wanyu/IteraTeR_v2 huggingface dataset:
Original:  The proposed technique is data-driven, and the RL agent learns from scratch on how to efficiently use the energy storage device under variable tariff-structures Contracting the concept of the "black box" where the techniques learned by the agent are ignored.
Revised:  The proposed technique is data-driven, and the RL agent learns from scratch on how to efficiently use the energy storage device given variable tariff structures. In most of the studies, the RL agent is considered as a black box, and how the agent has learned is often ignored.
Edit intent: clarity
Original: We design a high efficient exponential time differencing method (ETD) for simulating pulse-coupled Hodgkin-Huxley (HH) neuronal networks.
Revised: The exponential time differencing method (ETD) for simulating pulse-coupled Hodgkin-Huxley (HH) neuronal networks.
Edit intent: coherence
Original: We design a high efficient exponential time differencing method (ETD) for simulating pulse-coupled Hodgkin-Huxley (HH) neuronal networks.
Revised: We design a high efficient exponential time differencing (ETD) for simulating pulse-coupled Hodgkin-Huxley (HH) neuronal networks.
Edit intent: clarity
Original:  This high accuracy and efficiency can be robustly obtained without relying on the dynamical regimes, structural connectivity or the scale of networks .
Revised:  This high accuracy and efficiency can be robustly obtained and do not depend on the dynamical regimes, structural connectivity or the scale of networks .
Edit intent: clarity
Original:  This high accuracy and efficiency can be robustly obtained without relying on the dynamical regimes, structural connectivity or the scale of networks .
Revised:  This high accuracy and efficiency can be robustly obtained without relying on the dynamical regimes, connectivity structure or the network size .
Edit intent: clarity
Original: In this study we consider the shortest path problem , where the arc costs are subject to distributional uncertainty.
Revised: In this study we consider the shortest path problem where the arc costs are subject to distributional uncertainty.
Edit intent: fluency
Original:  Basically, the decision-maker attempts to minimize her worst-case expected regret over an ambiguity set (or a family) of candidate distributions that are consistent with the decision-maker's initial information.
Revised:  Basically, the decision-maker attempts to minimize her worst-case expected loss over an ambiguity set (or a family) of candidate distributions that are consistent with the decision-maker's initial information.
Edit intent: clarity
Original:  we also demonstrate that the MIP reformulation of DRSPP can be solved reasonably fast using off-the-shelf solvers.
Revised:  we also demonstrate that the MIP reformulation of DRSPP can be solved effectively using off-the-shelf solvers.
Edit intent: clarity
Original: In this study we consider the shortest path problem  where the arc costs are subject to distributional uncertainty.
Revised: In this study we consider the shortest path problem , where the arc costs are subject to distributional uncertainty.
Edit intent: fluency
Original:  Our distributional constraints can be designed from incomplete and partially observable data.
Edit intent: coherence
Original:  The robust reformulation is shown to be strongly NP-hard, whereas the problem without the first-order moment constraints is proved to be polynomially solvable.
Revised:  The robust reformulation is shown to be NP-hard, whereas the problem without the first-order moment constraints is proved to be polynomially solvable.
Edit intent: clarity
Original:  We perform numerical experiments to illustrate the advantages of the proposed approach;
Revised:  We perform numerical experiments to illustrate the advantages of the considered approach;
Edit intent: clarity
Original:  An LSTM processes the handcrafted input, while a novel LSTM architecture, denoted as Dual-Sequence LSTM (DS-LSTM), processes the two mel-spectrograms simultaneously.
Revised:  A standard LSTM processes the handcrafted input, while a novel LSTM architecture, denoted as Dual-Sequence LSTM (DS-LSTM), processes the two mel-spectrograms simultaneously.
Edit intent: clarity
Original:  Our proposed model achieves, on average, a weighted accuracy of 72.7\% and an unweighted accuracy of 73.3\% --- a 6\% improvement over current state-of-the-art models --- and is comparable with multimodal SER models that leverage textual information  .
