wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robot agents performing active search considering two sources of sensory information from monocular optical imagery and sonar tracking. NATS utilizes communications between robot agents in a decentralized manner that is robust to intermittent loss of communication links. Additionally, it takes into account object detection uncertainty from depth as well as environmental occlusions  . Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search.
Revised:  We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robot agents performing active search considering two sources of sensory information from monocular optical imagery and depth maps. By utilizing Thompson Sampling, NATS allows for decentralized coordination among multiple agents. NATS also considers object detection uncertainty from depth as well as environmental occlusions . Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search.
Edit intent: clarity
Original:  Additionally, it takes into account object detection uncertainty from depth as well as environmental occlusions  . Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search.
Revised:  Additionally, it takes into account object detection uncertainty from depth as well as environmental occlusions and operates while remaining agnostic of the number of objects of interest . Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search.
Edit intent: meaning-changed
Original: A timely understanding of the spatiotemporal pattern and development trend of COVID-19 in the early stage of the epidemic is highly significant for epidemic prevention and control early in the epidemic. Therefore, many scholars use reported cases to explore spatiotemporal characteristics of COVID-19 early in the epidemic.
Revised: A timely understanding of the spatiotemporal pattern and development trend of COVID-19 in the early stage of the epidemic is highly significant for epidemic prevention and control .
Edit intent: coherence
Original:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19  . The results show that: (1) the  estimated peak infection time of the reported cases lags behind the actual peak infection time .
Revised:  In this paper, confirmed cases of healthcare workers are considered as accurately small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 . The results show that: (1) the estimated peak infection time of the reported cases lags behind the actual peak infection time .
Edit intent: meaning-changed
Original:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19  . The results show that: (1) the  estimated peak infection time of the reported cases lags behind the actual peak infection time .
Revised:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives, namely: parameter estimation, spatiotemporal correlation, and spatiotemporal lag . The results show that: (1) the estimated peak infection time of the reported cases lags behind the actual peak infection time .
Edit intent: meaning-changed
Original:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19  . The results show that: (1) the  estimated peak infection time of the reported cases lags behind the actual peak infection time .
Revised:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 . The results show that: (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time. (2) The estimated peak infection time of the reported cases lags behind the actual peak infection time .
Edit intent: meaning-changed
Original:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19  . The results show that: (1) the  estimated peak infection time of the reported cases lags behind the actual peak infection time .
Revised:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 . The results show that: (1) the estimated peak infection time of the reported cases lags behind the peak infection time .
Edit intent: clarity
Original:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19  . The results show that: (1) the  estimated peak infection time of the reported cases lags behind the actual peak infection time . The infection peak time of the epidemic in Wuhan was January 29, not February 11 estimated by reported cases, and the infection peak time of the epidemic in Hubei (except Wuhan) was February 2, not February 4 estimated by reported cases.
Revised:  In this paper, confirmed cases of healthcare workers were used to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 . The results show that: (1) the estimated peak infection time of the reported cases lags behind the actual peak infection time of the healthcare worker cases, and the infection peak time of the epidemic in Hubei (except Wuhan) was February 2, not February 4 estimated by reported cases.
Edit intent: coherence
Original:  The infection peak time of the epidemic in Wuhan was January 29, not February 11 estimated by reported cases, and the infection peak time of the epidemic in Hubei (except Wuhan) was February 2, not February 4 estimated by reported cases. (2) The estimated infection time interval of the reported cases is smaller than the actual interval.
Revised:  The infection peak time of the epidemic in Wuhan was January 29, not February 11 estimated by reported cases, and the estimated infection time interval of the reported cases is smaller than the actual interval.
Edit intent: coherence
Original:  (2) The estimated infection time interval of the reported cases is smaller than the actual interval. The infection time interval of the epidemic in Wuhan was approximately 63.6 days, not 45.6 days estimated by reported cases, and the infection time interval of the epidemic in Hubei (except Wuhan) was about 58.8 days, not 40.8 days estimated by reported cases.
