wanyu/IteraTeR_v2 huggingface dataset:
Original: In this paper an algorithm for the forward interval propagation on the amplitude of the discrete Fourier transform (DFT) is presented .
Revised: In this paper an algorithm for the forward interval propagation on the amplitude of the discrete Fourier transform .
Edit intent: clarity
Original:  The algorithm yields best-possible bounds on the amplitude of the DFT for real and complex valued sequences.
Revised:  The algorithm yields best-possible bounds when computing the amplitude of the DFT for real and complex valued sequences.
Edit intent: clarity
Original:  We show that computing the exact bounds for the amplitude of the DFT can be achieved with an exhaustive examination of all possible corners of the interval-shaped domain.
Revised:  We show that computing the exact bounds of the amplitude can be achieved with an exhaustive examination of all possible corners of the interval-shaped domain.
Edit intent: clarity
Original:  We show that computing the exact bounds for the amplitude of the DFT can be achieved with an exhaustive examination of all possible corners of the interval-shaped domain.
Revised:  We show that computing the exact bounds for the amplitude of the DFT can be achieved with an exhaustive examination of all possible corners of the interval domain.
Edit intent: clarity
Original: The computational complexity of the MaxCut problem restricted to interval graphs has been open since the 80's, being one of the problems considered by Johnson on his Ongoing Guide to NP-completeness, and has been settled as NP-complete only recently by Adhikary, Bose, Mukherjee and Roy.
Revised: The computational complexity of the MaxCut problem restricted to interval graphs has been open since the 80's, being one of the problems proposed by Johnson on his Ongoing Guide to NP-completeness, and has been settled as NP-complete only recently by Adhikary, Bose, Mukherjee and Roy.
Edit intent: clarity
Original:  Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of ProdBERT andprod2vec  embeddings: while ProdBERT is found to be superior to traditional methods in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models.
Revised:  Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of ~ embeddings: while ProdBERT is found to be superior to traditional methods in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models.
Edit intent: clarity
Original:  Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of ProdBERT andprod2vec  embeddings: while ProdBERT is found to be superior to traditional methods in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models.
Revised:  Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of ProdBERT andprod2vec embeddings: while ~ is found to be superior to traditional methods in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models.
Edit intent: fluency
Original:  Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of ProdBERT andprod2vec  embeddings: while ProdBERT is found to be superior to traditional methods in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models.
Revised:  Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of ProdBERT andprod2vec embeddings: while ProdBERT is found to be superior in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models.
Edit intent: clarity
Original:  Finally, we conclude by providing guidelines for training embeddings under a variety of computational and data constraints.
Revised:  Finally, we provide guidelines to practitioners for training embeddings under a variety of computational and data constraints.
Edit intent: clarity
Original: We formally study how Ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model using Knowledge Distillation .
Revised: We formally study how ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model using Knowledge Distillation .
Edit intent: fluency
Original: We formally study how Ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model using Knowledge Distillation .
Revised: We formally study how Ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model using knowledge distillation .
Edit intent: fluency
Original:  In this paper, we study network measurement data collected by the Federal Communication Commission's (FCC) Measuring Broadband America (MBA) program before and during the pandemic in the United States  .
Revised:  In this paper, we study network measurement data collected by the Federal Communications Commission's (FCC) Measuring Broadband America (MBA) program before and during the pandemic in the United States .
Edit intent: fluency
Original:  In this paper, we study network measurement data collected by the Federal Communication Commission's (FCC) Measuring Broadband America (MBA) program before and during the pandemic in the United States  .
Revised:  In this paper, we study network measurement data collected by the Federal Communication Commission's Measuring Broadband America (MBA) program before and during the pandemic in the United States .
Edit intent: clarity
Original:  In this paper, we study network measurement data collected by the Federal Communication Commission's (FCC) Measuring Broadband America (MBA) program before and during the pandemic in the United States  .
Revised:  In this paper, we study network measurement data collected by the Federal Communication Commission's (FCC) Measuring Broadband America program before and during the pandemic in the United States .
Edit intent: clarity
Original:  In this paper, we study network measurement data collected by the Federal Communication Commission's (FCC) Measuring Broadband America (MBA) program before and during the pandemic in the United States  .
Revised:  In this paper, we study network measurement data collected by the Federal Communication Commission's (FCC) Measuring Broadband America (MBA) program before and during the pandemic in the United States (US) .
