wanyu/IteraTeR_v2 huggingface dataset:
Original:  In particular, these improve existing bounds on L(p,q)-labeling of interval and circular arc graphs and L(2,1)-labeling of permutation graphs.
Revised:  In particular, these improve existing bounds on L(p,q)-labeling of interval graphs and L(2,1)-labeling of permutation graphs.
Edit intent: clarity
Original:  However, two major open questions are: (i) Does the set of LP optimal dual prices of OLP converge to those of the "offline" LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative.
Revised:  However, two major open questions are: (i) Does the set of LP optimal dual prices in OLP converge to those of the "offline" LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative.
Edit intent: clarity
Original:  Virtually all current online algorithms were based on learning the dual optimal solutions/prices of the linear programs (LP), and their analyses were focused on the aggregate objective value and solving the packing LP where all coefficients in the constraint matrix and objective are nonnegative.
Revised:  Virtually all pre-existing online algorithms were based on learning the dual optimal solutions/prices of the linear programs (LP), and their analyses were focused on the aggregate objective value and solving the packing LP where all coefficients in the constraint matrix and objective are nonnegative.
Edit intent: clarity
Original:  However, two major open questions are : (i) Does the set of LP optimal dual prices in OLP converge to those of the "offline" LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative.
Revised:  However, two major open questions were : (i) Does the set of LP optimal dual prices in OLP converge to those of the "offline" LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative.
Edit intent: fluency
Original:  However, two major open questions are : (i) Does the set of LP optimal dual prices in OLP converge to those of the "offline" LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative.
Revised:  However, two major open questions are : (i) Does the set of LP optimal dual prices in OLP converge to those of the ``offline'' LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative.
Edit intent: fluency
Original:  Then we propose a new type of OLP algorithm, Action-History-Dependent Learning Algorithm, which improves the previous algorithm performances by taking into account the past input data as well as and decisions/actions already made.
Revised:  Then we propose a new type of OLP algorithm, Action-History-Dependent Learning Algorithm, which improves the previous algorithm performances by taking into account the past input data as well as decisions/actions already made.
Edit intent: fluency
Original:  We derive an O(\log n \log \log n) regret bound  for the proposed algorithm, against the O(n) bound for typical dual-price learning algorithms, and show that no dual-based thresholding algorithm achieves a worst-case regret smaller than O(\log n), where n is the number of decision variables.
Revised:  We derive an O(\log n \log \log n) regret bound for the proposed algorithm, against the O(n) bound for typical dual-price learning algorithms, where n is the number of decision variables.
Edit intent: coherence
Original:  Numerical experiments demonstrate the superior performance of the proposed algorithms and the effectiveness of our action-history-dependent design .
Revised:  Numerical experiments demonstrate the effectiveness of the proposed algorithms and the effectiveness of our action-history-dependent design .
Edit intent: clarity
Original:  Numerical experiments demonstrate the superior performance of the proposed algorithms and the effectiveness of our action-history-dependent design .
Revised:  Numerical experiments demonstrate the superior performance of the proposed algorithm and the action-history-dependent design .
Edit intent: clarity
Original:  However, two major open questions were: (i) Does the set of LP optimal dual prices learned in the pre-existing algorithms converge to those of the ``offline'' LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative.
Revised:  However, two major open questions were: (i) Does the set of LP optimal dual prices learned in the pre-existing algorithms converge to those of the "offline" LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative.
Edit intent: fluency
Original: We demonstrate that with an optimally tuned scheduling function, adiabatic quantum computing (AQC) can  solve a quantum linear system problem (QLSP) with O(\kappa (\log(\kappa} /\epsilon)  runtime, where \kappa is the condition number, and \epsilon is the target accuracy.
Revised: We demonstrate that with an optimally tuned scheduling function, adiabatic quantum computing (AQC) can readily solve a quantum linear system problem (QLSP) with O(\kappa (\log(\kappa} /\epsilon) runtime, where \kappa is the condition number, and \epsilon is the target accuracy.
Edit intent: clarity
Original: We demonstrate that with an optimally tuned scheduling function, adiabatic quantum computing (AQC) can  solve a quantum linear system problem (QLSP) with O(\kappa (\log(\kappa} /\epsilon)  runtime, where \kappa is the condition number, and \epsilon is the target accuracy.
Revised: We demonstrate that with an optimally tuned scheduling function, adiabatic quantum computing (AQC) can solve a quantum linear system problem (QLSP) with O(\kappa (\log(\kappa} /\epsilon) )) runtime, where \kappa is the condition number, and \epsilon is the target accuracy.
Edit intent: fluency
Original:  On the other hand such imaging remains challenging because of large data dimension and, typically, small pool of available training examples.
