wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with  lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Revised:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" is considered. This problem has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Edit intent: coherence
Original:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with  lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Revised:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects the k best subsets of objects with lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Edit intent: clarity
Original:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with  lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Revised:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with the lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Edit intent: fluency
Original:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with  lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Revised:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with lowest (or highest) overall scores are required. Given an input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Edit intent: clarity
Original:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with  lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Revised:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with lowest (or highest) overall scores . Given any input set R of n real numbers and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Edit intent: fluency
Original:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with  lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets (top-k Subset Sums) of R , whose sum of elements is minimized.
Revised:  In this article, we consider its top-k variation - the " Top-k Subset Sums problem" that has wide application in recommendation systems, where instead of k best objects they require k best subsets of objects with lowest (or highest) overall scores . Given any input set R of n real numbers , and a positive integer k, our target is to generate the k best subsets of R such that the sum of their elements is minimized.
Edit intent: clarity
Original:  In this article, our method is based on constructing a metadata structure G for  n. Each node of G stores a bit vector of size n from which a subset of R can be retrieved.
Revised:  In this article, our method is based on constructing a metadata structure G for a given n. Each node of G stores a bit vector of size n from which a subset of R can be retrieved.
Edit intent: clarity
Original:  We finally show that we do not need to explicitly construct G as a whole , rather to answer a query, we traverse only the required portion of G on demand that obviously helps to bypass the high space requirement of the pre-processing step .
Revised:  Here it is shown that the construction of the whole graph G is not needed. To answer a query, we traverse only the required portion of G on demand that obviously helps to bypass the high space requirement of the pre-processing step .
Edit intent: clarity
Original:  We finally show that we do not need to explicitly construct G as a whole , rather to answer a query, we traverse only the required portion of G on demand that obviously helps to bypass the high space requirement of the pre-processing step .
Revised:  We finally show that we do not need to explicitly construct G as a whole , rather to answer a query, only implicit traversal of the required portion of G on demand that obviously helps to bypass the high space requirement of the pre-processing step .
Edit intent: clarity
Original: When training control policies for robot manipulation via deep learning, sim-to-real transfer can help satisfy the large data requirements.
Edit intent: coherence
Original:  In this paper, we study the problem of zero-shot sim-to-real when the task requires both highly precise control , with sub-millimetre error tolerance, and full workspace generalisation.
Revised:  In this paper, we study the problem of zero-shot sim-to-real when the task requires both highly precise control with sub-millimetre error tolerance, and full workspace generalisation.
Edit intent: fluency
Original:  In this paper, we study the problem of zero-shot sim-to-real when the task requires both highly precise control , with sub-millimetre error tolerance, and full workspace generalisation.
Revised:  In this paper, we study the problem of zero-shot sim-to-real when the task requires both highly precise control , with sub-millimetre error tolerance, and wide task space generalisation.
Edit intent: clarity
Original:  Our framework involves a coarse-to-fine controller, where trajectories initially begin with classical motion planning based on pose estimation, and transition to an end-to-end controller which maps images to actions and is trained in simulation with domain randomisation.
Revised:  Our framework involves a coarse-to-fine controller, where trajectories begin with classical motion planning based on pose estimation, and transition to an end-to-end controller which maps images to actions and is trained in simulation with domain randomisation.
Edit intent: clarity
Original:  Our framework involves a coarse-to-fine controller, where trajectories initially begin with classical motion planning based on pose estimation, and transition to an end-to-end controller which maps images to actions and is trained in simulation with domain randomisation.
Revised:  Our framework involves a coarse-to-fine controller, where trajectories initially begin with classical motion planning based on pose estimation, and transition to a learned end-to-end controller which maps images to actions and is trained in simulation with domain randomisation.
Edit intent: clarity
Original:  In this way, we achieve precise control whilst also generalising the controller across the workspace and keeping the generality and robustness of vision-based, end-to-end control.
Revised:  In this way, we achieve precise control whilst also generalising the controller across wide task spaces, and keeping the generality and robustness of vision-based, end-to-end control.
Edit intent: clarity
Original:  In this way, we achieve precise control whilst also generalising the controller across the workspace and keeping the generality and robustness of vision-based, end-to-end control.
Revised:  In this way, we achieve precise control whilst also generalising the controller across the workspace and keeping the robustness of vision-based, end-to-end control.
Edit intent: coherence
Original:  Our evaluation platform provides flexible APIs to implement new FL algorithms and include new execution backends with minimal developer efforts.
Revised:  Our evaluation platform provides flexible APIs to implement new FL algorithms and includes new execution backends with minimal developer efforts.
Edit intent: fluency
Original:  Our experiments suggest that FedScale presents significant challenges of heterogeneity-aware co-optimizations of the system and statistical efficiency under realistic FL characteristics , indicating fruitful opportunities for future research .
Revised:  Our experiments suggest fruitful opportunities in heterogeneity-aware co-optimizations of the system and statistical efficiency under realistic FL characteristics , indicating fruitful opportunities for future research .
