wanyu/IteraTeR_full_sent huggingface dataset:
Original:  However, most of the existing deep learning-based methods either assume strong physics prior, or depend on specific initial and boundary conditions, or require data in dense regular grid making them inapt for modeling unknown PDEs from sparsely-observed data. This paper presents a deep learning-based collocation method for modeling dynamical systems driven by unknown PDEs when data sites are sparsely distributed .
Revised:  However, most of the existing deep learning-based methods either assume strong physics prior, or depend on specific initial and boundary conditions, or require data in a dense grid when the governing PDEs are unknown. In contrast, our method focuses on learning prediction models for unknown PDE-driven dynamics only from sparsely observed data .
Edit intent: clarity
Original:  This paper introduces a new 3-2/b(G)-approximation algorithm for the graph burning problem over general graphs  , where b(G) is the size of an optimal solution.
Revised:  This paper introduces a new 3-2/b(G)-approximation algorithm for the graph burning problem over general graphs . The approximation factor of this algorithm is 3-2/b(G) , where b(G) is the size of an optimal solution.
Edit intent: meaning-changed
Original:  This algorithm , named 'burning greedy permutation' algorithm, is based on greedy permutations and is easy to implement . According to a brief empirical analysis, the proposed algorithm generates competitive solutions , some of which are better than the best previously known .
Revised:  This algorithm , named 'burning greedy permutation' algorithm, is based on greedy permutations and is easy to implement , and according to a brief empirical analysis, the proposed algorithm generates competitive solutions , some of which are better than the best previously known .
Edit intent: coherence
Original:  We outline the fundamental attributes and emergent phenomena associated with dormancy and seed banks with the goal of developing a unifying framework that can address problems in the life sciences, ranging from global change to cancer biology.
Revised:  We outline the fundamental attributes and emergent phenomena associated with dormancy and seed banks , with the vision for a unifying and mathematically based framework that can address problems in the life sciences, ranging from global change to cancer biology.
Edit intent: meaning-changed
Original: We present a new  framework for solution of Partial Differential Equations that offers a consistent approach to an efficient treatment of almost any set of boundary conditions .
Revised: We present a new framework for solution of Partial Differential Equations (PDEs) that is based on an analytical transformation that moves the boundary constraints into the dynamics of the corresponding governing equation. The framework is based on a Partial Integral Equation (PIE) representation of PDEs, where a PDE equation is transformed into an equivalent PIE representation that does not require boundary conditions on its solution state. The PDE-PIE framework allows for a development of a generalized PIE-Galerkin approximation methodology for a broad class of linear PDEs with non-constant coefficients governed by non-periodic boundary conditions, including, e.g., Dirichlet, Neumann and Robin boundaries. The significance of this result is that solution to almost any linear PDE can now be constructed in a form of an analytical approximation based on a series expansion using a suitable set of basis functions, such as, e.g., Chebyshev polynomials of the first kind, irrespective of the boundary conditions. In many cases involving homogeneous or simple time-dependent boundary inputs, an analytical integration in time is also possible. We present several PDE solution examples in one spatial variable implemented with the developed PIE-Galerkin methodology using both analytical and numerical integration in time. The developed framework can be naturally extended to multiple spatial dimensions and, potentially, to nonlinear problems .
Edit intent: meaning-changed
Original: We extend the unfitted h-adaptive Finite Element Method (h-AgFEM) on parallel tree-based adaptive meshes, recently developed for linear scalar elliptic problems, to handle nonlinear problems in solid mechanics.
Revised: In this work, we bridge standard adaptive mesh refinement and coarsening on scalable octree background meshes and robust unfitted finite element formulations for the automatic and efficient solution of large-scale nonlinear solid mechanics problems posed on complex geometries, as an alternative to standard body-fitted formulations, unstructured mesh generation and graph partitioning strategies. We pay special attention to those aspects requiring a specialized treatment in the extension of the unfitted h-adaptive Finite Element Method (h-AgFEM) on parallel tree-based adaptive meshes, recently developed for linear scalar elliptic problems, to handle nonlinear problems in solid mechanics.
Edit intent: meaning-changed
Original: We extend the unfitted h-adaptive Finite Element Method (h-AgFEM) on parallel tree-based adaptive meshes, recently developed for linear scalar elliptic problems, to handle nonlinear problems in solid mechanics.
Revised: We extend the unfitted h-adaptive aggregated finite element method on parallel tree-based adaptive meshes, recently developed for linear scalar elliptic problems, to handle nonlinear problems in solid mechanics.
