wanyu/IteraTeR_v2 huggingface dataset:
Original: We introduce LTrack, a new tracking attack on LTE that allows an attacker to stealthily extract user devices' (UEs) permanent identifiers (IMSI) and locations .
Revised: We introduce LTrack, a new tracking attack on LTE that allows an attacker to stealthily extract user devices' locations and permanent identifiers (IMSI) and locations .
Edit intent: clarity
Original: We introduce LTrack, a new tracking attack on LTE that allows an attacker to stealthily extract user devices' (UEs) permanent identifiers (IMSI) and locations .
Revised: We introduce LTrack, a new tracking attack on LTE that allows an attacker to stealthily extract user devices' (UEs) permanent identifiers (IMSI) .
Edit intent: clarity
Original:  To remain stealthy, the localization of UEs in LTrack is fully passive .
Revised:  To remain stealthy, the localization of devices in LTrack is fully passive .
Edit intent: clarity
Original:  It relies on our new uplink/downlink sniffer implementation, which records both times of arrivals of LTE messages and contents of Timing Advance commands , based on which LTrack calculates UE locations.
Revised:  It relies on our new uplink/downlink sniffer . Our sniffer records both the times of arrival of arrivals of LTE messages and contents of Timing Advance commands , based on which LTrack calculates UE locations.
Edit intent: clarity
Original:  It relies on our new uplink/downlink sniffer implementation, which records both times of arrivals of LTE messages and contents of Timing Advance commands , based on which LTrack calculates UE locations.
Revised:  It relies on our new uplink/downlink sniffer implementation, which records both times of LTE messages and contents of Timing Advance commands , based on which LTrack calculates UE locations.
Edit intent: clarity
Original:  It relies on our new uplink/downlink sniffer implementation, which records both times of arrivals of LTE messages and contents of Timing Advance commands , based on which LTrack calculates UE locations.
Revised:  It relies on our new uplink/downlink sniffer implementation, which records both times of arrivals of LTE messages and the contents of the Timing Advance Commands , based on which LTrack calculates UE locations.
Edit intent: fluency
Original:  It relies on our new uplink/downlink sniffer implementation, which records both times of arrivals of LTE messages and contents of Timing Advance commands , based on which LTrack calculates UE locations.
Revised:  It relies on our new uplink/downlink sniffer implementation, which records both times of arrivals of LTE messages and contents of Timing Advance commands , based on which LTrack calculates locations.
Edit intent: clarity
Original:  Passive localization attacks reveal information about a UE's locations but can at best link these locations to a UE 's pseudonymous temporary identifier (TMSI), making tracking in dense areas  challenging.
Revised:  Passive localization attacks reveal a user's location traces but can at best link these locations to a UE 's pseudonymous temporary identifier (TMSI), making tracking in dense areas challenging.
Edit intent: clarity
Original:  Passive localization attacks reveal information about a UE's locations but can at best link these locations to a UE 's pseudonymous temporary identifier (TMSI), making tracking in dense areas  challenging.
Revised:  Passive localization attacks reveal information about a UE's locations but can at best link these traces to a device 's pseudonymous temporary identifier (TMSI), making tracking in dense areas challenging.
Edit intent: clarity
Original:  It extracts a UE's permanent identifier (IMSI ) and binds it to its current TMSI. Instead of relying on fake base stations like existing IMSI Catchers ( which are detectable due to their output power) , IMSI Extractor relies on our uplink/downlink sniffer enhanced with surgical message overshadowing.
Revised:  It extracts a device's IMSI and binds it to its current TMSI. Instead of relying on fake base stations like existing IMSI Catchers ( which are detectable due to their output power) , IMSI Extractor relies on our uplink/downlink sniffer enhanced with surgical message overshadowing.
Edit intent: clarity
Original:  It extracts a UE's permanent identifier (IMSI ) and binds it to its current TMSI. Instead of relying on fake base stations like existing IMSI Catchers ( which are detectable due to their output power) , IMSI Extractor relies on our uplink/downlink sniffer enhanced with surgical message overshadowing.
