wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Revised:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%% \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Edit intent: coherence
Original:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Revised:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times %DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%% \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Edit intent: fluency
Original:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Revised:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% We prove that a shallow neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%% \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Edit intent: meaning-changed
Original:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Revised:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with the logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%% \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Edit intent: clarity
Original:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Revised:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator in L^{2}( %DIFDELCMD < \RR %%% \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Edit intent: fluency
Original:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Revised:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%% \mathbb{R \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Edit intent: meaning-changed
Original:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Revised:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%% \times [0, 1] ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times } [0, 1] ^{n .
Edit intent: meaning-changed
Original:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
Revised:  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%% \times [0, 1] ^{n^+ \times } [0, 1] . This justifies the benefit of time value oriented decision functions in option pricing models .
Edit intent: meaning-changed
Original:  Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metric, which may cause obstacles for the discovery of weak and hidden complexes that are overshadowed by strong complexes.
Revised:  Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metrics, which causes obstacles for the discovery of weak and hidden complexes that are overshadowed by strong complexes.
Edit intent: coherence
Original:  Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metric, which may cause obstacles for the discovery of weak and hidden complexes that are overshadowed by strong complexes. Additionally , protein complexes not only have different densities but also a various range of scales, making them extremely difficult to be detected.
Revised:  Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metric, which may cause obstacles for the discovery of weak and hidden complexes shielded by stronger complexes. Also , protein complexes not only have different densities but also a various range of scales, making them extremely difficult to be detected.
Edit intent: clarity
Original:  We propose a meta-method called HirHide (Hierarchical Hidden Community Detection), which can adopt any standard community detection method as the base algorithm and enable it to discover hierarchical hidden communities as well as boosting the detection on hierarchical strong communities.
Revised:  We propose a method called HirHide (Hierarchical Hidden Community Detection), which can adopt any standard community detection method as the base algorithm and enable it to discover hierarchical hidden communities as well as boosting the detection on hierarchical strong communities.
Edit intent: clarity
Original:  We propose a meta-method called HirHide (Hierarchical Hidden Community Detection), which can adopt any standard community detection method as the base algorithm and enable it to discover hierarchical hidden communities as well as boosting the detection on hierarchical strong communities.
Revised:  We propose a meta-method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities as well as boosting the detection on hierarchical strong communities.
Edit intent: clarity
Original:  We propose a meta-method called HirHide (Hierarchical Hidden Community Detection), which can adopt any standard community detection method as the base algorithm and enable it to discover hierarchical hidden communities as well as boosting the detection on hierarchical strong communities. To our knowledge, this is the first combination of hierarchical structure with hidden structure, which provides a new perspective for finding protein complexes of various strengths and scales .
Revised:  We propose a meta-method called HirHide (Hierarchical Hidden Community Detection), which can adopt any standard community detection method as the base algorithm and enable it to discover hierarchical hidden communities . It is the first combination of hierarchical structure with hidden structure, which provides a new perspective for finding protein complexes of various strengths and scales .
Edit intent: coherence
Original:  Nonetheless, due to high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no much concern on its less practical, time-consuming, and data-hungry process.
Revised:  Nonetheless, there still exist BCI-illiterate subjects who struggle to use BCI systems, showing high intra-subject variabilities. Several methods have been proposed to enhance their performance via transfer learning. In such a case, high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no much concern on its less practical, time-consuming, and data-hungry process.
Edit intent: meaning-changed
Original:  Nonetheless, due to high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no much concern on its less practical, time-consuming, and data-hungry process. To tackle this problem, recent studies took advantage of transfer learning, especially using domain adaptation techniques .
Revised:  Nonetheless, due to high inter- and intrasubject variabilities are the points to be considered. Transfer learning, especially using domain adaptation techniques .
Edit intent: coherence
Original:  However, there still remain two challenging limitations ; i) most domain adaptation methods are designed for labeled source and unlabeled target domain whereas BCI tasks generally have multiple annotated domains. ii) most of the methods do not consider negatively transferable to disrupt generalization ability.
Revised:  However, there still remain two challenging limitations . (i) Most domain adaptation methods are designed for labeled source and unlabeled target domain whereas BCI tasks generally have multiple annotated domains. ii) most of the methods do not consider negatively transferable to disrupt generalization ability.
Edit intent: fluency
Original:  i) most domain adaptation methods are designed for labeled source and unlabeled target domain whereas BCI tasks generally have multiple annotated domains. ii) most of the methods do not consider negatively transferable to disrupt generalization ability.
