wanyu/IteraTeR_v2 huggingface dataset:
Original: We study the effect of norm based regularization on the size of coresets for the regularized regression problems.
Revised: We study the effect of norm based regularization on the size of coresets for regression problems.
Edit intent: clarity
Original:  We show that when r \neq s, no coreset for some regularized regression can have size smaller than the optimal coreset of the unregularized version.
Revised:  We show that when r \neq s, no coreset for regularized regression can have size smaller than the optimal coreset of the unregularized version.
Edit intent: clarity
Original:  Specifically, given a matrix A \in {R}^{n \times d} with n\gg d and a vector b \in R ^ n and \lambda > 0, we analyze the size of coresets for regularized versions of regression of the form \|Ax-b\|_p^r + \lambda\|{x}\|_q^s . Prior work has shown that for ridge regression (where p,q,r,s=2) we can obtain a coreset that is smaller than the coreset for the unregularized counterpart i.e. least squares regression (Avron et al . ).
Revised:  Specifically, given a matrix A \in {R}^{n \times d} with n\gg d and a vector b \in R ^ n and \lambda > 0, we analyze the size of coresets for regularized versions of regression of the form \|Ax-b\|_p^r + \lambda\|{x}\|_q^s . Prior work has shown that for ridge regression (where p,q,r,s=2) we can obtain a coreset that is smaller than the coreset for the unregularized counterpart i.e. least squares regression (Avron et al ).
Edit intent: fluency
Original:  Consequently, in contrast to traditional verification approaches that use design-specific assertions (often generated manually), SQED does not require a  formal design specification or manually-written properties.
Revised:  Consequently, in contrast to traditional verification approaches that use design-specific assertions (often generated manually), SQED does not require a full formal design specification or manually-written properties.
Edit intent: clarity
Original:  Case studies have shown that SQED is effective on commercial designs and  substantially improves design productivity.
Revised:  Case studies have shown that SQED is effective for commercial designs and substantially improves design productivity.
Edit intent: clarity
Original:  Case studies have shown that SQED is effective on commercial designs and  substantially improves design productivity.
Revised:  Case studies have shown that SQED is effective on commercial designs and that SQED substantially improves design productivity.
Edit intent: fluency
Original:  We prove the soundness of SQED, namely that any bug reported by SQED is in fact a real bug in the processor.
Revised:  We prove the soundness of SQED, i.e., that any bug reported by SQED is in fact a real bug in the processor.
Edit intent: coherence
Original:  Instead, this paper uses a nationwide natural experiment .
Revised:  Instead, this paper takes advantage of a rare nationwide natural experiment .
Edit intent: clarity
Original:  In April 1975, Sweden discontinued newborns BCG vaccination .
Revised:  In April 1975, where discontinuation of newborns BCG vaccination .
Edit intent: clarity
Original:  The vaccination rate fell from 92\% to 2\% . I compare the numbers of COVID-19 cases and hospitalizations for cohorts born just before and just after that change .
Revised:  The vaccination rate fell from 92\% to 2\% . I compare the numbers of COVID-19 cases and hospitalizations were recorded for birth cohorts born just before and just after that change .
Edit intent: clarity
Original:  Thus, ] ]\\%DIF > confidence that universal BCG  receiving the BCG vaccine at birth does not seem to have a protective effect against COVID-19.
Revised:  Thus, ] ]\\%DIF > confidence that universal BCG receiving the BCG vaccine at birth does not have a protective effect against COVID-19.
Edit intent: clarity
Original:  All five AI platforms significantly outperformed the human readers .
Revised:  All five AI products significantly outperformed the human readers .
Edit intent: clarity
Original: Considerable analytical work during the COVID-19 pandemic has been devoted to developing predictive models to evaluate the merits of potential governmental interventions versus the cost of not carrying them out.
Revised: During the COVID-19 pandemic has been devoted to developing predictive models to evaluate the merits of potential governmental interventions versus the cost of not carrying them out.
Edit intent: clarity
Original: Considerable analytical work during the COVID-19 pandemic has been devoted to developing predictive models to evaluate the merits of potential governmental interventions versus the cost of not carrying them out.
Revised: Considerable analytical work during the COVID-19 pandemic , the scientific community developed predictive models to evaluate the merits of potential governmental interventions versus the cost of not carrying them out.
Edit intent: clarity
Original: Considerable analytical work during the COVID-19 pandemic has been devoted to developing predictive models to evaluate the merits of potential governmental interventions versus the cost of not carrying them out.
