wanyu/IteraTeR_v2 huggingface dataset:
Original:  Finally, we discuss the potential of Komondor for simulating complex environments -- even with machine learning support -- in next-generation WLANs by easily developing new user-defined modules of code .
Revised:  Finally, we discuss the potential of Komondor for simulating complex environments -- even with machine learning support -- in next-generation WLANs by easily developing new user-defined modules of code 
Edit intent: fluency
Original:  [3] However, many of the attempts to model human behavior with quantum mechanics con ate the identity of a particle with its own wavefunction, which is incorrect and invalidates the analogy.
Revised:  [3] However, many of the attempts to model human behavior with quantum mechanics conflate the identity of a particle with its own wavefunction, which is incorrect and invalidates the analogy.
Edit intent: fluency
Original: We provide an approach to utilize topological priors to reconstruct the surface of a point scan.
Revised: We present an approach to utilize topological priors to reconstruct the surface of a point scan.
Edit intent: clarity
Original:  This novel topology-aware technique is useful to weed out topological noise from point scans, apart from capturing certain nuanced properties of the underlying shape , that could otherwise be lost while performing direct surface reconstruction.
Revised:  This novel topology-aware technique is useful to weed out topological noise from point scans, apart from capturing certain nuanced properties of the underlying shape which could otherwise be lost while performing direct surface reconstruction.
Edit intent: fluency
Original:  This novel topology-aware technique is useful to weed out topological noise from point scans, apart from capturing certain nuanced properties of the underlying shape , that could otherwise be lost while performing direct surface reconstruction.
Revised:  This novel topology-aware technique is useful to weed out topological noise from point scans, apart from capturing certain nuanced properties of the underlying shape , that could otherwise be lost while performing surface reconstruction.
Edit intent: clarity
Original:  We showcase results reconstructing shapes with multiple potential topologies, compare to other classical surface construction techniques and show completion of medical and real scan data.
Revised:  We showcase results reconstructing shapes with multiple potential topologies, compare to other classical surface construction techniques , and show the completion of real scan data.
Edit intent: clarity
Original:  We present empirical results from several domains, showing that X* outperforms existing state-of-the-art joint planners for sparse anytime multi-agent planning with optimality convergence .
Revised:  We present empirical results from several domains, showing that X* outperforms state-of-the-art joint planners for sparse anytime multi-agent planning with optimality convergence .
Edit intent: clarity
Original:  \% Using these insights, we present two algorithmic contributions: 1) a Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generates valid paths with suboptimality estimates and generates optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner.
Revised:  Using these insights, we present two algorithmic contributions: 1) a Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generates valid paths with suboptimality estimates and generates optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner.
Edit intent: coherence
Original:  \% Using these insights, we present two algorithmic contributions: 1) a Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generates valid paths with suboptimality estimates and generates optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner.
Revised:  \% Using these insights, we present two algorithmic contributions: 1) the Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generates valid paths with suboptimality estimates and generates optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner.
Edit intent: fluency
Original:  \% Using these insights, we present two algorithmic contributions: 1) a Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generates valid paths with suboptimality estimates and generates optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner.
Revised:  \% Using these insights, we present two algorithmic contributions: 1) a Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generate valid paths with suboptimality estimates and generates optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner.
Edit intent: fluency
Original:  \% Using these insights, we present two algorithmic contributions: 1) a Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generates valid paths with suboptimality estimates and generates optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner.
Revised:  \% Using these insights, we present two algorithmic contributions: 1) a Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generates valid paths with suboptimality estimates and generate optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner.
Edit intent: fluency
Original: Real-world multi-agent systems such as warehouse robots operate under significant time constraints -- in such settings, rather than spend time solving for optimal paths, it is instead preferable to find valid collision-free paths quickly, even if suboptimal, and given additional time, to iteratively refine such paths to improve their cost.
Revised: Real-world multi-agent systems such as warehouse robots operate under significant time constraints -- in such settings, rather than spending significant amounts of time solving for optimal paths, it is instead preferable to find valid collision-free paths quickly, even if suboptimal, and given additional time, to iteratively refine such paths to improve their cost.
Edit intent: clarity
Original:  The task generates certain amount of data at a sender periodically , and streams them to a receiver using multiple paths over a multi-hop network  .
Revised:  The task generates certain amount of data at a sender periodically , and streams them to a receiver over a multi-hop network .
Edit intent: clarity
Original:  The task generates certain amount of data at a sender periodically , and streams them to a receiver using multiple paths over a multi-hop network  .