Revised:  It extracts a UE's permanent identifier (IMSI ) and binds it to its current TMSI. Instead of relying on fake base stations like existing IMSI Catchers , which are detectable due to their output power) , IMSI Extractor relies on our uplink/downlink sniffer enhanced with surgical message overshadowing.
Edit intent: fluency
Original:  In addition, we successfully test our IMSI Extractor against a set of 17 modern smartphones connected to an industry-grade LTE testbed  .
Revised:  We successfully tested our IMSI Extractor against a set of 17 modern smartphones connected to an industry-grade LTE testbed .
Edit intent: coherence
Original:  In addition, we successfully test our IMSI Extractor against a set of 17 modern smartphones connected to an industry-grade LTE testbed  .
Revised:  In addition, we successfully test our IMSI Extractor against a set of 17 modern smartphones connected to our industry-grade LTE testbed .
Edit intent: fluency
Original: We consider the EBT algorithm (a particle method) for the non-local equation with  discontinuous interaction kernel.
Revised: We consider the EBT algorithm (a particle method) for the non-local equation with a discontinuous interaction kernel.
Edit intent: fluency
Original: This document introduces a hypothesized framework on the functional nature of primitive neural network.
Revised: Understanding the basic operational logics of the nervous system is essential to advancing neuroscientific research. However, theoretical efforts to tackle this fundamental problem are lacking, despite the abundant empirical data about the brain that has been collected in the past few decades. To address this shortcoming, this document introduces a hypothesized framework on the functional nature of primitive neural network.
Edit intent: coherence
Original: This document introduces a hypothesized framework on the functional nature of primitive neural network.
Revised: This document introduces a hypothetical framework for the functional nature of primitive neural network.
Edit intent: fluency
Original:  It discusses such an idea that the activity of neurons and synapses can symbolically reenact the dynamic changes in the world and  enable an adaptive system of behavior.
Revised:  It discusses such an idea that the activity of neurons and synapses can symbolically reenact the dynamic changes in the world and thus enable an adaptive system of behavior.
Edit intent: coherence
Original:  More specifically , the network achieves these without participating in an algorithmic structure.
Revised:  More significantly , the network achieves these without participating in an algorithmic structure.
Edit intent: clarity
Original:  More specifically , the network achieves these without participating in an algorithmic structure.
Revised:  More specifically , the network achieves this without participating in an algorithmic structure.
Edit intent: fluency
Original:  A neuron's activation is transformed to its postsynaptic targets as it fires , resulting in a chronological shift of the represented elements.
Revised:  As it fires, a neuron's activation is transformed to its postsynaptic targets as it fires , resulting in a chronological shift of the represented elements.
Edit intent: clarity
Original:  A neuron's activation is transformed to its postsynaptic targets as it fires , resulting in a chronological shift of the represented elements.
Revised:  A neuron's activation is transformed to its postsynaptic targets , resulting in a chronological shift of the represented elements.
Edit intent: clarity
Original:  Here, we introduce a graph neural network framework with a novel scalable Directed Mixed Path Aggregation(DIMPA) scheme to obtain node embeddings for directed networks in a self-supervised manner, including a novel probabilistic imbalance loss  .
Revised:  We introduce a graph neural network framework with a novel scalable Directed Mixed Path Aggregation(DIMPA) scheme to obtain node embeddings for directed networks in a self-supervised manner, including a novel probabilistic imbalance loss .
Edit intent: coherence
Original:  Here, we introduce a graph neural network framework with a novel scalable Directed Mixed Path Aggregation(DIMPA) scheme to obtain node embeddings for directed networks in a self-supervised manner, including a novel probabilistic imbalance loss  .
Revised:  Here, we introduce a graph neural network framework to obtain node embeddings for directed networks in a self-supervised manner, including a novel probabilistic imbalance loss .
