wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our experiments with in- and out-of-distribution samples selected from a subset of the Moments-in-Time (MiT) dataset show  more reliable confidence measure as compared to the non-Bayesian baseline  .
Revised:  Our experiments with in- and out-of-distribution samples selected from a subset of the Moments-in-Time (MiT) dataset show a more reliable confidence measure as compared to the non-Bayesian baseline .
Edit intent: fluency
Original:  We also demonstrate the uncertainty estimates obtained from this framework can identify out-of-distribution data on the UCF101 and MiT datasets.
Revised:  We also demonstrate the uncertainty estimates obtained from the proposed framework can identify out-of-distribution data on the UCF101 and MiT datasets.
Edit intent: clarity
Original:   Locality Sensitive Hashing (LSH) is a fast nearest neighbor search algorithm and the recent study shows that LSH enables quick retrieval of a relevant voice in the large-scale data in conjunction with i-vector while maintaining accuracy.
Revised:  A recent study shows that Locality Sensitive Hashing (LSH) is a fast nearest neighbor search algorithm and the recent study shows that LSH enables quick retrieval of a relevant voice in the large-scale data in conjunction with i-vector while maintaining accuracy.
Edit intent: coherence
Original:   Locality Sensitive Hashing (LSH) is a fast nearest neighbor search algorithm and the recent study shows that LSH enables quick retrieval of a relevant voice in the large-scale data in conjunction with i-vector while maintaining accuracy.
Revised:  Locality Sensitive Hashing (LSH) enables quick retrieval of a relevant voice in the large-scale data in conjunction with i-vector while maintaining accuracy.
Edit intent: coherence
Original:  Additionally, a random subset of the speaker in the training data was chosen for speaker label for LDA to produce multiple RSS . From the experimental result, the proposed approach shows 100 times and 7 times faster than the linear search and LSH, respectively .
Revised:  Additionally, a random subset of the speaker in the training data was chosen for speaker label for LDA to produce multiple RSS . From the experimental result, the proposed approach shows 100 times and 7 times faster than the linear search and LSH, respectively 
Edit intent: fluency
Original:  Since computing VNGE is required to find all eigenvalues, it is computationally demanding for a large-scale graph.
Revised:  However, computing VNGE is required to find all eigenvalues, it is computationally demanding for a large-scale graph.
Edit intent: fluency
Original:  Since computing VNGE is required to find all eigenvalues, it is computationally demanding for a large-scale graph.
Revised:  Since computing VNGE is extensively demanding for a large-scale graph.
Edit intent: clarity
Original:  Computational simulations on random graph models and various real network datasets demonstrate the superior performance.
Revised:  Computational simulations on random graph models and various real network datasets demonstrate superior performance.
Edit intent: fluency
Original: We consider fractional Sobolev spaces H^\theta(\Gamma), \theta \in [0,1]  on a 2D surface \Gamma.
Revised: We consider fractional Sobolev spaces H^\theta(\Gamma), \theta \in [0,1] , on a 2D surface \Gamma.
Edit intent: fluency
Original: In this paper, we present several new a posteriori error estimators for decoupled errors and two adaptive mixed finite element methods AMFEM1 and AMFEM2 for the Hodge Laplacian problem in finite element exterior calculus.
Revised: In this paper, we present several new a posteriori error estimators and two adaptive mixed finite element methods AMFEM1 and AMFEM2 for the Hodge Laplacian problem in finite element exterior calculus.
Edit intent: coherence
Original:  Comparing to existing literature, our results work on Lipschitz domains with nontrivial cohomology and provide the first norm convergence and optimality results.
Revised:  As opposed to existing literature, our results work on Lipschitz domains with nontrivial cohomology and provide the first norm convergence and optimality results.
Edit intent: coherence
Original:  To disentangle the factors without any supervision, our key idea is to use information theory to associate each factor to a latent code, and to condition the relationships between the codes in a specific way to induce the desired hierarchy.
Revised:  To disentangle the factors without supervision, our key idea is to use information theory to associate each factor to a latent code, and to condition the relationships between the codes in a specific way to induce the desired hierarchy.
Edit intent: clarity
Original:  In particular, we develop a numerical approach for estimating genotypes' fitnesses in locked-box  models describing bacterial evolution in experimental setups similar to the celebrated Lenski experiment .
Revised:  In particular, we develop a numerical approach for estimating genotypes' fitnesses in locked-box models describing bacterial genetic evolution similar to the celebrated Lenski experiment .
Edit intent: clarity
Original:  In the case where the trader is creating an instantaneous market impact, we show that transactions costs resulting from the optimal adaptive strategy are substantially lower than the corresponding costs of the optimal static strategy.
Revised:  When the trader creates an instantaneous market impact, we show that transactions costs resulting from the optimal adaptive strategy are substantially lower than the corresponding costs of the optimal static strategy.