Revised:  However, such imaging remains challenging because of large data dimension and, typically, small pool of available training examples.
Edit intent: coherence
Original:  Performed experiments show that it is very effective in improving the classification accuracy without being restricted to a particular image type or neural network architecture  .
Revised:  Performed experiments show that it is very effective at improving the classification accuracy without being restricted to a particular image type or neural network architecture .
Edit intent: fluency
Original:  To achieve this, we adapt the Douglas-Peucker algorithm to create a set of line segments whose specific subsets represent the lines at any variable LOD.
Revised:  To achieve this, we adapt the Douglas-Peucker line simplification principle, generating an exhaustive set of line segments whose specific subsets represent the lines at any variable LOD.
Edit intent: clarity
Original:  At run time, our algorithm supports screen-space adaptive LOD levels and creates an appropriate subset of the line segments accordingly.
Revised:  At run time, our algorithm supports screen-space adaptive LOD levels and the display of the correct subset of line segments accordingly.
Edit intent: clarity
Original:  Our implementation shows that we can simplify and render large line datasets interactively.
Revised:  Our implementation shows that we can simplify and display large line datasets interactively.
Edit intent: clarity
Original:  Additionally, we can successfully apply line style patterns, dynamic  lenses, and anti-aliasing techniques to our line rendering.
Revised:  We can successfully apply line style patterns, dynamic lenses, and anti-aliasing techniques to our line rendering.
Edit intent: coherence
Original:  The model introduces a feedback mechanism that modulates the neurons that are expected to respond to future portions of the sweeps, accelarating their onset response.
Edit intent: coherence
Original:  Combined, the experimental and modelling results suggest that predictive feedback modulation plays an important role in the neural encoding of FMeven at early stages of the processing hierarchy.
Revised:  Combined, the experimental and modelling results suggest that predictive feedback modulation plays an important role in the neural processing of FM. In the brain, this mechanism is likely to occur at early stages of the processing hierarchy.
Edit intent: clarity
Original:  Combined with the continued advancements in VR technology, the interest in platforms that generate immersive experiences has increased.
Edit intent: clarity
Original:  However, the available systems are either focusing on gaming context, featuring limited capabilities (embedded editors in game engines) or they support only content creation of virtual environments without any rapid prototyping and modification.
Revised:  However, the available systems are either focusing on gaming context, featuring limited capabilities or they support only content creation of virtual environments without any rapid prototyping and modification.
Edit intent: clarity
Original:  In this project we propose Scenior, an innovative coding-free, visual scripting platform to replicate gamified training scenarios through Rapid Prototyping via newly defined VR software design patterns.
Revised:  In this project , we propose a code-free, visual scripting platform to replicate gamified training scenarios through Rapid Prototyping via newly defined VR software design patterns.
Edit intent: clarity
Original:  In this project we propose Scenior, an innovative coding-free, visual scripting platform to replicate gamified training scenarios through Rapid Prototyping via newly defined VR software design patterns.
Revised:  In this project we propose Scenior, an innovative coding-free, visual scripting platform to replicate gamified training scenarios through rapid prototyping and VR software design patterns.
Edit intent: clarity
Original:  We implemented and compared three authoring tools: a) Prototyped scripting , b) Visual Scripting and c) VR Editor for rapid reconstruction of VR training scenarios.
Revised:  We implemented and compared three authoring tools: a) visual scripting and b) VR editor for the rapid reconstruction of VR training scenarios.
Edit intent: coherence
Original:  We also introduce Action Prototypes , a new software design pattern suitable to replicate behavioural tasks for VR experiences.
Revised:  We also introduce action prototypes , a new software design pattern suitable to replicate behavioural tasks for VR experiences.
Edit intent: fluency
Original:  We also introduce Action Prototypes , a new software design pattern suitable to replicate behavioural tasks for VR experiences.
Revised:  We also introduce Action Prototypes , a new software design pattern suitable to replicate behavioral tasks for VR experiences.
Edit intent: fluency
Original:  In a addition, we present the  scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum  .
Revised:  In addition, we present the scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum .
Edit intent: fluency
Original:  In a addition, we present the  scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum  .
Revised:  In a addition, we present the training scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum .
Edit intent: fluency
Original:  In this paper, we will investigate the causes of the GNNs' " suspended animation problem ", and analyze if such a problem also exists in all other GNN models or not.
Revised:  In this paper, we further identify the suspended animation problem ", and analyze if such a problem also exists in all other GNN models or not.
Edit intent: clarity
Original: In terms of source separation task, most of deep neural networks have two main types: one is modeling in the spectrogram  , and the other is in the waveform  .
Revised: In terms of source separation task, deep neural networks have two main types: one is modeling in the spectrogram , and the other is in the waveform .
