wanyu/IteraTeR_full_sent huggingface dataset:
Original: The use of machine learning to model motor learning mechanisms is still limited, while it could help to design novel interactive systems for movement learningor rehabilitation. This approach requires to account for the motor variabilityinduced by motor learning mechanisms. This represents specific challenges concerning fast adaptability of the computational models, from small variations to more drastic changes, including new movement classes. We propose a short reviewon machine learning based movement models and their existing adaptation mechanisms. We discuss the current challenges for applying these models in motor learning support systems, delineating promising research directions at the intersection of machine learning and motor learning .
Revised: Machine learning approaches have seen considerable applications in human movement modeling, but remain limited for motor learning. Motor learning requires accounting for motor variability, and poses new challenges as the algorithms need to be able to differentiate between new movements and variation of known ones. In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. To conclude, we discuss challenges for applying these models in motor learning support systems, delineating promising research directions at the intersection of machine learning and motor learning .
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Original: Data-driven predictive models are increasingly used to inform decisions that hold important consequences for individuals and society. As a result, decision makers are often obliged, even legally required, to provide explanations about their decisions. In this context, it has been increasingly argued that these explanations should help individuals understand what would have to change for these decisions to be beneficial ones.
Revised: Data-driven predictive models are increasingly used to inform decisions , it has been increasingly argued that these explanations should help individuals understand what would have to change for these decisions to be beneficial ones.
Edit intent: coherence
Original:  However, we further show that the corresponding objective is nondecreasing and satisfies submodularity . Therefore, a standard greedy algorithm offers an approximation factor of (1-1/e) at solving the problem.
Revised:  However, we further show that the corresponding objective is nondecreasing and satisfies submodularity and this allows a standard greedy algorithm offers an approximation factor of (1-1/e) at solving the problem.
Edit intent: coherence
Original:  Therefore, a standard greedy algorithm offers an approximation factor of (1-1/e) at solving the problem. Additionally, we also show that the problem of jointly finding both the optimal policy and set of counterfactual explanations reduces to maximizing a non-monotone submodular function.
Revised:  Therefore, a standard greedy algorithm to enjoy approximation guarantees. In addition, we further show that the problem of jointly finding both the optimal policy and set of counterfactual explanations reduces to maximizing a non-monotone submodular function.
Edit intent: clarity
Original:  As a result, we can use a recent randomized algorithm to solve the problem, which offers an approximation factor of 1/e . Finally, we illustrate our theoretical findings by performing experiments on synthetic and real lending data .
Revised:  As a result, we can use a recent randomized algorithm to solve the problem, which also offers approximation guarantees . Finally, we illustrate our theoretical findings by performing experiments on synthetic and real lending data .
Edit intent: clarity
Original:  As a result, we can use a recent randomized algorithm to solve the problem, which offers an approximation factor of 1/e . Finally, we illustrate our theoretical findings by performing experiments on synthetic and real lending data .
Revised:  As a result, we can use a recent randomized algorithm to solve the problem, which offers an approximation factor of 1/e . Finally, we demonstrate that, by incorporating a matroid constraint into the problem formulation, we can increase the diversity of the optimal set of counterfactual explanations and incentivize individuals across the whole spectrum of the population to self improve. Experiments on synthetic and real lending data .
Edit intent: meaning-changed
Original:  As a result, we can use a recent randomized algorithm to solve the problem, which offers an approximation factor of 1/e . Finally, we illustrate our theoretical findings by performing experiments on synthetic and real lending data .
Revised:  As a result, we can use a recent randomized algorithm to solve the problem, which offers an approximation factor of 1/e . Finally, we illustrate our theoretical findings by performing experiments on synthetic and real lending and credit card data illustrate our theoretical findings and show that the counterfactual explanations and decision policies found by our algorithms achieve higher utility than several competitive baselines .
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Original: This paper deals with parameterisation, identifiability, and maximum likelihood (ML) estimation of possibly non-invertible structural vector autoregressive moving average (SVARMA) models driven by independent and non-Gaussian shocks.
