wanyu/IteraTeR_v2 huggingface dataset:
Original: Underwater 3D reconstruction is challenging due to the refraction of light at the water-air interface (most electronic devices cannot be directly submerged in water).
Revised: Underwater 3D geometry of underwater scenes is challenging because of non-linear refraction of light at the water-air interface (most electronic devices cannot be directly submerged in water).
Edit intent: clarity
Original:  Extensive synthetic and real experiments demonstrate that our method can perform underwater 3D reconstruction with high accuracy .
Revised:  Extensive synthetic and real data show our method produces state-of-the-art reconstruction on static and dynamic underwater scenes .
Edit intent: clarity
Original: I introduce the Koha model, a new theory that aims to explain two unresolved phenomena within biological neural networks: How information is processed and stored within neural circuits, and how neurons learn to become pattern detectors.
Revised: This paper introduces the Koha model, a new theory that aims to explain two unresolved phenomena within biological neural networks: How information is processed and stored within neural circuits, and how neurons learn to become pattern detectors.
Edit intent: clarity
Original:  In the Koha model, dendritic spines serve as computational units that scan for precise spike patterns in their synaptic inputs.
Revised:  In the Koha model, the dendritic spines of a neuron serve as computational units that scan for precise spike patterns in their synaptic inputs.
Edit intent: clarity
Original:  The model proposes the existence of a temporal code within dendritic spines , which is used for the dampening or amplification of signals, depending on the temporal information of incoming spike trains.
Revised:  The model proposes the existence of a temporal code within each dendritic spine , which is used for the dampening or amplification of signals, depending on the temporal information of incoming spike trains.
Edit intent: clarity
Original:  A concrete process is described for how signal filtration occurs within spine necks and compelling evidence for the existence of such a mechanism within dendritic spines is presented .
Revised:  Compelling evidence is provided and a concrete process is described for how signal filtration occurs within spine necks and compelling evidence for the existence of such a mechanism within dendritic spines is presented .
Edit intent: coherence
Original:  A concrete process is described for how signal filtration occurs within spine necks and compelling evidence for the existence of such a mechanism within dendritic spines is presented .
Revised:  A concrete process is described for how signal filtration occurs within spine necks .
Edit intent: clarity
Original: This paper introduces the Koha model, a new theory that aims to explain two unresolved phenomena within biological neural networks: How information is processed and stored within neural circuits, and how neurons learn to become pattern detectors.
Revised: This work introduces the Koha model, a new theory that aims to explain two unresolved phenomena within biological neural networks: How information is processed and stored within neural circuits, and how neurons learn to become pattern detectors.
Edit intent: clarity
Original: Objective: To propose a novel deep neural network (DNN) architecture -- the filter bank convolutional neural network (FBCNN) -- to improve SSVEP classification in single-channel BCIs with small data lengths.
Revised: Objective: To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel BCIs with small data lengths.
Edit intent: clarity
Original: Objective: To propose a novel deep neural network (DNN) architecture -- the filter bank convolutional neural network (FBCNN) -- to improve SSVEP classification in single-channel BCIs with small data lengths.
Revised: Objective: To propose a novel deep neural network (DNN) architecture -- the filter bank convolutional neural network (FBCNN) -- to improve SSVEP classification in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths.
Edit intent: clarity
Original:  Methods : We propose two models: the FBCNN-2D and the FBCNN-3D.
Revised:  Approach : We propose two models: the FBCNN-2D and the FBCNN-3D.
Edit intent: fluency
Original:  a driver may behave in the same manner for different occupancy patterns ahead of them (e.g., a driver may move at constant speed in traffic or on an open road).
Revised:  a driver may behave similarly for different occupancy patterns ahead of them (e.g., a driver may move at constant speed in traffic or on an open road).
Edit intent: clarity
Original:  Notably, for adversarial textual attack, it is even better than the previous bound which only holds in special case.
Edit intent: coherence
Original:  Extensive experiments involving five NLP tasks, six datasets and eleven NLP models show that LS can largely reduce the number of queries usually by an order of magnitude to achieve high attack success rates.
Revised:  Extensive experiments involving 5 NLP tasks, six datasets and eleven NLP models show that LS can largely reduce the number of queries usually by an order of magnitude to achieve high attack success rates.
Edit intent: fluency
Original: Autonomy in parts of robot-assisted surgery is essential to reduce surgeons' cognitive load and eventually improve the overall surgical outcome.
Revised: Autonomy in robot-assisted surgery is essential to reduce surgeons' cognitive load and eventually improve the overall surgical outcome.
Edit intent: clarity
Original:  A key requirement to ensure safety in an Autonomous Robotic Surgical System (ARSS) lies in the generation of interpretable plans that rely on expert knowledge.
