow -n$
\Else
\State $X \Leftarrow x$
\State $N \Leftarrow n$
\EndIf
\While{$N \neq 0$}
\If{$N$ is even}
\State $X \Leftarrow X \times X$
\State $N \Leftarrow N /... | "https://arxiv.org/src/2312.05063" | "2312.05063.tar.gz" | "2024-02-25" | {
"title": "individualizing glioma radiotherapy planning by optimization of data and physics-informed discrete loss",
"id": "2312.05063",
"abstract": "brain tumor growth is unique to each patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. understanding thes... | "2024-03-15T03:04:49.138400" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sn-nature.bst": {
"toxicity_score": 0.014827953,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0034223017,
"insult_score": 0.008880884,
"pro... | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 96.32356487712283,
"hash": "003f3f7f1f1f3f3f",
"most_frequent_color_ratio": 96.32356487712283
}
} | [] | "algorithm" | "37f9ec91-081a-4799-9d19-7a6595228dc9" | 437 | easy | |
\begin{algorithmic}[1]
\State Compute the set
\begin{equation*}
I(\mathbf{x}) = \left\{i \in \{1, \dots, N\} : \mathbf{x} \in \mathcal{B}_i\right\}.
\end{equation*}
\If{$|I(\mathbf{x})|=1$}
\State Assign $C(\mathbf{x})$ the unique element of $I(\mathbf{x})$.
\EndIf
\If{$|I(\mathbf{x})| = ... | \begin{algorithmic}
[1]
\State Compute the set
\begin{equation*}
I(\mathbf{x}) = \left\{i \in \{1, \dots, N\} : \mathbf{x} \in \mathcal{B}_i\right\}.
\end{equation*}
\If{$|I(\mathbf{x})|=1$}
\State Assign $C(\mathbf{x})$ the unique element of $I(\mathbf{x})$.
\EndIf
\If{$|I(\mathbf{x})| = 0$}
\State $C(\mathbf{x}) = \a... | "https://arxiv.org/src/2301.09734" | "2301.09734.tar.gz" | "2024-02-08" | {
"title": "topological learning in multi-class data sets",
"id": "2301.09734",
"abstract": "we specialize techniques from topological data analysis to the problem of characterizing the topological complexity (as defined in the body of the paper) of a multi-class data set. as a by-product, a topological cla... | "2024-03-15T05:24:48.442243" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.01608456,
"severe_toxicity_score": 0.0013446808,
"identity_attack_score": 0.0022198714,
"insult_score": 0.008633883,
"profanity... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.60868197546398,
"hash": "011f1f009fbf3f7f",
"most_frequent_color_ratio": 91.60868197546398
}
} | [] | "algorithm" | "be38228b-6714-4f2a-987e-5364b1359ad6" | 490 | easy | |
\begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\While{\(k<k_{max}\)}
\State \(\hat{J}=-\infty\)
\For{\(\sigma_{\text{test}} \in [0,1]\)}
\If{\(J(\mathbf{w}, \sigma_{\text{test}}) > \hat{J}\)}
\State \(\sigma \gets \sigma_{\text{test}... | \begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\While{\(k<k_{max}\)}
\State \(\hat{J}=-\infty\)
\For{\(\sigma_{\text{test}} \in [0,1]\)}
\If{\(J(\mathbf{w}, \sigma_{\text{test}}) > \hat{J}\)}
\State \(\sigma \gets \sigma_{\text{test}}\)
\State \(\hat{J} \gets J(\mathbf{w}, \sigma_{\text{t... | "https://arxiv.org/src/2207.06392" | "2207.06392.tar.gz" | "2024-01-25" | {
"title": "relationship design for socially-aware behavior in static games",
"id": "2207.06392",
"abstract": "autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. we present a new approach to model socially-aware de... | "2024-03-15T08:38:27.674079" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sections/3_preliminaries.tex": {
"toxicity_score": 0.007382561,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0020071338,
"insult_score": 0.0063918694,
... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.73401572466993,
"hash": "0707821f07bf9f3f",
"most_frequent_color_ratio": 92.73401572466993
}
} | [] | "algorithm" | "b6fcbba0-8428-4ca6-90b8-c0ab8babb1b1" | 775 | medium | |
\begin{algorithm}[!ht]\caption{LSH, private procedures}\label{alg:LSH_private_app}
\begin{algorithmic}[1]
\State {\bf data structure} \textsc{LSH}
\State
\State {\bf private}
\Procedure{\textsc{ChooseHashFunc}}{$k,L\in \mathbb{N}_+$}\label{lin:choose_hash_func}
\For{$l \in [L]$}
\State \Comment{Amplify hash... | \begin{algorithm}[!ht]
\caption{LSH, private procedures}\begin{algorithmic}
[1]
\State {\bf data structure} \textsc{LSH}
\State
\State {\bf private}
\Procedure{\textsc{ChooseHashFunc}}{$k,L\in \mathbb{N}_+$} \For{$l \in [L]$}
\State \Comment{Amplify hash functions by concatenating}
\State $\mathcal{H}_{l} \leftarrow$ s... | "https://arxiv.org/src/2208.03915" | "2208.03915.tar.gz" | "2024-02-13" | {
"title": "dynamic maintenance of kernel density estimation data structure: from practice to theory",
"id": "2208.03915",
"abstract": "kernel density estimation (kde) stands out as a challenging task in machine learning. the problem is defined in the following way: given a kernel function $f(x,y)$ and a ... | "2024-03-15T05:43:39.046127" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"preli.tex": {
"toxicity_score": 0.00609454,
"severe_toxicity_score": 0.0007247925,
"identity_attack_score": 0.0016834025,
"insult_score": 0.0062493687,
"profani... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.56386257114235,
"hash": "34a0101f1f3f3f1f",
"most_frequent_color_ratio": 92.56386257114235
}
} | [] | "algorithm" | "df723daa-5bbd-4dcf-a0b8-28a7b34143bd" | 808 | medium | |
\begin{algorithmic}
\State $solutions \gets []$
\\ \Comment{Rule Generation}
\For{$k \gets 1, 2, 3, \ldots n$}
\If{$k = 1$}
\State $candidates \gets getNodeRules(model)$
\Else
\State $candidates \gets getNextStage(candidates, k)$
\EndIf
\\
\For{$r \gets candidates$}
\State $p... | \begin{algorithmic}
\State $solutions \gets []$
\\ \Comment{Rule Generation}
\For{$k \gets 1, 2, 3, \ldots n$}
\If{$k = 1$}
\State $candidates \gets getNodeRules(model)$
\Else
\State $candidates \gets getNextStage(candidates, k)$
\EndIf
\\
\For{$r \gets candidates$}
\State $p \gets getPrecision(r \implies positiveLabel... | "https://arxiv.org/src/2206.14359" | "2206.14359.tar.gz" | "2024-01-23" | {
"title": "te2rules: explaining tree ensembles using rules",
"id": "2206.14359",
"abstract": "tree ensemble (te) models, such as gradient boosted trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. this paper ... | "2024-03-15T09:04:28.850184" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"checklist.tex": {
"toxicity_score": 0.007288316,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0020811295,
"insult_score": 0.00654387,
"pro... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.70896211887897,
"hash": "001f0f8f8f8f3f1f",
"most_frequent_color_ratio": 94.70896211887897
}
} | [] | "algorithm" | "747ba3d5-8631-482c-94f1-e88f21f9e83d" | 551 | easy | |
\begin{algorithmic}[1] % The number [1] indicates that lines are numbered
\Statex \textbf{Input:} Classifiers $h_0^*$ and $h_1^*$
\Statex \textbf{Output:} A randomized classifier $h^*_{\text{Fair}}:\mathcal{X}\times\{0,1\}\rightarrow\{0,1\}$
\State Compute $\alpha = \mathbb{P}_{\mu^X_0} (h^*_0(X) = 1 )$ and $\beta = ... | \begin{algorithmic}[1]
% The number [1] indicates that lines are numbered
\Statex \textbf{Input:} Classifiers $h_0^*$ and $h_1^*$
\Statex \textbf{Output:} A randomized classifier $h^*_{\text{Fair}}:\mathcal{X}\times\{0,1\}\rightarrow\{0,1\}$
\State Compute $\alpha = \mathbb{P}_{\mu^X_0} (h^*_0(X) = 1 )$ and $\beta = \m... | "https://arxiv.org/src/2402.15603" | "2402.15603.tar.gz" | "2024-02-23" | {
"title": "differentially private fair binary classifications",
"id": "2402.15603",
"abstract": "in this work, we investigate binary classification under the constraints of both differential privacy and fairness. we first propose an algorithm based on the decoupling technique for learning a classifier with... | "2024-03-15T03:43:07.102159" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main_arXiv.tex": {
"toxicity_score": 0.009864358,
"severe_toxicity_score": 0.0009727478,
"identity_attack_score": 0.00310782,
"insult_score": 0.0071138735,
"pro... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.18799061699234,
"hash": "0f023f1fe3e3e17f",
"most_frequent_color_ratio": 91.18799061699234
}
} | [] | "algorithm" | "236edd78-153c-4be7-a691-f7c18995a882" | 1062 | medium | |
\begin{algorithm}[tb]
\caption{Working Alliance Analysis (WAA)}
\label{alg:waa}
\begin{algorithmic}[1]
\State {\bfseries }\textbf{for} i = 1,2,$\cdots$, T \textbf{do}
\State {\bfseries } \quad Automatically transcribe dialogue turn pairs $(S^p_i,S^t_i)$
\State {\bfseries }\quad \textbf{for} $(I^p_j, I^t_j) \in$ i... | \begin{algorithm}
[tb]
\caption{Working Alliance Analysis (WAA)}
\begin{algorithmic}
[1]
\State {\bfseries }\textbf{for} i = 1,2,$\cdots$, T \textbf{do}
\State {\bfseries } \quad Automatically transcribe dialogue turn pairs $(S^p_i,S^t_i)$
\State {\bfseries }\quad \textbf{for} $(I^p_j, I^t_j) \in$ inventories $(I^p, I^... | "https://arxiv.org/src/2402.14701" | "2402.14701.tar.gz" | "2024-02-22" | {
"title": "compass: computational mapping of patient-therapist alliance strategies with language modeling",
"id": "2402.14701",
"abstract": "the therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment. traditionally, working alliance assessment relies on que... | "2024-03-15T03:21:50.438155" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanit... | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.8658828308359,
"hash": "070f83a3bf830f3e",
"most_frequent_color_ratio": 88.8658828308359
}
} | [] | "algorithm" | "a010c2b9-6ba2-4b65-b5c3-2c168747e932" | 633 | easy | |
\begin{algorithmic}
\State \textbf{Input:} Independent initial samples $x_1^{(0)},...,x_N^{(0)}$ from $\mu_0$, momentum parameters $m_l\in[0,1)$ for $l=1,...,L$.
\State Initialize $(v_1,...,v_N)=0$.
\For{$l=1,...,L$}
\State - Set $(\tilde x_1^{(0)},...,\tilde x_N^{(0)})=(x_1^{(l-1)},...,x_N^{(l-1)})$.
\State - Simulate... | \begin{algorithmic}
\State \textbf{Input:} Independent initial samples $x_1^{(0)},...,x_N^{(0)}$ from $\mu_0$, momentum parameters $m_l\in[0,1)$ for $l=1,...,L$.
\State Initialize $(v_1,...,v_N)=0$.
\For{$l=1,...,L$}
\State - Set $(\tilde x_1^{(0)},...,\tilde x_N^{(0)})=(x_1^{(l-1)},...,x_N^{(l-1)})$.
\State - Simulate... | "https://arxiv.org/src/2305.11463" | "2305.11463.tar.gz" | "2024-02-20" | {
"title": "generative sliced mmd flows with riesz kernels",
"id": "2305.11463",
"abstract": "maximum mean discrepancy (mmd) flows suffer from high computational costs in large scale computations. in this paper, we show that mmd flows with riesz kernels $k(x,y) = - \\|x-y\\|^r$, $r \\in (0,2)$ have exceptio... | "2024-03-15T04:32:53.456595" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.33502252252252,
"hash": "0c07179f8f31cb55",
"most_frequent_color_ratio": 94.33502252252252
}
} | [] | "algorithm" | "078ffa9b-811e-4152-aa26-00a8190564a6" | 1124 | medium | |
\begin{algorithm}\label{alg:sbo2}
An equivalent formulation to Algorithm \ref{alg:sbo1} is to (i) scramble $A$ and map it onto an ordered set, then (ii) order the scrambled blocks by least element. Let $\sigma:A\to\{1,\dots,n\}$ be a uniform random bijection and order the blocks in increasing order of $\min \sigma(B)$ ... | \begin{algorithm}
An equivalent formulation to Algorithm \ref{alg:sbo1} is to (i) scramble $A$ and map it onto an ordered set, then (ii) order the scrambled blocks by least element. Let $\sigma:A\to\{1,\dots,n\}$ be a uniform random bijection and order the blocks in increasing order of $\min \sigma(B)$ as $(B_i)_{i=1}^... | "https://arxiv.org/src/2104.00193" | "2104.00193.tar.gz" | "2024-01-12" | {
"title": "takeover, fixation and identifiability in finite neutral genealogy models",
"id": "2104.00193",
"abstract": "for neutral genealogy models in a finite, possibly non-constant population, there is a convenient ordered rearrangement of the particles, known as the lookdown representation, that grea... | "2024-03-15T06:08:13.487657" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"EJP2104-001revised3.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.0012779236,
"identity_attack_score": 0.0026083488,
"insult_score": 0.009393888,
... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 83.32385938668662,
"hash": "005b4ffbfd20dfc7",
"most_frequent_color_ratio": 83.32385938668662
}
} | [] | "algorithm" | "4c89b80b-8597-4535-a477-f7125e3c7c7f" | 501 | easy | |
\begin{algorithm}
\caption{Dynamics-based Arrival Date Computation}\label{alg:variant_arrival}
\begin{algorithmic}[1]
\ForAll{$X_p$ in $\{X_1, X_2, \dots\}_t$}
\State Calculate and store delay predictions for $(X_p, Y)$ until the current date, $t$.
\EndFor
\If{$t \neq 0$}
\State Select top 3... | \begin{algorithm}
\caption{Dynamics-based Arrival Date Computation} \begin{algorithmic}
[1]
\ForAll{$X_p$ in $\{X_1, X_2, \dots\}_t$}
\State Calculate and store delay predictions for $(X_p, Y)$ until the current date, $t$.
\EndFor
\If{$t \neq 0$}
\State Select top 3 $X_p$ with the best performance at $t-1$
\Else
\State... | "https://arxiv.org/src/2401.03390" | "2401.03390.tar.gz" | "2024-01-07" | {
"title": "global prediction of covid-19 variant emergence using dynamics-informed graph neural networks",
"id": "2401.03390",
"abstract": "during the covid-19 pandemic, a major driver of new surges has been the emergence of new variants. when a new variant emerges in one or more countries, other nations... | "2024-03-15T07:51:28.358213" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"other/0_paper.tex": {
"toxicity_score": 0.009801528,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.0030153254,
"insult_score": 0.007132874,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.23877662220748,
"hash": "038c7f3f6f3f0304",
"most_frequent_color_ratio": 91.23877662220748
}
} | [] | "algorithm" | "5fcefa49-ac51-47dc-af4d-df5297782c70" | 583 | easy | |
\begin{algorithm}
\caption{Conventional Screening Applied to Graphs}
\label{alg:vs}
\begin{algorithmic}[1]
\Require{$\{(A_i,Y_i)\}_{i=1}^m$ and $c \in [0,1]$}{}
\For{$u \in V$ }
\State $X_i=A_{i}[u,\cdot]$
\State $\beta(u) = Dcor(\{X_i, Y_i\}_{i=1}^m )$
\EndFor
\State $\hat{S} = \{u \in V| \beta(u) > c\}... | \begin{algorithm}
\caption{Conventional Screening Applied to Graphs}
\begin{algorithmic}
[1]
\Require{$\{(A_i,Y_i)\}_{i=1}^m$ and $c \in [0,1]$}{}
\For{$u \in V$ }
\State $X_i=A_{i}[u,\cdot]$
\State $\beta(u) = Dcor(\{X_i, Y_i\}_{i=1}^m )$
\EndFor
\State $\hat{S} = \{u \in V| \beta(u) > c\}$.
