wanyu/IteraTeR_v2 huggingface dataset:
Original:  Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that  lexical representation of a word includes phonetic details associated with its prosodic prominence .
Revised:  Extending this interpretation, these results suggest that predictability is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its prosodic prominence .
Edit intent: clarity
Original:  Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that  lexical representation of a word includes phonetic details associated with its prosodic prominence .
Revised:  Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that the lexical representation of a word includes phonetic details associated with its prosodic prominence .
Edit intent: fluency
Original:  although its performance degrades significantly if the setup is not optimally configured in case of Poisson traffic .
Revised:  although its performance degrades significantly if the setup is not optimally configured .
Edit intent: clarity
Original:  To this end, this paper proposes a novel, yet practical smartphone based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the ambient air pressure and magnetic field environment matching.
Revised:  To this end, this paper proposes a novel, yet practical smartphone-based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the ambient air pressure and magnetic field environment matching.
Edit intent: fluency
Original:  To this end, this paper proposes a novel, yet practical smartphone based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the ambient air pressure and magnetic field environment matching.
Revised:  To this end, this paper proposes a novel, yet practical smartphone based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the air pressure and magnetic field environment matching.
Edit intent: clarity
Original:  To this end, this paper proposes a novel, yet practical smartphone based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the ambient air pressure and magnetic field environment matching.
Revised:  To this end, this paper proposes a novel, yet practical smartphone based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the ambient air pressure and the magnetic field for ambient environment matching.
Edit intent: fluency
Original:  In this paper , we present KGTK, a data science-centric toolkit  to represent, create, transform, enhance and analyze KGs. KGTK represents graphs in tables and leverages popular libraries developed for data science applications, enabling a wide audience of developers to easily construct knowledge graph pipelines for their applications.
Revised:  In this paper we present KGTK, a data science-centric toolkit to represent, create, transform, enhance and analyze KGs. KGTK represents graphs in tables and leverages popular libraries developed for data science applications, enabling a wide audience of developers to easily construct knowledge graph pipelines for their applications.
Edit intent: fluency
Original:  In this paper , we present KGTK, a data science-centric toolkit  to represent, create, transform, enhance and analyze KGs. KGTK represents graphs in tables and leverages popular libraries developed for data science applications, enabling a wide audience of developers to easily construct knowledge graph pipelines for their applications.
Revised:  In this paper , we present KGTK, a data science-centric toolkit designed to represent, create, transform, enhance and analyze KGs. KGTK represents graphs in tables and leverages popular libraries developed for data science applications, enabling a wide audience of developers to easily construct knowledge graph pipelines for their applications.
Edit intent: clarity
Original:  We illustrate KGTK with real-world scenarios in which we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet , in our own work .
Revised:  We illustrate the framework with real-world scenarios in which we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet , in our own work .
Edit intent: clarity
Original:  We illustrate KGTK with real-world scenarios in which we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet , in our own work .
Revised:  We illustrate KGTK with real-world scenarios where we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet , in our own work .
Edit intent: clarity
Original:  We illustrate KGTK with real-world scenarios in which we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet , in our own work .
Revised:  We illustrate KGTK with real-world scenarios in which we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet .
Edit intent: clarity
Original: The condensation of several mega base pair human chromosomes in a small cell volume is a spectacular phenomenon in biology .
Revised: The condensation of several mega base pair human chromosomes in a small nuclear volume is a spectacular phenomenon in biology .
Edit intent: clarity
Original: The condensation of several mega base pair human chromosomes in a small cell volume is a spectacular phenomenon in biology .
Revised: The condensation of several mega base pair human chromosomes in a small cell volume is a spectacular phenomenon .
Edit intent: clarity
Original:  This process , involving the formation of loops in chromosomes, is facilitated by ATP consuming motors (condensinand cohesin), that interact with chromatin segments thereby actively extruding loops.
Revised:  This process is driven by the formation of loops in chromosomes, is facilitated by ATP consuming motors (condensinand cohesin), that interact with chromatin segments thereby actively extruding loops.
Edit intent: clarity
Original:  This process , involving the formation of loops in chromosomes, is facilitated by ATP consuming motors (condensinand cohesin), that interact with chromatin segments thereby actively extruding loops.
Revised:  This process , involving the formation of chromosome loops. ATP consuming motor, condensin, interacts with chromatin segments thereby actively extruding loops.
Edit intent: clarity
Original:  This process , involving the formation of loops in chromosomes, is facilitated by ATP consuming motors (condensinand cohesin), that interact with chromatin segments thereby actively extruding loops.
Revised:  This process , involving the formation of loops in chromosomes, is facilitated by ATP consuming motors (condensinand cohesin), that interact with chromatin segments to actively extrude loops.