Revised:  The task generates certain amount of data at a sender periodically , and streams them to a receiver using multiple paths over a multi-hop network using multiple paths .
Edit intent: coherence
Original:  Our objective is to minimize either the peakAoI or the average AoI , by jointly optimizing the multi-path routing strategy and the task activation length.
Revised:  Our objective is to minimize either the peakAoI or the average AoI , by jointly optimizing throughput and multi-path routing strategy and the task activation length.
Edit intent: clarity
Original: We consider the scenario where a sender periodically sends a batch of data to a receiver over a multi-hop network  using multiple paths.
Revised: We consider the scenario where a sender periodically sends a batch of data to a receiver over a multi-hop network , possibly using multiple paths.
Edit intent: clarity
Original:  Our objective is to minimize peak/average Age-of-Information (AoI) subject to both a minimum and a maximum throughput requirement, by jointly optimizing throughput and multi-path routing strategy .
Revised:  Our objective is to minimize peak/average Age-of-Information (AoI) subject to throughput requirements .
Edit intent: clarity
Original:  The consideration of batch generation and multi-path communication differentiate us from existing studies.
Revised:  The consideration of batch generation and multi-path communication differentiates us from existing studies.
Edit intent: fluency
Original:  First, we show that our AoI minimization problems are NP-hard, but only in the weak sense  as we develop an optimal algorithm with a pseudo-polynomial time complexity.
Revised:  We first show that our AoI minimization problems are NP-hard, but only in the weak sense as we develop an optimal algorithm with a pseudo-polynomial time complexity.
Edit intent: coherence
Original:  First, we show that our AoI minimization problems are NP-hard, but only in the weak sense  as we develop an optimal algorithm with a pseudo-polynomial time complexity.
Revised:  First, we show that our AoI minimization problems are NP-hard, but only in the weak sense , as we develop an optimal algorithm with a pseudo-polynomial time complexity.
Edit intent: fluency
Original:  Next we show that minimizing AoI and minimizing maximum delay for a batch of data are " largely " compatible.
Revised:  Next we prove that minimizing AoI and minimizing maximum delay for a batch of data are " largely " compatible.
Edit intent: coherence
Original:  We leverage this understanding to develop a framework to adapt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to develop a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \beta, where \beta is a constant determined by input minimum and maximum throughput requirement .
Revised:  We leverage this understanding to develop a framework to adapt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to derive a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \beta, where \beta is a constant determined by input minimum and maximum throughput requirement .
Edit intent: clarity
Original:  We leverage this understanding to develop a framework to adapt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to develop a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \beta, where \beta is a constant determined by input minimum and maximum throughput requirement .
Revised:  We leverage this understanding to develop a framework to adapt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to develop a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \beta, where \beta is a constant determined by throughput requirements .
Edit intent: clarity
Original:  Extensive simulations that compare our approximation framework and our optimal algorithm over various network topologies validate the effectiveness of our proposed approaches.
Revised:  Extensive simulations over various network topologies validate the effectiveness of our proposed approaches.
Edit intent: clarity
Original:  Extensive simulations that compare our approximation framework and our optimal algorithm over various network topologies validate the effectiveness of our proposed approaches.
Revised:  Extensive simulations that compare our approximation framework and our optimal algorithm over various network topologies validate the effectiveness of our approaches.
Edit intent: clarity
Original:  The consideration of batch generation and multi-path communication differentiates us from existing studies .
Revised:  The consideration of batch generation and multi-path communication differentiates our AoI study from existing ones .
Edit intent: clarity
Original:  Next we prove that minimizing AoI and minimizing maximum delay for a batch of data are "roughly" equivalent, in the sense that any optimal solution of minimizing maximum delay is an approximate solution of minimizing AoI with bounded optimality loss.
Revised:  We then prove that minimizing AoI and minimizing maximum delay for a batch of data are "roughly" equivalent, in the sense that any optimal solution of minimizing maximum delay is an approximate solution of minimizing AoI with bounded optimality loss.
Edit intent: coherence
Original:  Next we prove that minimizing AoI and minimizing maximum delay for a batch of data are "roughly" equivalent, in the sense that any optimal solution of minimizing maximum delay is an approximate solution of minimizing AoI with bounded optimality loss.
Revised:  Next we prove that minimizing AoI and minimizing maximum delay are "roughly" equivalent, in the sense that any optimal solution of minimizing maximum delay is an approximate solution of minimizing AoI with bounded optimality loss.
