wanyu/IteraTeR_v2 huggingface dataset:
Original:  It turns out that the most difficult instances involve codes with non trivial stabilizer algebras {\em  .
Revised:  It turns out that the most difficult instances involve codes with non trivial {\em .
Edit intent: clarity
Original:  It turns out that the most difficult instances involve codes with non trivial stabilizer algebras {\em  .
Revised:  It turns out that the most difficult instances involve codes with non trivial stabilizer algebras {\em stabilizer algebras .
Edit intent: fluency
Original:  The resolution of the latter case will involve tools related to finite dimensional algebras and the so--called Wedderburn--Artin theory.
Revised:  The resolution of the latter case will involve tools related to finite dimensional algebras and Wedderburn--Artin theory.
Edit intent: clarity
Original:  This threshold is unphysical in generic systems with N=2 diffusing species, forcing experimental realizations of the instability to rely on fluctuations or additional non-diffusing species.
Revised:  This threshold is unphysical in most systems with N=2 diffusing species, forcing experimental realizations of the instability to rely on fluctuations or additional non-diffusing species.
Edit intent: clarity
Original:  This threshold is unphysical in generic systems with N=2 diffusing species, forcing experimental realizations of the instability to rely on fluctuations or additional non-diffusing species.
Revised:  This threshold is unphysical in generic systems with N=2 diffusing species, forcing experimental realizations of the instability to rely on fluctuations or additional nondiffusing species.
Edit intent: fluency
Original:  Inspired by May's analysis of the stability of random ecological communities, we analyze the threshold for reaction-diffusion systems whose linearized dynamics near a homogeneous fixed point are given by a random matrix .
Revised:  Inspired by May's analysis of the stability of random ecological communities, we analyze the threshold for reaction-diffusion systems whose linearized dynamics near a homogeneous fixed point .
Edit intent: clarity
Original:  Since validating drug combinations via direct screening is prohibitively expensive due to combinatorial explosion, recent approaches have applied machine learning to identify synergistic combinations for cancer .
Revised:  Recent approaches have applied machine learning to identify synergistic combinations for cancer .
Edit intent: coherence
Original:  Motivated by the fact that drug synergy is closely tied with biological targets, we propose a  model that jointly learns drug-target interaction and drug synergy.
Revised:  Given that drug synergy is closely tied with biological targets, we propose a model that jointly learns drug-target interaction and drug synergy.
Edit intent: coherence
Original:  Motivated by the fact that drug synergy is closely tied with biological targets, we propose a  model that jointly learns drug-target interaction and drug synergy.
Revised:  Motivated by the fact that drug synergy is closely tied to biological targets, we propose a model that jointly learns drug-target interaction and drug synergy.
Edit intent: fluency
Original:  Motivated by the fact that drug synergy is closely tied with biological targets, we propose a  model that jointly learns drug-target interaction and drug synergy.
Revised:  Motivated by the fact that drug synergy is closely tied with biological targets, we propose a model that jointly learns drug-target interaction and synergy.
Edit intent: fluency
Original:  The model , parametrized as a graph convolutional network, consists of two parts: a drug-target interaction and target-disease association module.
Revised:  The model consists of two parts: a drug-target interaction and target-disease association module.
Edit intent: coherence
Original:  This applies in particular to amenable graphs  such as graphs which are finite subsets of lattices.
Revised:  This applies in particular to amenable graphs , such as graphs which are finite subsets of lattices.
Edit intent: fluency
Original:  This representation is at the heart of the designing the polynomial time algorithms underlying the interpolation method itself.
Revised:  This representation is at the heart of the design of the polynomial time algorithms underlying the interpolation method itself.
Edit intent: fluency
Original:  We provide two key results towards unifying these approaches in terms of their interpretation and use.
Revised:  We provide two key results in terms of their interpretation and use.
Edit intent: clarity
Original:  We provide two key results towards unifying these approaches in terms of their interpretation and use.
Revised:  We provide two key results towards unifying these approaches in terms of their use.
Edit intent: clarity
Original:  As a result, we highlight the complementarity of these two approaches and provide an interpretation based on a causal inference framework .
Revised:  As a result, we highlight the complementarity of these two approaches .