Edit intent: clarity
Original: In terms of source separation task, most of deep neural networks have two main types: one is modeling in the spectrogram  , and the other is in the waveform  .
Revised: In terms of source separation task, most of deep neural networks have two major approaches: one approach is modeling in the spectrogram , and the other is in the waveform .
Edit intent: clarity
Original: In terms of source separation task, most of deep neural networks have two main types: one is modeling in the spectrogram  , and the other is in the waveform  .
Revised: In terms of source separation task, most of deep neural networks have two main types: one is modeling in the spectrogram domain , and the other is in the waveform .
Edit intent: clarity
Original: In terms of source separation task, most of deep neural networks have two main types: one is modeling in the spectrogram  , and the other is in the waveform  .
Revised: In terms of source separation task, most of deep neural networks have two main types: one is modeling in the spectrogram , and the other approach is modeling in the waveform .
Edit intent: clarity
Original: In terms of source separation task, most of deep neural networks have two main types: one is modeling in the spectrogram  , and the other is in the waveform  .
Revised: In terms of source separation task, most of deep neural networks have two main types: one is modeling in the spectrogram , and the other is in the waveform domain .
Edit intent: clarity
Original:  Most of them use CNNs , LSTMs, but due to the high sampling rate of audio, whether it is LSTMs with a long-distance dependent or CNNs with sliding windows is difficult to extract long-term input context.
Revised:  Most of the previous papers use CNNs or LSTMs. However, due to the high sampling rate of audio, whether it is LSTMs with a long-distance dependent or CNNs with sliding windows is difficult to extract long-term input context.
Edit intent: coherence
Original:  Most of them use CNNs , LSTMs, but due to the high sampling rate of audio, whether it is LSTMs with a long-distance dependent or CNNs with sliding windows is difficult to extract long-term input context.
Revised:  Most of them use CNNs , LSTMs, but due to the high sampling rate of audio, whether it is LSTMs with long-distance dependent or CNNs with sliding windows is difficult to extract long-term input context.
Edit intent: fluency
Original:  Most of them use CNNs , LSTMs, but due to the high sampling rate of audio, whether it is LSTMs with a long-distance dependent or CNNs with sliding windows is difficult to extract long-term input context.
Revised:  Most of them use CNNs , LSTMs, but due to the high sampling rate of audio, whether it is LSTMs with a long-distance dependent or CNNs with sliding windows , it is still difficult to extract long-term input context.
Edit intent: coherence
Original:  In this case, we propose an end-to-end network: Time Frequency Attention Net (TF-Attention-Net), to study the ability of the attention mechanism in the source separation task.
Revised:  In this case, we propose an end-to-end network: Time-Frequency Attention Net (TF-Attention-Net), to study the ability of the attention mechanism in the source separation task.
Edit intent: fluency
Original:  Later, we will introduce the Slice Attention, which can extract the acoustic features of time and frequency scales under different channels while the time complexity of which is less than Multi-head Attention.
Revised:  First, we introduce the Slice Attention, which can extract the acoustic features of time and frequency scales under different channels while the time complexity of which is less than Multi-head Attention.
Edit intent: coherence
Original:  Later, we will introduce the Slice Attention, which can extract the acoustic features of time and frequency scales under different channels while the time complexity of which is less than Multi-head Attention.
Revised:  Later, we will introduce the Slice Attention, which can extract the acoustic features of temporal and frequency scales under different channels while the time complexity of which is less than Multi-head Attention.
Edit intent: fluency
Original:  Also, attention mechanism can be efficiently parallelized while the LSTMs can not because of their time dependence .
Revised:  Also, attention mechanism can be parallel calculated, while LSTMs cannot, because of its time-dependent property .
Edit intent: clarity
Original:  Meanwhile, the receptive field of  attention mechanism is larger than the CNNs, which means  we can use shallower layers to extract deeper features.
Revised:  Meanwhile, the receptive field of the attention mechanism is larger than the CNNs, which means we can use shallower layers to extract deeper features.
Edit intent: fluency
Original:  Meanwhile, the receptive field of  attention mechanism is larger than the CNNs, which means  we can use shallower layers to extract deeper features.
Revised:  Meanwhile, the receptive field of attention mechanism is larger than the CNNs, which means that we can use shallower layers to extract deeper features.
Edit intent: fluency
Original:  Experiments for singing voice separation indicate that our model yields a better performance compared with the SotA model: spectrogram-based U-Net and  waveform-based Wave-U-Net , given the same data .
Revised:  Experiments for singing voice separation indicate that our model yields a better performance compared with the SotA model: spectrogram-based U-Net and the waveform-based Wave-U-Net , given the same data .