\end{algorithmic}
\end{alg... | "https://arxiv.org/src/1801.07683" | "1801.07683.tar.gz" | "2024-02-05" | {
"title": "discovering the signal subgraph: an iterative screening approach on graphs",
"id": "1801.07683",
"abstract": "supervised learning on graphs is a challenging task due to the high dimensionality and inherent structural dependencies in the data, where each edge depends on a pair of vertices. exis... | "2024-03-15T06:55:05.801196" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.0040142676,
"insult_score": 0.007531876,
"profanit... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.22004695043628,
"hash": "070fcf8780871f03",
"most_frequent_color_ratio": 91.22004695043628
}
} | [] | "algorithm" | "cc1b7c4e-d7c2-4dc0-bb8f-d35405db8b74" | 327 | easy | |
\begin{algorithm}
\caption{Modified Gradient Descent with Triggering Events}
\label{alg:inner loop}
\begin{algorithmic}[1]
\Procedure{ModifiedGD}{$\nabla F, \hat{L}_{i, 0}, \delta_{(i, \cdot)}, R, \theta_{i, 0}, \kappa_{(i,\cdot)}, T^*, \ell, \gamma$}
\State $ \Delta \leftarrow 0 $ \Comment... | \begin{algorithm}
\caption{Modified Gradient Descent with Triggering Events}
\begin{algorithmic}
[1]
\Procedure{ModifiedGD}{$\nabla F, \hat{L}_{i, 0}, \delta_{(i, \cdot)}, R, \theta_{i, 0}, \kappa_{(i,\cdot)}, T^*, \ell, \gamma$}
\State $ \Delta \leftarrow 0 $ \Comment{Measure of predicted decrease of objective}
\For{$... | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in dat... | "2024-03-15T05:06:27.333458" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"table/novel-step-size-param-table.tex": {
"toxicity_score": 0.011309455,
"severe_toxicity_score": 0.0012969971,
"identity_attack_score": 0.0034592997,
"insult_score": 0.0074558... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 98.56671985827441,
"hash": "3fbdb9bbb3af8880",
"most_frequent_color_ratio": 98.56671985827441
}
} | [] | "algorithm" | "c86adc1c-d642-421b-84bd-bb24d2165239" | 1540 | hard | |
\begin{algorithm}[htb]
\caption{Mixed precision variant of LSQR for \eqref{1.1}}\label{alg3}
\begin{algorithmic}[1]
\Require $A$, $b$, $x_{0}=\mathbf{0}$
\For{$k=1,2,\ldots,$}
\State Compute $p_k$, $q_k$, $\alpha_k$, $\beta_k$ by the LBFRO \algorithmiccomment{roundoff unit is $\mathbf{u}$}
\State Compute $\r... | \begin{algorithm}
[htb]
\caption{Mixed precision variant of LSQR for \eqref{1.1}} \begin{algorithmic}
[1]
\Require $A$, $b$, $x_{0}=\mathbf{0}$
\For{$k=1,2,\ldots,$}
\State Compute $p_k$, $q_k$, $\alpha_k$, $\beta_k$ by the LBFRO \algorithmiccomment{roundoff unit is $\mathbf{u}$}
\State Compute $\rho_k$, $\theta_{k+1}$... | "https://arxiv.org/src/2210.11025" | "2210.11025.tar.gz" | "2024-02-12" | {
"title": "double precision is not necessary for lsqr for solving discrete linear ill-posed problems",
"id": "2210.11025",
"abstract": "the growing availability and usage of low precision foating point formats has attracts many interests of developing lower or mixed precision algorithms for scientific co... | "2024-03-15T06:22:07.329432" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"manuscript.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"pr... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.04932088929357,
"hash": "000f1f9f8f9f9f3f",
"most_frequent_color_ratio": 94.04932088929357
}
} | [] | "algorithm" | "83abbcf3-a319-4f9d-ae9e-68b6ef556a1f" | 892 | medium | |
\begin{algorithmic}[1]
\State Perform Step 1 - 3 proposed in \textbf{Algorithm} \ref{alg:EFT}. Based on $\{\check{\Lambda}_{\mathbf{C}}(t_j)\}_{ j =1}^n$, obtain the Jackknife bias-corrected estimators $T_n(t) = \check{\Lambda}_{\mathbf{C}}(t) - f(t, \{\check{\Lambda}_{\mathbf{C}}(v_i)\}_{i =1}^k)$, and construct the c... | \begin{algorithmic}
[1]
\State Perform Step 1 - 3 proposed in \textbf{Algorithm} \ref{alg:EFT}. Based on $\{\check{\Lambda}_{\mathbf{C}}(t_j)\}_{ j =1}^n$, obtain the Jackknife bias-corrected estimators $T_n(t) = \check{\Lambda}_{\mathbf{C}}(t) - f(t, \{\check{\Lambda}_{\mathbf{C}}(v_i)\}_{i =1}^k)$, and construct the ... | "https://arxiv.org/src/2310.11724" | "2310.11724.tar.gz" | "2024-02-26" | {
"title": "simultaneous nonparametric inference of m-regression under complex temporal dynamics",
"id": "2310.11724",
"abstract": "the paper considers simultaneous nonparametric inference for a wide class of m-regression models with time-varying coefficients. the covariates and errors of the regression m... | "2024-03-15T03:19:25.303660" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Mregression.tex": {
"toxicity_score": 0.10175867,
"severe_toxicity_score": 0.005378723,
"identity_attack_score": 0.026408968,
"insult_score": 0.02173949,
"profa... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.16367265469061,
"hash": "0184838f0ffb7f03",
"most_frequent_color_ratio": 88.16367265469061
}
} | [] | "algorithm" | "46c3670f-20c1-4b32-a8c6-12c99bd2376d" | 1045 | medium | |
\begin{algorithm}
\caption{The market return fitting step in training GF-AGRU.} \label{algorithm_N}
\textbf{Hyperparameters}: learning rate $l_{\text{fix}}$ of the FIX-OPTIM sub-procedure, learning rate $l_{\text{tv}}$ of the TV-AGRU sub-procedure, number of maximum iterative steps $N_m$, number of training epochs $N_{... | \begin{algorithm}
\caption{The market return fitting step in training GF-AGRU.} \textbf{Hyperparameters}: learning rate $l_{\text{fix}}$ of the FIX-OPTIM sub-procedure, learning rate $l_{\text{tv}}$ of the TV-AGRU sub-procedure, number of maximum iterative steps $N_m$, number of training epochs $N_{\text{fix}}$ of FIX-... | "https://arxiv.org/src/2301.07318" | "2301.07318.tar.gz" | "2024-01-16" | {
"title": "dynamic cvar portfolio construction with attention-powered generative factor learning",
"id": "2301.07318",
"abstract": "the dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. to achieve this, we propose a dynamic generativ... | "2024-03-15T06:00:53.804038" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"elsarticle-harv.bst": {
"toxicity_score": 0.016838523,
"severe_toxicity_score": 0.0013065338,
"identity_attack_score": 0.004439743,
"insult_score": 0.0088618845,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.1748968745042,
"hash": "00003f9f9f9fbf3f",
"most_frequent_color_ratio": 89.1748968745042
}
} | [] | "algorithm" | "35dbd100-908a-49a6-a53b-82b8ac388509" | 1979 | hard | |
\begin{algorithm}[t]
\begin{algorithmic}
\State \textbf{Input:} Independent initial samples $x_1^{(0)},...,x_N^{(0)}$ from $\mu_0$, momentum parameters $m_l\in[0,1)$ for $l=1,...,L$.
\State Initialize $(v_1,...,v_N)=0$.
\For{$l=1,...,L$}
\State - Set $(\tilde x_1^{(0)},...,\tilde x_N^{(0)})=(x_1^{(l-1)},...,x_N^{(l-1)}... | \begin{algorithm}
[t]
\begin{algorithmic}
\State \textbf{Input:} Independent initial samples $x_1^{(0)},...,x_N^{(0)}$ from $\mu_0$, momentum parameters $m_l\in[0,1)$ for $l=1,...,L$.
\State Initialize $(v_1,...,v_N)=0$.
\For{$l=1,...,L$}
\State - Set $(\tilde x_1^{(0)},...,\tilde x_N^{(0)})=(x_1^{(l-1)},...,x_N^{(l-1)... | "https://arxiv.org/src/2305.11463" | "2305.11463.tar.gz" | "2024-02-20" | {
"title": "generative sliced mmd flows with riesz kernels",
"id": "2305.11463",
"abstract": "maximum mean discrepancy (mmd) flows suffer from high computational costs in large scale computations. in this paper, we show that mmd flows with riesz kernels $k(x,y) = - \\|x-y\\|^r$, $r \\in (0,2)$ have exceptio... | "2024-03-15T04:32:53.456595" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.33502252252252,
"hash": "0c07179f8f31cb55",
"most_frequent_color_ratio": 94.33502252252252
}
} | [] | "algorithm" | "80f4ac37-c1ba-4f04-8d61-69501d168731" | 1205 | hard | |
\begin{algorithmic}
\For{\texttt{k in} $1:5000$}
\State \text{Draw from joint prior: } $\boldsymbol{\theta}^{sim}_k \sim\pi (\boldsymbol{\theta})$
\State \text{Simulate data set with 1000 observations: } $\boldsymbol{y}^{sim}_k \sim \pi(\boldsymbol{y}|\boldsymbol{\theta}^... | \begin{algorithmic}
\For{\texttt{k in} $1:5000$}
\State \text{Draw from joint prior: } $\boldsymbol{\theta}^{sim}_k \sim\pi (\boldsymbol{\theta})$
\State \text{Simulate data set with 1000 observations: } $\boldsymbol{y}^{sim}_k \sim \pi(\boldsymbol{y}|\boldsymbol{\theta}^{sim}_k)$
\State \text{Draw 999 posterior sample... | "https://arxiv.org/src/2402.12384" | "2402.12384.tar.gz" | "2024-01-27" | {
"title": "comparing mcmc algorithms in stochastic volatility models using simulation based calibration",
"id": "2402.12384",
"abstract": "simulation based calibration (sbc) is applied to analyse two commonly used, competing markov chain monte carlo algorithms for estimating the posterior distribution of... | "2024-03-15T03:27:09.183136" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanit... | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.41802604820217,
"hash": "3f3f9f878180837f",
"most_frequent_color_ratio": 89.41802604820217
}
} | [] | "algorithm" | "bdeae886-45cf-4bc7-8de5-6c53fb4b2f0e" | 645 | easy | |
\begin{algorithmic}[1]
\State Initialise $(w_1^k)_0=(w_2^k)_0=,...,(w_M^k)_0=\frac{1}{M}$
\State $i \gets 0$
\State $\overline{\text{LogS}}_0^k \gets \frac{1}{|D_{val}^{k*}|} \sum_{y_{ij} \in D_{val}^{k*}} \ln (\sum_{m=1}^M (w_m^k)_0 \cdot \hat{f}_m(y_{i,j}))$
\While{$i < \text{MaxIters}$}
\State Calculate ... | \begin{algorithmic}
[1]
\State Initialise $(w_1^k)_0=(w_2^k)_0=,...,(w_M^k)_0=\frac{1}{M}$
\State $i \gets 0$
\State $\overline{\text{LogS}}_0^k \gets \frac{1}{|D_{val}^{k*}|} \sum_{y_{ij} \in D_{val}^{k*}} \ln (\sum_{m=1}^M (w_m^k)_0 \cdot \hat{f}_m(y_{i,j}))$
\While{$i < \text{MaxIters}$}
\State Calculate the Log Sco... | "https://arxiv.org/src/2206.08541" | "2206.08541.tar.gz" | "2024-02-19" | {
"title": "ensemble distributional forecasting for insurance loss reserving",
"id": "2206.08541",
"abstract": "loss reserving generally focuses on identifying a single model that can generate superior predictive performance. however, different loss reserving models specialise in capturing different aspects... | "2024-03-15T04:26:48.962440" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"elsarticle.cls": {
"toxicity_score": 0.011560776,
"severe_toxicity_score": 0.0010490417,
"identity_attack_score": 0.0031263188,
"insult_score": 0.007493876,
"pr... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.71980946001266,
"hash": "0f070108000f3f3f",
"most_frequent_color_ratio": 93.71980946001266
}
} | [] | "algorithm" | "ab3164e2-32b4-46f8-9f03-873eaaa5ab4c" | 962 | medium | |
\begin{algorithmic}[1]
\Require trajectory $\tau$
\For{layer $i$ in HKSL (begin with the lowest layer)}
\For{layer $j$ in HKSL (begin with the highest layer)}
\If{$i == j$}
\State break loop
\EndIf
\State Embed first observation $o$ in $\tau$ using layer ... | \begin{algorithmic}
[1]
\Require trajectory $\tau$
\For{layer $i$ in HKSL (begin with the lowest layer)}
\For{layer $j$ in HKSL (begin with the highest layer)}
\If{$i == j$}
\State break loop
\EndIf
\State Embed first observation $o$ in $\tau$ using layer $j$'s encoder
\If{layer $j$ is the top layer}
\For{step layer $j... | "https://arxiv.org/src/2206.11396" | "2206.11396.tar.gz" | "2024-01-29" | {
"title": "multi-horizon representations with hierarchical forward models for reinforcement learning",
"id": "2206.11396",
"abstract": "learning control from pixels is difficult for reinforcement learning (rl) agents because representation learning and policy learning are intertwined. previous approaches... | "2024-03-15T08:37:32.877079" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.011560776,
"severe_toxicity_score": 0.0011253357,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0074178753,
"profani... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.66482670393972,
"hash": "039f8f8f8f9f801f",
"most_frequent_color_ratio": 91.66482670393972
}
} | [] | "algorithm" | "49b1c20c-7c84-4acf-bc3b-44875eb26979" | 1227 | hard | |
\begin{algorithmic}[1]
\Require $A$, $b$, $x_{0}=\mathbf{0}$
\For{$k=1,2,\ldots,$}
\State Compute $p_k$, $q_k$, $\alpha_k$, $\beta_k$ by the LBFRO \algorithmiccomment{roundoff unit is $\mathbf{u}$}
\State Compute $\rho_k$, $\theta_{k+1}$, $\bar{\rho}_{k+1}$, $\phi_{k}$, $\bar{\phi}_{k+1}$ by the updating proc... | \begin{algorithmic}
[1]
\Require $A$, $b$, $x_{0}=\mathbf{0}$
\For{$k=1,2,\ldots,$}
\State Compute $p_k$, $q_k$, $\alpha_k$, $\beta_k$ by the LBFRO \algorithmiccomment{roundoff unit is $\mathbf{u}$}
\State Compute $\rho_k$, $\theta_{k+1}$, $\bar{\rho}_{k+1}$, $\phi_{k}$, $\bar{\phi}_{k+1}$ by the updating procedure \al... | "https://arxiv.org/src/2210.11025" | "2210.11025.tar.gz" | "2024-02-12" | {
"title": "double precision is not necessary for lsqr for solving discrete linear ill-posed problems",
"id": "2210.11025",
"abstract": "the growing availability and usage of low precision foating point formats has attracts many interests of developing lower or mixed precision algorithms for scientific co... | "2024-03-15T04:23:06.439214" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"manuscript.tex": {
"toxicity_score": 0.013445686,
"severe_toxicity_score": 0.0011014938,
"identity_attack_score": 0.0029598286,
"insult_score": 0.007968879,
"pr... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.902947342459,
"hash": "3f843b04839f3f58",
"most_frequent_color_ratio": 90.902947342459
}
} | [] | "algorithm" | "361d2e88-f516-4671-b1a7-cdde90b03105" | 794 | medium | |
\begin{algorithm}
\caption{Compute $c^*$}\label{alg:find_cstar}
\begin{algorithmic}
\Require $a_i, B_i, \theta_i$
\\
\State Sort the values $a_i, \theta_i$ according to $\frac{a_{ij}}{\theta_{ij}}$ in a descending order.
If there are goods with $\theta_{ij} = 0$, sort them separately according to $a_{ij}$ and place the... | \begin{algorithm}
\caption{Compute $c^*$}\begin{algorithmic}
\Require $a_i, B_i, \theta_i$
\\
\State Sort the values $a_i, \theta_i$ according to $\frac{a_{ij}}{\theta_{ij}}$ in a descending order.
If there are goods with $\theta_{ij} = 0$, sort them separately according to $a_{ij}$ and place them as a prefix (lower in... | "https://arxiv.org/src/2307.04108" | "2307.04108.tar.gz" | "2024-01-15" | {
"title": "asynchronous proportional response dynamics in markets with adversarial scheduling",
"id": "2307.04108",
"abstract": "we study proportional response dynamics (prd) in linear fisher markets where participants act asynchronously. we model this scenario as a sequential process in which in every s... | "2024-03-15T06:09:48.864469" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"camera_ready_with_prices_convergence.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.0071... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.6672459450513,
"hash": "3f00003f1f1fbf7f",
"most_frequent_color_ratio": 93.6672459450513
}
} | [] | "algorithm" | "0bacbbc9-4d09-4d9e-94ec-d435646ac257" | 721 | medium | |
\begin{algorithm}\label{alg: tau-best}
\begin{enumerate}
\item Set $J_0=J$ and $I=J^{\rm lower}$.
\item Perform Steps 3--5 of Algorithm~\ref{alg:marg sim CS} to obtain $R_n\equiv \prod_{j\in J_0} R_{n,j}$.
\item Construct $R^{\tau-\rm{best}}_n$ as defined in \eqref{eq: def proj tau-best}.
\end{enumerate}
\end{a... | \begin{algorithm}
\begin{enumerate}
\item Set $J_0=J$ and $I=J^{\rm lower}$.
\item Perform Steps 3--5 of Algorithm~\ref{alg:marg sim CS} to obtain $R_n\equiv \prod_{j\in J_0} R_{n,j}$.
\item Construct $R^{\tau-\rm{best}}_n$ as defined in \eqref{eq: def proj tau-best}.
\end{enumerate}
\end{algorithm} | "https://arxiv.org/src/2402.00192" | "2402.00192.tar.gz" | "2024-01-31" | {
"title": "finite- and large-sample inference for ranks using multinomial data with an application to ranking political parties",
"id": "2402.00192",
"abstract": "it is common to rank different categories by means of preferences that are revealed through data on choices. a prominent example is the rankin... | "2024-03-15T05:13:20.067590" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"AESSummaryStats.tex": {
"toxicity_score": 0.010555492,
"severe_toxicity_score": 0.00088214874,
"identity_attack_score": 0.0029413297,
"insult_score": 0.0074178753,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.98375344092406,
"hash": "00009fffa0a3ff00",
"most_frequent_color_ratio": 92.98375344092406
}
} | [] | "algorithm" | "ff73008d-5dfb-4af6-9bcc-04e80743973b" | 300 | easy | |
\begin{algorithmic}[1]
\State Construct the IV $z_{t-1}$ by equation (\ref{mulivz}).
\State Construct the IV estimators $\hat{\beta}_{ivx}$, $\hat{\beta}_a$ and $\hat{\beta}_b$ using the full sample and two subsamples by equations (\ref{defdeftwo2}), (\ref{muldef2new}) and (\ref{muldef3new}).
\State Eliminate the DE: C... | \begin{algorithmic}
[1]
\State Construct the IV $z_{t-1}$ by equation (\ref{mulivz}).
