wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This is a source (state) GGM hidden by the MEEG observation equation , i. e. equivalent to a frequency domain Linear State Space Model (LSSM) . It is unhidden due to the Type II Likelihood approximated representation: Expected Log-Likelihood (ELL).
Revised:  This is a source (state) GGM hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) . It is unhidden due to the Type II Likelihood approximated representation: Expected Log-Likelihood (ELL).
Edit intent: meaning-changed
Original:  This is a source (state) GGM hidden by the MEEG observation equation , i. e. equivalent to a frequency domain Linear State Space Model (LSSM) . It is unhidden due to the Type II Likelihood approximated representation: Expected Log-Likelihood (ELL). The essential contributions here are a theory for HGGM solvers, and its implementation for the subsequent Maximization step of the H-HGGM ELL. Its efficacy is demonstrated in high resolution EEG simulations and a Steady State Visual Evoked potential.
Revised:  This is a source (state) GGM hidden by the MEEG observation equation , i. e. equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked potential.
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Original:  Its efficacy is demonstrated in high resolution EEG simulations and a Steady State Visual Evoked potential.
Revised:  Its efficacy is demonstrated in high resolution EEG simulations and a Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey.
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Original:  In the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO asymptotically breaks cointegrated groups although it cannot wipe out all inactive cointegrating variables .
Revised:  In the presence of stationary, local unit root, and cointegrated predictors, we show that the adaptive LASSO asymptotically breaks cointegrated groups although it cannot wipe out all inactive cointegrating variables .
Edit intent: meaning-changed
Original:  In the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO asymptotically breaks cointegrated groups although it cannot wipe out all inactive cointegrating variables .
Revised:  In the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO cannot asymptotically eliminate all cointegrating variables with zero regression coefficients .
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Original:  This new finding motivates a simple but novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to fix variable selection inconsistency. TAlasso's penalty scheme accommodates the system of heterogeneous regressors, and it recovers the well-known oracle property that implies variable selection consistency and optimal rate of convergence for all three types of regressors.
Revised:  This new finding motivates a simple but novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to restore variable selection consistency. Accommodating the system of heterogeneous regressors, and it recovers the well-known oracle property that implies variable selection consistency and optimal rate of convergence for all three types of regressors.
Edit intent: clarity
Original:  TAlasso's penalty scheme accommodates the system of heterogeneous regressors, and it recovers the well-known oracle property that implies variable selection consistency and optimal rate of convergence for all three types of regressors.
Revised:  TAlasso's penalty scheme accommodates the system of heterogeneous regressors, TAlasso achieves the well-known oracle property that implies variable selection consistency and optimal rate of convergence for all three types of regressors.
Edit intent: clarity
Original:  TAlasso's penalty scheme accommodates the system of heterogeneous regressors, and it recovers the well-known oracle property that implies variable selection consistency and optimal rate of convergence for all three types of regressors. On the contrary , conventional LASSO fails to attain coefficient estimation consistency and variable screening in all components simultaneously , since its penalty is imposed according to the marginal behavior of each individual regressor only.
Revised:  TAlasso's penalty scheme accommodates the system of heterogeneous regressors, and it recovers the well-known oracle property . In contrast , conventional LASSO fails to attain coefficient estimation consistency and variable screening in all components simultaneously , since its penalty is imposed according to the marginal behavior of each individual regressor only.
Edit intent: coherence
Original:  On the contrary , conventional LASSO fails to attain coefficient estimation consistency and variable screening in all components simultaneously , since its penalty is imposed according to the marginal behavior of each individual regressor only. We demonstrate the theoretical properties via extensive Monte Carlo simulations. These LASSO-type methods are applied to evaluate short- and long-horizon predictability of S  P 500 excess return .
Revised:  On the contrary , conventional LASSO fails to attain coefficient estimation consistency and variable screening in all components simultaneously . We apply these LASSO methods to evaluate the short- and long-horizon predictability of S P 500 excess return .
