wanyu/IteraTeR_v2 huggingface dataset:
Original:  Not surprisingly, the manual design process becomes a particularly challenging barrier, as it demands sufficient prior experience, enormous effort, intuition  and perhaps some good luck.
Revised:  Not surprisingly, the manual design process becomes a particularly challenging barrier, as it demands sufficient prior experience, enormous effort, intuition , and perhaps some good luck.
Edit intent: fluency
Original:  Meanwhile, neural architecture search has gaining grounds in practical applications such as image segmentation, as a promising method in tackling the issue of automated search of feasible network structures.
Revised:  Meanwhile, neural architecture search has gaining grounds in practical applications as a promising method in tackling the issue of automated search of feasible network structures.
Edit intent: clarity
Original:  We aspire to alleviate this problem by leveraging bodily as well as contextual features, as part of a broader emotion recognition framework.
Revised:  We aspire to alleviate this problem , we leverage bodily as well as contextual features, as part of a broader emotion recognition framework.
Edit intent: fluency
Original:  A standard CNN-RNN cascade constitutes the backbone of our proposed model for sequence-to-sequence (seq2seq) learning.
Revised:  We choose to use a standard CNN-RNN cascade constitutes the backbone of our proposed model for sequence-to-sequence (seq2seq) learning.
Edit intent: coherence
Original:  A standard CNN-RNN cascade constitutes the backbone of our proposed model for sequence-to-sequence (seq2seq) learning.
Revised:  A standard CNN-RNN cascade as the backbone of our proposed model for sequence-to-sequence (seq2seq) learning.
Edit intent: clarity
Original:  Apart from learning through the RGB input modality, we construct an aural stream which operates on sequences of extracted mel-spectrograms.
Revised:  Apart from learning through the RGB input modality, we construct an aural stream which operates on sequences of extracted mel-spectrograms.
Edit intent: fluency
Original:  In order to alleviate this problem, we leverage bodily as well as contextual features, as part of a broader emotion recognition framework.
Revised:  In order to alleviate this problem, we leverage both bodily and contextual features, as part of a broader emotion recognition framework.
Edit intent: coherence
Original:  Our extensive experiments on the challenging and newly assembled Affect-in-the-wild-2 ( Aff-Wild2 ) dataset verify the superiority of our methods over existing approaches, while by properly incorporating all of the aforementioned modules in a network ensemble, we manage to surpass the previous best published recognition scores, in the official validation set .
Revised:  Our extensive experiments on the challenging and newly assembled Aff-Wild2 ) dataset verify the superiority of our methods over existing approaches, while by properly incorporating all of the aforementioned modules in a network ensemble, we manage to surpass the previous best published recognition scores, in the official validation set .
Edit intent: clarity
Original:  Our extensive experiments on the challenging and newly assembled Affect-in-the-wild-2 ( Aff-Wild2 ) dataset verify the superiority of our methods over existing approaches, while by properly incorporating all of the aforementioned modules in a network ensemble, we manage to surpass the previous best published recognition scores, in the official validation set .
Revised:  Our extensive experiments on the challenging and newly assembled Affect-in-the-wild-2 ( Aff-Wild2 dataset verify the superiority of our methods over existing approaches, while by properly incorporating all of the aforementioned modules in a network ensemble, we manage to surpass the previous best published recognition scores, in the official validation set .
Edit intent: fluency
Original:  All the code was implemented using URL and is publicly URL
Revised:  All the code was implemented using URL and is publicly available.
Edit intent: clarity
Original:  The code and model are available for research purposes at URL link .
Revised:  The code and model are available for research purposes at URL .
Edit intent: clarity
Original:  The code and model are available for research purposes at URL link .
Revised:  The code and model are available for research purposes at URL link 
Edit intent: fluency
Original: Affective Analysis is not a single task, and the valence-arousal value, expression class  and action unit can be predicted at the same time.
Revised: Affective Analysis is not a single task, and the valence-arousal value, expression class , and action unit can be predicted at the same time.
Edit intent: fluency
Original:  Previous researches failed to take them as a whole task or ignore the entanglement and hierarchical relation of this three facial attributes.
Revised:  Previous researches did not pay enough attention to the entanglement and hierarchical relation of this three facial attributes.
Edit intent: clarity
Original:  Previous researches failed to take them as a whole task or ignore the entanglement and hierarchical relation of this three facial attributes.
Revised:  Previous researches failed to take them as a whole task or ignore the entanglement and hierarchical relation of these three facial attributes.
Edit intent: fluency
Original:  The hierarchical features are extracted to predict three labels and we apply  teacher-student training strategy to learn from pretrained single-task models.