\State Construct the IV estimators $\hat{\beta}_{ivx}$, $\hat{\beta}_a$ and $\hat{\beta}_b$ using the full sample and two subsamples by equations (\ref{defdeftwo2}), (\ref{muldef2new}) and (\ref{muldef3new}).
\State Eliminate the DE: ... | "https://arxiv.org/src/2401.01064" | "2401.01064.tar.gz" | "2024-01-02" | {
"title": "robust inference for multiple predictive regressions with an application on bond risk premia",
"id": "2401.01064",
"abstract": "we propose a robust hypothesis testing procedure for the predictability of multiple predictors that could be highly persistent. our method improves the popular extend... | "2024-03-15T06:49:14.014471" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"agsm.bst": {
"toxicity_score": 0.02308189,
"severe_toxicity_score": 0.0014400482,
"identity_attack_score": 0.0073625734,
"insult_score": 0.011160898,
"profanity... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.93296188564327,
"hash": "070f0f8387830f0f",
"most_frequent_color_ratio": 91.93296188564327
}
} | [] | "algorithm" | "f1710741-93ba-4b6e-9b20-3a516fbf4d3b" | 2612 | hard | |
\begin{algorithmic}[1]
\item \textbf{Input:} $X = \langle X_{(t-T+1)}, X_{(t-T+2)}, \ldots, X_{t}\rangle; \quad X \in \mathbb{R}^{N \times T \times 1}$
\item \textbf{Output:} $ \widetilde{Y} = \langle X_{(t+1)}, X_{(t+2)}, \ldots, X_{\left(t+T^{\prime}\right)}\rangle; \quad \widetilde{Y} \in \mathbb{R}^{N \times... | \begin{algorithmic}
[1]
\item \textbf{Input:} $X = \langle X_{(t-T+1)}, X_{(t-T+2)}, \ldots, X_{t}\rangle; \quad X \in \mathbb{R}^{N \times T \times 1}$
\item \textbf{Output:} $ \widetilde{Y} = \langle X_{(t+1)}, X_{(t+2)}, \ldots, X_{\left(t+T^{\prime}\right)}\rangle; \quad \widetilde{Y} \in \mathbb{R}^{N \times T \ti... | "https://arxiv.org/src/2212.04548" | "2212.04548.tar.gz" | "2024-02-19" | {
"title": "stlgru: spatio-temporal lightweight graph gru for traffic flow prediction",
"id": "2212.04548",
"abstract": "reliable forecasting of traffic flow requires efficient modeling of traffic data. indeed, different correlations and influences arise in a dynamic traffic network, making modeling a com... | "2024-03-15T05:03:36.405412" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"IEEEbib.bst": {
"toxicity_score": 0.014827953,
"severe_toxicity_score": 0.0015163422,
"identity_attack_score": 0.0020163832,
"insult_score": 0.010020891,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.51631705653305,
"hash": "1b013f8d0f7f1c1f",
"most_frequent_color_ratio": 92.51631705653305
}
} | [] | "algorithm" | "b3e95324-18ae-4fa8-ab16-f98f466463cd" | 939 | medium | |
\begin{algorithmic}
\State \textbf{Input:} 1) fixed $L_s$; 2) current positions $X(t)$ of agents at time $t$; 3) a nondecreasing transition function $\xi:\mathbb{R}^+\to [0,1]$ (e.g. $\xi(s):=1-\sigma(s)$, see \textsection \ref{sec:weights})\\
\\
set $k=0$\\
set $Y=\emptyset$
\While{$true$}
\State for all $\mathbf... | \begin{algorithmic}
\State \textbf{Input:} 1) fixed $L_s$; 2) current positions $X(t)$ of agents at time $t$; 3) a nondecreasing transition function $\xi:\mathbb{R}^+\to [0,1]$ (e.g. $\xi(s):=1-\sigma(s)$, see \textsection \ref{sec:weights})\\
\\
set $k=0$\\
set $Y=\emptyset$
\While{$true$}
\State for all $\mathbf{\mat... | "https://arxiv.org/src/2111.03448" | "2111.03448.tar.gz" | "2024-02-13" | {
"title": "emergence of collective behaviors from local voronoi topological perception",
"id": "2111.03448",
"abstract": "this article addresses how diverse collective behaviors arise from simple and realistic decisions made entirely at the level of each agent's personal space in the sense of the voronoi... | "2024-03-15T04:08:22.815847" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"plane_data/data3.csv": {
"toxicity_score": 0.009424546,
"severe_toxicity_score": 0.0010538101,
"identity_attack_score": 0.0020996283,
"insult_score": 0.006771872,
... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.51191510342075,
"hash": "007f3ffb9f032f01",
"most_frequent_color_ratio": 92.51191510342075
}
} | [] | "algorithm" | "88895f2a-16d2-4871-8f56-b4703f063921" | 1353 | hard | |
\begin{algorithm}[htb!]
Step 1: Initialize the parameters $\boldsymbol{\theta}$ of the neural
network $\hat{u}(\boldsymbol{x};\boldsymbol{\theta})$.\\
Step 2: Set up the training sets
$\left\{\boldsymbol{x}_{\Omega}\right\}$
and $\left\{\boldsymbol{x}_{\partial \Omega}\right\}$ for the equation
and
boundary... | \begin{algorithm}
[htb!]
Step 1: Initialize the parameters $\boldsymbol{\theta}$ of the neural
network $\hat{u}(\boldsymbol{x};\boldsymbol{\theta})$.\\
Step 2: Set up the training sets
$\left\{\boldsymbol{x}_{\Omega}\right\}$
and $\left\{\boldsymbol{x}_{\partial \Omega}\right\}$ for the equation
and
boundary/initial co... | "https://arxiv.org/src/2401.04378" | "2401.04378.tar.gz" | "2024-01-09" | {
"title": "computing the gerber-shiu function with interest and a constant dividend barrier by physics-informed neural networks",
"id": "2401.04378",
"abstract": "in this paper, we propose a new efficient method for calculating the gerber-shiu discounted penalty function. generally, the gerber-shiu funct... | "2024-03-15T06:29:05.749339" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"20230911.bib": {
"toxicity_score": 0.01017851,
"severe_toxicity_score": 0.0018024445,
"identity_attack_score": 0.0015631594,
"insult_score": 0.007474876,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 1,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 85.25176343832595,
"hash": "003f011f1f03031f",
"most_frequent_color_ratio": 85.25176343832595
}
} | [] | "algorithm" | "d155ec32-f60d-4e38-905f-74344f56ca57" | 756 | medium | |
\begin{algorithm}[H]
\caption{Minorization-Maximisation algorithm for Log Score maximisation}\label{alg:algoMM}
\begin{algorithmic}[1]
\State Initialise $(w_1^k)_0=(w_2^k)_0=,...,(w_M^k)_0=\frac{1}{M}$
\State $i \gets 0$
\State $\overline{\text{LogS}}_0^k \gets \frac{1}{|D_{val}^{k*}|} \sum_{y_{ij} \in D_{val}... | \begin{algorithm}
[H]
\caption{Minorization-Maximisation algorithm for Log Score maximisation}\begin{algorithmic}
[1]
\State Initialise $(w_1^k)_0=(w_2^k)_0=,...,(w_M^k)_0=\frac{1}{M}$
\State $i \gets 0$
\State $\overline{\text{LogS}}_0^k \gets \frac{1}{|D_{val}^{k*}|} \sum_{y_{ij} \in D_{val}^{k*}} \ln (\sum_{m=1}^M (... | "https://arxiv.org/src/2206.08541" | "2206.08541.tar.gz" | "2024-02-19" | {
"title": "ensemble distributional forecasting for insurance loss reserving",
"id": "2206.08541",
"abstract": "loss reserving generally focuses on identifying a single model that can generate superior predictive performance. however, different loss reserving models specialise in capturing different aspects... | "2024-03-15T03:14:29.396980" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"libraries.bib": {
"toxicity_score": 0.015707577,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0058826595,
"insult_score": 0.008975885,
"pro... | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.19083617058111,
"hash": "03131f037f1f0303",
"most_frequent_color_ratio": 92.19083617058111
}
} | [] | "algorithm" | "d4abd2a9-2115-43a4-8aaa-b5b870594c51" | 1072 | medium | |
\begin{algorithmic}[1]
\State Select an initial estimate $\hat{\mathbf{c}}^{(1)}$, a maximum number of iterations $M_{\textnormal{iter}}$, and a tolerance factor $\epsilon$
\State $j\gets 1$, $\textnormal{flag}\gets 1$
\While{$j\leq M_{\textnormal{iter}}$ and $\textnormal{flag}=1$}
\State \textbf{E-ste... | \begin{algorithmic}
[1]
\State Select an initial estimate $\hat{\mathbf{c}}^{(1)}$, a maximum number of iterations $M_{\textnormal{iter}}$, and a tolerance factor $\epsilon$
\State $j\gets 1$, $\textnormal{flag}\gets 1$
\While{$j\leq M_{\textnormal{iter}}$ and $\textnormal{flag}=1$}
\State \textbf{E-step}: Compute the ... | "https://arxiv.org/src/2303.06045" | "2303.06045.tar.gz" | "2024-02-23" | {
"title": "kernel-based identification using lebesgue-sampled data",
"id": "2303.06045",
"abstract": "sampling in control applications is increasingly done non-equidistantly in time. this includes applications in motion control, networked control, resource-aware control, and event-based control. some of th... | "2024-03-15T03:38:24.648127" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"root_final.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"pr... | {
"num_done": {
"table": 2,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.07949649404411,
"hash": "100fc387e38f3f3f",
"most_frequent_color_ratio": 95.07949649404411
}
} | [] | "algorithm" | "1863a5a0-eb65-4a76-9517-b59c05e77190" | 1211 | hard | |
\begin{algorithmic}[1]
\Require{$\{(A_i,Y_i)\}_{i=1}^m$ and $c \in [0,1]$}{}
\For{$u \in V$ }
\State $X_i=A_{i}[u,\cdot]$
\State $\beta(u) = Dcor(\{X_i, Y_i\}_{i=1}^m )$
\EndFor
\State $\hat{S} = \{u \in V| \beta(u) > c\}$.
\end{algorithmic}
| \begin{algorithmic}
[1]
\Require{$\{(A_i,Y_i)\}_{i=1}^m$ and $c \in [0,1]$}{}
\For{$u \in V$ }
\State $X_i=A_{i}[u,\cdot]$
\State $\beta(u) = Dcor(\{X_i, Y_i\}_{i=1}^m )$
\EndFor
\State $\hat{S} = \{u \in V| \beta(u) > c\}$.
\end{algorithmic} | "https://arxiv.org/src/1801.07683" | "1801.07683.tar.gz" | "2024-02-05" | {
"title": "discovering the signal subgraph: an iterative screening approach on graphs",
"id": "1801.07683",
"abstract": "supervised learning on graphs is a challenging task due to the high dimensionality and inherent structural dependencies in the data, where each edge depends on a pair of vertices. exis... | "2024-03-15T06:55:05.801196" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.0040142676,
"insult_score": 0.007531876,
"profanit... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.22004695043628,
"hash": "070fcf8780871f03",
"most_frequent_color_ratio": 91.22004695043628
}
} | [] | "algorithm" | "12f11cf2-05ea-4e5c-a672-27103ff98ca7" | 242 | easy | |
\begin{algorithmic} % enter the algorithmic environment
\State Pick an initial state $(x_0,y_0,z_0) \sim \nu(x,y,z)$.
\For{$i = 0, 1, 2, \dots$}
\State Generate a random candidate state $(x', y', z') \sim \nu(x,y,z)$.
\State Calculate the acceptance probability $A(x_i, y_i, z_i, x', y', z') = \min\left\{1, \fra... | \begin{algorithmic}
% enter the algorithmic environment
\State Pick an initial state $(x_0,y_0,z_0) \sim \nu(x,y,z)$.
\For{$i = 0, 1, 2, \dots$}
\State Generate a random candidate state $(x', y', z') \sim \nu(x,y,z)$.
\State Calculate the acceptance probability $A(x_i, y_i, z_i, x', y', z') = \min\left\{1, \frac{\pi(x'... | "https://arxiv.org/src/1805.10721" | "1805.10721.tar.gz" | "2024-01-11" | {
"title": "bernstein's inequalities for general markov chains",
"id": "1805.10721",
"abstract": "we establish bernstein's inequalities for functions of general (general-state-space and possibly non-reversible) markov chains. these inequalities achieve sharp variance proxies and encompass the classical bern... | "2024-03-15T06:32:05.168692" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main_bernstein.tex": {
"toxicity_score": 0.01218908,
"severe_toxicity_score": 0.0011634827,
"identity_attack_score": 0.0037367835,
"insult_score": 0.007664877,
... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.30012306251277,
"hash": "1f0f000f0f3f077f",
"most_frequent_color_ratio": 92.30012306251277
}
} | [] | "algorithm" | "869ae36f-ec88-4a03-85c6-d8e631af69fa" | 709 | easy | |
\begin{algorithm}[]
\caption{Signal Proportion Estimator}\label{alg}
\begin{algorithmic}[1]
\Statex {\bf Input:} $p$-values of the observed test statistics and bounding sequences $c_{m, 0.5}$ and $c_{m, 1}$
\Statex {\bf Output:} a proportion estimate $\hat \pi$
\State Rank the variables by their $p$-values so ... | \begin{algorithm}
[]
\caption{Signal Proportion Estimator} \begin{algorithmic}
[1]
\Statex {\bf Input:} $p$-values of the observed test statistics and bounding sequences $c_{m, 0.5}$ and $c_{m, 1}$
\Statex {\bf Output:} a proportion estimate $\hat \pi$
\State Rank the variables by their $p$-values so that $p_{(1)} < p_... | "https://arxiv.org/src/2212.13574" | "2212.13574.tar.gz" | "2024-02-02" | {
"title": "weak signal inclusion under dependence and applications in genome-wide association study",
"id": "2212.13574",
"abstract": "motivated by the inquiries of weak signals in underpowered genome-wide association studies (gwass), we consider the problem of retaining true signals that are not strong ... | "2024-03-15T07:22:29.819002" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"aoas-FNC.bbl": {
"toxicity_score": 0.012817383,
"severe_toxicity_score": 0.0011634827,
"identity_attack_score": 0.0041067624,
"insult_score": 0.0076838774,
"pro... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.72568576155119,
"hash": "01211f07ff88ff1f",
"most_frequent_color_ratio": 92.72568576155119
}
} | [] | "algorithm" | "85eaac21-1a19-47ec-854c-a3876e72f479" | 673 | easy | |
\begin{algorithmic}[1]
\State \textbf{Input:} Query vector $q$; user-specified radius $R$; output from Algorithm~\ref{algo:index}
\State Compute $x_q: = q - \mu$
\State Compute the sorting score of $x_q$, i.e., $\alpha_q := x_q^T v_1$
\State Select candidate index range $J$ so that $|\alpha_j - \alp... | \begin{algorithmic}
[1]
\State \textbf{Input:} Query vector $q$; user-specified radius $R$; output from Algorithm~\ref{algo:index}
\State Compute $x_q: = q - \mu$
\State Compute the sorting score of $x_q$, i.e., $\alpha_q := x_q^T v_1$
\State Select candidate index range $J$ so that $|\alpha_j - \alpha_q| \le R$ for al... | "https://arxiv.org/src/2212.07679" | "2212.07679.tar.gz" | "2024-01-29" | {
"title": "fast and exact fixed-radius neighbor search based on sorting",
"id": "2212.07679",
"abstract": "fixed-radius near neighbor search is a fundamental data operation that retrieves all data points within a user-specified distance to a query point. there are efficient algorithms that can provide fast... | "2024-03-15T08:42:59.915070" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"preprint.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"prof... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 86.79174829261957,
"hash": "007f3f0709032101",
"most_frequent_color_ratio": 86.79174829261957
}
} | [] | "algorithm" | "0e5fcf47-636c-43fc-a429-da0dfb113895" | 559 | easy | |
\begin{algorithm}
\caption{PC Sampling (HFS-SDE).}
\label{alg:PC Sampling-HFS}
\begin{algorithmic}[1]
\Require{$\{\beta_i\}_{i=1}^N, \{\alpha_i\}_{i=1}^N, \text{csm}, \mathbf{\hat{y}}, \lambda_1, \lambda_2, r, N, M, \mathbf{M_u}$.} \Comment{$\text{csm} = \{\text{csm}_1, \cdots, \text{csm}_n\}$, $M_u$ is the und... | \begin{algorithm}
\caption{PC Sampling (HFS-SDE).}
\begin{algorithmic}
[1]
\Require{$\{\beta_i\}_{i=1}^N, \{\alpha_i\}_{i=1}^N, \text{csm}, \mathbf{\hat{y}}, \lambda_1, \lambda_2, r, N, M, \mathbf{M_u}$.} \Comment{$\text{csm} = \{\text{csm}_1, \cdots, \text{csm}_n\}$, $M_u$ is the undersampling mask}
\State{$\mathbf{x}... | "https://arxiv.org/src/2208.05481" | "2208.05481.tar.gz" | "2024-01-20" | {
"title": "high-frequency space diffusion models for accelerated mri",
"id": "2208.05481",
"abstract": "diffusion models with continuous stochastic differential equations (sdes) have shown superior performances in image generation. it can serve as a deep generative prior to solving the inverse problem in m... | "2024-03-15T09:17:42.639884" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"ieeecolor.cls": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0013828278,
"identity_attack_score": 0.004162259,
"insult_score": 0.009241886,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.90630891513204,
"hash": "031f879f9f871f3f",
"most_frequent_color_ratio": 93.90630891513204
}
} | [] | "algorithm" | "5bca4549-5341-43ec-9bf0-3329f33ef987" | 2032 | hard | |
\begin{algorithm}[H]
\caption{deep SMP-BSDE algorithm}
\begin{algorithmic}[1]
\State \textbf{Input:} Initial parameters $\left(\theta^P_0, \theta^Q_0,\ldots, \theta^Q_{N-1}\right)$, learning rate $\eta$; batch size $M$; number of iteration $K$.
