wanyu/IteraTeR_v2 huggingface dataset:
Original:  Also, we show that several variables that significantly correlate with transmissibility do not directly influence R_0 or affect it differently than suggested by naive analysis.
Revised:  Also, we argue that several variables that significantly correlate with transmissibility do not directly influence R_0 or affect it differently than suggested by naive analysis.
Edit intent: clarity
Original:  Our results unveil internal mechanisms of CBO that are responsible for the success of the method.
Revised:  The result unveils internal mechanisms of CBO that are responsible for the success of the method.
Edit intent: fluency
Original:  As a by-product of our analysis, we establish a quantitative nonasymptotic Laplace principle, which may be of independent interest.
Revised:  As a by-product of the analysis, we establish a quantitative nonasymptotic Laplace principle, which may be of independent interest.
Edit intent: fluency
Original:  We show that the GCNM has strong generalizability to different domain shapes  , out of distribution data as well as experimental data, from purely simulated training data  .
Revised:  We show that the GCNM has strong generalizability to different domain shapes and meshes , out of distribution data as well as experimental data, from purely simulated training data .
Edit intent: clarity
Original: Recently discovered simple quantitative relations knowns as bacterial growth laws hints on the existence of simple underlying principles at the heart of bacterial growth.
Revised: Recently discovered simple quantitative relations known as bacterial growth laws hints on the existence of simple underlying principles at the heart of bacterial growth.
Edit intent: fluency
Original:  We derive two kinds of growth laws per autocatalytic cycle,  relating growth rate to the relative fraction of the catalyst and its catalysis rate and relating growth rate to all the time-scales in the cycle.
Revised:  We derive two types of growth laws per autocatalytic cycle, relating growth rate to the relative fraction of the catalyst and its catalysis rate and relating growth rate to all the time-scales in the cycle.
Edit intent: clarity
Original:  We derive two kinds of growth laws per autocatalytic cycle,  relating growth rate to the relative fraction of the catalyst and its catalysis rate and relating growth rate to all the time-scales in the cycle.
Revised:  We derive two kinds of growth laws per autocatalytic cycle, relating growth rate to the relative fraction of the catalyst and its catalysis rate and relating growth rate to all the time--scales in the cycle.
Edit intent: fluency
Original:  The autocatalytic network structure defines multiple growth regimes , determined by the limiting components .
Revised:  The structure of the autocatalytic network generates numerous regimes in state space , determined by the limiting components .
Edit intent: clarity
Original:  The number of growth regimes can be very large , while the number of growth laws obtained can be much smaller.
Revised:  The number of growth regimes can be very large , while the number of growth laws can be much smaller.
Edit intent: clarity
Original:  We further derive growth laws for tRNA synthesis and charging  and predict how perturbations to ribosome assembly or membrane synthesisaffect the growth rate .
Revised:  We derive growth laws for tRNA synthesis and charging and predict how perturbations to ribosome assembly or membrane synthesisaffect the growth rate .
Edit intent: clarity
Original:  We further derive growth laws for tRNA synthesis and charging  and predict how perturbations to ribosome assembly or membrane synthesisaffect the growth rate .
Revised:  We further derive growth laws for tRNA synthesis and charging , and predict how perturbations to ribosome assembly or membrane synthesisaffect the growth rate .
Edit intent: fluency
Original:  We further derive growth laws for tRNA synthesis and charging  and predict how perturbations to ribosome assembly or membrane synthesisaffect the growth rate .
Revised:  We further derive growth laws for tRNA synthesis and charging and predict how perturbations to ribosome assembly and membrane synthesis .
Edit intent: clarity
Original: Recently discovered simple quantitative relations  known as bacterial growth laws hints on the existence of simple underlying principles at the heart of bacterial growth.
Revised: Recently discovered simple quantitative relations , known as bacterial growth laws hints on the existence of simple underlying principles at the heart of bacterial growth.
Edit intent: fluency
Original: Recently discovered simple quantitative relations  known as bacterial growth laws hints on the existence of simple underlying principles at the heart of bacterial growth.
Revised: Recently discovered simple quantitative relations known as bacterial growth laws , hint on the existence of simple underlying principles at the heart of bacterial growth.
Edit intent: fluency
Original:  We derive growth laws for tRNA synthesis and charging, and predict how growth rate depends on temperature, perturbation to ribosome assembly  and membrane synthesis.
Revised:  We derive growth laws for tRNA synthesis and charging, and predict how growth rate depends on temperature, perturbation to ribosome assembly , and membrane synthesis.
Edit intent: fluency
Original:  In this paper, we tackle the problem of dynamics mismatch and propose a distributionally robust optimal control formulation that alternates between two relative-entropy trust region optimization problems.
Revised:  In this paper, we tackle the problem of dynamics mismatch and propose a distributionally robust optimal control formulation that alternates between two relative entropy trust-region optimization problems.
