wanyu/IteraTeR_v2 huggingface dataset:
Original:  The experiments carried out are extensive, as we use two Alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer),  sequence lengths from a few thousand to millions  and different values of k  . As a result, and using power, we provide a characterization of those AF functions that is novel and informative.
Revised:  The experiments carried out are extensive, as we use two Alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), sequence lengths from a few thousand to millions and different values of k . As a result, we provide a characterization of those AF functions that is novel and informative.
Edit intent: coherence
Original:  In synthesis, and quite remarkably, of the fifteen functions that we have considered, only four stand out  .
Revised:  Remarkably, of the fifteen functions that we have considered, only four stand out .
Edit intent: coherence
Original:  Given d-dimensional data points, we show an efficient algorithm that finds an explainable clustering whose k-means cost is at most k^{1 - 2/d}  poly(d\log k) times the minimum cost achievable by a clustering without the explainability constraint, assuming k,d\ge 2. Combining this with an independent work by Makarychev and Shan (ICML '21), we get an improved bound of k^{1 - 2/d}  polylog(k), which we show is optimal for every choice of k,d\ge 2 up to a poly-logarithmic factor in k.
Revised:  Given d-dimensional data points, we show an efficient algorithm that finds an explainable clustering whose k-means cost is at most k^{1 - 2/d} poly(d\log k) times the minimum cost achievable by a clustering without the explainability constraint, assuming k,d\ge 2. Combining this with an independent work by Makarychev and Shan (ICML '21), we get an improved bound of k^{1 - 2/d} \, polylog(k), which we show is optimal for every choice of k,d\ge 2 up to a poly-logarithmic factor in k.
Edit intent: fluency
Original: High levels of sparsity and strong class imbalance are ubiquitous challenges that are often presented simultaneously in real-world time series data.
Revised: High levels of missing data and strong class imbalance are ubiquitous challenges that are often presented simultaneously in real-world time series data.
Edit intent: clarity
Original:  In this work, we propose leveraging a self-supervised learning method, specifically Autoregressive Predictive Coding (APC), to learn relevant hidden representations of time series data in the context of both missing data and class imbalance.
Revised:  In this work, we propose leveraging a self-supervised training method, namely Autoregressive Predictive Coding (APC), to learn relevant hidden representations of time series data in the context of both missing data and class imbalance.
Edit intent: clarity
Original:  We apply APC using either a GRU or GRU-D encoder on two real-world  datasets, and show that applying one-step-ahead prediction with APC improves the classification results in all settings .
Revised:  We apply APC using either a GRU or GRU-D encoder on two real-world datasets, and show that APC improves the classification results in all settings .
Edit intent: clarity
Original:  We apply APC using either a GRU or GRU-D encoder on two real-world  datasets, and show that applying one-step-ahead prediction with APC improves the classification results in all settings .
Revised:  We apply APC using either a GRU or GRU-D encoder on two real-world datasets, and show that applying one-step-ahead prediction with APC improves the classification performance in all settings .
Edit intent: clarity
Original:  Here we develop a novel, nearly-complete sample of public open source project repositories outside of centralized platforms like GitHub.
Revised:  Here we develop a novel, extensive sample of public open source project repositories outside of centralized platforms like GitHub.
Edit intent: clarity
Original:  Analysis suggests that learning to extract discriminative texture information and localization are equally crucial for self-supervised pre-training under fine-grained scenarios.
Revised:  Analysis suggests that learning to extract discriminative texture information and localization are equally crucial for self-supervised pre-training in fine-grained scenarios.
Edit intent: clarity
Original:  Based on our findings, we introduce Cross-view Saliency Alignment (CVSA), a contrastive learning framework that first crops and swaps saliency regions of images as a novel view generation and then guides the model to localize on the foreground object via a cross-view alignment loss.
Revised:  Based on our findings, we introduce cross-view saliency alignment (CVSA), a contrastive learning framework that first crops and swaps saliency regions of images as a novel view generation and then guides the model to localize on the foreground object via a cross-view alignment loss.
Edit intent: fluency
Original:  In this paper, we summarize this task as \textit{Class-Incremental Video Classification (CIVC) and propose a novel framework to address it.
Revised:  In this paper, we summarize this task as \textit{Class-Incremental Video Classification (CIVC) and propose a novel framework to address it.
Edit intent: fluency
Original:  As a subarea of incremental learning tasks, the challenge of \textit{catastrophic forgetting is unavoidable in CIVC.
Revised:  As a subarea of incremental learning tasks, the challenge of \textit{catastrophic forgetting is unavoidable in CIVC.
