wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Although a large group of packages shows some degree of connectivity (either depending on functions or suggesting the use of another tracking package), a third of tracking packages work on isolation, reflecting a fragmentation in the R Movement-Ecology programming community.
Revised:  Although a large group of packages shows some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the R Movement-Ecology programming community.
Edit intent: clarity
Original:  Although a large group of packages shows some degree of connectivity (either depending on functions or suggesting the use of another tracking package), a third of tracking packages work on isolation, reflecting a fragmentation in the R Movement-Ecology programming community.
Revised:  Although a large group of packages shows some degree of connectivity (either depending on functions or suggesting the use of another tracking package), a third of tracking packages work on isolation, reflecting a fragmentation in the R movement-ecology programming community.
Edit intent: fluency
Original:  In near-term fetal sheep, we aimed to identify fetal HRV measures reflecting the vagus nerve contributions to HRV. Fetal HRV was recorded during surgery and 72 hours later without (n=24) and with intra-surgical bilateral cervical vagotomy (n=15).
Revised:  In near-term fetal sheep, we aimed to identify fetal HRV measures reflecting the vagus nerve contributions to fHRV. In near-term ovine fetuses, a comprehensive set of 46 fHRV measures was computed from fetal pre-cordial electrocardiogram recorded during surgery and 72 hours later without (n=24) and with intra-surgical bilateral cervical vagotomy (n=15).
Edit intent: meaning-changed
Original:  We report the most encompassing set of fetal HRV measures known to-date and identify those HRV measures that are specific to the vagal modulation of HRV and show recovery dynamic due to fetal surgery.
Revised:  The fetal heart rate did not change due to vagotomy. We identify fHRV measures specific to the vagal modulation of HRV and show recovery dynamic due to fetal surgery.
Edit intent: meaning-changed
Original:  We report the most encompassing set of fetal HRV measures known to-date and identify those HRV measures that are specific to the vagal modulation of HRV and show recovery dynamic due to fetal surgery. Our findings have clinical significance for in utero monitoring of fetal health during surgery.
Revised:  We report the most encompassing set of fetal HRV measures known to-date and identify those HRV measures that are specific to the vagal modulation of fHRV: Multiscale time irreversibility asymmetry index (AsymI), Detrended fluctuation analysis (DFA) alpha1, Kullback-Leibler permutation entropy (KLPE) and Scale dependent Lyapunov exponent slope (SDLE alpha).
Edit intent: meaning-changed
Original:  We provide a systematic delineation of vagal contributions to HRV across signal-analytical domains which should be relevant for the emerging field of bioelectronic medicine and the deciphering of the vagus code  .
Revised:  We provide a systematic delineation of vagal contributions to fHRV across signal-analytical domains which should be relevant for the emerging field of bioelectronic medicine and the deciphering of the vagus code .
Edit intent: meaning-changed
Original:  We provide a systematic delineation of vagal contributions to HRV across signal-analytical domains which should be relevant for the emerging field of bioelectronic medicine and the deciphering of the vagus code  .
Revised:  We provide a systematic delineation of vagal contributions to HRV across signal-analytical domains which should be relevant for the emerging field of bioelectronic medicine and the deciphering of the vagus code . Our findings also have clinical significance for in utero monitoring of fetal health during surgery .
Edit intent: meaning-changed
Original: We deduce a simple closed formula for illiquid corporate coupon bond prices when liquid bonds with similar characteristics (e.g. maturity) are present in the market for the same issuer. The key model parameter is the time-to-liquidate a position, i.e. the time that an experienced bond trader takes to liquidate a given position on a corporate coupon bond. The option approach we propose for pricing bonds' illiquidity is reminiscent of the celebrated work of Longstaff (1995) on the non-marketability of some non-dividend-paying shares in IPOs.
Revised: We propose an option approach for pricing bond illiquidity that is reminiscent of the celebrated work of Longstaff (1995) on the non-marketability of some non-dividend-paying shares in IPOs.
Edit intent: clarity
Original:  The model considers interest rate and credit spread term structures and their dynamics. We show that illiquid bonds present an additional liquidity spread that depends on the time-to-liquidate aside from credit and interest rate parameters .
Revised:  The model considers interest rate and credit risks via a convenient reduced-form approach. We deduce a simple closed formula for illiquid corporate coupon bond prices when liquid bonds with similar characteristics (e.g. maturity) are present in the market for the same issuer. The key model parameter is the time-to-liquidate a position, i.e. the time that an experienced bond trader takes to liquidate a given position on a corporate coupon bond. We show that illiquid bonds present an additional liquidity spread that depends on the time-to-liquidate aside from credit and interest rate parameters .
Edit intent: meaning-changed
Original:  We explore the use of modern neural-based generative models trained on a limited quantity of EEG data collected from different subjects to generate supplementary synthetic EEG signal vectors  subsequently utilised to train an SSVEP classifier.