Edit intent: coherence
Original:  The method is end-to-end in combining embedding generation and clustering without an intermediate step.
Edit intent: coherence
Original:  cut flow imbalance is optimized without resorting to spectral methodsor cluster labels . Experimental results on synthetic data, in the form of directed stochastic block models  and real-world data at different scales, demonstrate that our method  attains state-of-the-art results on directed  clustering, for a wide range of noise and sparsity levels , as well as graph structures .
Revised:  cut flow imbalance is optimized without resorting to spectral methodsor cluster labels . Experimental results on synthetic data, in the form of directed stochastic block models , and real-world data at different scales, demonstrate that our method attains state-of-the-art results on directed clustering, for a wide range of noise and sparsity levels , as well as graph structures .
Edit intent: fluency
Original:  cut flow imbalance is optimized without resorting to spectral methodsor cluster labels . Experimental results on synthetic data, in the form of directed stochastic block models  and real-world data at different scales, demonstrate that our method  attains state-of-the-art results on directed  clustering, for a wide range of noise and sparsity levels , as well as graph structures .
Revised:  cut flow imbalance is optimized without resorting to spectral methodsor cluster labels . Experimental results on synthetic data, in the form of directed stochastic block models and real-world data at different scales, demonstrate that our method , based on flow imbalance, attains state-of-the-art results on directed clustering, for a wide range of noise and sparsity levels , as well as graph structures .
Edit intent: clarity
Original:  cut flow imbalance is optimized without resorting to spectral methodsor cluster labels . Experimental results on synthetic data, in the form of directed stochastic block models  and real-world data at different scales, demonstrate that our method  attains state-of-the-art results on directed  clustering, for a wide range of noise and sparsity levels , as well as graph structures .
Revised:  cut flow imbalance is optimized without resorting to spectral methodsor cluster labels . Experimental results on synthetic data, in the form of directed stochastic block models and real-world data at different scales, demonstrate that our method attains state-of-the-art results on directed graph clustering, for a wide range of noise and sparsity levels , as well as graph structures .
Edit intent: fluency
Original:  cut flow imbalance is optimized without resorting to spectral methodsor cluster labels . Experimental results on synthetic data, in the form of directed stochastic block models  and real-world data at different scales, demonstrate that our method  attains state-of-the-art results on directed  clustering, for a wide range of noise and sparsity levels , as well as graph structures .
Revised:  cut flow imbalance is optimized without resorting to spectral methodsor cluster labels . Experimental results on synthetic data, in the form of directed stochastic block models and real-world data at different scales, demonstrate that our method attains state-of-the-art results on directed clustering, for a wide range of noise and sparsity levels and graph structures and topologies .
Edit intent: clarity
Original: Cloth simulation has wide applications including computer animation, garment design, and robot-assisted dressing.
Revised: Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing.
Edit intent: clarity
Original:  In this work , we present a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications.
Revised:  This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications.
Edit intent: coherence
Original:  Our differentiable simulator extends the state-of-the-art cloth simulator based on Projective Dynamics  and with dry frictional contact governed by the Signorini-Coulomb law.
Revised:  Our differentiable simulator extends a state-of-the-art cloth simulator based on Projective Dynamics and with dry frictional contact governed by the Signorini-Coulomb law.
Edit intent: fluency
Original:  We  demonstrate the efficacy of our simulator in various applications, including system identification, manipulation, inverse design, and a real-to-sim task.
Revised:  We demonstrate the efficacy of our simulator in various applications, including system identification, manipulation, inverse design, and real-to-sim task.
Edit intent: fluency
Original:  Such problems are motivated by the recent swarm control technique that uses mean-field approximations to represent the collective effect of the swarm, wherein the mean-field density ( and its gradient) is usually used in feedback control design.
Revised:  Such problems are motivated by the recent swarm control technique that uses mean-field approximations to represent the collective effect of the swarm, wherein the mean-field density ( especially its gradient) is usually used in feedback control design.
