wanyu/IteraTeR_v2 huggingface dataset:
Original:  To encourage reproducibility in network measurement research and  serve as a starting point for future IPv6 studies, we publish source code, analysis tools, and data.
Revised:  To encourage reproducibility in network measurement research and to serve as a starting point for future IPv6 studies, we publish source code, analysis tools, and data.
Edit intent: fluency
Original:  The best nonlinear regression to averaged data and 60\% of individual data is Hull's model, while for the remaining 40\% of the subjects the dynamical model (i) provides a better fit .
Revised:  The best nonlinear regression to averaged data only for a minority of the subjects the dynamical model (i) provides a better fit .
Edit intent: clarity
Original: How stable and general is behavior once reached maximum learning ?
Revised: How stable and general is behavior once maximum learning is reached ?
Edit intent: fluency
Original:  There is weak to positive evidence that Hull model is the best nonlinear regression to averaged data only for a minority of the subjects, while its dynamical extension (i) can explain the  totality of data .
Revised:  There is weak to positive evidence that Hull model is the best nonlinear regression to data only for a minority of the subjects, while its dynamical extension (i) can explain the totality of data .
Edit intent: clarity
Original: Objective: Higher-order tensor decompositions have hardly been used in muscle activity analysis despite the fact that multichannel electromyography (EMG) datasets used in muscle synergy studies naturally present multi-way structure .
Revised: Objective: Higher-order tensor decompositions have hardly been used in muscle activity analysis despite the fact that multichannel electromyography (EMG) datasets naturally occurring as multi-way structure .
Edit intent: clarity
Original:  We then utilise the power tensor factorisation to create a novel direct method for shared and task-specific synergy estimation from two biomechanically related tasks by developing a constrained Tucker decomposition method .
Revised:  We then utilise the power tensor factorisation to create a novel direct method for shared and task-specific synergy estimation from two biomechanically related tasks .
Edit intent: coherence
Original:  Given a tree of n nodes, each with a label from [1..\sigma], and a fixed threshold 0< \tau<1, such a query gives two nodes u and v and asks for all the labels that appear more than \tau \cdot |P_{uv| times in the path P_{uv} from u to v. Note that the answer to any query is of size up to 1/\tau.
Revised:  Given a tree of n nodes, each with a label from [1..\sigma], and a fixed threshold 0< | times in the path P_{uv} from u to v. Note that the answer to any query is of size up to 1/\tau.
Edit intent: clarity
Original:  For any \kappa > } 1, we can also build a data structure that uses O(n\log^{[\kappa]} n) space, where \log^{[\kappa]} n is the iterated logarithm , and answers queries in time O((1/\tau)\log\log_w \sigma).
Revised:  For any \kappa > } 1, we can also build a structure that uses O(n\log^{[\kappa]} n) space, where \log^{[\kappa]} n is the iterated logarithm , and answers queries in time O((1/\tau)\log\log_w \sigma).
Edit intent: clarity
Original:  The construction time of both data structures is O(n\log n).
Revised:  The construction time of both structures is O(n\log n).
Edit intent: clarity
Original:  We also describe  succinct-space solutions , both reaching the same query time of the linear-space structure.
Revised:  We also describe succinct-space solutions with the same query time of the linear-space structure.
Edit intent: clarity
Original:  One uses 2nH + 4n + o(n)(H+1) bits, where H \le \lg\sigma is the entropy of the distribution of labels in T , and can be built in O(n\log n) time.
Revised:  One uses 2nH + 4n + o(n)(H+1) bits, where H \le \lg\sigma is the entropy of the label distribution , and can be built in O(n\log n) time.
Edit intent: clarity
Original: We study the stochastic model developed by Baar et al., 2015 for the modeling of immunotherapy against melanoma skin cancer.
Revised: We study the stochastic model developed by Baar et al., 2015 for modeling immunotherapy against melanoma skin cancer.
Edit intent: fluency
Original:  In the first part, we estimate the parameters of the deterministic limit of the model based on the biological data of tumor growth in mice which are provided in Landsberg et al. ,2012.
Revised:  In the first part, we estimate the parameters of the deterministic limit of the model based on biological data of tumor growth in mice which are provided in Landsberg et al. ,2012.
Edit intent: fluency
Original:  The main statistical tools we use are the NonLinear Mixed Effects Models (NLMEM) and the Stochastic Approximation Expectation Maximization (SAEM) algorithm.