Revised:  The hierarchical features are extracted to predict three labels and we apply a teacher-student training strategy to learn from pretrained single-task models.
Edit intent: fluency
Original:  Extensive experiment results demonstrate the proposed model outperform other models.
Revised:  Extensive experiment results demonstrate the proposed model outperforms other models.
Edit intent: fluency
Original:  This is a submission to The 2nd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW).
Revised:  This is a submission to The 2nd Workshop and Competition on Affective Behavior Analysis in the wild (ABAW).
Edit intent: fluency
Original: Results:We present MPS(T B) (Maximum Protein Similarity Topological and Biological) , a new algorithm that leverages both topological and biological information to predict protein-protein interactions.
Revised: Results:We present B) (Maximum Protein Similarity Topological and Biological) , a new algorithm that leverages both topological and biological information to predict protein-protein interactions.
Edit intent: coherence
Original: Results:We present MPS(T B) (Maximum Protein Similarity Topological and Biological) , a new algorithm that leverages both topological and biological information to predict protein-protein interactions.
Revised: Results:We present MPS(T , a new algorithm that leverages both topological and biological information to predict protein-protein interactions.
Edit intent: coherence
Original: Conclusion: MPS(T) and MPS(T B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome  .
Revised: Conclusion: B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome .
Edit intent: coherence
Original: Conclusion: MPS(T) and MPS(T B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome  .
Revised: Conclusion: MPS(T) and MPS(T We shown that topological plus sequence-based computational methods that can effectively predict the entire human interactome .
Edit intent: coherence
Original: Conclusion: MPS(T) and MPS(T B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome  .
Revised: Conclusion: MPS(T) and MPS(T B) are topological only and topological plus sequence-based computational methods can effectively predict the entire human interactome .
Edit intent: fluency
Original:  But we also find that some provisions of the draft AI Act have surprising legal implications, whilst others may be largely ineffective at achieving their stated goals.
Revised:  But we also find that some provisions of the Draft AI Act have surprising legal implications, whilst others may be largely ineffective at achieving their stated goals.
Edit intent: fluency
Original:  Several overarching aspects, including the enforcement regime and the effect of maximum harmonisation on the space for AI policymore generally , engender significant concern.
Revised:  Several overarching aspects, including the enforcement regime and the risks of maximum harmonisation on the space for AI policymore generally , engender significant concern.
Edit intent: clarity
Original:  Several overarching aspects, including the enforcement regime and the effect of maximum harmonisation on the space for AI policymore generally , engender significant concern.
Revised:  Several overarching aspects, including the enforcement regime and the effect of maximum harmonisation pre-empting legitimate national AI policy , engender significant concern.
Edit intent: clarity
Original: We present a lattice gas model of financial markets to explain previous empirical observations of the interplay of trends and reversion  .
Revised: Motivated by empirical observations on the interplay of trends and reversion .
Edit intent: clarity
Original:  Neighbors in the network tend to align their positions due to herding behavior.
Edit intent: clarity
Original:  The model is equivalent to the Ising model on this network, with the magnetization in the role of the deviation of the asset price from its value.
Revised:  The model is equivalent to the Ising model on this network, whose magnetization represents the deviation of the asset price from its value.
Edit intent: clarity
Original:  There, it is characterized by long-range correlations and universal critical exponents, in analogy with the second-order phase transition between water and steam.
Revised:  There, it is characterized by universal critical exponents, in analogy with the second-order phase transition between water and steam.
Edit intent: clarity
Original:  Using the renormalization group, we show that these critical exponents imply predictions for the auto-correlations of financial market returns.
Revised:  These critical exponents imply predictions for the auto-correlations of financial market returns.
Edit intent: coherence
Original:  For a simple network topology, consistency with observation implies a fractal dimension of the network of 3.3  and a correlation time of 10 years.
Revised:  For a simple network topology, consistency with observation implies a fractal network dimension of 3.3 and a correlation time of 10 years.
Edit intent: clarity
Original:  For a simple network topology, consistency with observation implies a fractal dimension of the network of 3.3  and a correlation time of 10 years.
Revised:  For a simple network topology, consistency with observation implies a fractal dimension of the network of 3.3 , and a correlation time of 10 years.
Edit intent: fluency
Original:  While the simple model agrees well with market data on long time scales, it cannot explain the observed market trends over time horizons from one month to one year.
Revised:  To also explain the observed market trends over time horizons from one month to one year.
Edit intent: clarity
Original: Leveraging the side information associated with entities (i.e. \ users and items) to enhance the performance of recommendation systems has been widely recognized as an important modelling dimension.
Revised: Leveraging the side information associated with entities (i.e. users and items) to enhance the performance of recommendation systems has been widely recognized as an important modelling dimension.
