wanyu/IteraTeR_v2 huggingface dataset:
Original:  Combining LSTM and autoencoder (AE), the proposed model can capture long-term dependencies across data points and uses features extracted from recent observations for augmenting LSTM at the same time . Based on comprehensive experiments , we show that the proposed methods significantly improves the state-of-art performance on chaotic time seriesbenchmark and also has better performance on real-world data.
Revised:  Combining LSTM and autoencoder (AE), the proposed model can capture long-term dependencies across data points and uses features extracted from recent observations for augmenting LSTM at the same time . Based on experiments with both synthetic and real-world data , we show that the proposed methods significantly improves the state-of-art performance on chaotic time seriesbenchmark and also has better performance on real-world data.
Edit intent: clarity
Original:  Combining LSTM and autoencoder (AE), the proposed model can capture long-term dependencies across data points and uses features extracted from recent observations for augmenting LSTM at the same time . Based on comprehensive experiments , we show that the proposed methods significantly improves the state-of-art performance on chaotic time seriesbenchmark and also has better performance on real-world data.
Revised:  Combining LSTM and autoencoder (AE), the proposed model can capture long-term dependencies across data points and uses features extracted from recent observations for augmenting LSTM at the same time . Based on comprehensive experiments , we show that the proposed method significantly improves the state-of-art performance on chaotic time seriesbenchmark and also has better performance on real-world data.
Edit intent: fluency
Original:  However, we still observe the missing piece of the research that dominates the current design, eg lacking knowledge in the middle to glue sensing and networking  holistically.
Revised:  However, we still observe the missing piece of the research that dominates the current design, e.g., lacking of the knowledge penetrated into both sensing and networking holistically.
Edit intent: clarity
Original:  The key insight of KCN is to leverage emerging machine learning  techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate communication with better controllability , improved quality of service  , and lower cost.
Revised:  The key insight of KCN is to leverage emerging machine learning techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate communication with better controllability , improved quality of service , communication with better controllability , and lower cost.
Edit intent: fluency
Original:  However, we still observe the missing piece of the research that dominates the current design , e.g., lacking of the knowledge penetrated into both sensing and networking to glue them holistically.
Revised:  However, we still observe the missing piece of the research that dominates the current networking traffic control and system management , e.g., lacking of the knowledge penetrated into both sensing and networking to glue them holistically.
Edit intent: clarity
Original:  The key insight of KCN is to leverage emerging machine learning or deep learning techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate  enhanced system intelligence and interactivity, improved quality of service , communication with better controllability , and lower cost.
Revised:  The key insight of KCN is to leverage emerging machine learning or deep learning techniques to create aspects of knowledge for facilitating the designs. In particular, we can extract knowledge from collected data to facilitate enhanced system intelligence and interactivity, improved quality of service , communication with better controllability , and lower cost.
Edit intent: clarity
Original:  The key insight of KCN is to leverage emerging machine learning or deep learning techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate  enhanced system intelligence and interactivity, improved quality of service , communication with better controllability , and lower cost.
Revised:  The key insight of KCN is to leverage emerging machine learning or deep learning techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate enhanced system intelligence and interactivity, improved service quality , communication with better controllability , and lower cost.
Edit intent: clarity
Original:  The key insight of KCN is to leverage emerging machine learning or deep learning techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate  enhanced system intelligence and interactivity, improved quality of service , communication with better controllability , and lower cost.
Revised:  The key insight of KCN is to leverage emerging machine learning or deep learning techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate enhanced system intelligence and interactivity, improved quality of service , communication with better controllability and lower cost.
Edit intent: fluency
Original:   This paper presents the KCN design rationale, the KCN benefitsand also the potential research opportunities.
Revised:  This paper presents KCN rationale, KCN benefits, related works and research opportunities.
Edit intent: clarity
Original:  This leads to a  simulation approach which works complementary different to the state-of-the-art  . Experimental evaluations show that the proposed solution is capable of simulating quantum computations with more qubits than before, and in significantly less run-time (several magnitudes faster compared to previously proposed simulators).
Revised:  This leads to a simulation approach which clearly outperforms state-of-the-art . Experimental evaluations show that the proposed solution is capable of simulating quantum computations with more qubits than before, and in significantly less run-time (several magnitudes faster compared to previously proposed simulators).
Edit intent: clarity
Original:  This leads to a  simulation approach which works complementary different to the state-of-the-art  . Experimental evaluations show that the proposed solution is capable of simulating quantum computations with more qubits than before, and in significantly less run-time (several magnitudes faster compared to previously proposed simulators).
