wanyu/IteraTeR_v2 huggingface dataset:
Original:   highlights further opportunities in terms of hardware/software/ algorithm co-design optimizations and joint optimizations among described hardware and software enhancement modules .
Revised:  highlights further opportunities in terms of hardware/software/ algorithm co-design optimizations (inter/intra-module) .
Edit intent: clarity
Original:  understanding enhancements in acceleration systems for supporting their efficient computations;
Revised:  understanding enhancements in accelerator systems for supporting their efficient computations;
Edit intent: fluency
Original:  analyzing trade-offs in opting for a specific type of design enhancement ;
Revised:  analyzing trade-offs in opting for a specific design choice for encoding, storing, extracting, communicating, computing, and load-balancing the non-zeros ;
Edit intent: clarity
Original:  Further, we show that CM has the lowest WAR for this objective in the class of strategyproof, anonymous , continuous mechanism .
Revised:  Further, we show that CM has the lowest WAR for this objective in the class of anonymous and strategyproof mechanisms .
Edit intent: clarity
Original:  For the p-norm social welfare objective, we find that the AR for CM is bounded above by 2^{3{2}-2{p}} for p\geq 2. Since it follows from \mbox{ feigenbaum_approximately_2017 any deterministic strategyproof mechanism must have AR at least 2^{1-1{p}}  , our upper bound guarantees that the CM mechanism is very close to being the best deterministic strategyproof mechanism for p\geq 2.
Revised:  For the p-norm social welfare objective, we find that the AR for CM is bounded above by 2^{3{2}-2{p}} for p\geq 2. Since any deterministic strategyproof mechanism must have AR at least 2^{1-1{p}} , our upper bound guarantees that the CM mechanism is very close to being the best deterministic strategyproof mechanism for p\geq 2.
Edit intent: coherence
Original:  For the p-norm social welfare objective, we find that the AR for CM is bounded above by 2^{3{2}-2{p}} for p\geq 2. Since it follows from \mbox{ feigenbaum_approximately_2017 any deterministic strategyproof mechanism must have AR at least 2^{1-1{p}}  , our upper bound guarantees that the CM mechanism is very close to being the best deterministic strategyproof mechanism for p\geq 2.
Revised:  For the p-norm social welfare objective, we find that the AR for CM is bounded above by 2^{3{2}-2{p}} for p\geq 2. Since it follows from \mbox{ feigenbaum_approximately_2017 any deterministic strategyproof mechanism must have AR at least 2^{1-1{p}} (\mbox{ feigenbaum_approximately_2017 , our upper bound guarantees that the CM mechanism is very close to being the best deterministic strategyproof mechanism for p\geq 2.
Edit intent: fluency
Original:  For the p-norm social welfare objective, we find that the AR for CM is bounded above by 2^{3{2}-2{p}} for p\geq 2. Since it follows from \mbox{ feigenbaum_approximately_2017 any deterministic strategyproof mechanism must have AR at least 2^{1-1{p}}  , our upper bound guarantees that the CM mechanism is very close to being the best deterministic strategyproof mechanism for p\geq 2.
Revised:  For the p-norm social welfare objective, we find that the AR for CM is bounded above by 2^{3{2}-2{p}} for p\geq 2. Since it follows from \mbox{ feigenbaum_approximately_2017 any deterministic strategyproof mechanism must have AR at least 2^{1-1{p}} , our upper bound suggests that the CM mechanism is very close to being the best deterministic strategyproof mechanism for p\geq 2.
Edit intent: clarity
Original:  In particular, for p=2 (miniSOS) and p=\infty (minimax), the AR of CM is in fact equal to the corresponding lower bound on any deterministic strategyproof mechanism of 2 and 2 respectively.
Revised:  In particular, for p=2 (miniSOS) and p=\infty (minimax), the AR of CM is in fact equal to the lower bound 2 respectively.
Edit intent: clarity
Original: For the DCASE 2020 Challenge Task 4, this paperpro-posed a combinative approach using Nonnegative Matrix Factorization (NMF) and Convolutional Neural Network (CNN)  .