Edit intent: clarity
Original:  Next we prove that minimizing AoI and minimizing maximum delay for a batch of data are "roughly" equivalent, in the sense that any optimal solution of minimizing maximum delay is an approximate solution of minimizing AoI with bounded optimality loss.
Revised:  Next we prove that minimizing AoI and minimizing maximum delay for a batch of data are "roughly" equivalent, in the sense that any optimal solution of the latter is an approximate solution of minimizing AoI with bounded optimality loss.
Edit intent: clarity
Original:  Next we prove that minimizing AoI and minimizing maximum delay for a batch of data are "roughly" equivalent, in the sense that any optimal solution of minimizing maximum delay is an approximate solution of minimizing AoI with bounded optimality loss.
Revised:  Next we prove that minimizing AoI and minimizing maximum delay for a batch of data are "roughly" equivalent, in the sense that any optimal solution of minimizing maximum delay is an approximate solution of the former with bounded optimality loss.
Edit intent: clarity
Original:  We leverage this understanding and design a general approximation framework for our AoI minimization problems.
Revised:  We leverage this understanding to design a general approximation framework for our AoI minimization problems.
Edit intent: coherence
Original:  We leverage this understanding and design a general approximation framework for our AoI minimization problems.
Revised:  We leverage this understanding and design a general approximation framework for our problems.
Edit intent: clarity
Original:  It can adopt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to derive a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \mathsf{c is a constant determined by throughput requirements.
Revised:  It can build upon any \alpha-approximation algorithm of the existing maximum delay minimization problem to derive a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \mathsf{c is a constant determined by throughput requirements.
Edit intent: clarity
Original:  It can adopt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to derive a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \mathsf{c is a constant determined by throughput requirements.
Revised:  It can adopt any polynomial-time \alpha-approximation algorithm of the maximum delay minimization problem to derive a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \mathsf{c is a constant determined by throughput requirements.
Edit intent: clarity
Original:  It can adopt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to derive a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \mathsf{c is a constant determined by throughput requirements.
Revised:  It can adopt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem , to construct an ( \alpha+ \mathsf{c is a constant determined by throughput requirements.
Edit intent: clarity
Original:  It can adopt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to derive a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \mathsf{c is a constant determined by throughput requirements.
Revised:  It can adopt any polynomial-time \alpha-approximation algorithm of the existing maximum delay minimization problem to derive a polynomial-time algorithm for our AoI minimization problems with an approximation ratio of \alpha+ \mathsf{c is a constant depending on the throughput requirements.
Edit intent: clarity
Original:  Extensive simulations over various network topologies validate the effectiveness of our approaches .
Revised:  Extensive simulations over various network topologies validate the effectiveness of our approach .
Edit intent: fluency
Original:  In this paper we present an extension of the Dynamic-Monge-Kantorovich model that considers a time derivative of the transport density that grows as a power law of the transport flux counterbalanced by a linear decay term that maintains the density bounded.
Revised:  In this paper we present an extension of this model that considers a time derivative of the transport density that grows as a power law of the transport flux counterbalanced by a linear decay term that maintains the density bounded.
Edit intent: clarity
Original:  In this paper we present an extension of the Dynamic-Monge-Kantorovich model that considers a time derivative of the transport density that grows as a power law of the transport flux counterbalanced by a linear decay term that maintains the density bounded.
Revised:  In this paper we present an extension of the Dynamic-Monge-Kantorovich model that considers a time derivative of the conductivity that grows as a power law of the transport flux counterbalanced by a linear decay term that maintains the density bounded.
Edit intent: clarity
Original:  In all situations, we derive a numerical discretization of our model that is accurate, efficient, and robust for a wide range of scenarios.
Revised:  In all situations, we derive a numerical discretization of the proposed model that is accurate, efficient, and robust for a wide range of scenarios.
Edit intent: clarity
Original:  Using ad-hoc linear solver strategies, we are able to obtain numerical approximations that show highly irregular and fractal-like formations  .
Revised:  For \beta>1 the numerical model is able to reproduce highly irregular and fractal-like formations .
Edit intent: clarity
Original:  Current semantic models trained on click-through data treat all historical clicked query-document pairs as equivalent.
Revised:  Current semantic models trained on click-through data which treats each clicked query-document pairs as equivalent.
Edit intent: clarity
Original:  Current semantic models trained on click-through data treat all historical clicked query-document pairs as equivalent.
Revised:  Current semantic models trained on click-through data treat all historical clicked query-document pair as equivalent.
