wanyu/IteraTeR_v2 huggingface dataset:
Original:  If code has  distance more than the diagonal distance  then it is degenerate  whereas if it is below the diagonal distance  then it is nondegenerate.
Revised:  If code has distance more than the diagonal distance then it is degenerate whereas if it is below the diagonal distance , then it is nondegenerate.
Edit intent: fluency
Original:  We show that most of the CWS codes without a cycle of length 4 attain the upper bound of diagonal distance d+1  where d is the minimum vertex degree of the associated graph.
Revised:  We show that most of the CWS codes without a cycle of length four attain the upper bound of diagonal distance d+1 where d is the minimum vertex degree of the associated graph.
Edit intent: fluency
Original:  We show that most of the CWS codes without a cycle of length 4 attain the upper bound of diagonal distance d+1  where d is the minimum vertex degree of the associated graph.
Revised:  We show that most of the CWS codes without a cycle of length 4 attain the upper bound of diagonal distance d+1 , where d is the minimum vertex degree of the associated graph.
Edit intent: fluency
Original:  Our initial results show that forest carbon estimates from satellite imagery can overestimate above-ground biomass by more than 10-times for tropical reforestation projects.
Revised:  Our initial results show that forest carbon estimates from satellite imagery can overestimate above-ground biomass by up to 10-times for tropical reforestation projects.
Edit intent: clarity
Original:   In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction.
Revised:  Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction.
Edit intent: fluency
Original:  We show that this framework generates synthetic noisy labels that exhibit important characteristics of the label noise in practical settings via comparison with the CIFAR10-H dataset . Equipped with controllable label noise, we study the negative impact of noisy labels across a few realistic settings to understand when label noise is more problematic.
Revised:  We show that the distribution of the synthetic noisy labels that exhibit important characteristics of the label noise in practical settings via comparison with the CIFAR10-H dataset . Equipped with controllable label noise, we study the negative impact of noisy labels across a few realistic settings to understand when label noise is more problematic.
Edit intent: clarity
Original: We propose a simulation framework for generating realistic instance-dependent noisy labels via a pseudo-labeling paradigm.
Revised: We propose a simulation framework for generating instance-dependent noisy labels via a pseudo-labeling paradigm.
Edit intent: clarity
Original:  Equipped with controllable label noise, we study the negative impact of noisy labels across a few realistic settings to understand when label noise is more problematic.
Revised:  Equipped with controllable label noise, we study the negative impact of noisy labels across a few practical settings to understand when label noise is more problematic.
Edit intent: clarity
Original: In this paper , we investigate the explicit solutions and stability properties of certain continuous-time homogeneous polynomial dynamical systems via tensor algebra.
Revised: This paper investigates the explicit solutions and stability properties of certain continuous-time homogeneous polynomial dynamical systems via tensor algebra.
Edit intent: fluency
Original:  In particular, if a system of homogeneous polynomial differential equations can be represented by an orthogonal decomposable tensor, we can write down its explicit solution in a simple fashion by exploiting tensor Z-eigenvalues and Z-eigenvectors.
Revised:  In particular, if a system of homogeneous polynomial differential equations can be represented by an orthogonally decomposable tensor, we can write down its explicit solution in a simple fashion by exploiting tensor Z-eigenvalues and Z-eigenvectors.
Edit intent: clarity
Original:  In particular, if a system of homogeneous polynomial differential equations can be represented by an orthogonal decomposable tensor, we can write down its explicit solution in a simple fashion by exploiting tensor Z-eigenvalues and Z-eigenvectors.
Revised:  In particular, if a system of homogeneous polynomial differential equations can be represented by an orthogonal decomposable tensor, we can construct its explicit solution in a simple fashion by exploiting tensor Z-eigenvalues and Z-eigenvectors.
Edit intent: clarity
Original:  In particular, if a system of homogeneous polynomial differential equations can be represented by an orthogonal decomposable tensor, we can write down its explicit solution in a simple fashion by exploiting tensor Z-eigenvalues and Z-eigenvectors.
Revised:  In particular, if a system of homogeneous polynomial differential equations can be represented by an orthogonal decomposable tensor, we can write down its explicit solution by exploiting tensor Z-eigenvalues and Z-eigenvectors.
Edit intent: clarity
Original:  In addition, according to the form of the explicit solution, we explore the stability properties of the homogeneous polynomial dynamical system.
Revised:  By utilizing the form of the explicit solution, we explore the stability properties of the homogeneous polynomial dynamical system.
Edit intent: coherence
Original:  In addition, according to the form of the explicit solution, we explore the stability properties of the homogeneous polynomial dynamical system.
Revised:  In addition, according to the form of the explicit solution, we are able to discuss the stability properties of the homogeneous polynomial dynamical system.