Revised: In this paper, a combinative approach using Nonnegative Matrix Factorization (NMF) and Convolutional Neural Network (CNN) .
Edit intent: coherence
Original:  The main idea begins with utilizing NMF to ap-proximate strong labels for the weakly labeled data.
Revised:  The main idea begins with the use of NMF to approximate strong labels for the weakly labeled data.
Edit intent: fluency
Original:  Sub-sequently, based on the approximated strongly labeled data, two different CNNs are trained using a semi-supervised framework where one CNN is used for clip-level prediction and the other for frame-level prediction.
Revised:  Subsequently, using the approximated strongly labeled data, two different CNNs are trained using a semi-supervised framework where one CNN is used for clip-level prediction and the other for frame-level prediction.
Edit intent: fluency
Original:  Sub-sequently, based on the approximated strongly labeled data, two different CNNs are trained using a semi-supervised framework where one CNN is used for clip-level prediction and the other for frame-level prediction.
Revised:  Sub-sequently, based on the approximated strongly labeled data, two different CNNs are trained in a semi-supervised framework where one CNN is used for clip-level prediction and the other for frame-level prediction.
Edit intent: clarity
Original:  Using this idea, the best model trained can achieve an event-based F1-score of 45.7\% on the  validation dataset.
Revised:  Based on this idea, the best model trained can achieve an event-based F1-score of 45.7\% on the validation dataset.
Edit intent: fluency
Original:  Using this idea, the best model trained can achieve an event-based F1-score of 45.7\% on the  validation dataset.
Revised:  Using this idea, our model can achieve an event-based F1-score of 45.7\% on the validation dataset.
Edit intent: clarity
Original:  By comparing with the base-line model, the proposed model outperforms the baseline model by a margin of over 8\% .
Revised:  By comparing with the baseline model, our proposed models outperform the baseline model by a margin of over 8\% .
Edit intent: clarity
Original:  By comparing with the base-line model, the proposed model outperforms the baseline model by a margin of over 8\% .
Revised:  By comparing with the base-line model, the proposed model outperforms the baseline model by over 8\% .
Edit intent: clarity
Original:  This criteria allows us to easily assert the feasibility by analyzing the unconstrained minimum of a certain convex function, that we form with the given sets.
Revised:  This criteria allows us to easily assert the feasibility by analyzing the unconstrained minimum of a speci?fic convex function, that we form with the given sets.
Edit intent: clarity
Original:  Next an algorithm is presented which extends the idea to a particular non-convex case: assert the inclusion of the  intersection of a set of balls with equal radii in another ball with a different radius . Given a certain condition on the radii is met, our method can decide if the inclusion happens or not.
Revised:  Next an algorithm is presented which extends the idea to a particular non-convex case: assert the inclusion of the fi?nite intersection of a set of balls with equal radii in another ball with a different radius . Given a certain condition on the radii is met, our method can decide if the inclusion happens or not.
Edit intent: fluency
Original: Behavioural distances provide a fine-grained measure of equivalence in systems involving quantitative data, such as probabilistic, fuzzy, or metric systems  . Like in the classical setting of crisp bisimulation-type equivalences , the wide variation found in system types creates a need for generic methods that apply to many system types at once.
Revised: In systems involving quantitative data, such as probabilistic, fuzzy, or metric systems . Like in the classical setting of crisp bisimulation-type equivalences , the wide variation found in system types creates a need for generic methods that apply to many system types at once.
Edit intent: clarity
Original:  An immediate benefit of the latter is that they allow bounding behavioural distance by means of fuzzy bisimulations that need not themselves be (pseudo-)metrics, in analogy to classical bisimulations ( which need not be equivalence relations).
Revised:  An immediate benefit of the latter is that they allow bounding behavioural distance by means of fuzzy (bi-)simulations that need not themselves be (pseudo-)metrics, in analogy to classical bisimulations ( which need not be equivalence relations).
Edit intent: fluency
Original:  An immediate benefit of the latter is that they allow bounding behavioural distance by means of fuzzy bisimulations that need not themselves be (pseudo-)metrics, in analogy to classical bisimulations ( which need not be equivalence relations).