Revised:  A key requirement for autonomy in a safety-critical scenario as surgery lies in the generation of interpretable plans that rely on expert knowledge.
Edit intent: clarity
Original:  Moreover, the ARSS must be able to reason on the dynamic and unpredictable anatomical environment, and quickly adapt the surgical plan in case of unexpected situations.
Revised:  Moreover, the Autonomous Robotic Surgical System (ARSS) must be able to reason on the dynamic and unpredictable anatomical environment, and quickly adapt the surgical plan in case of unexpected situations.
Edit intent: clarity
Original:  In this paper, we present the first cognitive modular framework for the autonomous planning and execution of surgical tasks in deformable anatomical environments.
Revised:  In this paper, we present a modular Framework for Robot-Assisted Surgery (FRAS) in deformable anatomical environments.
Edit intent: clarity
Original:  Here, we study frequency and voltage stability in the case of the simplest network (two nodes) and an extended all-to-all network via linear stability and bulk analysis.
Revised:  Here, we study frequency and voltage stability in the case of simple networks via linear stability and bulk analysis.
Edit intent: clarity
Original:  For small sample sizes, we show how to leverage  knowledge of the set of possible values to compute bounds that are stronger than for general random variables such as standard concentration inequalities.
Revised:  For small sample sizes, we show how to leverage the knowledge of the set of possible values to compute bounds that are stronger than for general random variables such as standard concentration inequalities.
Edit intent: fluency
Original:  However, most deep learning methods  fit a brute-force mapping relationship between the compressive and standard HSIs.
Revised:  However, most deep learning methods aimed to fit a brute-force mapping relationship between the compressive and standard HSIs.
Edit intent: clarity
Original:  However, most deep learning methods  fit a brute-force mapping relationship between the compressive and standard HSIs.
Revised:  However, most deep learning methods fit a brute-force mapping relationship between the captured compressed image and standard HSIs.
Edit intent: clarity
Original:  In addition , compared with most of the network-based methods, our system does not require a dedicated dataset for pre-training.
Revised:  Furthermore , compared with most of the network-based methods, our system does not require a dedicated dataset for pre-training.
Edit intent: fluency
Original:  We introduce and make publicly available the %DIFDELCMD < {\sc %%% WhyAct dataset, consisting of 1,077 visual actions manually annotated with their reasons.
Revised:  We introduce and make publicly available the %DIFDELCMD < {\sc %%% WhyAct dataset, consisting of 1,077 visual actions manually annotated with their reasons.
Edit intent: fluency
Original: As the demand for connected and automated vehicles emerges, so to does the need for quality testing environments to support their development.
Revised: The increasing demand for emerging mobility systems with connected and automated vehicles emerges, so to does the need for quality testing environments to support their development.
Edit intent: clarity
Original: As the demand for connected and automated vehicles emerges, so to does the need for quality testing environments to support their development.
Revised: As the demand for connected and automated vehicles has imposed the necessity for quality testing environments to support their development.
Edit intent: clarity
Original:  In this paper, we introduce a Unity-based virtual simulation environment for emerging mobility systems, called  Information and Decision Science Lab's Scaled Smart Digital City (IDS 3D City), intended to operate alongside its physical peer and its existing control framework.
Revised:  In this paper, we introduce a Unity-based virtual simulation environment for emerging mobility systems, called the Information and Decision Science Lab's Scaled Smart Digital City (IDS 3D City), intended to operate alongside its physical peer and its existing control framework.
Edit intent: fluency
Original:  In this paper, we introduce a Unity-based virtual simulation environment for emerging mobility systems, called  Information and Decision Science Lab's Scaled Smart Digital City (IDS 3D City), intended to operate alongside its physical peer and its existing control framework.
Revised:  In this paper, we introduce a Unity-based virtual simulation environment for emerging mobility systems, called Information and Decision Science Lab's Scaled Smart Digital City (IDS 3D City), intended to operate alongside its physical peer and its established control framework.
Edit intent: clarity
Original:  By utilizing the Robot Operation System, AirSim, and Unity, we have constructed a simulation environment capable of iteratively designing experiments significantly faster than  is possible in a physical testbed.
Revised:  By utilizing the Robot Operation System, AirSim, and Unity, we constructed a simulation environment capable of iteratively designing experiments significantly faster than is possible in a physical testbed.
Edit intent: coherence
Original:  By utilizing the Robot Operation System, AirSim, and Unity, we have constructed a simulation environment capable of iteratively designing experiments significantly faster than  is possible in a physical testbed.