Edit intent: fluency
Original:  Experiments for singing voice separation indicate that our model yields a better performance compared with the SotA model: spectrogram-based U-Net and  waveform-based Wave-U-Net , given the same data .
Revised:  Experiments for singing voice separation indicate that our model yields a better performance compared with the SotA model: spectrogram-based U-Net and waveform-based Wave-U-Net baselines .
Edit intent: clarity
Original: In terms of source separation task, deep neural networks have two major approaches: one approach is modeling in the spectrogram domain, and the other approach is modeling in the waveform domain.
Revised: Recent studies in deep learning-based source separation have two major approaches: one approach is modeling in the spectrogram domain, and the other approach is modeling in the waveform domain.
Edit intent: clarity
Original:  In this case , we propose an end-to-end network : Time-Frequency Attention Net (TF-Attention-Net) , to study the ability of the attention mechanism in the source separation task .
Revised:  In this paper , we propose an end-to-end network : Time-Frequency Attention Net (TF-Attention-Net) , to study the ability of the attention mechanism in the source separation task .
Edit intent: clarity
Original: Recent studies in deep learning-based source separation have two major approaches: one approach is modeling in the spectrogram domain, and the other approach is modeling in the time domain, but all of them used pure CNNor LSTM . In this paper, we propose a Sliced Attention-based neural network (Sams-Net) at the spectrogram domain for  music source separation task , which enables feature interactions from the magnitude spectrogram contribute differently to the separation.
Revised: Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based models with the input of spectrogram or waveforms are commonly used for deep learning based audio source separation . In this paper, we propose a Sliced Attention-based neural network (Sams-Net) at the spectrogram domain for music source separation task , which enables feature interactions from the magnitude spectrogram contribute differently to the separation.
Edit intent: clarity
Original: Recent studies in deep learning-based source separation have two major approaches: one approach is modeling in the spectrogram domain, and the other approach is modeling in the time domain, but all of them used pure CNNor LSTM . In this paper, we propose a Sliced Attention-based neural network (Sams-Net) at the spectrogram domain for  music source separation task , which enables feature interactions from the magnitude spectrogram contribute differently to the separation.
Revised: Recent studies in deep learning-based source separation have two major approaches: one approach is modeling in the spectrogram domain, and the other approach is modeling in the time domain, but all of them used pure CNNor LSTM . In this paper, we propose a Sliced Attention-based neural network (Sams-Net) in the spectrogram domain for music source separation task , which enables feature interactions from the magnitude spectrogram contribute differently to the separation.
Edit intent: fluency
Original: Recent studies in deep learning-based source separation have two major approaches: one approach is modeling in the spectrogram domain, and the other approach is modeling in the time domain, but all of them used pure CNNor LSTM . In this paper, we propose a Sliced Attention-based neural network (Sams-Net) at the spectrogram domain for  music source separation task , which enables feature interactions from the magnitude spectrogram contribute differently to the separation.
Revised: Recent studies in deep learning-based source separation have two major approaches: one approach is modeling in the spectrogram domain, and the other approach is modeling in the time domain, but all of them used pure CNNor LSTM . In this paper, we propose a Sliced Attention-based neural network (Sams-Net) at the spectrogram domain for the music source separation task , which enables feature interactions from the magnitude spectrogram contribute differently to the separation.
Edit intent: fluency
Original:  Our results demonstrate potential of the rtfMRI-EEG-nf  for treatment of MDD.
Revised:  Our results suggest that the rtfMRI-EEG-nf for treatment of MDD.
Edit intent: clarity
Original: As more and more application providers transition to the cloud and deliver their services on a Software as a Service (SaaS) basis, cloud providers need to make their provisioning systems agile enough to deliver on Service Level Agreements.
Revised: As more and more application providers transition to the cloud and deliver their services on a Software as a Service (SaaS) basis, cloud providers need to make their provisioning systems agile enough to meet Service Level Agreements.
Edit intent: clarity
Original:  To this end we propose SQLR, a dynamic provisioning system employing a customized model-free reinforcement learning algorithm that is capable of reusing contextual knowledge learned from one workload to optimize resource provisioning for different workload patterns.
Revised:  To this end we propose SQLR, a dynamic provisioning system employing a customized model-free reinforcement learning algorithm that is capable of reusing contextual knowledge learned from one workload to optimize resource provisioning for other workload patterns.
Edit intent: clarity
Original:  Our experiments show that we can reduce the amount of resources that need to be provisioned by almost 25\% with less than 1\% overall service unavailability  due to blocking , and still deliver similar response times as  an over-provisioned system.
Revised:  Our experiments show that we can reduce the amount of provisioned resources by almost 25\% with less than 1\% overall service unavailability due to blocking , and still deliver similar response times as an over-provisioned system.
Edit intent: clarity