Edit intent: clarity
Original:  In the case where the trader is creating an instantaneous market impact, we show that transactions costs resulting from the optimal adaptive strategy are substantially lower than the corresponding costs of the optimal static strategy.
Revised:  In the case where the trader is creating an instantaneous market impact, it is shown that transaction costs of optimal adaptive strategies are substantially lower than the corresponding costs of the optimal static strategy.
Edit intent: clarity
Original:  We show that strategies in which the trader is observing the signal a number of times during the trading period, can dramatically reduce the transaction costs and improve the performance of the optimal static strategy .
Revised:  We show that strategies in which the trader is observing the signal a number of times can dramatically reduce the transaction costs and improve the performance of the optimal static strategy .
Edit intent: clarity
Original:  These results answer a question which was raised by Brigo and Piat 6%DIFDELCMD < ]%%% , by analyzing two cases where adaptive strategies can improve the performance of the execution .
Revised:  These results answer a question which was raised by Brigo and Piat %DIFDELCMD < ]%%% , by analyzing two cases where adaptive strategies can improve the performance of the execution .
Edit intent: coherence
Original:  These results answer a question which was raised by Brigo and Piat 6%DIFDELCMD < ]%%% , by analyzing two cases where adaptive strategies can improve the performance of the execution .
Revised:  These results answer a question which was raised by Brigo and Piat 6%DIFDELCMD < ]%%% .
Edit intent: coherence
Original: As one type of complex networks widely-seen in real-world application, heterogeneous information networks (HINs ) often encapsulate higher-order interactions that crucially reflect the complex nature among nodes and edges in real-world data.
Revised: Heterogeneous information networks (HINs) with rich semantics are ubiquitous in real-world applications. For a given HIN, many reasonable clustering results with distinct semantic meaning can simultaneously exist. User-guided clustering is hence of great practical value for HINs where users provide labels to a small portion of nodes. To cater to a broad spectrum of user guidance evidenced by different expected clustering results, carefully exploiting the signals residing in the data is potentially useful. Meanwhile, as one type of complex networks widely-seen in real-world application, heterogeneous information networks (HINs ) often encapsulate higher-order interactions that crucially reflect the complex nature among nodes and edges in real-world data.
Edit intent: coherence
Original: As one type of complex networks widely-seen in real-world application, heterogeneous information networks (HINs ) often encapsulate higher-order interactions that crucially reflect the complex nature among nodes and edges in real-world data.
Revised: As one type of complex networks , HINs often encapsulate higher-order interactions that crucially reflect the complex nature among nodes and edges in real-world data.
Edit intent: clarity
Original: As one type of complex networks widely-seen in real-world application, heterogeneous information networks (HINs ) often encapsulate higher-order interactions that crucially reflect the complex nature among nodes and edges in real-world data.
Revised: As one type of complex networks widely-seen in real-world application, heterogeneous information networks (HINs ) often encapsulate higher-order interactions that reflect the interlocked nature among nodes and edges in real-world data.
Edit intent: clarity
Original:  At the same time, network motifs have been used extensively to reveal higher-order interactions and network semanticsin homogeneous networks.
Revised:  At the same time, network motifs have been used as tools to capture such higher-order interactions and network semanticsin homogeneous networks.
Edit intent: clarity
Original:  In our experiment, MoCHIN surpasses all baselines in three evaluation tasks under  different metrics.
Revised:  In our experiment, MoCHIN surpasses all baselines in three evaluation tasks under three different metrics.
Edit intent: fluency
Original:  The advantage of our model when the supervision is weak is also discussed in additional experiments .
Revised:  Additional experiments demonstrated the utility of motifs and the benefit of directly modeling higher-order information especially when user guidance is limited .
Edit intent: clarity
Original:  For this setup , we characterize the length of the shortest path between a typical vehicular user and its nearest facility that can be reached by traveling along the streets.
Revised:  For this setup , we characterize the length of the shortest path between the typical point and its nearest facility that can be reached by traveling along the streets.
Edit intent: clarity
Original:  For a typical vehicular user starting from an intersection, we derive the closed-form expression for the exact cumulative distribution function (CDF) of the length of the shortest path  to its nearest facility in the sense of path distance.
Revised:  For a typical vehicular user starting from an intersection, we derive the exact cumulative distribution function (CDF) of the length of the shortest path to its nearest facility in the sense of path distance.
Edit intent: clarity
Original:  For a typical vehicular user starting from an intersection, we derive the closed-form expression for the exact cumulative distribution function (CDF) of the length of the shortest path  to its nearest facility in the sense of path distance.
Revised:  For a typical vehicular user starting from an intersection, we derive the closed-form expression for the exact cumulative distribution function (CDF) of the length of the shortest path to its nearest neighbor in the sense of path distance.
