wanyu/IteraTeR_v2 huggingface dataset:
Original:  These approachestrain the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on ab-normal events.
Revised:  These approachestrain the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on abnormal events.
Edit intent: clarity
Original:  The architecture, however, comes with thecomputational burden as some anomaly detection tasks re-quire low computational cost without sacrificing perfor-mance .
Revised:  The architecture, however, comes with the computational burden as some anomaly detection tasks re-quire low computational cost without sacrificing perfor-mance .
Edit intent: fluency
Original:  The architecture, however, comes with thecomputational burden as some anomaly detection tasks re-quire low computational cost without sacrificing perfor-mance .
Revised:  The architecture, however, comes with thecomputational burden as some anomaly detection tasks require low computational cost without sacrificing perfor-mance .
Edit intent: fluency
Original:  The architecture, however, comes with thecomputational burden as some anomaly detection tasks re-quire low computational cost without sacrificing perfor-mance .
Revised:  The architecture, however, comes with thecomputational burden as some anomaly detection tasks re-quire low computational cost without sacrificing performance .
Edit intent: fluency
Original:  In this paper, Cross-Parallel Network (CPNet) forefficient anomaly detection is proposed here to minimizecomputations without performance drops.
Revised:  In this paper, Cross-Parallel Network (CPNet) for efficient anomaly detection is proposed here to minimizecomputations without performance drops.
Edit intent: fluency
Original:  In this paper, Cross-Parallel Network (CPNet) forefficient anomaly detection is proposed here to minimizecomputations without performance drops.
Revised:  In this paper, Cross-Parallel Network (CPNet) forefficient anomaly detection is proposed here to minimize computations without performance drops.
Edit intent: fluency
Original:  It consists ofNsmaller parallel U-Net, each of which is designed to handlea single input frame, to make the calculations significantlymore efficient.
Revised:  It consists of N smaller parallel U-Net, each of which is designed to handlea single input frame, to make the calculations significantlymore efficient.
Edit intent: fluency
Original:  It consists ofNsmaller parallel U-Net, each of which is designed to handlea single input frame, to make the calculations significantlymore efficient.
Revised:  It consists ofNsmaller parallel U-Net, each of which is designed to handle a single input frame, to make the calculations significantlymore efficient.
Edit intent: fluency
Original:  It consists ofNsmaller parallel U-Net, each of which is designed to handlea single input frame, to make the calculations significantlymore efficient.
Revised:  It consists ofNsmaller parallel U-Net, each of which is designed to handlea single input frame, to make the calculations significantly more efficient.
Edit intent: fluency
Original:  Additionally, an inter-network shift moduleis incorporated to capture temporal relationships among se-quential frames to enable more accurate future predictions.
Revised:  Additionally, an inter-network shift module is incorporated to capture temporal relationships among se-quential frames to enable more accurate future predictions.
Edit intent: fluency
Original:  Additionally, an inter-network shift moduleis incorporated to capture temporal relationships among se-quential frames to enable more accurate future predictions.
Revised:  Additionally, an inter-network shift moduleis incorporated to capture temporal relationships among sequential frames to enable more accurate future predictions.
Edit intent: fluency
Original: The quantitative results show that our model requires lesscomputational cost than the baseline U-Net while deliver-ing equivalent performance in anomaly detection.
Revised: The quantitative results show that our model requires less computational cost than the baseline U-Net while deliver-ing equivalent performance in anomaly detection.
Edit intent: fluency
Original: The quantitative results show that our model requires lesscomputational cost than the baseline U-Net while deliver-ing equivalent performance in anomaly detection.
Revised: The quantitative results show that our model requires lesscomputational cost than the baseline U-Net while delivering equivalent performance in anomaly detection.
Edit intent: fluency
Original:  Existing structural analysis methods need flattening  the hierarchical models into an equation system for analysis.
Revised:  Existing structural analysis methods need flattening of the hierarchical models into an equation system for analysis.