Revised:  (2) The estimated infection time interval of the reported cases is smaller than the infection time interval of the epidemic in Wuhan was approximately 63.6 days, not 45.6 days estimated by reported cases, and the infection time interval of the epidemic in Hubei (except Wuhan) was about 58.8 days, not 40.8 days estimated by reported cases.
Edit intent: clarity
Original:  The infection time interval of the epidemic in Wuhan was approximately 63.6 days, not 45.6 days estimated by reported cases, and the infection time interval of the epidemic in Hubei (except Wuhan) was about 58.8 days, not 40.8 days estimated by reported cases. ( 3  ) Although the number of reported cases is lower than the actual number of cases, a high spatial correlation exists between the cumulatively reported cases and the actual situation.
Revised:  The infection time interval of the healthcare worker cases. ( 3 ) Although the number of reported cases is lower than the actual number of cases, a high spatial correlation exists between the cumulatively reported cases and the actual situation.
Edit intent: meaning-changed
Original:  ( 3  ) Although the number of reported cases is lower than the actual number of cases, a high spatial correlation exists between the cumulatively reported cases and the actual situation.
Revised:  ( 3 ) The impact of under-reporting cases on the early stages of the pandemic is greater than that on its later stages, and the impact on the early onset area is greater than that on the late onset area. (4 ) Although the number of reported cases is lower than the actual number of cases, a high spatial correlation exists between the cumulatively reported cases and the actual situation.
Edit intent: meaning-changed
Original:  ( 3  ) Although the number of reported cases is lower than the actual number of cases, a high spatial correlation exists between the cumulatively reported cases and the actual situation. This work provides scientific support for researchers that explore the spatiotemporal distribution of COVID-19 using the data on reported cases.
Revised:  ( 3 ) Although the number of reported cases is lower than the actual number of cases, a high spatial correlation exists between the cumulatively reported cases and healthcare worker cases.
Edit intent: meaning-changed
Original:  We establish that if the interpolation method is valid for a family of graphs satisfying the self-reducibility property, then this family exhibits a form of correlation decay property which is asymptotic Strong Spatial Mixing (SSM) at distances \omega(\log ^{3 n), where n is the number of nodes of the graph.
Revised:  We establish that if the interpolation method is valid for a family of graphs satisfying the self-reducibility property, then this family exhibits a form of correlation decay property which is asymptotic Strong Spatial Mixing (SSM) at distances \omega(\log n), where n is the number of nodes of the graph.
Edit intent: meaning-changed
Original: This work proposes an end-to-end differentiable physics engine for tensegrity robots , which introduces a data-efficient linear contact model for accurately predicting collision responses that arise due to contacting surfaces, and a linear actuator model that can drive these robotsby expanding and contracting their flexible cables .
Revised: Learning policies in simulation is promising for reducing human effort when training robot controllers. This is especially true for soft robots that are more adaptive and safe but also more difficult to accurately model and control. The sim2real gap is the main barrier to successfully transfer policies from simulation to a real robot. System identification can be applied to reduce this gap but traditional identification methods require a lot of manual tuning. Data-driven alternatives can tune dynamical models directly from data but are often data hungry, which also incorporates human effort in collecting data. This work proposes a data-driven, end-to-end differentiable physics engine for tensegrity robots , which introduces a data-efficient linear contact model for accurately predicting collision responses that arise due to contacting surfaces, and a linear actuator model that can drive these robotsby expanding and contracting their flexible cables .
Edit intent: coherence
Original: This work proposes an end-to-end differentiable physics engine for tensegrity robots , which introduces a data-efficient linear contact model for accurately predicting collision responses that arise due to contacting surfaces, and a linear actuator model that can drive these robotsby expanding and contracting their flexible cables .
Revised: This work proposes an end-to-end differentiable simulator focused on the exciting but challenging domain of tensegrity robots .