Revised: This article deals with parameterisation, identifiability, and maximum likelihood (ML) estimation of possibly non-invertible structural vector autoregressive moving average (SVARMA) models driven by independent and non-Gaussian shocks.
Edit intent: fluency
Original:  We introduce a new parameterisation of the MA polynomial matrix based on the Wiener-Hopf factorisation (WHF) and show that the model is identified in this parametrisation for a generic set in the parameter space (when certain just-identifying restrictions are imposed).
Revised:  In contrast to previous literature, the novel representation of the MA polynomial matrix based on the Wiener-Hopf factorisation (WHF) and show that the model is identified in this parametrisation for a generic set in the parameter space (when certain just-identifying restrictions are imposed).
Edit intent: clarity
Original:  We introduce a new parameterisation of the MA polynomial matrix based on the Wiener-Hopf factorisation (WHF) and show that the model is identified in this parametrisation for a generic set in the parameter space (when certain just-identifying restrictions are imposed).
Revised:  We introduce a new parameterisation of the MA polynomial matrix using the Wiener-Hopf factorisation (WHF) and show that the model is identified in this parametrisation for a generic set in the parameter space (when certain just-identifying restrictions are imposed).
Edit intent: clarity
Original:  We introduce a new parameterisation of the MA polynomial matrix based on the Wiener-Hopf factorisation (WHF) and show that the model is identified in this parametrisation for a generic set in the parameter space (when certain just-identifying restrictions are imposed). When the SVARMA model is driven by Gaussian errors, neither the static shock transmission matrix, nor the location of the determinantal zeros of the MA polynomial matrix can be identified without imposing further identifying restrictions on the parameters. We characterise the classes of observational equivalence with respect to second moment information at different stages of the modelling process. Subsequently, cross-sectional and temporal independence and non-Gaussianity of the shocks is used to solve these identifiability problems and identify the true root location of the MA polynomial matrix as well as the static shock transmission matrix (up to permutation and scaling) .
Revised:  We introduce a new parameterisation of the MA polynomial matrix based on the Wiener-Hopf factorisation (WHF) focuses on the multivariate nature of the model, generates insights into its structure, and uses this structure for devising optimisation algorithms. In particular, it allows to parameterise the location of determinantal zeros inside and outside the unit circle, and it allows for MA zeros at zero, which can be interpreted as informational delays. This is highly relevant for data-driven evaluation of Dynamic Stochastic General Equilibrium (DSGE) models .
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Original:  Furthermore, we provide low level conditions for asymptotic normality of the ML estimator . The estimation procedure is illustrated with various examples from the economic literature and implemented as R-package.
Revised:  Furthermore, we provide low level conditions for asymptotic normality of the ML estimator and analytic expressions for the score and the information matrix. As application, we estimate the Blanchard and Quah model and show that our method provides further insights regarding non-invertibility using a standard macroeconometric model. These and further analyses are implemented in a well documented R-package.
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Original:  News articles along with other related components like news creators and news subjects can be modeled as a heterogeneous information network (HIN for short).
Revised:  Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language models and invalidating simple text-based models. In fact, news articles and other related components like news creators and news subjects can be modeled as a heterogeneous information network (HIN for short).
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Original:  In this paper, we focus on studying the HIN- based fake news detection problem. We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT) which employs a novel hierarchical attention mechanism to detect fake news by classifying news article nodes in the HIN.
Revised:  In this paper, we propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT) which employs a novel hierarchical attention mechanism to detect fake news by classifying news article nodes in the HIN.
Edit intent: coherence
Original:  We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT) which employs a novel hierarchical attention mechanism to detect fake news by classifying news article nodes in the HIN.
Revised:  We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT) , which uses a novel hierarchical attention mechanism to detect fake news by classifying news article nodes in the HIN.
Edit intent: fluency
Original:  We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT) which employs a novel hierarchical attention mechanism to detect fake news by classifying news article nodes in the HIN.
Revised:  We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT) which employs a novel hierarchical attention mechanism to perform node representation learning in HIN, and then detects fake news by classifying news article nodes in the HIN.