Revised:  i) most domain adaptation methods are designed for labeled source and unlabeled target domain whereas BCI tasks generally have multiple annotated domains. (ii) Most of the existing methods do not consider negatively transferable to disrupt generalization ability.
Edit intent: clarity
Original:  i) most domain adaptation methods are designed for labeled source and unlabeled target domain whereas BCI tasks generally have multiple annotated domains. ii) most of the methods do not consider negatively transferable to disrupt generalization ability.
Revised:  i) most domain adaptation methods are designed for labeled source and unlabeled target domain whereas BCI tasks generally have multiple annotated domains. ii) most of the methods do not consider a negative transfer to disrupt generalization ability.
Edit intent: clarity
Original: The growth process of fungal hyphae involves transport of nutrients from the sub-apical region of the fungus to the growing tip of hyphae. At the tip domain, the URLanelle -Spitzenkorper uses the nutrients that are packaged as membrane bound vesicles to synthesize fresh cell wall. This in turn leads to elongation and branching of the hyphae and formation of a single colony mycelium. We propose a driven lattice gas model which incorporates the processes of vesicle supply dependent linear extension and branching of the tips of the hyphae, apart from process of motor driven transport of vesicles along the hyphae towards the growing tips .
Revised: We present a minimal driven lattice gas model which incorporates the processes of vesicle supply dependent linear extension and branching of the tips of the hyphae, apart from process of motor driven transport of vesicles along the hyphae towards the growing tips .
Edit intent: clarity
Original:  We propose a driven lattice gas model which incorporates the processes of vesicle supply dependent linear extension and branching of the tips of the hyphae, apart from process of motor driven transport of vesicles along the hyphae towards the growing tips .
Revised:  We propose a driven lattice gas model which generates the morphological characteristics associated with single colony mycelium arising from the growth and branching process of fungal hyphae, which is fed by a single source of nutrients .
Edit intent: meaning-changed
Original:  We simulate the resultant 2-d morphology generated by the growing primary lattice , quantifying the motility behavior and morphological characteristics of the colony in terms of measures such as the Center of Mass, Radius of Gyration and Aspect Ratio .
Revised:  We simulate the resultant 2-d morphology generated by the growing primary hypha , quantifying the motility behavior and morphological characteristics of the colony in terms of measures such as the Center of Mass, Radius of Gyration and Aspect Ratio .
Edit intent: clarity
Original:  We simulate the resultant 2-d morphology generated by the growing primary lattice , quantifying the motility behavior and morphological characteristics of the colony in terms of measures such as the Center of Mass, Radius of Gyration and Aspect Ratio .
Revised:  We simulate the resultant 2-d morphology generated by the growing primary lattice , quantifying the motility behavior and morphological characteristics of the colony .
Edit intent: clarity
Original:  Analysis of the temporal behavior and morphological characteristics of the resultant 2-d morphology reveals a wide variability of these characteristics which depend on the input parameters which characterize the branching and elongation dynamics of the individual hyphae. We make quantitative comparison of the predictions of our model with experiments .
Revised:  Analysis of the temporal behavior and morphological characteristics of the resultant 2-d morphology reveals a wide variability of these characteristics which depend on the input parameters which characterize the branching and elongation dynamics of the hyphae. By calibrating the input parameters for our model, we make some quantitative comparison of the predictions of our model with experiments .
Edit intent: clarity
Original:  The database stores functional data for over 68 million genes, 52 million proteins, and 239 million domains with associated biological activity annotations from GeneOntology , KEGG, MetaCyc, and Reactome.
Revised:  The database stores functional data for over 68 million genes, 52 million proteins, and 239 million domains with associated biological activity annotations from Gene Ontology , KEGG, MetaCyc, and Reactome.
Edit intent: fluency
Original:  We describe the data selection, the pipeline to create and update OMXWare, and developer tools (Python SDK and Rest APIs) which allow researchers to efficiently study microbial life at scale.
Revised:  We describe the data selection, the pipeline to create and update the IBM Functional Genomics Platform, and the developer tools (Python SDK and Rest APIs) which allow researchers to efficiently study microbial life at scale.
Edit intent: meaning-changed
Original:  The controller enables switching between different Level(s) of Autonomy (LOA) during task execution initiated by either the human operator or/andthe robot. The controller is evaluated in two Search and Rescue (SAR) inspired experiments, one with a simulated robot and test arena  and one with a real robot in a realistic environment.