Edit intent: clarity
Original:  Leveraging h-AgFEM on locally-adapted, non-conforming, tree-based meshes, and its parallel distributed-memory implementation, we can tackle large-, multi-scale problems posed on complex geometries. On top of that, in order to accurately and efficiently capture localized phenomena that frequently occur in nonlinear solid mechanics problems, we propose an algorithm to perform pseudo time-stepping in combination with h-adaptive dynamic mesh refinement and re-balancing driven by a-posteriori error estimators.
Revised:  In order to accurately and efficiently capture localized phenomena that frequently occur in nonlinear solid mechanics problems, we propose an algorithm to perform pseudo time-stepping in combination with h-adaptive dynamic mesh refinement and re-balancing driven by a-posteriori error estimators.
Edit intent: coherence
Original:  On top of that, in order to accurately and efficiently capture localized phenomena that frequently occur in nonlinear solid mechanics problems, we propose an algorithm to perform pseudo time-stepping in combination with h-adaptive dynamic mesh refinement and re-balancing driven by a-posteriori error estimators.
Revised:  On top of that, in order to accurately and efficiently capture localized phenomena that frequently occur in nonlinear solid mechanics problems, we perform pseudo time-stepping in combination with h-adaptive dynamic mesh refinement and re-balancing driven by a-posteriori error estimators.
Edit intent: clarity
Original:  On top of that, in order to accurately and efficiently capture localized phenomena that frequently occur in nonlinear solid mechanics problems, we propose an algorithm to perform pseudo time-stepping in combination with h-adaptive dynamic mesh refinement and re-balancing driven by a-posteriori error estimators.
Revised:  On top of that, in order to accurately and efficiently capture localized phenomena that frequently occur in nonlinear solid mechanics problems, we propose an algorithm to perform pseudo time-stepping in combination with h-adaptive dynamic mesh refinement and rebalancing driven by a-posteriori error estimators.
Edit intent: fluency
Original:  In this work, we propose training a neural network through large-scale domain randomization to identify the articulation type of object parts from a single image observation. Training data is generated via photorealistic rendering in simulation .
Revised:  In this work, we propose training a neural network through large-scale domain randomization to identify the articulation type of object parts from a single frame of an RGB-D image and segmentation masks. The network is trained on 100k synthetic images of 149 articulated objects from 6 categories. Synthetic images are rendered via a photorealistic simulator with domain randomization .
Edit intent: meaning-changed
Original:  We train the network on six object categories with 149 objects and 100K rendered images, achieving an accuracy of 82.5\% . Experiments show our method generalizes to novel object categories in simulation and can be applied to real-world images without fine-tuning.
Revised:  The network achieves an articulation type classification accuracy of 82.5\% . Experiments show our method generalizes to novel object categories in simulation and can be applied to real-world images without fine-tuning.
Edit intent: meaning-changed
Original:  We train the network on six object categories with 149 objects and 100K rendered images, achieving an accuracy of 82.5\% . Experiments show our method generalizes to novel object categories in simulation and can be applied to real-world images without fine-tuning.
Revised:  We train the network on six object categories with 149 objects and 100K rendered images, achieving an accuracy of 82.5\% on novel object instances in trained categories. Experiments also show how this method enables generalization to novel categories and be applied to real-world images without fine-tuning.
Edit intent: meaning-changed
Original:  However, prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases . To address this problem, fair ranking algorithms (e.g., Det-Greedy) which increase exposure of underrepresented candidates have been proposed in recent literature .
Revised:  However, prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases , leading to the proposal of fair ranking algorithms (e.g., Det-Greedy) which increase exposure of underrepresented candidates have been proposed in recent literature .
Edit intent: coherence
Original:  In this work, we study how gender biases manifest in online hiring platforms and how they impact real world hiring decisions. More specifically, we analyze various sources of gender biases including the nature of the ranking algorithm, the job context ,  and inherent biases of employers , and establish how these factors interact and affect real world hiring decisions.
Revised:  In this work, we study how gender biases manifest in online hiring platforms , and inherent biases of employers , and establish how these factors interact and affect real world hiring decisions.
Edit intent: coherence
Original:  More specifically, we analyze various sources of gender biases including the nature of the ranking algorithm, the job context ,  and inherent biases of employers , and establish how these factors interact and affect real world hiring decisions.
Revised:  More specifically, we analyze various sources of gender biases including the nature of the ranking algorithm, the job context , including the job context and inherent biases of employers , and establish how these factors interact and affect real world hiring decisions.