Edit intent: meaning-changed
Original:  We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT) which employs a novel hierarchical attention mechanism to detect fake news by classifying news article nodes in the HIN. This method can effectively learn information from different types of related nodes through node-level and schema-level attention. Experiments with real-world fake news data show that our model can outperform text-based models and other network-based models.
Revised:  We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT) which employs a novel hierarchical attention mechanism to detect fake news by classifying news article nodes . Experiments on two real-world fake news data show that our model can outperform text-based models and other network-based models.
Edit intent: clarity
Original:  This approach has so far been applied primarily to situations of resource competition , in accord with the general focus of work on ecological interactions since the 1970's. In this manuscript, we expand contemporary niche theory to integrate mutualistic interactions through a systematic analysis of mutualistic plant-pollinator networks.
Revised:  This approach has so far been applied primarily to resource competition since its early beginnings. Here, we integrate mutualism into niche theory by expanding Tilman's graphical representation to the analysis of consumer-resource dynamics of plant-pollinator networks.
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Original:  In this manuscript, we expand contemporary niche theory to integrate mutualistic interactions through a systematic analysis of mutualistic plant-pollinator networks. Specifically, we investigate the dynamics of a consumer-resource model of plant-pollinator interactions that has been extensively studied in numerical simulations and whose predictions have been tested against field data. We provide simple explanations in graphical terms for the qualitative phenomena consistently found by extensive numerical simulations, including the effects of nestedness and adaptive foragingon community dynamics, and the effects of invasions by new pollinator species.
Revised:  In this manuscript, we expand contemporary niche theory to integrate mutualistic interactions through a systematic analysis of mutualistic plant-pollinator networks. We graphically explain the qualitative phenomena consistently found by extensive numerical simulations, including the effects of nestedness and adaptive foragingon community dynamics, and the effects of invasions by new pollinator species.
Edit intent: coherence
Original:  We provide simple explanations in graphical terms for the qualitative phenomena consistently found by extensive numerical simulations, including the effects of nestedness and adaptive foragingon community dynamics, and the effects of invasions by new pollinator species.
Revised:  We provide simple explanations in graphical terms for the qualitative phenomena previously found by numerical simulations, including the effects of nestedness and adaptive foragingon community dynamics, and the effects of invasions by new pollinator species.
Edit intent: clarity
Original:  We provide simple explanations in graphical terms for the qualitative phenomena consistently found by extensive numerical simulations, including the effects of nestedness and adaptive foragingon community dynamics, and the effects of invasions by new pollinator species. We also use this representation to highlight general similarities and differences between mutualistic systems and the more well-studied scenarios of pure competition .
Revised:  We provide simple explanations in graphical terms for the qualitative phenomena consistently found by extensive numerical simulations, including the effects on community dynamics of nestedness, adaptive foraging, and pollinator invasions. Our graphical approach promotes the unification of niche and network theories, and deepens the synthesis of different types of interactions within a consumer-resource framework .
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Original:  Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (  searching or traversing the network) to computing important hypergraph measures, to including more challenging algorithms (  community detection).
Revised:  Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms ( such as searching or traversing the network) to computing important hypergraph measures, to including more challenging algorithms ( community detection).
Edit intent: clarity
Original:  Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (  searching or traversing the network) to computing important hypergraph measures, to including more challenging algorithms (  community detection).
Revised:  Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms ( searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms ( community detection).
Edit intent: clarity
Original:  Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (  searching or traversing the network) to computing important hypergraph measures, to including more challenging algorithms (  community detection).
Revised:  Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms ( searching or traversing the network) to computing important hypergraph measures, to including more challenging algorithms ( such as community detection).
Edit intent: clarity
Original:  In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs  .
Revised:  In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation .
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Original:  To demonstrate how the library can be exploited in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series.
Revised:  In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series.
Edit intent: clarity
Original:  To test this method, we construct two types of data: 1) when variables and outcome were randomly determined and 2 ) when the outcome depends on one variable.