Edit intent: clarity
Original:  However, the large-scale equations in complex models make the structural analysis difficult.
Revised:  However, the large-scale equations in complex models make structural analysis difficult.
Edit intent: coherence
Original:  This method obtains the singularity of a hierarchical equation-oriented model by analyzing the dummy model constructed with the parts from the decomposing results of its components.
Revised:  This method obtains the singularity of a hierarchical equation-oriented model by analyzing a dummy model constructed with the parts from the decomposing results of its components.
Edit intent: fluency
Original:  This method can be adaptively applied to nonlinear algebraic and differential-algebraic equation models.
Revised:  This method can be adaptively applied to nonlinear-algebraic and differential-algebraic equation models.
Edit intent: fluency
Original:  The main algorithms, application cases , and comparison with the existing methods are present in the paper.
Revised:  The main algorithms, application cases and comparison with the existing methods are present in the paper.
Edit intent: fluency
Original:  Complexity analysis results show the enhanced efficiency of the proposed method in  structural analysis of complex equation-oriented models.
Revised:  Complexity analysis results show the enhanced efficiency of the proposed method in the structural analysis of complex equation-oriented models.
Edit intent: fluency
Original:  As compared with the existing methods, the time complexity of the proposed method is improved significantly.
Revised:  Compared with the existing methods, the time complexity of the proposed method is improved significantly.
Edit intent: coherence
Original:  We also provide detailed analysis topave the way for future research.
Revised:  We also provide detailed analysis to pave the way for future research.
Edit intent: fluency
Original:  By coping with the trope understanding task and enabling the deep cognition skills of machines, we are optimistic that data mining applications and algorithms could be taken to the next level.
Revised:  By coping with the trope understanding task and enabling the deep cognition skills of machines, data mining applications and algorithms could be taken to the next level.
Edit intent: coherence
Original:  To address this issue, we present instance-wise hard Negative Example Generation for Contrastive learning in Unpaired image-to-image Translation ~ (NEGCUT).
Revised:  To address this issue, we present instance-wise hard Negative Example Generation for Contrastive learning in Unpaired image-to-image Translation (NEGCUT).
Edit intent: fluency
Original: Pre-trained models for programming languages have proven their significant values in various code-related tasks, such as code search, code clone detection, and code translation.
Revised: Code pre-trained models have shown great success in various code-related tasks, such as code search, code clone detection, and code translation.
Edit intent: clarity
Original:  Currently, most pre-trained models  treat a code snippet as a  sequence of tokens or only focus on the data flow between code identifiers .
Revised:  Currently, most pre-trained models often treat a code snippet as a sequence of tokens or only focus on the data flow between code identifiers .
Edit intent: clarity
Original:  Currently, most pre-trained models  treat a code snippet as a  sequence of tokens or only focus on the data flow between code identifiers .
Revised:  Currently, most pre-trained models treat a code snippet as a sequence of tokens .
Edit intent: clarity
Original:  However, rich code syntax and hierarchy are ignored which can provide important structure information and semantic rules of codes to help enhance code representations.
Revised:  However, the inherent syntax and hierarchy are ignored which can provide important structure information and semantic rules of codes to help enhance code representations.
Edit intent: clarity
Original:  However, rich code syntax and hierarchy are ignored which can provide important structure information and semantic rules of codes to help enhance code representations.
Revised:  However, rich code syntax and hierarchy that provide important structure information and semantic rules of codes to help enhance code representations.
Edit intent: clarity
Original:  To address these problems , we propose CLSEBERT, a Constrastive Learning Framework for Syntax Enhanced Code Pre-Trained Model, to deal with various code intelligence tasks.
Revised:  To address these problems , we propose CLSEBERT, a Contrastive Learning Framework for Syntax Enhanced Code Pre-Trained Model, to deal with various code intelligence tasks.
Edit intent: fluency
Original:  Besides the masked language modeling (MLM)  , we also introduce two novel pre-training objectives .