Revised: Considerable analytical work during the COVID-19 pandemic has been devoted to developing predictive models to evaluate potential governmental interventions versus the cost of not carrying them out.
Edit intent: clarity
Original:  We use a sparse regression method to fit the dynamic progression of the data (the Rt parameter) with the least number of changes , allowing us to attribute discrete change events to specific intervention events.
Revised:  We use a regularized regression to find a parsimonious model that fits the data with the least number of changes , allowing us to attribute discrete change events to specific intervention events.
Edit intent: clarity
Original:  Since the sparse fit yields a discrete jump between two constant Rt , we follow do-operator prescriptions and simulate the counterfactual case by forcing Rt to stay at the pre-jump value.
Revised:  Since the sparse fit yields a discrete jump between two constant Rt , we follow do-operator prescriptions , we simulate the counterfactual case by forcing Rt to stay at the pre-jump value.
Edit intent: coherence
Original:  In this work, we present ReCOVery, a repository designed and constructed to facilitate the studies of combating such information regarding COVID-19.
Revised:  In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19.
Edit intent: clarity
Original:  Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be directly compared.
Revised:  Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared.
Edit intent: clarity
Original:  Our repository is available at URL which will be timely updated.
Revised:  Our repository is available at URL 
Edit intent: clarity
Original:  However, complex and unknown market dynamics pose enormous challenges for the development and validation of optimal execution strategies.
Revised:  However, complex and unknown market dynamics pose significant challenges for the development and validation of optimal execution strategies.
Edit intent: clarity
Original:  We propose a model-free approach by training Reinforcement Learning (RL) agents in a realistic market simulation environment with multiple agents.
Revised:  In this paper, we propose a model-free approach by training Reinforcement Learning (RL) agents in a realistic market simulation environment with multiple agents.
Edit intent: clarity
Original:  Second, we formulated the problem of optimal execution in an RL setting in which an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in HFT .
Revised:  Second, we formulate the problem of optimal execution in an RL setting in which an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in HFT .
Edit intent: fluency
Original:  Second, we formulated the problem of optimal execution in an RL setting in which an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in HFT .
Revised:  Second, we formulated the problem of optimal execution in an RL setting where an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in HFT .
Edit intent: fluency
Original:  Second, we formulated the problem of optimal execution in an RL setting in which an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in HFT .
Revised:  Second, we formulated the problem of optimal execution in an RL setting in which an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in High Frequency Trading (HFT) .
Edit intent: clarity
Original:  Third, we developed and trained an RL execution agent using the Double Deep Q-Learning (DDQL) algorithm in the ABIDES environment.
Revised:  Third, we develop and train an RL execution agent using the Double Deep Q-Learning (DDQL) algorithm in the ABIDES environment.
Edit intent: fluency
Original:  Finally, we evaluated the simulation with our RL agent by comparing the simulation on the actual market Limit Order Book (LOB) characteristics .
Revised:  Finally, we evaluate the simulation with our RL agent by comparing the simulation on the actual market Limit Order Book (LOB) characteristics .
Edit intent: coherence
Original:  Finally, we evaluated the simulation with our RL agent by comparing the simulation on the actual market Limit Order Book (LOB) characteristics .
Revised:  Finally, we evaluated the simulation with our RL agent by comparing it with a market replay simulation using real market Limit Order Book (LOB) characteristics .
Edit intent: clarity
Original:  Finally, we evaluated the simulation with our RL agent by comparing the simulation on the actual market Limit Order Book (LOB) characteristics .
Revised:  Finally, we evaluated the simulation with our RL agent by comparing the simulation on the actual market Limit Order Book (LOB) data .
Edit intent: clarity
Original:  We develop a novel Bayesian approach to such inference based on a probabilistic compartmental model and data of daily confirmed COVID-19 cases.
Revised:  We develop a novel Bayesian approach to such inference based on a probabilistic compartmental model using data of daily confirmed COVID-19 cases.
Edit intent: coherence
Original:  The construction requires no specific ad hoc tweaking for hanging nodes on l-irregular (l\geq 2) meshes thanks to the adoption of some novel ideas borrowed from virtual element families.
Revised:  The construction requires no specific ad hoc tweaking for hanging nodes on l-irregular (l\geq 2) meshes thanks to the adoption of virtual element families.
Edit intent: clarity
Original:  An efficient a posteriori error estimator is then constructed based on the recovered flux projected to a space with simpler structure , and its reliability is proved under common assumptions, both of which are further verified in numerics.
Revised:  An efficient a posteriori error estimator is then constructed based on the recovered flux , and its reliability is proved under common assumptions, both of which are further verified in numerics.