Edit intent: clarity
Original:  Our main insight is that simply shrink-wrapping a planar mesh sheet onto the input image, consistent with the learned intermediate depth, captures underlying geometry sufficient enough to generate photorealistic unseen views with arbitrarily large view-point changes.
Revised:  Our main insight is that simply shrink-wrapping a planar mesh sheet onto the input image, consistent with the learned intermediate depth, captures underlying geometry sufficient to generate photorealistic unseen views with arbitrarily large view-point changes.
Edit intent: fluency
Original:  Our main insight is that simply shrink-wrapping a planar mesh sheet onto the input image, consistent with the learned intermediate depth, captures underlying geometry sufficient enough to generate photorealistic unseen views with arbitrarily large view-point changes.
Revised:  Our main insight is that simply shrink-wrapping a planar mesh sheet onto the input image, consistent with the learned intermediate depth, captures underlying geometry sufficient enough to generate photorealistic unseen views with large viewpoint changes.
Edit intent: clarity
Original:  Our approach is category-agnostic, end-to-end trainable without using any 3D supervision  and requires a single image at test time.
Revised:  Our approach is category-agnostic, end-to-end trainable without using any 3D supervision , and requires a single image at test time.
Edit intent: fluency
Original:  Furthermore, this simple idea captures novel views surprisingly well on a wide range of high resolution in-the-wild images in converting them into a navigable 3D pop-up . Video results and code at URL
Revised:  Furthermore, this simple idea captures novel views surprisingly well on a wide range of high-resolution in-the-wild images in converting them into a navigable 3D pop-up . Video results and code at URL
Edit intent: fluency
Original:  Furthermore, this simple idea captures novel views surprisingly well on a wide range of high resolution in-the-wild images in converting them into a navigable 3D pop-up . Video results and code at URL
Revised:  Furthermore, this simple idea captures novel views surprisingly well on a wide range of high resolution in-the-wild images , converting them into a navigable 3D pop-up . Video results and code at URL
Edit intent: coherence
Original:  Furthermore, this simple idea captures novel views surprisingly well on a wide range of high resolution in-the-wild images in converting them into a navigable 3D pop-up . Video results and code at URL
Revised:  Furthermore, this simple idea captures novel views surprisingly well on a wide range of high resolution in-the-wild images in converting them into navigable 3D pop-up . Video results and code at URL
Edit intent: fluency
Original:  Furthermore, this simple idea captures novel views surprisingly well on a wide range of high resolution in-the-wild images in converting them into a navigable 3D pop-up . Video results and code at URL
Revised:  Furthermore, this simple idea captures novel views surprisingly well on a wide range of high resolution in-the-wild images in converting them into a navigable 3D pop-ups . Video results and code at URL
Edit intent: fluency
Original:  Video results and code  at URL
Revised:  Video results and code are available at URL
Edit intent: clarity
Original: We introduce the problem of perpetual view generation -- long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image.
Revised: We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image.
Edit intent: fluency
Original:  This is a challenging problem that goes far beyond the capabilities of current view synthesis methods, which work for a limited range of viewpoints and quickly degenerate when presented with a large camera motion.
Revised:  This is a challenging problem that goes far beyond the capabilities of current view synthesis methods, which quickly degenerate when presented with a large camera motion.
Edit intent: clarity
Original:  Methods designed for video generation also have limited ability to produce long video sequences and are often agnostic to scene geometry.
Revised:  Methods designed for video generation also have limited ability to produce long sequences and are often agnostic to scene geometry.
Edit intent: fluency
Original:  We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat  framework, allowing for long-range generation that cover large distances after hundreds of frames.
Revised:  We take a hybrid approach that integrates both geometry and image synthesis in an iterative ` framework, allowing for long-range generation that cover large distances after hundreds of frames.
Edit intent: coherence
Original:  We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat  framework, allowing for long-range generation that cover large distances after hundreds of frames.
Revised:  We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat, framework, allowing for long-range generation that cover large distances after hundreds of frames.
Edit intent: fluency
Original:  We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat  framework, allowing for long-range generation that cover large distances after hundreds of frames.
Revised:  We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat repeat framework, allowing for long-range generation that cover large distances after hundreds of frames.
Edit intent: coherence
Original:  We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat  framework, allowing for long-range generation that cover large distances after hundreds of frames.
Revised:  We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat ' framework, allowing for long-range generation that cover large distances after hundreds of frames.