Edit intent: coherence
Original:  Our evaluation on three benchmark datasets -- Adult Income , LendingClub, and GermanCredit -- confirm the complementarity.
Revised:  Our evaluation on three benchmark datasets -- Adult-Income , LendingClub, and GermanCredit -- confirm the complementarity.
Edit intent: fluency
Original:  Our evaluation on three benchmark datasets -- Adult Income , LendingClub, and GermanCredit -- confirm the complementarity.
Revised:  Our evaluation on three benchmark datasets -- Adult Income , LendingClub, and German-Credit -- confirm the complementarity.
Edit intent: fluency
Original:  Our evaluation on three benchmark datasets -- Adult Income , LendingClub, and GermanCredit -- confirm the complementarity.
Revised:  Our evaluation on three benchmark datasets -- Adult Income , LendingClub, and GermanCredit -- confirms the complementarity.
Edit intent: fluency
Original:  In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are  neither necessary nor sufficient explanations of a model's prediction.
Revised:  In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction.
Edit intent: clarity
Original: To explain a machine learning model, there are two main approaches: feature attributions that assign an importance score to each input feature, and counterfactual explanations that provide input examples with minimal changes to alter the model's prediction .
Revised: Feature attributions and counterfactual explanations are popular approaches to explain a ML model. The former assigns an importance score to each input feature, and counterfactual explanations that provide input examples with minimal changes to alter the model's prediction .
Edit intent: clarity
Original: To explain a machine learning model, there are two main approaches: feature attributions that assign an importance score to each input feature, and counterfactual explanations that provide input examples with minimal changes to alter the model's prediction .
Revised: To explain a machine learning model, there are two main approaches: feature attributions that assign an importance score to each input feature, while the latter provides input examples with minimal changes to alter the model's prediction .
Edit intent: clarity
Original: To explain a machine learning model, there are two main approaches: feature attributions that assign an importance score to each input feature, and counterfactual explanations that provide input examples with minimal changes to alter the model's prediction .
Revised: To explain a machine learning model, there are two main approaches: feature attributions that assign an importance score to each input feature, and counterfactual explanations that provide input examples with minimal changes to alter the model's predictions .
Edit intent: fluency
Original:  These feature attributions convey how important a feature is to changing the classification outcome of a model, especially on whether a subset of features is necessary and/or sufficient for that change, which feature attribution methods are unable to provide.
Revised:  These feature attributions convey how important a feature is to changing the classification outcome of a model, especially on whether a subset of features is necessary and/or sufficient for that change, which attribution-based methods are unable to provide.
Edit intent: clarity
Original:  Our evaluation on three benchmark datasets -- Adult-Income, LendingClub, and German-Credit -- confirms the complementarity.
Revised:  Our evaluation on three benchmark datasets - Adult-Income, LendingClub, and German-Credit -- confirms the complementarity.
Edit intent: fluency
Original:  Our evaluation on three benchmark datasets -- Adult-Income, LendingClub, and German-Credit -- confirms the complementarity.
Revised:  Our evaluation on three benchmark datasets -- Adult-Income, LendingClub, and German-Credit - confirms the complementarity.
Edit intent: fluency
Original:  Finally, we present a case study of different explanation methods on a real-world hospital triage problem .
Revised:  Finally, we present a case study of different explanation methods on a real-world hospital triage problem 
Edit intent: fluency
Original:  To the best of the authors' knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable modeling , contact, and actuation .
Revised:  To the best of the authors' knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable modeling , contact, and actuation .
Edit intent: fluency
Original:  To the best of the authors' knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable modeling , contact, and actuation .
Revised:  To the best of the authors' knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable , contact, and actuation .
Edit intent: clarity
Original:  In particular, after the engine has been trained with few trajectories to match a ground truth simulated model, then a policy learned on the\\%DIF > of ground truth data are needed to train  differentiable engine is shown to be transferable back to the ground-truth model.
Revised:  In particular, after the engine has been trained with few trajectories to match a ground truth simulated model, then a policy learned on the\\%DIF > of ground truth data are needed to train a policy that works on the ground truth system when the differentiable engine is shown to be transferable back to the ground-truth model.
