wanyu/IteraTeR_v2 huggingface dataset:
Original:  We aim to develop a node classification approach that can flexibly adapt to a range of settings wherein labels are correlated with graph structure, or node attributes, or both.
Revised:  We aim to develop a node classification approach that can flexibly adapt to a range of settings wherein unknown labels are predicted either from labels of proximate nodes, or individual node attributes, or both.
Edit intent: clarity
Original:  To this end , we propose JANE (Jointly using Attributes and Node Embeddings): a novel and principled approach based on a generative probabilistic model that  weighs the role of node proximity and attribute similarity in predicting labels.
Revised:  To this end , we propose a principled approach, JANE, based on a generative probabilistic model that weighs the role of node proximity and attribute similarity in predicting labels.
Edit intent: coherence
Original:  To this end , we propose JANE (Jointly using Attributes and Node Embeddings): a novel and principled approach based on a generative probabilistic model that  weighs the role of node proximity and attribute similarity in predicting labels.
Revised:  To this end , we propose JANE (Jointly using Attributes and Node Embeddings): a novel and principled approach based on a generative probabilistic model that weighs the role of attributes and node proximity via embeddings in predicting labels.
Edit intent: clarity
Original:  Our experiments on a variety of graph datasets and comparison with standard baselines demonstrate that JANE exhibits a superior combination of versatility and competitive performance  .
Revised:  Our experiments on a variety of network datasets demonstrate that JANE exhibits a superior combination of versatility and competitive performance .
Edit intent: clarity
Original:  Our experiments on a variety of graph datasets and comparison with standard baselines demonstrate that JANE exhibits a superior combination of versatility and competitive performance  .
Revised:  Our experiments on a variety of graph datasets and comparison with standard baselines demonstrate that JANE exhibits the desired combination of versatility and competitive performance .
Edit intent: clarity
Original:  Our experiments on a variety of graph datasets and comparison with standard baselines demonstrate that JANE exhibits a superior combination of versatility and competitive performance  .
Revised:  Our experiments on a variety of graph datasets and comparison with standard baselines demonstrate that JANE exhibits a superior combination of versatility and competitive performance compared to standard baselines .
Edit intent: clarity
Original:  To show how conclusions drawn from network analyses can get distorted due to such inaccuracies, we focus on the effects of edge-uncertainty on minority group representations in degree-based rankings.
Revised:  To show how conclusions drawn from network analyses on the network analysis task of minority representations in degree-based rankings.
Edit intent: clarity
Original:  For that purpose, we analyze synthetic and real networks with varying homophily  and group sizes .
Revised:  By analysing synthetic and real networks with varying homophily and group sizes .
Edit intent: clarity
Original:  For that purpose, we analyze synthetic and real networks with varying homophily  and group sizes .
Revised:  For that purpose, we analyze synthetic and real networks with varying homophily levels and group sizes .
Edit intent: clarity
Original:  We find that introducing edge uncertainty can significantly alter the relative density of networks and result both in a strongly increased or decreased ranking of the minority , depending on the type of edge error and homophily.
Revised:  We find that introducing systematic edge errors can result both in a strongly increased or decreased ranking of the minority , depending on the type of edge error and homophily.
Edit intent: clarity
Original:  We find that introducing edge uncertainty can significantly alter the relative density of networks and result both in a strongly increased or decreased ranking of the minority , depending on the type of edge error and homophily.
Revised:  We find that introducing edge uncertainty can significantly alter the relative density of networks and result both in a strongly increased or decreased ranking of the minority . The observed effect depends both on the type of edge error and homophily.
Edit intent: clarity
Original:  When this selective pressure is high the number of B-cells quickly decreases and the population risks extinction in what is known as a population bottleneck.
Revised:  In cases when this selective pressure is high the number of B-cells quickly decreases and the population risks extinction in what is known as a population bottleneck.
Edit intent: clarity
Original:  When this selective pressure is high the number of B-cells quickly decreases and the population risks extinction in what is known as a population bottleneck.
Revised:  When this selective pressure is high the number of B-cells might quickly decrease and the population risks extinction in what is known as a population bottleneck.
Edit intent: clarity
Original:  When this selective pressure is high the number of B-cells quickly decreases and the population risks extinction in what is known as a population bottleneck.
Revised:  When this selective pressure is high the number of B-cells quickly decreases and the population might risk extinction in what is known as a population bottleneck.
Edit intent: clarity
Original:  Here we study the probability for a B-cell lineage to survive this bottleneck  as a function of the progenitor affinity for the Ag.
Revised:  Here we study the probability for a B-cell lineage to survive this bottleneck scenario as a function of the progenitor affinity for the Ag.
Edit intent: clarity
Original:  We predict the pass-by instants of vehicles from local minima of a vehicle-to-microphone distance predicted from audio.
