wanyu/IteraTeR_v2 huggingface dataset:
Original:  The Vector Space (VS) family and the presently defined Linear Scalable Family (LSF)  lead to theoretically and practically high-quality guarantees, even for queries that combine multiple time series that have been independently compressed  .
Revised:  The Vector Space (VS) and Linear Scalable Family (LSF) lead to theoretically and practically high-quality guarantees, even for queries that combine multiple time series that have been independently compressed .
Edit intent: clarity
Original:  The Vector Space (VS) family and the presently defined Linear Scalable Family (LSF)  lead to theoretically and practically high-quality guarantees, even for queries that combine multiple time series that have been independently compressed  .
Revised:  The Vector Space (VS) family and the presently defined Linear Scalable Family (LSF) ), which lead to theoretically and practically high-quality guarantees, even for queries that combine multiple time series that have been independently compressed .
Edit intent: coherence
Original:  The Vector Space (VS) family and the presently defined Linear Scalable Family (LSF)  lead to theoretically and practically high-quality guarantees, even for queries that combine multiple time series that have been independently compressed  .
Revised:  The Vector Space (VS) family and the presently defined Linear Scalable Family (LSF) lead to theoretically and practically high-quality guarantees, even for expressions (eg correlation) that combine multiple time series that have been independently compressed .
Edit intent: clarity
Original:  Well-known function families (e.g., the polynomial function family) belong to LSF.
Edit intent: coherence
Original:  The experiments on four real-life datasets validated the importance of the Amplitude Independent (AI) error guarantees: When the novel AI guarantees were applicable, the guarantees could ensure that the approximate query results were  very close (typically 1\%) to the true results.
Revised:  The experiments on four real-life datasets showed that when the novel AI guarantees were applicable, the guarantees could ensure that the approximate query results were very close (typically 1\%) to the true results.
Edit intent: clarity
Original:  The experiments on four real-life datasets validated the importance of the Amplitude Independent (AI) error guarantees: When the novel AI guarantees were applicable, the guarantees could ensure that the approximate query results were  very close (typically 1\%) to the true results.
Revised:  The experiments on four real-life datasets validated the importance of the Amplitude Independent (AI) error guarantees: When the novel AI guarantees were applicable, the approximate query results were very close (typically 1\%) to the true results.
Edit intent: clarity
Original:  The experiments on four real-life datasets validated the importance of the Amplitude Independent (AI) error guarantees: When the novel AI guarantees were applicable, the guarantees could ensure that the approximate query results were  very close (typically 1\%) to the true results.
Revised:  The experiments on four real-life datasets validated the importance of the Amplitude Independent (AI) error guarantees: When the novel AI guarantees were applicable, the guarantees could ensure that the approximate query results were certified to be very close (typically 1\%) to the true results.
Edit intent: clarity
Original:  Observable agents influence each other both directly and indirectly through the influence they exert on the latent part .
Revised:  Observable agents influence each other both directly and indirectly through the influence they exert on the latent agents .
Edit intent: clarity
Original:  We provide an algorithm for estimating this matrix under an approximate sparsity condition, and obtain convergence rates for the proposed estimator despite the high-dimensionality that allows more agents than observations.
Revised:  We provide an algorithm for estimating this matrix under an approximate sparsity condition, and obtain convergence rates for the proposed estimator despite the high dimensionality that allows more agents than observations.
Edit intent: fluency
Original: The pancake graph of S_n , the symmetric group on n elements , has been shown to have many interesting properties that makes it a useful network scheme for parallel processors.
Revised: The pancake graph of S_n , the symmetric group on n elements , has been shown to have properties that makes it a useful network scheme for parallel processors.
Edit intent: clarity
Original:  The burnt pancake graph BP_n, which is obtained as the Cayley graph of the group B_n of signed permutations on n elements using prefix reversal as generators, has similar properties.