Revised:  We explore the use of modern neural-based generative models trained on a limited quantity of EEG data collected from different subjects to generate supplementary synthetic EEG signal vectors , subsequently utilised to train an SSVEP classifier.
Edit intent: fluency
Original:  Extensive experimental analyses demonstrate the efficacy of our generated data, leading to significant improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35\% with the use of  synthetic data.
Revised:  Extensive experimental analysis demonstrates the efficacy of our generated data, leading to significant improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35\% with the use of synthetic data.
Edit intent: fluency
Original:  Extensive experimental analyses demonstrate the efficacy of our generated data, leading to significant improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35\% with the use of  synthetic data.
Revised:  Extensive experimental analyses demonstrate the efficacy of our generated data, leading to improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35\% with the use of synthetic data.
Edit intent: clarity
Original:  Extensive experimental analyses demonstrate the efficacy of our generated data, leading to significant improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35\% with the use of  synthetic data.
Revised:  Extensive experimental analyses demonstrate the efficacy of our generated data, leading to significant improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35\% with the use of such synthetic data.
Edit intent: clarity
Original:  This leads us  the following question: will the hedge still be somewhat meaningful for models in the proximity of Q? If we measure proximity with the usual Wasserstein distance (say), the answer is NO. Models which are similar wrt Wasserstein distance may provide dramatically different information on which to base a hedging strategy.
Revised:  This leads us to the following question: will the hedge still be somewhat meaningful for models in the proximity of Q? If we measure proximity with the usual Wasserstein distance (say), the answer is NO. Models which are similar wrt Wasserstein distance may provide dramatically different information on which to base a hedging strategy.
Edit intent: fluency
Original:  This leads us  the following question: will the hedge still be somewhat meaningful for models in the proximity of Q? If we measure proximity with the usual Wasserstein distance (say), the answer is NO. Models which are similar wrt Wasserstein distance may provide dramatically different information on which to base a hedging strategy.
Revised:  This leads us the following question: will the hedge still be somewhat meaningful for models in the proximity of Q? If we measure proximity with the usual Wasserstein distance (say), the answer is NO. Models which are similar w.r.t. Wasserstein distance may provide dramatically different information on which to base a hedging strategy.
Edit intent: fluency
Original: Finding the causes to observed effects and establishing causal relationships between events is (and has been) an essential element of science and philosophy. Automated methods that can detect causal relationships would be very welcome, but practical methods that can infer causality are difficult to find, and the subject of ongoing research.
Revised: To infer information flow in any network of agents, it is important first and foremost to establish causal temporal relations between the nodes. Practical and automated methods that can detect causal relationships would be very welcome, but practical methods that can infer causality are difficult to find, and the subject of ongoing research.
Edit intent: meaning-changed
Original:   Our results suggest that whether or not transfer entropy measures of causality are misleading depends strongly on the cognitive task considered.
Revised:  We also test whether activity time-series recorded from behaving digital brains can infer information flow using the transfer entropy concept, when compared to a ground-truth model of causal influence constructed from connectivity and circuit logic. Our results suggest that whether or not transfer entropy measures of causality are misleading depends strongly on the cognitive task considered.
Edit intent: meaning-changed
Original: The spread of an epidemic is considered in the context of a SIR spatial stochastic model that includes a parameter 0\le p\le 1 that assigns weights p and 1-p to global and local infective contacts respectively.
Revised: The spread of an epidemic is considered in the context of an SIR spatial stochastic model that includes a parameter 0\le p\le 1 that assigns weights p and 1-p to global and local infective contacts respectively.
Edit intent: fluency
Original: The spread of an epidemic is considered in the context of a SIR spatial stochastic model that includes a parameter 0\le p\le 1 that assigns weights p and 1-p to global and local infective contacts respectively.
Revised: The spread of an epidemic is considered in the context of a SIR spatial stochastic model that includes a parameter 0\le p\le 1 that assigns weights p and 1- p to global and local infective contacts respectively.
Edit intent: fluency
Original: The spread of an epidemic  is considered in the context of an SIR spatial stochastic model that includes a parameter 0\le p\le 1 that assigns weights p and 1- p to global and local infective contacts respectively.
Revised: The spread of an epidemic process is considered in the context of an SIR spatial stochastic model that includes a parameter 0\le p\le 1 that assigns weights p and 1- p to global and local infective contacts respectively.
Edit intent: clarity
Original: The spread of an epidemic  is considered in the context of an SIR spatial stochastic model that includes a parameter 0\le p\le 1 that assigns weights p and 1- p to global and local infective contacts respectively.
Revised: The spread of an epidemic is considered in the context of a spatial SIR stochastic model that includes a parameter 0\le p\le 1 that assigns weights p and 1- p to global and local infective contacts respectively.