Edit intent: coherence
Original: Abnormal synchronisation has been associated with epileptic seizures, one of the most common brain disease. Due to this fact, a better understanding of neuronal synchronisation mechanisms can help in the epilepsytreatment.
Revised: Abnormal synchronisation has been associated with epileptic seizures, one of the most common brain diseases worldwide. A better understanding of neuronal synchronisation mechanisms can help in the epilepsytreatment.
Edit intent: clarity
Original:  Due to this fact, a better understanding of neuronal synchronisation mechanisms can help in the epilepsytreatment. We study neuronal synchronisation by means of a randomly network with excitatory and inhibitory synapses, where the neuron model is given by the adaptive exponential integrate-and-fire .
Revised:  Due to this fact, a better understanding of neuronal synchronisation mechanisms can thus help control or even treat epilepsy. In this paper, we study neural synchronisation in a random network where nodes are neurons with excitatory and inhibitory synapses, where the neuron model is given by the adaptive exponential integrate-and-fire .
Edit intent: clarity
Original:  We study neuronal synchronisation by means of a randomly network with excitatory and inhibitory synapses, where the neuron model is given by the adaptive exponential integrate-and-fire . In our network , we verify that the decrease in the influence of inhibition can generate synchronisation  from a pattern of desynchronised spikes.
Revised:  We study neuronal synchronisation by means of a randomly network with excitatory and inhibitory synapses, where the neuron model is given by the adaptive exponential integrate-and-fire model. In this framework , we verify that the decrease in the influence of inhibition can generate synchronisation from a pattern of desynchronised spikes.
Edit intent: clarity
Original:  The transition from desynchronous spikes to synchronous bursts activities , induced by varying the synaptic coupling, emerges in a hysteretic loop due to a bistable regime where normal (desynchronous) or abnormal ( synchronous) regimes exist.
Revised:  The transition from desynchronous spikes to synchronous bursts of activity , induced by varying the synaptic coupling, emerges in a hysteretic loop due to a bistable regime where normal (desynchronous) or abnormal ( synchronous) regimes exist.
Edit intent: clarity
Original:  The transition from desynchronous spikes to synchronous bursts activities , induced by varying the synaptic coupling, emerges in a hysteretic loop due to a bistable regime where normal (desynchronous) or abnormal ( synchronous) regimes exist.
Revised:  The transition from desynchronous spikes to synchronous bursts activities , induced by varying the synaptic coupling, emerges in a hysteresis loop due to a bistable regime where normal (desynchronous) or abnormal ( synchronous) regimes exist.
Edit intent: clarity
Original:  The transition from desynchronous spikes to synchronous bursts activities , induced by varying the synaptic coupling, emerges in a hysteretic loop due to a bistable regime where normal (desynchronous) or abnormal ( synchronous) regimes exist.
Revised:  The transition from desynchronous spikes to synchronous bursts activities , induced by varying the synaptic coupling, emerges in a hysteretic loop due to bistability where abnormal (excessively high synchronous) regimes exist.
Edit intent: meaning-changed
Original:  We show that, for parameters within the bistable region , a square current pulse can trigger the abnormalsynchronous regime , a process we claim to reproduce features of an epileptic seizure. We can also suppress it, as well. Suppression can be achieved through a current with small amplitude applying on less than 10 %DIFDELCMD < \\%%% %DIF < of neurons.
Revised:  We show that, for parameters within the bistable region , a square current pulse can trigger the abnormalsynchronous regime , a process we claim to reproduce features of epileptic seizures. Then, we show that it is possible to suppress such abnormal synchronisation by applying a small-amplitude external current on less than 10 %DIFDELCMD < \\%%% %DIF < of neurons.