Revised:  An immediate benefit of the latter is that they allow bounding behavioural distance by means of fuzzy bisimulations that need not themselves be hemi- or pseudometrics; this is analogous to classical simulations and bisimulations, which need not be equivalence relations).
Edit intent: clarity
Original:  For non-expansive fuzzy lax extensions, this allows for the extraction of quantitative modal logics that characterize behavioural distance, i.e. satisfy a quantitative version of the Hennessy-Milner theorem;
Revised:  For nonexpansive fuzzy lax extensions, this allows for the extraction of quantitative modal logics that characterize behavioural distance, i.e. satisfy a quantitative version of the Hennessy-Milner theorem;
Edit intent: fluency
Original:  In fact, the Bitcoin system is believed to consume more electricity than several small countries 5 . In this work we formally define three properties that are necessary for wasteless PoW systems: (1) solve "meaningful" problems (2) solve them efficiently and (3) be secure against double-spend attacks.
Revised:  In fact, the Bitcoin system is believed to consume more electricity than several small countries . In this work we formally define three properties that are necessary for wasteless PoW systems: (1) solve "meaningful" problems (2) solve them efficiently and (3) be secure against double-spend attacks.
Edit intent: clarity
Original:   We analyze these properties  and deduce constraints that impose on PoW systems.
Revised:  We analyze these properties , and deduce constraints that impose on PoW systems.
Edit intent: fluency
Original:   We analyze these properties  and deduce constraints that impose on PoW systems.
Revised:  We analyze these properties and deduce constraints that must apply to such PoW systems.
Edit intent: clarity
Original:  In particular , we conclude that under realistic assumptions, the set of allowed functions for mining must be preimage resistant functions .
Revised:  In our main result , we conclude that under realistic assumptions, the set of allowed functions for mining must be preimage resistant functions .
Edit intent: coherence
Original:  In particular , we conclude that under realistic assumptions, the set of allowed functions for mining must be preimage resistant functions .
Revised:  In particular , we conclude that under realistic assumptions, the set of allowed problems must be preimage resistant functions .
Edit intent: clarity
Original:  Through minimal input from humans, these algorithms are able to learn to perform complex tasks from scratch, just through  interaction with their environment.
Revised:  Through minimal input from humans, these algorithms can learn to perform complex tasks from scratch, just through interaction with their environment.
Edit intent: clarity
Original:  In this context , we review a number of findings that establish evidence of key elements of the RL problem and solution being represented in regions of the brain .
Revised:  In this paper , we review a number of findings that establish evidence of key elements of the RL problem and solution being represented in regions of the brain .
Edit intent: clarity
Original: Reinforcement learning methods have been recently been very successful in complex sequential tasks like playing Atari games, Go and Poker.
Revised: Reinforcement learning methods have recently been very successful in complex sequential tasks like playing Atari games, Go and Poker.
Edit intent: fluency
Original: Reinforcement learning methods have been recently been very successful in complex sequential tasks like playing Atari games, Go and Poker.
Revised: Reinforcement learning methods have been recently been very successful at performing complex sequential tasks like playing Atari games, Go and Poker.
Edit intent: clarity
Original:  Through minimal input from humans, these algorithms can learn to perform complex tasks from scratch, just through rewards obtained through interaction with their environment.
Revised:  These algorithms have outperformed humans in several tasks by learning from scratch, just through rewards obtained through interaction with their environment.
Edit intent: clarity
Original:  While there certainly has been considerable independent innovation in the area to produce such results, many core ideas in RL are inspired by  animal learning, psychology and neuroscience phenomena.
Revised:  While there certainly has been considerable independent innovation in the area to produce such results, many core ideas in reinforcement learning are inspired by animal learning, psychology and neuroscience phenomena.
Edit intent: clarity
Original:  While there certainly has been considerable independent innovation in the area to produce such results, many core ideas in RL are inspired by  animal learning, psychology and neuroscience phenomena.
Revised:  While there certainly has been considerable independent innovation in the area to produce such results, many core ideas in RL are inspired by phenomena of animal learning, psychology and neuroscience phenomena.