Revised:  Our proposed model achieves, on average, a weighted accuracy of 72.7\% and an unweighted accuracy of 73.3\% , a 6\% improvement over current state-of-the-art models --- and is comparable with multimodal SER models that leverage textual information .
Edit intent: fluency
Original:  Our proposed model achieves, on average, a weighted accuracy of 72.7\% and an unweighted accuracy of 73.3\% --- a 6\% improvement over current state-of-the-art models --- and is comparable with multimodal SER models that leverage textual information  .
Revised:  Our proposed model achieves, on average, a weighted accuracy of 72.7\% and an unweighted accuracy of 73.3\% --- a 6\% improvement over current state-of-the-art models , and is comparable with multimodal SER models that leverage textual information .
Edit intent: fluency
Original: Speech Emotion Recognition (SER) has emerged as a critical component of the next generation of human-machine interfacing technologies.
Revised: Speech Emotion Recognition (SER) has emerged as a critical component of the next generation human-machine interfacing technologies.
Edit intent: fluency
Original:  In this work, we propose a new dual-level model that predicts emotions based on both MFCC features and mel-spectrograms from audio signals.
Revised:  In this work, we propose a new dual-level model that predicts emotions based on both MFCC features and mel-spectrograms produced from raw audio signals.
Edit intent: clarity
Original:  Each utterance is preprocessed into a MFCC input and two mel-spectrograms at different time-frequency resolutions.
Revised:  Each utterance is preprocessed into MFCC features and two mel-spectrograms at different time-frequency resolutions.
Edit intent: clarity
Original:  A standard LSTM processes the MFCC input , while a novel LSTM architecture, denoted as Dual-Sequence LSTM (DS-LSTM), processes the two mel-spectrograms simultaneously.
Revised:  A standard LSTM processes the MFCC features , while a novel LSTM architecture, denoted as Dual-Sequence LSTM (DS-LSTM), processes the two mel-spectrograms simultaneously.
Edit intent: clarity
Original:  Our proposed model achieves, on average, a weighted accuracy of 72.7\% and an unweighted accuracy of 73.3\% , a 6\% improvement over current state-of-the-art models, and is comparable with multimodal SER models that leverage textual information as well as audio signals.
Revised:  Our proposed model achieves, on average, a weighted accuracy of 72.7\% and an unweighted accuracy of 73.3\% ---a 6\% improvement over current state-of-the-art models, and is comparable with multimodal SER models that leverage textual information as well as audio signals.
Edit intent: fluency
Original:  Our proposed model achieves, on average, a weighted accuracy of 72.7\% and an unweighted accuracy of 73.3\% , a 6\% improvement over current state-of-the-art models, and is comparable with multimodal SER models that leverage textual information as well as audio signals.
Revised:  Our proposed model achieves, on average, a weighted accuracy of 72.7\% and an unweighted accuracy of 73.3\% , a 6\% improvement over current state-of-the-art models, and is comparable with multimodal models that leverage textual information as well as audio signals.
Edit intent: clarity
Original:  In many cases, especially in pathological ones, the properties of the vocal folds often deviate from their generic values -- sometimes asymmetrically wherein the characteristics of the two vocal folds differ for the same individual.
Revised:  In many cases, especially in pathological ones, the properties of the vocal folds often deviate from their generic values---sometimes asymmetrically wherein the characteristics of the two vocal folds differ for the same individual.
Edit intent: fluency
Original:  We focus on an asymmetrical model, and show that for such models, differences in estimated parameters can be successfully used to discriminate between voices that are characteristic of different underlying pathologies of the vocal tract .
Revised:  We focus on an asymmetric model, and show that for such models, differences in estimated parameters can be successfully used to discriminate between voices that are characteristic of different underlying pathologies of the vocal tract .
Edit intent: fluency
Original:  We focus on an asymmetrical model, and show that for such models, differences in estimated parameters can be successfully used to discriminate between voices that are characteristic of different underlying pathologies of the vocal tract .