Revised:  Besides the masked language modeling (MLM) objective , we also introduce two novel pre-training objectives .
Edit intent: fluency
Original:  Through extensive experiments on four code intelligence tasks , we successfully show the effectiveness of our proposed model .
Revised:  Extensive experiments on four code intelligence tasks , we successfully show the effectiveness of our proposed model .
Edit intent: fluency
Original:  (2) ``Code Token Type Prediction (TTP)'' to predict the types of code tokens . Extensive experiments on four code intelligence tasks demonstrate the superior performance of CLSEBERT compared to state-of-the-art at the same pre-training corpus and parameter scale .
Revised:  (2) ``Code Token Type Prediction (TTP)'' to predict the types of code tokens . Extensive experiments on four downstream tasks related to code intelligence show that SynCoBERT advances the state-of-the-art at the same pre-training corpus and parameter scale .
Edit intent: clarity
Original:  (2) ``Code Token Type Prediction (TTP)'' to predict the types of code tokens . Extensive experiments on four code intelligence tasks demonstrate the superior performance of CLSEBERT compared to state-of-the-art at the same pre-training corpus and parameter scale .
Revised:  (2) ``Code Token Type Prediction (TTP)'' to predict the types of code tokens . Extensive experiments on four code intelligence tasks demonstrate the superior performance of CLSEBERT compared to state-of-the-art with the same pre-training corpus and parameter scale .
Edit intent: fluency
Original:  (2) ``Code Token Type Prediction (TTP)'' to predict the types of code tokens . Extensive experiments on four code intelligence tasks demonstrate the superior performance of CLSEBERT compared to state-of-the-art at the same pre-training corpus and parameter scale .
Revised:  (2) ``Code Token Type Prediction (TTP)'' to predict the types of code tokens . Extensive experiments on four code intelligence tasks demonstrate the superior performance of CLSEBERT compared to state-of-the-art at the same pre-training corpus and model size .
Edit intent: clarity
Original: General nonlinear continuous-time systems are considered for which the state is to be estimated via a packet-based communication network.
Revised: General nonlinear continuous-time systems are considered for which its state is estimated via a packet-based communication network.
Edit intent: clarity
Original:  We assume that the system has multiple sensor nodes, affected by measurement noise, which can transmit output data at discrete (non-equidistant) and asynchronous points in time.
Revised:  We assume that the system has multiple sensor nodes, affected by measurement noise, which can transmit at discrete (non-equidistant) and asynchronous points in time.
Edit intent: clarity
Original:  We assume that the system has multiple sensor nodes, affected by measurement noise, which can transmit output data at discrete (non-equidistant) and asynchronous points in time.
Revised:  We assume that the system has multiple sensor nodes, affected by measurement noise, which can transmit output data at discrete (non-equidistant) points in time.
Edit intent: clarity
Original:  For this general system setup, we develop a state estimation framework, where the transmission instances of the individual sensor nodes can be generated in both time-triggered and event-triggered fashions.
Revised:  For this general system setup, we develop a state estimation framework, where the transmission instances of the individual sensor nodes can be generated in either time-triggered and event-triggered fashions.
Edit intent: fluency
Original:  For this general system setup, we develop a state estimation framework, where the transmission instances of the individual sensor nodes can be generated in both time-triggered and event-triggered fashions.
Revised:  For this general system setup, we develop a state estimation framework, where the transmission instances of the individual sensor nodes can be generated in both time-triggered or event-triggered fashions.
Edit intent: fluency
Original:  The MAP estimator essentially coincides with the (regularised) variational solution to the inverse problem, seen as minimisation of the Onsager--Machlup functional of the posterior measure.
Revised:  The MAP estimator essentially coincides with the (regularised) variational solution to the inverse problem, seen as minimisation of the Onsager-Machlup functional of the posterior measure.