Edit intent: clarity
Original:  Our experiments suggest that FedScale presents significant challenges of heterogeneity-aware co-optimizations of the system and statistical efficiency under realistic FL characteristics , indicating fruitful opportunities for future research .
Revised:  Our experiments suggest that FedScale presents significant challenges of heterogeneity-aware co-optimizations of the system and statistical efficiency under realistic FL characteristics .
Edit intent: clarity
Original:  FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, language modeling, speech recognition, and reinforcement learning .
Revised:  FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, word prediction, and speech recognition .
Edit intent: clarity
Original:  Finally, we perform indepth benchmark experiments on these datasets.
Revised:  Finally, we perform in-depth benchmark experiments on these datasets.
Edit intent: fluency
Original:  FedScale is open-source with permissive licenses and actively maintained, 1 and we welcome feedback and contributions from the community.
Revised:  FedScale is open-source with permissive licenses and actively maintained, and we welcome feedback and contributions from the community.
Edit intent: fluency
Original:  Computational elements are networked at every scale and can communicate with each others and with humans.
Revised:  Computational elements are networked at every scale and can communicate with each other and with humans.
Edit intent: fluency
Original:  In this scenario, monitoring spatial and temporal properties plays a key role in  understanding of how complex behaviors can emerge from local and dynamic interactions.
Revised:  In this scenario, monitoring spatial and temporal properties plays a key role in the understanding of how complex behaviors can emerge from local and dynamic interactions.
Edit intent: fluency
Original:  We propose here the Spatio-Temporal Reach and Escape Logic , (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration.
Revised:  We propose here the Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration.
Edit intent: fluency
Original:  Both nodes and edges have attributes modelling physical and logical quantities that can evolve over time.
Revised:  Both nodes and edges have attributes modeling physical and logical quantities that can evolve over time.
Edit intent: fluency
Original:  From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere  and surround.
Revised:  From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere , and surround.
Edit intent: fluency
Original:  We propose both a qualitative and quantitative semantics based on constraint semirings, an algebraic structure suitable for constraint satisfaction and optimisation .
Revised:  We propose both qualitative and quantitative semantics based on constraint semirings, an algebraic structure suitable for constraint satisfaction and optimisation .
Edit intent: fluency
Original:  We propose both a qualitative and quantitative semantics based on constraint semirings, an algebraic structure suitable for constraint satisfaction and optimisation .
Revised:  We propose both a qualitative and quantitative semantics based on constraint semirings, an algebraic structure suitable for constraint satisfaction and optimization .
Edit intent: fluency
Original:  We propose here the Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration.
Revised:  We revisit here the Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration.
Edit intent: clarity
Original:  Both nodes and edges have attributes modeling physical and logical quantities that can evolve over time.
Revised:  Both nodes and edges include attributes modeling physical and logical quantities that can evolve over time.
Edit intent: clarity
Original:  From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere , and surround.
Revised:  From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere and surround.
Edit intent: fluency
Original:  We propose both qualitative and quantitative semantics based on constraint semirings, an algebraic structuresuitable for constraint satisfaction and optimization .
Revised:  We propose both qualitative and quantitative semantics based on constraint semiring algebraic structure .
Edit intent: clarity
Original:  Hybrid Analysis and Modeling (HAM) is an emerging modeling paradigm which aims to combine physics-based modeling (PBM) and data-driven modeling (DDM) to create generalizable, trustworthy, accurate, computationally efficient and self-evolving models.
Revised:  Hybrid Analysis and Modeling (HAM) . The objective of HAM is to combine physics-based modeling (PBM) and data-driven modeling (DDM) to create generalizable, trustworthy, accurate, computationally efficient and self-evolving models.
Edit intent: clarity
Original: - the Corrective Source Term Approach (CoSTA ) -- which augments the governing equation of a PBM model with a corrective source term generated by a deep neural network (DNN) . In a series of numerical experiments on one-dimensional heat diffusion, CoSTA is generally found to outperform comparable DDM and PBM models in terms of accuracy -- often reducing predictive errors by several orders of magnitude -- while also generalizing better than pure DDM.
Revised: - the Corrective Source Term Approach (CoSTA ) -- which augments the governing equation of a PBM model with a corrective source term generated using a deep neural network (DNN) . In a series of numerical experiments on one-dimensional heat diffusion, CoSTA is generally found to outperform comparable DDM and PBM models in terms of accuracy -- often reducing predictive errors by several orders of magnitude -- while also generalizing better than pure DDM.
Edit intent: clarity
Original: - the Corrective Source Term Approach (CoSTA ) -- which augments the governing equation of a PBM model with a corrective source term generated by a deep neural network (DNN) . In a series of numerical experiments on one-dimensional heat diffusion, CoSTA is generally found to outperform comparable DDM and PBM models in terms of accuracy -- often reducing predictive errors by several orders of magnitude -- while also generalizing better than pure DDM.