Edit intent: fluency
Original:  We prove that the local density filter is also convergent and remains close to the centralized one in the sense of ISS. Simulation results suggest that the (centralized ) suboptimal density filter is able to generate convergent density estimates, and the local density filter is able to converge and remain close to the centralized filter.
Revised:  We prove that the local density filter is also convergent and remains close to the centralized one in the sense of ISS. Simulation results suggest that the centralized suboptimal density filter is able to generate convergent density estimates, and the local density filter is able to converge and remain close to the centralized filter.
Edit intent: clarity
Original: In this paper we present a novel class of methods for high order accurate integration of multirate systems of ordinary differential equation initial-value problems.
Revised: In this paper we propose a novel class of methods for high order accurate integration of multirate systems of ordinary differential equation initial-value problems.
Edit intent: clarity
Original:  We then present numerical simulations to confirm these theoretical convergence rates, and to compare the efficiency of MERB methods against  recently-introduced multirate MERK and MRI-GARK methods.
Revised:  We then present numerical simulations to confirm these theoretical convergence rates, and to compare the efficiency of MERB methods against other recently-introduced multirate MERK and MRI-GARK methods.
Edit intent: clarity
Original:  Specifically, in our model  each point j has a set of other points S_j that it perceives as similar to itself, and it feels that it is fairly treated , if the quality of service it receives in the solution is \alpha-close  to that of the points in S_j. We begin our study by answering questions regarding the structure of the problem, namely for what values of \alpha the problem is well-defined, and what the behavior of the Price of Fairness (PoF) for it is.
Revised:  Specifically, in our model , each point j has a set of other points S_j that it perceives as similar to itself, and it feels that it is fairly treated , if the quality of service it receives in the solution is \alpha-close to that of the points in S_j. We begin our study by answering questions regarding the structure of the problem, namely for what values of \alpha the problem is well-defined, and what the behavior of the Price of Fairness (PoF) for it is.
Edit intent: fluency
Original:  Specifically, in our model  each point j has a set of other points S_j that it perceives as similar to itself, and it feels that it is fairly treated , if the quality of service it receives in the solution is \alpha-close  to that of the points in S_j. We begin our study by answering questions regarding the structure of the problem, namely for what values of \alpha the problem is well-defined, and what the behavior of the Price of Fairness (PoF) for it is.
Revised:  Specifically, in our model each point j has a set of other points S_j that it perceives as similar to itself, and it feels that it is fairly treated if the quality of service it receives in the solution is \alpha-close to that of the points in S_j. We begin our study by answering questions regarding the structure of the problem, namely for what values of \alpha the problem is well-defined, and what the behavior of the Price of Fairness (PoF) for it is.
Edit intent: fluency
Original:  Specifically, in our model  each point j has a set of other points S_j that it perceives as similar to itself, and it feels that it is fairly treated , if the quality of service it receives in the solution is \alpha-close  to that of the points in S_j. We begin our study by answering questions regarding the structure of the problem, namely for what values of \alpha the problem is well-defined, and what the behavior of the Price of Fairness (PoF) for it is.
Revised:  Specifically, in our model each point j has a set of other points S_j that it perceives as similar to itself, and it feels that it is fairly treated , if the quality of service it receives in the solution is \alpha-close to that of the points in S_j. We begin our study by answering questions regarding the structure of the problem, namely for what values of \alpha the problem is well-defined, and what the behavior of the Price of Fairness (PoF) for it is.
Edit intent: fluency
Original:  For the well-defined region of \alpha, we provide efficient and easily implementable approximation algorithms for the k-center objective, which in certain cases also enjoy bounded PoF guarantees.
Revised:  For the well-defined region of \alpha, we provide efficient and easily-implementable approximation algorithms for the k-center objective, which in certain cases also enjoy bounded PoF guarantees.
Edit intent: fluency
Original:  For the well-defined region of \alpha, we provide efficient and easily implementable approximation algorithms for the k-center objective, which in certain cases also enjoy bounded PoF guarantees.