Edit intent: clarity
Original:  The core idea is to learn plausible representations through the combined use of human neural activity evoked by natural imagesas a supervision mechanism for deep learning models. To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration  for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
Revised:  The core idea is to learn plausible representations through the combined use of human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
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Original:  To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration  for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
Revised:  To accomplish this, we propose a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
Edit intent: clarity
Original:  To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration  for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
Revised:  To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration , for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
Edit intent: fluency
Original:  To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration  for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation. The learned manifold is then used to perform image classification and saliency detection as well as to shed light on the possible representations generated by the human brain when perceiving the visual world.
Revised:  To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations. We then carry out image classification and saliency detection as well as to shed light on the possible representations generated by the human brain when perceiving the visual world.
Edit intent: clarity
Original:  The learned manifold is then used to perform image classification and saliency detection as well as to shed light on the possible representations generated by the human brain when perceiving the visual world. Performance analysis shows that neural signals can be used to effectively supervise the training of deep learning models, as demonstrated by the achieved performance in both image classification and saliency detection .
Revised:  The learned manifold is then used to perform image classification and saliency detection on the learned manifold. Performance analyses show that our approach satisfactorily decodes visual information from neural signals. This, in turn, can be used to effectively supervise the training of deep learning models, as demonstrated by the achieved performance in both image classification and saliency detection .
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Original:  Performance analysis shows that neural signals can be used to effectively supervise the training of deep learning models, as demonstrated by the achieved performance in both image classification and saliency detection . Furthermore, the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and , most importantly, highlights new associations between brain areas, image patches and computational kernels.
Revised:  Performance analysis shows that neural signals can be used to effectively supervise the training of deep learning models, as demonstrated by the achieved performance in both image classification and saliency detection on out-of-training classes. The obtained results show that the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and , most importantly, highlights new associations between brain areas, image patches and computational kernels.
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Original:  Furthermore, the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and , most importantly, highlights new associations between brain areas, image patches and computational kernels.
Revised:  Furthermore, the learned brain-visual features lead to improved performance and simultaneously bring deep models more in line with cognitive neuroscience literature about visual perception and , most importantly, highlights new associations between brain areas, image patches and computational kernels.
Edit intent: meaning-changed
Original:  Furthermore, the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and , most importantly, highlights new associations between brain areas, image patches and computational kernels. In particular, we are able to approximate brain responses to visual stimuli by training an artificial model with image features correlated to neural activity .
Revised:  Furthermore, the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and attention .
Edit intent: clarity
Original:  Therefore, the concept of " tree-based " phylogenetic networks, which was introduced by Francis and Steel, has attracted much attention of theoretical biologists in the last few years. In this context, spanning trees of a certain kind called "subdivision trees" play an essential role and there are many important computational problems about them, whose time complexity is still unclear. Against this backdrop, the present paperaims to provide a graph theoretical framework for solving different problems on subdivision trees in a simple and unified manner.
Revised:  Therefore, the concept of " tree-based " phylogenetic networks, which was introduced by Francis and Steel, has attracted much attention of theoretical biologists . However, the application of such networks is still not easy, due to many problems whose time complexities are not clearly understood. In this paper, we provide a general framework for solving different problems on subdivision trees in a simple and unified manner.