Edit intent: clarity
Original:  To the best of the authors' knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable modeling , contact, and actuation . This engine can be used inside an off-the-shelf, RL-based locomotion controller in order to provide training examples. This paper proposes a progressive training pipeline for the differentiable physics engine that helps avoid local optima during the training phase and reduces data requirements. It demonstrates the data-efficiency benefits of using the differentiable engine for learning locomotion policies for NASA's icosahedron SUPERballBot. In particular, after the engine has been trained with few trajectories to match a ground truth simulated model, then a policy learned on the differentiable engine is shown to be transferable back to the ground-truth model.
Revised:  To the best of the authors' knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable modeling , contact, and actuation modeling. The aim is to develop a reasonably simplified, data-driven simulation, which can learn approximate dynamics with limited ground truth data. The dynamics must be accurate enough to generate policies that can be transferred back to the ground-truth model.
Edit intent: clarity
Original:  In particular, after the engine has been trained with few trajectories to match a ground truth simulated model, then a policy learned on the differentiable engine is shown to be transferable back to the ground-truth model. Training the controller requires orders of magnitude more data than training the differential engine.
Revised:  In particular, after the engine has been trained with few trajectories to match a ground truth simulated model, then a policy learned on the differentiable engine is shown to be transferable back to the ground-truth system. As a first step in this direction, the current work demonstrates sim2sim transfer, where the unknown physical model of MuJoCo acts as a ground truth system. Two different tensegrity robots are used for evaluation and learning of locomotion policies, a 6-bar and a 3-bar tensegrity. The results indicate that only 0.25
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Original: This paper extends the work of Boudt and Pertitjean(2014) and investigates the trading patterns before price jumps in the stock market based on a new multivariate time classification technique .
Revised: Studying the micro-trading behaviors before stock price jumps is an important problem for financial regulations and investment decisions. In this study, we provide a new framework to study pre-jump trading behaviors based on multivariate time series analysis .
Edit intent: clarity
Original:  Different from Boudt and Pertitjean(2014), our analyzing scheme can explore the "time-series information " embedded in the trading-related attributes and provides a set of jump indicators for abnormal pattern recognition .
Revised:  Different from Boudt and Pertitjean(2014), our analyzing scheme can explore the "time-series information " embedded in the trading-related attributes and can better evaluate and compare the abnormality levels of different attributes. Moreover, it can explore the joint informativeness of the attributes as well as select a subset of highly informative but minimally redundant attributes to analyze the homogeneous and idiosyncratic patterns in the pre-jump trades of individual stocks .
Edit intent: meaning-changed
Original:  It is found that among all the candidate attributes, several volume and volatility-related attributes exhibit the most significant abnormality before price jumps . Though some of the abnormalities start just shortly before the occurrence of the jumps, some start much earlier. We also find that most of our attributes have low mutual dependencies with each other from the perspective of time-series analysis, which allows various perspectives to study the market trading behaviors.
Revised:  The individual and joint informativeness levels of the attributes in predicting price jumps are evaluated and compared.
Edit intent: clarity
Original:  as a method to predict the stability/reactivity and risk of degradation of sequences of RNA.  Reasonably accurate results were able to be generated , with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation under various circumstances (RMSE = 0.266)  . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Revised:  as a method to predict the stability/reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates. Reasonably accurate results were able to be generated , with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation under various circumstances (RMSE = 0.266) . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Edit intent: coherence
Original:  as a method to predict the stability/reactivity and risk of degradation of sequences of RNA.  Reasonably accurate results were able to be generated , with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation under various circumstances (RMSE = 0.266)  . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Revised:  as a method to predict the stability/reactivity and risk of degradation of sequences of RNA. Reasonably accurate results were able to be generated with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation under various circumstances (RMSE = 0.266) . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Edit intent: fluency
Original:  as a method to predict the stability/reactivity and risk of degradation of sequences of RNA.  Reasonably accurate results were able to be generated , with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation under various circumstances (RMSE = 0.266)  . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Revised:  as a method to predict the stability/reactivity and risk of degradation of sequences of RNA. Reasonably accurate results were able to be generated , with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation under various circumstances (RMSE = 0.266) . Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\% . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
Edit intent: meaning-changed
Original:  Excitingly, we are able to show how different network structures leave distinct signatures in the spectrum of fluctuations, opening the door to a new avenue of research in the age-old complexity/stability debate .