Revised:  We focus on an asymmetrical model, and show that for such models, differences in estimated parameters can be successfully used to discriminate between voices that are characteristic of different underlying vocal fold pathologies .
Edit intent: clarity
Original:  The parameters of these models, such as vocal fold elasticity, resistance  etc. are traditionally determined through the observation and measurement of the vocal fold vibrations in the larynx.
Revised:  The parameters of these models, such as vocal fold elasticity, resistance , etc. are traditionally determined through the observation and measurement of the vocal fold vibrations in the larynx.
Edit intent: fluency
Original:  We focus on an asymmetric model , and show that for such models, differences in estimated parameters can be successfully used to discriminate between voices that are characteristic of different underlying vocal fold pathologies.
Revised:  We focus on an asymmetric model and show that for such models, differences in estimated parameters can be successfully used to discriminate between voices that are characteristic of different underlying vocal fold pathologies.
Edit intent: fluency
Original:  We say that a directed, acyclic graph with n inputs and n outputs is (k_1, k_2) -ST-Robust if we can remove any k_1 nodes and there exists a subgraph containing at least k_2 inputs and k_2 outputs such that each of the k_2 inputs is connected to all of the k_2 outputs.
Revised:  We say that a directed, acyclic graph with n inputs and n outputs is (k_1, k_2) -ST-robust if we can remove any k_1 nodes and there exists a subgraph containing at least k_2 inputs and k_2 outputs such that each of the k_2 inputs is connected to all of the k_2 outputs.
Edit intent: fluency
Original: Given a directed acyclic graph (DAG) G = (V,E), we say that G is (e,d)-depth-robust (resp. (e,d)-edge-depth-robust) if for any set S \subseteq V (resp.
Revised: Given a directed acyclic graph (DAG) G = (V,E), we say that G is (e,d)-depth-robust (resp. (e,d)-edge-depth-robust) if for any set S \subset V (resp.
Edit intent: clarity
Original:  While edge-depth-robust graphs are potentially easier to construct , many applications in cryptography require node depth-robust graphs with small indegree.
Revised:  While edge-depth-robust graphs are potentially easier to construct many applications in cryptography require node depth-robust graphs with small indegree.
Edit intent: fluency
Original:  One immediate consequence of this result is the first construction of a provably (n \log \log n{\log n}, \frac{n{\log n (\log n)^{\log \log n}}} {(\log n)^{1 + \log \log n}}} )-depth-robust graph with constant indegree  had d = O(n^{1-\epsilon})} . Our reduction crucially relies on ST-robust graphs, a new graph property we introduce which may be of independent interest.
Revised:  One immediate consequence of this result is the first construction of a provably (n \log \log n{\log n}, {\log n (\log n)^{\log \log n}}} {(\log n)^{1 + \log \log n}}} )-depth-robust graph with constant indegree had d = O(n^{1-\epsilon})} . Our reduction crucially relies on ST-robust graphs, a new graph property we introduce which may be of independent interest.
Edit intent: clarity
Original:  One immediate consequence of this result is the first construction of a provably (n \log \log n{\log n}, \frac{n{\log n (\log n)^{\log \log n}}} {(\log n)^{1 + \log \log n}}} )-depth-robust graph with constant indegree  had d = O(n^{1-\epsilon})} . Our reduction crucially relies on ST-robust graphs, a new graph property we introduce which may be of independent interest.
Revised:  One immediate consequence of this result is the first construction of a provably (n \log \log n{\log n}, \frac{n{\log n (\log n)^{\log \log n}}} \frac{n{(\log n)^{1 + \log \log n}}} )-depth-robust graph with constant indegree had d = O(n^{1-\epsilon})} . Our reduction crucially relies on ST-robust graphs, a new graph property we introduce which may be of independent interest.
Edit intent: fluency
Original:  One immediate consequence of this result is the first construction of a provably (n \log \log n{\log n}, \frac{n{\log n (\log n)^{\log \log n}}} {(\log n)^{1 + \log \log n}}} )-depth-robust graph with constant indegree  had d = O(n^{1-\epsilon})} . Our reduction crucially relies on ST-robust graphs, a new graph property we introduce which may be of independent interest.
