wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Through our broad set of experiments, we show that ESM provides a general computation graph for embodied spatial reasoning, and the module forms a bridge between real-time mapping systems and differentiable memory architectures. 
Revised:  Through our broad set of experiments, we show that ESM provides a general computation graph for embodied spatial reasoning, and the module forms a bridge between real-time mapping systems and differentiable memory architectures. Implementation at: URL
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Original: Stochastic differential equation (SDE) models have been extensively used for the description of processes with uncertainties arising in the queueing theory and behavioral sciences . A large class of the problems from these domains is characterized by the necessity to deal with several distinct groups of populations, which are usually labeled as "active" and "passive".
Revised: Stochastic dynamic models have been extensively used for the description of processes with uncertainties arising in the queueing theory and behavioral sciences . A large class of the problems from these domains is characterized by the necessity to deal with several distinct groups of populations, which are usually labeled as "active" and "passive".
Edit intent: clarity
Original: Stochastic differential equation (SDE) models have been extensively used for the description of processes with uncertainties arising in the queueing theory and behavioral sciences . A large class of the problems from these domains is characterized by the necessity to deal with several distinct groups of populations, which are usually labeled as "active" and "passive".
Revised: Stochastic differential equation (SDE) models have been extensively used for the description of processes with uncertainties arising in the operations research, behavioral sciences, and many other application areas . A large class of the problems from these domains is characterized by the necessity to deal with several distinct groups of populations, which are usually labeled as "active" and "passive".
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Original:  We provide details of the model, as well as a representative numerical example , and discuss an intrinsic interconnection between active and passive customers in the underlying stochastic process.
Revised:  We provide details of the queueing theory and neuronal models and discuss a relationship between reflecting SDEs and a model of queueing theory via a limit theorem. Furthermore, we present several representative numerical examples , and discuss an intrinsic interconnection between active and passive customers in the underlying stochastic process.
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Original:  We propose a novel method for false discovery control using null bootstrapping.
Revised:  A problem of particular interest is in financial asset pricing, where the goal is to determine which ``factors" lead to excess returns out of a large number of potential factors. Our contribution is two-fold. First, we show the consistency of Fama and French's prominent method under multiple testing. Second, we propose a novel method for false discovery control using null bootstrapping.
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Original: Deep learning models are now being increasingly used for predictive process mining tasksin business processes. Modern approaches have been successful in achieving better performance for different predictive tasks , as compared to traditional approaches.
Revised: Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved better performance for different predictive tasks , as compared to traditional approaches.
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Original:  In this work, five different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next activity in a given process instance.
Revised:  We extend the existing body of research by testing four different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next activity in a given process instance.
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Original:  In this work, five different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next activity in a given process instance.
Revised:  In this work, five different variants of Graph Neural Networks (GNN) and a fully connected Multi-layer Perceptron (MLP) with dropout for the tasks of predicting the nature and timestamp of the next activity in a given process instance.
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Original:  In this work, five different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next activity in a given process instance. We have introduced a new method for representing feature vectors for any individual event in a given process instance, taking into consideration the structure of Directly-follows process graphs generated from the corresponding datasets. The adjacency matrix of the process graphs generated has been used as input to a Graph Convolutional Network (GCN). Different model variants make use of variations in the representation of the adjacency matrix. The performance of all the model variants have been tested at different stages of a process, determined by quartiles estimated based on the number of events and  the case duration.
Revised:  In this work, five different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next process activity. In contrast to existing studies, we evaluate our models' performance at different stages of a process, determined by quartiles estimated based on the number of events and the case duration.
Edit intent: clarity
Original:  Interestingly, it was observed that a linear Multi-Layer Perceptron (MLP) with dropout was able to outperform the GCN variants in both the  prediction tasks.
Revised:  Interestingly, it was observed that a linear Multi-Layer Perceptron (MLP) with dropout was able to outperform the GCN variants in both the simple MLP often outperforms more sophisticated deep-learning models in both prediction tasks.
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Original:  Deviating from this trend, we define and study the novel notion of interim envy-freeness (iEF) for lotteries over allocations, which aims to serve as a sweet spot between the too stringent notion of ex-post envy-freeness and the very weak notion of ex-ante envy-freeness.
Revised:  Deviating from this trend, we study the fairness notion of interim envy-freeness (iEF) for lotteries over allocations, which aims to serve as a sweet spot between the too stringent notion of ex-post envy-freeness and the very weak notion of ex-ante envy-freeness.