Revised:  The main statistical tools we use are the NonLinear Mixed Effects Models (NLMEM) and the Stochastic Approximation Expectation Maximization algorithm.
Edit intent: clarity
Original:  We show that for biologically reasonable valuesof the parameters , an early relapse is due to stochastic fluctuations (complete T cells exhaustion) with a non negligible probability.
Revised:  We show that for some relevant parameter values , an early relapse is due to stochastic fluctuations (complete T cells exhaustion) with a non negligible probability.
Edit intent: clarity
Original:  We show that for biologically reasonable valuesof the parameters , an early relapse is due to stochastic fluctuations (complete T cells exhaustion) with a non negligible probability.
Revised:  We show that for biologically reasonable valuesof the parameters , an early relapse is due to stochastic uctuations (complete T cells exhaustion) with a non negligible probability.
Edit intent: fluency
Original:  In the second part, assuming that the relapse is related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability at different stages of the disease evolution .
Revised:  In the second part, focusing on the relapse related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability at different stages of the disease evolution .
Edit intent: clarity
Original:  In the second part, assuming that the relapse is related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability at different stages of the disease evolution .
Revised:  In the second part, assuming that the relapse is related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability in the parameter estimation ranges .
Edit intent: clarity
Original:  We study a stochastic model developed by Baar et al. , 2015 for modeling immunotherapy against melanoma skin cancer.
Revised:  We study a stochastic model developed by Baar et al. in 2015 for modeling immunotherapy against melanoma skin cancer.
Edit intent: clarity
Original:  In the first part , we estimate the parameters of the deterministic limit of the model based on biological data of tumor growth in mice.
Revised:  First , we estimate the parameters of the deterministic limit of the model based on biological data of tumor growth in mice.
Edit intent: coherence
Original:  We show that for some relevant parameter values, an early relapse is due to stochastic uctuations (complete T cells exhaustion) with a non negligible probability.
Revised:  We show that for some relevant parameter values, an early relapse is due to stochastic fluctuations (complete T cells exhaustion) with a non negligible probability.
Edit intent: fluency
Original:  In the second part , focusing on the relapse related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability in the parameter estimation ranges.
Revised:  Then , focusing on the relapse related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability in the parameter estimation ranges.
Edit intent: coherence
Original:  Recently, a few approaches have been proposed to automatically search for the optimal structure of neural networks, however, they suffer from either prohibitive computation cost (e. g., 256 Hours on 250 GPU in 1%DIFDELCMD < ]%%% ) or unsatisfactory performance compared to those of hand-crafted neural networks.
Revised:  Recently, a few approaches have been proposed to automatically search for the optimal structure of neural networks, however, they suffer from either prohibitive computation cost (e. g., 256 Hours on 250 GPU in %DIFDELCMD < ]%%% ) or unsatisfactory performance compared to those of hand-crafted neural networks.
Edit intent: coherence
Original:  In this paper, we propose an Ecologically-Inspired GENetic approach for neural network structure search (EIGEN) , that includes succession, mimicry and gene duplication  . Specifically, we first use primary succession to rapidly evolve a community of poor initialized neural network structures into a more diverse community , followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession.
Revised:  In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) , that includes succession, mimicry and gene duplication . Specifically, we first use primary succession to rapidly evolve a community of poor initialized neural network structures into a more diverse community , followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession.
Edit intent: clarity
Original:  In this paper, we propose an Ecologically-Inspired GENetic approach for neural network structure search (EIGEN) , that includes succession, mimicry and gene duplication  . Specifically, we first use primary succession to rapidly evolve a community of poor initialized neural network structures into a more diverse community , followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession.
Revised:  In this paper, we propose an Ecologically-Inspired GENetic approach for neural network structure search (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication . Specifically, we first use primary succession to rapidly evolve a community of poor initialized neural network structures into a more diverse community , followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession.
Edit intent: clarity
Original:  In this paper, we propose an Ecologically-Inspired GENetic approach for neural network structure search (EIGEN) , that includes succession, mimicry and gene duplication  . Specifically, we first use primary succession to rapidly evolve a community of poor initialized neural network structures into a more diverse community , followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession.
Revised:  In this paper, we propose an Ecologically-Inspired GENetic approach for neural network structure search (EIGEN) , that includes succession, mimicry and gene duplication . Specifically, we first use primary succession to rapidly evolve a population of poorly initialized neural network structures into a more diverse community , followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession.
