wanyu/IteraTeR_v2 huggingface dataset:
Original:  Through extensive simulations and on-line training based on dense Wi-Fi deployments , this paper demonstrates the merits of using DRL by achieving up to a 20\% improvement in the performance of D-MIMO networks with DRL compared to when DRL is not used .
Revised:  Through extensive simulations and on-line training based on dense Wi-Fi deployments , this paper demonstrates the efficacy of DRL in achieving an improvement of 20\% improvement in the performance of D-MIMO networks with DRL compared to when DRL is not used .
Edit intent: clarity
Original:  Numerical tests on linear and nonlinear problems demonstrate the effectiveness of the approach.
Revised:  Numerical tests on linear and nonlinear problems demonstrate the effectiveness of the approach.
Edit intent: fluency
Original:  At each iteration of the combined algorithm, search directions implied by the coarse-grid correction  process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned) gradient and search directions involving the previous iterates (so-called history).
Revised:  At each iteration of the algorithm, search directions implied by the coarse-grid correction process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned) gradient and search directions involving the previous iterates (so-called history).
Edit intent: clarity
Original:  The resulting accelerated technique is called SESOP-MG. The asymptotic convergence rate of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems ( i.e., optimization of quadratic functionals ) . Numerical tests on linear and nonlinear problems demonstrate the effectiveness of the approach.
Revised:  The resulting accelerated technique is called SESOP-MG. The asymptotic convergence factor of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems ( i.e., optimization of quadratic functionals ) . Numerical tests on linear and nonlinear problems demonstrate the effectiveness of the approach.
Edit intent: clarity
Original:  The resulting accelerated technique is called SESOP-MG. The asymptotic convergence rate of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems ( i.e., optimization of quadratic functionals ) . Numerical tests on linear and nonlinear problems demonstrate the effectiveness of the approach.
Revised:  The resulting accelerated technique is called SESOP-MG. The asymptotic convergence rate of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems , i.e., optimization of quadratic functionals ) . Numerical tests on linear and nonlinear problems demonstrate the effectiveness of the approach.
Edit intent: fluency
Original:  The resulting accelerated technique is called SESOP-MG. The asymptotic convergence rate of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems ( i.e., optimization of quadratic functionals ) . Numerical tests on linear and nonlinear problems demonstrate the effectiveness of the approach.
Revised:  The resulting accelerated technique is called SESOP-MG. The asymptotic convergence rate of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems ( i.e., optimization of quadratic functionals . Numerical tests on linear and nonlinear problems demonstrate the effectiveness of the approach.
Edit intent: fluency
Original: A merger of two optimization frameworks is introduced: SEquential Subspace OPtimization (SESOP) with the MultiGrid (MG) optimization.
Revised: A merger of two optimization frameworks is introduced: SEquential Subspace OPtimization (SESOP) with MultiGrid (MG) optimization.
Edit intent: fluency
Original:  At each iteration of the algorithm, search directions implied by the coarse-grid correction (CGC) process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Revised:  At each iteration of the algorithm, the search direction implied by the coarse-grid correction (CGC) process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Edit intent: fluency
Original:  At each iteration of the algorithm, search directions implied by the coarse-grid correction (CGC) process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Revised:  At each iteration of the algorithm, search directions implied by the coarse-grid correction process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Edit intent: clarity
Original:  At each iteration of the algorithm, search directions implied by the coarse-grid correction (CGC) process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Revised:  At each iteration of the algorithm, search directions implied by the coarse-grid correction (CGC) process of MG is added to the low dimensional search-spaces of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Edit intent: fluency
Original:  At each iteration of the algorithm, search directions implied by the coarse-grid correction (CGC) process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Revised:  At each iteration of the algorithm, search directions implied by the coarse-grid correction (CGC) process of MG are added to the low dimensional search-space of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Edit intent: fluency
Original:  At each iteration of the algorithm, search directions implied by the coarse-grid correction (CGC) process of MG are added to the low dimensional search-spaces of SESOP, which include the (preconditioned ) gradient and search directions involving the previous iterates (so-called history).
Revised:  At each iteration of the algorithm, search directions implied by the coarse-grid correction (CGC) process of MG are added to the low dimensional search-spaces of SESOP, which includes the preconditioned gradient and search directions involving the previous iterates (so-called history).
