wanyu/IteraTeR_v2 huggingface dataset:
Original: In this paper, we propose to address the issue of the lack of strongly labeled data by using pseudo strongly labeled data that is approximated using Convolutive Nonnegative Matrix Factorization (CNMF)
Revised: In this paper, we propose to address the issue of the lack of strongly labeled data by using pseudo strongly labeled data approximated using Convolutive Nonnegative Matrix Factorization (CNMF)
Edit intent: coherence
Original: In this paper, we propose to address the issue of the lack of strongly labeled data by using pseudo strongly labeled data that is approximated using Convolutive Nonnegative Matrix Factorization (CNMF)
Revised: In this paper, we propose to address the issue of the lack of strongly labeled data by using pseudo strongly labeled data that is approximated using Convolutive Nonnegative Matrix Factorization 
Edit intent: clarity
Original:  . Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( MN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other  will provide the clip level prediction.
Revised:  . Using this set of data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( MN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other will provide the clip level prediction.
Edit intent: clarity
Original:  . Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( MN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other  will provide the clip level prediction.
Revised:  . Using this pseudo strongly labeled data, we then train a novel architecture called the Convolutional Macaron Net ( MN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other will provide the clip level prediction.
Edit intent: clarity
Original:  . Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( MN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other  will provide the clip level prediction.
Revised:  . Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( CMN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other will provide the clip level prediction.
Edit intent: clarity
Original:  . Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( MN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other  will provide the clip level prediction.
Revised:  . Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( MN ), which combines Convolutional Neural Network (CNN) with MN, in a semi-supervised manner. Instead of training only a single model or using the Mean-teacher approach, we train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other will provide the clip level prediction.
Edit intent: clarity
Original:  . Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( MN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other  will provide the clip level prediction.
Revised:  . Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net ( MN ), which we term it as Convolutional Macaron Net (CMN) . As opposed to the Mean-Teacher approachwhich trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other model will provide the clip level prediction.
Edit intent: fluency
Original:  The experiments reveal a new convergence pattern compared to the standard , non-conservative Galerkin methods.
Revised:  The experiments reveal a new convergence pattern compared to standard Galerkin methods.
Edit intent: clarity
Original:  Applications to SARS and COVID-19  using this combined method are also introduced.
Revised:  Applications to SARS and COVID-19 ) using this combined method are also introduced.
Edit intent: fluency
Original: In this work, we propose DiffWave, a versatile Diffusion probabilistic model for conditional and unconditional Waveform generation.
Revised: In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional Waveform generation.
Edit intent: fluency
Original: In this work, we propose DiffWave, a versatile Diffusion probabilistic model for conditional and unconditional Waveform generation.
Revised: In this work, we propose DiffWave, a versatile Diffusion probabilistic model for conditional and unconditional waveform generation.
Edit intent: fluency
Original:  The blurring of those PIPs by the quick fluctuations around any time series are treated via an additive decomposition theorem, due to Cartier and Perrin, and algebraic estimation techniques, which are already useful in automatic control and signal processing.
Revised:  The blurring of those PIPs by the quick fluctuations around any time series is treated via an additive decomposition theorem, due to Cartier and Perrin, and algebraic estimation techniques, which are already useful in automatic control and signal processing.
Edit intent: fluency
Original: The ubiquity of implicit feedback (e. g., click) makes it the default choice to train recommenders .
Revised: Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specifically, building predictive models based on the click behavior data. This is known as the Click-Through Rate (CTR) prediction, which has become the gold standard for building personalized recommendation service .
Edit intent: coherence
Original:  In this paper, we formulate the recommendation process as a causal graph that reflects the cause-effect factors in recommendation, and address the clickbait issue by performing counterfactual inference on the causal graph.
Revised:  In this paper, we formulate the recommendation models as a causal graph that reflects the cause-effect factors in recommendation, and address the clickbait issue by performing counterfactual inference on the causal graph.
Edit intent: clarity
Original: Contextual bandits provide an effective way to model this problem and leverage online (incremental) learning to keep adjusting the predictions based on changing environment.
Revised: Contextual bandits provide an effective way to model this problem and leverage online (incremental) learning to continuously adjust the predictions based on changing environment.