Edit intent: coherence
Original:  Motivated by real time videos of loop extrusion (LE)  , we created an analytically solvable model, which yields the LE velocity  as a function of external load acting on condensin .
Revised:  Motivated by real-time imaging of loop extrusion (LE) , we created an analytically solvable model, which yields the LE velocity as a function of external load acting on condensin .
Edit intent: clarity
Original:  Motivated by real time videos of loop extrusion (LE)  , we created an analytically solvable model, which yields the LE velocity  as a function of external load acting on condensin .
Revised:  Motivated by real time videos of loop extrusion (LE) , we created an analytically solvable model, predicting the LE velocity as a function of external load acting on condensin .
Edit intent: clarity
Original:  Motivated by real time videos of loop extrusion (LE)  , we created an analytically solvable model, which yields the LE velocity  as a function of external load acting on condensin .
Revised:  Motivated by real time videos of loop extrusion (LE) , we created an analytically solvable model, which yields the LE velocity as a function of external load .
Edit intent: clarity
Original:  The theory fits the experimental data quantitatively, and suggests that condensin must undergo a large conformational change, triggered by ATP binding and hydrolysis, that brings distant parts of the motor to proximity.
Revised:  The theory fits the experimental data quantitatively, and suggests that condensin must undergo a large conformational change, induced by ATP binding and hydrolysis, that brings distant parts of the motor to proximity.
Edit intent: clarity
Original:  The theory fits the experimental data quantitatively, and suggests that condensin must undergo a large conformational change, triggered by ATP binding and hydrolysis, that brings distant parts of the motor to proximity.
Revised:  The theory fits the experimental data quantitatively, and suggests that condensin must undergo a large conformational change, triggered by ATP binding , bringing distant parts of the motor to proximity.
Edit intent: coherence
Original:  Simulations using a simple model confirm that a transition between an open and closed states is necessary for LE . Changes in the orientation of the motor domain are transmitted over \sim 50 nm, connecting the motor head and the hinge, thus providing a plausible mechanism for LE.
Revised:  Simulations using a simple model confirm that the motor transitions between an open and closed states is necessary for LE . Changes in the orientation of the motor domain are transmitted over \sim 50 nm, connecting the motor head and the hinge, thus providing a plausible mechanism for LE.
Edit intent: clarity
Original:  Simulations using a simple model confirm that a transition between an open and closed states is necessary for LE . Changes in the orientation of the motor domain are transmitted over \sim 50 nm, connecting the motor head and the hinge, thus providing a plausible mechanism for LE.
Revised:  Simulations using a simple model confirm that a transition between an open and closed states is necessary for LE . Changes in the orientation of the motor domains are transmitted over \sim 50 nm, connecting the motor head and the hinge, thus providing a plausible mechanism for LE.
Edit intent: fluency
Original:  We present the first algorithm for LSS whose running time depends on other parameters, i.e., we show that an LSS of S can be computed in O(r \min\{n, M\}\log n{r} +  M \log n) time with O(M) space, where r is the length of an LSS of S and M is the number of matching points on S.
Revised:  We present the first algorithm for LSS whose running time depends on other parameters, i.e., we show that an LSS of S can be computed in O(r \min\{n, M\}\log n{r} + n + M \log n) time with O(M) space, where r is the length of an LSS of S and M is the number of matching points on S.
Edit intent: clarity
Original:  The  goal of CSM is to enhance RPL resilience against several routing attacks, with the ability to mitigate some of them.
Revised:  The main goal of CSM is to enhance RPL resilience against several routing attacks, with the ability to mitigate some of them.
Edit intent: coherence
Original:  The  goal of CSM is to enhance RPL resilience against several routing attacks, with the ability to mitigate some of them.
Revised:  The goal of CSM is to enhance RPL resilience against replay attacks, with the ability to mitigate some of them.
Edit intent: clarity
Original: As the de facto routing protocol for many Internet of Things (IoT) networks nowadays, and to assure the confidentiality and integrity of its control messages, the Routing Protocol for Low Power and Lossy Networks (RPL) incorporates three modes of security: the Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and the Authenticated Secure Mode (ASM).
Revised: The current standard of Routing Protocol for Low Power and Lossy Networks (RPL) incorporates three modes of security: the Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and the Authenticated Secure Mode (ASM).
Edit intent: clarity
Original:  In this paper, we propose a novel secure mode for RPL, the Chained Secure Mode (CSM), based on the concept of intra-flow Network Coding.
Revised:  In this paper, we propose a novel secure mode for RPL, the Chained Secure Mode (CSM), based on the concept of intraflow Network Coding.