Revised:  We predict the pass-by instants of vehicles from local minima of clipped vehicle-to-microphone distance predicted from audio.
Edit intent: fluency
Original:  The distance is predicted via a two-stage (coarse-fine) regression, both realised using neural networks (NNs).
Revised:  The distance is predicted via a two-stage (coarse-fine) regression, with both stages realised via neural networks (NNs).
Edit intent: clarity
Original:  Besides the minima-based counting, we propose a deep learning counting which %DIF > , -0.55\\%].
Revised:  %DIF > , -0.55\\%].
Edit intent: coherence
Original:  Because surrogate and high-fidelity models are used together, poor predictions by the surrogate models can be compensated with frequent recourse to high-fidelity models.
Revised:  Because surrogate and high-fidelity models are used together, poor predictions by surrogate models can be compensated with frequent recourse to high-fidelity models.
Edit intent: clarity
Original:  Thus, there is a trade-off between investing computational resources to improve surrogate models and the frequency of making recourse to expensive high-fidelity models;
Revised:  Thus, there is a trade-off between investing computational resources to improve the accuracy of surrogate models versus simply making more frequent recourse to expensive high-fidelity models;
Edit intent: clarity
Original:  This work considers multi-fidelity importance sampling and theoretically and computationally derives the optimal trade-off between improving the fidelity of surrogate models for constructing more accurate biasing densities and the number of samples that is required from the high-fidelity model to compensate poor biasing densities.
Revised:  This work considers multi-fidelity importance sampling and theoretically and computationally trades off increasing the fidelity of surrogate models for constructing more accurate biasing densities and the number of samples that is required from the high-fidelity model to compensate poor biasing densities.
Edit intent: clarity
Original:  This work considers multi-fidelity importance sampling and theoretically and computationally derives the optimal trade-off between improving the fidelity of surrogate models for constructing more accurate biasing densities and the number of samples that is required from the high-fidelity model to compensate poor biasing densities.
Revised:  This work considers multi-fidelity importance sampling and theoretically and computationally derives the optimal trade-off between improving the fidelity of surrogate models for constructing more accurate biasing densities and the numbers of samples that is required from the high-fidelity model to compensate poor biasing densities.
Edit intent: fluency
Original:  This work considers multi-fidelity importance sampling and theoretically and computationally derives the optimal trade-off between improving the fidelity of surrogate models for constructing more accurate biasing densities and the number of samples that is required from the high-fidelity model to compensate poor biasing densities.
Revised:  This work considers multi-fidelity importance sampling and theoretically and computationally derives the optimal trade-off between improving the fidelity of surrogate models for constructing more accurate biasing densities and the number of samples that are required from the high-fidelity model to compensate poor biasing densities.
Edit intent: fluency
Original:  This work considers multi-fidelity importance sampling and theoretically and computationally derives the optimal trade-off between improving the fidelity of surrogate models for constructing more accurate biasing densities and the number of samples that is required from the high-fidelity model to compensate poor biasing densities.
Revised:  This work considers multi-fidelity importance sampling and theoretically and computationally derives the optimal trade-off between improving the fidelity of surrogate models for constructing more accurate biasing densities and the number of samples that is required from the high-fidelity models to compensate poor biasing densities.
Edit intent: fluency
Original:  Numerical examples demonstrate that such optimal---context-aware---surrogate models for multi-fidelity importance sampling have lower fidelity than what typically is set as tolerance in traditional model reduction, leading to runtime speedups of up to one order of magnitude in the presented examples.
Revised:  Numerical examples demonstrate that such context-aware surrogate models for multi-fidelity importance sampling have lower fidelity than what typically is set as tolerance in traditional model reduction, leading to runtime speedups of up to one order of magnitude in the presented examples.
Edit intent: clarity
Original:  Convex ordering tools are used to establish that faster moving interferers result in a queue workload that is larger for the increasing convex stochastic order.
Revised:  Convex ordering tools are used to establish that faster moving interferers result in a queue workload that is smaller for the increasing-convex stochastic order.
Edit intent: clarity
Original:  Positive correlation between SINR level-crossing events at different time points is established and the autocorrelation function is determined .
Revised:  Positive correlation between SINR level-crossing events at different time points , and by determining the autocorrelation function is determined .
Edit intent: coherence
Original: Programs for multiprocessor machines commonly perform busy-waiting for synchronization.
Revised: Programs for multiprocessor machines commonly perform busy waiting for synchronization.
Edit intent: fluency
Original: Epidemiological forecasts are beset by uncertainties in the generative model for the disease , and the surveillance process through which data are acquired.
Revised: Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes , and the surveillance process through which data are acquired.
Edit intent: clarity
Original:  Nevertheless, most of the existing NN architectures can only deal with fixed and position-specific microphone arrays.