Revised:  One immediate consequence of this result is the first construction of a provably (n \log \log n{\log n}, \frac{n{\log n (\log n)^{\log \log n}}} {(\log n)^{1 + \log \log n}}} )-depth-robust graph with constant indegree had d = O(n^{1-\epsilon})} . Our reduction crucially relies on ST-Robust graphs, a new graph property we introduce which may be of independent interest.
Edit intent: fluency
Original:  We say that a directed, acyclic graph with n inputs and n outputs is (k_1, k_2) -ST-robust if we can remove any k_1 nodes and there exists a subgraph containing at least k_2 inputs and k_2 outputs such that each of the k_2 inputs is connected to all of the k_2 outputs.
Revised:  We say that a directed, acyclic graph with n inputs and n outputs is (k_1, k_2) -ST-Robust if we can remove any k_1 nodes and there exists a subgraph containing at least k_2 inputs and k_2 outputs such that each of the k_2 inputs is connected to all of the k_2 outputs.
Edit intent: fluency
Original:  If the graph if (k_1,n-k_1) -ST-robust for all k_1 \leq n we say that the graph is maximally ST-robust.
Revised:  If the graph if (k_1,n-k_1) -ST-Robust for all k_1 \leq n we say that the graph is maximally ST-robust.
Edit intent: fluency
Original:  This is not an artifact of continuous time: we prove that such equilibria arise as limits of equilibria in the discrete-time game.
Revised:  This is not an artifact of continuous time: we prove that efficient equilibria arise as limits of equilibria in the discrete-time game.
Edit intent: clarity
Original: Three variants of Kurt G\"odel's ontological argument, as proposed byDana Scott, C. Anthony Anderson and Melvin Fitting, are encoded and rigorously assessed on the computer.
Revised: Three variants of Kurt G\"odel's ontological argument, proposed by Dana Scott, C. Anthony Anderson and Melvin Fitting, are encoded and rigorously assessed on the computer.
Edit intent: clarity
Original:  In contrast to Scott's version of G\"odel's argument , the two variants contributed by Anderson and Fitting avoid modal collapse.
Revised:  In contrast to Scott's version of G\"odel's argument the two variants contributed by Anderson and Fitting avoid modal collapse.
Edit intent: fluency
Original:  Although they appear quite different on a cursory reading , they are in fact closely related , as our computer-supported formal analysis (conducted in the proof assistant system Isabelle/HOL) reveals . Key to our formal analysis is the utilization of suitably adapted notions of (modal) ultrafilters, and a careful distinction between extensions and intensions of positive properties.
Revised:  Although they appear quite different on a cursory reading they are in fact closely related , as our computer-supported formal analysis (conducted in the proof assistant system Isabelle/HOL) reveals . Key to our formal analysis is the utilization of suitably adapted notions of (modal) ultrafilters, and a careful distinction between extensions and intensions of positive properties.
Edit intent: fluency
Original:  Although they appear quite different on a cursory reading , they are in fact closely related , as our computer-supported formal analysis (conducted in the proof assistant system Isabelle/HOL) reveals . Key to our formal analysis is the utilization of suitably adapted notions of (modal) ultrafilters, and a careful distinction between extensions and intensions of positive properties.
Revised:  Although they appear quite different on a cursory reading , they are in fact closely related . This has been revealed in the computer-supported formal analysis (conducted in the proof assistant system Isabelle/HOL) reveals . Key to our formal analysis is the utilization of suitably adapted notions of (modal) ultrafilters, and a careful distinction between extensions and intensions of positive properties.
Edit intent: coherence
Original:  Although they appear quite different on a cursory reading , they are in fact closely related , as our computer-supported formal analysis (conducted in the proof assistant system Isabelle/HOL) reveals . Key to our formal analysis is the utilization of suitably adapted notions of (modal) ultrafilters, and a careful distinction between extensions and intensions of positive properties.