Edit intent: clarity
Original:  In this paper, we  review a number of key findings in neuroscience and human behavior that provide evidence for the involvement of reinforcement learning  in human learning .
Revised:  In this paper, we review a number of findings in neuroscience and human behavior that provide evidence for the involvement of reinforcement learning in human learning .
Edit intent: clarity
Original:  In this paper, we  review a number of key findings in neuroscience and human behavior that provide evidence for the involvement of reinforcement learning  in human learning .
Revised:  In this paper, we review a number of key findings in neuroscience and psychology that provide evidence for the involvement of reinforcement learning in human learning .
Edit intent: clarity
Original:  In this paper, we  review a number of key findings in neuroscience and human behavior that provide evidence for the involvement of reinforcement learning  in human learning .
Revised:  In this paper, we review a number of key findings in neuroscience and human behavior that provide evidence for the plausibility of reinforcement learning in human learning .
Edit intent: clarity
Original:  Finally, we discuss how these findings have provided inspiration for new RL algorithms .
Revised:  Finally, we discuss the implications of these findings and their role in advancing research in both AI and brain science .
Edit intent: clarity
Original:  While there certainly has been considerable independent innovation in the area to produce such results, many core ideas in reinforcement learning are inspired by phenomena of animal learning, psychology and neuroscience.
Revised:  While there certainly has been considerable independent innovation to produce such results, many core ideas in reinforcement learning are inspired by phenomena of animal learning, psychology and neuroscience.
Edit intent: clarity
Original:  While there certainly has been considerable independent innovation in the area to produce such results, many core ideas in reinforcement learning are inspired by phenomena of animal learning, psychology and neuroscience.
Revised:  While there certainly has been considerable independent innovation in the area to produce such results, many core ideas in reinforcement learning are inspired by phenomena in animal learning, psychology and neuroscience.
Edit intent: clarity
Original:  In this paper, we comprehensively review a  number of findings in  neuroscience and psychology that provide evidence for the plausibility of reinforcement learning being a promising model for phenomena in human learning , decision making and behavior.
Revised:  In this paper, we comprehensively review a number of findings in both neuroscience and psychology that provide evidence for the plausibility of reinforcement learning being a promising model for phenomena in human learning , decision making and behavior.
Edit intent: clarity
Original:  While such scans can identify public open ports, they are not able to provide details on configurations of the system related to legitimate Industrial Traffic passing the Internet (e. g. , source-based filtering in Network Address Translation or Firewalls ) . In this work, we complement Shodan-only analysis with large-scale traffic analysis at a local Internet Exchange Point (IXP), based on sFlow sampling.
Revised:  While such scans can identify publicly open ports, they are not able to provide details on configurations of the system related to legitimate Industrial Traffic passing the Internet (e. g. , source-based filtering in Network Address Translation or Firewalls ) . In this work, we complement Shodan-only analysis with large-scale traffic analysis at a local Internet Exchange Point (IXP), based on sFlow sampling.
Edit intent: fluency
Original:  While such scans can identify public open ports, they are not able to provide details on configurations of the system related to legitimate Industrial Traffic passing the Internet (e. g. , source-based filtering in Network Address Translation or Firewalls ) . In this work, we complement Shodan-only analysis with large-scale traffic analysis at a local Internet Exchange Point (IXP), based on sFlow sampling.
Revised:  While such scans can identify public open ports, they cannot identify legitimate use of insecure industrial traffic. In particular , source-based filtering in Network Address Translation or Firewalls ) . In this work, we complement Shodan-only analysis with large-scale traffic analysis at a local Internet Exchange Point (IXP), based on sFlow sampling.
Edit intent: clarity
Original:  While such scans can identify public open ports, they are not able to provide details on configurations of the system related to legitimate Industrial Traffic passing the Internet (e. g. , source-based filtering in Network Address Translation or Firewalls ) . In this work, we complement Shodan-only analysis with large-scale traffic analysis at a local Internet Exchange Point (IXP), based on sFlow sampling.