Edit intent: fluency
Original:  To address this problem, we propose a general convergence theory for modes that is based on the \Gamma-convergence of Onsager--Machlup functionals, and apply this theory to Bayesian inverse problems with Gaussian and edge-preserving Besov priors.
Revised:  To address this problem, we propose a general convergence theory for modes that is based on the \Gamma-convergence of Onsager-Machlup functionals, and apply this theory to Bayesian inverse problems with Gaussian and edge-preserving Besov priors.
Edit intent: fluency
Original: We derive Onsager--Machlup functionals for countable product measures on weighted \ell^p subspaces of the sequence space R^{N}. Each measure in the product is a shifted and scaled copy of a reference probability measure on R that admits a sufficiently regular Lebesgue density.
Revised: We derive Onsager-Machlup functionals for countable product measures on weighted \ell^p subspaces of the sequence space R^{N}. Each measure in the product is a shifted and scaled copy of a reference probability measure on R that admits a sufficiently regular Lebesgue density.
Edit intent: fluency
Original:  We study the equicoercivity and \Gamma-convergence of sequences of Onsager--Machlup functionals associated to convergent sequences of measures within this class.
Revised:  We study the equicoercivity and \Gamma-convergence of sequences of Onsager-Machlup functionals associated to convergent sequences of measures within this class.
Edit intent: fluency
Original:   In this paper, we  study turn-based stochastic two-player games on graphs where the winning condition is to satisfy at least one reachability or safety objective from a given set of alternatives with at least some desired probability.
Revised:  In this paper, we study turn-based stochastic two-player games on graphs where the winning condition is to guarantee at least one reachability or safety objective from a given set of alternatives with at least some desired probability.
Edit intent: clarity
Original:  Here, we focus on two such problems that are related : influence estimation, a \# P-hard counting problem, and influence maximization, an NP-hard problem.
Revised:  Here, we focus on two such problems : influence estimation, a \# P-hard counting problem, and influence maximization, an NP-hard problem.
Edit intent: clarity
Original:  Here, we focus on two such problems that are related : influence estimation, a \# P-hard counting problem, and influence maximization, an NP-hard problem.
Revised:  Here, we focus on two such problems that are related : influence estimation, a P-hard counting problem, and influence maximization, an NP-hard problem.
Edit intent: coherence
Original:  Experiments show that GLIE can provide accurate predictions faster than the alternatives for graphs 10 times larger than the train set.
Revised:  Experiments show that GLIE provides accurate influence estimation for real graphs up to 10 times larger than the train set.
Edit intent: clarity
Original:  More importantly, it can be used on arbitrary large graphs for influence maximization  , as the predictions can rank effectively seed sets even when the accuracy deteriorates.
Revised:  More importantly, it can be used for influence maximization , as the predictions can rank effectively seed sets even when the accuracy deteriorates.
Edit intent: clarity
Original:  We also transfer GLIE into a reinforcement learning model that learns how to choose seeds to maximize influence sequentially using GLIE's hidden representations and predictions.
Revised:  We also transfer GLIE into a reinforcement learning model that learns how to choose seeds sequentially using GLIE's hidden representations and predictions.
Edit intent: clarity
Original:  We also transfer GLIE into a reinforcement learning model that learns how to choose seeds to maximize influence sequentially using GLIE's hidden representations and predictions.
Revised:  We also transfer GLIE into a reinforcement learning model that learns how to choose seeds to maximize influence sequentially using GLIE's predictions.
Edit intent: clarity
Original:  More importantly, it can be used for influence maximization on considerably larger graphs, as the predictions ranking is not effected by the drop of accuracy.
Revised:  More importantly, it can be used for influence maximization on considerably larger graphs, as the predictions ranking is not affected by the drop of accuracy.
Edit intent: fluency
Original:  In this paper, bench scale batch reactors were set-up to investigate the effect of volatile suspended solid (VSS) concentration and pH on the VFArich stream formation;
Revised:  In this paper, bench scale batch reactors were set-up to investigate the effect of volatile suspended solid (VSS) and pH on the VFArich stream formation;
Edit intent: clarity
Original:  We name the problem as the `` submergence" of personalization.
