wanyu/IteraTeR_full_sent huggingface dataset:
Original: We present convergence rates of operator learning in [Chen and Chen 1995] and [Lu et al. 2020] when the operators are solution operators of differential equations. In particular , we consider solution operators of both linear and nonlinear advection-diffusion equations  .
Revised: We present convergence rates of operator learning in [Chen and Chen 1995] and [Lu et al. 2020] , where continuous operators are approximated by a sum of products of branch and trunk networks. In this work , we consider solution operators of both linear and nonlinear advection-diffusion equations .
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Original:  In this paper, we show that molecular IFCs  suffer from a hazardous instability called negative-equilibrium catastrophe (NEC), whereby all nonnegative equilibria vanish under the action of the controllers, and some of the underlying molecular abundances blow up.
Revised:  In this paper, we show that molecular IFCs can suffer from a hazardous instability called negative-equilibrium catastrophe (NEC), whereby all nonnegative equilibria vanish under the action of the controllers, and some of the underlying molecular abundances blow up.
Edit intent: clarity
Original:  In this paper, we show that molecular IFCs  suffer from a hazardous instability called negative-equilibrium catastrophe (NEC), whereby all nonnegative equilibria vanish under the action of the controllers, and some of the underlying molecular abundances blow up.
Revised:  In this paper, we show that molecular IFCs suffer from a hazardous instability called negative-equilibrium catastrophe (NEC), whereby all nonnegative equilibria vanish under the action of the controllers, and some of the molecular abundances blow up.
Edit intent: clarity
Original:  However, we show that, without a detailed knowledge of the controlled systems, NECs cannot be prevented when IFCs are applied on bimolecular, and hence the vast majority of intracellular, networks .
Revised:  However, we show that, without a detailed knowledge of the controlled systems, NECs cannot be prevented when IFCs are applied on uncertain bimolecular (and hence most intracellular) networks, we show that preventing NECs generally becomes an intractable problem as the number of interacting molecular species increases .
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Original:  To discuss such impacts on XVA from changes to instantaneous hazard rates we introduce a flexible and expressive parameterization to capture the path of this impact to climate change endpoints, and  transition effects.
Revised:  To discuss such impacts on CVA+FVA from changes to instantaneous hazard rates we introduce a flexible and expressive parameterization to capture the path of this impact to climate change endpoints, and transition effects.
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Original:  To discuss such impacts on XVA from changes to instantaneous hazard rates we introduce a flexible and expressive parameterization to capture the path of this impact to climate change endpoints, and  transition effects.
Revised:  To discuss such impacts on XVA from changes to instantaneous hazard rates we introduce a flexible and expressive parameterization to capture the path of this impact to climate change endpoints, and transient transition effects.
Edit intent: fluency
Original:  We find that even with the slowest possible uniform approach to a climate change impact in 2101 there can still be significant XVA impacts on interest rate swaps of 20 years or more maturity.
Revised:  We find that even with the slowest possible uniform approach to a climate change impact in 2101 there can still be significant CVA+FVA impacts on interest rate swaps of 20 years or more maturity.
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Original: This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches , surpassing the 50\% exact match on NaturalQuestions and EfficentQA datasets . Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever,  reranker, extractive reader, generative reader and a simple way to combine them.
Revised: This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches . Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever, reranker, extractive reader, generative reader and a simple way to combine them.
Edit intent: coherence
Original: This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches , surpassing the 50\% exact match on NaturalQuestions and EfficentQA datasets . Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever,  reranker, extractive reader, generative reader and a simple way to combine them.
Revised: This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches , surpassing the 50\% exact match on NaturalQuestions and EfficentQA datasets . Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever, passage reranker, extractive reader, generative reader and a simple way to combine them.
Edit intent: fluency
Original:  Compared to previous efforts on multi-task learning with transformers, we share the same model parameters to all tasks instead of separately fine-tuning task-specific models and handle a much higher variety of tasks across different domains.
Revised:  Compared to previous efforts on multi-task learning with transformers, we share the same model parameters across all tasks instead of separately fine-tuning task-specific models and handle a much higher variety of tasks across different domains.
Edit intent: clarity
Original:  LightCAKE  uses an iterative aggregation strategy to integrate the context information in multi-hop into the entity/relation embeddings , also explicitly models the graph context without introducing extra trainable parameters other than embeddings.
Revised:  LightCAKE explicitly models the graph context without introducing redundant trainable parameters, and uses an iterative aggregation strategy to integrate the context information in multi-hop into the entity/relation embeddings , also explicitly models the graph context without introducing extra trainable parameters other than embeddings.
Edit intent: coherence
Original:  LightCAKE  uses an iterative aggregation strategy to integrate the context information in multi-hop into the entity/relation embeddings , also explicitly models the graph context without introducing extra trainable parameters other than embeddings.
Revised:  LightCAKE uses an iterative aggregation strategy to integrate the context information into the entity/relation embeddings , also explicitly models the graph context without introducing extra trainable parameters other than embeddings.