Revised:  While such scans can identify public open ports, they are not able to provide details on configurations of the system related to legitimate Industrial Traffic passing the Internet (e. g. , source-based filtering in Network Address Translation or Firewalls ) . In this work, we compare Shodan-only analysis with large-scale traffic analysis at a local Internet Exchange Point (IXP), based on sFlow sampling.
Edit intent: clarity
Original:  This setup allows us to identify ICS endpoints actually exchanging Industrial Traffic over the Internet.
Revised:  This setup allows us to identify ICS endpoints actually exchanging industrial traffic over the Internet.
Edit intent: fluency
Original:  We show that 75.6\% of ICS hosts  rely on unencrypted communications without integrity protection, leaving those critical systems vulnerable to malicious attacks.
Revised:  We show that 75.6\% of ICS hosts still rely on unencrypted communications without integrity protection, leaving those critical systems vulnerable to malicious attacks.
Edit intent: clarity
Original:  Such solvers determine all finite eigenvalues in a prescribed region in the complex plane and the corresponding eigenvectors.
Revised:  Such solvers determine all finite eigenvalues in a given region in the complex plane and the corresponding eigenvectors.
Edit intent: clarity
Original:  The challenge is to extend the definition of a complex matrix moment to the non-square case .
Revised:  The challenge is to extend the definition of a complex moment matrix to the nonsquare case has posed a challenge .
Edit intent: clarity
Original:  This extension naively replaces the standard matrix inverse in the resolvent with the pseudoinverse.
Revised:  This extension replaces the standard matrix inverse in the resolvent with the pseudoinverse.
Edit intent: clarity
Original:  The extended method involves complex moments associated with contour integrals of generalized resolvents associated with rectangular matrices.
Revised:  The extended method involves complex moments given by contour integrals of generalized resolvents associated with rectangular matrices.
Edit intent: clarity
Original:  We give conditions such that this method gives all finite eigenvalues in a prescribed region in the complex plane and the corresponding eigenvectors.
Revised:  We give conditions such that the method gives all finite eigenvalues in a prescribed region in the complex plane and the corresponding eigenvectors.
Edit intent: fluency
Original:  In numerical computations, the contour integrals are approximated by using numerical quadratures.
Revised:  In numerical computations, the contour integrals are approximated using numerical quadratures.
Edit intent: fluency
Original:  The dominant cost is in the solutions of linear least squares problems that arise from quadrature points, and they can be readily parallelized in practice.
Revised:  The primary cost is in the solutions of linear least squares problems that arise from quadrature points, and they can be readily parallelized in practice.
Edit intent: clarity
Original:  Numerical experiments on large matrix pencils illustrate the method , comparing with previous methods, and show that the new method is  more robust and efficient , in particular, in parallel.
Revised:  Numerical experiments on large matrix pencils illustrate the method . As compared to previous methods, and show that the new method is more robust and efficient , in particular, in parallel.
Edit intent: coherence
Original:  Numerical experiments on large matrix pencils illustrate the method , comparing with previous methods, and show that the new method is  more robust and efficient , in particular, in parallel.
Revised:  Numerical experiments on large matrix pencils illustrate the method , comparing with previous methods, the new method is more robust and efficient , in particular, in parallel.
Edit intent: coherence
Original:  Numerical experiments on large matrix pencils illustrate the method , comparing with previous methods, and show that the new method is  more robust and efficient , in particular, in parallel.
Revised:  Numerical experiments on large matrix pencils illustrate the method , comparing with previous methods, and show that the new method is found to be more robust and efficient , in particular, in parallel.
Edit intent: coherence
Original:  Further , the proposed method does not fail for the existence of many pairs of extremely close eigenvalues and breaks the previous barrier of problem size .
Revised:  Further , the proposed method does not fail in cases involving pairs of extremely close eigenvalues and breaks the previous barrier of problem size .
Edit intent: clarity
Original:  Further , the proposed method does not fail for the existence of many pairs of extremely close eigenvalues and breaks the previous barrier of problem size .
Revised:  Further , the proposed method does not fail for the existence of many pairs of extremely close eigenvalues ; it overcomes the issue of problem size .
Edit intent: clarity