prithivMLmods/Callisto-OCR3-2B-Instruct
Image-Text-to-Text • 2B • Updated • 617 • 7
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\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" | {
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} | [] | "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{$P(u,v) = Z\left(\frac{1}{deg(u)-Cm(u, v)}\cdo... | "https://arxiv.org/src/2303.15702" | "2303.15702.tar.gz" | "2024-02-25" | {
"title": "distributed graph embedding with information-oriented random walks",
"id": "2303.15702",
"abstract": "graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. the increasing availability of billion-edge graphs underscores the importance of lea... | "2024-03-15T03:43:03.810720" | {
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} | [] | "algorithm" | "495ba26c-9d13-4916-8708-fb41c8065401" | 1121 | medium | |
\begin{algorithmic}[1]
\Procedure{ModifiedGD}{$\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" | {
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} | [] | "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" | {
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} | [] | "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" | {
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} | [] | "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" | {
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} | [] | "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" | {
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} | [] | "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" | {
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} | [] | "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" | {
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} | [] | "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" | {
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} | [] | "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" | {
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} | [] | "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 $N \Leftarrow N /... | "https://arxiv.org/src/2312.05063" | "2312.05063.tar.gz" | "2024-02-25" | {
"title": "individualizing glioma radiotherapy planning by optimization of data and physics-informed discrete loss",
"id": "2312.05063",
"abstract": "brain tumor growth is unique to each patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. understanding thes... | "2024-03-15T03:04:49.138400" | {
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} | [] | "algorithm" | "37f9ec91-081a-4799-9d19-7a6595228dc9" | 437 | easy | |
\begin{algorithmic}[1]
\State Compute the set
\begin{equation*}
I(\mathbf{x}) = \left\{i \in \{1, \dots, N\} : \mathbf{x} \in \mathcal{B}_i\right\}.
\end{equation*}
\If{$|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" | {
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} | [] | "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" | {
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} | [] | "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" | {
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End of preview. Expand in Data Studio
Img2Text-Algorithm-Retrieval Dataset
Dataset Overview
The Img2Text-Algorithm-Retrieval dataset is designed for retrieving text descriptions of algorithms from corresponding images. This dataset consists of structured text, raw text, algorithm images, and metadata such as source URLs and filenames. It can be useful for tasks like OCR-based text retrieval, image-to-text learning, and document understanding.
Dataset Details
- Modality: Image, Text
- Format: Parquet
- Size: ~33.8MB
- Total Rows: 300
Features
| Column Name | Data Type | Description |
|---|---|---|
| structure | String | LaTeX or structured representation of the algorithm |
| text | String | Extracted textual representation of the algorithm |
| image | Image | Algorithm snapshot (from research papers) |
| download_url | String | URL of the source document |
| instance_name | String | Name of the instance (e.g., paper ID) |
Usage
This dataset is useful for:
- Algorithm text-image retrieval: Matching textual algorithm descriptions to corresponding images.
- OCR and text recognition: Evaluating OCR models for structured text extraction.
- Machine Learning research: Training deep learning models for image-to-text conversion.
- Downloads last month
- 18
Size of downloaded dataset files:
33.8 MB
Size of the auto-converted Parquet files:
33.8 MB
Number of rows:
300