Edit intent: clarity
Original:  Deviating from this trend, we define and study the novel notion of interim envy-freeness (iEF) for lotteries over allocations, which aims to serve as a sweet spot between the too stringent notion of ex-post envy-freeness and the very weak notion of ex-ante envy-freeness.
Revised:  Deviating from this trend, we define and study the novel notion of interim envy-freeness (iEF) for lotteries over allocations, which serves as a sweet spot between the too stringent notion of ex-post envy-freeness and the very weak notion of ex-ante envy-freeness.
Edit intent: clarity
Original:  Our new fairness notion is a natural generalization of envy-freeness to random allocations in the sense that a deterministic envy-free allocation is iEF (when viewed as a degenerate lottery).
Revised:  iEF is a natural generalization of envy-freeness to random allocations in the sense that a deterministic envy-free allocation is iEF (when viewed as a degenerate lottery).
Edit intent: clarity
Original:  We show that MomentumNets can be interpreted in the infinitesimal step size regime as second-order ordinary differential equations (ODEs) and exactly characterize how adding momentum progressively increases the representation capabilities of MomentumNets .
Revised:  We show that Momentum ResNets can be interpreted in the infinitesimal step size regime as second-order ordinary differential equations (ODEs) and exactly characterize how adding momentum progressively increases the representation capabilities of MomentumNets .
Edit intent: fluency
Original:  We show that MomentumNets can be interpreted in the infinitesimal step size regime as second-order ordinary differential equations (ODEs) and exactly characterize how adding momentum progressively increases the representation capabilities of MomentumNets .
Revised:  We show that MomentumNets can be interpreted in the infinitesimal step size regime as second-order ordinary differential equations (ODEs) and exactly characterize how adding momentum progressively increases the representation capabilities of Momentum ResNets .
Edit intent: fluency
Original:  We show on CIFAR and ImageNet that MomentumNets have the same accuracy as ResNets, while having a much smaller memory footprint, and show that pre-trained MomentumNets are promising for fine-tuning models.
Revised:  We show on CIFAR and ImageNet that Momentum ResNets have the same accuracy as ResNets, while having a much smaller memory footprint, and show that pre-trained MomentumNets are promising for fine-tuning models.
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Original:  We show on CIFAR and ImageNet that MomentumNets have the same accuracy as ResNets, while having a much smaller memory footprint, and show that pre-trained MomentumNets are promising for fine-tuning models.
Revised:  We show on CIFAR and ImageNet that MomentumNets have the same accuracy as ResNets, while having a much smaller memory footprint, and show that pre-trained Momentum ResNets are promising for fine-tuning models.
Edit intent: clarity
Original:  In this work, we show that an underactuated system as a spin-rolling sphere on a plane with three inputs and five states can be transformed into a fully-actuated model by the given Darboux-frame transformation.
Revised:  In this work, we show that an underactuated model of a spin-rolling sphere on a plane with three inputs and five states can be transformed into a fully-actuated model by the given Darboux-frame transformation.
Edit intent: clarity
Original:  In this work, we show that an underactuated system as a spin-rolling sphere on a plane with three inputs and five states can be transformed into a fully-actuated model by the given Darboux-frame transformation.
Revised:  In this work, we show that an underactuated system as a spin-rolling sphere on a plane with five states and three inputs can be transformed into a fully-actuated model by the given Darboux-frame transformation.
Edit intent: fluency
Original:  In this work, we show that an underactuated system as a spin-rolling sphere on a plane with three inputs and five states can be transformed into a fully-actuated model by the given Darboux-frame transformation.
Revised:  In this work, we show that an underactuated system as a spin-rolling sphere on a plane with three inputs and five states can be transformed into a fully-actuated one by a given Darboux frame transformation.
Edit intent: clarity
Original: A plethora of real-world problems require utilization of hypergraphs and diffusion algorithms.Examples include recommendation systems, node ranking in food networks and community detection in social networks to mention a few. Due to the increased size and complexity of real hypergraphs, local and accurate diffusion algorithms that work with the most complex hypergraphs are in need. We propose the first local diffusion method that works on higher-order relations with only a submodularity assumption .
Revised: Recently, hypergraphs have attracted a lot of attention due to their ability to capture complex relations among entities. The insurgence of hypergraphs has resulted in data of increasing size and complexity that exhibit interesting small-scale and local structure, e.g., small-scale communities and localized node-ranking around a given set of seed nodes. Popular and principled ways to capture the local structure are the local hypergraph clustering problem and related seed set expansion problem. In this work, we propose the first local diffusion method that works on higher-order relations with only a submodularity assumption .