Edit intent: fluency
Original:  It argues that this approach is sub-optimal because of the noisy and long tail nature of click-through data (especially for sponsored search).
Revised:  It argues that this approach is sub-optimal because of its noisy and long-tail nature (especially for sponsored search).
Edit intent: clarity
Original:  Experimental evaluation is done on Bing sponsored search and  Amazon product recommendation .
Revised:  Experimental evaluation is done on Bing sponsored search and also on Amazon product recommendation .
Edit intent: clarity
Original:  Online A/B testing on live search engine traffic shows 11.8\\%DIF < higher click-through rateand 8.2%DIFDELCMD < \\%%% %DIF < lower bounce rate as compared to the %DIF > higher CTR) and as well as improvement in quality (8.2\\% lower  unweighted model.
Revised:  Online A/B testing on live search engine traffic showed improvements in clicks ( 11.8\\%DIF < higher click-through rateand 8.2%DIFDELCMD < \\%%% %DIF < lower bounce rate as compared to the %DIF > higher CTR) and as well as improvement in quality (8.2\\% lower unweighted model.
Edit intent: clarity
Original:  Online A/B testing on live search engine traffic shows 11.8\\%DIF < higher click-through rateand 8.2%DIFDELCMD < \\%%% %DIF < lower bounce rate as compared to the %DIF > higher CTR) and as well as improvement in quality (8.2\\% lower  unweighted model.
Revised:  Online A/B testing on live search engine traffic shows 11.8\\%DIF < %DIFDELCMD < \\%%% %DIF < lower bounce rate as compared to the %DIF > higher CTR) and as well as improvement in quality (8.2\\% lower unweighted model.
Edit intent: clarity
Original:  Online A/B testing on live search engine traffic shows 11.8\\%DIF < higher click-through rateand 8.2%DIFDELCMD < \\%%% %DIF < lower bounce rate as compared to the %DIF > higher CTR) and as well as improvement in quality (8.2\\% lower  unweighted model.
Revised:  Online A/B testing on live search engine traffic shows 11.8\\%DIF < higher click-through rateand 8.2%DIFDELCMD < \\%%% %DIF < lower bounce rate as compared to the %DIF > higher CTR) and as well as improvement in quality (8.2\\% lower bounce rate) when compared to the unweighted model.
Edit intent: clarity
Original:  Second, the weighted model trained on amazon co-purchased product recommendation dataset showed improvement of 0.27 at NDCG@1 and 0.25 at NDCG@3 as compared to the unweighted model .
Revised:  Second, the weighted model trained on amazon co-purchased product .
Edit intent: clarity
Original:  The result is for certain values of the fractional parameter \alpha, the transformed equation presents a smaller error and a larger rate of convergence , and for others,the un-transformed equation performs better.
Revised:  The main method derived in this paper has a rate of convergence , and for others,the un-transformed equation performs better.
Edit intent: clarity
Original: In this paper, we propose a heuristic recommendation system for interactive news, called the graph-based broad behavior-aware network (G-BBAN).
Revised: In this paper, we propose a heuristic recommendation system for interactive news, called the graph-based behavior-aware network (G-BBAN).
Edit intent: clarity
Original:  First, we build a structured user-dependent interaction behavior graph for multi-level and multi-category data as a preprocessing step.
Revised:  First, we build an interaction behavior graph for multi-level and multi-category data as a preprocessing step.
Edit intent: clarity
Original:  According to the definition of core and coritivity , this user-dependent feature provides useful insights into the concentration degree of his/her interestsand affects the trade-off between accuracy and diversity of the personalized recommendation . Last, we represent item (news ) information by entity semantics and environment semantics;
Revised:  According to the definition of core and coritivity , this user-dependent feature provides useful insights into the concentration degree of user's interests. These features affect the trade-off between accuracy and diversity of the personalized recommendation . Last, we represent item (news ) information by entity semantics and environment semantics;
Edit intent: clarity
Original: Let X and Y be domains of R^n equipped with  probability measures \mu and \nu , respectively .
Revised: Let X and Y be domains of R^n equipped with probability measures \mu and \nu .
Edit intent: clarity
Original:  We consider the problem of transporting \mu to \nu in a way that minimizes a cost c: X \times Y \to R of the\textit{  form c(x,y)= \Psi(x-y) for some convex function \Psi .
Revised:  We consider the problem of optimal transport from \mu to \nu in a way that minimizes a cost c: X \times Y \to R of the\textit{ form c(x,y)= \Psi(x-y) for some convex function \Psi .
Edit intent: clarity