Edit intent: fluency
Original:  In this work, we focus on some of the assumptions underlying rationality such as mutual consistency and best-response , and consider ways to relax these assumptions using concepts from level-k reasoning and quantal response equilibrium (QRE) respectively.
Revised:  In this work, we focus on some of the assumptions underlying rationality such as mutual consistency and best response , and consider ways to relax these assumptions using concepts from level-k reasoning and quantal response equilibrium (QRE) respectively.
Edit intent: fluency
Original:  Specifically, we provide an information-theoretic two-parameter model that can relax both mutual consistency and best-response , but can recover approximations of level-k, QRE, or typical Nash equilibrium behaviour in the limiting cases.
Revised:  Specifically, we provide an information-theoretic two-parameter model that can relax both mutual consistency and best response , but can recover approximations of level-k, QRE, or typical Nash equilibrium behaviour in the limiting cases.
Edit intent: fluency
Original:  The proposed approach is based on a recursive form of the variational free energy principle, representing self-referential games as (pseudo) sequential decisions.
Revised:  The proposed Quantal Hierarchy model is based on a recursive form of the variational free energy principle, representing self-referential games as (pseudo) sequential decisions.
Edit intent: clarity
Original:  The new value update is very similar to the fast iterative method in that we selectively update the points in the active list chosen by a convergence measure  .
Revised:  The new value update is very similar to the fast iterative method in that we selectively update the points , chosen by a convergence measure .
Edit intent: clarity
Original:  However, in order to save time, the improved algorithm does not do a convergence check of the neighboring points of the narrow band as the fast iterative method  does.
Revised:  However, in order to reduce running time, the improved algorithm does not do a convergence check of the neighboring points of the narrow band as the fast iterative method does.
Edit intent: clarity
Original:  However, in order to save time, the improved algorithm does not do a convergence check of the neighboring points of the narrow band as the fast iterative method  does.
Revised:  However, in order to save time, the improved algorithm does not run a convergence check of the neighboring points of the narrow band as the fast iterative method does.
Edit intent: clarity
Original:  However, in order to save time, the improved algorithm does not do a convergence check of the neighboring points of the narrow band as the fast iterative method  does.
Revised:  However, in order to save time, the improved algorithm does not do a convergence check of the neighboring points of the narrow band as the fast iterative method usually does.
Edit intent: clarity
Original:  In our experiments, we implemented our new algorithm in parallel on a GPU and compared the elapsed time with other current algorithms.
Revised:  In our experiments, we implemente our new algorithm in parallel on a GPU and compared the elapsed time with other current algorithms.
Edit intent: fluency
Original:  In our experiments, we implemented our new algorithm in parallel on a GPU and compared the elapsed time with other current algorithms.
Revised:  In our experiments, we implemented our new algorithm in parallel on a GPU and compare the elapsed time with other current algorithms.
Edit intent: fluency
Original:  The improved fast iterative method runs faster than the other algorithms in most cases in our experiments .
Revised:  The improved fast iterative method runs faster than the other algorithms in most cases through our numercal studies .
Edit intent: clarity
Original: Whole-slide-image cartography is the process of automatically detecting and outlining different tissue types in digitized histological specimen .
Revised: Automatic outlining of different tissue types in digitized histological specimen .
Edit intent: clarity
Original:  This semantic segmentation provides a basis for many follow-up analyses and can potentially guide subsequent medical decisions.
Revised:  This semantic segmentation provides a basis for follow-up analyses and can potentially guide subsequent medical decisions.
Edit intent: clarity
Original:  Due to their large size , whole-slide-images typically have to be divided into smaller patches which are then analyzed individually using machine learning-based approaches .
Revised:  The immense size of whole-slide-images typically have to be divided into smaller patches which are then analyzed individually using machine learning-based approaches .
Edit intent: clarity
Original:  Thereby, local dependencies of image regions get lost and since a whole-slide-image comprises many thousands of such patches this process is inherently slow.
Edit intent: coherence
Original:  We propose to subdivide the image into coherent regions prior to classification by grouping visually similar adjacent image pixels into larger segments, i.e. superpixels.
Revised:  We propose to subdivide the image into coherent regions prior to classification by grouping visually similar adjacent pixels into superpixels.
Edit intent: clarity
Original:  Afterwards, only a random subset of patches per superpixel is classified and patch labels are combined into a single superpixel label.
Revised:  Afterwards, only a random subset of patches per superpixel is classified and patch labels are combined into a superpixel label.
Edit intent: clarity
Original:   The algorithm has been developed and validated on a dataset of 159 hand-annotated whole-slide-images of colon resections and its performance has been compared to a standard patch-based approach .
Revised:  The algorithm has been developed on 159 hand-annotated whole-slide-images of colon resections and its performance has been compared to a standard patch-based approach .