Revised:  By utilizing the Robot Operation System, AirSim, and Unity, we have constructed a simulation environment capable of iteratively designing experiments significantly faster than it is possible in a physical testbed.
Edit intent: fluency
Original:  This provides us with an intermediate step to validate the effectiveness of our control framework prior to testing them in the physical testbed.
Revised:  This environment provides an intermediate step to validate the effectiveness of our control framework prior to testing them in the physical testbed.
Edit intent: clarity
Original:  This provides us with an intermediate step to validate the effectiveness of our control framework prior to testing them in the physical testbed.
Revised:  This provides us with an intermediate step to validate the effectiveness of our control algorithms prior to their implementation in the physical testbed.
Edit intent: clarity
Original:  Another benefit provided by the IDS 3D City is demonstrating that our control algorithms work independent of the physical vehicle dynamics, since the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city.
Revised:  The IDS 3D City is demonstrating that our control algorithms work independent of the physical vehicle dynamics, since the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city.
Edit intent: coherence
Original:  Another benefit provided by the IDS 3D City is demonstrating that our control algorithms work independent of the physical vehicle dynamics, since the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city.
Revised:  Another benefit provided by the IDS 3D City also enables us to demonstrate that our control algorithms work independent of the physical vehicle dynamics, since the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city.
Edit intent: clarity
Original:  Another benefit provided by the IDS 3D City is demonstrating that our control algorithms work independent of the physical vehicle dynamics, since the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city.
Revised:  Another benefit provided by the IDS 3D City is demonstrating that our control algorithms work independently of the underlying vehicle dynamics, since the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city.
Edit intent: clarity
Original:  Another benefit provided by the IDS 3D City is demonstrating that our control algorithms work independent of the physical vehicle dynamics, since the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city.
Revised:  Another benefit provided by the IDS 3D City is demonstrating that our control algorithms work independent of the physical vehicle dynamics, as the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city.
Edit intent: coherence
Original:  We finally demonstrate the effectiveness of our digital environment by performing an experiment in both  virtual and physical environments  .
Revised:  Finally, we demonstrate the behavior of our digital environment by performing an experiment in both virtual and physical environments .
Edit intent: clarity
Original:  We finally demonstrate the effectiveness of our digital environment by performing an experiment in both  virtual and physical environments  .
Revised:  We finally demonstrate the effectiveness of our digital environment by performing an experiment in both the virtual and physical environments .
Edit intent: fluency
Original:  To this end, there has been a growing surge of interest to learn simultaneously from multiple training domains - while enforcing different types of invariance across those domains.
Revised:  To this end, there has been a growing surge of interest to learn simultaneously from multiple training domains -- while enforcing different types of invariance across those domains.
Edit intent: fluency
Original:  Yet, all existing approaches fail to show systematic benefits under fair evaluation protocols.
Revised:  Yet, all existing approaches fail to show systematic benefits under controlled evaluation protocols.
Edit intent: clarity
Original:  In this paper, we propose a new learning scheme to enforce domain invariance in the space of the gradients of the loss function : specifically, we introduce a regularization term that matches the domain-level variances of gradients  across training domains.
Revised:  In this paper, we propose a new learning scheme to enforce domain invariance in the space of the gradients of the loss : specifically, we introduce a regularization term that matches the domain-level variances of gradients across training domains.
Edit intent: clarity
Original:  In this paper, we propose a new learning scheme to enforce domain invariance in the space of the gradients of the loss function : specifically, we introduce a regularization term that matches the domain-level variances of gradients  across training domains.
Revised:  In this paper, we propose a new learning scheme to enforce domain invariance in the space of the gradients of the loss function : specifically, the domain-level variances of gradients across training domains.
Edit intent: coherence
Original:  In this paper, we propose a new learning scheme to enforce domain invariance in the space of the gradients of the loss function : specifically, we introduce a regularization term that matches the domain-level variances of gradients  across training domains.
Revised:  In this paper, we propose a new learning scheme to enforce domain invariance in the space of the gradients of the loss function : specifically, we introduce a regularization term that matches the domain-level variances of gradients are matched across training domains.
Edit intent: clarity
Original:  In particular , Fishr improves the state of the art on the DomainBed benchmark and performs significantly better than Empirical Risk Minimization.
Revised:  Notably , Fishr improves the state of the art on the DomainBed benchmark and performs significantly better than Empirical Risk Minimization.
Edit intent: coherence
Original:  In particular , Fishr improves the state of the art on the DomainBed benchmark and performs significantly better than Empirical Risk Minimization.
Revised:  In particular , Fishr improves the state of the art on the DomainBed benchmark and performs consistently better than Empirical Risk Minimization.
Edit intent: clarity
Original:  Therefore, the proposed PLVC model learns to compress video towards good perceptual quality at low bit-rate .