Edit intent: clarity
Original: A number of studies have successfully developed speaker verification or spoofing detection systems.
Revised: A number of studies have successfully developed speaker verification or presentation attack detection systems.
Edit intent: clarity
Original:  In this paper, we propose two approaches for the integrated replay spoofing-aware speaker verification task : an end-to-end monolithic  and a back-end modular approach.
Revised:  In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection : an end-to-end monolithic and a back-end modular approach.
Edit intent: clarity
Original:  In this paper, we propose two approaches for the integrated replay spoofing-aware speaker verification task : an end-to-end monolithic  and a back-end modular approach.
Revised:  In this paper, we propose two approaches for the integrated replay spoofing-aware speaker verification task : an end-to-end monolithic approach and a back-end modular approach.
Edit intent: fluency
Original:  The first approach simultaneously trains speaker identification, replay spoofing detection, and the integrated system using multi-task learning with a common feature.
Revised:  The first approach simultaneously trains speaker identification, presentation attack detection, and the integrated system using multi-task learning with a common feature.
Edit intent: clarity
Original:  The first approach simultaneously trains speaker identification, replay spoofing detection, and the integrated system using multi-task learning with a common feature.
Revised:  The first approach simultaneously trains speaker identification, replay spoofing detection, and the integrated system using multi-task learning using a common feature.
Edit intent: fluency
Original:  However, through experiments, we hypothesize that the information required for performing speaker verification and replay spoofing detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings while replay spoofing exploits such information.
Revised:  However, through experiments, we hypothesize that the information required for performing speaker verification and presentation attack detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings while replay spoofing exploits such information.
Edit intent: clarity
Original:  However, through experiments, we hypothesize that the information required for performing speaker verification and replay spoofing detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings while replay spoofing exploits such information.
Revised:  However, through experiments, we hypothesize that the information required for performing speaker verification and replay spoofing detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings , while presentation attack detection systems exploit such information.
Edit intent: clarity
Original:  Therefore, we propose a back-end  approach using a  deep neural network that takes speaker embeddings extracted from enrollment and test utterances and a replay detection prediction on the test utterance as input . Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and replay spoofing detection.
Revised:  Therefore, we propose a back-end approach using a deep neural network that takes speaker embeddings extracted from enrollment and test each) and prediction of presentation attacks . Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and replay spoofing detection.
Edit intent: clarity
Original:  Therefore, we propose a back-end  approach using a  deep neural network that takes speaker embeddings extracted from enrollment and test utterances and a replay detection prediction on the test utterance as input . Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and replay spoofing detection.
Revised:  Therefore, we propose a back-end approach using a deep neural network that takes speaker embeddings extracted from enrollment and test utterances and a replay detection prediction on the test utterance as input . Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and presentation attack detection.
Edit intent: clarity
Original:  We introduce Adjoined networks as a training approach that can compress and regularize any CNN-based neural architecture.
Revised:  We introduce Adjoined networks as a training approach that can compress and regularize any CNN-based neural architecture.
Edit intent: fluency
Original:  We introduce Adjoined networks as a training approach that can compress and regularize any CNN-based neural architecture.
Revised:  We introduce Adjoined networks as a training approach that can regularize and compress any CNN-based neural architecture.
Edit intent: fluency
Original:  On both these datasets, the original network trained in the adjoint fashion gains about 3\\%DIF < in top-1 accuracy as compared to the same %DIF > in top-1 accuracy as compared to the same network trained in the standard fashion.
Revised:  On both these datasets, the original network trained in the adjoint fashion gains about 3\\%DIF < in top-1 accuracy as compared %DIF > in top-1 accuracy as compared to the same network trained in the standard fashion.
Edit intent: clarity
Original:  We introduce Adjoined networks as a training approach that can regularize and compress any CNN-based neural architecture.
Revised:  We introduce Adjoined networks as a training approach that can regularize and compress any CNN-based neural architecture.
Edit intent: fluency
Original:  On both these datasets, the original network trained in the adjoint fashion gains about 3\\%DIF < in top-1 accuracy as compared to the same network trained in the standard %DIF > in top-1 accuracy as compared to the same network trained in the standard fashion.
Revised:  On both these datasets, the original network trained in the adjoint fashion gains about 3\\%DIF < in top-1 accuracy as compared to the same %DIF > in top-1 accuracy as compared to the same network trained in the standard fashion.
Edit intent: clarity
Original: Recent research on compressing deep neural networks has focused on reducing the number of parameters.
Revised: Compressing deep neural networks has focused on reducing the number of parameters.