Edit intent: clarity
Original:  Building on this result , we derive an upper bound and a remarkably accurate but approximate lower bound on the CDF of the shortest path distance to the nearest facility for a typical vehicle starting from an arbitrary position on a road.
Revised:  Further , we derive an upper bound and a remarkably accurate but approximate lower bound on the CDF of the shortest path distance to the nearest facility for a typical vehicle starting from an arbitrary position on a road.
Edit intent: coherence
Original:  Building on this result , we derive an upper bound and a remarkably accurate but approximate lower bound on the CDF of the shortest path distance to the nearest facility for a typical vehicle starting from an arbitrary position on a road.
Revised:  Building on this result , we derive an upper bound and a remarkably accurate but approximate lower bound on the CDF of the shortest path distance for the typical point at an arbitrary position on a road.
Edit intent: clarity
Original:  We also discuss extension of this work to other cost metrics and possible applications to the areas of urban planning, personnel deployment and wireless communication .
Revised:  We also discuss the application of these results in infrastructure planning, wireless communication, and transportation networks .
Edit intent: clarity
Original:  In this paper, we characterize the length of the shortest path between the typical point and its nearest neighbor in the sense of path distance in a stationary Manhattan Poisson line Cox process (MPLCP), which is a variant of the PLCP.
Revised:  In this paper, we solve this problem for the stationary Manhattan Poisson line Cox process (MPLCP), which is a variant of the PLCP.
Edit intent: clarity
Original:  First , we derive the exact cumulative distribution function (CDF) of the length of the shortest path for the typical point at an intersection to its nearest neighbor in the sense of path distance .
Revised:  Specifically , we derive the exact cumulative distribution function (CDF) of the length of the shortest path for the typical point at an intersection to its nearest neighbor in the sense of path distance .
Edit intent: coherence
Original:  First , we derive the exact cumulative distribution function (CDF) of the length of the shortest path for the typical point at an intersection to its nearest neighbor in the sense of path distance .
Revised:  First , we derive the exact cumulative distribution function (CDF) for the length of the shortest path for the typical point at an intersection to its nearest neighbor in the sense of path distance .
Edit intent: fluency
Original:  Based on Betram et al 2004 model, we construct a kinetic models to analyze the thermodynamic characteristics of this system, to reveal how the ATP hydrolysis free energy affects the calcium oscillation.
Revised:  Based on Betram et . al. 2004 model, we construct a kinetic models to analyze the thermodynamic characteristics of this system, to reveal how the ATP hydrolysis free energy affects the calcium oscillation.
Edit intent: fluency
Original: The question whether one can recover the shape of a geometric object from its Laplacian spectrum ( ` hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications.
Revised: The question whether one can recover the shape of a geometric object from its Laplacian spectrum ( ' hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications.
Edit intent: fluency
Original:  In this paper, we introduce a numerical procedure called %DIFDELCMD < {\em %%% isospectralization , consisting of deforming one shape to make its Laplacian spectrum match that of another.
Revised:  In this paper, we introduce a numerical procedure called %DIFDELCMD < {\em %%% isospectralization , consisting of deforming one shape to make its Laplacian spectrum match that of another.
Edit intent: fluency
Original: An optimal experimental set-up maximizes the value of data for statistical inference and prediction, which is particularly important for experiments that are time consuming or expensive to perform.
Revised: An optimal experimental set-up maximizes the value of data for statistical inferences and predictions. An optimal set-up is particularly important for experiments that are time consuming or expensive to perform.
Edit intent: coherence
Original:  In the context of partial differential equations (PDEs), multilevel methods have been proven in many cases to dramatically reduce the computational complexity of their single-level counterparts.
Revised:  In the context of partial differential equations (PDEs), multilevel methods have been proven to dramatically reduce the computational complexity of their single-level counterparts.
Edit intent: clarity
Original:  Here, two multilevel methods are proposed to efficiently compute the expected information gain using a Kullback-Leibler divergence measure in simulation-based Bayesian optimal experimental design  .
Revised:  Here, two multilevel methods , which efficiently compute the expected information gain using a Kullback-Leibler divergence measure in simulation-based Bayesian optimal experimental design .
Edit intent: clarity
Original:  Here, two multilevel methods are proposed to efficiently compute the expected information gain using a Kullback-Leibler divergence measure in simulation-based Bayesian optimal experimental design  .
Revised:  Here, two multilevel methods are proposed to efficiently compute the expected information gain using a Kullback-Leibler divergence measure in simulation-based Bayesian optimal experimental design , are proposed .
Edit intent: coherence
Original:  The first method is a multilevel double loop Monte Carlo (MLDLMC) with importance sampling , which greatly reduces the computational work of the inner loop.
Revised:  The first method is a multilevel double loop Monte Carlo (MLDLMC) with importance sampling that greatly reduces the computational work of the inner loop.