Revised:  Although they appear quite different on a cursory reading , they are in fact closely related , as our computer-supported formal analysis presented in this article . Key to our formal analysis is the utilization of suitably adapted notions of (modal) ultrafilters, and a careful distinction between extensions and intensions of positive properties.
Edit intent: clarity
Original:  In the SNR problem, we are interested in only finding the  path that connects two terminals with the least routing cost on a special grid layout that contains obstacles.
Revised:  In the SNR problem, we are interested in only finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles.
Edit intent: clarity
Original:  The SneakySnake algorithm quickly and optimally solves the SNR problem and uses the found optimal path to decide whether performing sequence alignment is necessary.
Revised:  The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether performing sequence alignment is necessary.
Edit intent: clarity
Original:  The addition of Snake-on-Chip and Snake-on-GPU as a pre-alignment filter reduces the execution time of four state-of-the-art sequence aligners, designed for different computing platforms, by up to  689x ( 101x on average) .
Revised:  The addition of Snake-on-Chip and by up to 689x ( 101x on average) .
Edit intent: coherence
Original: Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for the computationally costly sequence alignment step .
Revised: Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment step .
Edit intent: fluency
Original: Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for the computationally costly sequence alignment step .
Revised: Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for the computationally costly sequence alignment .
Edit intent: clarity
Original:  In the SNR problem, we are interested in only finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles.
Revised:  In the SNR problem, we are interested in finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles.
Edit intent: clarity
Original:  The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether  performing sequence alignment is necessary.
Revised:  The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether or not performing sequence alignment is necessary.
Edit intent: coherence
Original:  Reducing the ASM problem into SNR also makes SneakySnake efficient to implement for all modern high-performance computing architectures ( CPUs, GPUs, and FPGAs ) . Results: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper, and SHD.
Revised:  Reducing the ASM problem into SNR also makes SneakySnake efficient to implement on CPUs, GPUs, and FPGAs ) . Results: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper, and SHD.
Edit intent: clarity
Original:  Reducing the ASM problem into SNR also makes SneakySnake efficient to implement for all modern high-performance computing architectures ( CPUs, GPUs, and FPGAs ) . Results: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper, and SHD.
Revised:  Reducing the ASM problem into SNR also makes SneakySnake efficient to implement for all modern high-performance computing architectures ( CPUs, GPUs, and FPGAs . Results: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper, and SHD.
Edit intent: fluency
Original:   SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail (  sequence aligner with  configurable scoring function), by up to 37.6 x and 43.9x (>12x on average), respectively, without requiring hardware acceleration , and by up to 413x and 689x (>400x on average), respectively, using hardware acceleration.
Revised:  SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail ( state-of-the-art sequence aligner with configurable scoring function), by up to 37.6 x and 43.9x (>12x on average), respectively, without requiring hardware acceleration , and by up to 413x and 689x (>400x on average), respectively, using hardware acceleration.
Edit intent: fluency
Original:   SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail (  sequence aligner with  configurable scoring function), by up to 37.6 x and 43.9x (>12x on average), respectively, without requiring hardware acceleration , and by up to 413x and 689x (>400x on average), respectively, using hardware acceleration.
Revised:  SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail ( sequence aligner with a configurable scoring function), by up to 37.6 x and 43.9x (>12x on average), respectively, without requiring hardware acceleration , and by up to 413x and 689x (>400x on average), respectively, using hardware acceleration.
Edit intent: fluency
Original:  As SneakySnake does not replace sequence alignment, users can still configure the aligner of their choice for different scoring functions, surpassing most existing efforts that aim to accelerate sequence alignment .
Revised:  As SneakySnake does not replace sequence alignment, users can still obtain all capabilities (e.g., configurable scoring functions) of the aligner of their choice for different scoring functions, surpassing most existing efforts that aim to accelerate sequence alignment .
Edit intent: clarity