Edit intent: clarity
Original:  We discover that the Z-eigenvalues from the orthogonal decomposition of the corresponding dynamic tensor can offer necessary and sufficient stability conditions, similar to these from linear systems theory.
Revised:  We illustrate that the Z-eigenvalues from the orthogonal decomposition of the corresponding dynamic tensor can offer necessary and sufficient stability conditions, similar to these from linear systems theory.
Edit intent: clarity
Original:  We discover that the Z-eigenvalues from the orthogonal decomposition of the corresponding dynamic tensor can offer necessary and sufficient stability conditions, similar to these from linear systems theory.
Revised:  We discover that the Z-eigenvalues from the orthogonal decomposition of the corresponding dynamic tensor can be utilized to establish necessary and sufficient stability conditions, similar to these from linear systems theory.
Edit intent: clarity
Original:  Techniques for raw data exploration, sketching, or physicalization can avoid the perils of tool development, but prevent direct analytical access to users' rich personal data streams .
Revised:  Techniques for raw data exploration, sketching, or physicalization can avoid the perils of tool development, but prevent direct analytical access to users' rich personal data .
Edit intent: clarity
Original:  This interview method incorporates an analyst to provide real-time analysis of personal data streams , granting interview participants opportunities to directly engage  their data, and interviewers to observe and ask questions throughout this engagement.
Revised:  This interview method incorporates an analyst to provide real-time personal data analysis , granting interview participants opportunities to directly engage their data, and interviewers to observe and ask questions throughout this engagement.
Edit intent: clarity
Original:  This interview method incorporates an analyst to provide real-time analysis of personal data streams , granting interview participants opportunities to directly engage  their data, and interviewers to observe and ask questions throughout this engagement.
Revised:  This interview method incorporates an analyst to provide real-time analysis of personal data streams , granting interview participants opportunities to directly engage with their data, and interviewers to observe and ask questions throughout this engagement.
Edit intent: fluency
Original:  This interview method incorporates an analyst to provide real-time personal data analysis, granting interview participants opportunities to directly engage with their data, and interviewers to observe and ask questions throughout this engagement.
Revised:  This interview method incorporates an analyst to provide real-time personal data analysis, granting interview participants the opportunity to directly engage with their data, and interviewers to observe and ask questions throughout this engagement.
Edit intent: fluency
Original:  The Genome Aggregation Database (gnomAD) is currently the largest and most widely-used publicly available collection of population variation from harmonized sequencing data.
Revised:  The Genome Aggregation Database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data.
Edit intent: fluency
Original:  We will introduce key features including allele frequency, per-base expression levels, and constraint scores, and provide guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease.
Revised:  We introduce key features including allele frequency, per-base expression levels, and constraint scores, and provide guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease.
Edit intent: coherence
Original:  We will introduce key features including allele frequency, per-base expression levels, and constraint scores, and provide guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease.
Revised:  We will introduce key features including allele frequency, per-base expression levels, constraint scores, and provide guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease.
Edit intent: fluency
Original:  Hyperbolic space , owing to its ability to embed hierarchical structures continuously with low distortion, has been applied for learning data with tree-like structures .
Revised:  Hyperbolic space , owing to its ability to embed hierarchical structures continuously .
Edit intent: clarity
Original:  The improved HNNs are able to achieve comparable performance with ENNs on standard image recognition datasets including MNIST, CIFAR10, CIFAR100 and ImageNet , while demonstrating more adversarial robustness and stronger out-of-distribution detection capability .
Revised:  The improved HNNs are able to achieve comparable performance with ENNs on MNIST, CIFAR10, CIFAR100 and ImageNet , while demonstrating more adversarial robustness and stronger out-of-distribution detection capability .
Edit intent: clarity
Original:  The improved HNNs are able to achieve comparable performance with ENNs on standard image recognition datasets including MNIST, CIFAR10, CIFAR100 and ImageNet , while demonstrating more adversarial robustness and stronger out-of-distribution detection capability .
Revised:  The improved HNNs are able to achieve comparable performance with ENNs on standard image recognition datasets including MNIST, CIFAR10, CIFAR100 and ImageNet benchmarks, with better adversarial robustness and stronger out-of-distribution detection capability .
Edit intent: clarity
Original:  The improved HNNs are able to achieve comparable performance with ENNs on standard image recognition datasets including MNIST, CIFAR10, CIFAR100 and ImageNet , while demonstrating more adversarial robustness and stronger out-of-distribution detection capability .
Revised:  The improved HNNs are able to achieve comparable performance with ENNs on standard image recognition datasets including MNIST, CIFAR10, CIFAR100 and ImageNet , while demonstrating more adversarial robustness and out-of-distribution detection capability .
Edit intent: clarity
Original:  The improved HNNs are able to achieve comparable performance with ENNs on standard image recognition datasets including MNIST, CIFAR10, CIFAR100 and ImageNet , while demonstrating more adversarial robustness and stronger out-of-distribution detection capability .