\State \textbf{Data:} Simulated Brownian increments $\left\{ \... | \begin{algorithm}
[H]
\caption{deep SMP-BSDE algorithm}
\begin{algorithmic}[1]
\State \textbf{Input:} Initial parameters $\left(\theta^P_0, \theta^Q_0,\ldots, \theta^Q_{N-1}\right)$, learning rate $\eta$; batch size $M$; number of iteration $K$.
\State \textbf{Data:} Simulated Brownian increments $\left\{ \Delta W_{t_i... | "https://arxiv.org/src/2401.17472" | "2401.17472.tar.gz" | "2024-01-30" | {
"title": "convergence of the deep bsde method for stochastic control problems formulated through the stochastic maximum principle",
"id": "2401.17472",
"abstract": "it is well-known that decision-making problems from stochastic control can be formulated by means of forward-backward stochastic differenti... | "2024-03-15T05:11:50.224129" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"figs/table_ex1.tex": {
"toxicity_score": 0.0154562555,
"severe_toxicity_score": 0.001411438,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008367881,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.14958863126402,
"hash": "00070f9fcfa18f2b",
"most_frequent_color_ratio": 92.14958863126402
}
} | [] | "algorithm" | "0cab6e3d-362d-4954-bf2d-7ff3b62fed5f" | 1466 | hard | |
\begin{algorithm}[t]
\caption{MEP-ONMF}
\label{alg}
\begin{algorithmic}[1]
\Require data matrix $X \in \mathbb{R}^{d \times n}_+$, number of features $k_{\text{max}}$, $\Gamma$, $\beta_{\text{max}}$, $c_j$ [for the $\ell_0$-forced version]
\State \textbf{Initialization} $\beta_{init}$%\leftarrow\frac{1}{2\lamb... | \begin{algorithm}
[t]
\caption{MEP-ONMF}
\begin{algorithmic}
[1]
\Require data matrix $X \in \mathbb{R}^{d \times n}_+$, number of features $k_{\text{max}}$, $\Gamma$, $\beta_{\text{max}}$, $c_j$ [for the $\ell_0$-forced version]
\State \textbf{Initialization} $\beta_{init}$%\leftarrow\frac{1}{2\lambda_{\text{max}}C_{x... | "https://arxiv.org/src/2210.02672" | "2210.02672.tar.gz" | "2024-01-18" | {
"title": "a novel maximum-entropy-driven technique for low-rank orthogonal nonnegative matrix factorization with $\\ell_0$-norm sparsity constraint",
"id": "2210.02672",
"abstract": "in data-driven control and machine learning, a common requirement involves breaking down large matrices into smaller, low... | "2024-03-15T05:57:43.061097" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"root.tex": {
"toxicity_score": 0.020607091,
"severe_toxicity_score": 0.0013256073,
"identity_attack_score": 0.0026453468,
"insult_score": 0.00983089,
"profanity... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.82458474439606,
"hash": "003f3f1f878f3f0f",
"most_frequent_color_ratio": 93.82458474439606
}
} | [] | "algorithm" | "db47442e-db5f-4ce3-b041-b23a0196675a" | 1311 | hard | |
\begin{algorithm}
\caption{Terminal set construction via symbolic model}\label{safetygame}
\textbf{Input:} $\mathcal{X}$ (specification set), $\mathcal{D}_{N}$ (training dataset), $\eta_x, \eta_u, \varepsilon, \widetilde{\gamma}$ (some parameters for symbolic model), $\gamma_i$ for $i \in \mathbb{N}_{1: n_x}$ (a set of... | \begin{algorithm}
\caption{Terminal set construction via symbolic model}\textbf{Input:} $\mathcal{X}$ (specification set), $\mathcal{D}_{N}$ (training dataset), $\eta_x, \eta_u, \varepsilon, \widetilde{\gamma}$ (some parameters for symbolic model), $\gamma_i$ for $i \in \mathbb{N}_{1: n_x}$ (a set of scalars);
\textbf{... | "https://arxiv.org/src/2110.12214" | "2110.12214.tar.gz" | "2024-01-01" | {
"title": "learning-based event-triggered mpc with gaussian processes under terminal constraints",
"id": "2110.12214",
"abstract": "event-triggered control strategy is capable of significantly reducing the number of control task executions without sacrificing control performance. in this paper, we propos... | "2024-03-15T07:05:39.786368" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.008293601,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0026083488,
"insult_score": 0.0068003717,
"profani... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.70674214514631,
"hash": "1f1f0f1f3f010107",
"most_frequent_color_ratio": 92.70674214514631
}
} | [] | "algorithm" | "450937fb-9168-43f8-91b6-6a7f464cc6c9" | 2132 | hard | |
\begin{algorithm}[htbp] \label{ag:core}
\small
\caption{Training Process of RESTC}
\begin{algorithmic}[1]\label{ag:core}
\Require
Sessions $\mathbf{S}$, item embeddings $\mathbf{V_{s}}$
\Ensure
Top-k recommendation items
\State Transform session data into spatial and temporal view
\... | \begin{algorithm}[htbp]
\small
\caption{Training Process of RESTC}
\begin{algorithmic}[1]
\Require
Sessions $\mathbf{S}$, item embeddings $\mathbf{V_{s}}$
\Ensure
Top-k recommendation items
\State Transform session data into spatial and temporal view
\State Construct CFG overall sessions
\For{epoch in range(Epoches)}
\... | "https://arxiv.org/src/2209.11461" | "2209.11461.tar.gz" | "2024-02-17" | {
"title": "spatio-temporal contrastive learning enhanced gnns for session-based recommendation",
"id": "2209.11461",
"abstract": "session-based recommendation (sbr) systems aim to utilize the user's short-term behavior sequence to predict the next item without the detailed user profile. most recent works... | "2024-03-15T04:57:08.518692" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"ACM-Reference-Format.bst": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0016021729,
"identity_attack_score": 0.0023863618,
"insult_score": 0.00815888,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.92489779155078,
"hash": "070f87c0cfc1bf27",
"most_frequent_color_ratio": 88.92489779155078
}
} | [] | "algorithm" | "9911b2ea-f400-4613-936e-e4e5438295ee" | 1144 | medium | |
\begin{algorithmic}[1]
\For{$b=1:N_m$}
\State (TV-AGRU)
\State Fix $\nu_M$ given by FIX-OPTIM.
\For{$j=1:N_{\text{tv}}$}
\State Compute $\Theta^t_M = \{\alpha^t_M,\beta^t_M\} = \text{AGRU}(F_{<t};\theta_{\text{AGRU}})$.
\State Compute the NLL loss $L=\sum_{t=1}^S (-\ell_M^t)$ and its partial derivatives with re... | \begin{algorithmic}
[1]
\For{$b=1:N_m$}
\State (TV-AGRU)
\State Fix $\nu_M$ given by FIX-OPTIM.
\For{$j=1:N_{\text{tv}}$}
\State Compute $\Theta^t_M = \{\alpha^t_M,\beta^t_M\} = \text{AGRU}(F_{<t};\theta_{\text{AGRU}})$.
\State Compute the NLL loss $L=\sum_{t=1}^S (-\ell_M^t)$ and its partial derivatives with respect t... | "https://arxiv.org/src/2301.07318" | "2301.07318.tar.gz" | "2024-01-16" | {
"title": "dynamic cvar portfolio construction with attention-powered generative factor learning",
"id": "2301.07318",
"abstract": "the dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. to achieve this, we propose a dynamic generativ... | "2024-03-15T06:06:03.391456" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"elsarticle-harv.bst": {
"toxicity_score": 0.016838523,
"severe_toxicity_score": 0.0013065338,
"identity_attack_score": 0.004439743,
"insult_score": 0.0088618845,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.48617305976806,
"hash": "3f8f803f0f018f3f",
"most_frequent_color_ratio": 92.48617305976806
}
} | [] | "algorithm" | "f5b6538c-e04d-444e-b226-68bfba511f0d" | 927 | medium | |
\begin{algorithm}[H]
\caption{Our gradient estimation sampler}
\label{alg:second-order}
\begin{algorithmic}
\Require $(\sigma_N, \ldots, \sigma_0)$, $x_N \sim \mathcal{N}(0, I)$, $\epsilon_\theta$
\Ensure Compute $x_0$ with $N$ evaluations of $\epsilon_\theta$
\State $x_{N-... | \begin{algorithm}
[H]
\caption{Our gradient estimation sampler}
\begin{algorithmic}
\Require $(\sigma_N, \ldots, \sigma_0)$, $x_N \sim \mathcal{N}(0, I)$, $\epsilon_\theta$
\Ensure Compute $x_0$ with $N$ evaluations of $\epsilon_\theta$
\State $x_{N-1} \gets x_N + (\sigma_{N-1} - \sigma_N)\epsilon_\theta(x_N, \sigma_N)... | "https://arxiv.org/src/2306.04848" | "2306.04848.tar.gz" | "2024-02-13" | {
"title": "interpreting and improving diffusion models using the euclidean distance function",
"id": "2306.04848",
"abstract": "denoising is intuitively related to projection. indeed, under the manifold hypothesis, adding random noise is approximately equivalent to orthogonal perturbation. hence, learnin... | "2024-03-15T06:03:56.844522" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.43492463907516,
"hash": "2100002fd33f3f3f",
"most_frequent_color_ratio": 90.43492463907516
}
} | [] | "algorithm" | "f3c57f48-601e-4ee5-b119-8948e83f714c" | 588 | easy | |
\begin{algorithmic}[1]
\State \textbf{Input:} Each function has $W$ time points, then construct $Wp$-dimensional random vector ${f}$ ($W$: Full-time points) for $p$ functions, a set of its variable subscripts $U$ and a $Wp \times n$ data
matrix as $F$, initialize an ordered list of functions $K=\emptyset$ and $m:=1$;
... | \begin{algorithmic}
[1]
\State \textbf{Input:} Each function has $W$ time points, then construct $Wp$-dimensional random vector ${f}$ ($W$: Full-time points) for $p$ functions, a set of its variable subscripts $U$ and a $Wp \times n$ data
matrix as $F$, initialize an ordered list of functions $K=\emptyset$ and $m:=1$;
... | "https://arxiv.org/src/2401.09641" | "2401.09641.tar.gz" | "2024-01-17" | {
"title": "functional linear non-gaussian acyclic model for causal discovery",
"id": "2401.09641",
"abstract": "in causal discovery, non-gaussianity has been used to characterize the complete configuration of a linear non-gaussian acyclic model (lingam), encompassing both the causal ordering of variables a... | "2024-03-15T07:19:39.274028" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sample.tex": {
"toxicity_score": 0.01218908,
"severe_toxicity_score": 0.0011825562,
"identity_attack_score": 0.00414376,
"insult_score": 0.0076838774,
"profanit... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.37682325369897,
"hash": "87076f3c042f037f",
"most_frequent_color_ratio": 93.37682325369897
}
} | [] | "algorithm" | "c4695c9d-e7ff-4634-a8b4-f3e0281ce914" | 1613 | hard | |
\begin{algorithm}[H]
\caption{Expansion ranges}
\label{alg:expansionrange}\begin{algorithmic}[1]
\State Input:
\begin{enumerate}
\item[1] Difference equation system $\Sigma$ with algebraic or analytic transcendental functions.
\item[2] For each parameter $\mu_i$ appearing as an argument of a transcendental fu... | \begin{algorithm}
[H]
\caption{Expansion ranges}
\begin{algorithmic}
[1]
\State Input:
\begin{enumerate}
\item[1] Difference equation system $\Sigma$ with algebraic or analytic transcendental functions.
\item[2] For each parameter $\mu_i$ appearing as an argument of a transcendental function, user-specified acceptable ... | "https://arxiv.org/src/2401.16220" | "2401.16220.tar.gz" | "2024-01-29" | {
"title": "symbolic-numeric algorithm for parameter estimation in discrete-time models with $\\exp$",
"id": "2401.16220",
"abstract": "determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. discrete-time dynamic... | "2024-03-15T06:38:23.919697" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"arxiv-submission-Jan-29-2024.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.2402906931312,
"hash": "1fbe800f201fdf3f",
"most_frequent_color_ratio": 91.2402906931312
}
} | [] | "algorithm" | "adc38873-615d-489f-a7ba-a1c43e5cfe9c" | 1049 | medium | |
\begin{algorithm}[t]
\caption{Constructing a pseudo-image from a whole genome sample for a given k-mer size $k$.}\label{alg:cap}
\begin{algorithmic}
\Require Sequence reads $X_i = \{X_0, X_1, \ldots X_N\}, \forall X_i \in \mathcal{X}_i$
\Ensure Pseudo-image $I_r$
\Procedure{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\Sta... | \begin{algorithm}
[t]
\caption{Constructing a pseudo-image from a whole genome sample for a given k-mer size $k$.}\begin{algorithmic}
\Require Sequence reads $X_i = \{X_0, X_1, \ldots X_N\}, \forall X_i \in \mathcal{X}_i$
\Ensure Pseudo-image $I_r$
\Procedure{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\State $e_{i,j} \ge... | "https://arxiv.org/src/2401.13219" | "2401.13219.tar.gz" | "2024-01-23" | {
"title": "tepi: taxonomy-aware embedding and pseudo-imaging for scarcely-labeled zero-shot genome classification",
"id": "2401.13219",
"abstract": "a species' genetic code or genome encodes valuable evolutionary, biological, and phylogenetic information that aids in species recognition, taxonomic classi... | "2024-03-15T06:48:41.356948" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"arxiv.tex": {
"toxicity_score": 0.008293601,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0026083488,
"insult_score": 0.0068003717,
"profan... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.8674953616059,
"hash": "03383f1f9f3f381f",
"most_frequent_color_ratio": 92.8674953616059
}
} | [] | "algorithm" | "eeb16cae-98ff-4486-91cf-495a6c4ec7df" | 1483 | hard | |
\begin{algorithmic}[1]
\Statex \textbf{Input:}\texttt{ Set of sequences(S)}
\Statex \textbf{Output:}\texttt{ Distance Matrix(D)}
\For{\texttt{ $s_{1}$ in S\hspace{0.2cm}}}
\State \texttt{ $Es_{1} \gets encoded \hspace{0.2cm} s_{1}$}
\State \texttt{ $Cs_{1} \gets Gzip\hspace{0.2cm} compressed \hspace{0.2cm... | \begin{algorithmic}
[1]
\Statex \textbf{Input:}\texttt{ Set of sequences(S)}
\Statex \textbf{Output:}\texttt{ Distance Matrix(D)}
\For{\texttt{ $s_{1}$ in S\hspace{0.2cm}}}
\State \texttt{ $Es_{1} \gets encoded \hspace{0.2cm} s_{1}$}
\State \texttt{ $Cs_{1} \gets Gzip\hspace{0.2cm} compressed \hspace{0.2cm} Es_{1}$}
\S... | "https://arxiv.org/src/2402.08117" | "2402.08117.tar.gz" | "2024-02-12" | {
"title": "a universal non-parametric approach for improved molecular sequence analysis",
"id": "2402.08117",
"abstract": "in the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. the classification of molecular sequences has seen widesp... | "2024-03-15T04:40:32.096340" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"llncs.tex": {
"toxicity_score": 0.0032200534,
"severe_toxicity_score": 0.0004005432,
"identity_attack_score": 0.0009619443,
"insult_score": 0.005869366,
"profan... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.66365870479558,
"hash": "001f1f1f9f8f1f3f",
"most_frequent_color_ratio": 93.66365870479558
}
} | [] | "algorithm" | "232fef09-6702-461f-b049-5dffc6042b2c" | 1216 | hard | |
\begin{algorithmic}[1]
\Require
Training set $\mathcal{D}$, validation set size $M_{\mathrm{val}}$, learning rate $\zeta$, training iteration $T$, PGD function for finding adversarial validation data.
\Ensure An infinite-width adversarially robust DNN.
\State Randomly separate $\mathcal D$ into subsets $\mathcal D_... | \begin{algorithmic}
[1]
\Require
Training set $\mathcal{D}$, validation set size $M_{\mathrm{val}}$, learning rate $\zeta$, training iteration $T$, PGD function for finding adversarial validation data.
\Ensure An infinite-width adversarially robust DNN.
