wanyu/IteraTeR_full_sent huggingface dataset:
Original:  With our proposed initialization, regularizers and architectural changes combined, we are able to obtain 65.03\% verified error on CIFAR-10 (\epsilon=\frac{8}{255}) and 82.13 \% verified error on TinyImageNet (\epsilon=\frac{1}{255}) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with a few hundreds or thousands epochs  .
Revised:  With our proposed initialization, regularizers and architectural changes combined, we are able to obtain 65.03\% verified error on CIFAR-10 (\epsilon=\frac{8}{255}) and 82.13 \% verified error on TinyImageNet (\epsilon=\frac{1}{255}) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with hundreds or thousands epochs .
Edit intent: clarity
Original:  With our proposed initialization, regularizers and architectural changes combined, we are able to obtain 65.03\% verified error on CIFAR-10 (\epsilon=\frac{8}{255}) and 82.13 \% verified error on TinyImageNet (\epsilon=\frac{1}{255}) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with a few hundreds or thousands epochs  .
Revised:  With our proposed initialization, regularizers and architectural changes combined, we are able to obtain 65.03\% verified error on CIFAR-10 (\epsilon=\frac{8}{255}) and 82.13 \% verified error on TinyImageNet (\epsilon=\frac{1}{255}) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with a few hundreds or thousands epochs under the same network architecture .
Edit intent: clarity
Original:  The work we review illustrates the progress and promise that population neurophysiology holds for cognitive neuroscience-for delivering new insight into attention, working memory, decision-making, executive function, and learning .
Revised:  The work we review illustrates the progress and promise that population neurophysiology holds for cognitive neuroscience-for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing .
Edit intent: meaning-changed
Original:   Furthermore, we propose a feature map for approximating the convolutional counterpart of the NTK (Arora et al., 2019), which can transform any image using a runtime that is only linear in the number of pixels.
Revised:  Our transformation works by sketching the polynomial expansions of arc-cosine kernels. Furthermore, we propose a feature map for approximating the convolutional counterpart of the NTK (Arora et al., 2019), which can transform any image using a runtime that is only linear in the number of pixels.
Edit intent: meaning-changed
Original:   Furthermore, we propose a feature map for approximating the convolutional counterpart of the NTK (Arora et al., 2019), which can transform any image using a runtime that is only linear in the number of pixels.
Revised:  Furthermore, we propose a feature map for approximating the convolutional counterpart of the NTK , which can transform any image using a runtime that is only linear in the number of pixels.
Edit intent: clarity
Original:  Emerging new test procedures , such as antigen or RT-LAMP tests, might enable us to protect nursing home residents by means of preventive screening strategies .
Revised:  Emerging new test procedures , such as antigen or RT-LAMP tests, and increasing availability of vaccinations, nursing homes might be able to safely relax prohibitory measures while controlling the spread of infections (meaning an average of one or less secondary infections per index case) .
Edit intent: meaning-changed
Original:  We find that the effectiveness of preventive screenings depends critically on the timespan between test and test result, the detection threshold of the viral load for the test to give a positive result, and the screening frequencies of residents and employees.
Revised:  We find that the effectiveness of mitigation testing depends critically on the timespan between test and test result, the detection threshold of the viral load for the test to give a positive result, and the screening frequencies of residents and employees.
Edit intent: clarity
Original:  Under realistic conditions  , we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes (on average, one or less secondary infections per index case), provided that turnover times and detection thresholds of the tests are low enough.
Revised:  Under realistic conditions and in absence of an effective vaccine , we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes (on average, one or less secondary infections per index case), provided that turnover times and detection thresholds of the tests are low enough.
Edit intent: meaning-changed
Original:  Under realistic conditions  , we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes (on average, one or less secondary infections per index case), provided that turnover times and detection thresholds of the tests are low enough.
Revised:  Under realistic conditions , we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes , provided that turnover times and detection thresholds of the tests are low enough.
Edit intent: coherence
Original:  For the tests considered in this study, we conclude that same-day turnover PCR and RT-LAMP procedures allow for more effective prevention compared to antigen and PCR tests .
Revised:  If vaccines that are moderately effective against infection and transmission are available, control is achieved if 80\% or more of inhabitants are vaccinated, even if no preventive testing is in place and residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular voluntary screening and sequencing of virus genomes is advised to enable early identification of new variants of concern .
Edit intent: meaning-changed
Original:  If vaccines that are moderately effective against infection and transmission are available, control is achieved if 80\% or more of  inhabitants are vaccinated, even if no preventive testing is in place and residents are allowed to have visitors.