Revised:  This leads to a simulation approach which works complementary different to the state-of-the-art . Experimental evaluations show that the proposed solution is capable of simulating quantum computations for more qubits than before, and in significantly less run-time (several magnitudes faster compared to previously proposed simulators).
Edit intent: fluency
Original: Quantum computation is a promising emerging technology which, compared to classical computation, allows for substantial speed-ups e.g. for integer factorization or database search.
Revised: Quantum computation is a promising emerging technology which, compared to conventional computation, allows for substantial speed-ups e.g. for integer factorization or database search.
Edit intent: clarity
Original:  However, since physical realizations of quantum computers are in their infancy, a significant amount of research in this domain still relies on simulations of quantum computations on classical machines.
Revised:  However, since physical realizations of quantum computers are in their infancy, a significant amount of research in this domain still relies on simulations of quantum computations on conventional machines.
Edit intent: clarity
Original:  This leads to a new graph-based simulation approach which clearly outperforms state-of-the-art simulators (array-based as well as graph-based).
Revised:  This leads to a new graph-based simulation approach which outperforms state-of-the-art simulators (array-based as well as graph-based).
Edit intent: clarity
Original: Cycles (loops) on networks represent feedback processes which play a central role in dynamical self-regulation and resiliency against perturbations in complex systems.
Revised: Networks are versatile representations of the interactions between entities in complex systems. Cycles on such networks represent feedback processes which play a central role in dynamical self-regulation and resiliency against perturbations in complex systems.
Edit intent: clarity
Original:  We introduce a centrality measure to this effect, which quantifies the fraction of the total information flow of the network passing through a loop .
Revised:  We introduce a centrality measure to this effect, which quantifies the fraction of the total information flow of the network passing through the cycle .
Edit intent: clarity
Original:  As an illustration, we study the centrality of strategic ensembles of sectors in the input-output macro-economic model of four countries over the 2000-2014 period.
Revised:  We demonstrate that this measure accurately reflects the impact of events on strategic ensembles of sectors in the input-output macro-economic model of four countries over the 2000-2014 period.
Edit intent: clarity
Original: Individual and national wealth accumulation is here modelled as a recurrently played game between randomly matched pairs of individuals from a large population.
Revised: The accumulation of individual fitness or wealth is modelled as a recurrently played game between randomly matched pairs of individuals from a large population.
Edit intent: clarity
Original: Individual and national wealth accumulation is here modelled as a recurrently played game between randomly matched pairs of individuals from a large population.
Revised: Individual and national wealth accumulation is here modelled as a population game in which pairs of individuals from a large population.
Edit intent: clarity
Original: Graph data model is widely used in many areas, for example, bioinformatics, graph databases , RDF. One of the most common graph queries are navigational queries.
Revised: Graph data models are widely used in many areas, for example, bioinformatics, graph databases , RDF. One of the most common graph queries are navigational queries.
Edit intent: fluency
Original:  The result of query evaluation are implicit relations between nodes of the graph, i.e. paths in the graph.
Revised:  The result of query evaluation is a set of implicit relations between nodes of the graph, i.e. paths in the graph.
Edit intent: clarity
Original:  The answer to the context-free path queries in this approach is usually a set of triples (A, m, n) such that there is a path from  node m to node n whose labeling is derived from a non-terminal A of the given context-free grammar.
Revised:  An answer to a context-free path queries in this approach is usually a set of triples (A, m, n) such that there is a path from node m to node n whose labeling is derived from a non-terminal A of the given context-free grammar.
Edit intent: fluency
Original:  The answer to the context-free path queries in this approach is usually a set of triples (A, m, n) such that there is a path from  node m to node n whose labeling is derived from a non-terminal A of the given context-free grammar.
Revised:  The answer to the context-free path query in this approach is usually a set of triples (A, m, n) such that there is a path from node m to node n whose labeling is derived from a non-terminal A of the given context-free grammar.
Edit intent: fluency
Original:  The answer to the context-free path queries in this approach is usually a set of triples (A, m, n) such that there is a path from  node m to node n whose labeling is derived from a non-terminal A of the given context-free grammar.
Revised:  The answer to the context-free path queries in this approach is usually a set of triples (A, m, n) such that there is a path from the node m to node n whose labeling is derived from a non-terminal A of the given context-free grammar.
