wanyu/IteraTeR_v2 huggingface dataset:
Original:  Closed-form solutions for the steady state probabilities of the models are obtained, which are then utilized to derive expressions for key performance indicators of the two communicating technologies.
Revised:  Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for key performance indicators of the two communicating technologies.
Edit intent: fluency
Original: 11p is superior in terms of average delay, where as C-V2X Mode 4 excels in collision resolution.
Revised: 11p is superior in terms of average delay, whereas C-V2X Mode 4 excels in collision resolution.
Edit intent: coherence
Original: Vehicle to vehicle (V2V) communication plays a pivotal role in intelligent transport systems (ITS) , with cellular-vehicle to everything (C-V2X) and IEEE 802.11p being the two competing enabling technologies.
Revised: Vehicle-to-vehicle (V2V) communication plays a pivotal role in intelligent transport systems (ITS) , with cellular-vehicle to everything (C-V2X) and IEEE 802.11p being the two competing enabling technologies.
Edit intent: fluency
Original: Vehicle to vehicle (V2V) communication plays a pivotal role in intelligent transport systems (ITS) , with cellular-vehicle to everything (C-V2X) and IEEE 802.11p being the two competing enabling technologies.
Revised: Vehicle to vehicle (V2V) communication plays a pivotal role in intelligent transport systems (ITS) with cellular-vehicle to everything (C-V2X) and IEEE 802.11p being the two competing enabling technologies.
Edit intent: fluency
Original:  This paper presents multi-dimensional discrete time Markov chain (DTMC) based models to study the medium access control (MAC) layer performance of the ETSI ITS-G5 version of IEEE 802.
Revised:  This paper presents multi-dimensional discrete-time Markov chain (DTMC) based models to study the medium access control (MAC) layer performance of the ETSI ITS-G5 version of IEEE 802.
Edit intent: fluency
Original:  This paper presents multi-dimensional discrete time Markov chain (DTMC) based models to study the medium access control (MAC) layer performance of the ETSI ITS-G5 version of IEEE 802.
Revised:  This paper presents multi-dimensional discrete time Markov chain (DTMC) based models to study the medium access control (MAC) layer performance of the IEEE 802.
Edit intent: coherence
Original:  The models are coupled with an appropriate DTMC based queue model, that consists of traffic generators for periodic cooperative awareness messages (CAMs) and event driven decentralized environmental notification messages (DENMs).
Revised:  These models are coupled with an appropriate DTMC based queue model, that consists of traffic generators for periodic cooperative awareness messages (CAMs) and event driven decentralized environmental notification messages (DENMs).
Edit intent: fluency
Original:  The models are coupled with an appropriate DTMC based queue model, that consists of traffic generators for periodic cooperative awareness messages (CAMs) and event driven decentralized environmental notification messages (DENMs).
Revised:  The models are coupled with an appropriate DTMC based queuing model, and traffic generators for periodic cooperative awareness messages (CAMs) and event driven decentralized environmental notification messages (DENMs).
Edit intent: coherence
Original:  The models are coupled with an appropriate DTMC based queue model, that consists of traffic generators for periodic cooperative awareness messages (CAMs) and event driven decentralized environmental notification messages (DENMs).
Revised:  The models are coupled with an appropriate DTMC based queue model, that consists of traffic generators for periodic cooperative awareness messages (CAMs) and event-driven decentralized environmental notification messages (DENMs).
Edit intent: fluency
Original:  Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for key performance indicators of the two communicating technologies .
Revised:  Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for several key performance metrics .
Edit intent: clarity
Original:  An application for a highway scenario is presented to provide numerical results , and to draw insights on the performance.
Revised:  An application for a highway scenario is presented to provide numerical results and to draw insights on the performance.
Edit intent: fluency
Original:  However, there remains a long-term absence for a systematic divergence analysis .\newline \indent In this paper, we utilize 3, Theorem 2.2 (c)%DIFDELCMD < ] %%% to give a systematic divergence analysis for (SIE)with least squares, dual least squares, Babunov-Galerkin method in trigonometric basis .
Revised:  However, there remains a long-term absence for a systematic divergence analysis .\newline \indent In this paper, we 3, Theorem 2.2 (c)%DIFDELCMD < ] %%% to give a systematic divergence analysis for (SIE)with least squares, dual least squares, Babunov-Galerkin method in trigonometric basis .