Revised:  Nevertheless, most existing NN architectures can only deal with fixed and position-specific microphone arrays.
Edit intent: coherence
Original:  In this paper, we present an NN architecture that can cope with microphone arrays on which no prior knowledge is presumed , and demonstrate its applicability on the speech dereverberation problem .
Revised:  In this paper, we present an NN architecture that can cope with microphone arrays on which no prior knowledge is presumed , and demonstrate its applicability in the speech dereverberation problem .
Edit intent: fluency
Original:  In this paper, we present an NN architecture that can cope with microphone arrays on which no prior knowledge is presumed , and demonstrate its applicability on the speech dereverberation problem .
Revised:  In this paper, we present an NN architecture that can cope with microphone arrays on which no prior knowledge is presumed , and demonstrate its applicability on the speech dereverberation task .
Edit intent: clarity
Original:  To this end, our approach harnesses recent advances in the Deep Sets framework to design an architecture that enhances the reverberant log-spectrum.
Revised:  To this end, our approach harnesses recent advances in deep learning on set-structured data to design an architecture that enhances the reverberant log-spectrum.
Edit intent: clarity
Original:  We provide a setup for training and testing such a network .
Revised:  We use noisy and noiseless versions of a simulated reverberant dataset to test the proposed architecture .
Edit intent: clarity
Original:  Our experiments , using REVERB challenge datasets, show that the proposed position-agnostic setup performs comparably with the position-aware frameworkand sometimes slightly better, even with fewer microphones.
Revised:  Our experiments on the noisy data show that the proposed position-agnostic setup performs comparably with the position-aware frameworkand sometimes slightly better, even with fewer microphones.
Edit intent: clarity
Original:  Our experiments , using REVERB challenge datasets, show that the proposed position-agnostic setup performs comparably with the position-aware frameworkand sometimes slightly better, even with fewer microphones.
Revised:  Our experiments , using REVERB challenge datasets, show that the proposed scene-agnostic setup outperforms a powerful scene-aware framework, sometimes even with fewer microphones.
Edit intent: clarity
Original:  Generalizing a recent beautiful work of Malach and Shalev-Shwartz \mbox{ malach2020hardness who gave the first tight correlational SQ (CSQ) lower bounds for learning DNF formulas, we show that lower bounds on the threshold or approximate degree of any function class directly imply CSQ lower bounds for PAC or agnostic learning respectively.
Revised:  Generalizing a recent beautiful work of Malach and Shalev-Shwartz (2020) who gave the first tight correlational SQ (CSQ) lower bounds for learning DNF formulas, we show that lower bounds on the threshold or approximate degree of any function class directly imply CSQ lower bounds for PAC or agnostic learning respectively.
Edit intent: clarity
Original:  it is through multiple modalities , i.e., language, voice, and facial expressions, that opinions and emotions are expressed.
Revised:  it is through multiple modalities such as language, voice, and facial expressions, that opinions and emotions are expressed.
Edit intent: clarity
Original:  MTGAT is an interpretable graph-based neural model that provides a suitable framework for analyzing this type of multimodal sequential data.
Revised:  MTGAT is an interpretable graph-based neural model that provides a suitable framework for analyzing multimodal sequential data.
Edit intent: clarity
Original:  We first introduce a procedure to convert unaligned multimodal sequence data into a graph with heterogeneous nodes and edges that captures the rich interactions between different modalities through time.
Revised:  We first introduce a procedure to convert unaligned multimodal sequence data into a graph with heterogeneous nodes and edges that captures the rich interactions across modalities and through time.
Edit intent: clarity
Original:  With a one-hour open-source singing voice database, we explore the impact of the PE loss on various mainstream sequence-to-sequence models, including the RNN-based model , transformer-based model , and conformer-based model .
Revised:  With a one-hour open-source singing voice database, we explore the impact of the PE loss on various mainstream sequence-to-sequence models, including the RNN-based , transformer-based model , and conformer-based model .
Edit intent: fluency
Original:  With a one-hour open-source singing voice database, we explore the impact of the PE loss on various mainstream sequence-to-sequence models, including the RNN-based model , transformer-based model , and conformer-based model .
Revised:  With a one-hour open-source singing voice database, we explore the impact of the PE loss on various mainstream sequence-to-sequence models, including the RNN-based model , transformer-based , and conformer-based model .
Edit intent: fluency
Original:  With a one-hour open-source singing voice database, we explore the impact of the PE loss on various mainstream sequence-to-sequence models, including the RNN-based model , transformer-based model , and conformer-based model .
Revised:  With a one-hour open-source singing voice database, we explore the impact of the PE loss on various mainstream sequence-to-sequence models, including the RNN-based model , transformer-based model , and conformer-based models .