Edit intent: fluency
Original:  Our method finds the worst-case maximum-entropy Gaussian posterior over the dynamics parameters and the corresponding robust optimal policy.
Revised:  Our method finds the worst-case maximum entropy Gaussian posterior over the dynamics parameters and the corresponding robust optimal policy.
Edit intent: fluency
Original:  Our method finds the worst-case maximum-entropy Gaussian posterior over the dynamics parameters and the corresponding robust optimal policy.
Revised:  Our method finds the worst-case maximum-entropy Gaussian posterior over the dynamics parameters and the corresponding robust policy.
Edit intent: clarity
Original:  Despite their success, such approaches are still susceptible to catastrophic errors that may arise due to statistical learning biases, unmodeled disturbances  or even directed adversarial attacks.
Revised:  Despite their success, such approaches are still susceptible to catastrophic errors that may arise due to statistical learning biases, unmodeled disturbances , or even directed adversarial attacks.
Edit intent: fluency
Original:  We show that our approach admits a closed-form backward-pass for a certain class of systems and demonstrate the resulting robustness on linear and nonlinear numerical examples.
Revised:  Furthermore, we show that our approach admits a closed-form backward-pass for a certain class of systems and demonstrate the resulting robustness on linear and nonlinear numerical examples.
Edit intent: coherence
Original:  We show that our approach admits a closed-form backward-pass for a certain class of systems and demonstrate the resulting robustness on linear and nonlinear numerical examples.
Revised:  We show that our approach admits a closed-form backward-pass for a certain class of systems . Finally, we demonstrate the resulting robustness on linear and nonlinear numerical examples.
Edit intent: coherence
Original:  However, the current design of Rust also brings side effects which may increase the risk of dangling pointers .
Revised:  However, the current design of Rust also brings side effects which may increase the risk of memory-safety issues .
Edit intent: clarity
Original:  Our approach analyzes each public API of a Rust crate iteratively by traversing the control-flow graph and extracting all aliases of each data flow .
Revised:  Our approach analyzes each API of a Rust crate iteratively by traversing the control-flow graph and extracting all aliases of each data flow .
Edit intent: clarity
Original:  Our approach analyzes each public API of a Rust crate iteratively by traversing the control-flow graph and extracting all aliases of each data flow .
Revised:  Our approach analyzes each public API of a Rust crate iteratively by traversing the control-flow graph and extracting all aliases of each data-flow .
Edit intent: fluency
Original:  The analysis overhead ranges from 4.4\% to 87.2 \% in comparison with the original program compilation time.
Revised:  The analysis overhead ranges from 4.4\% to 87.2 \% in comparison with the original compilation time.
Edit intent: clarity
Original:  We measure the spike and decay of attention following death and model them as the interplay of communicative and cultural memory.
Revised:  We measure the sharp spike and rapid decay of attention following death and model them as the interplay of communicative and cultural memory.
Edit intent: clarity
Original:  We measure the spike and decay of attention following death and model them as the interplay of communicative and cultural memory.
Revised:  We measure the spike and decay of attention following death and model collective memory as a composition of communicative and cultural memory.
Edit intent: clarity
Original:  Clustering reveals four patterns of post-mortem memory, and regression analysis shows that boosts in media attention are largest for pre-mortem popular anglophones of any gender who died a young, unnatural death;
Revised:  Clustering reveals four patterns of post-mortem memory, and regression analysis shows that boosts in media attention are largest for pre-mortem popular anglophones who died a young, unnatural death;
Edit intent: clarity
Original:  Meanwhile, the disease may continue to spreads exponentially unless checked by Non Pharmacological Interventions (NPI).
Revised:  Meanwhile, the disease may continue to spread exponentially unless checked by Non Pharmacological Interventions (NPI).
Edit intent: fluency
Original:  We finally cast simulation of simultaneous impacts as a linear complementarity problem (LCP), and develop a probabilistically-complete algorithm for approximating the post-impact velocity set  .
Revised:  We finally cast simulation of simultaneous impacts as a linear complementarity problem (LCP), and develop an algorithm for tight approximation of the post-impact velocity set .
Edit intent: clarity
Original: Interventional causal models describe  joint distributions over some variables used to describe a system, one for each intervention setting.
Revised: Interventional causal models describe several joint distributions over some variables used to describe a system, one for each intervention setting.
Edit intent: clarity
Original:  Yet, it is difficult to formalise how we may change the underlying variables used to describe the system, say  from fine-grained to coarse-grained variables.
Revised:  Yet, it is difficult to formalise how we may change the underlying variables used to describe the system, say moving from fine-grained to coarse-grained variables.
Edit intent: clarity
Original:  Here, we argue that compositionality is a desideratum for  model transformations and the associated errors .
