wanyu/IteraTeR_full_sent huggingface dataset:
Original: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced  burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l  , a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of  inhibition to favor the cluster burst synchronization.
Revised: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l ~(\simeq 0.78) , a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of inhibition to favor the cluster burst synchronization.
Edit intent: meaning-changed
Original: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced  burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l  , a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of  inhibition to favor the cluster burst synchronization.
Revised: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l , a transition to 3-cluster burst synchronization occurs due to a constructive role of inhibition to favor the cluster burst synchronization.
Edit intent: clarity
Original: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced  burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l  , a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of  inhibition to favor the cluster burst synchronization.
Revised: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l , a transition from desynchronization to 3-cluster burst synchronization occurs due to another constructive role of inhibition to favor the cluster burst synchronization.
Edit intent: clarity
Original: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced  burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l  , a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of  inhibition to favor the cluster burst synchronization.
Revised: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l , a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of synaptic inhibition to favor the cluster burst synchronization.
Edit intent: clarity
Original: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced  burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l  , a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of  inhibition to favor the cluster burst synchronization. For this case, the whole population is segregated into 3 clusters, and HR neurons in each cluster exhibit burst synchronization.
Revised: We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced burst synchronization by varying the average coupling strength J_0. When passing a lower threshold J^*_l , a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of inhibition to favor population synchronization. In this case, the whole population is segregated into 3 clusters, and HR neurons in each cluster exhibit burst synchronization.
Edit intent: clarity
Original:  However, as J_0 is increased and passes an intermediate threshold J^*_m  , HR neurons begin to make intermittent hoppings between the 3 clusters.
Revised:  However, as J_0 is increased and passes an intermediate threshold J^*_m ~(\simeq 5.2) , HR neurons begin to make intermittent hoppings between the 3 clusters.
Edit intent: meaning-changed
Original:  In spite of break-up of the 3 clusters,  burst synchronization persists in the whole population, which is well visualized in the raster plot of burst onset times where bursting stripes (composed of burst onset times and indicating burst synchronization) appear successively.
Revised:  In spite of break-up of the 3 clusters, (non-cluster) burst synchronization persists in the whole population, which is well visualized in the raster plot of burst onset times where bursting stripes (composed of burst onset times and indicating burst synchronization) appear successively.
Edit intent: meaning-changed
Original:  With further increase in J_0, intercluster hoppings are intensified, and bursting stripes also become smeared more and more due to a destructive role of  inhibition to spoil the burst synchronization.
Revised:  With further increase in J_0, intercluster hoppings are intensified, and bursting stripes also become smeared more and more due to a destructive role of synaptic inhibition to spoil the burst synchronization.
Edit intent: meaning-changed
Original: In this paper, we study how temporal discounting determines sequential decision making. We analyze decision time distribution induced by all sequential information acquisition strategies that 1) implements a target information structure, 2) satisfies a constrainton flow informativeness of signal.
Revised: I consider the sequential implementation of a target information structure. I characterize the set of decision time distributions induced by all sequential information acquisition strategies that 1) implements a target information structure, 2) satisfies a constrainton flow informativeness of signal.
Edit intent: clarity
Original:  We analyze decision time distribution induced by all sequential information acquisition strategies that 1) implements a target information structure, 2) satisfies a constrainton flow informativeness of signal. The main result is that decisive Poisson signal creates the most dispersed decision time distribution (in mean-preserving spread order ), and the pure accumulation of information creates the least dispersed.
Revised:  We analyze decision time distribution induced by all signal processes that satisfy a per-period learning capacity constraint. I find that all decision time distributions have the same expectation, and the maximal and minimal elements by mean-preserving spread order ), and the pure accumulation of information creates the least dispersed.
Edit intent: meaning-changed
Original:  The main result is that decisive Poisson signal creates the most dispersed decision time distribution (in mean-preserving spread order ), and the pure accumulation of information creates the least dispersed. This implies that for a decision maker with convex discount function, decisive Poisson signal is the optimal learning strategy .
Revised:  The main result is that decisive Poisson signal creates the most dispersed decision time distribution (in mean-preserving spread order are deterministic distribution and exponential distribution. The result implies that when time preference is risk loving (e.g. standard or hyperbolic discounting), Poisson signal is the optimal learning strategy .