Edit intent: clarity
Original: The paper introduces strong proximity join-semilattice, a predicative notion of continuous lattice which arises as the Karoubi envelop of the category of algebraic lattices.
Revised: We introduce a notion of strong proximity join-semilattice, a predicative notion of continuous lattice which arises as the Karoubi envelop of the category of algebraic lattices.
Edit intent: clarity
Original:  Strong proximity join-semilattices can be characterised by the coalgebras of the lower powerlocale on the wider category of proximity posets (  known as abstract bases and R-structure ).
Revised:  Strong proximity join-semilattices can be characterised by the coalgebras of the lower powerlocale on the wider category of proximity posets ( also known as abstract bases and R-structure ).
Edit intent: coherence
Original:  Strong proximity join-semilattices can be characterised by the coalgebras of the lower powerlocale on the wider category of proximity posets (  known as abstract bases and R-structure ).
Revised:  Strong proximity join-semilattices can be characterised by the coalgebras of the lower powerlocale on the wider category of proximity posets ( known as abstract bases or R-structures ).
Edit intent: fluency
Original:  The paper also introduces more logical characterisation of a strong proximity join-semilattice, called a strong continuous finitary cover, which uses an entailment relation to present the underlying join-semilattice.
Revised:  We also provide more logical characterisation of a strong proximity join-semilattice, called a strong continuous finitary cover, which uses an entailment relation to present the underlying join-semilattice.
Edit intent: clarity
Original:  It is shown that this structure naturally corresponds to the notion of continuous lattice in the predicative pointfree topology.
Revised:  We show that this structure naturally corresponds to the notion of continuous lattice in the predicative pointfree topology.
Edit intent: clarity
Original: The study of network robustness is a critical tool in the characterization and understanding of complex interconnected systems such as transportation, infrastructure, communication, and computer networks.
Revised: Network robustness plays a crucial role in our understanding of complex interconnected systems such as transportation, infrastructure, communication, and computer networks.
Edit intent: clarity
Original: The study of network robustness is a critical tool in the characterization and understanding of complex interconnected systems such as transportation, infrastructure, communication, and computer networks.
Revised: The study of network robustness is a critical tool in the characterization and understanding of complex interconnected systems such as transportation, communication, and computer networks.
Edit intent: clarity
Original:  Through analyzing and understanding the robustness of these networks we can:(1) quantify network vulnerability and robustness,(2) augment a network's structure to resist attacks and recover from failure, and (3) control the dissemination of entities on the network (e.g., viruses, propaganda).
Edit intent: coherence
Original:  While significant research has been conducted on all of these tasks , no comprehensive open-source toolbox currently exists to assist researchers and practitioners in this important topic.
Revised:  While significant research has been conducted in the area of network robustness , no comprehensive open-source toolbox currently exists to assist researchers and practitioners in this important topic.
Edit intent: clarity
Original:  15 heuristic and optimization based defense techniques;
Revised:  15 heuristic and optimization-based defense techniques;
Edit intent: fluency
Original:  Further improvements to our bounds would imply a breakthrough in circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019).
Revised:  Further improvements to our bounds would imply new classical circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019).
Edit intent: clarity
Original:  Manually optimized tensor operators have limitations in terms of supporting new operators or supporting new hardware platforms.
Revised:  Manually optimizing tensor operators has limitations in terms of supporting new operators or supporting new hardware platforms.
Edit intent: fluency
Original:  Manually optimized tensor operators have limitations in terms of supporting new operators or supporting new hardware platforms.
Revised:  Manually optimized tensor operators have limitations in terms of supporting new operators or hardware platforms.
Edit intent: fluency
Original:  In this work, we propose a novel evolutionary method, OpEvo, which efficiently explores the search spaces of tensor operators by introducing a topology-aware mutation operation based on q-random walk distribution to leverage the topological structures over the search spaces.
Revised:  In this work, we propose a novel evolutionary method, OpEvo, which efficiently explores the search spaces of tensor operators by introducing a topology-aware mutation operation based on q-random walk to leverage the topological structures over the search spaces.
Edit intent: clarity
Original:  Our comprehensive experiment results show that  OpEvo can find the best configuration with the least number of trials and the lowest variance compared with state-of-the-art methods .
Revised:  Our comprehensive experiment results show that OpEvo can find the best configuration with the least number of trials and wall-clock time .
Edit intent: clarity
Original: Designing an incentive compatible auction that maximizes expected revenue is a centralproblem in Auction Design.
Revised: Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design.
Edit intent: fluency