Edit intent: fluency
Original: Edge computing servers like cloudlets from different service providers that compensate scarce computational, memory, and energy resources of mobile devices, are distributed across access networks.
Revised: Edge computing servers like cloudlets from different service providers compensate scarce computational, memory, and energy resources of mobile devices, are distributed across access networks.
Edit intent: coherence
Original:  Thus, in this paper, we propose an economic and non-cooperative load balancing game for low-latency applications among neighboring cloudlets, from same as well as different service providers  . Firstly, we propose a centralized incentive mechanism to compute the unique Nash equilibrium load balancing strategies of the cloudlets under the supervision of a neutral mediator.
Revised:  Thus, in this paper, we propose an economic and non-cooperative load balancing game for low-latency applications among federated neighboring cloudlets from the same as well as different service providers . Firstly, we propose a centralized incentive mechanism to compute the unique Nash equilibrium load balancing strategies of the cloudlets under the supervision of a neutral mediator.
Edit intent: clarity
Original:   Additional solution classes in variations on rock-paper-scissors are also studied.
Revised:  Additional solution classes in variations of rock-paper-scissors are also studied.
Edit intent: fluency
Original:  We study the differences in the two equations as they pertain to the one-dimensional rock-paper-scissors game .
Revised:  We study the differences in the two equations as they pertain to a one-dimensional rock-paper-scissors game .
Edit intent: fluency
Original:  Numerical examples illustrate the implementation of the method and verify the theoretical findings .
Revised:  Numerical examples illustrate the implementation of the method and verify the theoretical results .
Edit intent: clarity
Original:  In this paper we introduce a novel structure Cross-Time Delay Neural Network (CTDNN) updating the performance of  TDNN.
Revised:  In this paper we introduce a novel structure Crossed-Time Delay Neural Network (CTDNN) updating the performance of TDNN.
Edit intent: fluency
Original:  In this paper we introduce a novel structure Cross-Time Delay Neural Network (CTDNN) updating the performance of  TDNN.
Revised:  In this paper we introduce a novel structure Cross-Time Delay Neural Network (CTDNN) to enhance the performance of TDNN.
Edit intent: clarity
Original:  Inspired by the multi-filters setting of convolution layer from convolution neural network, we set different time delay units at the bottom layer each with different context size  and construct a multilayer parallel network.
Revised:  Inspired by the multi-filters setting of convolution layer from convolution neural network, we set multiple time delay units at the bottom layer each with different context size and construct a multilayer parallel network.
Edit intent: clarity
Original:  Inspired by the multi-filters setting of convolution layer from convolution neural network, we set different time delay units at the bottom layer each with different context size  and construct a multilayer parallel network.
Revised:  Inspired by the multi-filters setting of convolution layer from convolution neural network, we set different time delay units each with different context size and construct a multilayer parallel network.
Edit intent: clarity
Original:  It gives significant improvements over original TDNN as we tested in both speaker verification and identification tasks.
Revised:  It gives significant improvements over original TDNN on both speaker verification and identification tasks.
Edit intent: clarity
Original:  It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6\% absolute EER improvement.
Revised:  It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6\% absolute Equal Error Rate improvement.
Edit intent: clarity
Original:  In a numerical analysis we show that the stabilization is fundamental to achieve stable approximations of the sea-ice velocity field and a bounded energy of the sea-ice system .
Revised:  In a numerical analysis we show that the stabilization is fundamental to achieve stable approximation of the sea-ice velocity field and a bounded energy of the sea-ice system .
Edit intent: fluency
Original:  In a numerical analysis we show that the stabilization is fundamental to achieve stable approximations of the sea-ice velocity field and a bounded energy of the sea-ice system .
Revised:  In a numerical analysis we show that the stabilization is fundamental to achieve stable approximations of the sea-ice velocity field .
Edit intent: clarity
Original:  [ESA 2014], who for the same problem achieved update time f(d) for a non-elementary function f(d)}.
Revised:  [ESA 2014], who for the same problem achieved update time f(d) for some non-elementary function f(d)}.
Edit intent: clarity
Original:  In the second case, the amortized update time is 2^{{\cal O}(k^4)} + }{\cal   .
Revised:  In the second case, the amortized update time is 2^{{\cal O}(k^4)} + }{\cal O .
Edit intent: fluency
Original: Linear value decomposition is a widely-used structure to scale up multi-agent Q-learning algorithms in cooperative settings.
Revised: Value decomposition is a widely-used structure to scale up multi-agent Q-learning algorithms in cooperative settings.
Edit intent: clarity
Original: Linear value decomposition is a widely-used structure to scale up multi-agent Q-learning algorithms in cooperative settings.
