wanyu/IteraTeR_v2 huggingface dataset:
Original:  An essence of deep learning is the information propagation through the layers, suggesting a connection between deep neural networks and dynamical systems , in the sense that the information propagation is explicitly modeled by the time-evolution of dynamical systems.
Revised:  An essence of deep learning is the information propagation through the layers, suggesting a connection between deep neural networks and dynamical systems in the sense that the information propagation is explicitly modeled by the time-evolution of dynamical systems.
Edit intent: fluency
Original:  An essence of deep learning is the information propagation through the layers, suggesting a connection between deep neural networks and dynamical systems , in the sense that the information propagation is explicitly modeled by the time-evolution of dynamical systems.
Revised:  An essence of deep learning is the information propagation through the layers, suggesting a connection between deep neural networks and dynamical systems , in the sense that information propagation is explicitly modeled by the time-evolution of dynamical systems.
Edit intent: fluency
Original:  Here, we present a pattern recognition based on  optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation.
Revised:  In this study, we perform pattern recognition based on optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation.
Edit intent: clarity
Original:  Here, we present a pattern recognition based on  optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation.
Revised:  Here, we present a pattern recognition based on the optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation.
Edit intent: fluency
Original:  The learning is based on the adjoint method to optimally control dynamical systems, and the deep (virtual) network structures based on the time evolution of the systems can be used for processing input information.
Revised:  The learning is based on the adjoint method to optimally control dynamical systems, and the deep (virtual) network structures based on the time evolution of the systems are used for processing input information.
Edit intent: clarity
Original:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters  .
Revised:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and demonstrate that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters .
Edit intent: fluency
Original:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters  .
Revised:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system allows for image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters .
Edit intent: clarity
Original:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters  .
Revised:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications using only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters .
Edit intent: fluency
Original:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters  .
Revised:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals . This is in contrast to conventional multilayer neural networks which require training of a large number of weight parameters .
Edit intent: coherence
Original:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters  .
Revised:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks , which require a large number of weight parameters .
Edit intent: clarity
Original:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters  .
Revised:  As an example, we apply the dynamics-based recognition approach to an optoelectronic delay system and show that the use of the delay system enables image recognition and nonlinear classifications with only a few control signals , in contrast to conventional multilayer neural networks which require training of a large number of weight parameters to be trained .
Edit intent: clarity
Original:  The proposed approach enables to gain insight into mechanisms of deep network processing in the framework of an optimal control problem and opens a novel pathway to realize physical computing hardware.
Revised:  The proposed approach provides insight into the mechanisms of deep network processing in the framework of an optimal control problem and opens a novel pathway to realize physical computing hardware.
Edit intent: clarity
Original:  The proposed approach enables to gain insight into mechanisms of deep network processing in the framework of an optimal control problem and opens a novel pathway to realize physical computing hardware.
Revised:  The proposed approach enables to gain insight into mechanisms of deep network processing in the framework of an optimal control problem and presents a pathway for realizing physical computing hardware.
Edit intent: clarity
Original:  In a simplified model, we derive a transparent formula that connects the extent of superspreading to the width of credible intervals for the reproduction number  .
Revised:  Of independent interest is the derivation of a transparent formula that connects the extent of superspreading to the width of credible intervals for the reproduction number .
Edit intent: clarity
Original: A cluster of research in Human-Computer Interaction (HCI) suggests that it is possible to infer some characteristics of users' mental states by analyzing electrophysiological responses in real-time.
Revised: A cluster of research in Human-Computer Interaction (HCI) suggests that it is possible to infer some characteristics of users' affective states by analyzing electrophysiological responses in real-time.
Edit intent: clarity
Original: A cluster of research in Human-Computer Interaction (HCI) suggests that it is possible to infer some characteristics of users' mental states by analyzing electrophysiological responses in real-time.
Revised: A cluster of research in Human-Computer Interaction (HCI) suggests that it is possible to infer some characteristics of users' mental states by analyzing their electrophysiological activity in real-time.
Edit intent: clarity
Original:  However, it is not clear how to use the information extracted from electrophysiological signals to create visual representations of the emotional states of Virtual Reality (VR) users.