Edit intent: fluency
Original:  The resulting accelerated technique is called SESOP-MG. The{\em   asymptotic convergence factor of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems, i.e., optimization of quadratic functionals.
Revised:  The resulting accelerated technique is called SESOP-MG. The{\em history asymptotic convergence factor of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems, i.e., optimization of quadratic functionals.
Edit intent: fluency
Original:  The resulting accelerated technique is called SESOP-MG. The{\em   asymptotic convergence factor of the two-level version of SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear problems, i.e., optimization of quadratic functionals.
Revised:  The resulting accelerated technique is called SESOP-MG. The{\em asymptotic convergence factor of the two-level version of SESOP-MG (dubbed SESOP-TG) for optimization of quadratic functionals.
Edit intent: coherence
Original:  We analyze how the dynamics of nodes (such as single neurons) and recurrent connections interact to shape the effective dynamics in large randomly connected networks.
Revised:  We analyze how the dynamical properties of nodes (such as single neurons) and recurrent connections interact to shape the effective dynamics in large randomly connected networks.
Edit intent: clarity
Original:  Our theory for network analysis can be applied to numerous other mechanisms at the level of single nodes, such as synaptic filtering, refractoriness or spike synchronization.
Revised:  Our theory for network analysis can be applied to other mechanisms at the level of single nodes, such as synaptic filtering, refractoriness or spike synchronization.
Edit intent: clarity
Original: One of the main aims of indoor visible light communication (VLC) systems is to deliver a high data rate services in a single user scenario and in multiuser scenarios.
Revised: One of the main aims of indoor visible light communication (VLC) systems is to deliver a high data rate service in single user and in multiuser scenarios.
Edit intent: fluency
Original:  This task is challenging because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85\% of the slices in comparison.
Revised:  This task is challenging because, in contrast to research-quality scans, clinical images are often sparse, missing up to 85\% of the slices in comparison.
Edit intent: clarity
Original:  This task is challenging because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85\% of the slices in comparison.
Revised:  This task is challenging because, in contrast to high-resolution research-quality scans, clinical scans are often sparse, missing up to 85\% of the slices in comparison.
Edit intent: clarity
Original:  Because of its short runtimes and accurate behavior, SparseVM can enable clinical analyses not previously possible.
Revised:  Because of this, SparseVM enables clinical analyses that were not previously possible.
Edit intent: coherence
Original: We introduce SparseVM, a method to register clinical 3D scans faster and more accurately than previously possible.
Revised: We introduce SparseVM, a method that registers clinical-quality 3D scans faster and more accurately than previously possible.
Edit intent: clarity
Original:  Clinical scans present unique challenges because, in contrast to research-quality scans, clinical scans are often sparse, missing up to 85 \% of the slices available in research scans.
Revised:  Clinical scans present unique challenges because, in contrast to research-quality scans, clinical scans are often sparse, missing up to 86 \% of the slices available in research scans.
Edit intent: fluency
Original: The determination of the asymptotic algebraic complexity of matrix multiplication, succinctly represented by the matrix multiplication exponent \omega, is a central problem in algebraic complexity theory.
Revised: Determining the asymptotic algebraic complexity of matrix multiplication, succinctly represented by the matrix multiplication exponent \omega, is a central problem in algebraic complexity theory.
Edit intent: fluency
Original:  The best upper bounds on \omega, leading to the state-of the-art \omega \leq 2.37.., have been obtained via the laser method of Strassen and its generalization by Coppersmith and Winograd.
Revised:  The best upper bounds on \omega, leading to the state-of-the-art \omega \leq 2.37.., have been obtained via the laser method of Strassen and its generalization by Coppersmith and Winograd.
Edit intent: fluency
Original:  If no absolutely irreducible component with maximal dimension of X is a shift of another component, then the answer to the question is "yes".
Revised:  If no absolutely irreducible component is a shift of another component, then the answer to the question is "yes".
Edit intent: clarity
Original:  In data lakes, there has been considerable research on dataset or table discovery using search.
Revised:  In data lakes, there has been considerable research on dataset discovery using search.
Edit intent: clarity
Original:  We define an organization as a navigation structure (graph) containing nodes representing sets of attributes (from tables or from semi-structured documents) within a data lake.