Revised:  If vaccines that are moderately effective against infection and transmission are available, control is achieved if 80\% or more of the inhabitants are vaccinated, even if no preventive testing is in place and residents are allowed to have visitors.
Edit intent: fluency
Original:  We propose a theoretical framework for this problem setting based on autoregressively modelling sequences of nested sets, using latent variables to better capture multimodal distributions over future sets of sets. We present a new model architecture which we call a Nested Set Transformer which employs multi-head self-attention blocks over sets of sets that serve as a form of social attention between the elements of the sets at every timestep.
Revised:  Many existing methods treat social and temporal information separately and therefore fall short of modelling the joint future trajectories of all agents in a socially consistent way. To address this, we propose a new class of Latent Variable Sequential Set Transformers which autoregressively model multi-agent trajectories. We refer to these architectures as "AutoBots". AutoBots model the contents of sets (e.g. representing the properties of agents in a scene) over time and employ multi-head self-attention blocks over sets of sets that serve as a form of social attention between the elements of the sets at every timestep.
Edit intent: meaning-changed
Original:  We present a new model architecture which we call a Nested Set Transformer which employs multi-head self-attention blocks over sets of sets that serve as a form of social attention between the elements of the sets at every timestep. Our approach can produce a distribution over  future trajectories for all agents under consideration , or focus upon the trajectory of an ego-agent.
Revised:  We present a new model architecture which we call a Nested Set Transformer which employs multi-head self-attention blocks over these sequences of sets to encode the sociotemporal relationships between the different actors of a scene. This produces either the trajectory of one ego-agent or a distribution over future trajectories for all agents under consideration , or focus upon the trajectory of an ego-agent.
Edit intent: clarity
Original:  Our approach can produce a distribution over  future trajectories for all agents under consideration , or focus upon the trajectory of an ego-agent. We validate the Nested Set Transformer for autonomous driving settings which we refer to as ("AutoBot"), where we model the trajectory of an ego-agent based on the sequential observations of key attributes of multiple agents in a scene. AutoBot produces results better than state-of-the-art published prior work on the challenging nuScenes vehicle trajectory modeling benchmark. We also examine the multi-agent prediction version of our model and jointly forecast an ego-agent's future trajectory along with the other agents in the scene. We validate the behavior of our proposed Nested Set Transformer for scene level forecasting with a pedestrian trajectory dataset .
Revised:  Our approach can produce a distribution over future trajectories for all agents under consideration . Our approach works for general sequences of sets and we provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. For the single-agent prediction case, we validate our model on the NuScenes motion prediction task and achieve competitive results on the global leaderboard. In the multi-agent forecasting setting, we validate our model on TrajNet. We find that our method outperforms physical extrapolation and recurrent network baselines and generates scene-consistent trajectories .
Edit intent: meaning-changed
Original: We consider the classical problem of optimal portfolio construction with the constraint that no short position is allowed, or equivalently the valid equilibria of multispecies Lotka-Volterra equations , in the special case where the interaction matrix is of unit rank, corresponding to a single-resource MacArthur model .
Revised: We consider the classical problem of optimal portfolio construction with the constraint that no short position is allowed, or equivalently the valid equilibria of multispecies Lotka-Volterra equations with self-regulation in the special case where the interaction matrix is of unit rank, corresponding to a single-resource MacArthur model .
Edit intent: meaning-changed
Original: We consider the classical problem of optimal portfolio construction with the constraint that no short position is allowed, or equivalently the valid equilibria of multispecies Lotka-Volterra equations , in the special case where the interaction matrix is of unit rank, corresponding to a single-resource MacArthur model .
Revised: We consider the classical problem of optimal portfolio construction with the constraint that no short position is allowed, or equivalently the valid equilibria of multispecies Lotka-Volterra equations , in the special case where the interaction matrix is of unit rank, corresponding to species competing for a common resource .
Edit intent: clarity
Original:  Model-free methods based on deep-learning provide an attractive alternative, as they can in principle learn the optimal filter from data, but to the best of our knowledge were never compared to current state-of-the-art Bayesian filters, specially not in contexts where accurate models are available.
Revised:  Model-free methods based on deep-learning provide an attractive alternative, as they can , in principle, learn the optimal filter from data, but to the best of our knowledge were never compared to current state-of-the-art Bayesian filters, specially not in contexts where accurate models are available.
Edit intent: fluency
Original:  We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while macthing their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime.
Revised:  We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime.
Edit intent: fluency
Original:  We explain the setup of the Kermack-McKendrick theory (passing to a discrete approach) and use medical information to derive a different model which we call {\em adapted K-McK-model}.
Revised:  We explain the setup of the Kermack-McKendrick theory (passing to a discrete approach) and use medical information for specializing to a model which we call {\em adapted K-McK-model}.