Revised:  The improved HNNs are able to achieve comparable performance with ENNs on standard image recognition datasets including MNIST, CIFAR10, CIFAR100 and ImageNet , while demonstrating more adversarial robustness and stronger out-of-distribution detection .
Edit intent: clarity
Original:  Intuitively, recompositions of existing visual concepts ( i.e. \ie , attributes and objects) can generate unseen compositions in the training set, which will promote VQA models to generalize to OOD samples.
Revised:  Intuitively, recompositions of existing visual concepts ( \ie , attributes and objects) can generate unseen compositions in the training set, which will promote VQA models to generalize to OOD samples.
Edit intent: coherence
Original:  In this paper, we formulate OOD generalization in VQA as a compositional generalization problem and propose a graph generative modeling-based training scheme (X-GGM) to handle the problemimplicitly . X-GGM leverages graph generative modeling to iteratively generate a relation matrix and node representations for the predefined graph that utilizes attribute-object pairs as nodes.
Revised:  In this paper, we formulate OOD generalization in VQA as a compositional generalization problem and propose a graph generative modeling-based training scheme (X-GGM) to implicitly model the problem . X-GGM leverages graph generative modeling to iteratively generate a relation matrix and node representations for the predefined graph that utilizes attribute-object pairs as nodes.
Edit intent: clarity
Original:  The baseline VQA model (LXMERT) trained with the X-GGM scheme achieves state-of-the-art OOD performance on two standard VQA OOD benchmarks, i.e. \ie , VQA-CP v2 and GQA-OOD.
Revised:  The baseline VQA model (LXMERT) trained with the X-GGM scheme achieves state-of-the-art OOD performance on two standard VQA OOD benchmarks, \ie , VQA-CP v2 and GQA-OOD.
Edit intent: coherence
Original: A blockchain is a linked list of immutable tamper-proof blocks, which is stored at each participating node.
Revised: A blockchain is a linked list of immutable tamper-proof blocks, which is maintained at each participating node.
Edit intent: clarity
Original:  Each block records a set of transactions and the associated metadata.
Revised:  Each block records a set of transactions and their associated metadata.
Edit intent: clarity
Original:  Blockchain was first perceived by Satoshi Nakamoto , as a peer-to-peer money exchange system.
Revised:  Blockchain was first perceived by Satoshi Nakamoto as a peer-to-peer money exchange system.
Edit intent: fluency
Original:  Nakamoto referred to the transactional tokens exchanged among clients in his system as Bitcoins .
Revised:  Blockchains received traction due to their inherent property of immutability-once a block is accepted, it cannot be reverted .
Edit intent: clarity
Original:  Cooperative WF control of both turbine yaw and axis-induction are considered  .
Revised:  Cooperative WF control of both turbine yaw and axis-induction are considered in the WF layout design .
Edit intent: clarity
Original:  In contrast to existing fixation-level or object-level saliency detection tasks, we focus on multi-modal salient object detection (SOD), which mimics human attention mechanism by segmenting salient objects with the guidance of audio-visual cues .
Revised:  In contrast to existing fixation-/ object-level saliency detection tasks, we focus on multi-modal salient object detection (SOD), which mimics human attention mechanism by segmenting salient objects with the guidance of audio-visual cues .
Edit intent: fluency
Original:  In contrast to existing fixation-level or object-level saliency detection tasks, we focus on multi-modal salient object detection (SOD), which mimics human attention mechanism by segmenting salient objects with the guidance of audio-visual cues .
Revised:  In contrast to existing fixation-level or object-level saliency detection tasks, we focus on audio-induced salient object detection (SOD), which mimics human attention mechanism by segmenting salient objects with the guidance of audio-visual cues .
Edit intent: clarity
Original:  In contrast to existing fixation-level or object-level saliency detection tasks, we focus on multi-modal salient object detection (SOD), which mimics human attention mechanism by segmenting salient objects with the guidance of audio-visual cues .
Revised:  In contrast to existing fixation-level or object-level saliency detection tasks, we focus on multi-modal salient object detection (SOD), where the salient objects are labeled with the guidance of audio-visual cues .
Edit intent: clarity
Original:  In contrast to existing fixation-level or object-level saliency detection tasks, we focus on multi-modal salient object detection (SOD), which mimics human attention mechanism by segmenting salient objects with the guidance of audio-visual cues .
Revised:  In contrast to existing fixation-level or object-level saliency detection tasks, we focus on multi-modal salient object detection (SOD), which mimics human attention mechanism by segmenting salient objects with the guidance of audio-induced eye movements .
Edit intent: clarity
Original:  These coarse-to-fine annotations enable detailed analysis for PV-SOD modeling , e.g., determining the major challenges for existing SOD models, and predicting scanpaths to study the long-term eye fixation behaviors of humans.