Revised:  However, it is not clear how to use the information extracted from electrophysiological signals to create visual representations of the affective states of Virtual Reality (VR) users.
Edit intent: clarity
Original:  Visualization of users' emotions in VR can lead to biofeedback therapies for training emotion self-regulation .
Revised:  Visualization of users' affective states in VR can lead to biofeedback therapies for training emotion self-regulation .
Edit intent: clarity
Original:  Visualization of users' emotions in VR can lead to biofeedback therapies for training emotion self-regulation .
Revised:  Visualization of users' emotions in VR can lead to biofeedback therapies for mental health care .
Edit intent: clarity
Original:  Understanding how to visualize emotions in VR requires an interdisciplinary approach that integrates disciplines such as psychology, electrophysiology, and audiovisual design.
Revised:  Understanding how to visualize affective states in VR requires an interdisciplinary approach that integrates disciplines such as psychology, electrophysiology, and audiovisual design.
Edit intent: clarity
Original:  Understanding how to visualize emotions in VR requires an interdisciplinary approach that integrates disciplines such as psychology, electrophysiology, and audiovisual design.
Revised:  Understanding how to visualize emotions in VR requires an interdisciplinary approach that integrates psychology, electrophysiology, and audiovisual design.
Edit intent: clarity
Original:  Understanding how to visualize emotions in VR requires an interdisciplinary approach that integrates disciplines such as psychology, electrophysiology, and audiovisual design.
Revised:  Understanding how to visualize emotions in VR requires an interdisciplinary approach that integrates disciplines such as psychology, electrophysiology, and audio-visual design.
Edit intent: clarity
Original:  This manuscript addresses this challenge in three sections: First, theories related to emotion and affect are compared .
Revised:  This manuscript addresses this challenge in three sections: First, theories related to emotion and affect are summarized .
Edit intent: clarity
Original:  Second, evidence suggesting that specific visual and sound cues tend to be associated with particular emotions are discussed.
Revised:  Second, evidence suggesting that visual and sound cues tend to be associated with particular emotions are discussed.
Edit intent: clarity
Original:  Second, evidence suggesting that specific visual and sound cues tend to be associated with particular emotions are discussed.
Revised:  Second, evidence suggesting that specific visual and sound cues tend to be associated with affective states are discussed.
Edit intent: clarity
Original:  And third , some of the available methods for assessing emotions are described .
Revised:  Third , some of the available methods for assessing emotions are described .
Edit intent: clarity
Original: This work presents DONNA (Distilling Optimal Neural Network Architectures), a novel pipeline for rapid neural architecture search and search space exploration, targeting multiple different hardware platforms and user scenarios.
Revised: This work presents DONNA (Distilling Optimal Neural Network Architectures), a novel pipeline for rapid and diverse NAS, that scales to many user scenarios.
Edit intent: clarity
Original:  First, an accuracy predictor is built for a diverse search space using blockwise knowledge distillation.
Revised:  First, an accuracy predictor is built using blockwise knowledge distillation.
Edit intent: clarity
Original:  This predictor enables searching across diverse  macro-architectural network parameters such as layer types , attention mechanisms , and channel widths, as well as across micro-architectural parameters such as block repeats , kernel sizes, and expansion rates.
Revised:  This predictor enables searching across diverse macro-architectural parameters such as layer types , attention mechanisms , and channel widths, as well as across micro-architectural parameters such as block repeats , kernel sizes, and expansion rates.
Edit intent: clarity
Original:  This predictor enables searching across diverse  macro-architectural network parameters such as layer types , attention mechanisms , and channel widths, as well as across micro-architectural parameters such as block repeats , kernel sizes, and expansion rates.
Revised:  This predictor enables searching across diverse macro-architectural network parameters such as layer types and attention mechanisms as well as across micro-architectural parameters such as block repeats , kernel sizes, and expansion rates.
Edit intent: clarity
Original:  This predictor enables searching across diverse  macro-architectural network parameters such as layer types , attention mechanisms , and channel widths, as well as across micro-architectural parameters such as block repeats , kernel sizes, and expansion rates.
Revised:  This predictor enables searching across diverse macro-architectural network parameters such as layer types , attention mechanisms , and channel widths, as well as across micro-architectural parameters such as block repeats and expansion rates.