Edit intent: fluency
Original:  Our approach can be trained from a set of monocular video sequences without any manual annotation .
Revised:  Our approach can be trained from a set of monocular video sequences .
Edit intent: clarity
Original:  We propose a dataset of aerial footage of natural coastal scenes, and compare our method with recent view synthesis and conditional video generation baselines, showing that it can generate plausible scenes for much longer time horizons over large camera trajectories compared to existing methods.
Revised:  We propose a dataset of aerial footage of coastal scenes, and compare our method with recent view synthesis and conditional video generation baselines, showing that it can generate plausible scenes for much longer time horizons over large camera trajectories compared to existing methods.
Edit intent: clarity
Original:  Please visit our project page at URL
Revised:  Project page at URL
Edit intent: clarity
Original: Code that is highly optimized poses a problem for program-level verification . Programmers can employ various clever tricks that are non-trivial to reason about.
Revised: Code that is highly optimized poses a problem for program-level verification : programmers can employ various clever tricks that are non-trivial to reason about.
Edit intent: fluency
Original:  Building on earlier work, we use the Why3 platform to model the code and generate verification conditions, which are proven using automated provers.
Revised:  Building on earlier work, we use the Why3 platform to model the code and prove verification conditions, which are proven using automated provers.
Edit intent: clarity
Original:  Building on earlier work, we use the Why3 platform to model the code and generate verification conditions, which are proven using automated provers.
Revised:  Building on earlier work, we use the Why3 platform to model the code and generate verification conditions, using automated provers.
Edit intent: coherence
Original:  The approach is re-usable and adaptable, and  allows for validation.
Revised:  We expect the approach to be re-usable and adaptable, and allows for validation.
Edit intent: clarity
Original:  The approach is re-usable and adaptable, and  allows for validation.
Revised:  The approach is re-usable and adaptable, and it allows for validation.
Edit intent: fluency
Original: Finding ways to overcome the temptation to exploit one another is still an enigma in behavioural science .
Revised: Finding ways to overcome the temptation to exploit one another is still a challenge in behavioural sciences .
Edit intent: clarity
Original:  In the framework of Evolutionary Game Theory , punishing strategies are frequently used to promote cooperation in competitive environments.
Revised:  In the framework of evolutionary game theory , punishing strategies are frequently used to promote cooperation in competitive environments.
Edit intent: fluency
Original:  Here, we introduce altruistic punishers in the spatial public goods game which punish all defectors in their vicinity , while bearing a cost to do so.
Revised:  Here, we introduce altruistic punishers in the spatial public goods game which punish all defectors in their vicinity while bearing a cost to do so.
Edit intent: fluency
Original: An unsupervised process is described for clustering automatic detections in an acoustically active coral reef soundscape .
Revised: Deep clustering was applied to unlabeled, automatically detected signals in a coral reef soundscape .
Edit intent: clarity
Original:  This dependency suggests a potential source of leverage  : by reducing, stopping, redirecting, or otherwise manipulating data contributions, people can influence and impact the effectiveness of these technologies.
Revised:  This dependency suggests a potential source of leverage : by reducing, stopping, redirecting, or otherwise manipulating data contributions, the public can reduce the effectiveness of these technologies.
Edit intent: clarity
Original:  This dependency suggests a potential source of leverage  : by reducing, stopping, redirecting, or otherwise manipulating data contributions, people can influence and impact the effectiveness of these technologies.
Revised:  This dependency suggests a potential source of leverage : by reducing, stopping, redirecting, or otherwise manipulating data contributions, people can influence and impact the effectiveness of many lucrative technologies.
Edit intent: clarity
Original:  In this paper, we synthesize emerging research that helps people better understand and  action this data leverage.
Revised:  In this paper, we synthesize emerging research that seeks to better understand and action this data leverage.
Edit intent: clarity
Original:  In this paper, we synthesize emerging research that helps people better understand and  action this data leverage.
Revised:  In this paper, we synthesize emerging research that helps people better understand and help people action this data leverage.
Edit intent: clarity
Original:  Our framework also points towards ways that policymakers can augment data leverage as a means of changing the balance of power between the public and tech companies.
Revised:  Our framework also points towards ways that policymakers can bolster data leverage as a means of changing the balance of power between the public and tech companies.
Edit intent: clarity
Original: NeurST is an open-source toolkit for neural speech translation developed by ByteDance AI Lab .