Edit intent: clarity
Original:  We explore how contextual banditsproblem is modelled as an extension of the reinforcement learning (RL) problem and build a novel algorithm to solve the problem using an array of action-based learners.
Revised:  We explore details on contextual bandits, an extension to the traditional reinforcement learning (RL) problem and build a novel algorithm to solve the problem using an array of action-based learners.
Edit intent: clarity
Original:  In this paper, we undertake a foundational study of  finding proofs that use induction to reason with these logics.
Revised:  In this paper, we undertake a foundational study of finding proofs that use induction to reason in these logics.
Edit intent: clarity
Original:  By treating proofs as purely FO proofs punctuated by declarations of induction lemmas, we separate proofs into deductively reasoned components that can be automated and statements of lemmas that need to be divined, respectively .
Revised:  By treating proofs as pure FO proofs that are punctuated by declarations of induction lemmas, we separate proofs into deductively reasoned components that can be automated and statements of lemmas that need to be divined, respectively .
Edit intent: fluency
Original:  By treating proofs as purely FO proofs punctuated by declarations of induction lemmas, we separate proofs into deductively reasoned components that can be automated and statements of lemmas that need to be divined, respectively .
Revised:  By treating proofs as purely FO proofs punctuated by declarations of induction lemmas, we separate proofs into deductively reasoned components and statements of lemmas that need to be divined, respectively .
Edit intent: clarity
Original:  By treating proofs as purely FO proofs punctuated by declarations of induction lemmas, we separate proofs into deductively reasoned components that can be automated and statements of lemmas that need to be divined, respectively .
Revised:  By treating proofs as purely FO proofs punctuated by declarations of induction lemmas, we separate proofs into deductively reasoned components that can be automated and statements of lemmas that need to be synthesized .
Edit intent: clarity
Original:  While humans divine such lemmas with intuition, we propose a counterexample driven technique that guides the synthesis of such lemmas , where counterexamples are finite models that witness inability of proving the theorem as well as other proposed lemmas.
Revised:  While humans divine such lemmas with intuition, we propose a technique that guides the synthesis of such lemmas , where counterexamples are finite models that witness inability of proving the theorem as well as other proposed lemmas.
Edit intent: clarity
Original:  While humans divine such lemmas with intuition, we propose a counterexample driven technique that guides the synthesis of such lemmas , where counterexamples are finite models that witness inability of proving the theorem as well as other proposed lemmas.
Revised:  While humans divine such lemmas with intuition, we propose a counterexample driven technique that guides the synthesis of such lemmas , where counterexamples are finite models that witness the help required for proving a goal theorem as well as other proposed lemmas.
Edit intent: clarity
Original:  We develop relatively complete procedures for synthesizing such lemmas using techniques and tools from program/expression synthesis, for powerful FO+lfp logics that have background sorts constrained by natural theories such as arithmetic and set theory.
Revised:  We develop relatively complete procedures for synthesizing lemmas for powerful FO+lfp logics that have background sorts constrained by natural theories such as arithmetic and set theory.
Edit intent: clarity
Original:  We evaluate our procedures and show that over a class of theorems that require finding inductive proofs, our automatic synthesis procedure is effective in proving them .
Revised:  We evaluate our procedures and evaluate them over a class of theorems that require finding inductive proofs, our automatic synthesis procedure is effective in proving them .
Edit intent: clarity
Original: Design of Cyber-physical systems (CPSs) is a challenging task that involves searching over a large search space of various CPS configurations and possible values of components composing the system.
Revised: Design of cyber-physical systems (CPSs) is a challenging task that involves searching over a large search space of various CPS configurations and possible values of components composing the system.
Edit intent: fluency
Original:  In this paper, we first describe the data collection and prepossessing procedures followed by the description of the database specifications.
Revised:  In this paper, we first describe the data collection and preprocessing procedures followed by the description of the database specifications.
Edit intent: clarity
Original:  In this paper, we first describe the data collection and prepossessing procedures followed by the description of the database specifications.
Revised:  In this paper, we first describe the data collection and prepossessing procedures followed by a description of the database specifications.