Edit intent: clarity
Original:  In this paper, we propose an Ecologically-Inspired GENetic approach for neural network structure search (EIGEN) , that includes succession, mimicry and gene duplication  . Specifically, we first use primary succession to rapidly evolve a community of poor initialized neural network structures into a more diverse community , followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession.
Revised:  In this paper, we propose an Ecologically-Inspired GENetic approach for neural network structure search (EIGEN) , that includes succession, mimicry and gene duplication . Specifically, we first use primary succession to rapidly evolve a community of poor initialized neural network structures into a more diverse population , followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession.
Edit intent: clarity
Original:  Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find the better network structures.
Revised:  Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find better network structures.
Edit intent: fluency
Original:  Extensive experimental results show that our proposed approach can achieve the similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost.
Revised:  Experimental results show that our proposed approach can achieve the similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost.
Edit intent: clarity
Original:  Extensive experimental results show that our proposed approach can achieve the similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost.
Revised:  Extensive experimental results show that our proposed approach can achieve similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost.
Edit intent: clarity
Original: Despite of the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for discrete-continuous mixture random variables .
Revised: Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for discrete-continuous mixture random variables .
Edit intent: fluency
Original: Despite of the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for discrete-continuous mixture random variables .
Revised: Despite of the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements .
Edit intent: clarity
Original:  We develop the notion of measure-theoretic Bayesian networks (MTBNs) , and use it to provide more general semantics for PPLs with arbitrarily many random variables defined over arbitrary measure spaces.
Revised:  We develop the notion of measure-theoretic Bayesian networks (MTBNs) and use it to provide more general semantics for PPLs with arbitrarily many random variables defined over arbitrary measure spaces.
Edit intent: fluency
Original:  We develop two new general sampling algorithms which are provably correct under the MTBN framework:  lexicographic likelihood weighting (LLW) for general MTBNs and  lexicographic particle filter (LPF), a specialized algorithm for state space models.
Revised:  We develop two new general sampling algorithms that are provably correct under the MTBN framework: lexicographic likelihood weighting (LLW) for general MTBNs and lexicographic particle filter (LPF), a specialized algorithm for state space models.
Edit intent: fluency
Original:  We develop two new general sampling algorithms which are provably correct under the MTBN framework:  lexicographic likelihood weighting (LLW) for general MTBNs and  lexicographic particle filter (LPF), a specialized algorithm for state space models.
Revised:  We develop two new general sampling algorithms which are provably correct under the MTBN framework: the lexicographic likelihood weighting (LLW) for general MTBNs and lexicographic particle filter (LPF), a specialized algorithm for state space models.
Edit intent: clarity
Original:  We develop two new general sampling algorithms which are provably correct under the MTBN framework:  lexicographic likelihood weighting (LLW) for general MTBNs and  lexicographic particle filter (LPF), a specialized algorithm for state space models.
Revised:  We develop two new general sampling algorithms which are provably correct under the MTBN framework: lexicographic likelihood weighting (LLW) for general MTBNs and the lexicographic particle filter (LPF), a specialized algorithm for state space models.
Edit intent: clarity
Original:  We develop two new general sampling algorithms which are provably correct under the MTBN framework:  lexicographic likelihood weighting (LLW) for general MTBNs and  lexicographic particle filter (LPF), a specialized algorithm for state space models.
Revised:  We develop two new general sampling algorithms which are provably correct under the MTBN framework: lexicographic likelihood weighting (LLW) for general MTBNs and lexicographic particle filter (LPF), a specialized algorithm for state-space models.
Edit intent: fluency
Original:  We further integrate MTBN into a widely used PPL system, BLOG, and verify the effectiveness of our new inference algorithms through representative examples.
Revised:  We further integrate MTBNs into a widely used PPL system, BLOG, and verify the effectiveness of our new inference algorithms through representative examples.
Edit intent: clarity
Original:  We further integrate MTBN into a widely used PPL system, BLOG, and verify the effectiveness of our new inference algorithms through representative examples.
Revised:  We further integrate MTBN into a widely used PPL system, BLOG, and verify the effectiveness of the new inference algorithms through representative examples.
Edit intent: fluency
Original:  Reconstructed velocity fields are generated up to 700x faster than traditional CPU solvers , while achieving compression rates of over 1300x.
Revised:  Reconstructed velocity fields are generated up to 700x faster than traditional CPU solvers , while achieving compression rates of up to 1300x.