\State Randomly separate $\mathcal D$ into subsets $\mathcal D_{\m... | "https://arxiv.org/src/2310.06112" | "2310.06112.tar.gz" | "2024-02-04" | {
"title": "theoretical analysis of robust overfitting for wide dnns: an ntk approach",
"id": "2310.06112",
"abstract": "adversarial training (at) is a canonical method for enhancing the robustness of deep neural networks (dnns). however, recent studies empirically demonstrated that it suffers from robust... | "2024-03-15T07:33:05.157801" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.60280373831776,
"hash": "0103031f959a3f7f",
"most_frequent_color_ratio": 89.60280373831776
}
} | [] | "algorithm" | "e78d3b34-9831-449d-826f-0822071bca10" | 1040 | medium | |
\begin{algorithm}
\caption{Credal Bayesian Deep Learning (CBDL) -- Training and Inference}\label{algo-1}
\begin{algorithmic}
\item
\textit{During Training}
\item
\textbf{Step 1} Specify $K$ priors $\text{ex}\mathcal{P}_\text{prior}=\{P^\text{ex}_k\}_{k=1}^K$
\item
\textbf{Step 2} Specify $S$ likelihoods $\text{ex}... | \begin{algorithm}
\caption{Credal Bayesian Deep Learning (CBDL) -- Training and Inference}\begin{algorithmic}
\item
\textit{During Training}
\item
\textbf{Step 1} Specify $K$ priors $\text{ex}\mathcal{P}_\text{prior}=\{P^\text{ex}_k\}_{k=1}^K$
\item
\textbf{Step 2} Specify $S$ likelihoods $\text{ex}\mathcal{P}_\text{li... | "https://arxiv.org/src/2302.09656" | "2302.09656.tar.gz" | "2024-02-22" | {
"title": "credal bayesian deep learning",
"id": "2302.09656",
"abstract": "uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. although bayesian neural networks (bnns) allow for uncertainty in the predictions to be assessed,... | "2024-03-15T04:08:18.406723" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanit... | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.70760079043332,
"hash": "0007073f8f031f03",
"most_frequent_color_ratio": 88.70760079043332
}
} | [] | "algorithm" | "e2271d09-3594-4200-b0f8-d7d460ea5c65" | 1329 | hard | |
\begin{algorithm}
\caption{Training GANs-based auction features generator}
\begin{algorithmic}[1]
\State $D_{train} \gets$ Initialize training set
\While {$C(fake)>threshold$} \Comment{The critic can be optimized until $C(fake)$ is near $0$. }
\State Randomly select a discrete variable ... | \begin{algorithm}
\caption{Training GANs-based auction features generator}
\begin{algorithmic}
[1]
\State $D_{train} \gets$ Initialize training set
\While {$C(fake)>threshold$} \Comment{The critic can be optimized until $C(fake)$ is near $0$. }
\State Randomly select a discrete variable $c$ with equal probability
\Stat... | "https://arxiv.org/src/2207.12255" | "2207.12255.tar.gz" | "2024-02-15" | {
"title": "implementing a hierarchical deep learning approach for simulating multi-level auction data",
"id": "2207.12255",
"abstract": "we present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. the complexities encountered in thi... | "2024-03-15T03:57:59.120702" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"manuscript.bbl": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0010728836,
"identity_attack_score": 0.003311308,
"insult_score": 0.0073798755,
"pr... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.32547615623267,
"hash": "1f81809c94c0a83f",
"most_frequent_color_ratio": 90.32547615623267
}
} | [] | "algorithm" | "f989ce39-7f87-47e1-b412-145869315f0f" | 1537 | hard | |
\begin{algorithmic}[1]
\Require Bell sample $r \leftarrow P_\rho$.
\If{$\pi_Y(r) = 1$}
\State Declare an error and abort.
\EndIf
\Ensure $r$
\end{algorithmic}
| \begin{algorithmic}
[1]
\Require Bell sample $r \leftarrow P_\rho$.
\If{$\pi_Y(r) = 1$}
\State Declare an error and abort.
\EndIf
\Ensure $r$
\end{algorithmic} | "https://arxiv.org/src/2306.00083" | "2306.00083.tar.gz" | "2024-01-31" | {
"title": "bell sampling from quantum circuits",
"id": "2306.00083",
"abstract": "a central challenge in the verification of quantum computers is benchmarking their performance as a whole and demonstrating their computational capabilities. in this work, we find a universal model of quantum computation, bel... | "2024-03-15T06:26:35.245596" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"bu2.bbl": {
"toxicity_score": 0.06025757,
"severe_toxicity_score": 0.004005432,
"identity_attack_score": 0.0076955543,
"insult_score": 0.017163089,
"profanity_s... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.0557792992348,
"hash": "001f1f3f0f3f7f7f",
"most_frequent_color_ratio": 93.0557792992348
}
} | [] | "algorithm" | "64af60f6-fa98-471f-ad09-514444e64eaa" | 159 | easy | |
\begin{algorithm}[h!]
\caption{MIP-CDA.}
\label{al:mip_cda}
Constants: $\epsilon_{OBJ}=10^{-5}$, $MAXITER=20$\\
\begin{algorithmic}[1]
\State $\omega^{(0)}\leftarrow~\texttt{Relax}()$, $\text{OBJ}^{(0)}\leftarrow \infty$; \hfill (See Algorithm \ref{al:relax})
\State $(\omega^{(1)}, \gamma^{(1)}, \beta^{(1)}, \delta^{(... | \begin{algorithm}
[h!]
\caption{MIP-CDA.}
Constants: $\epsilon_{OBJ}=10^{-5}$, $MAXITER=20$\\
\begin{algorithmic}
[1]
\State $\omega^{(0)}\leftarrow~\texttt{Relax}()$, $\text{OBJ}^{(0)}\leftarrow \infty$; \hfill (See Algorithm \ref{al:relax})
\State $(\omega^{(1)}, \gamma^{(1)}, \beta^{(1)}, \delta^{(1)})\leftarrow~\te... | "https://arxiv.org/src/2206.06140" | "2206.06140.tar.gz" | "2024-01-13" | {
"title": "inference for change-plane regression",
"id": "2206.06140",
"abstract": "a key challenge in analyzing the behavior of change-plane estimators is that the objective function has multiple minimizers. two estimators are proposed to deal with this non-uniqueness. for each estimator, an n-rate of con... | "2024-03-15T06:15:14.775372" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.01237757,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.004180758,
"insult_score": 0.007607877,
"profanity_... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.89463350785341,
"hash": "0f8d0f3f80073c3f",
"most_frequent_color_ratio": 91.89463350785341
}
} | [] | "algorithm" | "de619293-f51b-435f-bbaf-47e448c7aaa3" | 1819 | hard | |
\begin{algorithmic}[1]
\State \textbf{Input}: matrix observations $\{\mathbf{X}_t\}_{t=1}^T$, factor numbers $k_1$ and $k_2$.
\State Estimate loading matrices by equations \eqref{estimator_R} and \eqref{estimator_C}.
\State Estimate factor matrices and the signal part by equations \eqref{factormatrix_RaDFaM} and \eqref... | \begin{algorithmic}
[1]
\State \textbf{Input}: matrix observations $\{\mathbf{X}_t\}_{t=1}^T$, factor numbers $k_1$ and $k_2$.
\State Estimate loading matrices by equations \eqref{estimator_R} and \eqref{estimator_C}.
\State Estimate factor matrices and the signal part by equations \eqref{factormatrix_RaDFaM} and \eqre... | "https://arxiv.org/src/2209.14846" | "2209.14846.tar.gz" | "2024-02-12" | {
"title": "modeling and learning on high-dimensional matrix-variate sequences",
"id": "2209.14846",
"abstract": "we propose a new matrix factor model, named radfam, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each b... | "2024-03-15T05:49:17.008547" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"radfam.out": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0019550323,
"identity_attack_score": 0.001812895,
"insult_score": 0.0069713728,
"profan... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 87.15488855116514,
"hash": "0103070003ffbf03",
"most_frequent_color_ratio": 87.15488855116514
}
} | [] | "algorithm" | "d853d5f8-1195-4891-871c-ae4f9fc20530" | 597 | easy | |
\begin{algorithmic}
\For{$t\in\{-1,\dots,-T^{traceback}\}$}\Comment{Retrieve the values of $P_{u,t}$}
\State $P_{u,t}\leftarrow\textrm{{\tt Power.GetValue}}(t)$
\EndFor
\For{$t\in\{-1,\dots,-T^{traceback}\}$}\Comment{Initial conditions on the state variables}
\If{$P_{u,t} > \textrm{{\tt MinimumPower.GetValue}}(t)$}
... | \begin{algorithmic}
\For{$t\in\{-1,\dots,-T^{traceback}\}$}\Comment{Retrieve the values of $P_{u,t}$}
\State $P_{u,t}\leftarrow\textrm{{\tt Power.GetValue}}(t)$
\EndFor
\For{$t\in\{-1,\dots,-T^{traceback}\}$}\Comment{Initial conditions on the state variables}
\If{$P_{u,t} > \textrm{{\tt MinimumPower.GetValue}}(t)$}
\St... | "https://arxiv.org/src/2402.12848" | "2402.12848.tar.gz" | "2024-02-20" | {
"title": "atlas: a model of short-term european electricity market processes under uncertainty",
"id": "2402.12848",
"abstract": "the atlas model simulates the various stages of the electricity market chain in europe, including the formulation of offers by different market actors, the coupling of europe... | "2024-03-15T03:29:36.738175" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Sections/OrderCreation.tex": {
"toxicity_score": 0.0062516155,
"severe_toxicity_score": 0.00064849854,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0062303683,
... | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.38618005222442,
"hash": "063f3f0f3f3f1f5b",
"most_frequent_color_ratio": 94.38618005222442
}
} | [] | "algorithm" | "56d94178-c64a-400a-8ad1-dc94af078bcd" | 1526 | hard | |
\begin{algorithm}[H]
\caption{Basic elastic formulation slice division process (negative balancing needs)}\label{alg:elastic_slice_division_neg}
\begin{algorithmic}
\State \textbf{\textit{Initialization of the set of time steps}}
\State $T_{div} \gets {t \in T_{m} \mkern9mu | \mkern9mu bn_{t} < ... | \begin{algorithm}
[H]
\caption{Basic elastic formulation slice division process (negative balancing needs)} \begin{algorithmic}
\State \textbf{\textit{Initialization of the set of time steps}}
\State $T_{div} \gets {t \in T_{m} \mkern9mu | \mkern9mu bn_{t} < 0}$\\
\State \textbf{\textit{Initialization of the first slic... | "https://arxiv.org/src/2402.12859" | "2402.12859.tar.gz" | "2024-02-20" | {
"title": "atlas: a model of short-term european electricity market processes under uncertainty -- balancing modules",
"id": "2402.12859",
"abstract": "the atlas model simulates the various stages of the electricity market chain in europe, including the formulation of offers by different market actors, t... | "2024-03-15T03:21:13.620616" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanit... | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.28531714775518,
"hash": "000f3f0fbf80bf3f",
"most_frequent_color_ratio": 94.28531714775518
}
} | [] | "algorithm" | "9a709d7b-4c26-44c4-9cab-8d4c9b748615" | 1115 | medium | |
\begin{algorithmic}
\State 1. Considering the dataset of all applicants to both programs, randomly split the dataset into train and test with equal probability at the applicant level, denote the resulting sets as $I^{train} = \left\{1,..,\mathcal{I}^{train}\right\}$ and $I^{test}= \left\{1,..,\mathcal{I}^{test}\right\}... | \begin{algorithmic}
\State 1. Considering the dataset of all applicants to both programs, randomly split the dataset into train and test with equal probability at the applicant level, denote the resulting sets as $I^{train} = \left\{1,..,\mathcal{I}^{train}\right\}$ and $I^{test}= \left\{1,..,\mathcal{I}^{test}\right\}... | "https://arxiv.org/src/2211.09968" | "2211.09968.tar.gz" | "2024-01-03" | {
"title": "effective and scalable programs to facilitate labor market transitions for women in technology",
"id": "2211.09968",
"abstract": "we describe the design, implementation, and evaluation of a low-cost (approximately $15 per person) and scalable program, called challenges, aimed at aiding women i... | "2024-03-15T06:43:58.018752" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"appendix.tex": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.001335144,
"identity_attack_score": 0.0032003147,
"insult_score": 0.007892879,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 86.27615965480044,
"hash": "006b00ffff006a7f",
"most_frequent_color_ratio": 86.27615965480044
}
} | [] | "algorithm" | "3b37cbbc-c141-49d2-9c5f-1c13033cb6a7" | 2692 | hard | |
\begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\State Set \(\sigma=0, J_{\text{prev}} = J(\mathbf{w},\sigma)\)
\While{\(k<k_{max}\)}
\State Compute gradient \(\nabla_\mathbf{w} J(\mathbf{w},\sigma)\)
\If{\(\left\|\nabla_\mathbf{w} J(\mathbf{w},\sigma) - \fr... | \begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\State Set \(\sigma=0, J_{\text{prev}} = J(\mathbf{w},\sigma)\)
\While{\(k<k_{max}\)}
\State Compute gradient \(\nabla_\mathbf{w} J(\mathbf{w},\sigma)\)
\If{\(\left\|\nabla_\mathbf{w} J(\mathbf{w},\sigma) - \frac{\nabla_\mathbf{w} J(\mathbf{w... | "https://arxiv.org/src/2207.06392" | "2207.06392.tar.gz" | "2024-01-25" | {
"title": "relationship design for socially-aware behavior in static games",
"id": "2207.06392",
"abstract": "autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. we present a new approach to model socially-aware de... | "2024-03-15T08:38:27.674079" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sections/3_preliminaries.tex": {
"toxicity_score": 0.007382561,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0020071338,
"insult_score": 0.0063918694,
... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.73401572466993,
"hash": "0707821f07bf9f3f",
"most_frequent_color_ratio": 92.73401572466993
}
} | [] | "algorithm" | "9176bd5b-eb5b-4137-a59e-c29b8db642a9" | 830 | medium | |
\begin{algorithm}[!h]
\caption{HKSL Learning Loop}\label{alg:hksl}
\begin{algorithmic}[1]
\Require trajectory $\tau$
\For{layer $i$ in HKSL (begin with the lowest layer)}
\For{layer $j$ in HKSL (begin with the highest layer)}
\If{$i == j$}
\State break loop
\EndIf
... | \begin{algorithm}
[!h]
\caption{HKSL Learning Loop}\begin{algorithmic}
[1]
\Require trajectory $\tau$
\For{layer $i$ in HKSL (begin with the lowest layer)}
\For{layer $j$ in HKSL (begin with the highest layer)}
\If{$i == j$}
\State break loop
\EndIf
\State Embed first observation $o$ in $\tau$ using layer $j$'s encoder... | "https://arxiv.org/src/2206.11396" | "2206.11396.tar.gz" | "2024-01-29" | {
"title": "multi-horizon representations with hierarchical forward models for reinforcement learning",
"id": "2206.11396",
"abstract": "learning control from pixels is difficult for reinforcement learning (rl) agents because representation learning and policy learning are intertwined. previous approaches... | "2024-03-15T08:37:32.877079" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.011560776,
"severe_toxicity_score": 0.0011253357,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0074178753,
"profani... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.66482670393972,
"hash": "039f8f8f8f9f801f",
"most_frequent_color_ratio": 91.66482670393972
}
} | [] | "algorithm" | "1eb15f76-7aa4-4b51-a942-c42ee02d3f72" | 1294 | hard | |
\begin{algorithmic}
\Require adjacency matrix $e(j,k)$, $j, k \in X$ and $o\in B$
\Ensure $p(d,j) \in \{0, 1\}$, $d = 1,\dots, n - 1$, $j \in X \setminus \{o\}$, satisfies (P)
\ForAll{$j \in X \setminus \{o\}$}
\If{$e(o,j)=1$}
\State $p(1,j) \gets 1$
\Else
\State $p(1,j) \gets 0$
\EndIf
\End... | \begin{algorithmic}
\Require adjacency matrix $e(j,k)$, $j, k \in X$ and $o\in B$
\Ensure $p(d,j) \in \{0, 1\}$, $d = 1,\dots, n - 1$, $j \in X \setminus \{o\}$, satisfies (P)
\ForAll{$j \in X \setminus \{o\}$}
\If{$e(o,j)=1$}
\State $p(1,j) \gets 1$
\Else
\State $p(1,j) \gets 0$
\EndIf
\EndFor
\For{$d = 2,\dots,n - 1$... | "https://arxiv.org/src/2306.05253" | "2306.05253.tar.gz" | "2024-02-12" | {
"title": "quantum computing algorithms for inverse problems on graphs and an np-complete inverse problem",
"id": "2306.05253",
"abstract": "we consider an inverse problem for a finite graph $(x,e)$ where we are given a subset of vertices $b\\subset x$ and the distances $d_{(x,e)}(b_1,b_2)$ of all vertic... | "2024-03-15T05:16:33.731462" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.01608456,
"severe_toxicity_score": 0.0018119812,
"identity_attack_score": 0.005660672,
"insult_score": 0.008329881,
"profanity_... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 67.97341204952325,
"hash": "3c3cbde7e7fd383c",
"most_frequent_color_ratio": 67.97341204952325
}
} | [] | "algorithm" | "5ee302ce-de10-47e1-910c-e2887f6fbafd" | 663 | easy | |
\begin{algorithm}
\caption{Definite Not Defectives (DND) \cite{kautz1964nonrandom,chen2008survey, chan2011non,aldridge2014group}}\label{alg:cap_coma}
\hspace*{\algorithmicindent} \textbf{Input: $\mathbf{X}, Y$} \\
\hspace*{\algorithmicindent} \textbf{Output: $\mathcal{P}^{(DND)}$}
\begin{algorithmic}[1]
\State... | \begin{algorithm}
\caption{Definite Not Defectives (DND) \cite{kautz1964nonrandom,chen2008survey, chan2011non,aldridge2014group}}\hspace*{\algorithmicindent} \textbf{Input: $\mathbf{X}, Y$} \\
\hspace*{\algorithmicindent} \textbf{Output: $\mathcal{P}^{(DND)}$}
\begin{algorithmic}
[1]
\State $\mathcal{P}^{(DND)} \gets \... | "https://arxiv.org/src/2402.10018" | "2402.10018.tar.gz" | "2024-02-15" | {
"title": "multi-stage algorithm for group testing with prior statistics",
"id": "2402.10018",
"abstract": "in this paper, we propose an efficient multi-stage algorithm for non-adaptive group testing (gt) with general correlated prior statistics. the proposed solution can be applied to any correlated stati... | "2024-03-15T04:32:28.811580" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"GT_with_prior_statistics_for_ISIT.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.00699987... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.56252899463186,
"hash": "003f3f3f073f3f3f",
"most_frequent_color_ratio": 93.56252899463186
}
} | [] | "algorithm" | "b0e9e600-a456-4cdf-ad05-aa1d68e0d505" | 563 | easy | |
\begin{algorithmic}[1]
\State{\textbf{Input:} set of arms $\mathcal{I}$, horizon length $n_{Q}$, $P$-data $\mathcal{D}^{P}$.}
\For{$s \in \mathcal{S}$}
\State{Initialize the policy $\widetilde{\pi}(s)$ by Procedure~\ref{alg:EA-TL-tabular}$\big(s,\mathcal{I},\mathcal{D}^{P} \big)$.}
\State{Initialize $N(s)\gets0$.}
... | \begin{algorithmic}
[1]
\State{\textbf{Input:} set of arms $\mathcal{I}$, horizon length $n_{Q}$, $P$-data $\mathcal{D}^{P}$.}
\For{$s \in \mathcal{S}$}
\State{Initialize the policy $\widetilde{\pi}(s)$ by Procedure~\ref{alg:EA-TL-tabular}$\big(s,\mathcal{I},\mathcal{D}^{P} \big)$.}
\State{Initialize $N(s)\gets0$.}
\Co... | "https://arxiv.org/src/2211.12612" | "2211.12612.tar.gz" | "2024-01-24" | {
"title": "transfer learning for contextual multi-armed bandits",
"id": "2211.12612",
"abstract": "motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on ... | "2024-03-15T05:41:18.863431" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"notation.tex": {
"toxicity_score": 0.011623607,
"severe_toxicity_score": 0.0011873245,
"identity_attack_score": 0.0037182847,
"insult_score": 0.007607877,
"prof... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.62309024556194,
"hash": "071c3f83843cbf3f",
"most_frequent_color_ratio": 92.62309024556194
}
} | [] | "algorithm" | "610bd165-ccf4-412b-99b3-38a858cb7a63" | 748 | medium | |
\begin{algorithmic}[1]
\State Motifs = $\emptyset$
\State N = length($\textbf{T}$[0])
\Comment{Number of time series samples}
\State m = length($\textbf{T}$[0][0])
\Comment{Length of time series}
\For{$\textbf{T}_i$ $\leftarrow$ $\textbf{T}_1$ to $\textbf{T}_N$}
\State Motifs $\gets$ $\emptyset$
\For{l in [0.3... | \begin{algorithmic}
[1]
\State Motifs = $\emptyset$
\State N = length($\textbf{T}$[0])
\Comment{Number of time series samples}
\State m = length($\textbf{T}$[0][0])
\Comment{Length of time series}
\For{$\textbf{T}_i$ $\leftarrow$ $\textbf{T}_1$ to $\textbf{T}_N$}
\State Motifs $\gets$ $\emptyset$
\For{l in [0.3m, 0.5m,... | "https://arxiv.org/src/2211.04411" | "2211.04411.tar.gz" | "2024-02-01" | {
"title": "motif-guided time series counterfactual explanations",
"id": "2211.04411",
"abstract": "with the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. to improve th... | "2024-03-15T08:04:45.521940" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"mybib.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profani... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.91747693841936,
"hash": "033f3f8f87833f01",
"most_frequent_color_ratio": 90.91747693841936
}
} | [] | "algorithm" | "cc1709f0-b7c4-4162-a6e1-e0c3597f3e9e" | 905 | medium | |
\begin{algorithm}[H]
\caption{Approximate Minimal Sub-Cover - 2}
\label{alg:AMSCr}
\begin{algorithmic}[1]
\State Sort the elements of $C_i$ by order of decreasing radius.