Revised:  Excitingly, we are able to show how different network structures leave distinct signatures in the spectrum of fluctuations, and demonstrate the application of our theory to the analysis ecological timeseries data of plankton abundances .
Edit intent: meaning-changed
Original: Large openly available neuroimaging datasets, although highly valuable, do not typically provide the community with subject-level whole-brain connectivity matrices that are ready to be used brain connectivity analysis. In the pursuit of open science, we have made available fully processed and parcellated connectomes and timeseries for the Human Connectome Project - Young Adult (HCP-YA)dataset to the neuroimaging and computational neuroscience community .
Revised: The assessment of brain fingerprints has emerged in the recent years as an important tool to study individual differences and to infer quality of neuroimaging datasets. Studies so far have mainly focused on connectivity fingerprints between different brain scans of the same individual. Here, we extend the concept of brain connectivity fingerprints beyond test/retest and assess fingerprint gradients in young adults by developing an extension of the differential identifiability framework. To do so, we look at the similarity between not only the multiple scans of an individual (subject fingerprint), but also between the scans of monozygotic and dizygotic twins (twin fingerprint) .
Edit intent: meaning-changed
Original:  We have processed and parcellated the 8 fMRI conditions for the 1200 from subjects part of the HCP-YA datasetinto to the Schaefer parcellations, which are further subdivisions of the resting state functional networks.
Revised:  We have carried out this analysis on the 8 fMRI conditions for the 1200 from subjects part of the HCP-YA datasetinto to the Schaefer parcellations, which are further subdivisions of the resting state functional networks.
Edit intent: clarity
Original:  We have processed and parcellated the 8 fMRI conditions for the 1200 from subjects part of the HCP-YA datasetinto to the Schaefer parcellations, which are further subdivisions of the resting state functional networks.
Revised:  We have processed and parcellated the 8 fMRI conditions present in the Human Connectome Project -- Young Adult dataset, which we processed into functional connectomes (FCs) and timeseries parcellated according to the Schaefer parcellations, which are further subdivisions of the resting state functional networks.
Edit intent: meaning-changed
Original:  We have processed and parcellated the 8 fMRI conditions for the 1200 from subjects part of the HCP-YA datasetinto to the Schaefer parcellations, which are further subdivisions of the resting state functional networks. We also provide a detailed fingerprinting analysis of these parcellated connectomes and timeseries data using the differential identifiability framework on three different cohorts: Unrelated subjects, monozygotic twins, and dizygotic twins. Overall, results indicate that combining fine-grained parcellations with the differential identifiability framework uncovers fingerprints for all cohorts and fMRI conditions evaluated .
Revised:  We have processed and parcellated the 8 fMRI conditions for the 1200 from subjects part of the HCP-YA datasetinto to the Schaefer Atlas scheme, which has multiple levels of resolution. Our differential identifiability results show that the fingerprint gradients based on genetic and environmental similarities are indeed present when comparing FCs for all parcellations and fMRI conditions. Importantly, only when assessing optimally reconstructed FCs, we fully uncover fingerprints present in higher resolution atlases. We also study the effect of scanning length on subject fingerprint of resting-state FCs to analyze the effect of scanning length and parcellation. In the pursuit of open science, we have also made available the processed and parcellated FCs and timeseries for all conditions for ~1200 subjects part of the HCP-YA dataset to the scientific community .
Edit intent: meaning-changed
Original:  We give a necessary and sufficient condition in terms of solutions to a system of monomial equations whose variables are in the binary octahedral group  .
Revised:  We give a necessary and sufficient condition in terms of solutions to a system of monomial equations whose variables are in the binary octahedral group when a boundary frame field and singularity graph have been fixed. This is phrased with respect to general cellular decompositions of the volume, which allows some flexibility in simplifying these systems .
Edit intent: meaning-changed