wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In mammals, the receptor neurons are replaced regularly, and the receptor distribution changes in response to olfactory experience.
Revised:  We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a little-studied regime of efficient coding that is sensitive to the global context of correlated sensor responses. This model predicts further that in mammals, where olfactory sensory neurons are replaced regularly, and the receptor distribution changes in response to olfactory experience.
Edit intent: meaning-changed
Original:  We propose a theoretical model that explains these observations: the receptor distribution is tuned to efficiently encode information about the olfactory environment. This adaptation occurs before olfactory signals are decorrelated, leading to a new regime of efficient coding in which the optimal receptor distribution is sensitive to the global context of the correlatedresponses of all receptor types in the population. We demonstrate how this effect reproduces the context-dependence seen in experiments.
Revised:  We show that efficient coding theory predicts precisely such a context-dependence seen in experiments.
Edit intent: clarity
Original:  We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a little-studied regime of efficient coding that is sensitive to the global context of correlated sensor responses.
Revised:  We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a regime of efficient coding that is sensitive to the global context of correlated sensor responses.
Edit intent: clarity
Original: We introduce the exit time finite state projection (ETFSP) scheme, a truncation-based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of a continuous-time Markov chain .
Revised: We introduce the exit time finite state projection (ETFSP) scheme, a truncation-based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of time-homogeneous continuous-time Markov chain .
Edit intent: meaning-changed
Original: We introduce the exit time finite state projection (ETFSP) scheme, a truncation-based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of a continuous-time Markov chain .
Revised: We introduce the exit time finite state projection (ETFSP) scheme, a truncation-based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of a continuous-time Markov chains .
Edit intent: fluency
Original: Decoding specific task states from human fMRI ( functional magnetic resonance imaging ) signals constitutes a major goal of neuroimaging. Previous studies of task-state classification have focused largely on processed fMRI characteristics (e.g. functional connectivity patterns, activity patterns) or a single fMRI volume. Here, we assembled a novel deep neural network (DNN) for classifying directly from 4-D fMRI time series, retaining both spatial and temporal information of fMRI signals .
Revised: Decoding specific task states from human fMRI ( functional magnetic resonance imaging (fMRI) of the human brain. However, the number of dimensions of fMRI signals are too large (hundreds of thousands of voxels in one volume) to be efficiently learnt by the MVPA. Researchers thus need to reduce the features to tens or hundreds of dimensions through feature selection/extraction that requires expert knowledge and may lead to a selection bias. In this study, we propose a deep neural network (DNN) for classifying directly from 4-D fMRI time series, retaining both spatial and temporal information of fMRI signals .
Edit intent: meaning-changed
Original:  We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with 91.75\% accuracy in test.
Revised:  We trained and tested the DNN classifier using task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with 91.75\% accuracy in test.
Edit intent: clarity
Original:  We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with 91.75\% accuracy in test.
Revised:  We trained and tested the DNN classifier by task fMRI data from the Human Connectome Project's S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with 91.75\% accuracy in test.
Edit intent: clarity
Original:  We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with 91.75\% accuracy in test.
Revised:  We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset (N=1034). In tests to verify its performance, the proposed classification method identified the seven tasks with 91.75\% accuracy in test.
Edit intent: meaning-changed
Original:  We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with 91.75\% accuracy in test.
Revised:  We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified seven tasks with 91.75\% accuracy in test.
Edit intent: clarity
Original:  We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with 91.75\% accuracy in test. Then we extracted task heatmaps of each time series by a guided backpropagation approach, and found that the heatmaps had a high similarity with the group average result of traditional contrast of parameter estimate (COPE). Furthermore, the DNN could be applied to small dataset. The features from the intermediate layer of the DNN were trained and used to train a support vector machine (SVM) to classify four conditions within 300 blocks of task-fMRI scans, data amount comparable to a usual neuroimaging research, and obtained a 95\% accuracy. Our reported findings were the first to demonstrate the ability of DNN to classify and extract heatmaps from a 4-D fMRI time series, as well as its ability to be transfer trained by a small dataset. This approach should enable decoding and mapping subject's task status and furthermore help with classification and diagnosis of neuropsychiatric diseases .
Revised:  We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with an average accuracy of 93.7\%. We also showed the general applicability of the DNN for transfer learning to small datasets (N=43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89\% and 94.7\% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2\% and 68.6\% obtained by the MVPA. A visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers .
Edit intent: coherence
Original:  However, the number of dimensions of fMRI signals are too large (hundreds of thousands of voxels in one volume) to be efficiently learnt by the MVPA. Researchers thus need to reduce the features to tens or hundreds of dimensions through feature selection/extraction that requires expert knowledgeand may lead to a selection bias .
Revised:  Conventionally, the SVM-MVPA requires careful feature selection/extraction that requires expert knowledgeand may lead to a selection bias .
Edit intent: clarity
Original:  The proposed method achieved an average accuracy of 89 \% and 94.7\% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2\% and 68.6\% obtained by the MVPA.
Revised:  The proposed method achieved an average accuracy of 89.0 \% and 94.7\% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2\% and 68.6\% obtained by the MVPA.
Edit intent: meaning-changed
Original:  The proposed method achieved an average accuracy of 89 \% and 94.7\% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2\% and 68.6\% obtained by the MVPA. A visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task.