Edit intent: clarity
Original:  Second, a rapid evolutionary search phase finds a Pareto-optimal set of architectures in terms of accuracy and latency for any scenario using the  predictor and on-device measurements.
Revised:  Second, a rapid evolutionary search phase finds a set of Pareto-optimal architectures for any scenario using the predictor and on-device measurements.
Edit intent: clarity
Original:  Third, Pareto-optimal models can be quickly finetuned to full accuracy.
Revised:  Third, optimal models are quickly finetuned to full accuracy.
Edit intent: clarity
Original:  Third, Pareto-optimal models can be quickly finetuned to full accuracy.
Revised:  Third, Pareto-optimal models can be quickly finetuned to training-from-scratch accuracy.
Edit intent: clarity
Original:  In ImageNetclassification, architectures found by DONNA are 20\% faster than EfficientNet-B0 and MobileNetV2 on a Nvidia V100 GPU at similar accuracy and 10\% faster with 0.5\% higher accuracy than MobileNetV2-1.4x on a Samsung S20 smartphone.
Revised:  In ImageNetclassification, architectures found by DONNA are 20\% faster than EfficientNet-B0 and MobileNetV2 on a Nvidia V100 GPU and 10\% faster with 0.5\% higher accuracy than MobileNetV2-1.4x on a Samsung S20 smartphone.
Edit intent: clarity
Original:  In addition to neural architecture search , DONNA is used for search-space exploration and hardware-aware model compression.
Revised:  In addition to NAS , DONNA is used for search-space exploration and hardware-aware model compression.
Edit intent: clarity
Original: Today, state-of-the-art Neural Architecture Search (NAS) methods cannot scale to many hardware platformsor scenarios at a low training costs and/or can only handle non-diverse, heavily constrained architectural search-spaces.
Revised: Current state-of-the-art Neural Architecture Search (NAS) methods cannot scale to many hardware platformsor scenarios at a low training costs and/or can only handle non-diverse, heavily constrained architectural search-spaces.
Edit intent: clarity
Original: Today, state-of-the-art Neural Architecture Search (NAS) methods cannot scale to many hardware platformsor scenarios at a low training costs and/or can only handle non-diverse, heavily constrained architectural search-spaces.
Revised: Today, state-of-the-art Neural Architecture Search (NAS) methods neither efficiently scale to multiple hardware platforms, nor handle diverse architectural search-spaces.
Edit intent: clarity
Original:  To solve these issues , we present DONNA (Distilling Optimal Neural Network Architectures), a novel pipeline for rapid  and diverse NAS, that scales to many user scenarios.
Revised:  To remedy this , we present DONNA (Distilling Optimal Neural Network Architectures), a novel pipeline for rapid and diverse NAS, that scales to many user scenarios.
Edit intent: clarity
Original:  In DONNA , a search consists of three phases.
Revised:  DONNA consists of three phases.
Edit intent: clarity
Original:  This predictor enables searching across diverse networks with varying macro-architectural parameters such as layer types and attention mechanisms  as well as across micro-architectural parameters such as block repeats and expansion rates.
Revised:  This predictor enables searching across diverse networks with varying macro-architectural parameters such as layer types and attention mechanisms , as well as across micro-architectural parameters such as block repeats and expansion rates.
Edit intent: fluency
Original:  Second, a rapid evolutionary search phase finds a set of Pareto-optimal architectures for any scenario using the accuracy predictor and on-device measurements.
Revised:  Second, a rapid evolutionary search finds a set of Pareto-optimal architectures for any scenario using the accuracy predictor and on-device measurements.
Edit intent: clarity
Original:  Second, a rapid evolutionary search phase finds a set of Pareto-optimal architectures for any scenario using the accuracy predictor and on-device measurements.
Revised:  Second, a rapid evolutionary search phase finds a set of pareto-optimal architectures for any scenario using the accuracy predictor and on-device measurements.
Edit intent: fluency
Original:  With this approach, DONNA is up to 100x faster than MNasNet in finding state-of-the-art architectures on-device.
Revised:  DONNA is up to 100x faster than MNasNet in finding state-of-the-art architectures on-device.