Edit intent: meaning-changed
Original:  LightCAKE  uses an iterative aggregation strategy to integrate the context information in multi-hop into the entity/relation embeddings , also explicitly models the graph context without introducing extra trainable parameters other than embeddings. Moreover , extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework.
Revised:  LightCAKE uses an iterative aggregation strategy to integrate the context information in multi-hop into the entity/relation embeddings . As a generic framework, it can be used with many simple KGE models to achieve excellent results. Finally , extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework.
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Original: Almost all visual transformers such as ViT or DeiT rely on predefined positional encodings to incorporate the order of each input token. These encodings are often implemented as learnable fixed-dimension vectors or sinusoidal functions of different frequencies , which are not possible to accommodate variable-length input sequences.
Revised: We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings , which are not possible to accommodate variable-length input sequences.
Edit intent: clarity
Original:  These encodings are often implemented as learnable fixed-dimension vectors or sinusoidal functions of different frequencies , which are not possible to accommodate variable-length input sequences. This inevitably limits a wider application of transformers in vision, where many tasks require changing the input size on-the-fly. In this paper, we propose to employ a conditional position encoding scheme, which is conditioned on the local neighborhood of the input token.
Revised:  These encodings are often implemented as learnable fixed-dimension vectors or sinusoidal functions of different frequencies , which are pre-defined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input token.
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Original:  In this paper, we propose to employ a conditional position encoding scheme, which is conditioned on the local neighborhood of the input token. It is effortlessly implemented as what we call Position Encoding Generator (PEG), which can be seamlessly incorporated into the current transformer framework.
Revised:  In this paper, we propose to employ a conditional position encoding scheme, which is conditioned on the local neighborhood of the input tokens. As a result, CPE can easily generalize to the input sequences that are longer than what the model has ever seen during training. Besides, CPE can keep the desired translation-invariance in the image classification task, resulting in improved classification accuracy. CPE can be effortlessly implemented with a simple Position Encoding Generator (PEG), which can be seamlessly incorporated into the current transformer framework.
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Original:  It is effortlessly implemented as what we call Position Encoding Generator (PEG), which can be seamlessly incorporated into the current transformer framework. Our new model with PEGis named Conditional Position encoding Visual Transformer (CPVT) and can naturally process the input sequences of arbitrary length .
Revised:  It is effortlessly implemented as what we call Position Encoding Generator (PEG), which can be seamlessly incorporated into the current Transformer framework. Built on PEG, we present Conditional Position encoding Visual Transformer (CPVT) and can naturally process the input sequences of arbitrary length .
Edit intent: clarity
Original: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns ,%DIFDELCMD < {\em %%% verbs , and%DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Revised: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% ,%DIFDELCMD < {\em %%% verbs , and%DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Edit intent: clarity
Original: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns ,%DIFDELCMD < {\em %%% verbs , and%DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Revised: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns %DIFDELCMD < {\em %%% verbs , and%DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Edit intent: fluency
Original: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns ,%DIFDELCMD < {\em %%% verbs , and%DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Revised: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns ,%DIFDELCMD < {\em %%% , and%DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Edit intent: clarity
Original: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns ,%DIFDELCMD < {\em %%% verbs , and%DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Revised: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns ,%DIFDELCMD < {\em %%% verbs %DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Edit intent: fluency
Original: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns ,%DIFDELCMD < {\em %%% verbs , and%DIFDELCMD < {\em %%% others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Revised: We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes ( %DIFDELCMD < {\em %%% nouns ,%DIFDELCMD < {\em %%% verbs , and%DIFDELCMD < {\em %%% nouns, verbs, and others ) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role.
Edit intent: clarity
Original: Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans may still be difficult for neural models. Humans can extrapolate simple reasoning strategies to solve difficult problems using long sequences of abstract manipulations, i. e., harder problems are solved by thinking for longer. In contrast, the sequential computing budget of feed-forward networks is limited by their depth, and networks trained on simple problems have no way of extending their reasoning capabilities without retraining.
Revised: It is widely believed that deep neural networks contain layer specialization, wherein networks extract hierarchical features representing edges and patterns in shallow layers and complete objects in deeper layers. Unlike common feed-forward networks is limited by their depth, and networks trained on simple problems have no way of extending their reasoning capabilities without retraining.
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Original:  In contrast, the sequential computing budget of feed-forward networks is limited by their depth, and networks trained on simple problems have no way of extending their reasoning capabilities without retraining. In this work, we observe that recurrent networks have the uncanny ability to closely emulate the behavior of non-recurrent deep models, often doing so with far fewer parameters , on both image classification and maze solving tasks.
Revised:  In contrast, the sequential computing budget of feed-forward models that have distinct filters at each layer, recurrent networks reuse the same parameters at various depths. In this work, we observe that recurrent models exhibit the same hierarchical behaviors and the same performance benefits as depth despite reusing the same filters at every recurrence. By training models of various feed-forward and recurrent architectures on several datasets for image classification as well as maze solving, we show that recurrent networks have the uncanny ability to closely emulate the behavior of non-recurrent deep models, often doing so with far fewer parameters , on both image classification and maze solving tasks.
