wanyu/IteraTeR_v2 huggingface dataset:
Original:  While standard transfer learning assumes that the representations learned in one task will be useful for another task, anti-transfer learning avoids learning representations that have been learned for a different task, which is not relevant and potentially misleading for the new taskand should be ignored.
Revised:  While transfer learning assumes that the representations learned in one task will be useful for another task, anti-transfer learning avoids learning representations that have been learned for a different task, which is not relevant and potentially misleading for the new taskand should be ignored.
Edit intent: clarity
Original:  While standard transfer learning assumes that the representations learned in one task will be useful for another task, anti-transfer learning avoids learning representations that have been learned for a different task, which is not relevant and potentially misleading for the new taskand should be ignored.
Revised:  While standard transfer learning assumes that the learning process for a target task will benefit from re-using representations learned for another task, anti-transfer learning avoids learning representations that have been learned for a different task, which is not relevant and potentially misleading for the new taskand should be ignored.
Edit intent: clarity
Original:  While standard transfer learning assumes that the representations learned in one task will be useful for another task, anti-transfer learning avoids learning representations that have been learned for a different task, which is not relevant and potentially misleading for the new taskand should be ignored.
Revised:  While standard transfer learning assumes that the representations learned in one task will be useful for another task, anti-transfer avoids the learning of representations that have been learned for a different task, which is not relevant and potentially misleading for the new taskand should be ignored.
Edit intent: clarity
Original: We implemented anti-transfer learning with different similarity metrics and aggregation functions .
Revised: We implemented anti-transfer for convolutional neural networks in different configurations with several similarity metrics and aggregation functions .
Edit intent: clarity
Original: This paper aims to derive a quantitative model to evaluate the impact of information flow on the effectiveness of delivering federal assistance to the community .
Revised: This paper aims to derive a quantitative model to evaluate the impact of information flow on the effectiveness of disaster response .
Edit intent: clarity
Original:  Based on the model, the probability of community satisfaction is computed to reflect the effectiveness of the disaster responseprocess.
Revised:  Using the proposed model, the probability of community satisfaction is computed to reflect the effectiveness of the disaster responseprocess.
Edit intent: clarity
Original:  Feigenbaum et al.  showed that it is not always possible to provide perfect resilience and showed how to tolerate a single failure in any network .
Revised:  Feigenbaum et al. showed that it is not always possible to provide perfect resilience .
Edit intent: coherence
Original:  On the positive side, we show that graph families which are closed under the subdivision of links, can allow for simple and efficient failover algorithms which simply skip failed links.
Revised:  On the positive side, we show that graph families closed under link subdivision allow for simple and efficient failover algorithms which simply skip failed links.
Edit intent: clarity
Original:  Due to their ability to adjust the channels through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks.
Revised:  Due to their ability to adjust the behavior of interacting electromagnetic (EM) waves through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks.
Edit intent: clarity
Original:  From a system level perspective, LIS have also been proven to be a low cost , green, sustainable, and energy-efficient 6G solution .
Revised:  From a system level perspective, LIS have also been proven to be a low-cost , green, sustainable, and energy-efficient 6G solution .
Edit intent: fluency
Original:  From a system level perspective, LIS have also been proven to be a low cost , green, sustainable, and energy-efficient 6G solution .
Revised:  From a system level perspective, LIS have also been proven to be a low cost , green, sustainable, and energy-efficient solution for 6G systems .
Edit intent: clarity
Original:  Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid RF / VLC systems, health considerations, and localization.
Revised:  Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid radio frequency / VLC systems, health considerations, and localization.
Edit intent: fluency
Original:  Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid RF / VLC systems, health considerations, and localization.
Revised:  Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid RF / visible light communication (RF-VLC) systems, health considerations, and localization.
Edit intent: clarity
Original:  In this work, we propose a Scalable Actor Critic framework that applies in settings where the dependencies are non-local and  provide a finite-time error bound that shows how the convergence rate depends on the depth of the dependencies in the network.
Revised:  In this work, we propose a Scalable Actor Critic framework that applies in settings where the dependencies can be non-local and provide a finite-time error bound that shows how the convergence rate depends on the depth of the dependencies in the network.
Edit intent: clarity
Original:  In this work, we propose a Scalable Actor Critic framework that applies in settings where the dependencies are non-local and  provide a finite-time error bound that shows how the convergence rate depends on the depth of the dependencies in the network.
Revised:  In this work, we propose a Scalable Actor Critic framework that applies in settings where the dependencies are non-local and provide a finite-time error bound that shows how the convergence rate depends on the speed of information spread in the network.
Edit intent: clarity
Original:  Additionally, as a byproduct of our analysis, we obtain novel finite-time convergence results for a general stochastic approximation scheme and for temporal difference learning with state aggregation that apply beyond the setting of RL in networked systems.
