wanyu/IteraTeR_full_sent huggingface dataset:
Original:  However, a significant generalization gap in the robustness obtained from AT has been problematic, making practitioners to consider a bag of tricks for a successful training, e.g., early stopping  .
Revised:  However, the phenomenon of robust overfitting, i.e., the robustness starts to decrease significantly during AT, has been problematic, making practitioners to consider a bag of tricks for a successful training, e.g., early stopping .
Edit intent: meaning-changed
Original:  However, a significant generalization gap in the robustness obtained from AT has been problematic, making practitioners to consider a bag of tricks for a successful training, e.g., early stopping  .
Revised:  However, a significant generalization gap in the robustness obtained from AT has been problematic, making practitioners to consider a bag of tricks for a successful training, e.g., early stopping , but also incurring a significant generalization gap in the robustness .
Edit intent: coherence
Original:  In this paper, we investigate data augmentation (DA) techniques to address the issue. In contrast to the previous reports in the literature that DA is not effective for regularizing AT, we discover that DA can mitigate overfitting in AT surprisingly well, but they should be chosen deliberately. To utilize the effect of DA further, we propose a simple yet effective auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations  to be similar to each other.
Revised:  In this paper, we propose an effective regularization technique that prevents robust overfitting by optimizing an auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations to be similar to each other.
Edit intent: clarity
Original:  To utilize the effect of DA further, we propose a simple yet effective auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations  to be similar to each other.
Revised:  To utilize the effect of DA further, we propose a simple yet effective auxiliary 'consistency' regularization loss during AT. Specifically, it forces the predictive distributions after attacking from two different augmentations to be similar to each other.
Edit intent: clarity
Original:  To utilize the effect of DA further, we propose a simple yet effective auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations  to be similar to each other.
Revised:  To utilize the effect of DA further, we propose a simple yet effective auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations of the same instance to be similar to each other.
Edit intent: meaning-changed
Original:  To utilize the effect of DA further, we propose a simple yet effective auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations  to be similar to each other.
Revised:  To utilize the effect of DA further, we propose a simple yet effective auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations to be similar with each other.
Edit intent: fluency
Original:  Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among the governmental parties: an example is provided by the effects of the 2019 Italian government crisis on a number of center-left leaning users - the future members of the Italy Alive party (`Italia Viva') - whose Twitter activity become markedly different from that of their former party members - the supporters of the Democratic Party (`Partito Democratico').On the `semantic ' side, instead, the networks of hashtags are characterized by a mesoscale structure which appears to be core-periphery - a seemingly recurrent pattern that is present also in the semantic networks characterizing the online debate in the run up of the 2018 Italian elections .
Revised:  Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among different political forces.As regards the semantic analysis, our networks of hashtags are characterized by a mesoscale structure which appears to be core-periphery - a seemingly recurrent pattern that is present also in the semantic networks characterizing the online debate in the run up of the 2018 Italian elections .
Edit intent: clarity
Original: On the `semantic ' side, instead, the networks of hashtags are characterized by a mesoscale structure which appears to be core-periphery - a seemingly recurrent pattern that is present also in the semantic networks characterizing the online debate in the run up of the 2018 Italian elections .
Revised: On the `semantic ' side, instead, the networks of hashtags display a mesoscale URLanized in a core-periphery - a seemingly recurrent pattern that is present also in the semantic networks characterizing the online debate in the run up of the 2018 Italian elections .
Edit intent: clarity
Original: On the `semantic ' side, instead, the networks of hashtags are characterized by a mesoscale structure which appears to be core-periphery - a seemingly recurrent pattern that is present also in the semantic networks characterizing the online debate in the run up of the 2018 Italian elections .
Revised: On the `semantic ' side, instead, the networks of hashtags are characterized by a mesoscale structure which appears to be core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment .
Edit intent: meaning-changed
Original: We present an approach for procedural content generation (PCG), and improving generalization in reinforcement learning (RL) agents, by using adversarial deep RL . Training RL agents for generalization over novel environments is a notoriously difficult task.