\ForAll{$(x_{i_j},r_{i_j}) \in C_i$}
\If{there does not exist $(x_{i_k}, r_{i_k}) \in C_i^*$ that covers $(x_{i_j}, r_{i_j})$}
\St... | \begin{algorithm}
[H]
\caption{Approximate Minimal Sub-Cover - 2}
\begin{algorithmic}
[1]
\State Sort the elements of $C_i$ by order of decreasing radius.
\ForAll{$(x_{i_j},r_{i_j}) \in C_i$}
\If{there does not exist $(x_{i_k}, r_{i_k}) \in C_i^*$ that covers $(x_{i_j}, r_{i_j})$}
\State Add $(x_{i_j}, r_{i_j})$ to $C_... | "https://arxiv.org/src/2301.09734" | "2301.09734.tar.gz" | "2024-02-08" | {
"title": "topological learning in multi-class data sets",
"id": "2301.09734",
"abstract": "we specialize techniques from topological data analysis to the problem of characterizing the topological complexity (as defined in the body of the paper) of a multi-class data set. as a by-product, a topological cla... | "2024-03-15T07:16:13.009881" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"TopologyPaper.bib": {
"toxicity_score": 0.015707577,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0058826595,
"insult_score": 0.008975885,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.67934565487079,
"hash": "073f3f07800f8f3f",
"most_frequent_color_ratio": 89.67934565487079
}
} | [] | "algorithm" | "3ffecae1-9530-42f6-8fba-3bffa79c2654" | 374 | easy | |
\begin{algorithm}\label{mainalgorithm} (See Algorithm 5.1 of \cite{Potra})
Given $\beta_1 < \beta_2$ with $\beta_2^2/(2(1-\beta_2)^2) \leq
\beta_1 < \beta_2 < \beta_2/(1-\beta_2) < 1$. Choose $(X_0,y_0,Y_0,
\tau_0,\kappa_0) \in \mathcal{N}(\beta_1, \mu_0)$ with $(n+1) \mu_0
= {\rm{Tr}}(X_0 Y_0) + \tau_0 \kappa_0$. For... | \begin{algorithm}
(See Algorithm 5.1 of \cite{Potra})
Given $\beta_1 < \beta_2$ with $\beta_2^2/(2(1-\beta_2)^2) \leq
\beta_1 < \beta_2 < \beta_2/(1-\beta_2) < 1$. Choose $(X_0,y_0,Y_0,
\tau_0,\kappa_0) \in \mathcal{N}(\beta_1, \mu_0)$ with $(n+1) \mu_0
= {\rm{Tr}}(X_0 Y_0) + \tau_0 \kappa_0$. For $k = 0, 1, \ldots$,
{... | "https://arxiv.org/src/2211.08215" | "2211.08215.tar.gz" | "2024-01-12" | {
"title": "superlinear convergence of an interior point algorithm on linear semi-definite feasibility problems with application to linear matrix inequalities",
"id": "2211.08215",
"abstract": "in the literature, besides the assumption of strict complementarity, superlinear convergence of implementable ... | "2024-03-15T06:24:29.659435" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Reference_Sim.bib": {
"toxicity_score": 0.012503231,
"severe_toxicity_score": 0.0015163422,
"identity_attack_score": 0.0015169121,
"insult_score": 0.0081208795,
... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.05148933594356,
"hash": "03ef0101ff9fefef",
"most_frequent_color_ratio": 95.05148933594356
}
} | [] | "algorithm" | "d6e75587-aa76-4145-855f-04f2a7573dc6" | 3604 | hard | |
\begin{algorithm}
\caption{ESTIMATE on a single interval $I_m$, i.e. time-stepping based}
\begin{algorithmic}
\Require $\hat{u}_{kh}$ on $I_{m-1}$ and $I_m$ and $\hat{z}_{kh}$ on $I_{m-1}$, $I_m$ and $I_{m+1}$
\State interpolate/reconstruct $\tilde{u}$, $u_k$, $u_{kh}$ as well as
$\tilde{z}$, $z_k$, $z_{kh}$ at quadra... | \begin{algorithm}
\caption{ESTIMATE on a single interval $I_m$, i.e. time-stepping based}
\begin{algorithmic}
\Require $\hat{u}_{kh}$ on $I_{m-1}$ and $I_m$ and $\hat{z}_{kh}$ on $I_{m-1}$, $I_m$ and $I_{m+1}$
\State interpolate/reconstruct $\tilde{u}$, $u_k$, $u_{kh}$ as well as
$\tilde{z}$, $z_k$, $z_{kh}$ at quadrat... | "https://arxiv.org/src/2207.04764" | "2207.04764.tar.gz" | "2024-02-04" | {
"title": "numerical modeling and open-source implementation of variational partition-of-unity localizations of space-time dual-weighted residual estimators for parabolic problems",
"id": "2207.04764",
"abstract": "in this work, we consider space-time goal-oriented a posteriori error estimation for par... | "2024-03-15T04:49:36.236742" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.fdb_latexmk": {
"toxicity_score": 0.009173225,
"severe_toxicity_score": 0.0010585785,
"identity_attack_score": 0.0026453468,
"insult_score": 0.007474876,
"... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 85.58447442609747,
"hash": "00092f142707ff7f",
"most_frequent_color_ratio": 85.58447442609747
}
} | [] | "algorithm" | "2db594cd-07bf-4cb3-9bdb-45c1106c895a" | 612 | easy | |
\begin{algorithm}
\caption{Bernoulli factory for continuous $f : [0, 1] \mapsto [0, 1]$ satisfying \eqref{polynomially_bounded}.}
\label{alg_1}
\begin{algorithmic}[1]
\Require Sequences $\{ f_k \}_{k \geq 1}$, $\{ \eta( f, k ) \}_{k \geq 1}$.
\State Sample $L \sim \operatorname{Geo}(1 / 4)$.
\State Sample $X_{\eta( f,... | \begin{algorithm}
\caption{Bernoulli factory for continuous $f : [0, 1] \mapsto [0, 1]$ satisfying \eqref{polynomially_bounded}.}
\begin{algorithmic}
[1]
\Require Sequences $\{ f_k \}_{k \geq 1}$, $\{ \eta( f, k ) \}_{k \geq 1}$.
\State Sample $L \sim \operatorname{Geo}(1 / 4)$.
\State Sample $X_{\eta( f, L ) }( p ) \s... | "https://arxiv.org/src/2306.03539" | "2306.03539.tar.gz" | "2024-02-01" | {
"title": "bernoulli factories and duality in wright-fisher and allen-cahn models of population genetics",
"id": "2306.03539",
"abstract": "mathematical models of genetic evolution often come in pairs, connected by a so-called duality relation. the most seminal example are the wright-fisher diffusion and... | "2024-03-15T05:56:16.398949" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"wfb-diffusion_Google.tex": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010824203,
"identity_attack_score": 0.0035517942,
"insult_score": 0.007322875,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.32758476489971,
"hash": "000f1f0f3f7f3f3f",
"most_frequent_color_ratio": 92.32758476489971
}
} | [] | "algorithm" | "e3e1d65e-83c7-4b98-ade5-fcdf139940ad" | 494 | easy | |
\begin{algorithmic}
\State $e_1$ = Minimized energy wrt vertex positions
\State Swap cells i and j
\State $e_2$ = Minimized energy wrt vertex positions
\If{$e_2 < e_1$}
\State Accept this swap move
\Else
\State Reject this swap move
\EndIf
\end{algorithmic}
| \begin{algorithmic}
\State $e_1$ = Minimized energy wrt vertex positions
\State Swap cells i and j
\State $e_2$ = Minimized energy wrt vertex positions
\If{$e_2 < e_1$}
\State Accept this swap move
\Else
\State Reject this swap move
\EndIf
\end{algorithmic} | "https://arxiv.org/src/2312.11683" | "2312.11683.tar.gz" | "2024-01-07" | {
"title": "tuning for fluidity using fluctuations in biological tissue models",
"id": "2312.11683",
"abstract": "how do biological systems tune emergent properties at the scale of tissues? one class of such emergent behaviors, important to biological functions such as body-axis elongation, involves rigidit... | "2024-03-15T07:49:18.144761" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.01633588,
"severe_toxicity_score": 0.0014877319,
"identity_attack_score": 0.0029968263,
"insult_score": 0.008272881,
"profanity... | {
"num_done": {
"figure": 0,
"algorithm": 1,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.1006786486688,
"hash": "001f001f877f073f",
"most_frequent_color_ratio": 90.1006786486688
}
} | [] | "algorithm" | "5a73de89-9917-4d1c-8f2b-836f173ac6c8" | 257 | easy | |
\label{alg:UCB-TL} \begin{algorithmic}[1]
\State{\textbf{Input:} arm set $\mathcal{I}$, horizon length
$n_{Q}$, smoothness parameters $\beta, C_{\beta}$, transfer parameters $\gamma$, exploration coefficient $\kappa$, $P$-data $\mathcal{D}^{P}$.}
\State{Initialize $\mathcal{L}_{1}\gets\{\mathcal{X}\}$, $\mathcal{I}(\ma... | \begin{algorithmic}
[1]
\State{\textbf{Input:} arm set $\mathcal{I}$, horizon length
$n_{Q}$, smoothness parameters $\beta, C_{\beta}$, transfer parameters $\gamma$, exploration coefficient $\kappa$, $P$-data $\mathcal{D}^{P}$.}
\State{Initialize $\mathcal{L}_{1}\gets\{\mathcal{X}\}$, $\mathcal{I}(\mathcal{X})\gets\mat... | "https://arxiv.org/src/2211.12612" | "2211.12612.tar.gz" | "2024-01-24" | {
"title": "transfer learning for contextual multi-armed bandits",
"id": "2211.12612",
"abstract": "motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on ... | "2024-03-15T05:41:18.863431" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"notation.tex": {
"toxicity_score": 0.011623607,
"severe_toxicity_score": 0.0011873245,
"identity_attack_score": 0.0037182847,
"insult_score": 0.007607877,
"prof... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.62309024556194,
"hash": "071c3f83843cbf3f",
"most_frequent_color_ratio": 92.62309024556194
}
} | [] | "algorithm" | "30c4349b-761b-4614-b9c0-1247edaaaf90" | 3082 | hard | |
\begin{algorithmic}[1]
\State {\bfseries } Learned topics $T$ as references
\State {\bfseries }\textbf{for} i = 1,2,$\cdots$, N \textbf{do}
\State {\bfseries } \quad Automatically transcribe dialogue turn pairs $(S^p_i,S^t_i)$
\State {\bfseries }\quad \textbf{for} $T_j \in$ topics $T$ \textbf{do}
\State {\bfseries... | \begin{algorithmic}
[1]
\State {\bfseries } Learned topics $T$ as references
\State {\bfseries }\textbf{for} i = 1,2,$\cdots$, N \textbf{do}
\State {\bfseries } \quad Automatically transcribe dialogue turn pairs $(S^p_i,S^t_i)$
\State {\bfseries }\quad \textbf{for} $T_j \in$ topics $T$ \textbf{do}
\State {\bfseries }\q... | "https://arxiv.org/src/2402.14701" | "2402.14701.tar.gz" | "2024-02-22" | {
"title": "compass: computational mapping of patient-therapist alliance strategies with language modeling",
"id": "2402.14701",
"abstract": "the therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment. traditionally, working alliance assessment relies on que... | "2024-03-15T03:37:09.328472" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanit... | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.50578064502115,
"hash": "075f800f8080bf3f",
"most_frequent_color_ratio": 89.50578064502115
}
} | [] | "algorithm" | "53dbce17-15be-4982-b262-83e99c02bec4" | 588 | easy | |
\begin{algorithmic}[1]
\Require
\Statex$\tau$: Scale of coordinate noise
\Statex$GNN_{\theta}$: Graph Neural Network with parameter $\theta$
\Statex ${\rm NoiseHead}_{\theta_{n}}$: Network module with parameter $\theta_{n}$ for prediction of node-level noise of each atom
\Statex ${\rm LabelHead}_{\theta_{l}}$: Network ... | \begin{algorithmic}
[1]
\Require
\Statex$\tau$: Scale of coordinate noise
\Statex$GNN_{\theta}$: Graph Neural Network with parameter $\theta$
\Statex ${\rm NoiseHead}_{\theta_{n}}$: Network module with parameter $\theta_{n}$ for prediction of node-level noise of each atom
\Statex ${\rm LabelHead}_{\theta_{l}}$: Network... | "https://arxiv.org/src/2307.10683" | "2307.10683.tar.gz" | "2024-02-26" | {
"title": "fractional denoising for 3d molecular pre-training",
"id": "2307.10683",
"abstract": "coordinate denoising is a promising 3d molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. theoretically, the objective is equivalent to learning... | "2024-03-15T02:31:31.211598" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bib": {
"toxicity_score": 0.0060317097,
"severe_toxicity_score": 0.00049829483,
"identity_attack_score": 0.0013781702,
"insult_score": 0.0068003717,
"profa... | {
"num_done": {
"equation": 3,
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.80857252484745,
"hash": "0f033f3f0f1d8f3f",
"most_frequent_color_ratio": 91.80857252484745
}
} | [] | "algorithm" | "7e1c230e-b2f9-46cb-9d37-e0e427b71cca" | 1287 | hard | |
\begin{algorithmic}[1]
\Statex Input: The underlying linear dynamical systems matrices $\tilde{B}$ and $\tilde{B'}$ and the correlations between each of the coordinates (corresponding to genes) and the two phenotypes of interest.
\Statex Output: A set of pathways of a given length $L$ that are prominently different bet... | \begin{algorithmic}
[1]
\Statex Input: The underlying linear dynamical systems matrices $\tilde{B}$ and $\tilde{B'}$ and the correlations between each of the coordinates (corresponding to genes) and the two phenotypes of interest.