Revised:  Here, we argue that compositionality is a desideratum for such model transformations and the associated errors .
Edit intent: clarity
Original:  We develop a framework for model transformations and abstractions with a notion of error that is compositional: when abstracting a reference model M modularly , first obtaining M' and then further simplifying that to obtain M'', then the composite transformation from M to M'' exists and its error can be bounded by the errors incurred by each individual transformation step.
Revised:  We develop a framework for model transformations and abstractions with a notion of error that is compositional: when abstracting a reference model M iteratively , first obtaining M' and then further simplifying that to obtain M'', then the composite transformation from M to M'' exists and its error can be bounded by the errors incurred by each individual transformation step.
Edit intent: clarity
Original:  We develop a framework for model transformations and abstractions with a notion of error that is compositional: when abstracting a reference model M modularly , first obtaining M' and then further simplifying that to obtain M'', then the composite transformation from M to M'' exists and its error can be bounded by the errors incurred by each individual transformation step.
Revised:  We develop a framework for model transformations and abstractions with a notion of error that is compositional: when abstracting a reference model M modularly , first obtaining M' and then further simplifying that to obtain M'', we expect the composite transformation from M to M'' exists and its error can be bounded by the errors incurred by each individual transformation step.
Edit intent: clarity
Original:  We develop a framework for model transformations and abstractions with a notion of error that is compositional: when abstracting a reference model M modularly , first obtaining M' and then further simplifying that to obtain M'', then the composite transformation from M to M'' exists and its error can be bounded by the errors incurred by each individual transformation step.
Revised:  We develop a framework for model transformations and abstractions with a notion of error that is compositional: when abstracting a reference model M modularly , first obtaining M' and then further simplifying that to obtain M'', then the composite transformation from M to M'' to exist and its error can be bounded by the errors incurred by each individual transformation step.
Edit intent: clarity
Original:  We develop a framework for model transformations and abstractions with a notion of error that is compositional: when abstracting a reference model M modularly , first obtaining M' and then further simplifying that to obtain M'', then the composite transformation from M to M'' exists and its error can be bounded by the errors incurred by each individual transformation step.
Revised:  We develop a framework for model transformations and abstractions with a notion of error that is compositional: when abstracting a reference model M modularly , first obtaining M' and then further simplifying that to obtain M'', then the composite transformation from M to M'' exists and its error to be bounded by the errors incurred by each individual transformation step.
Edit intent: coherence
Original:  Category theory, the study of mathematical objects via the compositional transformations between them, offers a natural language for developing our framework .
Revised:  Category theory, the study of mathematical objects via compositional transformations between them, offers a natural language for developing our framework .
Edit intent: fluency
Original:  We introduce a category of finite interventional causal models and, leveraging theory of enriched categories, prove that our framework enjoys the desired compositionality properties  .
Revised:  We introduce a category of finite interventional causal models and, leveraging theory of enriched categories, prove the desired compositionality properties .
Edit intent: clarity
Original:  We introduce a category of finite interventional causal models and, leveraging theory of enriched categories, prove that our framework enjoys the desired compositionality properties  .
Revised:  We introduce a category of finite interventional causal models and, leveraging theory of enriched categories, prove that our framework enjoys the desired compositionality properties for our framework .
Edit intent: clarity
Original:  Motivated by this broader vision for NAS , we study the problem of enabling users to discover the right neural operations given data from their specific domain.
Revised:  Motivated by this goal , we study the problem of enabling users to discover the right neural operations given data from their specific domain.
Edit intent: clarity
Original:  We introduce a search space of neural operations called XD-Operations that mimic the inductive bias of standard multichannel convolutions while being much more expressive: we prove that XD-operations include many named operations across several application areas.
Revised:  We introduce a search space of operations called XD-Operations that mimic the inductive bias of standard multichannel convolutions while being much more expressive: we prove that XD-operations include many named operations across several application areas.
Edit intent: clarity
Original:  We introduce a search space of neural operations called XD-Operations that mimic the inductive bias of standard multichannel convolutions while being much more expressive: we prove that XD-operations include many named operations across several application areas.
Revised:  We introduce a search space of neural operations called XD-Operations that mimic the inductive bias of standard multi-channel convolutions while being much more expressive: we prove that XD-operations include many named operations across several application areas.
Edit intent: fluency
Original:  We introduce a search space of neural operations called XD-Operations that mimic the inductive bias of standard multichannel convolutions while being much more expressive: we prove that XD-operations include many named operations across several application areas.
Revised:  We introduce a search space of neural operations called XD-Operations that mimic the inductive bias of standard multichannel convolutions while being much more expressive: we prove that it includes many named operations across several application areas.
Edit intent: clarity
Original:  We introduce a search space of neural operations called XD-Operations that mimic the inductive bias of standard multichannel convolutions while being much more expressive: we prove that XD-operations include many named operations across several application areas.