Edit intent: coherence
Original:  However, there remains a long-term absence for a systematic divergence analysis .\newline \indent In this paper, we utilize 3, Theorem 2.2 (c)%DIFDELCMD < ] %%% to give a systematic divergence analysis for (SIE)with least squares, dual least squares, Babunov-Galerkin method in trigonometric basis .
Revised:  However, there remains a long-term absence for a systematic divergence analysis .\newline \indent In this paper, we utilize %DIFDELCMD < ] %%% to give a systematic divergence analysis for (SIE)with least squares, dual least squares, Babunov-Galerkin method in trigonometric basis .
Edit intent: coherence
Original:  by 2\pi periodic regular parametric representation of boundary curve \partial \Omega ,  complete convergence and error analysis are done in L^2 setting for least squares, dual least squares, Bubnov-Galerkin methods with trigonometric polynomials into Symm's integral equation of the first kind K \Psi = g when g \in H^r(0,2\pi), \ r \geq 1 . %DIFDELCMD < \newline \indent %%% In this paper, we focus on the numerical behavior of (LS), (DLS), (BG)
Revised:  by 2\pi periodic parametric representation of boundary curve \partial \Omega , complete convergence and error analysis are done in L^2 setting for least squares, dual least squares, Bubnov-Galerkin methods with trigonometric polynomials into Symm's integral equation of the first kind K \Psi = g when g \in H^r(0,2\pi), \ r \geq 1 . %DIFDELCMD < \newline \indent %%% In this paper, we focus on the numerical behavior of (LS), (DLS), (BG)
Edit intent: clarity
Original:  by 2\pi periodic regular parametric representation of boundary curve \partial \Omega ,  complete convergence and error analysis are done in L^2 setting for least squares, dual least squares, Bubnov-Galerkin methods with trigonometric polynomials into Symm's integral equation of the first kind K \Psi = g when g \in H^r(0,2\pi), \ r \geq 1 . %DIFDELCMD < \newline \indent %%% In this paper, we focus on the numerical behavior of (LS), (DLS), (BG)
Revised:  by 2\pi periodic regular parametric representation of boundary curve \partial \Omega , complete convergence and error analysis in L^2 setting for least squares, dual least squares, Bubnov-Galerkin methods with trigonometric polynomials into Symm's integral equation of the first kind K \Psi = g when g \in H^r(0,2\pi), \ r \geq 1 . %DIFDELCMD < \newline \indent %%% In this paper, we focus on the numerical behavior of (LS), (DLS), (BG)
Edit intent: clarity
Original:  Here, we propose a variational mean-field theory in which only the distribution of synaptic weight is considered.
Revised:  Here, we propose a variational mean-field theory in which the distribution of synaptic weight is considered.
Edit intent: clarity
Original:  Here, we propose a variational mean-field theory in which only the distribution of synaptic weight is considered.
Revised:  Here, we propose a variational mean-field theory in which only the distribution of synaptic weights is considered.
Edit intent: fluency
Original:  Therefore, our framework explains how data (or sensory inputs), synapses and neural activities interact with each other to achieve the goal of extracting statistical regularities in sensory inputs.
Revised:  Therefore, our framework provides insights on how data (or sensory inputs), synapses and neural activities interact with each other to achieve the goal of extracting statistical regularities in sensory inputs.
Edit intent: clarity
Original:  This variational framework is verified in restricted Boltzmann machines with planted synaptic weights and learning handwritten digits .
Revised:  This variational framework is verified in restricted Boltzmann machines with planted synaptic weights and handwritten-digits learning .
Edit intent: clarity
Original: Given a point set P , are k closed convex polygons sufficient to partition the convex hull of P , such that the interior of a convex set contains no point in P?
Revised: We consider the Minimum Convex Partition problem: Given a set P , such that the interior of a convex set contains no point in P?
Edit intent: clarity
Original:  In this paper, we propose an incentive mechanism called FEITE {\em  to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand.
Revised:  In this paper, we propose an incentive mechanism called {\em to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand.
Edit intent: clarity
Original:  Furthermore, we prove that  when the payment budget is tight, the overall revenue will still exceed or equal the budget.