Edit intent: fluency
Original:  While many existing approaches focus on the integration scheme to incorporate entity side information -- by combining the recommendation loss function with an extra side information-aware loss -- in this paper, we propose instead a novel pre-training scheme for leveraging the side information.
Revised:  While many existing approaches focus on the integration scheme to incorporate entity side information -- by combining the recommendation loss function with an extra side information-aware loss -- in this paper, we propose instead a novel pre-training scheme for leveraging the side information.
Edit intent: fluency
Original:  An extensive evaluation of our pre-training models fine-tuned under four general representation-based recommender models, i.e. \ MF, NCF, NGCF and LightGCN, shows that effectively pre-training embeddings with both the user's and item's side information can significantly improve these original models in terms of both effectiveness and stability.
Revised:  An extensive evaluation of our pre-training models fine-tuned under four general representation-based recommender models, i.e. MF, NCF, NGCF and LightGCN, shows that effectively pre-training embeddings with both the user's and item's side information can significantly improve these original models in terms of both effectiveness and stability.
Edit intent: fluency
Original: Even with state-of-the-art defense mechanisms, cyberattacks in the electric power distribution sector are commonplace.
Revised: Cyberattacks in the energy sector are commonplace.
Edit intent: clarity
Original:  Particularly alarming are load-altering (demand-side) cyberattacks launched through high-wattage assets, which are not continuously monitored by electric power utilities.
Revised:  Load-altering cyberattacks launched via the manipulations of high-wattage assets, which are not continuously monitored by electric power utilities.
Edit intent: clarity
Original:  Particularly alarming are load-altering (demand-side) cyberattacks launched through high-wattage assets, which are not continuously monitored by electric power utilities.
Revised:  Particularly alarming are load-altering (demand-side) cyberattacks launched through high-wattage appliances and assets are particularly alarming, as they are not continuously monitored by electric power utilities.
Edit intent: clarity
Original:   Electric Vehicle Charging Stations (EVCSs) are among such high-wattage assets and, therefore, cyber insurance can be an effective mechanism to protect EVCSs from economic losses caused by cyberattacks .
Revised:  Public Electric Vehicle Charging Stations (EVCSs) are among such high-wattage assets and, therefore, cyber insurance can be an effective mechanism to protect EVCSs from economic losses caused by cyberattacks .
Edit intent: fluency
Original:  Under some mildly restrictive assumptions, we derive an optimal cyber insurance premium.
Revised:  Under mild modeling assumptions, we derive an optimal cyber insurance premium.
Edit intent: clarity
Original:  Then, we robustify this optimal premium against uncertainty in data and investigate the risk of insuring the EVCSs using  Conditional Value-at-Risk  . A case study with data from EVCSs in Manhattan, New York illustrates our results  .
Revised:  Then, we ensure the robustness of this optimal premium against uncertainty in data and investigate the risk of insuring the EVCSs using Conditional Value-at-Risk . A case study with data from EVCSs in Manhattan, New York illustrates our results .
Edit intent: clarity
Original:  Then, we robustify this optimal premium against uncertainty in data and investigate the risk of insuring the EVCSs using  Conditional Value-at-Risk  . A case study with data from EVCSs in Manhattan, New York illustrates our results  .
Revised:  Then, we robustify this optimal premium and investigate the risk of insuring the EVCSs using Conditional Value-at-Risk . A case study with data from EVCSs in Manhattan, New York illustrates our results .
Edit intent: clarity
Original:  Then, we robustify this optimal premium against uncertainty in data and investigate the risk of insuring the EVCSs using  Conditional Value-at-Risk  . A case study with data from EVCSs in Manhattan, New York illustrates our results  .
Revised:  Then, we robustify this optimal premium against uncertainty in data and investigate the risk of insuring the EVCSs using Conditional Value-at-Risk ) . A case study with data from EVCSs in Manhattan, New York illustrates our results .
Edit intent: fluency
Original: Complex sensors like video camerasinclude tens of configurable parameters, which can be set by end-users to customize the sensors to specific application scenarios.
Revised: Complex sensors like video camerasinclude tens of configurable parameter settings that can be set by end-users to customize the sensors to specific application scenarios.
Edit intent: clarity
Original:  Although parameter settings significantly affect the quality of the sensor output and the accuracy of insights derived from sensor data, most end-users use a fixed parameter setting because they lack the skill or understanding to appropriately configure these parameters.
Revised:  Although parameter settings significantly affect the quality of the sensor output and the accuracy of insights derived from sensor data, most end-users lack the skill or understanding to appropriately configure these parameters.