Revised:  The proposed method achieved an average accuracy of 89 \% and 94.7\% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2\% and 68.6\% obtained by the SVM-MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task.
Edit intent: clarity
Original:  This solution is in excellent agreement with both coarse-grained simulations of DNA minicircles and experimental structural data for nucleosomal DNA , which is bent and wrapped around histone proteins in a superhelical conformation.
Revised:  This solution is in excellent agreement with both coarse-grained simulations of minicircles and nucleosomal DNA data , which is bent and wrapped around histone proteins in a superhelical conformation.
Edit intent: clarity
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Revised:  In this way, we aim to capture key `system-wide' effects on risk. The resulting mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Edit intent: clarity
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit is characterized uniquely by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Edit intent: clarity
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Edit intent: fluency
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition . The density of this SPDE gives the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Edit intent: clarity
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a non-standard `conditional' McKean--Vlasov diffusion, for which we provide a novel upper Dirichlet heat kernel type estimate that is essential to the proofs . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Edit intent: meaning-changed
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the strength of the mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition ) which governs the conditional law of a 'conditionalMcKean-Vlasov' type diffusion . Depending on the realizations of the common noise and the rate of mean reversion, the SPDE can exhibit rapid accelerations in the loss of mass at the boundary.
Edit intent: clarity
Original:  Here, we present a new concept of rational choice as  hyper-rational choice which in this concept, the actor thinks about profit or loss of other actors in addition to his personal profit or loss and then will choose an action which is desirable to him.
Revised:  Here, we present a new concept of rational choice as a hyper-rational choice which in this concept, the actor thinks about profit or loss of other actors in addition to his personal profit or loss and then will choose an action which is desirable to him.
Edit intent: fluency
Original: Though the big data benchmark suites like BigDataBench and CloudSuite have been used in architecture and system researches, we have not yet answered the fundamental issue-- what are abstractions of frequently-appearing units of computation in big data analytics, which we call big data dwarfs. For the first time, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation  while being reasonably divorced from individual implementations  among a wide variety of big data analytics workloads .
Revised: The complexity and diversity of big data and AI workloads make understanding them difficult and challenging. This paper proposes a new approach to characterizing big data and AI workloads. We consider each big data and AI workload as a pipeline of one or more classes of unit of computations performed on different initial or intermediate data inputs. Each class of unit of computation while being reasonably divorced from individual implementations among a wide variety of big data analytics workloads .
Edit intent: clarity
Original:  For the first time, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation  while being reasonably divorced from individual implementations  among a wide variety of big data analytics workloads .
Revised:  For the first time, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation captures the common requirements while being reasonably divorced from individual implementations among a wide variety of big data analytics workloads .
Edit intent: meaning-changed
Original:  For the first time, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation  while being reasonably divorced from individual implementations  among a wide variety of big data analytics workloads .
Revised:  For the first time, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation while being reasonably divorced from individual implementations , and hence we call it a data dwarf. For the first time, among a wide variety of big data analytics workloads .
Edit intent: meaning-changed
Original:  For the first time, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation  while being reasonably divorced from individual implementations  among a wide variety of big data analytics workloads .
Revised:  For the first time, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation while being reasonably divorced from individual implementations among a wide variety of big data and AI workloads, we identify eight data dwarfs that takes up most of run time, including Matrix, Sampling, Logic, Transform, Set, Graph, Sort and Statistic .
Edit intent: meaning-changed
Original:  We implement the eight  dwarfs on different software stacks as the dwarf components. We present the application of the big data dwarfs to construct big data proxy benchmarks using the directed acyclic graph (DAG)-like combinations of the dwarf components with different weights to mimic the benchmarks in BigDataBench. Our proxy benchmarks shorten the execution time by 100s times on the real systems while they are qualified for both earlier architecture design and later system evaluation across different architectures .
Revised:  We implement the eight dwarfs on different software stacks as the micro benchmarks of an open-source big data and AI benchmark suite, and perform comprehensive characterization of those data dwarfs from perspective of data sizes, types, sources, and patterns as a lens towards fully understanding big data and AI workloads .
Edit intent: clarity
Original:  In many studies, the goal is not only to describe a network, but to investigate how this network is changing under different conditions, with respect to certain diseases, or between different species. While methods for comparing two networks exist, this is not the case for comparing multiple networks, although this is a common aim of many studies.
Revised:  In many studies, the goal is investigating complex-phenotypes under varying conditions, it comes naturally to employ comparative network methods. While approaches for comparing two networks exist, this is not the case for comparing multiple networks, although this is a common aim of many studies.
Edit intent: clarity
Original:  Here we present a method for the  comparison of an unlimited number of networks: Co-expression Differential Network Analysis (CoDiNA) . Our method distinguishes between links that are common to all networks, links that are specific to only one of the compared networks, and links that are different in that their sign changes between networks.
Revised:  Here we present a method for the comparison of an unlimited number of networks: Co-expression Differential Network Analysis (CoDiNA) for detecting links and nodes that are common to all networks, links that are specific to only one of the compared networks, and links that are different in that their sign changes between networks.
Edit intent: meaning-changed