Revised: We present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with an auxiliary input as a control variable . Training RL agents for generalization over novel environments is a notoriously difficult task.
Edit intent: meaning-changed
Original: We present an approach for procedural content generation (PCG), and improving generalization in reinforcement learning (RL) agents, by using adversarial deep RL . Training RL agents for generalization over novel environments is a notoriously difficult task.
Revised: We present an approach for procedural content generation (PCG), and improving generalization in reinforcement learning (RL) agents, by using adversarial deep RL . Training RL agents over novel environments is a notoriously difficult task.
Edit intent: clarity
Original:  The benefit is mainly two-fold: Firstly, the Solver achieves better generalization through the generated challenges from the Generator .
Revised:  The Generator receives a reward signal based on the Solver's performance, which encourages the environment design to be challenging but not impossible. To further drive diversity and control of the environment generation, we propose using auxiliary inputs for the Generator. The benefit is two-fold: Firstly, the Solver achieves better generalization through the generated challenges from the Generator .
Edit intent: meaning-changed
Original:  The Generator receives a reward signal based on the performance of the Solver which encourages the environment design to be challenging but not impossible. To further drive diversity and control of the environment generation, we propose the use of auxiliary inputs for the Generator. Thus, we propose adversarial RL for procedural content generation (ARLPCG ), an adversarial approach which procedurally generates previously unseen environments with an auxiliary input as a control variable. Herein we describe this concept in detail and compare it with previous methods showing improved generalization, as well as a new method to create novel environments.
Revised:  We create two types of 3D environments to validate our model, representing two popular game genres: a third-person platformer and a racing game. In these cases, we shows that ARLPCG has a significantly better solve ratio, and that the auxiliary inputs renders the levels creation controllable to a certain degree. For a video compilation of the results please visit URL
Edit intent: meaning-changed
Original:  The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. games with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security)  efficient MCTS application often requires either its problem-dependent modification or its integration with other techniques.
Revised:  The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. those with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) efficient MCTS application often requires either its problem-dependent modification or its integration with other techniques.
Edit intent: clarity
Original:  The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. games with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security)  efficient MCTS application often requires either its problem-dependent modification or its integration with other techniques.
Revised:  The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. games with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires either its problem-dependent modification or its integration with other techniques.
Edit intent: fluency
Original:  The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. games with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security)  efficient MCTS application often requires either its problem-dependent modification or its integration with other techniques.
Revised:  The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. games with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) efficient MCTS application often requires its problem-dependent modification or its integration with other techniques.
Edit intent: fluency
Original:  The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. games with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security)  efficient MCTS application often requires either its problem-dependent modification or its integration with other techniques.
Revised:  The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. games with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) efficient MCTS application often requires either its problem-dependent modification or integration with other techniques.
Edit intent: fluency
Original:  In contrast, PCR tests detect RNA genomes and sometimes react to a COVID-19 infection before the infectious phase but also to remnants of the virus genome, even weeks after virus replication has ended.
Revised:  In contrast, PCR tests detect RNA genomes . They can react to a COVID-19 infection before the infectious phase but also to remnants of the virus genome, even weeks after virus replication has ended.
Edit intent: coherence
Original:  In this article, we discuss  relative advantages of the different testing procedures and summarise evidence that shows that using antigen tests 2-3 times per week could become a powerful tool to suppress the COVID-19 epidemic .
Revised:  In this article, we discuss the relative advantages of the different testing procedures and summarise evidence that shows that using antigen tests 2-3 times per week could become a powerful tool to suppress the COVID-19 epidemic .
Edit intent: fluency
Original:  In this article, we discuss  relative advantages of the different testing procedures and summarise evidence that shows that using antigen tests 2-3 times per week could become a powerful tool to suppress the COVID-19 epidemic .
Revised:  In this article, we discuss relative advantages of the different testing procedures and summarise evidence that shows that using antigen tests 2-3 times per week could become a powerful tool to suppress the COVID-19 pandemic .