Revised:  The controller enables switching between different Level(s) of Autonomy (LOA) during task execution initiated by either the human operator and/or the EMICS. The EMICS is evaluated in two Search and Rescue (SAR) inspired experiments, one with a simulated robot and test arena and one with a real robot in a realistic environment.
Edit intent: meaning-changed
Original:  Our hypothesis is that timely switching between different LOAs will enable the system to overcome various performance degrading factors and thus enable superior performance compared to systems in which LOAs cannot switch on-the-fly. Statistically validated analyses from the two experiments provide evidence that: a) Human-Initiative (HI) systems outperform purely teleoperated or autonomous systems in navigation tasks;
Revised:  Analyses from the two experiments provide evidence that: a) Human-Initiative (HI) systems outperform purely teleoperated or autonomous systems in navigation tasks;
Edit intent: clarity
Original:  b) MI systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload  . Results also reinforce previous Human-Robot interaction (HRI) evidence regarding the importance of the operator's personality traits and their trust in the autonomous system.
Revised:  b) in the context of the simulated robot experiment, Mixed-Initiative (MI) systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload . Results also reinforce previous Human-Robot interaction (HRI) evidence regarding the importance of the operator's personality traits and their trust in the autonomous system.
Edit intent: meaning-changed
Original:  b) MI systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload  . Results also reinforce previous Human-Robot interaction (HRI) evidence regarding the importance of the operator's personality traits and their trust in the autonomous system.
Revised:  b) MI systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload compared to HI . Results also reinforce previous Human-Robot interaction (HRI) evidence regarding the importance of the operator's personality traits and their trust in the autonomous system.
Edit intent: meaning-changed
Original:  b) MI systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload  . Results also reinforce previous Human-Robot interaction (HRI) evidence regarding the importance of the operator's personality traits and their trust in the autonomous system.
Revised:  b) MI systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload . Results also reinforce previous human-robot interaction evidence regarding the importance of the operator's personality traits and their trust in the autonomous system.
Edit intent: clarity
Original: The enormous amount of data and computation required to train DNNshave led to the rise of various parallelization strategies . Broadly, there are two strategies: 1) Data-Parallelism -- replicating the DNN on multiple processes and training on different training samples, and 2) Model-Parallelism -- dividing elements of the DNN itself into partitions across different processes .
Revised: To reduce training time of large-scale DNNs, scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism .
Edit intent: clarity
Original:  While data-parallelism has been extensively studied and developed,  model-parallelism has received less attention as it is non-trivial to split the model across processes . In this paper, we propose HyPar-Flow : a framework for scalable and user-transparent parallel training of very large DNNs (up to 5, 000 layers).
Revised:  While data-parallelism has been extensively studied and developed, model-parallelism has received less attention as it is non-trivial to split the model across processes , 2) implementing forward/back-propagation across process boundaries that requires explicit communication, 3) obtaining parallel speedup on an inherently sequential task, and 4) achieving scalability without losing out on a model's accuracy. To address these problems, we create HyPar-Flow : a framework for scalable and user-transparent parallel training of very large DNNs (up to 5, 000 layers).
Edit intent: meaning-changed
Original:  In this paper, we propose HyPar-Flow : a framework for scalable and user-transparent parallel training of very large DNNs (up to 5, 000 layers).
Revised:  In this paper, we propose HyPar-Flow --- a model-size/-type agnostic, scalable, practical, and user-transparent parallel training of very large DNNs (up to 5, 000 layers).
Edit intent: meaning-changed
Original:  In this paper, we propose HyPar-Flow : a framework for scalable and user-transparent parallel training of very large DNNs (up to 5, 000 layers). We exploit TensorFlow's Eager Execution features and KerasAPIs for model definition and distribution .
Revised:  In this paper, we propose HyPar-Flow : a framework for scalable and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow .
Edit intent: meaning-changed
Original:  HyPar-Flow exposes a simple API to offer data, model, and hybrid (model + data) parallel training for models defined using the Keras API. Under the hood, we introduce MPI communication primitives like send and recv on layer boundaries for data exchange between model-partitions and allreduce for gradient exchange across model-replicas.
Revised:  HyPar-Flow exposes a simple API to offer data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF's Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. Between model partitions, we use send and recv on layer boundaries for data exchange between model-partitions and allreduce for gradient exchange across model-replicas.
Edit intent: meaning-changed