Edit intent: clarity
Original:  In this work, our goal is instead to fill the gap between affordance discovery and affordance-based policy learning by integrating the two objectives in an end-to-end imitation learning framework based on deep neural networks.
Revised:  In this work, our goal is instead to integrate the two objectives of affordance discovery and affordance-based policy learning by integrating the two objectives in an end-to-end imitation learning framework based on deep neural networks.
Edit intent: clarity
Original:  First, as a result of a collaboration of three fields, machine learning, vision and language reasoning, and data analytics, the work lead to a direct impact on the design and training of a neural model for VQA, improving modelperformance as a consequence .
Revised:  First, as a result of a collaboration of three fields, machine learning, vision and language reasoning, and data analytics, the work lead to a better understanding of bias exploitation of neural models for VQA, which eventually resulted in an impact on its design and training of a neural model for VQA, improving modelperformance as a consequence .
Edit intent: meaning-changed
Original:  First, as a result of a collaboration of three fields, machine learning, vision and language reasoning, and data analytics, the work lead to a direct impact on the design and training of a neural model for VQA, improving modelperformance as a consequence .
Revised:  First, as a result of a collaboration of three fields, machine learning, vision and language reasoning, and data analytics, the work lead to a direct impact on the design and training through the proposition of a method for the transfer of reasoning patterns from an oracle model .
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Original:  Furthermore, the spectrum of Drosophila ICPs includes virtually all observed insect coordination patterns ; this raises the exciting possibility that the controller in Drosophila is common to all insects, and perhaps even more generally .
Revised:  Furthermore, the spectrum of Drosophila stepping patterns includes all canonical coordination patterns observed during forward walking in insects. This raises the exciting possibility that the controller in Drosophila is common to all insects, and perhaps even more generally .
Edit intent: meaning-changed
Original:  We discuss implications  for the design of human-AI partnerships , arguing for participatory approaches to research  in this area, and for principled approaches to studying human-AI decision-making in real-world contexts.
Revised:  We discuss implications of this research for the design of human-AI partnerships , arguing for participatory approaches to research in this area, and for principled approaches to studying human-AI decision-making in real-world contexts.
Edit intent: clarity
Original:  We discuss implications  for the design of human-AI partnerships , arguing for participatory approaches to research  in this area, and for principled approaches to studying human-AI decision-making in real-world contexts.
Revised:  We discuss implications for the design of human-AI partnerships . We argue for more participatory approaches to research in this area, and for principled approaches to studying human-AI decision-making in real-world contexts.
Edit intent: clarity
Original:  We discuss implications  for the design of human-AI partnerships , arguing for participatory approaches to research  in this area, and for principled approaches to studying human-AI decision-making in real-world contexts.
Revised:  We discuss implications for the design of human-AI partnerships , arguing for participatory approaches to research and design in this area, and for principled approaches to studying human-AI decision-making in real-world contexts.
Edit intent: meaning-changed
Original:  We discuss implications  for the design of human-AI partnerships , arguing for participatory approaches to research  in this area, and for principled approaches to studying human-AI decision-making in real-world contexts.
Revised:  We discuss implications for the design of human-AI partnerships , arguing for participatory approaches to research in this area, in which practitioners and other stakeholders are deeply, meaningfully involved throughout the process. Furthermore, we advocate for theory-building and for principled approaches to studying human-AI decision-making in real-world contexts.
Edit intent: meaning-changed
Original:  We discuss implications  for the design of human-AI partnerships , arguing for participatory approaches to research  in this area, and for principled approaches to studying human-AI decision-making in real-world contexts.
Revised:  We discuss implications for the design of human-AI partnerships , arguing for participatory approaches to research in this area, and for principled approaches to the study of human-AI decision-making in real-world contexts.
Edit intent: fluency
Original: Human manipulation skills can be transferred to robots conveniently through learning from demonstrations (LfD) methods. However, most of these works either only encode motion trajectories or suffer from the complexity and incompleteness when estimating stiffness profiles. To solve these problems , we propose a simple and effective stiffness estimation method that estimates a complete end-effector stiffness matrix from the variation of demonstrations.
Revised: Learning Variable Impedance Control(VIC) policy can help robot assistants to intelligently adapt their manipulation compliance to ensure both safe interaction and proper task completion when operating in human-robot interaction environments. In this paper , we propose a simple and effective stiffness estimation method that estimates a complete end-effector stiffness matrix from the variation of demonstrations.
Edit intent: clarity
Original:  To solve these problems , we propose a simple and effective stiffness estimation method that estimates a complete end-effector stiffness matrix from the variation of demonstrations.