Edit intent: clarity
Original:  Against this backdrop, the present paperaims to provide a graph theoretical framework for solving different problems on subdivision trees in a simple and unified manner. To this end, we focus on a structure called the maximal zig-zag trail decomposition that is inherent in any rooted binary phylogenetic network N and prove a structure theorem that characterizes the collection of all subdivision trees of N . Our theorem does not only imply and unify various results in the literature but also yield linear time (for enumeration, linear delay) algorithms for the following problems:
Revised:  Against this backdrop, the present paperaims to provide a graph theoretical framework for solving those various old or new problems from a coherent perspective, rather than analyzing the complexity of each individual problem or developing an algorithm one by one. More precisely, we establish a structure theorem that gives a way to canonically decompose any rooted binary phylogenetic network N and prove a structure theorem that characterizes the collection of all subdivision trees of N . Our theorem does not only imply and unify various results in the literature but also yield linear time (for enumeration, linear delay) algorithms for the following problems:
Edit intent: meaning-changed
Original:  To this end, we focus on a structure called the maximal zig-zag trail decomposition that is inherent in any rooted binary phylogenetic network N and prove a structure theorem that characterizes the collection of all subdivision trees of N . Our theorem does not only imply and unify various results in the literature but also yield linear time (for enumeration, linear delay) algorithms for the following problems:
Revised:  To this end, we focus on a structure called the maximal zig-zag trail decomposition that is inherent in any rooted binary phylogenetic network N into maximal zig-zag trails that are uniquely determined, and use it to characterize the set of subdivision trees of N . Our theorem does not only imply and unify various results in the literature but also yield linear time (for enumeration, linear delay) algorithms for the following problems:
Edit intent: clarity
Original:  To this end, we focus on a structure called the maximal zig-zag trail decomposition that is inherent in any rooted binary phylogenetic network N and prove a structure theorem that characterizes the collection of all subdivision trees of N . Our theorem does not only imply and unify various results in the literature but also yield linear time (for enumeration, linear delay) algorithms for the following problems:
Revised:  To this end, we focus on a structure called the maximal zig-zag trail decomposition that is inherent in any rooted binary phylogenetic network N and prove a structure theorem that characterizes the collection of all subdivision trees of N in the form of a direct product, in a way reminiscent of the structure theorem for finitely generated Abelian groups. From the main results, we derive a series of linear time and linear time delay algorithms for the following problems:
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Original:  and 4 ) find a subdivision tree to maximize or minimize a prescribed objective function (optimization problem). Importantly, the results and algorithms in this paper still hold true for some non-binary phylogenetic networks and this generalization gives a partial answer to an open question from Pons, Semple, and Steel.
Revised:  and 4 ) find a subdivision tree to maximize or minimize a prescribed objective function . All algorithms proposed here are optimal in terms of time complexity. Our results do not only imply and unify various known results, but also answer many open questions and moreover enable novel applications, such as the estimation of a maximum likelihood tree underlying a tree-based network. The results and algorithms in this paper still hold true for some non-binary phylogenetic networks and this generalization gives a partial answer to an open question from Pons, Semple, and Steel.
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Original:  Importantly, the results and algorithms in this paper still hold true for some non-binary phylogenetic networks and this generalization gives a partial answer to an open question from Pons, Semple, and Steel.
Revised:  Importantly, the results and algorithms in this paper still hold true for a special class of rooted non-binary phylogenetic networks and this generalization gives a partial answer to an open question from Pons, Semple, and Steel.
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Original:  We show that deciding whether there exists a realization of a given graph as unit disk graphs on straight lines is NP-hard, even if the given lines are parallel to either x-axis or y-axis.
Revised:  In this paper, we first describe a polynomial-time reduction which shows that deciding whether there exists a realization of a given graph as unit disk graphs on straight lines is NP-hard, even if the given lines are parallel to either x-axis or y-axis.
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Original:  We show that deciding whether there exists a realization of a given graph as unit disk graphs on straight lines is NP-hard, even if the given lines are parallel to either x-axis or y-axis.
Revised:  We show that deciding whether there exists a realization of a given graph as unit disk graphs on straight lines is NP-hard, when the given lines are parallel to either x-axis or y-axis.
Edit intent: coherence
Original:  The limited applicability of these methods to smaller protein families has limited their use for structural annotation of whole genomes , however. Most recently , deep learning techniques have shown promise in allowing  residue-residue contacts to be predicted accurately even for shallow sequence alignments.
Revised:  The limited applicability of these methods to smaller protein families has limited their use for structural annotation of whole genomes . Recently , deep learning techniques have shown promise in allowing residue-residue contacts to be predicted accurately even for shallow sequence alignments.
Edit intent: coherence