Edit intent: coherence
Original:  Our method is based on a primal-dual optimization formulation where the primal problem has a natural network flow interpretation, and the dual problem has a cut-based interpretation using the \ell_2-norm penalty for general submodular cut-costs.
Revised:  Our method is based on a primal-dual optimization formulation where the primal problem has a natural network flow interpretation, and the dual problem has a cut-based interpretation using the \ell_2-norm penalty on associated cut-costs.
Edit intent: clarity
Original:  We prove that the proposed formulation achieves quadratic approximation error for the problem of local hypergraph clustering. We demonstrate that the new technique is significantly better than state-of-the-art methods over a range of real datasets for the local hypergraph clustering and node ranking problems .
Revised:  We demonstrate the new technique is significantly better than state-of-the-art methods over a range of real datasets for the local hypergraph clustering and node ranking problems .
Edit intent: coherence
Original:  Specifically, our discovered model family, AlphaNet, outperforms prior-art models on a wide range of FLOPs regimes, including BigNAS, Once-for-All networks, FBNetV3, and AttentiveNAS.
Revised:  Specifically, our discovered model family, AlphaNet, outperforms prior-art models on a wide range of FLOPs regimes, including BigNAS, Once-for-All networks, and AttentiveNAS.
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Original: An external human machine interface (eHMI) can be viewed as an explicit communication method for providing driving intentions of an automated driving vehicle (AV) to pedestrians .
Revised: Compared to a manual driving vehicle (MV), an automated driving vehicle lacks a way to communicate with the pedestrian through the driver when it interacts with the pedestrian because the driver usually does not participate in driving tasks. Thus, an external human machine interface (eHMI) can be viewed as an explicit communication method for providing driving intentions of an automated driving vehicle (AV) to pedestrians .
Edit intent: coherence
Original: An external human machine interface (eHMI) can be viewed as an explicit communication method for providing driving intentions of an automated driving vehicle (AV) to pedestrians .
Revised: An external human machine interface (eHMI) can be viewed as an explicit communication method for providing driving intentions of an automated driving vehicle (AV) to pedestrians when they need to negotiate in an interaction, e.g., an encountering scene.
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Original:  In this paper, we proposed that the instruction of the eHMI's rationale can help pedestrians correctly understand the driving intentions and predict the behavior of the AV, and thus their subjective feelings (i.e., dangerous feeling, trust in the AV, and feeling of relief) and decision-making are also improved.
Revised:  In this paper, we propose that the instruction of the eHMI's rationale can help pedestrians correctly understand the driving intentions and predict the behavior of the AV, and thus their subjective feelings (i.e., dangerous feeling, trust in the AV, and feeling of relief) and decision-making are also improved.
Edit intent: fluency
Original:  The results of an interaction experiment in a road-crossing scene indicate that the participants were more difficult to be aware of the situation when they encountered an AV w/o eHMI compared to when they encountered a manual driving vehicle (MV) ;
Revised:  The results of an interaction experiment in a road-crossing scene indicate that the participants were more difficult to be aware of the situation when they encountered an AV w/o eHMI compared to when they encountered an MV ;
Edit intent: clarity
Original:  Being a balanced combination of math and art, music in collaboration with A.I. can simplify the generation process for new musical pieces, and ease the interpretation of it to a tangible level. On the other hand, the artistic nature of music and its mingling with the senses and feelings of the composer makes the artificial generation and mathematical modeling of it infeasible. In fact, there are no clear evaluation measures that can combine the objective music grammarand structure with the subjective audience satisfactiongoal .
Revised:  One of the main challenges in AMG is that there is no clear objective evaluation criterion that can measure the music grammar, structural rules, and audience satisfaction .
Edit intent: clarity
Original:  Therefore, in this paper, a method based on a genetic multi-objective evolutionary optimization algorithm for the generation of polyphonic music (melody with rhythm and harmony or appropriate chords) is introduced in which three specific goals determine the qualifications of the music generated. One of the goals is the rules and regulations of music, which, along with the other two goals, including the scores of music experts and ordinary listeners , fits the cycle of evolution to get the most optimal response .
Revised:  Therefore, in this paper, we propose a Multi-Objective Genetic Algorithm (MO-GA) to generate polyphonic music pieces, considering grammar and listener satisfaction. In this method, we use three objective functions. The first objective function is the accuracy of the generated music piece, based on music theory; and the other two goals, including the scores of music experts and ordinary listeners , fits the cycle of evolution to get the most optimal response .