Edit intent: clarity
Original:  More specifically, we analyze various sources of gender biases including the nature of the ranking algorithm, the job context ,  and inherent biases of employers , and establish how these factors interact and affect real world hiring decisions.
Revised:  More specifically, we analyze various sources of gender biases including the nature of the ranking algorithm, the job context , and inherent biases of employers and establish how these factors interact and affect real world hiring decisions.
Edit intent: fluency
Original:  More specifically, we analyze various sources of gender biases including the nature of the ranking algorithm, the job context ,  and inherent biases of employers , and establish how these factors interact and affect real world hiring decisions.
Revised:  More specifically, we analyze various sources of gender biases including the nature of the ranking algorithm, the job context , and inherent biases of employers , and establish how these factors interact with ranking algorithms to affect hiring decisions.
Edit intent: clarity
Original:  To this end, we experiment with three different ranking algorithms on three different job contexts using real world data from TaskRabbit. We simulate the hiring scenarios on TaskRabbit by carrying out a large-scale user study with Amazon Mechanical Turk. We then leverage the responses from this study to understand the effect of each of the aforementioned factors .
Revised:  To the best of our knowledge, this work makes the first attempt at studying the interplay between the aforementioned factors in the context of online hiring. We carry out a largescale user study simulating online hiring scenarios with data from TaskRabbit, a popular online freelancing site .
Edit intent: clarity
Original:  In this paper, we systematically survey %DIFDELCMD < \paperNum %%%  ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?"
Revised:  In this paper, we systematically survey %DIFDELCMD < \paperNum %%% 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?"
Edit intent: meaning-changed
Original:  This survey reveals six main processes where the employment of ML techniques can benefit visualizations: VIS-driven Data Processing, Data Presentation, Insight Communication , Style Imitation, VIS Interaction, VIS Perception.
Revised:  This survey reveals seven main processes where the employment of ML techniques can benefit visualizations: VIS-driven Data Processing, Data Presentation, Insight Communication , Style Imitation, VIS Interaction, VIS Perception.
Edit intent: meaning-changed
Original:  This survey reveals six main processes where the employment of ML techniques can benefit visualizations: VIS-driven Data Processing, Data Presentation, Insight Communication , Style Imitation, VIS Interaction, VIS Perception.
Revised:  This survey reveals six main processes where the employment of ML techniques can benefit visualizations: Data Processing4VIS, Data-VIS Mapping, InsightCommunication , Style Imitation, VIS Interaction, VIS Perception.
Edit intent: meaning-changed
Original:  This survey reveals six main processes where the employment of ML techniques can benefit visualizations: VIS-driven Data Processing, Data Presentation, Insight Communication , Style Imitation, VIS Interaction, VIS Perception. The six processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.
Revised:  This survey reveals six main processes where the employment of ML techniques can benefit visualizations: VIS-driven Data Processing, Data Presentation, Insight Communication , Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.
Edit intent: meaning-changed
Original:  Some pathogens become so deadly that they put the whole technological advancement of humans under question-such as the case of SARS-CoV-2, however, this is not the only time.
Revised:  Some of these pathogens, such as SARS-CoV-2, become so deadly that they put the whole technological advancement of humans under question-such as the case of SARS-CoV-2, however, this is not the only time.
Edit intent: clarity
Original:  Some pathogens become so deadly that they put the whole technological advancement of humans under question-such as the case of SARS-CoV-2, however, this is not the only time. SARS-CoV-2 has become a pandemic but within this very decade several other deadly virus outbreaks were witnessed by mankind like Zika virus, Ebola virus, MERS etc.
Revised:  Some pathogens become so deadly that they put the whole technological advancement of healthcare under challenge. Within this very decade several other deadly virus outbreaks were witnessed by mankind like Zika virus, Ebola virus, MERS etc.
Edit intent: clarity
Original:  SARS-CoV-2 has become a pandemic but within this very decade several other deadly virus outbreaks were witnessed by mankind like Zika virus, Ebola virus, MERS etc.
Revised:  SARS-CoV-2 has become a pandemic but within this very decade several other deadly virus outbreaks were witnessed by humans such as Zika virus, Ebola virus, MERS etc.
Edit intent: fluency
Original:  SARS-CoV-2 has become a pandemic but within this very decade several other deadly virus outbreaks were witnessed by mankind like Zika virus, Ebola virus, MERS etc.
Revised:  SARS-CoV-2 has become a pandemic but within this very decade several other deadly virus outbreaks were witnessed by mankind like Zika virus, Ebola virus, MERS-coronavirus etc.