Edit intent: meaning-changed
Original: Many objects of interest can be expressed as an L2 continuous functional of a regression, including average treatment effects, economic average consumer surplus, expected conditional covariances, and discrete choice parameters that depend on expectations. Debiased machine learning (DML) of these objects requires a learninga Riesz representer (RR). We provide here Lasso and Dantzig learners of the RR and corresponding learners of affine and other nonlinear functionals.
Revised: Many objects of interest can be expressed as an L2 continuous functional of a regression, including average treatment effects, policy effects, average derivatives, regression decompositions, economic average equivalent variation, and parameters of economic structural models. The regressions may be high dimensional. Plugging machine learners into identifying equations can lead to poor inference due to bias and/or model selection. This paper gives automatic debiasing for estimating equations and valid asymptotic inference for the estimators of effects of interest. The debiasing is automatic in that its construction uses the identifying equations without the full form of the bias correction and is performed by machine learning. Novel results include convergence rates for Lasso and Dantzig learners of the RR and corresponding learners of affine and other nonlinear functionals.
Edit intent: meaning-changed
Original:  We provide here Lasso and Dantzig learners of the RR and corresponding learners of affine and other nonlinear functionals. We give an asymptotic variance estimator for DML. We allow for a wide variety of regression learners that can converge at relatively slow rates.
Revised:  We provide here Lasso and Dantzig learners of the bias correction, primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners that can converge at relatively slow rates.
Edit intent: meaning-changed
Original:  We give an asymptotic variance estimator for DML. We allow for a wide variety of regression learners that can converge at relatively slow rates. We give conditions for root-n consistency and asymptotic normality of the functional learner. We give results for non affine functionals in addition to affine functionals .
Revised:  We give an asymptotic variance estimator for DML. We allow for a wide variety of regression learners and identifying equations are covered. Automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income .
Edit intent: meaning-changed
Original:  The debiasing is automatic in that its construction uses the identifying equations without the full form of the bias correction and is performed by machine learning. Novel results include convergence rates for Lasso and Dantzig learners of the bias correction,  primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered.
Revised:  The debiasing is automatic in that its construction uses the identifying equations without the full form of the bias correction . The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high dimensional methods. In addition to providing the bias correction we give standard errors that are robust to misspecification, convergence rates for Lasso and Dantzig learners of the bias correction, primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered.
Edit intent: meaning-changed
Original:  Novel results include convergence rates for Lasso and Dantzig learners of the bias correction,  primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered.
Revised:  Novel results include convergence rates for the bias correction, primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered.
Edit intent: clarity
Original:  Novel results include convergence rates for Lasso and Dantzig learners of the bias correction,  primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered.
Revised:  Novel results include convergence rates for Lasso and Dantzig learners of the bias correction, and primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered.
Edit intent: fluency
Original:  Novel results include convergence rates for Lasso and Dantzig learners of the bias correction,  primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered. Automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.
Revised:  Novel results include convergence rates for Lasso and Dantzig learners of the bias correction, primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.
Edit intent: clarity
Original:  Automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.
Revised:  Automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.
Edit intent: clarity
Original:  Automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.
Revised:  Automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.
Edit intent: fluency
Original: The size of publicly available data in cognitive neuro-imaging has increased a lot in recent years, thanks to strong research and community efforts . Exploiting this wealth of data demands new methods to turn the heterogeneous cognitive information held in different task-fMRI studies into common-universal-cognitive models.
Revised: We show in this paper how to extract shared brain representations that predict mental processes across many cognitive neuroimaging studies. Focused cognitive-neuroimaging experiments study precise mental processes with carefully-designed cognitive paradigms; however the cost of imaging limits their statistical power. On the other hand, large-scale databasing efforts increase considerably the sample sizes, but cannot ask precise cognitive questions. To address this tension, we develop new methods that turn the heterogeneous cognitive information held in different task-fMRI studies into common-universal-cognitive models.