Revised:  To solve these problems , we propose a DMP-based framework that learns and generalizes variable impedance manipulation skills from human demonstrations. This framework improves robots' adaptability to environment changes(i.e. the weight and shape changes of the robot end-effector stiffness matrix from the variation of demonstrations.
Edit intent: meaning-changed
Original:  To solve these problems , we propose a simple and effective stiffness estimation method that estimates a complete end-effector stiffness matrix from the variation of demonstrations. To that end, Gaussian Mixture Regression (GMR) is applied to extract the reference pose trajectory and the variability. Quaternion logarithmic map is integrated to generate complete rotational stiffness . Besides, the Dynamic Movement Primitives (DMPs) model is further developed to encode and schedule both the movement trajectory and stiffness profiles in task space simultaneously.
Revised:  To solve these problems , we propose a simple and effective stiffness estimation method that estimates a complete end-effector ) and inherits the efficiency of demonstration variance-based stiffness estimation method . Besides, the Dynamic Movement Primitives (DMPs) model is further developed to encode and schedule both the movement trajectory and stiffness profiles in task space simultaneously.
Edit intent: clarity
Original:  Quaternion logarithmic map is integrated to generate complete rotational stiffness . Besides, the Dynamic Movement Primitives (DMPs) model is further developed to encode and schedule both the movement trajectory and stiffness profiles in task space simultaneously. Finally, the effectiveness of our approach is validated on a real-world 7 DoF robot with the variable impedance controller .
Revised:  Quaternion logarithmic map is integrated to generate complete rotational stiffness . Besides, with our stiffness estimation method, we generate not only translational stiffness profiles, but also rotational stiffness profiles that are ignored or incomplete in most learning VIC papers. Real-world experiments on a 7 DoF robot with the variable impedance controller .
Edit intent: meaning-changed
Original:  Resetting is a sudden interruption of a process such that the dynamicsis renewed intermittently. Quite surprisingly , although resetting renders GBM stationary, the resulting process remains non-ergodic.
Revised:  Resetting is a sudden interruption of a process , which consecutively renews its dynamics. We show that , although resetting renders GBM stationary, the resulting process remains non-ergodic.
Edit intent: clarity
Original:  We observe three different long-time regimes: a quenched state, an unstable and a stable annealed state depending on the resetting strength.
Revised:  Quite surprisingly, the effect of resetting is pivotal in manifesting the non-ergodic behavior. In particular, we observe three different long-time regimes: a quenched state, an unstable and a stable annealed state depending on the resetting strength.
Edit intent: meaning-changed
Original:   Crucially, the  regimes are well separated by a self-averaging time period which can be minimized by an optimal resetting rate.
Revised:  Notably, in the last regime, the system is self-averaging and thus the sample average will always mimic ergodic behavior establishing a stand alone feature for GBM under resetting. Crucially, the regimes are well separated by a self-averaging time period which can be minimized by an optimal resetting rate.
Edit intent: meaning-changed
Original:  In particular, when specialized to the \varepsilon-LDP model, our protocol achieves an error of k/(e^{\Theta(\varepsilon)}-1) for all \varepsilon, while the previous protocol (Chen et al., 2020) has error O(\sqrt{k/\min}  \{ \varepsilon, \sqrt{\varepsilon\}) } {\varepsilon} \}) bits of communication and O(k \log u) bits of public randomness, where u is the size of the domain} .
Revised:  In particular, when specialized to the \varepsilon-LDP model, our protocol achieves an error of k/(e^{\Theta(\varepsilon)}-1) /\min} \{ \varepsilon, \sqrt{\varepsilon\}) } {\varepsilon} \}) bits of communication and O(k \log u) bits of public randomness, where u is the size of the domain} .
Edit intent: meaning-changed
Original:  In particular, when specialized to the \varepsilon-LDP model, our protocol achieves an error of k/(e^{\Theta(\varepsilon)}-1) for all \varepsilon, while the previous protocol (Chen et al., 2020) has error O(\sqrt{k/\min}  \{ \varepsilon, \sqrt{\varepsilon\}) } {\varepsilon} \}) bits of communication and O(k \log u) bits of public randomness, where u is the size of the domain} .
Revised:  In particular, when specialized to the \varepsilon-LDP model, our protocol achieves an error of k/(e^{\Theta(\varepsilon)}-1) for all \varepsilon, while the previous protocol (Chen et al., 2020) has error O(\sqrt{k/\min} using O(k\max \{ \varepsilon, \sqrt{\varepsilon\}) } {\varepsilon} \}) bits of communication and O(k \log u) bits of public randomness, where u is the size of the domain} .
Edit intent: meaning-changed