Edit intent: meaning-changed
Original:  Though conventional techniques of virus detection have succeeded in detecting these viruses to some extent, they are time consuming, expensive and require a lot of manpower. Plasmonic biosensors can become a promising alternative to the conventional techniques .
Revised:  Though conventional techniques of virus detection have succeeded in detecting these viruses to some extent, these techniques are time-consuming, costly, and require trained human-resources. Plasmonic metamaterial-based biosensors might pave the way to low-cost rapid virus detection .
Edit intent: clarity
Original:  The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional magnetic resonance imaging (fMRI) onto the template structural brain network obtained through the diffusion tensor imaging (DTI ).
Revised:  The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional MRI (fMRI) onto the template structural brain network obtained through the diffusion tensor imaging (DTI ).
Edit intent: clarity
Original:  The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional magnetic resonance imaging (fMRI) onto the template structural brain network obtained through the diffusion tensor imaging (DTI ).
Revised:  The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional magnetic resonance imaging (fMRI) onto the template structural brain network obtained through the diffusion MRI (dMRI ).
Edit intent: clarity
Original:  We present an online algorithm that is (1/6)-competitive with respect to the value of the optimal-in-hindsight policy, for arbitrary weighted graphs . This is the first result to achieve a constant competitive ratio when both arrivals and departures are random and unannounced .
Revised:  We present an online algorithm that is (1/6)-competitive with respect to the value of the optimal-in-hindsight policy, for arbitrary weighted graphs .
Edit intent: coherence
Original:  We demonstrate the expressivity of Sigma Worksheet with several usage examples and report on its accessibility through user feedback we elicited since its deployment. Our user feedback suggests that Sigma Worksheet provides an easier to use and learn interface for data warehouses, improving the  data analysis and exploration experience for both expert and non-expert enterprise users  .
Revised:  We demonstrate the expressivity of Sigma Worksheet with several usage examples and report on user feedback that we elicit through a 100-person survey followed by a semi-structured interview study with 70 participants. We find that Sigma Worksheet provides an easier to use and learn interface for data warehouses, improving the data analysis and exploration experience for both expert and non-expert enterprise users .
Edit intent: meaning-changed
Original:  Our user feedback suggests that Sigma Worksheet provides an easier to use and learn interface for data warehouses, improving the  data analysis and exploration experience for both expert and non-expert enterprise users  .
Revised:  Our user feedback suggests that Sigma Worksheet provides an easier to use and learn interface for accessing, transforming, exploring, and analyzing data in cloud data warehouses, improving the data analysis and exploration experience for both expert and non-expert enterprise users .
Edit intent: meaning-changed
Original:  Our user feedback suggests that Sigma Worksheet provides an easier to use and learn interface for data warehouses, improving the  data analysis and exploration experience for both expert and non-expert enterprise users  .
Revised:  Our user feedback suggests that Sigma Worksheet provides an easier to use and learn interface for data warehouses, improving the data analysis and exploration experience for both expert and non-expert enterprise users . Our findings also suggest Sigma Worksheet can further improve user experience by providing guidance to users at various steps of data analysis .
Edit intent: meaning-changed
Original:   To control in a data-driven way for  potentially high dimensional pre-treatment covariatesthat motivate the selection-on-observables assumptions , we adapt the double machine learning framework  to sample selection problems.
Revised:  We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for potentially high dimensional pre-treatment covariatesthat motivate the selection-on-observables assumptions , we adapt the double machine learning framework to sample selection problems.
Edit intent: meaning-changed
Original:   To control in a data-driven way for  potentially high dimensional pre-treatment covariatesthat motivate the selection-on-observables assumptions , we adapt the double machine learning framework  to sample selection problems.
Revised:  To control in a data-driven way for a potentially high dimensional pre-treatment covariatesthat motivate the selection-on-observables assumptions , we adapt the double machine learning framework to sample selection problems.
Edit intent: fluency
Original:   To control in a data-driven way for  potentially high dimensional pre-treatment covariatesthat motivate the selection-on-observables assumptions , we adapt the double machine learning framework  to sample selection problems.
Revised:  To control in a data-driven way for potentially high dimensional pre-treatment covariatesthat motivate the selection-on-observables assumptions , we adapt the double machine learning framework for treatment evaluation to sample selection problems.
Edit intent: clarity
Original:  That is, we make use of (a) Neyman-orthogonal and doubly robust score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias.
Revised:  We make use of (a) Neyman-orthogonal and doubly robust score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias.
Edit intent: coherence