Edit intent: coherence
Original:  Although parameter settings significantly affect the quality of the sensor output and the accuracy of insights derived from sensor data, most end-users use a fixed parameter setting because they lack the skill or understanding to appropriately configure these parameters.
Revised:  Although parameter settings significantly affect the quality of the sensor output and the accuracy of insights derived from sensor data, most end-users use a fixed parameter setting because they lack the skill or understanding to reconfigure these parameters.
Edit intent: clarity
Original:  By removing the need to provide reward labels for the online experience, our approach can be more practical to use in settings where reward supervision would otherwise be provided manually .
Revised:  By removing the need to provide reward labels for online collection, this data can be much cheaper to collect .
Edit intent: clarity
Original: In spite of intensive efforts it has remained an open problem to what extent current Artificial Intelligence (AI) methods that employ Deep Neural Networks (DNNs) can be implemented more energy-efficiently on spike-based neuromorphic hardware .
Revised: Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) can be implemented more energy-efficiently on spike-based neuromorphic hardware .
Edit intent: clarity
Original:  In this paper, we introduce LocoTransformer, an end-to-end RL method for quadrupedal locomotion that leverages a Transformer-based model for fusing proprioceptive states and visual observations  .
Revised:  In this paper, we introduce LocoTransformer, an end-to-end RL method that leverages both proprioceptive states and visual observations .
Edit intent: clarity
Original:  In this paper, we introduce LocoTransformer, an end-to-end RL method for quadrupedal locomotion that leverages a Transformer-based model for fusing proprioceptive states and visual observations  .
Revised:  In this paper, we introduce LocoTransformer, an end-to-end RL method for quadrupedal locomotion that leverages a Transformer-based model for fusing proprioceptive states and visual observations for locomotion control .
Edit intent: clarity
Original:  We show that our method obtains significant improvements over policies with only proprioceptive state inputs, and that Transformer-based models further improve generalization across environments .
Revised:  We transfer our learned policy from simulation to a real robot by running it indoor and in-the-wild with unseen obstacles and terrain. Our method not only significantly improves over baselines, but also achieves far better generalization performance, especially when transferred to the real robot .
Edit intent: clarity
Original: The current exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data.
Revised: The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data.
Edit intent: clarity
Original:  Effective dimensionality has emerged as a key concept for describing neural activity at the collective level , yet different studies rely on a variety of definitions of it . Here we focus on the complementary notions of intrinsic and embedding dimensionality, and argue that they provide a useful framework for extracting computational principles from data.
Revised:  Effective dimensionality has emerged as an important property of neural activity across populations of neurons , yet different studies rely on a variety of definitions of it . Here we focus on the complementary notions of intrinsic and embedding dimensionality, and argue that they provide a useful framework for extracting computational principles from data.
Edit intent: clarity
Original:  Effective dimensionality has emerged as a key concept for describing neural activity at the collective level , yet different studies rely on a variety of definitions of it . Here we focus on the complementary notions of intrinsic and embedding dimensionality, and argue that they provide a useful framework for extracting computational principles from data.
Revised:  Effective dimensionality has emerged as a key concept for describing neural activity at the collective level , yet different studies rely on different definitions and interpretations of this quantity . Here we focus on the complementary notions of intrinsic and embedding dimensionality, and argue that they provide a useful framework for extracting computational principles from data.
Edit intent: clarity
Original:  Effective dimensionality has emerged as a key concept for describing neural activity at the collective level , yet different studies rely on a variety of definitions of it . Here we focus on the complementary notions of intrinsic and embedding dimensionality, and argue that they provide a useful framework for extracting computational principles from data.
Revised:  Effective dimensionality has emerged as a key concept for describing neural activity at the collective level , yet different studies rely on a variety of definitions of it . Here we focus on intrinsic and embedding dimensionality, and argue that they provide a useful framework for extracting computational principles from data.
Edit intent: clarity
Original:  Effective dimensionality has emerged as a key concept for describing neural activity at the collective level , yet different studies rely on a variety of definitions of it . Here we focus on the complementary notions of intrinsic and embedding dimensionality, and argue that they provide a useful framework for extracting computational principles from data.
Revised:  Effective dimensionality has emerged as a key concept for describing neural activity at the collective level , yet different studies rely on a variety of definitions of it . Here we focus on the complementary notions of intrinsic and embedding dimensionality, and discuss how they might reveal computational principles from data.
Edit intent: clarity
Original:  Network models form an ideal substrate for testing more specifically the hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
Revised:  We conclude by highlighting the role of network models as an ideal substrate for testing more specifically the hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
Edit intent: clarity
Original:  Network models form an ideal substrate for testing more specifically the hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
Revised:  Network models form an ideal substrate for testing more specifically various hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
Edit intent: clarity