Edit intent: clarity
Original:  We investigate these graphs in the setting of relaxed  evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature.
Revised:  We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature.
Edit intent: coherence
Original:  The conserve is not true, however.
Revised:  The converse is not true, however.
Edit intent: coherence
Original:  A 400 example mixed language English-Romanian dataset is created for cross-lingual transfer learning evaluation .
Revised:  A 400 example mixed English-Romanian dataset is created for cross-lingual transfer learning evaluation .
Edit intent: clarity
Original:  A 400 example mixed language English-Romanian dataset is created for cross-lingual transfer learning evaluation .
Revised:  A 400 example mixed language English-Romanian dataset is made available for cross-lingual transfer learning evaluation .
Edit intent: clarity
Original: Learning sophisticated feature interactions is crucial for Click-Through Rate (CTR) prediction in recommender systems.
Revised:  Click-Through Rate (CTR) prediction in recommender systems.
Edit intent: coherence
Original:  Various deep CTR models follow an Embedding  Feature Interaction paradigm.
Revised:  Various deep CTR models follow an Embedding \& Feature Interaction paradigm.
Edit intent: fluency
Original:  The majority focus on designing network architectures in Feature Interaction module to better model feature interactions while the Embedding module, serving as a bottleneck between data and Feature Interaction module , has been overlooked.
Revised:  The majority focus on designing network architectures to better capture feature interactions while the Embedding module, serving as a bottleneck between data and Feature Interaction module , has been overlooked.
Edit intent: clarity
Original:  To fill the gap of representing numerical features, in this paper, we propose AutoDis, a framework that discretizes features in numerical fields automatically and is optimized with CTR models in an end-to-end manner.
Revised:  To fill the gap of representing numerical features, in this paper, we propose a novel embedding learning framework for numerical features in CTR prediction (AutoDis) with high model capacity, end-to-end manner.
Edit intent: clarity
Original:  Specifically, we introduce a set of meta-embeddings for each numerical field to model the relationship among the intra-field features and propose an automatic differentiable discretization and aggregation approach to capture the correlations between the numerical features and meta-embeddings.
Revised:  Specifically, we propose meta-embeddings for each numerical field to model the relationship among the intra-field features and propose an automatic differentiable discretization and aggregation approach to capture the correlations between the numerical features and meta-embeddings.
Edit intent: clarity
Original:  We defend the idea that non harmonious connectives model the information erasure, the non-reversibility, and the non-determinism that occur, among other places, in quantum measurement.
Revised:  We defend that non harmonious connectives model the information erasure, the non-reversibility, and the non-determinism that occur, among other places, in quantum measurement.
Edit intent: clarity
Original: We investigate an unsuspected connection between non harmonious logical connectives, such as Prior's tonk, and quantum computing.
Revised: We investigate an unsuspected connection between non-harmonious logical connectives, such as Prior's tonk, and quantum computing.
Edit intent: fluency
Original:  We defend that non harmonious connectives model the information erasure, the non-reversibility, and the non-determinism that occur, among other places, in quantum measurement.
Revised:  We argue that non-harmonious connectives model the information erasure, the non-reversibility, and the non-determinism that occur, among other places, in quantum measurement.
Edit intent: clarity
Original:  More concretely, we introduce a propositional logic with a non harmonious connective \odot (read: "sup ", for "superposition") and show that its proof language forms the core of a quantum programming language.
Revised:  We introduce a propositional logic with a non harmonious connective \odot (read: "sup ", for "superposition") and show that its proof language forms the core of a quantum programming language.
Edit intent: coherence
Original:  More concretely, we introduce a propositional logic with a non harmonious connective \odot (read: "sup ", for "superposition") and show that its proof language forms the core of a quantum programming language.
Revised:  More concretely, we introduce a propositional logic with a non-harmonious connective sup and show that its proof language forms the core of a quantum programming language.
Edit intent: coherence
Original:   We propose an integration of device-centric wireless networks and NGO that is not driven by a precise knowledge of the presence of the links , but by their potential to support the requested demand and service which is estimated using context information available in cellular network .
Revised:  We propose an integration of device-centric wireless networks and NGO that is not driven by a precise knowledge of the presence of the links .
Edit intent: coherence