Edit intent: clarity
Original:  One of the goals is the rules and regulations of music, which, along with the other two goals, including the scores of music experts and ordinary listeners , fits the cycle of evolution to get the most optimal response .
Revised:  One of the goals is the rules and regulations of music, which, along with the other two objective functions are modeled scores provided by music experts and ordinary listeners , fits the cycle of evolution to get the most optimal response .
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Original: U-Net based convolutional neural networks with deep feature representation and skip-connections have significantly boosted the performance of medical image segmentation. In this paper, we study the more challenging problem of improving efficiency in modeling global contexts without losing localization ability for low-level details.
Revised: U-Net based convolutional neural networks (CNNs). However, it exhibits general limitations on modeling explicit long-range relation, and existing cures, resorting to building deep encoders along with aggressive downsampling operations, leads to redundant deepened networks and loss of localized details. Hence, the segmentation task awaits a better solution to improve the efficiency of modeling global contexts without losing localization ability for low-level details.
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Original:  TransFuse, a novel two-branch architectureis proposed, which combines Transformers and CNNs in a parallel style . With TransFuse, both global dependency and low-level spatial details can be efficiently captured in a much shallower manner.
Revised:  TransFuse, a novel two-branch architectureis proposed, which combines Transformers and CNNs in a parallel style , where both global dependency and low-level spatial details can be efficiently captured in a much shallower manner.
Edit intent: coherence
Original:  TransFuse achieves the newest state-of-the-arts on polyp segmentation task, with 20%DIFDELCMD < \\%%% %DIF < fewer parameters and the fastest inference speed at about 98.7 FPS.abstract 
Revised:  TransFuse achieves the newest %DIFDELCMD < \\%%% %DIF < fewer parameters and the fastest inference speed at about 98.7 FPS.abstract 
Edit intent: clarity
Original:  TransFuse achieves the newest state-of-the-arts on polyp segmentation task, with 20%DIFDELCMD < \\%%% %DIF < fewer parameters and the fastest inference speed at about 98.7 FPS.abstract 
Revised:  TransFuse achieves the newest state-of-the-arts on polyp segmentation task, with 20%DIFDELCMD < \\%%% %DIF < fewer parameters and the fastest inference speed at about 98.7 FPS.abstract state-of-the-art results on both 2D and 3D medical image sets including polyp, skin lesion, hip, and prostate segmentation, with significant parameter decrease and inference speed improvement.
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Original: We consider the interaction of an incompressible fluid described by a Leray model with a hyperelastic structure modeled as a Saint Venant-Kirchhoff material.
Revised: The goal of this paper is to test solids4Foam, the fluid-structure interaction (FSI) toolbox developed for foam-extend (a branch of OpenFOAM), and assess its flexibility in handling more complex flows. For this purpose, we consider the interaction of an incompressible fluid described by a Leray model with a hyperelastic structure modeled as a Saint Venant-Kirchhoff material.
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Original:  For the implementation of the Leray model, which features a nonlinear differential low-pass filter, we adopt a three-step algorithm called Evolve-Filter-Relax . All the computational results presented in this article have been obtained with solids4Foam, the FSI toolbox developed for foam-extend, a branch of OpenFOAM . We validate our approach against numerical data available in the literature for the 3D cross flow past a cantilever beam at Reynolds number 100 and 400.
Revised:  For the implementation of the Leray model, which features a nonlinear differential low-pass filter, we adopt a three-step algorithm called Evolve-Filter-Relax . We validate our approach against numerical data available in the literature for the 3D cross flow past a cantilever beam at Reynolds number 100 and 400.
Edit intent: coherence
Original:  This improves over the well-known randomized algorithm of Lenzen and Wattenhofer[DISC2010] in two ways: it is deterministic and the logarithmic factor depends only on the maximum degree \Delta, not the number of vertices n .
Revised:  This improves over the well-known randomized algorithm of Lenzen and Wattenhofer[DISC2010] by making it a deterministic algorithm .
Edit intent: clarity
Original:  Therefore, this paper provides quantitative evidence which demonstrates how current 5G policy affects universal broadband, as well as drawing conclusions over how  decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies.
Revised:  Therefore, this paper provides quantitative evidence which demonstrates how current 5G policy affects universal broadband, as well as drawing conclusions over how 4G and 5G decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies.
Edit intent: clarity