Edit intent: clarity
Original:  In contrast, PCR tests detect RNA genomes . They can react to a COVID-19 infection before the infectious phase but also  to remnants of the virus genome, even weeks after virus replication has ended .
Revised:  In contrast, PCR tests detect RNA genomes : they can diagnose COVID-19 infection before the infectious phase but also to remnants of the virus genome, even weeks after virus replication has ended .
Edit intent: clarity
Original:  Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art  techniques by a large margin even while dealing with reverse loops.
Revised:  Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art loop closure detection and point cloud registration techniques by a large margin even while dealing with reverse loops.
Edit intent: meaning-changed
Original:  Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art  techniques by a large margin even while dealing with reverse loops.
Revised:  Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art techniques by a large margin , especially while dealing with reverse loops.
Edit intent: clarity
Original:  In contrast to prior works which investigate finetuning on the same modality as the pretraining dataset, we show that pretraining on natural language improves performance and compute efficiency on non-language downstream tasks.
Revised:  In contrast to prior works which investigate finetuning on the same modality as the pretraining dataset, we show that pretraining on natural language can improve performance and compute efficiency on non-language downstream tasks.
Edit intent: clarity
Original:  In particular, we find that such pretraining enables FPT to generalize in zero-shot to these modalities, matching the performance of a transformer fully trained on these tasks.
Revised:  In particular, we find that such pretraining enables FPT to generalize in zero-shot to these modalities, matching the performance of a random initialized transformer to a random LSTM. Combining the two insights, we find language-pretrained transformers can obtain strong performance on a variety of non-language tasks.
Edit intent: meaning-changed
Original: We present NaroNet, a Machine Learning framework that integrates the multiscale spatial, in situ analysis of the tumor microenvironment (TME) with patient-level predictions into a seamless end-to-end learning pipeline.
Revised: Many efforts have been made to discover tumor-specific microenvironment elements (TMEs) from immunostained tissue sections. However, the tumor microenvironment (TME) with patient-level predictions into a seamless end-to-end learning pipeline.
Edit intent: meaning-changed
Original: We present NaroNet, a Machine Learning framework that integrates the multiscale spatial, in situ analysis of the tumor microenvironment (TME) with patient-level predictions into a seamless end-to-end learning pipeline. Trained only with patient-level labels, NaroNetquantifies the phenotypes, neighborhoods, and neighborhood interactions that have the highest influence on the predictive task .
Revised: We present NaroNet, a Machine Learning framework that integrates the multiscale spatial, in situ analysis of the identification of yet unknown but relevant TMEs from multiplex immunostained tissues remains a challenge, due to the number of markers involved (tens) and the complexity of their spatial interactions. We present NaroNet, which uses machine learning to identify and annotate known as well as novel TMEs from self-supervised embeddings of URLanized at different levels (local cell phenotypes and cellular neighborhoods). Then it uses the abundance of TMEs to classify patients based on biological or clinical features .
Edit intent: meaning-changed
Original:  We validate NaroNet using synthetic data simulating multiplex-immunostained images with adjustable probabilistic incidence of different TMEs . Then we apply our model to two real sets of patient tumors, one consisting of 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancers, and the other consisting of 372 35-plex mass cytometry images from 283 breast cancer patients .
Revised:  We validate NaroNet using synthetic patient cohorts with adjustable incidence of different TMEs . Then we apply our model to two real sets of patient tumors, one consisting of 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancers, and the other consisting of 372 35-plex mass cytometry images from 283 breast cancer patients .
Edit intent: clarity
Original:  We validate NaroNet using synthetic data simulating multiplex-immunostained images with adjustable probabilistic incidence of different TMEs . Then we apply our model to two real sets of patient tumors, one consisting of 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancers, and the other consisting of 372 35-plex mass cytometry images from 283 breast cancer patients .