Revised:  We define an organization as a navigation structure (graph) containing nodes representing sets of attributes within a data lake.
Edit intent: clarity
Original:  We propose a novel problem, the data lake organization problem where the goal is to find an organization that allows a user to most efficiently find attributes or tables .
Revised:  We propose a novel problem, the data lake organization problem where the goal is to find an organization that allows a user to most efficiently find attributes .
Edit intent: clarity
Original:  We present a new probabilistic model of how users interact with an organization and define the likelihood of a user finding an attribute or a table using the organization.
Revised:  We present a new probabilistic model of how users interact with an organization and define the likelihood of a user finding an attribute using the organization.
Edit intent: clarity
Original:  Our approach uses the attribute values and metadata (when available).
Revised:  Our approach uses attribute values and metadata (when available).
Edit intent: coherence
Original:  Finally, we construct an organization on tables of a real data lake containing data from federal Open Data portals and show that the organization dramatically improves the expected probability of discovering tables over a baseline.
Revised:  Finally, we construct an organization on attributes of a real data lake containing data from federal Open Data portals and show that the organization dramatically improves the expected probability of discovering tables over a baseline.
Edit intent: clarity
Original:  Finally, we construct an organization on tables of a real data lake containing data from federal Open Data portals and show that the organization dramatically improves the expected probability of discovering tables over a baseline.
Revised:  Finally, we construct an organization on tables of a real data lake containing data from federal Open Data portals and show that the organization improves the expected probability of discovering tables over a baseline.
Edit intent: clarity
Original:  Finally, we construct an organization on tables of a real data lake containing data from federal Open Data portals and show that the organization dramatically improves the expected probability of discovering tables over a baseline.
Revised:  Finally, we construct an organization on tables of a real data lake containing data from federal Open Data portals and show that the organization dramatically improves the expected probability of discovering attributes over a baseline.
Edit intent: clarity
Original:  We consider the complementary problem of creating an effective navigation structure over a data lake.
Revised:  We consider the problem of creating an effective navigation structure over a data lake.
Edit intent: clarity
Original:  We define an organization as a navigation structure (graph ) containing nodes representing sets of attributes within a data lake .
Revised:  We define an organization as a graph that contains nodes representing sets of attributes within a data lake .
Edit intent: clarity
Original: Neural networks have become the key technology of Artificial Intelligence (AI) that contributed to breakthroughs in several machine learning tasks, primarily owing to advances in  Artificial Neural Networks (ANNs).
Revised: Neural networks have become the key technology of artificial intelligence and have contributed to breakthroughs in several machine learning tasks, primarily owing to advances in Artificial Neural Networks (ANNs).
Edit intent: coherence
Original:  Neural networks incorporating spiking neurons have held great promise because of their brain-inspired temporal dynamics and high power efficiency, however scaling out and training deep Spiking Neural Networks (SNNs ) remained significant challenges - they still fall behind ANNs in terms of accuracy on many traditional learning tasks.
Revised:  Neural networks incorporating spiking neurons have held great promise because of their rich temporal dynamics and high power efficiency, however scaling out and training deep Spiking Neural Networks (SNNs ) remained significant challenges - they still fall behind ANNs in terms of accuracy on many traditional learning tasks.
Edit intent: clarity
Original:  In this paper, we propose an alternative perspective on the spiking neuron  as a particular ANN construct called Spiking Neural Unit (SNU) . Specifically, the SNU casts the stateful temporal spiking neuron, of a LIF type, as a recurrent ANN unit, analogous to LSTMs and GRUs.
Revised:  In this paper, we propose an alternative perspective on the spiking neuron that casts it as a particular ANN construct called Spiking Neural Unit (SNU) . Specifically, the SNU casts the stateful temporal spiking neuron, of a LIF type, as a recurrent ANN unit, analogous to LSTMs and GRUs.
Edit intent: clarity
Original:  Secondly, for the task of polyphonic music prediction on the JSB dataset,an sSNU-based network surpasses the state-of-the-art performance of RNNs, LSTM- and GRU-based networks.
Revised:  Secondly, for the task of polyphonic music prediction on the JSB dataset,an sSNU-based network surpasses the state-of-the-art ANNs, and GRU-based networks.