Revised: - the Corrective Source Term Approach (CoSTA ) -- which augments the governing equation of a PBM model with a corrective source term generated by a deep neural network . In a series of numerical experiments on one-dimensional heat diffusion, CoSTA is generally found to outperform comparable DDM and PBM models in terms of accuracy -- often reducing predictive errors by several orders of magnitude -- while also generalizing better than pure DDM.
Edit intent: clarity
Original: - the Corrective Source Term Approach (CoSTA ) -- which augments the governing equation of a PBM model with a corrective source term generated by a deep neural network (DNN) . In a series of numerical experiments on one-dimensional heat diffusion, CoSTA is generally found to outperform comparable DDM and PBM models in terms of accuracy -- often reducing predictive errors by several orders of magnitude -- while also generalizing better than pure DDM.
Revised: - the Corrective Source Term Approach (CoSTA ) -- which augments the governing equation of a PBM model with a corrective source term generated by a deep neural network (DNN) . In a series of numerical experiments on one-dimensional heat diffusion, CoSTA is found to outperform comparable DDM and PBM models in terms of accuracy -- often reducing predictive errors by several orders of magnitude -- while also generalizing better than pure DDM.
Edit intent: coherence
Original:  To achieve this goal, we apply a \emph{doubly randomized scheme (see, e.g.,~\mbox{ ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \mbox{ jacob2 .
Revised:  To achieve this goal, we apply a \emph{ (see, e.g.,~\mbox{ ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \mbox{ jacob2 .
Edit intent: clarity
Original:  To achieve this goal, we apply a \emph{doubly randomized scheme (see, e.g.,~\mbox{ ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \mbox{ jacob2 .
Revised:  To achieve this goal, we apply a \emph{doubly randomized scheme (see, e.g.,~\mbox{ ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization .
Edit intent: coherence
Original:  We illustrate our methodology in the context of stochastic gradient descent (SGD) in several numerical examples and compare with the Rhee \& Glynn estimator\mbox{ rhee,vihola .
Revised:  We illustrate our methodology in the context of stochastic gradient descent (SGD) in several numerical examples and compare with the Rhee Glynn estimator .
Edit intent: clarity
Original:  This property allows them to be ranked based on their reactivity to changes in relatively even assemblages (changes among dominant species) or changes in relatively uneven ones (changes among rare species) .
Revised:  This property allows them to be ranked based on their reactivity to changes in relatively even assemblages or changes in relatively uneven ones (changes among rare species) .
Edit intent: clarity
Original:  This property allows them to be ranked based on their reactivity to changes in relatively even assemblages (changes among dominant species) or changes in relatively uneven ones (changes among rare species) .
Revised:  This property allows them to be ranked based on their reactivity to changes in relatively even assemblages (changes among dominant species) or changes in relatively uneven ones .
Edit intent: clarity
Original:  We establish this ranking and expand it to include the distance-based family when order q= 2.
Revised:  We establish this ranking and explore how the distance-based family when order q= 2.
Edit intent: clarity
Original:  We establish this ranking and expand it to include the distance-based family when order q= 2.
Revised:  We establish this ranking and expand it to include the distance-based measures relate to it for both order q= 2.
Edit intent: clarity
Original:  This family turns out to be the most reactive to changes in uneven assemblagesof any known evenness measure .
Revised:  This family turns out to be the most reactive to changes in even assemblages .
Edit intent: clarity
Original:  A consensus on measuring evenness will require empirical or theoretical research that uncovers which kind of divergence is most appropriate for reflecting the underlying concept.
Revised:  A deeper understanding of how to measure evenness will require empirical or theoretical research that uncovers which kind of divergence is most appropriate for reflecting the underlying concept.
Edit intent: clarity
Original:  A consensus on measuring evenness will require empirical or theoretical research that uncovers which kind of divergence is most appropriate for reflecting the underlying concept.
Revised:  A consensus on measuring evenness will require empirical or theoretical research that uncovers which kind of divergence best reflects the underlying concept.
Edit intent: clarity
Original:  Powered prosthesis can reduce this instability of passive prostheses.
Revised:  Powered prostheses can reduce this instability of passive prostheses.
Edit intent: fluency
Original:  The algorithm relies on computing an upper bound on the supremum of a nonlinear function over a rectangular domain,  traditionally done using Bernstein polynomials.
Revised:  The algorithm relies on computing an upper bound on the supremum of a nonlinear function over a rectangular domain, which has been traditionally done using Bernstein polynomials.
Edit intent: coherence
Original:  Further , we show that changing templates dynamically during computations can improve accuracy.
Revised:  Furthermore , we show that changing templates dynamically during computations can improve accuracy.
Edit intent: fluency
Original:  Further , we show that changing templates dynamically during computations can improve accuracy.
Revised:  Further , we show that changing templates dynamically during computations cans improve accuracy.
Edit intent: fluency