Revised:  The burnt pancake graph BP_n, which is the Cayley graph of the group B_n of signed permutations on n elements using prefix reversal as generators, has similar properties.
Edit intent: clarity
Original:  The burnt pancake graph BP_n, which is obtained as the Cayley graph of the group B_n of signed permutations on n elements using prefix reversal as generators, has similar properties.
Revised:  The burnt pancake graph BP_n, which is obtained as the Cayley graph of the group of signed permutations on n elements using prefix reversal as generators, has similar properties.
Edit intent: clarity
Original:  The burnt pancake graph BP_n, which is obtained as the Cayley graph of the group B_n of signed permutations on n elements using prefix reversal as generators, has similar properties.
Revised:  The burnt pancake graph BP_n, which is obtained as the Cayley graph of the group B_n of signed permutations B_n using prefix reversals as generators, has similar properties.
Edit intent: clarity
Original:  By constraining the actual default intensity of the counterparty within an uncertainty interval , we derive both upper and lower bounds for the XVA process .
Revised:  By constraining the actual default intensity of the counterparty within an uncertainty interval . We derive both upper and lower bounds for the XVA process .
Edit intent: fluency
Original:  The presence of collateralization and closeout payoffs introduce fundamental differences relative to classical credit risk valuation.
Revised:  The presence of collateralization and closeout payoffs leads to fundamental differences with respect to classical credit risk valuation.
Edit intent: clarity
Original:  An investor trades a credit default swap portfolio with a risky counterparty, and hedges credit risk by taking a position in the counterparty bond .
Revised:  An investor trades a credit default swap portfolio with a risky counterparty, and hedges credit risk by taking a position in defaultable bonds .
Edit intent: clarity
Original:  The investor does not know the expected rate of return of the counterparty bond, but he is confident that it lies within an uncertainty interval.
Revised:  The investor does not know the return rate of her counterparty bond, but he is confident that it lies within an uncertainty interval.
Edit intent: clarity
Original:  The investor does not know the expected rate of return of the counterparty bond, but he is confident that it lies within an uncertainty interval.
Revised:  The investor does not know the expected rate of return of the counterparty bond, but is confident that it lies within an uncertainty interval.
Edit intent: clarity
Original:  The investor does not know the expected rate of return of the counterparty bond, but he is confident that it lies within an uncertainty interval.
Revised:  The investor does not know the expected rate of return of the counterparty bond, but he is confident that it lies within a uncertainty interval.
Edit intent: fluency
Original:  The presence of collateralization and closeout payoffs leads to fundamental differences with respect to classical credit risk valuation.
Revised:  The presence of collateralization and closeout payoffs leads to important differences with respect to classical credit risk valuation.
Edit intent: clarity
Original:  The investor does not know the return rate of her counterparty bond, but is confident that it lies within a uncertainty interval.
Revised:  The investor does not know the return rate of her counterparty bond, but is confident that it lies within an uncertainty interval.
Edit intent: fluency
Original:  The investor does not know the return rate of her counterparty  bond, but is confident that it lies within an uncertainty interval.
Revised:  The investor does not know the return rate of her counterparty 's bond, but is confident that it lies within an uncertainty interval.
Edit intent: fluency
Original:  We propose to not use the multimedia content but to find external information in other news platforms pivoting on it.
Revised:  We propose not to use the multimedia content but to find external information in other news platforms pivoting on it.
Edit intent: fluency
Original:  We then propose a new features set, cross-lingual cross-platform features that leverage the semantic similarity between the rumors and the external information.
Revised:  We introduce a new features set, cross-lingual cross-platform features that leverage the semantic similarity between the rumors and the external information.
Edit intent: coherence
Original:  Machine learning methods utilizing such features achieved the state-of-the-art rumor verification results.
Revised:  When implemented, machine learning methods utilizing such features achieved the state-of-the-art rumor verification results.
Edit intent: clarity
Original:  We show that for tasks of classification, segmentation and non-rigid registration , surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features  .