Edit intent: clarity
Original:  Diffix is a new framework for anonymous database query that adds noise based on the filter conditions in the query.
Revised:  Diffix is a framework for anonymous database query that adds noise based on the filter conditions in the query.
Edit intent: clarity
Original:  To understand large-scale structure in a network, a common task is to cluster a network's nodes into sets called "communities"  such that there are dense connections within communities but sparse connections between them.
Revised:  To understand large-scale structure in a network, a common task is to cluster a network's nodes into sets called "communities" , such that there are dense connections within communities but sparse connections between them.
Edit intent: fluency
Original:  To illustrate the utility of this bridge , we implement network analogs of three surface-tension algorithms, with which we successfully recover planted community structure in synthetic networks and which yield fascinating insights on empirical networks from the field of hyperspectral video segmentation .
Revised:  To illustrate the utility of this relationship , we implement network analogs of three surface-tension algorithms, with which we successfully recover planted community structure in synthetic networks and which yield fascinating insights on empirical networks from the field of hyperspectral video segmentation .
Edit intent: clarity
Original:  To illustrate the utility of this bridge , we implement network analogs of three surface-tension algorithms, with which we successfully recover planted community structure in synthetic networks and which yield fascinating insights on empirical networks from the field of hyperspectral video segmentation .
Revised:  To illustrate the utility of this bridge , we implement network analogs of three surface-tension algorithms, with which we successfully recover planted community structure in synthetic networks and which yield fascinating insights on empirical networks that we construct from hyperspectral videos .
Edit intent: clarity
Original:  EC2 experiments against popular benchmarks show that LB-BSP can effectively accelerate the training of deep models, with up to 2x speedup.
Revised:  EC2 deployment confirms that LB-BSP can effectively accelerate the training of deep models, with up to 2x speedup.
Edit intent: clarity
Original: Most existing sentiment analysis approaches heavily rely on a large amount of labeled data that usually involve time-consuming and error-prone manual annotations.
Revised: Sentiment classification typically relies on a large amount of labeled data that usually involve time-consuming and error-prone manual annotations.
Edit intent: clarity
Original:  The distribution of this labeled data is significantly imbalanced among languages, e.g., more English texts are labeled than texts in  other languages, which presents a major challenge to cross-lingual sentiment analysis.
Revised:  The distribution of this labeled data is significantly imbalanced among languages, e.g., more English texts are labeled than texts in any other languages, which presents a major challenge to cross-lingual sentiment analysis.
Edit intent: clarity
Original:  There have been several cross-lingual representation learning techniques that transfer the knowledge learned from a language with abundant labeled examples  to another language with much fewer labels .
Revised:  There have been several cross-lingual representation learning techniques that transfer the knowledge learned from one language that has abundant labeled examples to another language with much fewer labels .
Edit intent: clarity
Original:  In this paper, we employ emojis, a ubiquitous and emotional language , as a new bridge for sentiment analysis across languages.
Revised:  In this paper, we employ emojis, which are widely available in many languages , as a new bridge for sentiment analysis across languages.
Edit intent: clarity
Original:  The learned representations are then utilized to facilitate cross-lingual sentiment classification.
Revised:  The learned representations are then integrated to facilitate cross-lingual sentiment classification.
Edit intent: clarity
Original:  We demonstrate the effectiveness and efficiency of our approach on a representative Amazon review data set that covers three languages and three domains .
Revised:  The proposed method demonstrates state-of-the-art performance on benchmark datasets, which is sustained even when sentiment labels are scarce .
Edit intent: clarity
Original:  Under consideration in Theory and Practice of Logic Programming (TPLP).
Edit intent: clarity
Original:  We show that on classes of bounded expansion another small modification leads to a constant factor approximation.
Edit intent: coherence
Original: With the growing use of underwater acoustic communication (UWAC) for both industrial and military operations, there is a need to ensure communication security.
Revised: With the growing use of underwater acoustic communications (UWAC) for both industrial and military operations, there is a need to ensure communication security.
Edit intent: fluency
Original:  We propose a new algorithm for message authentication in an UWAN setting.
Revised:  In this paper, we propose a new algorithm for message authentication in an UWAN setting.
Edit intent: clarity
Original:  We propose a new algorithm for message authentication in an UWAN setting.
Revised:  We propose a new algorithm for message authentication in a UWAN setting.
Edit intent: fluency