Revised:  We introduce a search space of neural operations called XD-Operations that mimic the inductive bias of standard multichannel convolutions while being much more expressive: we prove that XD-operations include many named operations across multiple application areas.
Edit intent: clarity
Original:  Starting with any standard backbone network such as LeNet or ResNet, we show how to transform it into an architecture search space over XD-operations and how to traverse the space using a simple weight-sharing scheme.
Revised:  Starting with any standard backbone such as ResNet, we show how to transform it into an architecture search space over XD-operations and how to traverse the space using a simple weight-sharing scheme.
Edit intent: clarity
Original:  Starting with any standard backbone network such as LeNet or ResNet, we show how to transform it into an architecture search space over XD-operations and how to traverse the space using a simple weight-sharing scheme.
Revised:  Starting with any standard backbone network such as LeNet or ResNet, we show how to transform it into a search space over XD-operations and how to traverse the space using a simple weight-sharing scheme.
Edit intent: clarity
Original:  On a diverse set of applications--image classification, solving partial differential equations (PDEs), and sequence modeling--our approach consistently yields models with lower error than baseline networks and sometimes even lower error than expert-designed domain-specific approaches.
Revised:  On a diverse set of applications--image classification, solving partial differential equations (PDEs), and sequence modeling--our approach consistently yields models with lower error than baseline networks and often even lower error than expert-designed domain-specific approaches.
Edit intent: clarity
Original: Combining two elementary proofs  we decrease the gap between the upper and lower bounds by 0.2\\%DIF < in a classical combinatorial number theory problem.
Revised: Combining two elementary proofs , we decrease the gap between the upper and lower bounds by 0.2\\%DIF < in a classical combinatorial number theory problem.
Edit intent: fluency
Original:  The distributed algorithm has applications in cloud computing and spectrum sharing. keywords : Distributed learning, congestion games, poly-logarithmic regret.
Revised:  The distributed algorithm has applications in cloud computing and spectrum sharing. Keywords : Distributed learning, congestion games, poly-logarithmic regret.
Edit intent: fluency
Original:  We consider the scenario of the recent CORSMAL Containers Manipulation dataset and consider two actions (shaking and pouring), and seven  material and filling level combinations .
Revised:  We use the scenario of the recent CORSMAL Containers Manipulation dataset and consider two actions (shaking and pouring), and seven material and filling level combinations .
Edit intent: fluency
Original:  We consider the scenario of the recent CORSMAL Containers Manipulation dataset and consider two actions (shaking and pouring), and seven  material and filling level combinations .
Revised:  We consider the scenario of the recent CORSMAL Containers Manipulation dataset and consider two actions (shaking and pouring), and seven combinations of material and filling level combinations .
Edit intent: clarity
Original:  We consider the scenario of the recent CORSMAL Containers Manipulation dataset and consider two actions (shaking and pouring), and seven  material and filling level combinations .
Revised:  We consider the scenario of the recent CORSMAL Containers Manipulation dataset and consider two actions (shaking and pouring), and seven material and filling level .
Edit intent: clarity
Original:  The first step identifies the action a person performs while manipulating a container.
Revised:  The first step identifies the action performed by a person with the container.
Edit intent: clarity
Original:  The second step is an appropriate classifier trained for the specific interaction identified by the first step to classify the amount and type of content  . Experiments show that the proposed model achieves 76.02, 78.24, and 41.89 weighted average F1 score on the three test sets, respectively, and outperforms baselines and existing approaches that classify either independently content level and content type or directly the combination of content type and level together .
Revised:  The second step determines the amount and type of content . Experiments show that the proposed model achieves 76.02, 78.24, and 41.89 weighted average F1 score on the three test sets, respectively, and outperforms baselines and existing approaches that classify either independently content level and content type or directly the combination of content type and level together .
Edit intent: clarity
Original:  The second step is an appropriate classifier trained for the specific interaction identified by the first step to classify the amount and type of content  . Experiments show that the proposed model achieves 76.02, 78.24, and 41.89 weighted average F1 score on the three test sets, respectively, and outperforms baselines and existing approaches that classify either independently content level and content type or directly the combination of content type and level together .
Revised:  The second step is an appropriate classifier trained for the specific interaction identified by the first step to classify the amount and type of content . Experiments show that the proposed model achieves 76.02, 78.24, and 41.89 weighted average F1 score on the three test sets, respectively, and outperforms baselines and existing approaches that classify the content amount and type either independently or jointly .
Edit intent: clarity
Original:  consequently, the only way to interpret these values is by comparison with other academics within the same field and of similar seniority.
Revised:  consequently, the only way to interpret these values is by comparison with other academics within the same field who are of similar seniority.
Edit intent: clarity