wanyu/IteraTeR_v2 huggingface dataset:
Original:  We propose a novel trajectory generation model that employs both a generative adversarial network and a dynamics prediction model to generate pseudo trajectories from all learned tasks in the new task learning process to achieve continual imitation learning ability .
Revised:  We propose a novel trajectory generation model that employs both a generative adversarial network and a dynamics prediction model to generate pseudo trajectories from all learned tasks in the new task learning process .
Edit intent: coherence
Original:  Our experiments on both simulation and real world manipulation tasks demonstrate the effectiveness of our method.
Revised:  Our experiments on both simulation and real-world manipulation tasks demonstrate the effectiveness of our method.
Edit intent: fluency
Original:  First, we develop a method for minimizing the maximum of N functions, improving on recent results and matching a lower bound up  logarithmic factors.
Revised:  First, we develop a method for minimizing the maximum of N functions, improving on recent results and matching a lower bound up to logarithmic factors.
Edit intent: fluency
Original: With the rapid development of artificial intelligence, a series of machine learning algorithms, e.g., graph neural networks , have been proposed to facilitate network analysis or graph data mining.
Revised: With the rapid development of artificial intelligence, a number of machine learning algorithms, e.g., graph neural networks , have been proposed to facilitate network analysis or graph data mining.
Edit intent: clarity
Original: With the rapid development of artificial intelligence, a series of machine learning algorithms, e.g., graph neural networks , have been proposed to facilitate network analysis or graph data mining.
Revised: With the rapid development of artificial intelligence, a series of machine learning algorithms, such as graph neural networks , have been proposed to facilitate network analysis or graph data mining.
Edit intent: clarity
Original: With the rapid development of artificial intelligence, a series of machine learning algorithms, e.g., graph neural networks , have been proposed to facilitate network analysis or graph data mining.
Revised: With the rapid development of artificial intelligence, a series of machine learning algorithms, e.g., graph neural networks have been proposed to facilitate network analysis or graph data mining.
Edit intent: fluency
Original:  Unfortunately , recent studies indicate that such advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are purposely changed.
Revised:  Although effective , recent studies indicate that such advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are purposely changed.
Edit intent: clarity
Original:  Unfortunately , recent studies indicate that such advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are purposely changed.
Revised:  Unfortunately , recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are purposely changed.
Edit intent: clarity
Original:  Based on these findings, we further utilize the network attributes  to design machine learning models for adversarial sample detection and attack method recognition , achieving the outstanding performance.
Revised:  Based on these findings, the network attributes to design machine learning models for adversarial sample detection and attack method recognition , achieving the outstanding performance.
Edit intent: coherence
Original:  Based on these findings, we further utilize the network attributes  to design machine learning models for adversarial sample detection and attack method recognition , achieving the outstanding performance.
Revised:  Based on these findings, we further utilize the network attributes are utilized to design machine learning models for adversarial sample detection and attack method recognition , achieving the outstanding performance.
Edit intent: clarity
Original:  Based on these findings, we further utilize the network attributes  to design machine learning models for adversarial sample detection and attack method recognition , achieving the outstanding performance.
Revised:  Based on these findings, we further utilize the network attributes to design machine learning models for adversarial sample detection and attack method recognition with outstanding performance.
Edit intent: clarity
Original: Predicting chemical properties from the structure of a molecule is of great importance in many applications  including drug discovery and material design.
Revised: Predicting the chemical properties of a molecule is of great importance in many applications including drug discovery and material design.
Edit intent: clarity
Original: Predicting chemical properties from the structure of a molecule is of great importance in many applications  including drug discovery and material design.
Revised: Predicting chemical properties from the structure of a molecule is of great importance in many applications , including drug discovery and material design.
Edit intent: fluency
Original:  Machine learning based molecular property prediction holds the promise of enabling accurate predictions at much less complexity, when compared to, for example  Density Functional Theory (DFT) calculations.
Revised:  Machine learning based molecular property prediction holds the promise of enabling accurate predictions at much less computationally complex cost when compared to, for example Density Functional Theory (DFT) calculations.
Edit intent: clarity
Original:  Machine learning based molecular property prediction holds the promise of enabling accurate predictions at much less complexity, when compared to, for example  Density Functional Theory (DFT) calculations.
Revised:  Machine learning based molecular property prediction holds the promise of enabling accurate predictions at much less complexity, when compared to, for example , Density Functional Theory (DFT) calculations.
Edit intent: fluency
Original:  Features extracted from molecular graphs, using graph neural nets in a supervised manner , have emerged as strong baselines for such tasks.