Revised:  The experimental results show that the proposed PLVC model learns to compress video towards good perceptual quality at low bit-rate .
Edit intent: coherence
Original: Learning to play table tennis is a challenging task for robots, due to the variety of the strokes required.
Revised: Learning to play table tennis is a challenging task for robots, as a wide variety of strokes required.
Edit intent: coherence
Original:  Current advances in deep Reinforcement Learning (RL) have shown potential in learning the optimal strokes .
Revised:  Recent advances have shown that deep Reinforcement Learning (RL) have shown potential in learning the optimal strokes .
Edit intent: clarity
Original:  However, the large amount of exploration still limits the applicability when utilizing RL in real scenarios  .
Revised:  However, the applicability of RL in real scenarios .
Edit intent: coherence
Original:  In this paper, we first propose a realistic simulation environment where several models are built for the ball's dynamics and the robot's kinematics .
Revised:  In this work, we propose a realistic simulation environment where several models are built for the ball's dynamics and the robot's kinematics .
Edit intent: clarity
Original:  In this paper, we first propose a realistic simulation environment where several models are built for the ball's dynamics and the robot's kinematics .
Revised:  In this paper, we first propose a realistic simulation environment in which multiple models are built for the ball's dynamics and the robot's kinematics .
Edit intent: fluency
Original:  In this paper, we first propose a realistic simulation environment where several models are built for the ball's dynamics and the robot's kinematics .
Revised:  In this paper, we first propose a realistic simulation environment where several models are built for the dynamics of the ball and the kinematics of the robot .
Edit intent: clarity
Original:  A novel policy gradient approach with TD3 backbone is proposed for the second stage .
Revised:  A novel policy gradient approach with TD3 backbone is proposed to learn the racket strokes based on the predicted state of the ball at the hitting time .
Edit intent: clarity
Original:  To cross the domain from simulation to reality, we develop an efficient retraining method and test  in three real scenarios with a success rate of 98\%  .
Revised:  Furthermore, to cross the domain from simulation to reality, we develop an efficient retraining method and test in three real scenarios with a success rate of 98\% .
Edit intent: coherence
Original:  To cross the domain from simulation to reality, we develop an efficient retraining method and test  in three real scenarios with a success rate of 98\%  .
Revised:  To cross the domain from simulation to reality, we adopt an efficient retraining method and test in three real scenarios with a success rate of 98\% .
Edit intent: clarity
Original:  To cross the domain from simulation to reality, we develop an efficient retraining method and test  in three real scenarios with a success rate of 98\%  .
Revised:  To cross the domain from simulation to reality, we develop an efficient retraining method and test it in three real scenarios with a success rate of 98\% .
Edit intent: clarity
Original:  To cross the domain from simulation to reality, we develop an efficient retraining method and test  in three real scenarios with a success rate of 98\%  .
Revised:  To cross the domain from simulation to reality, we develop an efficient retraining method and test in three real scenarios . The resulting success rate is 98\% .
Edit intent: coherence
Original: This article presents a different way to study the theory of regularized learning for generalized data  including representer theorems and convergence theorems.
Revised: This article presents a new way to study the theory of regularized learning for generalized data including representer theorems and convergence theorems.
Edit intent: fluency
Original:  According to the techniques of regularization, the global solutions are approximated by minimizing the regularized empirical risks over the Banach spaces.
Revised:  According to the techniques of regularization, the exact solutions are approximated by minimizing the regularized empirical risks over the Banach spaces.
Edit intent: clarity
Original:  The Banach spaces are adaptively chosen to endow the generalized input data with compactness such that the existence and convergence of the approximate solutions are guaranteed by the weak* topology .
Revised:  The existence and convergence of the approximate solutions are guaranteed by the weak* topology .
Edit intent: coherence
Original:  In particular, the paper makes several contributions: a) It studies a novel problem of network fairness that considers equitability of access to influencers - nodes that influence access to opportunity for others in the graph;
Revised:  In particular, the paper makes several contributions: a) It studies a novel problem of network fairness that considers equitability of access to influencers , nodes that influence access to opportunity for others in the graph;
Edit intent: fluency
Original: In this paper, we propose a self-supervised approach for tumor segmentation.
Revised: In this paper, we target self-supervised approach for tumor segmentation.
Edit intent: clarity
Original:  Specifically , we advocate a zero-shot setting, where models from self-supervised learning should be directly applicable for the downstream task, without using any manual annotations whatsoever.
Revised:  Specifically , we advocate a zero-shot setting, where models from pre-training should be directly applicable for the downstream task, without using any manual annotations whatsoever.
Edit intent: clarity