Edit intent: fluency
Original:  The second proposed method is a multilevel double loop stochastic collocation (MLDLSC) with importance sampling, which is high-dimensional integration by deterministic quadrature on sparse grids.
Revised:  The second proposed method is a multilevel double loop stochastic collocation (MLDLSC) with importance sampling, which performs a high-dimensional integration by deterministic quadrature on sparse grids.
Edit intent: clarity
Original:  In both methods, the Laplace approximation is used as an effective means of importance sampling, and the optimal values for method parameters are determined by minimizing the average computational work  subject to a desired error tolerance.
Revised:  In both methods, the Laplace approximation is used as an effective means of importance sampling, and the optimal values of the method parameters are determined by minimizing the average computational work subject to a desired error tolerance.
Edit intent: clarity
Original:  In both methods, the Laplace approximation is used as an effective means of importance sampling, and the optimal values for method parameters are determined by minimizing the average computational work  subject to a desired error tolerance.
Revised:  In both methods, the Laplace approximation is used as an effective means of importance sampling, and the optimal values for method parameters are determined by minimizing the average computational work , subject to a desired error tolerance.
Edit intent: fluency
Original:  The computational efficiencies of the methods are demonstrated for computing the expected information gain for Bayesian inversion to infer the fiber orientation in composite laminate materials by an electrical impedance tomography experiment, given a particular set-up of the electrode configuration.
Revised:  The computational efficiencies of the methods are demonstrated by computing the expected information gain for Bayesian inversion to infer the fiber orientation in composite laminate materials by an electrical impedance tomography experiment, given a particular set-up of the electrode configuration.
Edit intent: fluency
Original:  The computational efficiencies of the methods are demonstrated for computing the expected information gain for Bayesian inversion to infer the fiber orientation in composite laminate materials by an electrical impedance tomography experiment, given a particular set-up of the electrode configuration.
Revised:  The computational efficiencies of the methods are demonstrated for computing the expected information gain from an electrical impedance tomography experiment where the fiber orientation in composite laminate materials by an electrical impedance tomography experiment, given a particular set-up of the electrode configuration.
Edit intent: clarity
Original:  MLDLSC shows a better performance than MLDLMC by exploiting the regularity of the underlying computational model  with respect to the additive noise and the unknown parameters to be statistically inferred .
Revised:  MLDLSC shows a better performance than MLDLMC by exploiting the regularity of the underlying computational model , with respect to the additive noise and the unknown parameters to be statistically inferred .
Edit intent: fluency
Original:  MLDLSC shows a better performance than MLDLMC by exploiting the regularity of the underlying computational model  with respect to the additive noise and the unknown parameters to be statistically inferred .
Revised:  MLDLSC shows a better performance than MLDLMC by exploiting the regularity of the underlying computational model with respect to the additive noise and the unknown parameters , can be exploited .
Edit intent: clarity
Original:  An optimal set-up is particularly important for experiments that are time consuming or expensive to perform.
Revised:  The efficiency of strategies for finding optimal experimental set-ups is particularly important for experiments that are time consuming or expensive to perform.
Edit intent: clarity
Original:  An optimal set-up is particularly important for experiments that are time consuming or expensive to perform.
Revised:  An optimal set-up is particularly important for experiments that are time-consuming or expensive to perform.
Edit intent: fluency
Original:  In the context of partial differential equations (PDEs), multilevel methods have been proven to dramatically reduce the computational complexity of their single-level counterparts .
Revised:  For instance, in the situation when the experiments are modeled by Partial Differential Equations (PDEs), multilevel methods have been proven to dramatically reduce the computational complexity of their single-level counterparts .
Edit intent: clarity
Original:  Here, two multilevel methods , which efficiently compute the expected information gain using a Kullback-Leibler divergence measure in simulation-based Bayesian optimal experimental design , are proposed.
Revised:  Here, two multilevel methods for estimating a popular design criterion known as the expected information gain using a Kullback-Leibler divergence measure in simulation-based Bayesian optimal experimental design , are proposed.
Edit intent: clarity
Original:  In both methods, the Laplace approximation is used as an effective means of importance sampling , and the optimal values of the method parameters are determined by minimizing the average computational work, subject to a desired error tolerance.
Revised:  For both methods, the Laplace approximation is used as an effective means of importance sampling , and the optimal values of the method parameters are determined by minimizing the average computational work, subject to a desired error tolerance.
Edit intent: fluency
Original:  In both methods, the Laplace approximation is used as an effective means of importance sampling , and the optimal values of the method parameters are determined by minimizing the average computational work, subject to a desired error tolerance.
Revised:  In both methods, the Laplace approximation is used as an effective means of importance sampling , and the optimal values of the method parameters are determined by minimizing the average computational work, subject to satisfying the desired error tolerance.
Edit intent: clarity