Revised:  Various representation learning methods in a supervised setting, including the features extracted using graph neural nets in a supervised manner , have emerged as strong baselines for such tasks.
Edit intent: clarity
Original:  Features extracted from molecular graphs, using graph neural nets in a supervised manner , have emerged as strong baselines for such tasks.
Revised:  Features extracted from molecular graphs, using graph neural nets , have emerged as strong baselines for such tasks.
Edit intent: clarity
Original:  Features extracted from molecular graphs, using graph neural nets in a supervised manner , have emerged as strong baselines for such tasks.
Revised:  Features extracted from molecular graphs, using graph neural nets in a supervised manner , have emerged for such tasks.
Edit intent: clarity
Original:  However, the vast chemical space together with the limited availability of labels makes supervised learning challenging, calling for learning a general-purpose molecular representation.
Revised:  However, the vast chemical space and the limited availability of labels makes supervised learning challenging, calling for learning a general-purpose molecular representation.
Edit intent: coherence
Original:  However, the vast chemical space together with the limited availability of labels makes supervised learning challenging, calling for learning a general-purpose molecular representation.
Revised:  However, the vast chemical space together with the limited availability of labels make supervised learning challenging, calling for learning a general-purpose molecular representation.
Edit intent: fluency
Original:  Recently, pre-trained transformer-based language models (PTLMs) on large unlabeled corpus have produced state-of-the-art results in many downstream natural language processing tasks.
Revised:  Recently, pre-trained transformer-based language models on large unlabeled corpus have produced state-of-the-art results in many downstream natural language processing tasks.
Edit intent: clarity
Original:  Inspired by this development, here we present molecular embeddings obtained by training an efficient transformer encoder model, referred to as MoLFormer.
Revised:  Inspired by this development, we present molecular embeddings obtained by training an efficient transformer encoder model, referred to as MoLFormer.
Edit intent: coherence
Original:  Inspired by this development, here we present molecular embeddings obtained by training an efficient transformer encoder model, referred to as MoLFormer.
Revised:  Inspired by this development, here we present molecular embeddings obtained by training an efficient transformer encoder model, MoLFormer.
Edit intent: coherence
Original:  This model was employed with a linear attention mechanism and highly paralleized training on 1D SMILES sequences of 1.1 billion unlabeled molecules from the PubChem and ZINC datasets.
Revised:  This model employs a linear attention mechanism and highly paralleized training on 1D SMILES sequences of 1.1 billion unlabeled molecules from the PubChem and ZINC datasets.
Edit intent: clarity
Original:  This model was employed with a linear attention mechanism and highly paralleized training on 1D SMILES sequences of 1.1 billion unlabeled molecules from the PubChem and ZINC datasets.
Revised:  This model was employed with a linear attention mechanism coupled with highly parallelized training on SMILES sequences of 1.1 billion unlabeled molecules from the PubChem and ZINC datasets.
Edit intent: clarity
Original:  These results provide encouraging evidence that large-scale molecular language models can capture sufficient structural information to be able to accurately predict quantum chemical propertiesand beyond.
Revised:  These results provide encouraging evidence that large-scale molecular language models can capture sufficient structural information to be able to predict diverse molecular properties, including quantum-chemical properties
Edit intent: clarity
Original:  We identify limitations of previous work on audiovisual on-screen sound separation, including the simplicity and coarse resolution of spatio-temporal attention, and poor convergence of the audio separation model.
Revised:  We identify limitations of previous work on audio-visual on-screen sound separation, including the simplicity and coarse resolution of spatio-temporal attention, and poor convergence of the audio separation model.
Edit intent: fluency
Original:   For evaluation and semi-supervised training, we collected human annotations of on-screen audio from a large database of in-the-wild videos ( YFCC100M ).
Revised:  For evaluation and semi-supervised training, we collected human annotations of on-screen audio from a large database of in-the-wild videos ( YFCC100m ).
Edit intent: fluency
Original: Rethinking cities is now more imperative than ever, as society is facing challenges such as population growth and climate change.
Revised: Rethinking cities is now more imperative than ever, as society faces global challenges such as population growth and climate change.
Edit intent: clarity
Original:  An autonomous bicycle-sharing system that combines the benefits of vehicle sharing, electrification, autonomy, and micro-mobility could increase the efficiency and convenience of bicycle-sharing systems  incentivizing more people to bike and enjoy their cities in an environmentally friendly way.
Revised:  An autonomous bicycle-sharing system that combines the benefits of vehicle sharing, electrification, autonomy, and micro-mobility , increasing the efficiency and convenience of bicycle-sharing systems incentivizing more people to bike and enjoy their cities in an environmentally friendly way.
