wanyu/IteraTeR_v2 huggingface dataset:
Original:  In addition, our simulation results illustrate the potential of our proposed solutions for enabling SR in future WLANs, showing that substantial improvements on network performance are achieved regarding throughput and fairness. t
Revised:  In addition, our simulation results illustrate the potential of our proposed solutions for enabling SR in future WLANs, showing that substantial improvements on network performance are achieved regarding throughput and fairness. 
Edit intent: fluency
Original: Spatial Reuse (SR) has recently gained attention for performance maximization in IEEE 802.11 Wireless Local Area Networks (WLANs).
Revised: Spatial Reuse (SR) has recently gained attention to maximize the performance of IEEE 802.11 Wireless Local Area Networks (WLANs).
Edit intent: clarity
Original:  While the former stands for pure individual behavior, the second one aims to consider the performance experienced by surrounding networks, thus taking into account the impact of individual actions on the environment.
Revised:  While the former stands for pure individual behavior, the second one considers the performance experienced by surrounding networks, thus taking into account the impact of individual actions on the environment.
Edit intent: clarity
Original:  Our simulation results illustrate the potential of the proposed solutions for enabling SR in future WLANs , showing that substantial improvements on network performance can be achieved regarding throughput and fairness.
Revised:  Our simulation results illustrate the potential of the proposed solutions for enabling SR in future WLANs . We show that substantial improvements on network performance can be achieved regarding throughput and fairness.
Edit intent: coherence
Original: We present strongly convergent explicit and semi-implicit  numerical schemes for systems of stiff stochastic differential equations (SDEs) where both the drift and diffusion coefficients are non-globally Lipschitz continuous.
Revised: We present strongly convergent explicit and semi-implicit numerical schemes for systems of stiff stochastic differential equations (SDEs) where both the drift and diffusion are non-globally Lipschitz continuous.
Edit intent: clarity
Original:  Typical examples arise from the space discretisation of an SPDE or, in finance, from certain stochastic volatility models.
Revised:  Typical applications arise from the space discretisation of an SPDE or, in finance, from certain stochastic volatility models.
Edit intent: clarity
Original:  Typical examples arise from the space discretisation of an SPDE or, in finance, from certain stochastic volatility models.
Revised:  Typical examples arise from the space discretisation of an SPDE , stochastic volatility models in finance, from certain stochastic volatility models.
Edit intent: fluency
Original:  A strategy that adapts the time step based on the drift alone is sufficient to control growth and to prove strong convergence .
Revised:  A strategy that adapts the stepsize based on the drift alone is sufficient to control growth and to prove strong convergence .
Edit intent: clarity
Original:  More details can be found at URL 
Revised:  More details can be found at URL .
Edit intent: fluency
Original:  We leverage the result in several papers that most congestion in datacenter networks occurs at the receiver.
Revised:  Several papers have shown that even in oversubscribed datacenter networks most congestion occurs at the receiver.
Edit intent: clarity
Original:  For receiver congestion, Dart proposes direct apportioning of sending rates (DASR) in which a receiver for n senders directs each sender to cut its rate by a factor of n, converging in only one RTT.
Revised:  For receiver congestion, we propose direct apportioning of sending rates (DASR) in which a receiver for n senders directs each sender to cut its rate by a factor of n, converging in only one RTT.
Edit intent: fluency
Original:  For the spatially-localized case, Dart employs deflection by adding novel switch hardware for in-order flow deflection (IOFD) because RDMA disallows packet reordering , providing fast (under one RTT), light-weight response .
Revised:  For the spatially-localized case, Dart provides fast (under one RTT) response by adding novel switch hardware for in-order flow deflection (IOFD) because RDMA disallows packet reordering , providing fast (under one RTT), light-weight response .
Edit intent: clarity
Original:  For the spatially-localized case, Dart employs deflection by adding novel switch hardware for in-order flow deflection (IOFD) because RDMA disallows packet reordering , providing fast (under one RTT), light-weight response .
Revised:  For the spatially-localized case, Dart employs deflection by adding novel switch hardware for in-order flow deflection (IOFD) because RDMA disallows packet reordering on which previous load balancing schemes rely .