Revised:  For the well-defined region of \alpha, we provide efficient and easily implementable approximation algorithms for the k-center objective, which in certain cases enjoy bounded-PoF guarantees.
Edit intent: clarity
Original: Graph cut problems form a fundamental problem type in combinatorial optimization , and are a central object of study in both theory and practice.
Revised: Graph cut problems are fundamental in Combinatorial Optimization , and are a central object of study in both theory and practice.
Edit intent: clarity
Original:  In addition , the study of fairness in Algorithmic Design and Machine Learning has recently received significant attention, with many different notions proposed and analyzed in a variety of contexts.
Revised:  Furthermore , the study of fairness in Algorithmic Design and Machine Learning has recently received significant attention, with many different notions proposed and analyzed in a variety of contexts.
Edit intent: fluency
Original:  In addition , the study of fairness in Algorithmic Design and Machine Learning has recently received significant attention, with many different notions proposed and analyzed in a variety of contexts.
Revised:  In addition , the study of fairness in Algorithmic Design and Machine Learning has recently received significant attention, with many different notions proposed and analyzed in a variety of contexts.
Edit intent: fluency
Original:  In addition , the study of fairness in Algorithmic Design and Machine Learning has recently received significant attention, with many different notions proposed and analyzed in a variety of contexts.
Revised:  In addition , the study of fairness in Algorithmic Design and Machine Learning has recently received significant attention, with many different notions proposed and analyzed for a variety of contexts.
Edit intent: fluency
Original:  Specifically, we incorporate two different definitions of fairness, namely demographic and probabilistic individual \emph{ fairness, in a particular cut problem modeling disaster containment scenarios.
Revised:  Specifically, we incorporate two different notions of fairness, namely demographic and probabilistic individual \emph{ fairness, in a particular cut problem modeling disaster containment scenarios.
Edit intent: clarity
Original:  Specifically, we incorporate two different definitions of fairness, namely demographic and probabilistic individual \emph{ fairness, in a particular cut problem modeling disaster containment scenarios.
Revised:  Specifically, we incorporate two different definitions of fairness, namely demographic and probabilistic individual and\emph{ fairness, in a particular cut problem modeling disaster containment scenarios.
Edit intent: fluency
Original:  Specifically, we incorporate two different definitions of fairness, namely demographic and probabilistic individual \emph{ fairness, in a particular cut problem modeling disaster containment scenarios.
Revised:  Specifically, we incorporate two different definitions of fairness, namely demographic and probabilistic individual \emph{ fairness, in a particular cut problem that models disaster containment scenarios.
Edit intent: fluency
Original: Coulomb interaction, following an inverse-square force-law, quantifies the amount of force between two stationary , electrically charged particles.
Revised: Coulomb interaction, following an inverse-square force-law, quantifies the amount of force between two stationary and electrically charged particles.
Edit intent: fluency
Original:  Various algorithms aim to speed up the pairwise Coulomb interactions to a linear scaling but the poor scalability limits the size of simulated systems.
Revised:  Various algorithms are developed to calculate the pairwise Coulomb interactions to a linear scaling but the poor scalability limits the size of simulated systems.
Edit intent: clarity
Original:  Here, we conduct an efficient molecular dynamics algorithm with the random batch Ewald method on all-atom systems where the complete Fourier components in the Coulomb interaction are replaced by randomly selected mini batches .
Revised:  Here, we conduct an efficient molecular dynamics algorithm with the random batch Ewald method on all-atom systems where the complete Fourier components in the Coulomb interaction are replaced by randomly selected mini-batches .
Edit intent: fluency
Original:  To tackle this issue , we leverage the theory of infinite-width neural networks to model neural discriminator training for a wide range of adversarial losses via its Neural Tangent Kernel (NTK).
Revised:  To tackle this issue , we leverage the theory of infinite-width neural networks for the discriminator via its Neural Tangent Kernel (NTK).
Edit intent: clarity