Revised: Linear value decomposition is a popular and promising approach to scaling up multi-agent Q-learning algorithms in cooperative settings.
Edit intent: clarity
Original: Linear value decomposition is a widely-used structure to scale up multi-agent Q-learning algorithms in cooperative settings.
Revised: Linear value decomposition is a widely-used structure to scale up multi-agent reinforcement learning in cooperative settings.
Edit intent: clarity
Original:  Our main results reveal two novel insights: ( 1) Linear value decomposition structure implicitly implements a classical  credit assignment called difference rewards.
Revised:  Our main results reveal two novel insights: ( 1) linear value decomposition implicitly implements a classical credit assignment called difference rewards.
Edit intent: clarity
Original:  In the empirical study, our experiments also demonstrate that most deep multi-agent Q-learning algorithms using linear value decomposition structure cannot efficiently utilize off-policy samples  .
Revised:  In the empirical study, our experiments also demonstrate that most deep multi-agent Q-learning algorithms using linear value decomposition cannot efficiently utilize off-policy samples .
Edit intent: clarity
Original:  and 2)  multi-agent Q-learning with linear value decomposition requires on-policy data distribution to achieve numerical stability .
Revised:  and 2) multi-agent Q-learning .
Edit intent: clarity
Original:  In this paper, we introduce a variant of the fitted Q-iteration framework for analyzing  multi-agent Q-learning with value decomposition . Based on this framework, we derive a closed-form solution to the empirical Bellman error minimization with linear value decomposition.
Revised:  In this paper, we formalize a multi-agent fitted Q-iteration framework for analyzing multi-agent Q-learning with value decomposition . Based on this framework, we derive a closed-form solution to the empirical Bellman error minimization with linear value decomposition.
Edit intent: clarity
Original:  In this paper, we introduce a variant of the fitted Q-iteration framework for analyzing  multi-agent Q-learning with value decomposition . Based on this framework, we derive a closed-form solution to the empirical Bellman error minimization with linear value decomposition.
Revised:  In this paper, we introduce a variant of the fitted Q-iteration framework for analyzing multi-agent Q-learning . Based on this framework, we derive a closed-form solution to the empirical Bellman error minimization with linear value decomposition.
Edit intent: fluency
Original:  In the empirical study, our experiments demonstrate the realizability of our theoretical closed-form formulation and implications in the didactic examples and a broad set of StarCraft II unit micromanagement tasks , respectively .
Revised:  In the empirical study, our experiments demonstrate the realizability of our theoretical closed-form formulation and implications in the didactic examples and a broad set of StarCraft II unit micromanagement tasks .
Edit intent: clarity
Original:  Finally, to support and extend our theoretical implications to practical realization, we conduct an empirical analysis of state-of-the-art deep multi-agent Q-learning algorithms on didactic examples and a broad set of StarCraft II unit micromanagement tasks.
Revised:  Finally, to support our theoretical implications to practical realization, we conduct an empirical analysis of state-of-the-art deep multi-agent Q-learning algorithms on didactic examples and a broad set of StarCraft II unit micromanagement tasks.
Edit intent: clarity
Original:  Finally, to support and extend our theoretical implications to practical realization, we conduct an empirical analysis of state-of-the-art deep multi-agent Q-learning algorithms on didactic examples and a broad set of StarCraft II unit micromanagement tasks.
Revised:  Finally, to support and extend our theoretical implications in practical realization, we conduct an empirical analysis of state-of-the-art deep multi-agent Q-learning algorithms on didactic examples and a broad set of StarCraft II unit micromanagement tasks.
Edit intent: fluency
Original: In this paper, we consider minimizing the L1/L2 term on the gradient for a limit-angle scanning problem in computed tomography (CT) reconstruction.
Revised: In this paper, we consider minimizing the L1/L2 term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction.
Edit intent: fluency
Original:  Numerical results on both synthetic and experimental datasets demonstrate the efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limited-angle CT reconstruction.
Revised:  Numerical results on both synthetic and experimental datasets demonstrate the effectiveness of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limited-angle CT reconstruction.
Edit intent: clarity
Original: We consider online algorithms for the k-servers problem on trees.
Revised: We consider online algorithms for the k-server problem on trees.
Edit intent: fluency
Original:  There is an k-competitive algorithm for this problem, and it is the best competitive ratio.
Revised:  There is a k-competitive algorithm for this problem, and it is the best competitive ratio.
Edit intent: fluency
Original:  M. Chrobak and L. Larmore suggested it.
Revised:  M. Chrobak and L. Larmore provided it.
Edit intent: clarity
Original:  We suggest a new time-efficient implementation of the algorithm.
Revised:  We provide a new time-efficient implementation of the algorithm.
Edit intent: clarity