Revised:  Additionally, as a byproduct of our analysis, we obtain novel finite-time convergence results for a general stochastic approximation scheme and for temporal difference learning with state aggregation , which apply beyond the setting of RL in networked systems.
Edit intent: fluency
Original:  Strategic additional Peerlock-lite deployment at all large ISPs (fewer than 1 percent of all networks), in tandem with Peerlock within the peering clique as deployed, completely mitigates 80\% of simulated Tier 1 route leaks.
Revised:  Strategic additional Peerlock-lite deployment at all large ISPs (fewer than 1 \% of all networks), in tandem with Peerlock within the peering clique as deployed, completely mitigates 80\% of simulated Tier 1 route leaks.
Edit intent: fluency
Original: Reaction-diffusion processes across layered media arise in several scientific domains such as pattern-forming E. coli on agar substrates, epidermal-mesenchymal coupling in development, and symmetry-breaking in cell polarisation.
Revised: Reaction-diffusion processes across layered media arise in several scientific domains such as pattern-forming E. coli on agar substrates, epidermal-mesenchymal coupling in development, and symmetry-breaking in cell polarisation.
Edit intent: fluency
Original: Real-time video inference on compute-limited edge devices like mobile phones and drones is challenging due to the high computation cost of Deep Neural Network models.
Revised: Real-time video inference on edge devices like mobile phones and drones is challenging due to the high computation cost of Deep Neural Network models.
Edit intent: clarity
Original:  The key idea in AMS is to use online learning to continually adapt a lightweight model running on an edge deviceto boost its performance on the video scenes in real-time.
Revised:  AMS uses a remote server to continually train and adapt a small model running on an edge deviceto boost its performance on the video scenes in real-time.
Edit intent: clarity
Original:  The key idea in AMS is to use online learning to continually adapt a lightweight model running on an edge deviceto boost its performance on the video scenes in real-time.
Revised:  The key idea in AMS is to use online learning to continually adapt a lightweight model running on the edge device, boosting its performance on the video scenes in real-time.
Edit intent: fluency
Original:  We discuss the challenges of online learning for video and present a practical design that takes into account the edge device, cloud server, and network bandwidth resource limitations .
Revised:  We discuss the challenges of online learning for video and adaptive sampling of training frames at edge devices .
Edit intent: clarity
Original:  On the task of video semantic segmentation, our experimental results show 5.1 --17 .0 percent mean Intersection-over-Union improvement compared to a pre-trained model on several real-world videos .
Revised:  On the task of video semantic segmentation, our experimental results show 5.1 --17 .0 percent mean Intersection-over-Union improvement compared to a pre-trained model across several video datasets .
Edit intent: clarity
Original:  This proof holds for deep homogeneous networks -- a broad class of networks allowing for ReLU, max pooling , linear, and convolutional layers -- and we additionally provide empirical support not just close to the theory (e.g., the AlexNet), but also on non-homogeneous networks (e.g., the ResNet ).
Revised:  This proof holds for deep homogeneous networks -- a broad class of networks allowing for ReLU, max-pooling , linear, and convolutional layers -- and we additionally provide empirical support not just close to the theory (e.g., the AlexNet), but also on non-homogeneous networks (e.g., the ResNet ).
Edit intent: fluency
Original:  If the network further has locally Lipschitz gradients, we show that these gradients  converge in direction, and asymptotically align with the gradient flow path, with consequences on margin maximization  .
Revised:  If the network further has locally Lipschitz gradients, we show that these gradients also converge in direction, and asymptotically align with the gradient flow path, with consequences on margin maximization .
Edit intent: clarity
Original:  The proof proceeds by developing a theory of unbounded nonsmooth Kurdyka-Lojasiewicz } inequalities for functions definable in an o-minimal structure, and is also applicable outside deep learning.
Revised:  The proof proceeds by developing a theory of unbounded nonsmooth Kurdyka- } inequalities for functions definable in an o-minimal structure, and is also applicable outside deep learning.
Edit intent: clarity
Original:  In view of these limitations, we propose a novel and more reliable iterative reweighting strategy that incorporates information from higher-order neighborhoods by exploiting the graph connection Laplacian.
Revised:  In view of these limitations, we suggest a novel and more reliable iterative reweighting strategy that incorporates information from higher-order neighborhoods by exploiting the graph connection Laplacian.
Edit intent: fluency
Original:  To facilitate our analysis, we introduce a second-order ordinary differential equation (ODE) and demonstrate that this ODE is the limiting case of PAGD as the step size tends to zero.
Revised:  We introduce a second-order ordinary differential equation (ODE) and demonstrate that this ODE is the limiting case of PAGD as the step size tends to zero.