Edit intent: clarity
Original:  The novel family of units introduced in this paper bridges the biologically-inspired SNNs with ANNs.
Revised:  The novel family of units introduced in this paper incorporate biologically-inspired SNNs with ANNs.
Edit intent: clarity
Original:  It provides a systematic methodology for implementing and training deep networks incorporating spiking dynamics that achieve accuracies as high, or better than, state-of-the-art ANNs . Thus, it opens a new avenue for the widespread adoption of SNNs to practical applications.
Revised:  It provides a systematic methodology for training neuromorphic computing hardware . Thus, it opens a new avenue for the widespread adoption of SNNs to practical applications.
Edit intent: clarity
Original:  It provides a systematic methodology for implementing and training deep networks incorporating spiking dynamics that achieve accuracies as high, or better than, state-of-the-art ANNs . Thus, it opens a new avenue for the widespread adoption of SNNs to practical applications.
Revised:  It provides a systematic methodology for implementing and training deep networks incorporating spiking dynamics that achieve accuracies as high, or better than, state-of-the-art ANNs . Thus, they open a new avenue for the widespread adoption of SNNs to practical applications.
Edit intent: clarity
Original:  It provides a systematic methodology for implementing and training deep networks incorporating spiking dynamics that achieve accuracies as high, or better than, state-of-the-art ANNs . Thus, it opens a new avenue for the widespread adoption of SNNs to practical applications.
Revised:  It provides a systematic methodology for implementing and training deep networks incorporating spiking dynamics that achieve accuracies as high, or better than, state-of-the-art ANNs . Thus, it opens a new avenue for a widespread adoption of SNNs to practical applications.
Edit intent: fluency
Original:  It provides a systematic methodology for implementing and training deep networks incorporating spiking dynamics that achieve accuracies as high, or better than, state-of-the-art ANNs . Thus, it opens a new avenue for the widespread adoption of SNNs to practical applications.
Revised:  It provides a systematic methodology for implementing and training deep networks incorporating spiking dynamics that achieve accuracies as high, or better than, state-of-the-art ANNs . Thus, it opens a new avenue for the widespread adoption of SNNs in practical applications.
Edit intent: clarity
Original:  In this paper we investigate how these techniques can be extended, and more specifically we give upper and lower bounds on the complexity of algorithms using universal graphs for solving mean payoff games.
Revised:  The notion naturally extends to mean payoff games and yields a class of algorithms for solving mean payoff games.
Edit intent: clarity
Original:  Our first algorithm improves the best known complexity by reducing the dependence on Nfrom N to N^{1 - 1/n . Our second algorithm runs in polynomial time for a fixed number k of weights , more specifically in O(m n^k), where m is the number of edges in the graph .
Revised:  Our first algorithm improves the best known complexity by reducing the dependence on Nfrom N to N^{1 - 1/n . Our second algorithm runs in polynomial time for a fixed number k of weights .
Edit intent: coherence
Original:  We show that using universal graphs we cannot hope to improve on the N^{1 - 1/n dependence in N nor break the O(n^{\Omega(k) . In particular, universal graphs do not yield a quasipolynomial algorithm for solving mean payoff games.
Revised:  We show that we cannot hope to improve on the N^{1 - 1/n dependence in N nor break the O(n^{\Omega(k) . In particular, universal graphs do not yield a quasipolynomial algorithm for solving mean payoff games.
Edit intent: clarity
Original: Information seeking techniques, satisfying users' information needs by suggesting users ' personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem on the Web.
Revised: Information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem on the Web.
Edit intent: fluency
Original: Search, recommendation, and  advertising are the three most important information-providing mechanisms  .
Revised: Search, recommendation, and online advertising are the three most important information-providing mechanisms .
Edit intent: clarity
Original:  These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem on the Web . With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information seeking techniques.
Revised:  These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem . With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information seeking techniques.
Edit intent: clarity
Original:  In this survey , we give an overview about deep reinforcement learning for search, recommendations, and advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.
Revised:  In this paper , we give an overview about deep reinforcement learning for search, recommendations, and advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.
Edit intent: clarity
Original:  In this survey , we give an overview about deep reinforcement learning for search, recommendations, and advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.
Revised:  In this survey , we give an overview of deep reinforcement learning for search, recommendations, and advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.
Edit intent: fluency