Revised:  We show that for tasks including classification, segmentation and non-rigid registration , surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features .
Edit intent: clarity
Original:  Thus, the major advantage of reservoir computing is fast and simple learning compared to other recurrent neural networks  .
Revised:  Thus, the major advantage of reservoir computing compared to other recurrent neural networks .
Edit intent: coherence
Original:  Another advantage is that the reservoir can be realized using physical systems, substrates, and devices , instead of recurrent neural networks .
Revised:  Another advantage is that the reservoir can be realized using physical systems, substrates, and devices .
Edit intent: coherence
Original:  In fact, such physical reservoir computing has attracted increasing attention in various fields of research.
Revised:  In fact, such physical reservoir computing has attracted increasing attention in diverse fields of research.
Edit intent: clarity
Original: Fixed point iterations are widely used for the analysis and numerical treatment of nonlinear problems .
Revised: A wide variety of (fixed-point) iterative methods for the solution of nonlinear equations (in Hilbert spaces) exists .
Edit intent: clarity
Original:  Furthermore, in order to solve nonlinear problems numerically , we propose a combination of the iterative linearization approach and the classical Galerkin discretization method, thereby giving rise to the so-called iterative linearization Galerkin (ILG) methodology.
Revised:  Furthermore, in the context of numerical solution schemes for nonlinear partial differential equations , we propose a combination of the iterative linearization approach and the classical Galerkin discretization method, thereby giving rise to the so-called iterative linearization Galerkin (ILG) methodology.
Edit intent: clarity
Original:  Finally, our abstract theory and the performance of the adaptive ILG approach are illustrated by means of a finite element discretization scheme for various examples of quasilinear stationary conservation laws .
Revised:  In addition, the ILG approach will be applied to the specific context of finite element discretizations of quasilinear elliptic equations, and some numerical experiments will be performed .
Edit intent: clarity
Original:  This is because of the kinetic constraints from the cooperativity via the competition between enzymes, which tend to bind to molecules with the limited structure.
Revised:  This is because of the kinetic constraints from the cooperativity via the competition for enzymes, which tend to bind to molecules with the limited structure.
Edit intent: clarity
Original:  This is because of the kinetic constraints from the cooperativity via the competition between enzymes, which tend to bind to molecules with the limited structure.
Revised:  This is because of the kinetic constraints from the cooperativity via the competition between enzymes, which tend to bind to molecules with limited structure.
Edit intent: fluency
Original:  Through Monte Carlo simulations and theoretical analysis , we found that the relaxation dynamics are much slower than the elementary reactions and are logarithmic in time with several plateaus, as is commonly observed for glasses.
Revised:  Through Monte Carlo simulations , we found that the relaxation dynamics are much slower than the elementary reactions and are logarithmic in time with several plateaus, as is commonly observed for glasses.
Edit intent: clarity
Original:  Our model showed symmetry breaking in the relaxation trajectories that led to inherently kinetic phase transitions without any correspondence to the equilibrium state.
Revised:  Our model showed symmetry breaking in the relaxation trajectories that led to inherently kinetic transitions without any correspondence to the equilibrium state.
Edit intent: clarity
Original:  StreamBox-TZ addresses two major challenges: (1) executing high-throughput, low-delay stream analytics in a single TEE, which is constrained by a low trusted computing base (TCB) and limited physical memory;
Revised:  This approach faces two major challenges: (1) executing high-throughput, low-delay stream analytics in a single TEE, which is constrained by a low trusted computing base (TCB) and limited physical memory;
Edit intent: clarity
Original:  Built on ARM TrustZone, StreamBox-TZ only adds 42.5 KB executable to the trusted computing base (16\% of  entire TCB).
Revised:  Built on ARM TrustZone, StreamBox-TZ only adds 42.5 KB executable to the TCB (16\% of entire TCB).