\Statex Output: A set of pathways of a given length $L$ that are prominently different be... | "https://arxiv.org/src/2401.11858" | "2401.11858.tar.gz" | "2024-01-22" | {
"title": "approximating a linear dynamical system from non-sequential data",
"id": "2401.11858",
"abstract": "given non-sequential snapshots from instances of a dynamical system, we design a compressed sensing based algorithm that reconstructs the dynamical system. we formally prove that successful recons... | "2024-03-15T07:04:04.342410" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"recomb_pathways.ipynb": {
"toxicity_score": 0.026145924,
"severe_toxicity_score": 0.0021266937,
"identity_attack_score": 0.00828752,
"insult_score": 0.011996903,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.1675392670157,
"hash": "00801f1f001f3f3f",
"most_frequent_color_ratio": 88.1675392670157
}
} | [] | "algorithm" | "6712150d-8079-4798-b74b-d08a5d1ff345" | 896 | medium | |
\begin{algorithm}
\caption{Anchored MFL-DA}\label{alg:cap}
\begin{algorithmic}
\For{$\ell =0,\cdots,T_\text{out}-1$}
\For{$k=0,\cdots,T_\text{in}-1$} (concurrently for all $s\in\mathcal{S}$)
\State $\theta_{k+1}^{(i)} \gets (1-\eta\lambda)\theta_k^{(i)}-2\eta \nabla_{\theta,k}^{(i)}+\eta\widetilde{\nabla}_\theta^{(i)} ... | \begin{algorithm}
\caption{Anchored MFL-DA}\begin{algorithmic}
\For{$\ell =0,\cdots,T_\text{out}-1$}
\For{$k=0,\cdots,T_\text{in}-1$} (concurrently for all $s\in\mathcal{S}$)
\State $\theta_{k+1}^{(i)} \gets (1-\eta\lambda)\theta_k^{(i)}-2\eta \nabla_{\theta,k}^{(i)}+\eta\widetilde{\nabla}_\theta^{(i)} +\sqrt{2\lambda\... | "https://arxiv.org/src/2312.01127" | "2312.01127.tar.gz" | "2024-02-16" | {
"title": "symmetric mean-field langevin dynamics for distributional minimax problems",
"id": "2312.01127",
"abstract": "in this paper, we extend mean-field langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates. we propo... | "2024-03-15T05:16:00.811582" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Misc/rebuttal.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.06504280033691,
"hash": "0780808fbf3f7f3f",
"most_frequent_color_ratio": 92.06504280033691
}
} | [] | "algorithm" | "8cf050b7-602d-453a-9cf8-4c7c2439fd12" | 1120 | medium | |
\begin{algorithm}[htb]
\caption{Forward propagation for each convolutional 3D layer. $M_w$, $\Sigma_w$ are the means and variances of each layer, $H$ is the input layer, and NF(·) is the masked
RealNVP normalising flow applied over samples initially drawn from a Gaussian distribution $q$. $D_f$ is the number of filters... | \begin{algorithm}
[htb]
\caption{Forward propagation for each convolutional 3D layer. $M_w$, $\Sigma_w$ are the means and variances of each layer, $H$ is the input layer, and NF(·) is the masked
RealNVP normalising flow applied over samples initially drawn from a Gaussian distribution $q$. $D_f$ is the number of filter... | "https://arxiv.org/src/2309.00612" | "2309.00612.tar.gz" | "2024-02-12" | {
"title": "bayesian deep learning for cosmic volumes with modified gravity",
"id": "2309.00612",
"abstract": "the new generation of galaxy surveys will provide unprecedented data allowing us to test gravity at cosmological scales. a robust cosmological analysis of the large-scale structure demands exploiti... | "2024-03-15T06:33:59.935340" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"aa.cls": {
"toxicity_score": 0.01225191,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.0027748393,
"insult_score": 0.007892879,
"profanity_s... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.49287588031044,
"hash": "0000030f7f3f7f07",
"most_frequent_color_ratio": 89.49287588031044
}
} | [] | "algorithm" | "cfe5e46c-8548-48bf-a2f1-e941bdbb3275" | 916 | medium | |
\begin{algorithmic}
\For{$\ell =0,\cdots,T_\text{out}-1$}
\For{$k=0,\cdots,T_\text{in}-1$} (concurrently for all $s\in\mathcal{S}$)
\State $\theta_{k+1}^{(i)} \gets (1-\eta\lambda)\theta_k^{(i)}-2\eta \nabla_{\theta,k}^{(i)}+\eta\widetilde{\nabla}_\theta^{(i)} +\sqrt{2\lambda\eta} \cdot\omega_k^{(i)}$, $\omega_k^{(i)}\... | \begin{algorithmic}
\For{$\ell =0,\cdots,T_\text{out}-1$}
\For{$k=0,\cdots,T_\text{in}-1$} (concurrently for all $s\in\mathcal{S}$)
\State $\theta_{k+1}^{(i)} \gets (1-\eta\lambda)\theta_k^{(i)}-2\eta \nabla_{\theta,k}^{(i)}+\eta\widetilde{\nabla}_\theta^{(i)} +\sqrt{2\lambda\eta} \cdot\omega_k^{(i)}$, $\omega_k^{(i)}\... | "https://arxiv.org/src/2312.01127" | "2312.01127.tar.gz" | "2024-02-16" | {
"title": "symmetric mean-field langevin dynamics for distributional minimax problems",
"id": "2312.01127",
"abstract": "in this paper, we extend mean-field langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates. we propo... | "2024-03-15T05:16:00.811582" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Misc/rebuttal.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.06504280033691,
"hash": "0780808fbf3f7f3f",
"most_frequent_color_ratio": 92.06504280033691
}
} | [] | "algorithm" | "e09a4ad9-8746-4e8c-a653-07fa6e89c508" | 1061 | medium | |
\begin{algorithmic}
\State Pick uniformly at random a permutation $\pi$ over $[n]$.
\State For each $i \in [n]$ sample the $x_{i \pi(i)}$-coin. If any sample is $0$, restart.
\State Pick uniformly at random a spanning tree of the complete graph $K_n$.
\State Let $T$ be the set of edges $(i,j)$ of the tree oriented t... | \begin{algorithmic}
\State Pick uniformly at random a permutation $\pi$ over $[n]$.
\State For each $i \in [n]$ sample the $x_{i \pi(i)}$-coin. If any sample is $0$, restart.
\State Pick uniformly at random a spanning tree of the complete graph $K_n$.
\State Let $T$ be the set of edges $(i,j)$ of the tree oriented towa... | "https://arxiv.org/src/2011.03865" | "2011.03865.tar.gz" | "2024-02-19" | {
"title": "combinatorial bernoulli factories",
"id": "2011.03865",
"abstract": "a bernoulli factory is an algorithmic procedure for exact sampling of certain random variables having only bernoulli access to their parameters. bernoulli access to a parameter $p \\in [0,1]$ means the algorithm does not know $... | "2024-03-15T04:22:53.719484" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"tex_journal/appendix_impossibility_journal.tex": {
"toxicity_score": 0.032863233,
"severe_toxicity_score": 0.0017929077,
"identity_attack_score": 0.010729378,
"insult_score": 0... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.44030004110152,
"hash": "fff7e6e666666666",
"most_frequent_color_ratio": 95.44030004110152
}
} | [] | "algorithm" | "ca7c0927-7aae-4fcd-809f-9f008b25aa4d" | 508 | easy | |
\begin{algorithmic}[1]
\Procedure{CSP}{$m$, $n$, $D$, $B$, $F$}
\Comment{$D$: data register of size $m - n$}
\Comment{$B$: buffer register of size $\frac{N}{M} - 1$}
\Comment{$F$: flag register of size $\frac{N}{M} - 1$}
\For{$k$ \textbf{in} range($2^m$)}
\For{$s$... | \begin{algorithmic}
[1]
\Procedure{CSP}{$m$, $n$, $D$, $B$, $F$}
\Comment{$D$: data register of size $m - n$}
\Comment{$B$: buffer register of size $\frac{N}{M} - 1$}
\Comment{$F$: flag register of size $\frac{N}{M} - 1$}
\For{$k$ \textbf{in} range($2^m$)}
\For{$s$ \textbf{in} range($n - m$) \textbf{and} $p$ \textbf{in... | "https://arxiv.org/src/2303.02131" | "2303.02131.tar.gz" | "2024-02-09" | {
"title": "spacetime-efficient low-depth quantum state preparation with applications",
"id": "2303.02131",
"abstract": "we propose a novel deterministic method for preparing arbitrary quantum states. when our protocol is compiled into cnot and arbitrary single-qubit gates, it prepares an $n$-dimensional ... | "2024-03-15T03:55:47.804290" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"quantum_template.tex": {
"toxicity_score": 0.0071626552,
"severe_toxicity_score": 0.0007247925,
"identity_attack_score": 0.0022753682,
"insult_score": 0.00649637,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.09071459135643,
"hash": "f899870f033f073f",
"most_frequent_color_ratio": 92.09071459135643
}
} | [] | "algorithm" | "e6f610a7-349d-4692-b6d3-757b2d3fa6ff" | 1178 | hard | |
\begin{algorithm}[ht]
\caption{Existence of a connected strongly-proportional allocation for $n$ agents.}
\label{alg:general}
\begin{algorithmic}[1]
\For{each permutation $\sigma : [n] \to [n]$}
\State \algorithmicif \ $\textsc{Mark}_{\sigma}(0, 1/n) < 1$ \algorithmicthen \ \Return true
\EndFor
... | \begin{algorithm}
[ht]
\caption{Existence of a connected strongly-proportional allocation for $n$ agents.}
\begin{algorithmic}
[1]
\For{each permutation $\sigma : [n] \to [n]$}
\State \algorithmicif \ $\textsc{Mark}_{\sigma}(0, 1/n) < 1$ \algorithmicthen \ \Return true
\EndFor
\State \Return false
\end{algorithmic}
\en... | "https://arxiv.org/src/2312.15326" | "2312.15326.tar.gz" | "2024-02-13" | {
"title": "on connected strongly-proportional cake-cutting",
"id": "2312.15326",
"abstract": "we investigate the problem of fairly dividing a divisible heterogeneous resource, also known as a cake, among a set of agents. we characterize the existence of an allocation in which every agent receives a contigu... | "2024-03-15T04:11:23.042663" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanit... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.90757715919604,
"hash": "1f9c1f841f3f7f3f",
"most_frequent_color_ratio": 93.90757715919604
}
} | [] | "algorithm" | "ed521259-70a8-411f-93b9-d08074c75150" | 332 | easy | |
\begin{algorithmic}[1]
\Require $\theta_0 \in \mathbb{R}^n, m \in (0, \infty)$
\State $z_0 = \theta_0, B_0 = 0, A_0 = 1/m$
\For{$t = 0,...$}
\State $B_{t+1} = B_t + .5(1+\sqrt{4B_t+1})$
\State $A_{t+1} = B_{t+1} + \frac{1}{m}$
\State $y_t = \theta_t + (1-... | \begin{algorithmic}
[1]
\Require $\theta_0 \in \mathbb{R}^n, m \in (0, \infty)$
\State $z_0 = \theta_0, B_0 = 0, A_0 = 1/m$
\For{$t = 0,...$}
\State $B_{t+1} = B_t + .5(1+\sqrt{4B_t+1})$
\State $A_{t+1} = B_{t+1} + \frac{1}{m}$
\State $y_t = \theta_t + (1-\frac{A_t}{A_{t+1}})(z_t-\theta_t)$
\State $\theta_{t+1} = y_t -... | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in dat... | "2024-03-15T05:06:27.333458" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"table/novel-step-size-param-table.tex": {
"toxicity_score": 0.011309455,
"severe_toxicity_score": 0.0012969971,
"identity_attack_score": 0.0034592997,
"insult_score": 0.0074558... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 98.56671985827441,
"hash": "3fbdb9bbb3af8880",
"most_frequent_color_ratio": 98.56671985827441
}
} | [] | "algorithm" | "04c9daad-b238-4624-967b-f511e1706777" | 413 | easy | |
\begin{algorithm}
\begin{algorithmic}
\Require{($\hat{x}_k,\hat{u}_k\,\hat{Q}_k,\hat{K}_k$)}
\For{$i=1\ldots N_{max}$}
\State{optimize $\bar{x}_k,\bar{u}_k$ by \eqref{eq:traj_update}}
\State{estimate $\gamma_k$ via \eqref{eq:gamma_update} or \eqref{eq:approximate_outer_optimization}}
\State{optimize $Q_k,K_k$ ... | \begin{algorithm}
\begin{algorithmic}
\Require{($\hat{x}_k,\hat{u}_k\,\hat{Q}_k,\hat{K}_k$)}
\For{$i=1\ldots N_{max}$}
\State{optimize $\bar{x}_k,\bar{u}_k$ by \eqref{eq:traj_update}}
\State{estimate $\gamma_k$ via \eqref{eq:gamma_update} or \eqref{eq:approximate_outer_optimization}}
\State{optimize $Q_k,K_k$ by \eqref... | "https://arxiv.org/src/2209.03535" | "2209.03535.tar.gz" | "2024-01-12" | {
"title": "joint synthesis of trajectory and controlled invariant funnel for discrete-time systems with locally lipschitz nonlinearities",
"id": "2209.03535",
"abstract": "this paper presents a joint synthesis algorithm of trajectory and controlled invariant funnel (cif) for locally lipschitz nonlinear s... | "2024-03-15T06:18:49.073482" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"NJDnatbib.sty": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.002231598,
"identity_attack_score": 0.0035887922,
"insult_score": 0.0107048955,
"pro... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.85126919927545,
"hash": "001f1f1f1f3f071f",
"most_frequent_color_ratio": 93.85126919927545
}
} | [] | "algorithm" | "231c18d7-9786-4050-8973-c72e5d7b2ad8" | 612 | easy | |
\begin{algorithm}[H]
\caption{{\sc Bernoulli Factory for $\P_{\alpha,n}$ for non-integer $\alpha$} (version 1)}
\label{alg:sampford_generic}
\begin{algorithmic}
\State Pick a random vertex $v$
\State For each index such that $v_i = 1$, sample the $x_i$-coin and restart if it is $0$.
\State For each index such that $v_... | \begin{algorithm}
[H]
\caption{{\sc Bernoulli Factory for $\P_{\alpha,n}$ for non-integer $\alpha$} (version 1)}
\begin{algorithmic}
\State Pick a random vertex $v$
\State For each index such that $v_i = 1$, sample the $x_i$-coin and restart if it is $0$.
\State For each index such that $v_i = 0$, sample the $x_i$-coin... | "https://arxiv.org/src/2011.03865" | "2011.03865.tar.gz" | "2024-02-19" | {
"title": "combinatorial bernoulli factories",
"id": "2011.03865",
"abstract": "a bernoulli factory is an algorithmic procedure for exact sampling of certain random variables having only bernoulli access to their parameters. bernoulli access to a parameter $p \\in [0,1]$ means the algorithm does not know $... | "2024-03-15T03:22:39.599567" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main-Bernoulli.bbl": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0011444092,
"identity_attack_score": 0.0039957687,
"insult_score": 0.007493876,
... | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.44030004110152,
"hash": "fff7e6e666666666",
"most_frequent_color_ratio": 95.44030004110152
}
} | [] | "algorithm" | "0d772c71-fcef-4b02-a6f9-a2ce5bdb3a9d" | 518 | easy | |
\begin{algorithm}
\caption{Schematic outline of the steps required to obtain a mock galaxy catalogue with ScamPy.}
\begin{algorithmic}
\vspace{1mm}
\State{// \texttt{Load Halo/Subhalo hierarchy}}
\State{// \texttt{(e.g. from SUBFIND algorithm)}}
\State{halo\_cat = catalogue( \emph{ chosen from file } )}
\vspace{3mm}
\S... | \begin{algorithm}
\caption{Schematic outline of the steps required to obtain a mock galaxy catalogue with ScamPy.}
\begin{algorithmic}
\vspace{1mm}
\State{// \texttt{Load Halo/Subhalo hierarchy}}
\State{// \texttt{(e.g. from SUBFIND algorithm)}}
\State{halo\_cat = catalogue( \emph{ chosen from file } )}
\vspace{3mm}
\S... | "https://arxiv.org/src/2002.07179" | "2002.07179.tar.gz" | "2024-02-14" | {
"title": "scampy -- a sub-halo clustering & abundance matching based python interface for painting galaxies on the dark matter halo/sub-halo hierarchy",
"id": "2002.07179",
"abstract": "we present a computational framework for \"painting\" galaxies on top of the dark matter halo/sub-halo hierarchy obtai... | "2024-03-15T04:33:05.687769" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"tab/python_modules_tab.tex": {
"toxicity_score": 0.013634177,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.002830336,
"insult_score": 0.007474876,
... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.92788902682571,
"hash": "000f0f071f1f1f07",
"most_frequent_color_ratio": 91.92788902682571
}
} | [] | "algorithm" | "3192311a-2514-458b-af56-7f71784f7937" | 690 | easy | |
\begin{algorithm}[H]
\centering
\caption{\textbf{-V.I:} Kernel CUSUM (KCUSUM)}\label{kcusumAlgo}
\begin{algorithmic}
\State \textbf{input:}Thresholds \(h\geq 0,\delta \ge \) and data \(x_1,x_2,\ldots\)
\State \textbf{initialize} \(Z_1=0\)
\State \textbf{For} \(n = 2,3,\ldots\)\textbf{do}... | \begin{algorithm}
[H]
\centering
\caption{\textbf{-V.I:} Kernel CUSUM (KCUSUM)} \begin{algorithmic}
\State \textbf{input:}Thresholds \(h\geq 0,\delta \ge \) and data \(x_1,x_2,\ldots\)
\State \textbf{initialize} \(Z_1=0\)
\State \textbf{For} \(n = 2,3,\ldots\)\textbf{do}
\State \hspace{3mm} \textbf{sample} \(y_n\) from... | "https://arxiv.org/src/2402.10291" | "2402.10291.tar.gz" | "2024-02-15" | {
"title": "an evaluation of real-time adaptive sampling change point detection algorithm using kcusum",
"id": "2402.10291",
"abstract": "detecting abrupt changes in real-time data streams from scientific simulations presents a challenging task, demanding the deployment of accurate and efficient algorithm... | "2024-03-15T05:28:54.125577" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.001001358,
"identity_attack_score": 0.002885833,
"insult_score": 0.007360875,
"profanity... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.15168396256446,
"hash": "101f1f5383001f7f",
"most_frequent_color_ratio": 90.15168396256446
}
} | [] | "algorithm" | "d351871d-75b1-4f8f-ba24-0241ed734e01" | 807 | medium | |
\begin{algorithm}[H]
\footnotesize
\caption{\label{algorithm1}The instructions for the construction of the test statistics ${Q_m^t}$ and ${Q_m}$}
\hspace*{0.01in}
\begin{algorithmic}[1]
\State Construct the IV $z_{t-1}$ by equation (\ref{mulivz}).