Edit intent: fluency
Original:  To demonstrate the reliability of the database, we performed the preliminary speech recognition experiments.
Revised:  To demonstrate the reliability of the database, we performed preliminary speech recognition experiments.
Edit intent: fluency
Original:  Diversity in academia is bolstered by smaller institutions and research groups that may have less incentives to `race ' and lower levels of collaboration with the private sector.
Revised:  Diversity in academia is bolstered by smaller institutions and research groups that may have less incentives to race and lower levels of collaboration with the private sector.
Edit intent: fluency
Original: Video moment retrieval targets at retrieving a golden moment in a video for a given natural language query.
Revised: Video moment retrieval targets at retrieving a moment in a video for a given natural language query.
Edit intent: clarity
Original: Video moment retrieval targets at retrieving a golden moment in a video for a given natural language query.
Revised: Video moment retrieval targets at retrieving a golden moment in a video for a given language query.
Edit intent: clarity
Original:  The main challenges of this task include 1) the requirement of accurately localizing (i.e., the start time and the end time of) the relevant moment in an untrimmed video stream , and 2) bridging the semantic gap between textual query and video contents.
Revised:  The challenges of this task include 1) the requirement of accurately localizing (i.e., the start time and the end time of) the relevant moment in an untrimmed video stream , and 2) bridging the semantic gap between textual query and video contents.
Edit intent: coherence
Original:  The main challenges of this task include 1) the requirement of accurately localizing (i.e., the start time and the end time of) the relevant moment in an untrimmed video stream , and 2) bridging the semantic gap between textual query and video contents.
Revised:  The main challenges of this task include 1) the requirement of localizing the relevant moment in an untrimmed video stream , and 2) bridging the semantic gap between textual query and video contents.
Edit intent: clarity
Original:  The main challenges of this task include 1) the requirement of accurately localizing (i.e., the start time and the end time of) the relevant moment in an untrimmed video stream , and 2) bridging the semantic gap between textual query and video contents.
Revised:  The main challenges of this task include 1) the requirement of accurately localizing (i.e., the start time and the end time of) the relevant moment in an untrimmed video , and 2) bridging the semantic gap between textual query and video contents.
Edit intent: clarity
Original:  In this paper, we propose an Attentive Cross-modal Relevance Matching (ACRM) model which predicts the temporal bounders based on an interaction modeling between two modalities .
Revised:  In this paper, we propose an Attentive Cross-modal Relevance Matching (ACRM) model which predicts the temporal boundaries based on an interaction modeling between two modalities .
Edit intent: fluency
Original:  In this paper, we propose an Attentive Cross-modal Relevance Matching (ACRM) model which predicts the temporal bounders based on an interaction modeling between two modalities .
Revised:  In this paper, we propose an Attentive Cross-modal Relevance Matching (ACRM) model which predicts the temporal bounders based on an interaction modeling .
Edit intent: clarity
Original:  In addition, an attention module is introduced to automatically assign higher weights to query words with richer semantic cues, which are considered to be more important for finding relevant video contents.
Revised:  In addition, an attention module is introduced to assign higher weights to query words with richer semantic cues, which are considered to be more important for finding relevant video contents.
Edit intent: clarity
Original:  The L(p,1)-labeling problem is to minimize the span of f (i.e.,u\in V(f(u)) - \min_{u\in V}(f(u))+1).
Revised:  The L(p,1)-labeling problem is to minimize the span of f (i.e.,u\in V(f(u)) - \min_{u\in V}(f(u))+1).
Edit intent: fluency
Original:  Our analysis may have interesting policy implications for the ecology of fishing areas  .
Revised:  Our analysis has interesting policy implications for the ecology of fishing areas .
Edit intent: clarity
Original:  As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the quantity of voters that are strongly pro and against the considered parties.
Revised:  As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the proportion of voters that are strongly pro and against the considered parties.
Edit intent: clarity
Original:  As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the quantity of voters that are strongly pro and against the considered parties.
Revised:  As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the quantity of voters that are strong supporters of each of the considered parties.
Edit intent: clarity
Original:  Our initial results strongly indicate that generated indexes are the next step in designing "index structures" .
Revised:  Our initial results strongly indicate that generated indexes are the next step in designing index structures .