Edit intent: clarity
Original:  Built on ARM TrustZone, StreamBox-TZ only adds 42.5 KB executable to the trusted computing base (16\% of  entire TCB).
Revised:  Built on ARM TrustZone, StreamBox-TZ only adds 42.5 KB executable to the trusted computing base (16\% of the entire TCB).
Edit intent: fluency
Original:  On an octa core ARMv8 platform, it processes input events up to 140 MB/sec (12M events/sec) with sub-second delay , outperforming popular engines by one order of magnitude in throughput .
Revised:  On an octa core ARMv8 platform, it processes input events up to 140 MB/sec (12M events/sec) with sub-second delay .
Edit intent: clarity
Original:  The overhead incurred by StreamBox-TZ 's security mechanism is less than 25\%.
Revised:  The overhead incurred by SBT 's security mechanism is less than 25\%.
Edit intent: clarity
Original:  In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the signal to interference-plus-noise ratio (SINR) for indoor and outdoor environments.
Revised:  In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environments.
Edit intent: clarity
Original:  By leveraging the ability of Q-learning to estimate future SINR improvement rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed loop power control (PC)  and (2) self-organizing network (SON) fault management.
Revised:  By leveraging the ability of Q-learning to estimate future performance improvement rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed loop power control (PC) and (2) self-organizing network (SON) fault management.
Edit intent: clarity
Original:  By leveraging the ability of Q-learning to estimate future SINR improvement rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed loop power control (PC)  and (2) self-organizing network (SON) fault management.
Revised:  By leveraging the ability of Q-learning to estimate future SINR improvement rewards, we propose two algorithms: (1) closed loop power control (PC) and (2) self-organizing network (SON) fault management.
Edit intent: clarity
Original:  By leveraging the ability of Q-learning to estimate future SINR improvement rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed loop power control (PC)  and (2) self-organizing network (SON) fault management.
Revised:  By leveraging the ability of Q-learning to estimate future SINR improvement rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed loop power control (PC) for downlink voice over LTE (VoLTE) and (2) self-organizing network (SON) fault management.
Edit intent: clarity
Original:  Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex SINR optimization problem using RL. Simulation results show that our proposed RL based algorithms outperform the industry standards today in realistic cellular communication environments.
Revised:  Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex performance optimization problem using RL. Simulation results show that our proposed RL based algorithms outperform the industry standards today in realistic cellular communication environments.
Edit intent: clarity
Original:  Furthermore, adopting the symbol error rate as performance metric, we show that using magnetic nanoparticles enables reliable communication in the presence of disruptive fluid flow.
Revised:  Furthermore, adopting the symbol error rate as performance metric, we show that using magnetic nanoparticles can enable reliable communication in the presence of disruptive fluid flow.
Edit intent: clarity
Original: The free rider problem is one of the most well studied problems in economics.
Revised: The free rider problem is one of the most well-studied problems in economics.
Edit intent: fluency
Original:  In this work  we introduce quantum strategies and also study the problem in the thermodynamic limit by drawing analogies with the 1D Ising model.
Revised:  In this work , we introduce quantum strategies and also study the problem in the thermodynamic limit by drawing analogies with the 1D Ising model.
Edit intent: fluency
Original:  In this work  we introduce quantum strategies and also study the problem in the thermodynamic limit by drawing analogies with the 1D Ising model.
Revised:  In this work we introduce quantum strategies and study the problem in the thermodynamic limit by drawing analogies with the 1D Ising model.
Edit intent: coherence
Original:  Our main results are the following: }%DIFDELCMD < {\bf %%% Theorem.
Revised:  Our main results are the following: }%DIFDELCMD < {\bf %%% 
Edit intent: clarity
Original:  (1) Any cluster point of \{z_n\} is a critical point of f. (2 ) Either \lim _{n\rightarrow\infty}||z_n||=\infty or \lim _{n\rightarrow\infty}||z_{n+1}-z_n||=0. ( 3 ) Assume that f has at most countably many critical points.