Edit intent: clarity
Original:  Assuming this restriction, an exponential rate of convergence of the PAGD sequence is demonstrated, by mimicking the convergence of the solution to the ODE via energy methods.
Revised:  Assuming this restriction, an exponential rate of convergence of the PAGD scheme is derived by mimicking the convergence of the solution to the ODE via energy methods.
Edit intent: clarity
Original:  Application of the PAGD method is made in the context of solving certain nonlinear elliptic PDE using pseudo-spectral methods  , and several numerical experiments are conducted.
Revised:  Application of the PAGD method to numerical PDEs are demonstrated with certain nonlinear elliptic PDE using pseudo-spectral methods , and several numerical experiments are conducted.
Edit intent: clarity
Original:  Application of the PAGD method is made in the context of solving certain nonlinear elliptic PDE using pseudo-spectral methods  , and several numerical experiments are conducted.
Revised:  Application of the PAGD method is made in the context of solving certain nonlinear elliptic PDEs using pseudo-spectral methods , and several numerical experiments are conducted.
Edit intent: fluency
Original:  For many classes of utility functions, equilibria are captured by convex programs.
Revised:  For many classes of utility functions, equilibria can be captured by convex programs.
Edit intent: clarity
Original:  We develop simple first-order methods that are suitable for solving these  programs for large-scale markets.
Revised:  We develop simple first-order methods suitable for solving these programs for large-scale markets.
Edit intent: coherence
Original:  Using structural properties of a market equilibrium under each utility class, we show that the corresponding convex programs can be reformulated as optimization of a structured smooth convex function over a polyhedral set, for which projected gradient achieves linear convergence.
Revised:  Using structural properties of market equilibria under each utility class, we show that the corresponding convex programs can be reformulated as optimization of a structured smooth convex function over a polyhedral set, for which projected gradient achieves linear convergence.
Edit intent: clarity
Original:  We then show that proximal gradient (a generalization of projected gradient) with a practical version of linesearch achieves linear convergence under the Proximal-PL condition  .
Revised:  We then show that proximal gradient (a generalization of projected gradient) with a practical linesearch scheme achieves linear convergence under the Proximal-PL condition .
Edit intent: clarity
Original:  For quasilinear utilities, we show that Mirror Descent applied to a specific convex program achieves sublinear last-iterate convergence and recovers the Proportional Response dynamics, an elegant and efficient algorithm for computing market equilibrium under linear utilities.
Revised:  For quasilinear utilities, we show that Mirror Descent applied to a new convex program achieves sublinear last-iterate convergence and recovers the Proportional Response dynamics, an elegant and efficient algorithm for computing market equilibrium under linear utilities.
Edit intent: clarity
Original:  For quasilinear utilities, we show that Mirror Descent applied to a specific convex program achieves sublinear last-iterate convergence and recovers the Proportional Response dynamics, an elegant and efficient algorithm for computing market equilibrium under linear utilities.
Revised:  For quasilinear utilities, we show that Mirror Descent applied to a specific convex program achieves sublinear last-iterate convergence and yields a form of Proportional Response dynamics, an elegant and efficient algorithm for computing market equilibrium under linear utilities.
Edit intent: clarity
Original:  Numerical experiments show that proportional response is highly efficient for computing an approximate solution , while projected gradient with linesearch can be much faster when higher accuracy is required .
Revised:  Numerical experiments show that proportional response is highly efficient for computing an approximate solution , while projected gradient with linesearch can be much faster when higher-accuracy solutions are needed .
Edit intent: fluency
Original:  Consequently, researchers have to spend an unnecessarily long time implementing their simulation and have to compromise either on model resolution or system size.
Revised:  Results:
Edit intent: clarity
Original:  We present a novel simulation platform called BioDynaMo that alleviates both of these problems researchers face in computer simulation of complex biological systems . BioDynaMo features a general-purpose and high-performance simulation engine.
Revised:  We present a novel simulation platform called BioDynaMo that alleviates both of these problems . BioDynaMo features a general-purpose and high-performance simulation engine.
Edit intent: clarity
Original:  An example of a polynomial matrix which exhibits all relevant structural features is considered to illustrate the main mathematical concepts and the capabilities of implemented tools.
Revised:  An example of a polynomial matrix which exhibits all relevant structural features , are considered to illustrate the main mathematical concepts and the capabilities of implemented tools.
Edit intent: fluency
Original:  As a result, commonly used supervised learning techniques suffer severe performance deterioration when attempting to detect behaviors not observed in the training data.
Revised:  As a result, supervised learning techniques suffer severe performance deterioration when attempting to detect behaviors not observed in the training data.
Edit intent: clarity
Original:  The ensemble of specialized classifiers (ESC) can better generalize, leading to an average improvement of 56 \% in F1 score for \\%DIF > in F1 score for unseen accounts across datasets.