Edit intent: coherence
Original:  An autonomous bicycle-sharing system that combines the benefits of vehicle sharing, electrification, autonomy, and micro-mobility could increase the efficiency and convenience of bicycle-sharing systems  incentivizing more people to bike and enjoy their cities in an environmentally friendly way.
Revised:  An autonomous bicycle-sharing system that combines the benefits of vehicle sharing, electrification, autonomy, and micro-mobility could increase the efficiency and convenience of bicycle-sharing systems and incentivizing more people to bike and enjoy their cities in an environmentally friendly way.
Edit intent: coherence
Original:  This paper presents an ad-hoc agent-based , discrete event simulator that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in the most realistic possible scenarios, including a rebalancing system based on demand prediction.
Revised:  This paper presents an ad-hoc agent-based simulator that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in the most realistic possible scenarios, including a rebalancing system based on demand prediction.
Edit intent: clarity
Original:  This paper presents an ad-hoc agent-based , discrete event simulator that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in the most realistic possible scenarios, including a rebalancing system based on demand prediction.
Revised:  This paper presents an ad-hoc agent-based , discrete event simulator that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in realistic scenarios, including a rebalancing system based on demand prediction.
Edit intent: clarity
Original:  In addition, this work  quantifies the extent to which an autonomous system would outperform current bicycle-sharing schemes and describes the impact of different parameters on system efficiency and service quality.
Revised:  In addition, this work describes the impact of different parameters on system efficiency and service quality and quantifies the extent to which an autonomous system would outperform current bicycle-sharing schemes and describes the impact of different parameters on system efficiency and service quality.
Edit intent: fluency
Original: In the field of data integration, data quality problems are often encountered when extracting, combining, and merging data .
Revised: Data quality problems are often encountered when extracting, combining, and merging data .
Edit intent: clarity
Original:  Following the global pushback against key escrow systems, client-side scanning based on perceptual hashing has been recently proposed by  governments and researchers to detect illegal content in E2EE communications.
Revised:  Following the global pushback against key escrow systems, client-side scanning based on perceptual hashing has been recently proposed by tech companies, governments and researchers to detect illegal content in E2EE communications.
Edit intent: clarity
Original:  More specifically, we propose three adversarial attacks -- a general black-box attack and two white-box attacks for discrete cosine-based-based algorithms -- against perceptual hashing algorithms.
Revised:  More specifically, we propose three adversarial attacks--a general black-box attack and two white-box attacks for discrete cosine-based-based algorithms -- against perceptual hashing algorithms.
Edit intent: fluency
Original:  More specifically, we propose three adversarial attacks -- a general black-box attack and two white-box attacks for discrete cosine-based-based algorithms -- against perceptual hashing algorithms.
Revised:  More specifically, we propose three adversarial attacks -- a general black-box attack and two white-box attacks for discrete cosine transform-based algorithms--against perceptual hashing algorithms.
Edit intent: clarity
Original:  In a large-scale evaluation, we show perceptual hashing-based client-side scanning mechanisms to be highly vulnerable to detection avoidance attacks in a black-box setting, with more than 99.9 \% of images successfully \\%DIF > of images successfully attacked while preserving the content of the image.
Revised:  In a large-scale evaluation, we show perceptual hashing-based client-side scanning mechanisms to be highly vulnerable to detection avoidance attacks in a black-box setting, with more than 99.9 \\%DIF > of images successfully attacked while preserving the content of the image.
Edit intent: clarity
Original:  This harm can take the form of a gradual degradation of public discourse;
Revised:  This harm may manifest as a gradual degradation of public discourse;
Edit intent: clarity
Original:  However, filtering disinformation is costly, not only for implementing filtering algorithms or employing manual filtering effort , but also because removing such highly viral content impacts user growth and thus potential advertising revenue.
Revised:  However, mitigating disinformation is costly, not only for implementing filtering algorithms or employing manual filtering effort , but also because removing such highly viral content impacts user growth and thus potential advertising revenue.
Edit intent: clarity
Original:  However, filtering disinformation is costly, not only for implementing filtering algorithms or employing manual filtering effort , but also because removing such highly viral content impacts user growth and thus potential advertising revenue.
Revised:  However, filtering disinformation is costly, not only for implementing filtering algorithms or employing manual detection , but also because removing such highly viral content impacts user growth and thus potential advertising revenue.
Edit intent: clarity
Original:  However, filtering disinformation is costly, not only for implementing filtering algorithms or employing manual filtering effort , but also because removing such highly viral content impacts user growth and thus potential advertising revenue.