Edit intent: clarity
Original:  Small- scale testbed measurements and at-scale simulations, respectively, show that Dart achieves 60\% (2.5x) and 79\% (4.8x) lower 99th percentile latency, and similar and 58\% higher throughput than RoCE , and TIMELY and DCQCN 
Revised:  Small-scale testbed measurements and at-scale simulations, respectively, show that Dart achieves 60\% (2.5x) and 79\% (4.8x) lower 99th percentile latency, and similar and 58\% higher throughput than RoCE , and TIMELY and DCQCN 
Edit intent: fluency
Original:  Small- scale testbed measurements and at-scale simulations, respectively, show that Dart achieves 60\% (2.5x) and 79\% (4.8x) lower 99th percentile latency, and similar and 58\% higher throughput than RoCE , and TIMELY and DCQCN 
Revised:  Small- scale testbed measurements and at-scale simulations, respectively, show that Dart achieves 60\% (2.5x) and 79\% (4.8x) lower 99th-percentile latency, and similar and 58\% higher throughput than RoCE , and TIMELY and DCQCN 
Edit intent: fluency
Original:  Small- scale testbed measurements and at-scale simulations, respectively, show that Dart achieves 60\% (2.5x) and 79\% (4.8x) lower 99th percentile latency, and similar and 58\% higher throughput than RoCE , and TIMELY and DCQCN 
Revised:  Small- scale testbed measurements and at-scale simulations, respectively, show that Dart achieves 60\% (2.5x) and 79\% (4.8x) lower 99th percentile latency, and similar and 58\% higher throughput than RoCE , and TIMELY and DCQCN .
Edit intent: fluency
Original:  As long as the optimization criterion is additive, the problem can be solved exactly in O(n^2k) time using a classic dynamic program.
Revised:  As long as the optimization criterion is additive, Seg can be solved exactly in O(n^2k) time using a classic dynamic program.
Edit intent: clarity
Original:  In addition, we consider a cumulative version of the problem , where we are asked to discover the optimal segmentation for each prefix of the input sequence.
Revised:  In addition, we consider a cumulative version of Seg , where we are asked to discover the optimal segmentation for each prefix of the input sequence.
Edit intent: clarity
Original:  Finally, we consider a cumulative version of the maximum segmentation , and show that this can be solved in O(nk \log k) time.
Revised:  Finally, we consider a cumulative version of MaxSeg , and show that this can be solved in O(nk \log k) time.
Edit intent: clarity
Original:  Finally, we consider a cumulative version of the maximum segmentation , and show that this can be solved in O(nk \log k) time.
Revised:  Finally, we consider a cumulative version of the maximum segmentation , and show that we can solve the problem in O(nk \log k) time.
Edit intent: clarity
Original: 3M IPv6 router interfaces from a single vantage point -- an order of magnitude more than produced by current state-of-the-art mapping systems that use hundreds of vantages .
Revised: 3M IPv6 router interface addresses from a single vantage point -- an order of magnitude more than produced by current state-of-the-art mapping systems that use hundreds of vantages .
Edit intent: clarity
Original: 3M IPv6 router interfaces from a single vantage point -- an order of magnitude more than produced by current state-of-the-art mapping systems that use hundreds of vantages .
Revised: 3M IPv6 router interfaces from a single vantage point .
Edit intent: clarity
Original:  Finally, we publicly share our prober implementation, synthesized target lists, and discovered IPv6 topology results.
Revised:  Finally, we share our prober implementation, synthesized target lists, and discovered IPv6 topology results.
Edit intent: clarity
Original: Motivation: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms, such as matrix completion (MC).
Revised:  The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms, such as matrix completion (MC).
Edit intent: coherence
Original:  By comparing several metrics on conformation evaluation between our results and the PDBdeposits , we conclude that our method is consistent with the popular used methods.
Revised:  By comparing several metrics in the conformation evaluation on our results with Protein Data Bank (PDB) structures , we conclude that our method is consistent with the popular used methods.
Edit intent: clarity
Original:  By comparing several metrics on conformation evaluation between our results and the PDBdeposits , we conclude that our method is consistent with the popular used methods.
Revised:  By comparing several metrics on conformation evaluation between our results and the PDBdeposits , we conclude that our method is consistent with the popularly used methods.
Edit intent: fluency
Original:  In particular, our results show higher validities in MPscore, Molprobity clash-score, Procheck dihedral angles G-factor than PDB models.
Revised:  In particular, our results show higher validities in Procheck dihedral angles G-factor than PDB models.
Edit intent: coherence
Original:  In the end, we compared our calculation results with PDB models by checking the structural similarity to X-ray crystallographic structure for a special dataset.
Revised:  In the end, we compared our calculation results with PDB structures by examining the structural similarity to X-ray crystallographic structure for a special dataset.
Edit intent: clarity
Original:  In the end, we compared our calculation results with PDB models by checking the structural similarity to X-ray crystallographic structure for a special dataset.
Revised:  In the end, we compared our calculation results with PDB models by checking the structural similarity to X-ray crystallographic structures in a special dataset.