Revised:  Furthermore, we prove that even when the payment budget is tight, the overall revenue will still exceed or equal the budget.
Edit intent: coherence
Original:  Furthermore, we prove that  when the payment budget is tight, the overall revenue will still exceed or equal the budget.
Revised:  Furthermore, we prove that when the payment budget is tight, the total revenue still exceeds or equals the budget.
Edit intent: clarity
Original:  Using an industrial dataset obtained from a large bike-sharing company, our experiments show that FEITE is effective in rebalancing bike supply and demand and generating high revenue as a result, which outperforms several benchmark mechanisms.
Revised:  Using an industrial dataset obtained from a large bike-sharing company, our experiments show that FEITE is effective in rebalancing bike supply and demand and , as a result, which outperforms several benchmark mechanisms.
Edit intent: clarity
Original:  Using an industrial dataset obtained from a large bike-sharing company, our experiments show that FEITE is effective in rebalancing bike supply and demand and generating high revenue as a result, which outperforms several benchmark mechanisms.
Revised:  Using an industrial dataset obtained from a large bike-sharing company, our experiments show that FEITE is effective in rebalancing bike supply and demand and generating high revenue as a result, generates high revenue that outperforms several benchmark mechanisms.
Edit intent: coherence
Original:  Instead of searching for the 1-best architecture for a given task, we focus on building a population of neural networks that are then ensembled into a neural network committee, an approach we dub Neural Committee Search .
Revised:  Instead of searching for the 1-best architecture for a given task, we focus on building a population of neural networks that are then ensembled into a neural network committee, an approach we dub 'Neural Committee Search' .
Edit intent: fluency
Original:  First, we answer a longstanding open question by showing that the Consistency of Local Density Matrices problem is QMA-complete under Karp reductions.
Revised:  Our central contribution is proving a longstanding conjecture that the Consistency of Local Density Matrices problem is QMA-complete under Karp reductions.
Edit intent: clarity
Original:  We also show for the first time a ] ] commit-and-open computational zero-knowledge proof system for all of QMA  as a quantum analogue of a "sigma" protocol.
Revised:  We also show for the first time a ] ] commit-and-open computational zero-knowledge proof system for all of QMA , as a quantum analogue of a "sigma" protocol.
Edit intent: fluency
Original:  However,  MA-RRT* cannot effectively find solutions in relatively dense environments, cause some random samples in the free space cannot be explored by the rapidly random tree , which hinders the application of MA-RRT* in a more complicated real-world .
Revised:  However, in a relatively dense environment, the application of MA-RRT* cannot effectively find solutions in relatively dense environments, cause some random samples in the free space cannot be explored by the rapidly random tree , which hinders the application of MA-RRT* in a more complicated real-world .
Edit intent: clarity
Original:  However,  MA-RRT* cannot effectively find solutions in relatively dense environments, cause some random samples in the free space cannot be explored by the rapidly random tree , which hinders the application of MA-RRT* in a more complicated real-world .
Revised:  However, MA-RRT* is hindered, because some of the random samples in the free space cannot be explored by the rapidly random tree , which hinders the application of MA-RRT* in a more complicated real-world .
Edit intent: clarity
Original:  However,  MA-RRT* cannot effectively find solutions in relatively dense environments, cause some random samples in the free space cannot be explored by the rapidly random tree , which hinders the application of MA-RRT* in a more complicated real-world .
Revised:  However, MA-RRT* cannot effectively find solutions in relatively dense environments, cause some random samples in the free space cannot be explored by the rapidly random tree .
Edit intent: coherence
Original:  This paper proposes an improved version of MA-RRT * , called Multi-agent RRT* Potential Field (MA-RRT*PF), an anytime algorithm that can efficiently guide the rapidly random tree to the free space in relatively dense environments.
Revised:  This paper proposes an improved version of MA-RRT* , called Multi-agent RRT* Potential Field (MA-RRT*PF), an anytime algorithm that can efficiently guide the rapidly random tree to the free space in relatively dense environments.
Edit intent: fluency
Original: Problems that claim several agents to find no-conflicts paths from their start locations to their destinations are named as cooperative pathfinding problems .
Revised: In cooperative pathfinding problems, non-conflict paths that bring several agents from their start locations to their destinations are named as cooperative pathfinding problems .