Revised:  To test this method, we randomly assigned boolean operators between binary variables and focused on frequencies of boolean operators which could explain outcomes correctly. We determined trends of boolean operators by chi-square test. As an application, we performed this method for a dataset which included patient's age, body mass index (BMI), mean arterial pressure (MAP) and the quantitative measure of diabetes progression (Y). We set cutoff to each variable and considered these as binary variables. Interactions of age, BMI and MAP were determined as age and BMI and MAP (p<0.0001 for both operators) in estimating Y and sensitivity, specificity, positive and negative predicting value were 0.50, 0.85. 0.59, 0.80 respectively. We may be able to detemine trends of boolean operators and construct a 2 ) when the outcome depends on one variable.
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Original:  To test this method, we construct two types of data: 1) when variables and outcome were randomly determined and 2 ) when the outcome depends on one variable. Analysis of the first type of data by this method showed that there was no significant result. Analysis of the second type of data reflected the bias of the data. As far as we know, this is the first attempt to use a frequentist approach to randomly assigned Boolean operators .
Revised:  To test this method, we construct two types of data: 1) when variables and outcome were randomly determined and 2 by 2 contingency table in multivariate situation by this method .
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Original: Dynamic algorithms capable of supporting batches of updates are increasingly relevant today due to the emergence of rapidly-evolving dynamic datasets.
Revised: The dynamic trees problem is to maintain a forest subject to edge insertions and deletions while facilitating queries such as connectivity, path weights, and subtree weights. Dynamic trees are a fundamental building block of a large number of graph algorithms. Although traditionally studied in the single-update setting, dynamic algorithms capable of supporting batches of updates are increasingly relevant today due to the emergence of rapidly-evolving dynamic datasets.
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Original:  In this paper, motivated by the difficulty in designing parallel batch-dynamic algorithms by hand, we propose a framework for algorithmically dynamizing static round-synchronous algorithms  to obtain parallel batch-dynamic algorithms with good bounds on their work and span.
Revised:  In this work, we design the first work-efficient parallel batch-dynamic algorithms by hand, we propose a framework for algorithmically dynamizing static round-synchronous algorithms to obtain parallel batch-dynamic algorithms with good bounds on their work and span.
Edit intent: meaning-changed
Original:  In this paper, motivated by the difficulty in designing parallel batch-dynamic algorithms by hand, we propose a framework for algorithmically dynamizing static round-synchronous algorithms  to obtain parallel batch-dynamic algorithms with good bounds on their work and span.
Revised:  In this paper, motivated by the difficulty in designing parallel batch-dynamic algorithm for dynamic trees that is capable of supporting both path queries and subtree queries, as well as a variety of non-local queries. To achieve this, we propose a framework for algorithmically dynamizing static round-synchronous algorithms to obtain parallel batch-dynamic algorithms with good bounds on their work and span.
Edit intent: meaning-changed
Original:  In this paper, motivated by the difficulty in designing parallel batch-dynamic algorithms by hand, we propose a framework for algorithmically dynamizing static round-synchronous algorithms  to obtain parallel batch-dynamic algorithms with good bounds on their work and span.
Revised:  In this paper, motivated by the difficulty in designing parallel batch-dynamic algorithms by hand, we propose a framework for algorithmically dynamizing static round-synchronous algorithms that allows us to obtain parallel batch-dynamic algorithms with good bounds on their work and span.
Edit intent: coherence
Original:  Using this framework , we develop the first work-efficient parallel batch-dynamic algorithm for dynamic trees that supports both subtree queries and path queries, as well as a variety of nonlocal queries such as centers and medians. We further investigate the applicability of the framework by analyzing map-reduce-based computations, and a random-mate list contraction algorithm , which, when dynamized, yields a simple solution to the batch-dynamic lists problem that matches the work bounds of the best known hand-crafted data structure .
Revised:  Our dynamic trees algorithm is obtained by applying our dynamization framework to the parallel tree contraction algorithm of Miller and Reif, and then performing a novel analysis of the computation distance of this algorithm under batch updates. We show that k updates can be performed in O(k \log(1+n/k)) work in expectation, which matches an existing algorithm of Tseng et al. while providing support for a substantially larger number of queries and applications .
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