Edit intent: clarity
Original:  The software and its MATLAB source codes are available by URL
Revised:  The software and its MATLAB source codes are available in URL
Edit intent: clarity
Original:  For a single cooperator ( or a single defector) and a network described by a regular graph the question of fixation can be addressed by a single parameter, the structure coefficient.
Revised:  For a single cooperator ( and a single defector) and a network described by a regular graph the question of fixation can be addressed by a single parameter, the structure coefficient.
Edit intent: fluency
Original:  As this quantity is generic to any regular graph, we may call it the generic structure coefficient.
Revised:  As this quantity is generic for any regular graph, we may call it the generic structure coefficient.
Edit intent: fluency
Original:  These structure coefficients, however, depend on the arrangement of cooperators and defectors which we  interpret as a configuration of the game.
Revised:  These structure coefficients, however, depend on the arrangement of cooperators and defectors which we may interpret as a configuration of the game.
Edit intent: clarity
Original:  Moreover, the coefficients are specific to a given interaction network modeled as regular graph, which is why we may call them specific structure coefficients.
Revised:  Moreover, the coefficients are specific for a given interaction network modeled as regular graph, which is why we may call them specific structure coefficients.
Edit intent: fluency
Original: This work studies the problem of learning under both large data and large feature space scenarios.
Revised: This work studies the problem of learning under both large datasets and large-dimensional feature space scenarios.
Edit intent: clarity
Original:  Through local cooperation, the agents are supposed to interact with each other to solve the inference problem and converge towards the global minimizer of the empirical risk.
Revised:  Through local cooperation, the agents are supposed to interact with each other to solve an inference problem and converge towards the global minimizer of the empirical risk.
Edit intent: fluency
Original:  Through local cooperation, the agents are supposed to interact with each other to solve the inference problem and converge towards the global minimizer of the empirical risk.
Revised:  Through local cooperation, the agents are supposed to interact with each other to solve the inference problem and converge towards the global minimizer of an empirical risk.
Edit intent: fluency
Original:  On the positive side, we show that in any graph G with edge costs and bounded treewidth, a popular matching of minimum cost can be found efficiently .
Revised:  On the positive side, we show that in any graph G with edge costs and bounded treewidth, a popular matching of minimum cost in a graph with edge costs and bounded treewidth .
Edit intent: clarity
Original:  We find that (i) top lists generally overestimate results compared to the general population by a significant margin, often even a magnitude, and (ii) some top lists have surprising change characteristics, causing high day-to-day fluctuation and leading to result instability.
Revised:  We find that (i) top lists generally overestimate results compared to the general population by a significant margin, often even an order of magnitude, and (ii) some top lists have surprising change characteristics, causing high day-to-day fluctuation and leading to result instability.
Edit intent: clarity
Original: SQL declaratively specifies what (not how) the desired output of a query is.
Revised: SQL declaratively specifies what the desired output of a query is.
Edit intent: clarity
Original:  We derive such data provenance for very rich SQL dialects \text{--- including recursion, windowed aggregates, and user-defined functions \text{--- at the fine-grained level of individual table cells.
Revised:  We derive such data provenance for very rich SQL dialects ( including recursion, windowed aggregates, and user-defined functions \text{--- at the fine-grained level of individual table cells.
Edit intent: fluency
Original:  We derive such data provenance for very rich SQL dialects \text{--- including recursion, windowed aggregates, and user-defined functions \text{--- at the fine-grained level of individual table cells.
Revised:  We derive such data provenance for very rich SQL dialects \text{--- including recursion, windowed aggregates, and user-defined functions ) at the fine-grained level of individual table cells.
Edit intent: fluency
Original:  In addition, we propose an improved variant of Difference Target Propagation, another algorithm that comes from the same family of algorithms as Local Representation Alignment.
Revised:  In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as Local Representation Alignment.
Edit intent: clarity
Original:  In this paper, we propose a simple learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery.
Revised:  In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery.
Edit intent: clarity
Original:  We compare our learning procedures to several other biologically-motivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation.
Revised:  We compare our procedures to several other biologically-motivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation.
Edit intent: clarity
Original:  In two benchmark datasets , we find that both of our proposed learning algorithms yield stable performance and strong generalization abilities in comparison to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
Revised:  In two benchmarks , we find that both of our proposed learning algorithms yield stable performance and strong generalization abilities in comparison to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
Edit intent: clarity
Original:  In two benchmark datasets , we find that both of our proposed learning algorithms yield stable performance and strong generalization abilities in comparison to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
Revised:  In two benchmark datasets , we find that both of our proposed algorithms yield stable performance and strong generalization abilities in comparison to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
Edit intent: clarity
Original:  In two benchmark datasets , we find that both of our proposed learning algorithms yield stable performance and strong generalization abilities in comparison to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
Revised:  In two benchmark datasets , we find that both of our proposed learning algorithms yield stable performance and strong generalization compared to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
Edit intent: clarity
Original: The von Neumann graph entropy (VNGE) facilitates the measure of information divergence and distance between graphs in a graph sequence and has successfully been applied to various network learning tasks .