Revised:  However, filtering disinformation is costly, not only for implementing filtering algorithms or employing manual filtering effort , but also because limiting such highly viral content impacts user growth and thus potential advertising revenue.
Edit intent: clarity
Original:  Since the costs of harmful content are borne by other entities, the platform will therefore have no incentive to filter at a socially-optimal level  .
Revised:  Since the costs of harmful content are borne by other entities, the platform will therefore have no incentive to exercise the socially-optimal level .
Edit intent: clarity
Original:  Since the costs of harmful content are borne by other entities, the platform will therefore have no incentive to filter at a socially-optimal level  .
Revised:  Since the costs of harmful content are borne by other entities, the platform will therefore have no incentive to filter at a socially-optimal level of effort .
Edit intent: clarity
Original:  Encoding our domain as a Markov decision process, we demonstrate that no penalty based on a static rule, no matter how large, can incentivize adequate filtering by the platform.
Revised:  Encoding our domain as a Markov decision process, we demonstrate that no penalty based on a static rule, no matter how large, can incentivize adequate effort by the platform.
Edit intent: clarity
Original: The spread of disinformation on social media platforms such as Facebook is harmful to society.
Revised: The spread of disinformation on social media platforms is harmful to society.
Edit intent: clarity
Original:  However, mitigating disinformation is costly, not only for implementing classification algorithms or employing manual detection , but also because limiting such highly viral content impacts user growth and thus potential advertising revenue.
Revised:  However, mitigating disinformation is costly, not only for implementing detection algorithms or employing manual detection , but also because limiting such highly viral content impacts user growth and thus potential advertising revenue.
Edit intent: clarity
Original:  However, mitigating disinformation is costly, not only for implementing classification algorithms or employing manual detection , but also because limiting such highly viral content impacts user growth and thus potential advertising revenue.
Revised:  However, mitigating disinformation is costly, not only for implementing classification algorithms or employing manual effort , but also because limiting such highly viral content impacts user growth and thus potential advertising revenue.
Edit intent: clarity
Original:  However, mitigating disinformation is costly, not only for implementing classification algorithms or employing manual detection , but also because limiting such highly viral content impacts user growth and thus potential advertising revenue.
Revised:  However, mitigating disinformation is costly, not only for implementing classification algorithms or employing manual detection , but also because limiting such highly viral content impacts user engagement and thus potential advertising revenue.
Edit intent: clarity
Original:  This problem is similar to the problem of environmental regulation, in which the costs of adverse events are not directly borne by a firm, the mitigation effort of a firm is not observable, and the causal link between a harmful consequence and a specific failure is difficult to prove.
Revised:  This problem is similar to that of environmental regulation, in which the costs of adverse events are not directly borne by a firm, the mitigation effort of a firm is not observable, and the causal link between a harmful consequence and a specific failure is difficult to prove.
Edit intent: clarity
Original:  In the environmental regulationdomain , one solution to this issue is to perform costly monitoring to ensure that the firm takes adequate precautions according  a specified rule.
Revised:  For environmental regulation , one solution to this issue is to perform costly monitoring to ensure that the firm takes adequate precautions according a specified rule.
Edit intent: fluency
Original:  In the environmental regulationdomain , one solution to this issue is to perform costly monitoring to ensure that the firm takes adequate precautions according  a specified rule.
Revised:  In the environmental regulationdomain , one solution is to perform costly monitoring to ensure that the firm takes adequate precautions according a specified rule.
Edit intent: clarity
Original:  In the environmental regulationdomain , one solution to this issue is to perform costly monitoring to ensure that the firm takes adequate precautions according  a specified rule.
Revised:  In the environmental regulationdomain , one solution to this issue is to perform costly monitoring to ensure that the firm takes adequate precautions according to a specified rule.
Edit intent: fluency
Original:  However, classifying disinformation is performative, and thus a fixed rule  becomes less effective over time  . Encoding our domain as a Markov decision process, we demonstrate that no penalty based on a static rule, no matter how large, can incentivize adequate effort by the platform . Penalties based on an adaptive rule can incentivize optimal effort, but counterintuitively, only if the regulator sufficiently overreacts to harmful events by requiring a greater-than-optimal level of effort.
Revised:  However, a fixed rule becomes less effective over time . Encoding our domain as a Markov decision process, we demonstrate that no penalty based on a static rule, no matter how large, can incentivize adequate effort by the platform . Penalties based on an adaptive rule can incentivize optimal effort, but counterintuitively, only if the regulator sufficiently overreacts to harmful events by requiring a greater-than-optimal level of effort.
Edit intent: coherence