Edit intent: clarity
Original: Problems that claim several agents to find no-conflicts paths from their start locations to their destinations are named as cooperative pathfinding problems .
Revised: Problems that claim several agents to find no-conflicts paths from their start location to their destination need to be planned .
Edit intent: clarity
Original:  However, in a relatively dense environment, the application of MA-RRT* is hindered , because some of the random samples in the free space cannot be explored by the rapidly random tree .
Revised:  However, the implementation of this algorithm is hindered in systems with limited memory because the number of nodes in the tree of RRT* grows indefinitely as the paths get optimized .
Edit intent: clarity
Original:  The results show that MA-RRT*PF performs much better than MA-RRT* in relatively dense environments in terms of scalability while still maintaining the solution quality .
Revised:  The results show that MA-RRT*PF performs much better than MA-RRT* in terms of scalability while still maintaining the solution quality .
Edit intent: clarity
Original:  The computational complexity of recent algorithms is \mathcal O ( \left( N\log^2 (N)) in 1 dimension \left(\right)\right)  and \mathcal O ( \left( N^2\log^2 (N)) in two dimensions \left(\right)\right)  .
Revised:  The computational complexity of recent algorithms is \mathcal O \left( N\log^2 (N)) in 1 dimension \left(\right)\right) and \mathcal O ( \left( N^2\log^2 (N)) in two dimensions \left(\right)\right) .
Edit intent: fluency
Original:  The computational complexity of recent algorithms is \mathcal O ( \left( N\log^2 (N)) in 1 dimension \left(\right)\right)  and \mathcal O ( \left( N^2\log^2 (N)) in two dimensions \left(\right)\right)  .
Revised:  The computational complexity of recent algorithms is \mathcal O ( \left( N\log^2 (N)) in 1 dimension \left(\right)\right) and \mathcal O \left( N^2\log^2 (N)) in two dimensions \left(\right)\right) .
Edit intent: fluency
Original:  The computational complexity of recent algorithms is \mathcal O ( \left( N\log^2 (N)) in 1 dimension \left(\right)\right)  and \mathcal O ( \left( N^2\log^2 (N)) in two dimensions \left(\right)\right)  .
Revised:  The computational complexity of recent algorithms is \mathcal O ( \left( N\log^2 (N)) in 1 dimension \left(\right)\right) and \mathcal O ( \left( N^2\log^2 (N)) in two dimensions \left(N\right)\right) .
Edit intent: fluency
Original:  We show that  the compact support of B-splines enables improved complexity for multivariate approximations, namely \mathcal O(N) in 1-D, \mathcal O (N^{3/2 \left(}\right) in 2-D and more generally \mathcal O (N^{3(d-1)/d \left(}\right) in d-D with d>1.
Revised:  We show that the compact support of B-splines enables improved complexity for multivariate approximations, namely \mathcal O(N) in 1-D, \mathcal O (N^{3/2 \left(N^{3/2}\right) in 2-D and more generally \mathcal O (N^{3(d-1)/d \left(}\right) in d-D with d>1.
Edit intent: fluency
Original:  We show that  the compact support of B-splines enables improved complexity for multivariate approximations, namely \mathcal O(N) in 1-D, \mathcal O (N^{3/2 \left(}\right) in 2-D and more generally \mathcal O (N^{3(d-1)/d \left(}\right) in d-D with d>1.
Revised:  We show that the compact support of B-splines enables improved complexity for multivariate approximations, namely \mathcal O(N) in 1-D, \mathcal O (N^{3/2 \left(}\right) in 2-D and more generally \mathcal O (N^{3(d-1)/d \left(N^{3(d-1)/d}\right) in d-D with d>1.
Edit intent: fluency
Original:  Secondly, we train various classical convolutional neural networks (CNNs) including VGGish, ResNet _50 and Mobilenet _v2.
Revised:  Secondly, we train various classical convolutional neural networks (CNNs) including VGGish, ResNet 50 and Mobilenet v2.
Edit intent: fluency
Original: In this paper, we present a new cluster-wise unsupervised hashing (CUH)approach to learn compact binary codes for cross-modal similarity retrieval.
Revised: Large-scale cross-modal similarity retrieval.