Edit intent: fluency
Original:  The answer to the context-free path queries in this approach is usually a set of triples (A, m, n) such that there is a path from  node m to node n whose labeling is derived from a non-terminal A of the given context-free grammar.
Revised:  The answer to the context-free path queries in this approach is usually a set of triples (A, m, n) such that there is a path from node m to the node n, whose labeling is derived from a non-terminal A of the given context-free grammar.
Edit intent: fluency
Original:   There is a number of algorithms for query evaluation which use such semantics but they have computational problems with big data .
Revised:  There is a number of algorithms for query evaluation which use these semantics but all of them perform poorly on large graphs .
Edit intent: clarity
Original:  One of the most common technique for efficient big data processing is GPGPU , but these algorithms do not allow to use this technique effectively .
Revised:  One of the most common technique for efficient big data processing is the use of a graphics processing unit (GPU) to perform computations , but these algorithms do not allow to use this technique effectively .
Edit intent: clarity
Original:  One of the most common technique for efficient big data processing is GPGPU , but these algorithms do not allow to use this technique effectively .
Revised:  One of the most common technique for efficient big data processing is GPGPU , but these algorithms do not allow to use this technique efficiently .
Edit intent: clarity
Original:  In this paper we propose a graph parsing algorithm for query evaluation which use relational query semantics and context-free grammars, and is based on matrix operations which allows to speed up computations by means of GPGPU .
Revised:  In this paper we propose a graph parsing algorithm for query evaluation which uses relational query semantics and context-free grammars, and is based on matrix operations which allows to speed up computations by means of GPGPU .
Edit intent: fluency
Original:  In this paper we propose a graph parsing algorithm for query evaluation which use relational query semantics and context-free grammars, and is based on matrix operations which allows to speed up computations by means of GPGPU .
Revised:  In this paper we propose a graph parsing algorithm for query evaluation which use relational query semantics and is based on matrix operations which allows to speed up computations by means of GPGPU .
Edit intent: clarity
Original:  In this paper we propose a graph parsing algorithm for query evaluation which use relational query semantics and context-free grammars, and is based on matrix operations which allows to speed up computations by means of GPGPU .
Revised:  In this paper we propose a graph parsing algorithm for query evaluation which use relational query semantics and context-free grammars, and is based on matrix operations that make it possible to speed up computations by means of GPGPU .
Edit intent: clarity
Original:  In this paper we propose a graph parsing algorithm for query evaluation which use relational query semantics and context-free grammars, and is based on matrix operations which allows to speed up computations by means of GPGPU .
Revised:  In this paper we propose a graph parsing algorithm for query evaluation which use relational query semantics and context-free grammars, and is based on matrix operations which allows to speed up computations by using a GPU .
Edit intent: clarity
Original:  We construct artificial agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving (following a bad turn try to return to mutual cooperation).
Revised:  We construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving (following a bad turn try to return to mutual cooperation).
Edit intent: clarity
Original:  We show both theoretically and experimentally that generalized tit-for-tat agents can maintain cooperation in more complex environments.
Revised:  We show both theoretically and experimentally that such agents can maintain cooperation in more complex environments.
Edit intent: clarity
Original:   In contrast, we show that employing purely reactive training techniques can lead to agents whose behavior results in socially inefficient outcomes .
Revised:  In contrast, we show that standard 'reactive self-play' paradigms, which have made great strides in zero-sum games, may not be enough for constructing agents that maintain cooperation in positive-sum interactions .
Edit intent: clarity
Original: In social dilemmas individuals face a temptation to increase their payoffs in the short run at a cost to the long run total welfare.
Revised: Social dilemmas are situations where individuals face a temptation to increase their payoffs in the short run at a cost to the long run total welfare.
Edit intent: clarity
Original: In social dilemmas individuals face a temptation to increase their payoffs in the short run at a cost to the long run total welfare.
Revised: In social dilemmas individuals face a temptation to increase their payoffs at a cost to the long run total welfare.
Edit intent: clarity
Original: In social dilemmas individuals face a temptation to increase their payoffs in the short run at a cost to the long run total welfare.
Revised: In social dilemmas individuals face a temptation to increase their payoffs in the short run at a cost to total welfare.
Edit intent: clarity
Original:  We show how to use modern reinforcement learning methods to generalize a highly successful Prisoner's Dilemma strategy: tit-for-tat.
Revised:  We show how to modify modern reinforcement learning methods to generalize a highly successful Prisoner's Dilemma strategy: tit-for-tat.
Edit intent: clarity
Original:  We construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving ( following a bad turn try to return to mutual cooperation).