Edit intent: clarity
Original:   The algorithm has been developed and validated on a dataset of 159 hand-annotated whole-slide-images of colon resections and its performance has been compared to a standard patch-based approach .
Revised:  The algorithm has been developed and validated on a dataset of 159 hand-annotated WSIs of colon resections and its performance has been compared to a standard patch-based approach .
Edit intent: clarity
Original:  The algorithm shows an average speed-up of 41\% on the test data and the overall accuracy is increased from 93.8\% to 95.7\%.
Revised:  The algorithm shows an average speed-up of 41\% and an increase in accuracy from 93.8\% to 95.7\%.
Edit intent: clarity
Original:  For hearing research and  evaluation of hearing systems  simulated CAEs using virtual reality techniques have gained interest in the context of ecologically validity.
Revised:  For hearing research and the evaluation of hearing systems simulated CAEs using virtual reality techniques have gained interest in the context of ecologically validity.
Edit intent: fluency
Original:  For hearing research and  evaluation of hearing systems  simulated CAEs using virtual reality techniques have gained interest in the context of ecologically validity.
Revised:  For hearing research and evaluation of hearing systems , simulated CAEs using virtual reality techniques have gained interest in the context of ecologically validity.
Edit intent: fluency
Original:  For hearing research and  evaluation of hearing systems  simulated CAEs using virtual reality techniques have gained interest in the context of ecologically validity.
Revised:  For hearing research and evaluation of hearing systems simulated CAEs using virtual reality techniques have gained interest in the context of ecological validity.
Edit intent: fluency
Original:  The scene consisted of a ring of eight (virtual) loudspeakers which played a target speech stimulus and non-sense speech interferers in several spatial conditions.
Revised:  The scene consisted of a ring of eight (virtual) loudspeakers which played a target speech stimulus and nonsense speech interferers in several spatial conditions.
Edit intent: clarity
Original:  In addition to varying the number of interferers, scene complexity was varied by assessing the psychoacoustic measures in isolated consecutive measurements or simultaneously . Results showed no significant effect of wearing the HMD on the data.
Revised:  In addition to varying the number of interferers, scene complexity was varied by assessing the psychoacoustic measures in isolated consecutive measurements orcsimultaneously . Results showed no significant effect of wearing the HMD on the data.
Edit intent: fluency
Original:  While the extreme cases of homophily or heterophily (J= \pm 1) are easily understood to result in complete polarization or anti-polarization, intermediate values of J lead to interesting behavior of the network.
Revised:  While the extreme cases of homophily or heterophily (J= \pm 1) are easily understood to result in complete polarization or anti-polarization, intermediate values of J lead to interesting features of the network.
Edit intent: clarity
Original:  As a dynamical model, agents can endlessly create and delete links to satisfy a preferred degree, and the network is shaped by homophily , a form of social interaction.
Revised:  As a dynamical model, agents can endlessly create and delete links to satisfy a preferred degree, and the network is shaped by homophily , a form of social interaction.
Edit intent: fluency
Original:  Characterized by the parameter J \in [-1,1], the latter plays a role similar to Ising spins: agents create links to others of the same opinion with probability (1+J)/2, and delete them with probability (1-J)/2. Using Monte Carlo simulations and mean field theory, we focus on the network structure in the steady state.
Revised:  Characterized by the parameter J \in [-1,1], the latter plays a role similar to Ising spins: agents create links to others of the same opinion with probability (1+J)/2, and delete them with probability (1-J)/2. Using Monte Carlo simulations and mean-field theory, we focus on the network structure in the steady state.
Edit intent: fluency
Original:  In addition, we introduce a novel measure of polarization which displays distinct advantages over the commonly used average edge homogeneity.
Revised:  In addition, we introduce an original measure of polarization which displays distinct advantages over the commonly used average edge homogeneity.
Edit intent: clarity
Original:  Following the success of deep learning-based models for various sequence learning tasks, these models increasingly replace classic approaches in object tracking applications for inferring the object motions state .
Revised:  Following the success of deep learning-based models for various sequence learning tasks, these models increasingly replace classic approaches in object tracking applications for inferring the objects' motion states .
Edit intent: fluency
Original:  The model's abilities are demonstrated on synthetic data and real-world data reflecting prototypical object tracking scenarios.
Revised:  The abilities of the approach are demonstrated on synthetic data and real-world data reflecting prototypical object tracking scenarios.
Edit intent: clarity
Original: Multi-modal imaging is a key healthcare technology in the diagnosis and management of disease, but it is often underutilized due to costs associated with multiple separate scans.