Edit intent: clarity
Original:  Moreover, considering the semantic consistency between observed modalities, our CUHgenerates one unified hash code for all observed modalities of any instance . Specifically, we construct a co-training framework for learning to hash, in which we simultaneously realize the multi-view clustering and the learning of hash.
Revised:  Moreover, considering the semantic consistency between observed modalities, our CUHgenerates one unified hash code for all observed modalities of any instance . Specifically, CUH jointly performs the multi-view clustering and the learning of hash.
Edit intent: clarity
Original:  We explore multiple ways of computing time-varying covariance matrices to improve beamforming , including factorizing the spatial covariance into a time-varying amplitude component and  time-invariant spatial component  .
Revised:  We explore multiple ways of computing time-varying covariance matrices , including factorizing the spatial covariance into a time-varying amplitude component and time-invariant spatial component .
Edit intent: clarity
Original:  We explore multiple ways of computing time-varying covariance matrices to improve beamforming , including factorizing the spatial covariance into a time-varying amplitude component and  time-invariant spatial component  .
Revised:  We explore multiple ways of computing time-varying covariance matrices to improve beamforming , including factorizing the spatial covariance into a time-varying amplitude component and a time-invariant spatial component .
Edit intent: fluency
Original:  For the subsequent mask-based filtering, we consider different modes, including masking the noisy input, masking the beamformer output, and a hybrid approach combining both.
Edit intent: coherence
Original:  Our best method first uses spectral separation , then spatial beamforming , and finally a spectral post-filter, and demonstrates an average improvement of 2.8 dB over baseline mask-based separation , across four different reverberant speech enhancement and separation tasks.
Revised:  Our best method first uses spectral separation , then spatial beamforming , and finally a spectral post-filter, and demonstrates an average improvement of 2.8 dB over baseline mask-based separation , across four challenging reverberant speech enhancement and separation tasks.
Edit intent: clarity
Original:  Our best method utilizes a sequence of three neural separation and multi-frame time-invariant spatial beamforming stages, and demonstrates an average improvement of 2.75 dB in scale-invariant signal-to-noise ratio and 14.2\% absolute reduction in differential word error rate over strong baselines, across four challenging reverberant speech enhancement and separation tasks  .
Revised:  Our best method utilizes a sequence of three neural separation and multi-frame time-invariant spatial beamforming stages, and demonstrates an average improvement of 2.75 dB in scale-invariant signal-to-noise ratio and 14.2\% absolute reduction in a comparative speech recognition metric across four challenging reverberant speech enhancement and separation tasks .
Edit intent: clarity
Original: Crowd counting using deep convolutional neural networks (CNN) has achieved encouraging progress in the last couple of years.
Revised: Automatic crowd behaviour analysis is an important task for intelligent transportation systems to enable effective flow control and dynamic route planning for varying road participants. Crowd counting is one of the keys to automatic crowd behaviour analysis. Crowd counting using deep convolutional neural networks (CNN) has achieved encouraging progress in the last couple of years.
Edit intent: coherence
Original:  Recent works have shown the effectiveness ofInception modules in crowd counting due to its ability to capture multi-scale visual information via the fusion of features from multi-column networks.
Revised:  Recent works have shown the effectiveness of modules in crowd counting due to its ability to capture multi-scale visual information via the fusion of features from multi-column networks.
Edit intent: clarity
Original:  However , the existing crowd counting networks built with Inception modules usually have a small number of layers and only employ the basic type ofInception modules.
Revised:  However , the existing crowd counting networks built with modules usually have a small number of layers and only employ the basic type ofInception modules.
Edit intent: clarity
Original:  However , the existing crowd counting networks built with Inception modules usually have a small number of layers and only employ the basic type ofInception modules.
Revised:  However , the existing crowd counting networks built with Inception modules usually have a small number of layers Inception modules.
Edit intent: clarity
Original:  However , the existing crowd counting networks built with Inception modules usually have a small number of layers and only employ the basic type ofInception modules.
Revised:  However , the existing crowd counting networks built with Inception modules usually have a small number of layers and only employ the basic type of modules.
Edit intent: fluency
Original:  In this paper, we investigate the use of pre-trainedInception model for crowd counting.
Revised:  In this paper, we Inception model for crowd counting.
Edit intent: clarity