Revised:  We construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving ( try to return to mutual cooperation).
Edit intent: coherence
Original:  In both cases, the common basic ingredient to achieve interactive frame rates is an acceleration structure based on a k-d forest together with suitable data structures .
Revised:  In both cases, our main contribution to achieve interactive frame rates is an acceleration structure based on a k-d forest together with suitable data structures .
Edit intent: clarity
Original:  As both (unbounded) stochastic volatility models and rough volatility models are examples of fully incomplete markets, one can interpret the buy-and-hold property when super-replicating Markovian claims as a natural phenomenon in incomplete markets.
Revised:  Since both (unbounded) stochastic volatility models and rough volatility models are examples of fully incomplete markets, one can interpret the buy-and-hold property when super-replicating Markovian claims as a natural phenomenon in incomplete markets.
Edit intent: fluency
Original:  By forcing some of the factors to zero, we can safely remove the corresponding structures, thus prune the unimportant parts of a CNN. Compared with other structure selection methods that may need thousands of trials or iterative fine-tuning, our method is trained fully end-to-end in one training pass without bells and whistles.
Revised:  By forcing some of the factors to zero, we can safely remove the corresponding structures, thus prune the unimportant parts of a CNN. Comparing with other structure selection methods that may need thousands of trials or iterative fine-tuning, our method is trained fully end-to-end in one training pass without bells and whistles.
Edit intent: fluency
Original:  We evaluate our method, Sparse Structure Selection with two state-of-the-art CNNs ResNet and ResNeXt , and demonstrate very promising results with adaptive depth and width selection.
Revised:  We evaluate our method, Sparse Structure Selection with two state-of-the-art CNNs , and demonstrate very promising results with adaptive depth and width selection.
Edit intent: coherence
Original: Despite deep neural networks have demonstrated extraordinary power in various applications, their superior performances are at expenses of high storage and computational cost . Consequently, the acceleration and compression of neural networks have attracted much attention recently.
Revised: Despite deep neural networks have demonstrated extraordinary power in various applications, their superior performances are at expense of high storage and computational cost . Consequently, the acceleration and compression of neural networks have attracted much attention recently.
Edit intent: fluency
Original: Despite deep neural networks have demonstrated extraordinary power in various applications, their superior performances are at expenses of high storage and computational cost . Consequently, the acceleration and compression of neural networks have attracted much attention recently.
Revised: Despite deep neural networks have demonstrated extraordinary power in various applications, their superior performances are at expenses of high storage and computational costs . Consequently, the acceleration and compression of neural networks have attracted much attention recently.
Edit intent: fluency
Original:  These latest progresses greatly facilitate the developments in various areas such as computer vision , natural language processing , etc .
Revised:  These latest progresses greatly facilitate the developments in various areas such as computer vision and natural language processing , etc .
Edit intent: fluency
Original:  These latest progresses greatly facilitate the developments in various areas such as computer vision , natural language processing , etc .
Revised:  These latest progresses greatly facilitate the developments in various areas such as computer vision , natural language processing .
Edit intent: coherence
Original:  To transfer them, we bring the  learning to rank  technique into deep metric learning formulation.
Revised:  To transfer them, we bring the " learning to rank technique into deep metric learning formulation.
Edit intent: fluency
Original:  To transfer them, we bring the  learning to rank  technique into deep metric learning formulation.
Revised:  To transfer them, we bring the learning to rank " technique into deep metric learning formulation.
Edit intent: fluency
Original:  Our DarkRank can improve over the baseline method by a large margin.
Revised:  Our method can improve over the baseline method by a large margin.
Edit intent: clarity
Original: We investigate how robust are results of committee elections  to small changes in the input preference orders, depending on the voting rules used.
Revised: We investigate how robust the results of committee elections to small changes in the input preference orders, depending on the voting rules used.
Edit intent: clarity
Original: We investigate how robust are results of committee elections  to small changes in the input preference orders, depending on the voting rules used.
Revised: We investigate how robust are results of committee elections are to small changes in the input preference orders, depending on the voting rules used.
Edit intent: fluency
Original:  We find that for typical rules the effect of making a single swap of adjacent candidates in a single preference order is either that (1) at most one committee member can be replaced, or (2) it is possible that the whole committee can be replaced.
Revised:  We find that for typical rules the effect of making a single swap of adjacent candidates in a single preference order is either that (1) at most one committee member might be replaced, or (2) it is possible that the whole committee can be replaced.
Edit intent: clarity