Edit intent: clarity
Original:  Different from Boudt and Pertitjean(2014), our analyzing scheme can explore the "time-series information " embedded in the trading-related attributes and provides a set of jump indicators for abnormal pattern recognition .
Revised:  Different from the existing literature, our methodology takes into account the temporal information embedded in the trading-related attributes and provides a set of jump indicators for abnormal pattern recognition .
Edit intent: clarity
Original:  In addition to the commonly used liquidity measures , our analysis also involves a set of technical indicators to describe the micro-trading behaviors.
Revised:  In addition , our analysis also involves a set of technical indicators to describe the micro-trading behaviors.
Edit intent: clarity
Original:  In addition to the commonly used liquidity measures , our analysis also involves a set of technical indicators to describe the micro-trading behaviors.
Revised:  In addition to the commonly used liquidity measures , our analysis involves a set of technical indicators to describe the micro-trading behaviors.
Edit intent: clarity
Original:  In addition to the commonly used liquidity measures , our analysis also involves a set of technical indicators to describe the micro-trading behaviors.
Revised:  In addition to the commonly used liquidity measures , our analysis also involves a set of technical indicators to describe micro-trading behaviors.
Edit intent: fluency
Original:  An empirical study is conducted on the level-2 data of the constituent stocks of  China Security Index 300.
Revised:  An empirical study is conducted on the level-2 data of the constituent stocks of the China Security Index 300.
Edit intent: fluency
Original:  In this paper, a general method is proposed to collect Chinese music in the form of numbered musical notation, and a Dizi dataset is collected using the method.
Revised:  In this paper, a general method is proposed to collect Chinese music in the form of numbered musical notation, and a Dizi dataset is collected using this method.
Edit intent: fluency
Original:  Based on the collected Dizi dataset, we conduct research on the Dizi music style of the Northern school and the Southern School.
Revised:  Based on the collected Dizi dataset, we conduct research on the Dizi music styles of the Northern school and the Southern School.
Edit intent: fluency
Original:  A reconstruction example, music style transfer which includes melody transfer and playing techniques transfer is given and subjective evaluation is done to evaluate the reconstruction results.
Revised:  A reconstruction example, music style transfer which includes melody transfer and playing techniques transfer is given and audience evaluation is done to evaluate the reconstruction results.
Edit intent: clarity
Original: We extend the general operator preconditioning framework [R. Hiptmair, Comput.
Revised: We extend the operator preconditioning framework [R. Hiptmair, Comput.
Edit intent: clarity
Original:  By explicitly considering different perturbation parameters for the original equation and its preconditioning , our bi-parametric abstract setting leads to endomorphisms.
Revised:  By explicitly considering different perturbation parameters for the original form and its preconditioner , our bi-parametric abstract setting leads to endomorphisms.
Edit intent: clarity
Original: Multimedia applications, like video streaming, teleconference, tele-control of vehicles/drones, tactile internet, just to mention a few, require guaranteed transmission resources at regular intervals .
Revised: Multimedia applications, like video streaming, eXtended Reality or remote robot control, are characterized by periodic data transmissions with strict latency and reliability constraints .
Edit intent: clarity
Original:  In an effort to meet the stringent demand of such quasi-periodic QoS-constrained traffic sources, the Wireless Gigabit (WiGig ) standard supports a contention-free channel access mechanism  that makes it possible to allocate dedicated time intervals , named Service Periods (SPs), to traffic sources that require guaranteed access to transmission resources at regular intervals.
Revised:  In an effort to meet the stringent demand of such traffic sources, the Wireless Gigabit (WiGig ) standard supports a contention-free channel access mechanism that makes it possible to allocate dedicated time intervals , named Service Periods (SPs), to traffic sources that require guaranteed access to transmission resources at regular intervals.
Edit intent: clarity
Original:  In an effort to meet the stringent demand of such quasi-periodic QoS-constrained traffic sources, the Wireless Gigabit (WiGig ) standard supports a contention-free channel access mechanism  that makes it possible to allocate dedicated time intervals , named Service Periods (SPs), to traffic sources that require guaranteed access to transmission resources at regular intervals.