Edit intent: meaning-changed
Original:  In this paper, we pool data from large fMRI repositories to predict psychological conditions from statistical brain maps across different studies and subjects. We leverage advances in deep learning, intermediate representations and multi-task learning to learn universal interpretable low-dimensional representations of brain images, usable for predicting psychological stimuli in all input studies. The method improves decoding performance for 80\% of studies, by permitting cognitive information to flow from every study to the others: it notably gives a strong performance boost when decoding studies of small size.
Revised:  Our approach does not assume any prior knowledge of the commonalities shared by the studies in the corpus; those are inferred during model training. The method uses deep-learning techniques to extract representations - task-optimized networks - that form a set of basis cognitive dimensions relevant to the psychological manipulations. In this sense, it forms a novel kind of functional atlas, optimized to capture mental state across many functional-imaging experiments. As it bridges information on the neural support of mental processes, this representation improves decoding performance for 80\% of studies, by permitting cognitive information to flow from every study to the others: it notably gives a strong performance boost when decoding studies of small size.
Edit intent: coherence
Original:  The method improves decoding performance for 80\% of studies, by permitting cognitive information to flow from every study to the others: it notably gives a strong performance boost when decoding studies of small size. The trained low-dimensional representation-task-optimized networks-is interpretable as a set of basis cognitive dimensions relevant to meaningful categories of cognitive stimuli.
Revised:  The method improves decoding performance for 80\% of the 35 widely-different functional imaging studies that we consider.
Edit intent: meaning-changed
Original:  On the other hand, large-scale databasing efforts increase considerably the sample sizes, but cannot ask precise cognitive questions. To address this tension, we develop new methods that turn the heterogeneous cognitive information held in different task-fMRI studies into common-universal-cognitive models. Our approach does not assume any prior knowledge of the commonalities shared by the studies in the corpus; those are inferred during model training. The method uses deep-learning techniques to extract representations - task-optimized networks - that form a set of basis cognitive dimensions relevant to the psychological manipulations. In this sense, it forms a novel kind of functional atlas, optimized to capture mental state across many functional-imaging experiments. As it bridges information on the neural support of mental processes, this representation improves decoding performance for 80\% of the 35 widely-different functional imaging studiesthat we consider.
Revised:  Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80\% of the 35 widely-different functional imaging studiesthat we consider.
Edit intent: coherence
Original:  As it bridges information on the neural support of mental processes, this representation improves decoding performance for 80\% of the 35 widely-different functional imaging studiesthat we consider. Our approach opens new ways of extracting information from brain maps, increasing statistical power even for focused cognitive neuroimaging studies, in particular for those with few subjects .
Revised:  As it bridges information on the neural support of mental processes, this representation improves decoding performance for 80\% of the 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data .
Edit intent: meaning-changed
Original: Alzheimer Disease (AD) is the most common form of dementia affecting the elderly population worldwide. Many neuroimaging modalities have been used to check the detection and progression of AD of which structural Magnetic Resonance Imaging (sMRI) is an important one. The recent rise in the popularity of deep learning methods with applications in computer vision, reinforcement learning and artificial intelligence has created a resurgence in the application of these methods to the classification of AD through different imaging modalities. In this study, by utilizing the concept of transfer learning in deep learning, we propose a classification framework to differentiate subjects with Clinical Dementia Rating (CDR) of zero from subjects with CDR greater than zero by using deep learning architecturessuch as Xception and Inception version 3 in the Keras deep learning library.
Revised: Alzheimer Disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) plays an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 in the Keras deep learning library.
Edit intent: coherence
Original:  In this study, by utilizing the concept of transfer learning in deep learning, we propose a classification framework to differentiate subjects with Clinical Dementia Rating (CDR) of zero from subjects with CDR greater than zero by using deep learning architecturessuch as Xception and Inception version 3 in the Keras deep learning library. The attained validation set accuracies are as high as 99.12\% for the Inception version 3 network and 97.97\% for the Xception network.
Revised:  In this study, by utilizing the concept of transfer learning in deep learning, we propose a classification framework to differentiate subjects with Clinical Dementia Rating (CDR) of zero from subjects with CDR greater than zero by using deep learning architecturessuch as Xception and Inception version 3 and Xception; as well as custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the Xception network.
Edit intent: meaning-changed