Revised: Multi-modal imaging is a key healthcare technology that is often underutilized due to costs associated with multiple separate scans.
Edit intent: coherence
Original:  However, GANs are characteristically based on convolutional neural network (CNN) backbones that perform local processing with compact filters.
Revised:  GANs are characteristically based on convolutional neural network (CNN) backbones that perform local processing with compact filters.
Edit intent: coherence
Original:  This inductive bias , in turn , compromises learning of long-range spatial dependencies.
Revised:  This inductive bias in turn compromises learning of long-range spatial dependencies.
Edit intent: fluency
Original:  Based on an encoder-decoder architecture, ResViT employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine convolutional and transformer modules.
Revised:  ResViT employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine convolutional and transformer modules.
Edit intent: clarity
Original:  Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI  and CT images from MRI.
Revised:  Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI , and CT images from MRI.
Edit intent: fluency
Original:  Our results indicate the superiority of ResViT against competing methods in terms of qualitative observations and quantitative metrics.
Revised:  Our results indicate superiority of ResViT against competing methods in terms of qualitative observations and quantitative metrics.
Edit intent: fluency
Original: Departing from the assumption that AI needs to be transparent to be trusted , we find that users trustfully take ethical advice from a transparent and an opaque AI-powered algorithm alike.
Revised: Departing from the claim that AI needs to be transparent to be trusted , we find that users trustfully take ethical advice from a transparent and an opaque AI-powered algorithm alike.
Edit intent: clarity
Original: Departing from the assumption that AI needs to be transparent to be trusted , we find that users trustfully take ethical advice from a transparent and an opaque AI-powered algorithm alike.
Revised: Departing from the assumption that AI needs to be trustworthy , we find that users trustfully take ethical advice from a transparent and an opaque AI-powered algorithm alike.
Edit intent: coherence
Original: Departing from the assumption that AI needs to be transparent to be trusted , we find that users trustfully take ethical advice from a transparent and an opaque AI-powered algorithm alike.
Revised: Departing from the assumption that AI needs to be transparent to be trusted , we find that ethical advice from a transparent and an opaque AI-powered algorithm alike.
Edit intent: clarity
Original: Departing from the assumption that AI needs to be transparent to be trusted , we find that users trustfully take ethical advice from a transparent and an opaque AI-powered algorithm alike.
Revised: Departing from the assumption that AI needs to be transparent to be trusted , we find that users trustfully take ethical advice from an AI-powered algorithm alike.
Edit intent: clarity
Original:  We conducted online experiments where the participants took the role of decision-makers who received AI-powered advice on how to deal with an ethical dilemma.
Revised:  We conducted online experiments where the subjects took the role of decision-makers who received AI-powered advice on how to deal with an ethical dilemma.
Edit intent: clarity
Original:  We conducted online experiments where the participants took the role of decision-makers who received AI-powered advice on how to deal with an ethical dilemma.
Revised:  We conducted online experiments where the participants took the role of decision-makers who received advice from an algorithm on how to deal with an ethical dilemma.
Edit intent: clarity
Original:  We manipulated  information about the algorithm to study its influence.
Revised:  We manipulated the information about the algorithm to study its influence.
Edit intent: fluency
Original:  We manipulated  information about the algorithm to study its influence.
Revised:  We manipulated information about the algorithm and studied its influence.
Edit intent: coherence
Original:  Our findings suggest that AI is overtrusted rather than distrusted , and that users need digital literacy to benefit from transparency .
Revised:  Our findings suggest that AI is overtrusted rather than distrusted . We suggest digital literacy as a potential remedy to ensure the responsible use of AI .
Edit intent: clarity
Original: Facial expressions are the most  universal forms of body language and automatic facial expression recognition is one of the challenging tasks due to different uncertainties.
Revised: Facial expressions are the most common universal forms of body language and automatic facial expression recognition is one of the challenging tasks due to different uncertainties.
Edit intent: clarity
Original:  However, it has been an active field of research for many years.
Revised:  However, it has been an active field of research . However, it is still a challenging task due to different uncertainties and complications.
Edit intent: coherence
Original:  EmoXNet is an ensemble learning technique for learning convoluted facial representations, whereas EmoXNetLite is a distillation technique  for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels to detect expressions effectively in real-time.
Revised:  EmoXNet is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels to detect expressions effectively in real-time.
Edit intent: coherence
Original:  EmoXNet is an ensemble learning technique for learning convoluted facial representations, whereas EmoXNetLite is a distillation technique  for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels to detect expressions effectively in real-time.
Revised:  EmoXNet is an ensemble learning technique for learning convoluted facial representations, whereas EmoXNetLite is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels to detect expressions effectively in real-time.
Edit intent: clarity