Revised:  We name the problem as the " submergence" of personalization.
Edit intent: fluency
Original: We introduce BIDCD - the Bosch Industrial Depth Completion Dataset.
Revised: We introduce BIDCD -- the Bosch Industrial Depth Completion Dataset.
Edit intent: fluency
Original: In this paper , we consider three related cost-sparsity induced optimal input selection problems for structural controllability using a unifying linear programming (LP) framework.
Revised: This paper investigates several cost-sparsity induced optimal input selection problems for structural controllability using a unifying linear programming (LP) framework.
Edit intent: clarity
Original:  and selecting the input links with the cost as small as possible while their cardinalityis not exceeding a prescribed number , all to ensure structural controllability of the resulting systems . Current studies show that in the dedicated input case (i.e., each input can actuate only a state variable), the first and second problems are polynomially solvable by some graph-theoretic algorithms, while the general nontrivial constrained case is largely unexploited.
Revised:  and selecting the input links with the smallest possible cost with a bound on their cardinality , all to ensure structural controllability of the resulting systems . Current studies show that in the dedicated input case (i.e., each input can actuate only a state variable), the first and second problems are polynomially solvable by some graph-theoretic algorithms, while the general nontrivial constrained case is largely unexploited.
Edit intent: clarity
Original:  and selecting the input links with the cost as small as possible while their cardinalityis not exceeding a prescribed number , all to ensure structural controllability of the resulting systems . Current studies show that in the dedicated input case (i.e., each input can actuate only a state variable), the first and second problems are polynomially solvable by some graph-theoretic algorithms, while the general nontrivial constrained case is largely unexploited.
Revised:  and selecting the input links with the cost as small as possible while their cardinalityis not exceeding a prescribed number , all to ensure system structural controllability . Current studies show that in the dedicated input case (i.e., each input can actuate only a state variable), the first and second problems are polynomially solvable by some graph-theoretic algorithms, while the general nontrivial constrained case is largely unexploited.
Edit intent: clarity
Original:  In this paper, we formulate these problems as equivalent integer linear programming (ILP) problems.
Revised:  We show these problems can be formulated as equivalent integer linear programming (ILP) problems.
Edit intent: clarity
Original:  Under a weaker constraint on the prescribed input configurations than most of the currently known ones with which the first two problems are reportedly polynomially solvable, we show these ILPs can be solved by simply removing the integer constraints and solving the corresponding LPproblems, thus providing a unifying algebraic method , rather than graph-theoretic, for these problems with polynomial time complexity.
Revised:  Subject to a certain condition on the prescribed input configurations than most of the currently known ones with which the first two problems are reportedly polynomially solvable, we show these ILPs can be solved by simply removing the integer constraints and solving the corresponding LPproblems, thus providing a unifying algebraic method , rather than graph-theoretic, for these problems with polynomial time complexity.
Edit intent: clarity
Original:  Under a weaker constraint on the prescribed input configurations than most of the currently known ones with which the first two problems are reportedly polynomially solvable, we show these ILPs can be solved by simply removing the integer constraints and solving the corresponding LPproblems, thus providing a unifying algebraic method , rather than graph-theoretic, for these problems with polynomial time complexity.
Revised:  Under a weaker constraint on the prescribed input configurations than most of the currently known ones with which the first two problems are reportedly polynomially solvable, we show these ILPs can be solved by simply removing the integer constraints and solving the corresponding LPproblems, thus providing a unifying algebraic method for these problems with polynomial time complexity.
Edit intent: coherence
Original: Despite the sophisticated phishing email detection systems, and training and awareness programs, humans continue to be tricked by phishing emails.
Revised: Despite sophisticated phishing email detection systems, and training and awareness programs, humans continue to be tricked by phishing emails.
Edit intent: fluency