Revised:  (1) Any cluster point of \{z_n\} is a critical point of f. (2 ) Either \lim _{n\rightarrow\infty}||z_n||=\infty or \lim _{n\rightarrow\infty}||z_{n+1}-z_n||=0. ( 2 ) Assume that f has at most countably many critical points.
Edit intent: coherence
Original:  Concerning what type of critical points z_{\infty may appear, we prove in another theorem that it is very rare for a cluster point of the sequence \{z_n\} to be a saddle point}  .
Revised:  may appear, we prove in another theorem that it is very rare for a cluster point of the sequence \{z_n\} to be a saddle point} .
Edit intent: clarity
Original:  We then introduce a new method mixing between the usual gradient descent method and (a slight modification of ) the backtracking gradient descent method, aiming to increase good features while reducing bad features of the two methods . Experiments with the MNIST and CIFAR10 data sets show that the new method is on par with current state-of-the-art methods such as MMT, NAG, Adagrad, Adadelta, RMSProp and Adam, in particular in the long run .
Revised:  We then introduce a new method mixing between the usual gradient descent method and (a slight modification of ) the backtracking gradient descent method, aiming to increase good features while reducing bad features of the two methods . Experiments with datasets CIFAR10 data sets show that the new method is on par with current state-of-the-art methods such as MMT, NAG, Adagrad, Adadelta, RMSProp and Adam, in particular in the long run .
Edit intent: coherence
Original:  We then introduce a new method mixing between the usual gradient descent method and (a slight modification of ) the backtracking gradient descent method, aiming to increase good features while reducing bad features of the two methods . Experiments with the MNIST and CIFAR10 data sets show that the new method is on par with current state-of-the-art methods such as MMT, NAG, Adagrad, Adadelta, RMSProp and Adam, in particular in the long run .
Revised:  We then introduce a new method mixing between the usual gradient descent method and (a slight modification of ) the backtracking gradient descent method, aiming to increase good features while reducing bad features of the two methods . Experiments with the MNIST and CIFAR10 data sets show that the new method is on par with current state-of-the-art methods such as MMT, NAG, Adagrad, Adadelta, RMSProp , Adam and Adamax .
Edit intent: clarity
Original: Approximations during program analysis are a necessary evil, as they ensure essential properties, such as analysis soundness and termination  , but they also imply that the analysis is not guaranteed to always produce useful results.
Revised: Approximations during program analysis are a necessary evil, as they ensure essential properties, such as soundness and termination , but they also imply that the analysis is not guaranteed to always produce useful results.
Edit intent: fluency
Original: Approximations during program analysis are a necessary evil, as they ensure essential properties, such as analysis soundness and termination  , but they also imply that the analysis is not guaranteed to always produce useful results.
Revised: Approximations during program analysis are a necessary evil, as they ensure essential properties, such as analysis soundness and termination of the analysis , but they also imply that the analysis is not guaranteed to always produce useful results.
Edit intent: clarity
Original: Approximations during program analysis are a necessary evil, as they ensure essential properties, such as analysis soundness and termination  , but they also imply that the analysis is not guaranteed to always produce useful results.
Revised: Approximations during program analysis are a necessary evil, as they ensure essential properties, such as analysis soundness and termination , but they also imply not always producing useful results.
Edit intent: clarity
Original:  In such cases it is necessary to have some means for users to provide information to guide analysis and thus to improve precision and/or shorten analysis time.
Revised:  In such cases it is necessary to have some means for users to provide information to guide analysis and thus improve precision and/or shorten analysis time.
Edit intent: fluency
Original:  We show how the proposed approach can be applied to both improving precision and accelerating convergence, and provide a procedure to decide whether the invariants used to guide the analyzer are checked, incompatible, or neither .
Revised:  We show how the proposed approach can be applied to both improving precision and accelerating analysis .
Edit intent: clarity