Edit intent: fluency
Original:  In this way, for p values lower than the proposed one, the probability of a major outbreak becomes negligible even when R_0 remains above 1.
Revised:  The dynamics of the average number of infected individuals and the average size of outbreaks show a behavior across the threshold that is well described by the deterministic approximation. The distributions of the outbreak sizes at the threshold present common features for all the cases considered corresponding to different values of \alpha> 1.
Edit intent: meaning-changed
Original:  The obtained results turn out to be relevant for infectious diseases with low R_0 but high mortality rates such as Ebola or H1N1 influenza. This study highlights the importance of control measures that minimize the possibility of global contacts, warning that a small reduction of them could produce a drastic reduction in the probability of huge outbreaks .
Revised:  These features are otherwise already known to hold for the standard stochastic SIR model at its threshold, \alpha=1: (i) the probability of having an outbreak of size n goes asymptotically as n^{-3/2 .
Edit intent: meaning-changed
Original: Using a novel network analysis of spontaneous low-frequency functional MRI data recorded at rest, we study the functional network that describes the extent of synchronization among different areas of the brain. Comparing forty-four medicated  patients and forty healthy subjects, we detected significant differences in the robustness of these functional networks.
Revised: Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the URLanization. We investigated the robustness of the brain functional architecture in forty-four medicated patients and forty healthy subjects, we detected significant differences in the robustness of these functional networks.
Edit intent: meaning-changed
Original:  Comparing forty-four medicated  patients and forty healthy subjects, we detected significant differences in the robustness of these functional networks. Such differences resulted in a larger resistance to edge removal (disconnection ) in the graph of schizophrenic patients as compared to healthy controls. This paper shows that the distribution of connectivity strength among brain regions is spatially more homogeneous in schizophrenic patients with respect to healthy ones. As a consequence, the precise hierarchical modularity of healthy brains  is crumbled in schizophrenic ones, making possible a peculiar arrangement of region-to-region interaction that, in turns, produces several topologically equivalent backbones of the whole functional brain network.
Revised:  Comparing forty-four medicated patients and forty healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal URLanization between the two groups, the expected hierarchy of healthy brains is crumbled in schizophrenic ones, making possible a peculiar arrangement of region-to-region interaction that, in turns, produces several topologically equivalent backbones of the whole functional brain network.
Edit intent: meaning-changed
Original:  As a consequence, the precise hierarchical modularity of healthy brains  is crumbled in schizophrenic ones, making possible a peculiar arrangement of region-to-region interaction that, in turns, produces several topologically equivalent backbones of the whole functional brain network.
Revised:  As a consequence, the precise hierarchical modularity of healthy brains URLanization is crumbled in schizophrenic ones, making possible a peculiar arrangement of region-to-region interaction that, in turns, produces several topologically equivalent backbones of the whole functional brain network.
Edit intent: meaning-changed
Original:  As a consequence, the precise hierarchical modularity of healthy brains  is crumbled in schizophrenic ones, making possible a peculiar arrangement of region-to-region interaction that, in turns, produces several topologically equivalent backbones of the whole functional brain network.
Revised:  As a consequence, the precise hierarchical modularity of healthy brains is crumbled in schizophrenia, showing a peculiar arrangement of region-to-region interaction that, in turns, produces several topologically equivalent backbones of the whole functional brain network.
Edit intent: clarity
Original:  As a consequence, the precise hierarchical modularity of healthy brains  is crumbled in schizophrenic ones, making possible a peculiar arrangement of region-to-region interaction that, in turns, produces several topologically equivalent backbones of the whole functional brain network. We hypothesize that the manifold nature of the basal scheme of URLanization within the brain, together with its altered hierarchical modularity, contributes to positive symptoms of schizophrenia. Our work also fits the disconnection hypothesis that describes schizophrenia as a brain disorder, characterized by abnormal functional integration among brain regions .
Revised:  As a consequence, the precise hierarchical modularity of healthy brains is crumbled in schizophrenic ones, making possible a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones .
Edit intent: coherence
Original: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification.
Revised: Space partitions of \mathbb{R nearest neighbor search problem (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification.
Edit intent: coherence
Original: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification.
Revised: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification.
Edit intent: clarity
Original: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification.
Revised: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) algorithms . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification.
Edit intent: clarity
Original: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification.
Revised: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions reducing the problem to balanced graph partitioning followed by supervised classification.
Edit intent: clarity
Original: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification.
Revised: Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space \mathbb{R . Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning
Edit intent: clarity
Original:   We instantiate this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH).
Revised:  followed by We instantiate this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH).
Edit intent: coherence
Original:   We instantiate this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH).
Revised:  supervised classification. We instantiate this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH).
Edit intent: meaning-changed