Revised:  In an effort to meet the stringent demand of such quasi-periodic QoS-constrained traffic sources, the WiGig standards support a contention-free channel access mechanism that makes it possible to allocate dedicated time intervals , named Service Periods (SPs), to traffic sources that require guaranteed access to transmission resources at regular intervals.
Edit intent: clarity
Original:  As usual, however, the standard only covers the fundamental aspects that ensure interoperability, while the actual schedule logic is left open to different implementations .
Revised:  As usual, however, the standard only covers the fundamental aspects that ensure interoperability, while the actual schedule logic is left to vendors .
Edit intent: clarity
Original:  In this paper, we propose two algorithms for joint admission control and scheduling of periodic traffic streams with opposite performance objectives, specifically acceptance rate vs allocation time .
Revised:  In this paper, we propose two algorithms for joint admission control and scheduling of periodic traffic streams with contrasting performance objectives, specifically acceptance rate vs allocation time .
Edit intent: clarity
Original:  The schemes are compared in two different scenarios, in order to shed light on some fundamental trade-offs.
Revised:  The schemes are compared in two different scenarios, in order to characterize and highlight some fundamental trade-offs.
Edit intent: clarity
Original:  This paper proposes and evaluates three deep learning models (Long Short Term Memory networks , Gated Recurrent Unit networks , and Graph Convolutional Networks) as a method to predict the stability/ reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates.
Revised:  This paper proposes and evaluates three deep learning models (Long Short Term Memory , Gated Recurrent Unit networks , and Graph Convolutional Networks) as a method to predict the stability/ reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates.
Edit intent: fluency
Original:  This paper proposes and evaluates three deep learning models (Long Short Term Memory networks , Gated Recurrent Unit networks , and Graph Convolutional Networks) as a method to predict the stability/ reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates.
Revised:  This paper proposes and evaluates three deep learning models (Long Short Term Memory networks , Gated Recurrent Unit , and Graph Convolutional Networks) as a method to predict the stability/ reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates.
Edit intent: fluency
Original:  This paper proposes and evaluates three deep learning models (Long Short Term Memory networks , Gated Recurrent Unit networks , and Graph Convolutional Networks) as a method to predict the stability/ reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates.
Revised:  This paper proposes and evaluates three deep learning models (Long Short Term Memory networks , Gated Recurrent Unit networks , and Graph Convolutional Networks) as methods to predict the stability/ reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates.
Edit intent: fluency
Original:  This paper proposes and evaluates three deep learning models (Long Short Term Memory networks , Gated Recurrent Unit networks , and Graph Convolutional Networks) as a method to predict the stability/ reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates.
Revised:  This paper proposes and evaluates three deep learning models (Long Short Term Memory networks , Gated Recurrent Unit networks , and Graph Convolutional Networks) as a method to predict the reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates.
Edit intent: coherence
Original:  Reasonably accurate results were able to be generated with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation under various circumstances (RMSE = 0.266).
Revised:  Reasonably accurate results were able to be generated with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation (RMSE = 0.266).
Edit intent: clarity
Original:  Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\%  . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Revised:  Combining all target variables, the GRU performed the best with an accuracy value of 76\% . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Edit intent: coherence
Original:  Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\%  . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Revised:  Overall, combining all target variables, the GRU performed the best with 76\% . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Edit intent: clarity
Original:  Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\%  . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Revised:  Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\% accuracy . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Edit intent: clarity
Original:  Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\%  . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Revised:  Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\% . Results suggest these models can be applied to understand and predict the chemical stability of mRNA in the near future.
Edit intent: clarity
Original: Understanding the relationship between complexity and stability in large dynamical systems - such as ecosystems - remains a key open question in complexity theory which has inspired a rich body of work developed over more than fifty years.
Revised: Understanding the relationship between complexity and stability in large dynamical systems -- such as ecosystems - remains a key open question in complexity theory which has inspired a rich body of work developed over more than fifty years.
Edit intent: fluency
Original: Understanding the relationship between complexity and stability in large dynamical systems - such as ecosystems - remains a key open question in complexity theory which has inspired a rich body of work developed over more than fifty years.
Revised: Understanding the relationship between complexity and stability in large dynamical systems - such as ecosystems -- remains a key open question in complexity theory which has inspired a rich body of work developed over more than fifty years.
Edit intent: fluency