Revised: The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence and has successfully been applied to various network learning tasks .
Edit intent: fluency
Original: The von Neumann graph entropy (VNGE) facilitates the measure of information divergence and distance between graphs in a graph sequence and has successfully been applied to various network learning tasks .
Revised: The von Neumann graph entropy (VNGE) facilitates the measure of information divergence and distance between graphs in a graph sequence . It has been successfully applied to various network learning tasks .
Edit intent: coherence
Original:  Albeit its effectiveness, it is computationally demanding by requiring the full eigenspectrum of the graph Laplacian matrix.
Revised:  Albeit its effectiveness, it is computationally demanding as it requires the full eigenspectrum of the graph Laplacian matrix.
Edit intent: coherence
Original:  In this paper, we propose a  Fast Incremental von Neumann Graph EntRopy (FINGER) framework , which approaches VNGE with a performance guarantee.
Revised:  In this paper, we propose a new computational framework, Fast Incremental von Neumann Graph EntRopy (FINGER) framework , which approaches VNGE with a performance guarantee.
Edit intent: clarity
Original:  In this paper, we propose a  Fast Incremental von Neumann Graph EntRopy (FINGER) framework , which approaches VNGE with a performance guarantee.
Revised:  In this paper, we propose a Fast Incremental von Neumann Graph EntRopy (FINGER) , which approaches VNGE with a performance guarantee.
Edit intent: clarity
Original:  We also show asymptotic consistency of FINGER to the exact VNGE, and derive its approximation error bounds.
Revised:  We also show asymptotic equivalence of FINGER to the exact VNGE, and derive its approximation error bounds.
Edit intent: clarity
Original:  Based on FINGER, we propose ultra-efficient algorithms for computing Jensen-Shannon distance between graphs.
Revised:  Based on FINGER, we propose efficient algorithms for computing Jensen-Shannon distance between graphs.
Edit intent: clarity
Original:  Our experimental results on different random graph models demonstrate the computational efficiency and the asymptotic consistency of FINGER.
Revised:  Our experimental results on different random graph models demonstrate the computational efficiency and the asymptotic equivalence of FINGER.
Edit intent: clarity
Original:  In addition, we also apply FINGER to two real-world applications and one synthesized  dataset, and corroborate its superior performance over seven baseline graph similarity methods.
Revised:  In addition, we apply FINGER to two real-world applications and one synthesized dataset, and corroborate its superior performance over seven baseline graph similarity methods.
Edit intent: coherence
Original:  In this context, steepest ascent or descent is often suggested as a biolog- ically implemented optimization algorithm.
Revised:  In this context, steepest ascent or descent is often suggested as a biologically implemented optimization algorithm.
Edit intent: fluency
Original: The idea that the brain functions in a way that minimizes certain costs pervades theoretical neuroscience.
Revised: The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience.
Edit intent: clarity
Original:  Since a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method are usually made in order to obtain predictions about physiological quantities.
Revised:  Since a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method need to be made to predict the dynamics of physiological quantities.
Edit intent: clarity
Original:  However, the  notion of a steepest direction depends on the choice of a Riemannian metric.
Revised:  However, the notion of a steepest direction depend on the choice of a Riemannian metric.
Edit intent: fluency
Original:  However, the  notion of a steepest direction depends on the choice of a Riemannian metric.
Revised:  However, the notion of a steepest direction depends on the choice of a metric.
Edit intent: clarity
Original:  Since this choice involves infinite degrees of freedom, the predictive power of models that are based on this principle can be called into question .
Revised:  Since this choice involves infinite degrees of freedom, the predictive power of models that are based on gradient descent must be called into question .
Edit intent: clarity
Original: In this paper, we analyze the convergence rates of Tikhonov regularization for solving the nonlinear ill-posed problems by using the Holder stability estimatesas the smoothness condition.
Revised: In this paper, we analyze the convergence rates of well known Tikhonov regularization scheme for solving the nonlinear ill-posed problems by using the Holder stability estimatesas the smoothness condition.
Edit intent: clarity
Original:  The first approach is the standard one which is to obtain the convergence rates in terms of Bregman distance and the second one is to obtain the convergence rates in weaker norms.
Revised:  The first approach is the standard one via Bregman distances and the second one is to obtain the convergence rates in weaker norms.
Edit intent: clarity