Edit intent: fluency
Original: Recently, significant progress has been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from the neural network (NN) speaker classifiers for clustering speech segments.
Revised: Significant progress has recently been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from the neural network (NN) speaker classifiers for clustering speech segments.
Edit intent: fluency
Original: Recently, significant progress has been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from the neural network (NN) speaker classifiers for clustering speech segments.
Revised: Recently, significant progress has been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from neural network (NN) speaker classifiers for clustering speech segments.
Edit intent: fluency
Original:  These numerical observations are in coherence with some recent theoretical results obtained by Banica and Vega.
Revised:  These numerical observations are in agreement with some recent theoretical results obtained by Banica and Vega.
Edit intent: clarity
Original: Block Gram-Schmidt algorithms comprise essential kernels in many scientific computing applications, but for many commonly used variants, a rigorous treatment of their stability properties remains open.
Revised: Block Gram-Schmidt algorithms serve as essential kernels in many scientific computing applications, but for many commonly used variants, a rigorous treatment of their stability properties remains open.
Edit intent: clarity
Original:  This survey provides a comprehensive categorization of block Gram-Schmidt algorithms, especially those used in Krylov subspace methods to build orthonormal bases one block vector at a time.
Revised:  This survey provides a comprehensive categorization of block Gram-Schmidt algorithms, particularly those used in Krylov subspace methods to build orthonormal bases one block vector at a time.
Edit intent: fluency
Original:  This survey provides a comprehensive categorization of block Gram-Schmidt algorithms, particularly those used in Krylov subspace methods to build orthonormal bases one block vector at a time.
Revised:  This work provides a comprehensive categorization of block Gram-Schmidt algorithms, particularly those used in Krylov subspace methods to build orthonormal bases one block vector at a time.
Edit intent: clarity
Original:  All known stability results are assembled, and new results are summarized or conjectured for important communication-reducing variants.
Revised:  Known stability results are assembled, and new results are summarized or conjectured for important communication-reducing variants.
Edit intent: clarity
Original:  new block versions of low-synchronization variants are derived, and their efficacy and stability are demonstrated for a wide range of challenging examples . Low-synchronization variants appear remarkably stable for s-step-like matrices built with Newton polynomials, pointing towards a new stable and efficient backbone for Krylov subspace methods . Numerical examples are computed with a versatile MATLAB package hosted at URL and scripts for reproducing all results in the paper are provided.
Revised:  new block versions of low-synchronization variants are derived, and their efficacy and stability are demonstrated for a wide range of challenging examples . Numerical examples are computed with a versatile MATLAB package hosted at URL and scripts for reproducing all results in the paper are provided.
Edit intent: coherence
Original: We study the effectiveness of external control in selecting a desired final state of Boolean dynamics when that state is among the original attractors of the dynamics.
Revised: We study the effectiveness of external control in selecting a desired final state when that state is among the original attractors of the dynamics.
Edit intent: clarity
Original:  This scaling suggests that biological networks may be typically easy to control even when the number of interacting components is large.
Revised:  This suggests that biological networks may be typically easy to control even when the number of interacting components is large.
Edit intent: clarity
Original:  We provide a theoretical explanation of this scaling by separating controlling nodes into three types: those that act as inputs, those that distinguish among attractors, and any remaining nodes.
Revised:  We provide a theoretical explanation of the scaling by separating controlling nodes into three types: those that act as inputs, those that distinguish among attractors, and any remaining nodes.
Edit intent: fluency
Original:  This framework identifies those characteristics of dynamics that can invalidate this behavior, hinting at a broader applicability of this scaling to both biological and non-biological systems.
Revised:  We further identify characteristics of dynamics that can invalidate this behavior, hinting at a broader applicability of this scaling to both biological and non-biological systems.
Edit intent: clarity
Original:  This framework identifies those characteristics of dynamics that can invalidate this behavior, hinting at a broader applicability of this scaling to both biological and non-biological systems.
Revised:  This framework identifies those characteristics of dynamics that can invalidate this scaling, and speculate about how this relates more broadly to non-biological systems.
Edit intent: clarity
Original:  We show that for a random n \times n matrix with entries chosen independently from the range \{-m,\ldots,m\}, the probability that it is singular is at most m^{-cn} for some absolute constant c>0. 
Revised:  We show that for a random n \times n matrix with entries chosen independently from the range \{-m,\ldots,m\}, the probability that it is singular is m^{-cn} for some absolute constant c>0. 
Edit intent: clarity
Original:  Thanks to this adaptation mechanism, the proposed methods automatically detect the problem 's smoothness properties , without requiring any prior tuning by the optimizer.
Revised:  Thanks to this adaptation mechanism, the proposed method automatically detects whether the problem is smooth or not , without requiring any prior tuning by the optimizer.
Edit intent: clarity