Edit intent: fluency
Original: In a neural network (NN), weight matrices linearly transform inputs into preactivations that are then transformed nonlinearly into activations .
Revised: In a neural network (NN), *weight matrices* linearly transform inputs into preactivations that are then transformed nonlinearly into activations .
Edit intent: clarity
Original: In a neural network (NN), weight matrices linearly transform inputs into preactivations that are then transformed nonlinearly into activations .
Revised: In a neural network (NN), weight matrices linearly transform inputs into *preactivations* that are then transformed nonlinearly into activations .
Edit intent: clarity
Original: In a neural network (NN), weight matrices linearly transform inputs into preactivations that are then transformed nonlinearly into activations .
Revised: In a neural network (NN), weight matrices linearly transform inputs into preactivations that are then transformed nonlinearly into *activations* .
Edit intent: clarity
Original:  We show that, surprisingly, (pre-)activations of a randomly initialized NN become independent from the weights as the NN's widths tend to infinity, in the sense of asymptotic freeness in random matrix theory.
Revised:  We show that, surprisingly, (pre-)activations of a randomly initialized NN become *independent* from the weights as the NN's widths tend to infinity, in the sense of asymptotic freeness in random matrix theory.
Edit intent: fluency
Original:  We show that, surprisingly, (pre-)activations of a randomly initialized NN become independent from the weights as the NN's widths tend to infinity, in the sense of asymptotic freeness in random matrix theory.
Revised:  We show that, surprisingly, (pre-)activations of a randomly initialized NN become independent from the weights as the NN's widths tend to infinity, in the sense of asymptotic freeness in random matrix theory.
Edit intent: fluency
Original:  We call this the \emph{Free Independence Principle (FIP) , which has these consequences: 1) It rigorously justifies the calculation of asymptotic Jacobian singular value distribution of an NN in Pennington et al.
Revised:  We call this the \emph{Free Independence Principle (FIP) , which has these consequences: 1) It rigorously justifies the calculation of asymptotic Jacobian singular value distribution of an NN in Pennington et al.
Edit intent: fluency
Original:  2) It gives a new justification of gradient independence assumption used for calculating the Neural Tangent Kernel of a neural network.
Revised:  2) It gives a new justification of gradient independence assumption used for calculating the Neural Tangent Kernel of a neural network.
Edit intent: fluency
Original:  In this paper, we bring the power of Fourier analysis to the design of iterative combinatorial auctions .
Revised:  In this paper, we bring the power of Fourier analysis to the design of combinatorial auctions (CAs) .
Edit intent: clarity
Original:  The key idea is to approximate the bidders' value functions using Fourier-sparse set functions, which can be computed using a relatively small number of queries.
Revised:  The key idea is to approximate bidders' value functions using Fourier-sparse set functions, which can be computed using a relatively small number of queries.
Edit intent: fluency
Original:  Since this number is still too large for real-world auctions , we propose a novel hybrid auction design: we first use neural networks to learn bidders' values and then apply Fourier analysis to those learned representations.
Revised:  Since this number is still too large for real-world CAs , we propose a novel hybrid auction design: we first use neural networks to learn bidders' values and then apply Fourier analysis to those learned representations.
Edit intent: clarity
Original:  Since this number is still too large for real-world auctions , we propose a novel hybrid auction design: we first use neural networks to learn bidders' values and then apply Fourier analysis to those learned representations.
Revised:  Since this number is still too large for real-world auctions , we propose a new hybrid design: we first use neural networks to learn bidders' values and then apply Fourier analysis to those learned representations.
Edit intent: clarity
Original:  Since this number is still too large for real-world auctions , we propose a novel hybrid auction design: we first use neural networks to learn bidders' values and then apply Fourier analysis to those learned representations.
Revised:  Since this number is still too large for real-world auctions , we propose a novel hybrid auction design: we first use neural networks to learn bidders' values and then apply Fourier analysis to the learned representations.
Edit intent: fluency
Original:  Based on this, we devise an iterative mechanism that asks Fourier-based queries.
Revised:  Based on this, we devise an iterative CA that asks Fourier-based queries.
Edit intent: clarity