Revised: NeurST is an open-source toolkit for neural speech translation .
Edit intent: clarity
Original:  NeurST aims at facilitating the speech translation research for NLP researchers and provides a complete setup for speech translation benchmarks, including feature extraction, data preprocessing, distributed training, and evaluation.
Revised:  NeurST aims at facilitating the speech translation research for NLP researchers and building reliable benchmarks for this field. It provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation.
Edit intent: clarity
Original:  It also uses the attention mechanism to fuse the embeddingsof x generated from various meta-paths to obtain x's final embedding .
Revised:  It also uses the attention mechanism to fuse such embeddings .
Edit intent: clarity
Original: Current standards for player characterisation in tokenised state machine replication protocols allow for honest players who will always follow the protocol, regardless of possible token increases for deviating.
Revised: Existing standards for player characterisation in tokenised state machine replication protocols allow for honest players who will always follow the protocol, regardless of possible token increases for deviating.
Edit intent: clarity
Original: Current standards for player characterisation in tokenised state machine replication protocols allow for honest players who will always follow the protocol, regardless of possible token increases for deviating.
Revised: Current standards for player characterisation in tokenised state machine replication protocols depend on honest players who will always follow the protocol, regardless of possible token increases for deviating.
Edit intent: clarity
Original:  Away of improving the user experience is by exploiting their buffers and the ability to provide a constant data rate to everyone, as one of the features of 5G networks.
Revised:  A way of improving the user experience is by exploiting their buffers and the ability to provide a constant data rate to everyone, as one of the features of 5G networks.
Edit intent: clarity
Original:  To this end, in this paper we provide a theoretical-analysis framework for resource allocation in 5G networks that leads to an optimized performance for live videostreaming .
Revised:  To this end, in this paper we provide a theoretical-analysis framework for resource allocation in 5G networks that leads to an improved user experience when watching live video .
Edit intent: clarity
Original:  The analysis is validated by simulations that are run on trace data .
Revised:  The analysis is validated by simulations that are run on traces .
Edit intent: clarity
Original:  We show that memory-n zero-determinant strategies in repeated games can be used to control conditional averages of payoffs.
Revised:  We show that memory-n zero-determinant strategies in repeated games can be used to control conditional expectations of payoffs.
Edit intent: clarity
Original:  Equivalently, they can be used to control average payoffs in biased ensembles.
Revised:  Equivalently, they can be used to control expected payoffs in biased ensembles.
Edit intent: clarity
Original:  We provide several examples of memory-n zero-determinant strategies in  repeated prisoner's dilemma game.
Revised:  We provide several examples of memory-n zero-determinant strategies in the repeated prisoner's dilemma game.
Edit intent: fluency
Original:  Various Hilbert spaces,  figures of merit, types of constructions, and search methods are covered by the tool.
Revised:  We summarize what are the various Hilbert spaces, figures of merit, types of constructions, and search methods are covered by the tool.
Edit intent: clarity
Original:  Here, we investigate if the shape of the system's nonlinear transfer function can mitigate amplitude inhomogeneity and improve computational performance.
Revised:  Here, we investigate how the system's nonlinear transfer function can mitigate amplitude inhomogeneity and improve computational performance.
Edit intent: clarity
Original:  For periodic, sigmoid and clipped transfer functions, we report order-ofmagnitude improvements in the time-to-solution compared to conventional polynomial models, which we link to the suppression of amplitude inhomogeneity induced by saturation of the transfer function.
Revised:  For periodic, sigmoid and clipped transfer functions, we report order-of-magnitude improvements in the time-to-solution compared to conventional polynomial models, which we link to the suppression of amplitude inhomogeneity induced by saturation of the transfer function.
Edit intent: fluency
Original:  We examine all the relationships between the different ranks, and discuss when the factorizations are unique.
Revised:  We examine the relationships between the different ranks, and discuss when the factorizations are unique.
Edit intent: clarity
Original:  We examine all the relationships between the different ranks, and discuss when the factorizations are unique.
Revised:  We examine all the relationships between the different ranks, and discuss when factorization is unique.
Edit intent: fluency
Original:  These entities interact to satisfy their Quality of Service demands , by playing a non-cooperative game.
Revised:  These entities interact to satisfy their Quality of Service demands by playing a non-cooperative game.
Edit intent: fluency