Revised:  These coarse-to-fine annotations enable detailed analysis for PV-SOD modelling , e.g., determining the major challenges for existing SOD models, and predicting scanpaths to study the long-term eye fixation behaviors of humans.
Edit intent: fluency
Original: We propose Rank & Sort (RS) Loss, as a ranking-based loss function to train deep object detection and instance segmentation methods (i.e. visual detectors).
Revised: We propose Rank & Sort (RS) Loss, a ranking-based loss function to train deep object detection and instance segmentation methods (i.e. visual detectors).
Edit intent: coherence
Original:  RS Loss supervises the classifier, a sub-network of these methods, to rank each positive above all negatives as well as to sort positives among themselves with respect to (wrt.) their continuous localisation qualities (e.g. Intersection-over-Union - IoU).
Revised:  RS Loss supervises the classifier, a sub-network of these methods, to rank each positive above all negatives as well as to sort positives among themselves with respect to (wrt.) their localisation qualities (e.g. Intersection-over-Union - IoU).
Edit intent: clarity
Original:  Code available at URL
Revised:  Code is available at: URL
Edit intent: clarity
Original:  The high speed of  procedure allows pathologists to rapidly assess the key microscopic features, such as tumor margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation.
Revised:  The high speed of the procedure allows pathologists to rapidly assess the key microscopic features, such as tumor margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation.
Edit intent: fluency
Original:  The high speed of  procedure allows pathologists to rapidly assess the key microscopic features, such as tumor margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation.
Revised:  The high speed of procedure allows pathologists to rapidly assess the key microscopic features, such as tumour margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation.
Edit intent: fluency
Original:  AI-FFPE rectifies FS artefacts with the guidance of an attention-mechanism that puts a particular emphasis on artefacts while utilising a self-regularization mechanism established between FS input image and synthesized FFPE-style image that preserves clinically relevant features.
Revised:  AI-FFPE rectifies FS artefacts with the guidance of an attention mechanism that puts a particular emphasis on artefacts while utilising a self-regularization mechanism established between FS input image and synthesized FFPE-style image that preserves clinically relevant features.
Edit intent: fluency
Original:  In the previous works, high-complexity digital signal processing (DSP) is required to resist the CD-caused spectral nulls.
Edit intent: coherence
Original:  Based on the characteristics of dispersive channel, Nyquist-SCM with multi-rate subcarriers is proposed to keep away from the CD-caused spectral nulls flexibly.
Revised:  Based on the prior knowledge of the dispersive channel, Nyquist-SCM with multi-rate subcarriers is proposed to keep away from the CD-caused spectral nulls flexibly.
Edit intent: clarity
Original:  Signal on each subcarrier can be individually recovered by a DSP with an acceptable complexity , including the feed-forward equalizer with no more than 31 taps, a two-tap post filter, and maximum likelihood sequence estimation with one memory length.
Revised:  Signal on each subcarrier can be individually recovered by a digital signal processing , including the feed-forward equalizer with no more than 31 taps, a two-tap post filter, and maximum likelihood sequence estimation with one memory length.
Edit intent: clarity
Original:  Several approaches exist, yet at large scale we face a combinatorial explosion of possible solutions and state-of-the-art methods like RANSAC, Hough transform or Branch \& Bound require a minimum inlier ratio or prior knowledge to remain practical.
Revised:  Several approaches exist, yet at large scale we face a combinatorial explosion of possible solutions and state-of-the-art methods like RANSAC, Hough transform or Branch Bound require a minimum inlier ratio or prior knowledge to remain practical.
Edit intent: fluency
Original: Despite the many advances in the use of weakly-compressible smoothed particle hydrodynamics (SPH) to simulate incompressible fluid flow, it is still challenging to obtain second-order numerical convergence .
Revised: Despite the many advances in the use of weakly-compressible smoothed particle hydrodynamics (SPH) for the simulation of incompressible fluid flow, it is still challenging to obtain second-order numerical convergence .
Edit intent: style
Original: Despite the many advances in the use of weakly-compressible smoothed particle hydrodynamics (SPH) to simulate incompressible fluid flow, it is still challenging to obtain second-order numerical convergence .
Revised: Despite the many advances in the use of weakly-compressible smoothed particle hydrodynamics (SPH) to simulate incompressible fluid flow, it is still challenging to obtain second-order convergence numerically .
Edit intent: clarity
Original:  To alleviate these problems, we propose Spatio-Temporal Representation Factorization module (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.
Revised:  To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.
Edit intent: clarity
Original:  We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results using standard person re-identification evaluation protocols on three benchmarks.
Revised:  We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results using standard person re-ID evaluation protocols on three benchmarks.
Edit intent: clarity