Revised:  We validate NaroNet using synthetic data simulating multiplex-immunostained images with adjustable probabilistic incidence of different TMEs and two cancer patient datasets .
Edit intent: clarity
Original:  In both synthetic and real datasets, NaroNet provides outstanding predictions while associating those predictions to the presence of specific TMEs. This inherent interpretability could be of great value both in a clinical setting and as a tool to discover novel biomarker signatures.
Revised:  In both synthetic and real datasets, NaroNet unsupervisedly identifies novel TMEs, relevant for the user-defined classification task. As NaroNet requires only patient-level information, it renders state-of-the-art computational methods accessible to a broad audience, accelerating the discovery of biomarker signatures.
Edit intent: meaning-changed
Original:  Planning-based prediction, on the other hand, can reliably output decent-but-not-great predictions: it is much more stable in the face of distribution shift  , but it has high inductive bias, missing important aspects that drive human decisions, and ignoring cognitive biases that make human behavior suboptimal.
Revised:  Planning-based prediction, on the other hand, can reliably output decent-but-not-great predictions: it is much more stable in the face of distribution shift (as we verify in this work) , but it has high inductive bias, missing important aspects that drive human decisions, and ignoring cognitive biases that make human behavior suboptimal.
Edit intent: meaning-changed
Original:  To address these concerns, we introduce the Entropic Reactive Control Improvisation (ERCI) framework and algorithm that supports synthesizing control policies for stochastic games that are declaratively specified by (i) a hard constraint specifying what must occur  (ii) a soft constraint specifying what typically occurs, and (iii) a randomization constraint specifying the unpredictability and variety of the controller, as quantified using causal entropy.
Revised:  To address these concerns, we introduce the Entropic Reactive Control Improvisation (ERCI) framework and algorithm which supports synthesizing control policies for stochastic games that are declaratively specified by (i) a hard constraint specifying what must occur (ii) a soft constraint specifying what typically occurs, and (iii) a randomization constraint specifying the unpredictability and variety of the controller, as quantified using causal entropy.
Edit intent: fluency
Original:  To address these concerns, we introduce the Entropic Reactive Control Improvisation (ERCI) framework and algorithm that supports synthesizing control policies for stochastic games that are declaratively specified by (i) a hard constraint specifying what must occur  (ii) a soft constraint specifying what typically occurs, and (iii) a randomization constraint specifying the unpredictability and variety of the controller, as quantified using causal entropy.
Revised:  To address these concerns, we introduce the Entropic Reactive Control Improvisation (ERCI) framework and algorithm that supports synthesizing control policies for stochastic games that are declaratively specified by (i) a hard constraint specifying what must occur , (ii) a soft constraint specifying what typically occurs, and (iii) a randomization constraint specifying the unpredictability and variety of the controller, as quantified using causal entropy.
Edit intent: fluency
Original:  This framework, which extends the state-of-the-art by supporting arbitrary combinations of adversarial and probabilistic uncertainty in the environment , enables a flexible modeling formalism which we argue, theoretically and empirically, remains tractable.
Revised:  This framework, extends the state of the art by supporting arbitrary combinations of adversarial and probabilistic uncertainty in the environment , enables a flexible modeling formalism which we argue, theoretically and empirically, remains tractable.
Edit intent: fluency
Original:  This framework, which extends the state-of-the-art by supporting arbitrary combinations of adversarial and probabilistic uncertainty in the environment , enables a flexible modeling formalism which we argue, theoretically and empirically, remains tractable.
Revised:  This framework, which extends the state-of-the-art by supporting arbitrary combinations of adversarial and probabilistic uncertainty in the environment . ERCI enables a flexible modeling formalism which we argue, theoretically and empirically, remains tractable.
Edit intent: meaning-changed
Original:  This work aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-bene?t tradeoff ? that can otherwise be an inhibitor for the fast development of aff?ordable systems.
Revised:  This book aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-bene?t tradeoff ? that can otherwise be an inhibitor for the fast development of aff?ordable systems.
Edit intent: clarity