Revised:  The ensemble of specialized classifiers (ESC) can better generalize, leading to an average improvement of 56 \\%DIF > in F1 score for unseen accounts across datasets.
Edit intent: fluency
Original:  We are deploying ESC in the newest version of Botometer, a popular tool to detect social bots in the wild  .
Revised:  We deployed ESC in the newest version of Botometer, a popular tool to detect social bots in the wild .
Edit intent: clarity
Original: We introduce a new mathematical model of multi-agent reinforcement learning, the Multi-Agent Informational Learning Process or "MAILP" model.
Revised: We introduce a new mathematical model of multi-agent reinforcement learning, the Multi-Agent Informational Learning Processor "MAILP" model.
Edit intent: clarity
Original:  The model is based on the notion that agents have policies for a certain amount of information, models how this information iteratively evolves and propagates through manyagents .
Revised:  The model is based on the notion that agents have policies for a certain amount of information, models how this information iteratively evolves and propagates through many agents .
Edit intent: fluency
Original:  We find that uniformly increasing or decreasing the pitch with FastPitch generates speech that resembles the voluntary modulation of voice.
Revised:  We find that uniformly increasing or decreasing pitch with FastPitch generates speech that resembles the voluntary modulation of voice.
Edit intent: fluency
Original:  Conditioning on frequency contours improves the  quality of synthesized speech, making it comparable to state-of-the-art.
Revised:  Conditioning on frequency contours improves the overall quality of synthesized speech, making it comparable to state-of-the-art.
Edit intent: clarity
Original: We develop a framework to leverage the elegant "worst average-case" idea in smoothed complexity analysis to social choice, motivated by modern applications of social choice powered by AI and ML. Using our framework, we characterize the smoothed likelihood of some fundamental paradoxes and impossibility theorems as the number of agents increases.
Revised: We develop a framework that leverages the smoothed complexity analysis to social choice, motivated by modern applications of social choice powered by AI and ML. Using our framework, we characterize the smoothed likelihood of some fundamental paradoxes and impossibility theorems as the number of agents increases.
Edit intent: coherence
Original:  Our results illustrate the smoothed possibility of social choice---even though the paradox and the impossibility theorem hold in the worst case, they may not be a big concern in practice in certain natural settings .
Revised:  Our results illustrate the smoothed possibility of social choice---even though the paradox and the impossibility theorem hold in the worst case, they may not be a big concern in practice .
Edit intent: clarity
Original:  Our results illustrate the smoothed possibility of social choice---even though the paradox and the impossibility theorem hold in the worst case, they may not be a big concern in practice.
Revised:  Our results illustrate the smoothed possibility of social choice -- even though the paradox and the impossibility theorem hold in the worst case, they may not be a big concern in practice.
Edit intent: fluency
Original:  Many successful experimental results have been recently achieved using the empirical straight-through estimation approach.
Revised:  Many successful experimental results have been achieved with empirical straight-through estimation approach.
Edit intent: clarity
Original:  We put such methods on a solid basis by obtaining them as viable approximations in the stochastic binary network (SBN) model with Bernoulli weights.
Revised:  At the same time, ST methods can be truly derived as estimators in the stochastic binary network (SBN) model with Bernoulli weights.
Edit intent: clarity
Original: Decision-making about pandemic mitigation often relies upon mathematical modelling.
Revised: Decision-making about pandemic mitigation often relies upon simulation modelling.
Edit intent: clarity
Original:  In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect communities in contact networks.
Revised:  In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks.
Edit intent: clarity
Original:  Under verifiable conditions on the model and performance measure, we establish strong consistency and a (martingale) central limit theorem for  resulting performance estimates .
Revised:  Under verifiable conditions on the model and performance measure, we establish strong consistency and a (martingale) central limit theorem for the resulting performance estimates .
Edit intent: fluency
Original:  Experiments on seven datasets demonstrate an average mean-squared error superior to state-of-the-art samplers on fixed label budgets.
Revised:  Experiments on seven datasets demonstrate an average MSE superior to state-of-the-art samplers on fixed label budgets.
Edit intent: clarity
Original:  Experiments on seven datasets demonstrate an average mean-squared error superior to state-of-the-art samplers on fixed label budgets.
Revised:  Experiments on seven datasets demonstrate an average mean-squared error superior to state-of-the-art on fixed label budgets.
Edit intent: clarity
Original: Quantitative mechanistic models based on reaction networks with stochastic chemical kinetics can help elucidate fundamental biological process where random fluctuations are relevant , such as in single cells.
Revised: Stochastic reaction networks are a fundamental model to describe interactions between species where random fluctuations are relevant , such as in single cells.
Edit intent: clarity