\State Construct the IV estimators $\hat{\beta}_{ivx}$, $\hat{\beta}_a$ ... | \begin{algorithm}
[H]
\footnotesize
\caption{The instructions for the construction of the test statistics ${Q_m^t}$ and ${Q_m}$}
\hspace*{0.01in}
\begin{algorithmic}
[1]
\State Construct the IV $z_{t-1}$ by equation (\ref{mulivz}).
\State Construct the IV estimators $\hat{\beta}_{ivx}$, $\hat{\beta}_a$ and $\hat{\beta}... | "https://arxiv.org/src/2401.01064" | "2401.01064.tar.gz" | "2024-01-02" | {
"title": "robust inference for multiple predictive regressions with an application on bond risk premia",
"id": "2401.01064",
"abstract": "we propose a robust hypothesis testing procedure for the predictability of multiple predictors that could be highly persistent. our method improves the popular extend... | "2024-03-15T06:49:14.014471" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"agsm.bst": {
"toxicity_score": 0.02308189,
"severe_toxicity_score": 0.0014400482,
"identity_attack_score": 0.0073625734,
"insult_score": 0.011160898,
"profanity... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.93296188564327,
"hash": "070f0f8387830f0f",
"most_frequent_color_ratio": 91.93296188564327
}
} | [] | "algorithm" | "fa60dee1-5185-4a5f-b279-eb310b9af968" | 2774 | hard | |
\begin{algorithm}
\caption{Kalman filter algorithm}\label{euclidKF1}
\begin{algorithmic}[1]
\State Initialize with initial state $\Hat{X}_{(0)} = x_{(0)}$ and $\Hat{\boldsymbol{P}}_{(0)} = \boldsymbol{Q}^*$ at $t=0$;
\For {$t = 1,...,\textit{T}$}
\State $X_{(t)}^{t-1} = \boldsymbol{A}^* \Hat{X}_{(t-1)} + \boldsymbol{B... | \begin{algorithm}
\caption{Kalman filter algorithm}\begin{algorithmic}
[1]
\State Initialize with initial state $\Hat{X}_{(0)} = x_{(0)}$ and $\Hat{\boldsymbol{P}}_{(0)} = \boldsymbol{Q}^*$ at $t=0$;
\For {$t = 1,...,\textit{T}$}
\State $X_{(t)}^{t-1} = \boldsymbol{A}^* \Hat{X}_{(t-1)} + \boldsymbol{B}^* u_{(t)}$, \qua... | "https://arxiv.org/src/2105.04789" | "2105.04789.tar.gz" | "2024-02-10" | {
"title": "innovative approaches in soil carbon sequestration modelling for better prediction with limited data",
"id": "2105.04789",
"abstract": "soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the pari... | "2024-03-15T06:18:35.682065" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"references.bib": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.003348306,
"insult_score": 0.007664877,
"pro... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.02737925251014,
"hash": "033f0b0be7c3bf3f",
"most_frequent_color_ratio": 92.02737925251014
}
} | [] | "algorithm" | "b0dbd789-9d91-496d-8614-4a5ac3a491fe" | 1412 | hard | |
\begin{algorithmic}[1]
\Procedure{penalizedG}{$\vec A, \vec H, \vec Y, \lambda$,\,corstr,\,$\kappa$} %Put comment if you want: \Comment{The g.c.d. of a and b}
\State Compute $E(\vec A_i|\vec H_i)$ for $i=1,\ldots,n$ \Comment{logistic regression on the pooled data}
\State $\vec\theta^{\text{up}} \gets \{\sum_{i=... | \begin{algorithmic}
[1]
\Procedure{penalizedG}{$\vec A, \vec H, \vec Y, \lambda$,\,corstr,\,$\kappa$} %Put comment if you want: \Comment{The g.c.d. of a and b}
\State Compute $E(\vec A_i|\vec H_i)$ for $i=1,\ldots,n$ \Comment{logistic regression on the pooled data}
\State $\vec\theta^{\text{up}} \gets \{\sum_{i=1}^n\ve... | "https://arxiv.org/src/2402.00154" | "2402.00154.tar.gz" | "2024-02-16" | {
"title": "penalized g-estimation for effect modifier selection in a structural nested mean model for repeated outcomes",
"id": "2402.00154",
"abstract": "effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. mo... | "2024-03-15T05:18:15.887063" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"bibliography.bib": {
"toxicity_score": 0.007916619,
"severe_toxicity_score": 0.00084877014,
"identity_attack_score": 0.0021273769,
"insult_score": 0.0069713728,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.48792751874008,
"hash": "002f8f86839f8f3f",
"most_frequent_color_ratio": 92.48792751874008
}
} | [] | "algorithm" | "53b6c355-7382-4f4e-8a61-656a6ce5f704" | 1862 | hard | |
\begin{algorithm}[t]
\caption{Local structure-aware graph refinement in the $t$-th epoch}
\label{algorithm2}
\scriptsize
\begin{algorithmic}[1]
\Require $\mathcal{G} = (V, E) $, $\mathbf{H}^{(t-1)}=\left[\mathbf{h}_1^{(t-1)},\cdots \mathbf{h}_n^{(t-1)}\right]$, $\mathbf{A}^{(0)}$;
\Ensure $\mathbf{A}... | \begin{algorithm}
[t]
\caption{Local structure-aware graph refinement in the $t$-th epoch}
\scriptsize
\begin{algorithmic}[1]
\Require $\mathcal{G} = (V, E) $, $\mathbf{H}^{(t-1)}=\left[\mathbf{h}_1^{(t-1)},\cdots \mathbf{h}_n^{(t-1)}\right]$, $\mathbf{A}^{(0)}$;
\Ensure $\mathbf{A}^{(t)}$;
\renewcommand{\algorithmicen... | "https://arxiv.org/src/2103.07295" | "2103.07295.tar.gz" | "2024-01-24" | {
"title": "adversarial graph disentanglement",
"id": "2103.07295",
"abstract": "a real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. however, most existing methods lack consideration of the intrinsic differences in relations between n... | "2024-03-15T08:52:54.851311" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"TAI_template.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"p... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.87942177277327,
"hash": "003f3f1f1f0f0f1f",
"most_frequent_color_ratio": 92.87942177277327
}
} | [] | "algorithm" | "31ba0e21-4de4-48e4-bda1-4fd662693665" | 1664 | hard | |
\begin{algorithm}
\label{fig: step_shrink}
A hybrid slice sampling transition of the stepping-out and shrinkage procedure from $x$ to $y$, i.e. input $x$ and output $y$.
The stepping-out procedure has input $x$ (current state), $t$ (chosen level),
$w>0$ (step size parameter from $\mathcal{R}_w$) and outputs an inte... | \begin{algorithm}
A hybrid slice sampling transition of the stepping-out and shrinkage procedure from $x$ to $y$, i.e. input $x$ and output $y$.
The stepping-out procedure has input $x$ (current state), $t$ (chosen level),
$w>0$ (step size parameter from $\mathcal{R}_w$) and outputs an interval $[L,R]$.
The shrinkage p... | "https://arxiv.org/src/1409.2709" | "1409.2709.tar.gz" | "2024-02-09" | {
"title": "convergence of hybrid slice sampling via spectral gap",
"id": "1409.2709",
"abstract": "it is known that the simple slice sampler has robust convergence properties, however the class of problems where it can be implemented is limited. in contrast, we consider hybrid slice samplers which are easi... | "2024-03-15T06:20:58.276849" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Lat_Ru_hybrid.tex": {
"toxicity_score": 0.010618322,
"severe_toxicity_score": 0.0010490417,
"identity_attack_score": 0.0034408006,
"insult_score": 0.007360875,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.25828970331588,
"hash": "000f9f83839fbfc3",
"most_frequent_color_ratio": 91.25828970331588
}
} | [] | "algorithm" | "42b76476-eeb3-4178-9697-30b2aae426d3" | 1153 | medium | |
\begin{algorithm}
\caption{Generation of realizable degree sequence from a prescribed distribution}
\label{alg: Seq}
Consider size $N$ and the prescribed distribution given by $\mathbb{P}(D=d), d\in \mathbb{Z}_{>0}$
\begin{enumerate}
\item Following the algorithm described by \cite{Newman:2010}, we first draw a set o... | \begin{algorithm}
\caption{Generation of realizable degree sequence from a prescribed distribution}
Consider size $N$ and the prescribed distribution given by $\mathbb{P}(D=d), d\in \mathbb{Z}_{>0}$
\begin{enumerate}
\item Following the algorithm described by \cite{Newman:2010}, we first draw a set of $N$ positive inte... | "https://arxiv.org/src/2401.06872" | "2401.06872.tar.gz" | "2024-01-12" | {
"title": "disease transmission on random graphs using edge-based percolation",
"id": "2401.06872",
"abstract": "edge-based percolation methods can be used to analyze disease transmission on complex social networks. this allows us to include complex social heterogeneity in our models while maintaining trac... | "2024-03-15T07:30:11.562367" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"figure1.tex": {
"toxicity_score": 0.011497946,
"severe_toxicity_score": 0.0011205673,
"identity_attack_score": 0.0035887922,
"insult_score": 0.007493876,
"profa... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.69585849870577,
"hash": "d39f7f13d0fffede",
"most_frequent_color_ratio": 94.69585849870577
}
} | [] | "algorithm" | "1737d0c0-567d-4d5b-b7be-2c69d1f38dbd" | 1053 | medium | |
\begin{algorithm}[H]
\begin{algorithmic}
\For{$t\in\{-1,\dots,-T^{traceback}\}$}\Comment{Retrieve the values of $P_{u,t}$}
\State $P_{u,t}\leftarrow\textrm{{\tt Power.GetValue}}(t)$
\EndFor
\For{$t\in\{-1,\dots,-T^{traceback}\}$}\Comment{Initial conditions on the state variables}
\If{$P_{u,t} > \textrm{{\tt MinimumP... | \begin{algorithm}
[H]
\begin{algorithmic}
\For{$t\in\{-1,\dots,-T^{traceback}\}$}\Comment{Retrieve the values of $P_{u,t}$}
\State $P_{u,t}\leftarrow\textrm{{\tt Power.GetValue}}(t)$
\EndFor
\For{$t\in\{-1,\dots,-T^{traceback}\}$}\Comment{Initial conditions on the state variables}
\If{$P_{u,t} > \textrm{{\tt MinimumPow... | "https://arxiv.org/src/2402.12848" | "2402.12848.tar.gz" | "2024-02-20" | {
"title": "atlas: a model of short-term european electricity market processes under uncertainty",
"id": "2402.12848",
"abstract": "the atlas model simulates the various stages of the electricity market chain in europe, including the formulation of offers by different market actors, the coupling of europe... | "2024-03-15T03:38:34.361656" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Sections/FurtherWork.tex": {
"toxicity_score": 0.0073197307,
"severe_toxicity_score": 0.00074386597,
"identity_attack_score": 0.0024603575,
"insult_score": 0.006325369,
... | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.90016142143322,
"hash": "00079f9f9f9fbf3f",
"most_frequent_color_ratio": 94.90016142143322
}
} | [] | "algorithm" | "e51006a5-6848-4672-acd2-96726cd529b0" | 1623 | hard | |
\begin{algorithm}[t] % enter the algorithm environment
\caption{Independent Metropolis-Hastings (IMH) Algorithm} % give the algorithm a caption
\label{alg1} % and a label for \ref{} commands later in the document
\begin{algorithmic} % enter the algorithmic environment
\State Pick an initial state $(x_0,y_0,z_0) \sim \n... | \begin{algorithm}[t]
% enter the algorithm environment
\caption{Independent Metropolis-Hastings (IMH) Algorithm} % give the algorithm a caption
% and a label for \ref{} commands later in the document
\begin{algorithmic}
% enter the algorithmic environment
\State Pick an initial state $(x_0,y_0,z_0) \sim \nu(x,y,z)$.
\F... | "https://arxiv.org/src/1805.10721" | "1805.10721.tar.gz" | "2024-01-11" | {
"title": "bernstein's inequalities for general markov chains",
"id": "1805.10721",
"abstract": "we establish bernstein's inequalities for functions of general (general-state-space and possibly non-reversible) markov chains. these inequalities achieve sharp variance proxies and encompass the classical bern... | "2024-03-15T06:32:05.168692" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main_bernstein.tex": {
"toxicity_score": 0.01218908,
"severe_toxicity_score": 0.0011634827,
"identity_attack_score": 0.0037367835,
"insult_score": 0.007664877,
... | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.30012306251277,
"hash": "1f0f000f0f3f077f",
"most_frequent_color_ratio": 92.30012306251277
}
} | [] | "algorithm" | "a48f11a3-ada8-42d2-bcd3-c2a00e6baa12" | 925 | medium | |
\begin{algorithmic}[1]
\Function {makeGraph}{$reads$}
\ForAll{$r$ in $reads$}
\ForAll{$kmer$ in $r$}
\State $left \gets s_{0}s_{1}\dots{}s_{k-2}$ //prefix
\State $right \gets s_{1}\dots{}s_{k-2}s_{k-1}$ //suffix
\State $addLeftVertex(left, s_{k-1})$
\State $addRightVertex(right)$
\State {// now both left and right vert... | \begin{algorithmic}
[1]
\Function {makeGraph}{$reads$}
\ForAll{$r$ in $reads$}
\ForAll{$kmer$ in $r$}
\State $left \gets s_{0}s_{1}\dots{}s_{k-2}$ //prefix
\State $right \gets s_{1}\dots{}s_{k-2}s_{k-1}$ //suffix
\State $addLeftVertex(left, s_{k-1})$
\State $addRightVertex(right)$
\State {// now both left and right ver... | "https://arxiv.org/src/2401.02756" | "2401.02756.tar.gz" | "2024-01-05" | {
"title": "lock-free de bruijn graph",
"id": "2401.02756",
"abstract": "de bruijn graph is one of the most important data structures used in de-novo genome assembly algorithms, especially for ngs data. there is a growing need for parallel data structures and algorithms due to the increasing number of cores... | "2024-03-15T07:57:44.023666" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"lfdb.bib": {
"toxicity_score": 0.005026425,
"severe_toxicity_score": 0.00088214874,
"identity_attack_score": 0.0010451894,
"insult_score": 0.0061448677,
"profan... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.0524161150859,
"hash": "03c081e1cfcf8f0f",
"most_frequent_color_ratio": 90.0524161150859
}
} | [] | "algorithm" | "69d6fd6f-05c1-4ab0-89b7-a3a067759544" | 529 | easy | |
\begin{algorithmic}[1]
\State \textbf{Input:} initial point $w_0$, learning rate $\eta$, preconditioner $\hat{D}_0$, probability $p$
\State $v_0 = \nabla P(w_0)$ \label{SARAHbegin}
\For{$t = 0,1,2,\ldots$}
\State $w_{t+1} = w_t - \eta \hat{D}^{-1}_{t}v_t$\label{SARAHpreconditionedstep}
\State Generate indepe... | \begin{algorithmic}
[1]
\State \textbf{Input:} initial point $w_0$, learning rate $\eta$, preconditioner $\hat{D}_0$, probability $p$
\State $v_0 = \nabla P(w_0)$ \For{$t = 0,1,2,\ldots$}
\State $w_{t+1} = w_t - \eta \hat{D}^{-1}_{t}v_t$ \State Generate independently batches $i_{t+1}$ for $v_{t+1}$ and $\mathcal{J}_... | "https://arxiv.org/src/2206.00285" | "2206.00285.tar.gz" | "2024-01-14" | {
"title": "stochastic gradient methods with preconditioned updates",
"id": "2206.00285",
"abstract": "this work considers the non-convex finite sum minimization problem. there are several algorithms for such problems, but existing methods often work poorly when the problem is badly scaled and/or ill-condit... | "2024-03-15T06:13:46.815677" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"arxiv.bbl": {
"toxicity_score": 0.01237757,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.004180758,
"insult_score": 0.007607877,
"profanity... | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.3059250831437,
"hash": "001f9fcf85830f07",
"most_frequent_color_ratio": 92.3059250831437
}
} | [] | "algorithm" | "ca38e228-41f8-4053-831c-8c671d72d632" | 750 | medium | |
\begin{algorithm}
\caption{The Queue Shuffle } \label{algo:qs}
\begin{algorithmic}[1]
\Require $\mathcal{T}$ \Comment{Current Tree}
\Require $\mathcal{N} = \{\nu_0,\nu_1,...\}$ \Comment{set of all non-root nodes}
\State $Q = [\nu_0,\nu_1]$ \Comment{"queue" of nodes to process}
\State $L = \{\nu_0 : 0, \nu_1 : 1\}$ \Co... | \begin{algorithm}
\caption{The Queue Shuffle } \begin{algorithmic}
[1]
\Require $\mathcal{T}$ \Comment{Current Tree}
\Require $\mathcal{N} = \{\nu_0,\nu_1,...\}$ \Comment{set of all non-root nodes}
\State $Q = [\nu_0,\nu_1]$ \Comment{"queue" of nodes to process}
\State $L = \{\nu_0 : 0, \nu_1 : 1\}$ \Comment{node:label... | "https://arxiv.org/src/2306.05739" | "2306.05739.tar.gz" | "2024-01-23" | {
"title": "leaping through tree space: continuous phylogenetic inference for rooted and unrooted trees",
"id": "2306.05739",
"abstract": "phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. however, finding suit... | "2024-03-15T06:52:58.742216" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"tab/table1_new.tex": {
"toxicity_score": 0.011058134,
"severe_toxicity_score": 0.0014781952,
"identity_attack_score": 0.0034408006,
"insult_score": 0.0073798755,
... | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.10342417889588,
"hash": "387c3c3c3f383c3c",
"most_frequent_color_ratio": 92.10342417889588
}
} | [] | "algorithm" | "37ec7dd0-6573-48a5-bac8-af3e272d9b7b" | 1213 | hard |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.