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Original:  In this work, we observe that recurrent networks have the uncanny ability to closely emulate the behavior of non-recurrent deep models, often doing so with far fewer parameters , on both image classification and maze solving tasks.
Revised:  In this work, we observe that recurrent networks have the ability to closely emulate the behavior of non-recurrent deep models, often doing so with far fewer parameters , on both image classification and maze solving tasks.
Edit intent: clarity
Original:  In this work, we observe that recurrent networks have the uncanny ability to closely emulate the behavior of non-recurrent deep models, often doing so with far fewer parameters , on both image classification and maze solving tasks. We also explore whether recurrent networks can make the generalization leap from simple problems to hard problems simply by increasing the number of recurrent iterations used at test time. To this end, we show that recurrent networks that are trained to solve simple mazes with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference .
Revised:  In this work, we observe that recurrent networks have the uncanny ability to closely emulate the behavior of non-recurrent deep models, often doing so with far fewer parameters .
Edit intent: coherence
Original:  The navigation problem is broken up into generating a  reference path to the goal and then using a local planner to track the reference path and avoid obstacles by generating velocity and steering references for a control system to execute.
Revised:  The navigation problem is broken up into generating a global reference path to the goal and then using a local planner to track the reference path and avoid obstacles by generating velocity and steering references for a control system to execute.
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Original:  This paper presents a novel local planner architecture for path following and obstacle avoidance by proposing a hybrid system that uses a classic path following algorithm in parallel with a neural network. Our solution, called the modification architecture, uses a path following algorithm to follow a reference path and a neural network to modify the references generated by the path follower in order to prevent collisions.
Revised:  This paper presents a novel local planner architecture for path following and obstacle avoidance by proposing a hybrid system that uses a classic path following algorithm with a deep reinforcement learning agent. The novel "modification planner" uses the path follower to track the global plan and the deep reinforcement learning agent to modify the references generated by the path follower in order to prevent collisions.
Edit intent: clarity
Original:  Our solution, called the modification architecture, uses a path following algorithm to follow a reference path and a neural network to modify the references generated by the path follower in order to prevent collisions. The neural network is trained using reinforcement learning to deviate from the reference trajectory just enough to avoid crashing. We present our local planner in a hierarchical planning framework, nested between a global reference planner and a low-level control system. The system is evaluated in the context of F1/10th scale autonomous racing in random forests and on a race track .
Revised:  Our solution, called the modification architecture, uses a path following algorithm to follow a reference path and a neural network to modify the references generated by the path follower to avoid obstacles. Importantly, our architecture does not require an updated obstacle map and only 10 laser range finders to avoid obstacles. The modification planner is evaluated in the context of F1/10th scale autonomous racing in random forests and on a race track .
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Original:  The results demonstrate that our solution is able to follow a reference trajectory while avoiding obstacles using only 10 sparse laser range finder readings. Our hybrid system overcomes previous limitations by not requiring an obstacle map, human demonstrations, or an instrumented training setup .
Revised:  The results show that the modification planner can achieve faster average times compared to the baseline mapless navigation planner and a 94\% success rate which is similar to the baseline .
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Original:  We are confident that a widespread use of automated psychotherapy rating tools in the near future will augment experts' capabilities by providing an avenue for more effective training and skill improvement and will eventually lead to more positive clinical outcomes.
Revised:  We are confident that a widespread use of automated psychotherapy rating tools in the near future will augment experts' capabilities by providing an avenue for more effective training and skill improvement , eventually leading to more positive clinical outcomes.
Edit intent: coherence
Original:  The method is based on a very flexible linear model for panel data, called factor-augmented regression model with both observable, latent common factors, as well as idiosyncratic components as high-dimensional covariate variables.
Revised:  The method is based on a flexible model for panel data, called factor-augmented regression model with both observable, latent common factors, as well as idiosyncratic components as high-dimensional covariate variables.
Edit intent: coherence
Original:  The theory and methods are further supported by an extensive simulation study and applications to the construction of a partial covariance network of the financial returns for the constituents of the S\&P500 index and prediction exercise for a large panel of macroeconomic time series from FRED-MD database.
Revised:  The theory and methods are further supported by an extensive simulation study and applications to the construction of a partial covariance network of the financial returns and a prediction exercise for a large panel of macroeconomic time series from FRED-MD database.
Edit intent: clarity
Original: We consider the problem of domain generalization , in which a predictor is trained on data drawn from a family of related training domains and tested on a distinct and unseen test domain.
Revised: Despite remarkable success in a variety of applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data. Toward addressing this challenge, we consider the domain generalization problem, wherein predictors are trained using data drawn from a family of related training domains and tested on a distinct and unseen test domain.
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Original:  While a variety of approaches have been proposed for this setting, it was recently shown that no existing algorithm can consistently outperform empirical risk minimization (ERM) over the training domains. To this end, in this paper we propose a novel approach for the domain generalization problem called Model-Based Domain Generalization.
Revised:  We show that under a natural model of data generation and a concomitant invariance condition, the domain generalization problem called Model-Based Domain Generalization.
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