wanyu/IteraTeR_v2 huggingface dataset:
Original:  It strictly decreases with the denominator of the irreducible fraction that equals p/q. Finally , we prove an APX-hardness result for the problem\classAPX with a lower bound on the ratio of 1.000015 .
Revised:  It strictly decreases with the denominator of the irreducible fraction that equals p/q. Finally , we prove an APX-hardness result for the problem\classAPX with a lower bound of 1.000015 .
Edit intent: clarity
Original: Distribution networks (DNs) that contain large amounts of distributed energy resources (DER) may experience peer-to-peer power flows between customers.
Revised: Electricity distribution networks that contain large amounts of distributed energy resources (DER) may experience peer-to-peer power flows between customers.
Edit intent: clarity
Original:  These power flows can create technical challenges and their pricing poses socio-economic questions regarding fairness and equity .
Revised:  These may create both technical and socio-economic questions regarding fairness and equity .
Edit intent: clarity
Original:  These power flows can create technical challenges and their pricing poses socio-economic questions regarding fairness and equity .
Revised:  These power flows can create technical challenges and their pricing poses socio-economic challenges .
Edit intent: clarity
Original:  This paper demonstrates how these challenges can be addressed in unison by deploying Community-scale Energy Storage (CES) and updating DN pricing structures with a tier of local network tariffs.
Revised:  This paper establishes how these challenges can be addressed in unison by deploying Community-scale Energy Storage (CES) and updating DN pricing structures with a tier of local network tariffs.
Edit intent: clarity
Original:  This paper demonstrates how these challenges can be addressed in unison by deploying Community-scale Energy Storage (CES) and updating DN pricing structures with a tier of local network tariffs.
Revised:  This paper demonstrates how these challenges can be addressed through the combined deployment of Community-scale Energy Storage (CES) and updating DN pricing structures with a tier of local network tariffs.
Edit intent: clarity
Original:  This paper demonstrates how these challenges can be addressed in unison by deploying Community-scale Energy Storage (CES) and updating DN pricing structures with a tier of local network tariffs.
Revised:  This paper demonstrates how these challenges can be addressed in unison by deploying Community-scale Energy Storage (CES) and local network tariffs.
Edit intent: clarity
Original:  While both of these interventions have been studied in isolation, we for the first time show how their combination can be symbiotic and resolve the shortcomings that each faces when deployed in isolation - the feasibility of CES is no longer blocked by network tariffs and local network tariffs are no longer a zero-sum trade off between DN operators and customer segments.
Edit intent: coherence
Original: Decomposing a complex time series into trend, seasonality, and remainder components is an important primitive that facilitates time series anomaly detection, change point detection  and forecasting.
Revised: Decomposing a complex time series into trend, seasonality, and remainder components is an important primitive that facilitates time series anomaly detection, change point detection , and forecasting.
Edit intent: fluency
Original:  In this paper, we propose OnlineSTL, a novel online algorithm for time series decomposition which solves the scalability problem and is deployed for real-time metrics monitoring on high resolution, high ingest rate data.
Revised:  In this paper, we propose OnlineSTL, a novel online algorithm for time series decomposition which is highly scalable and is deployed for real-time metrics monitoring on high resolution, high ingest rate data.
Edit intent: clarity
Original:  In this paper, we propose OnlineSTL, a novel online algorithm for time series decomposition which solves the scalability problem and is deployed for real-time metrics monitoring on high resolution, high ingest rate data.
Revised:  In this paper, we propose OnlineSTL, a novel online algorithm for time series decomposition which solves the scalability problem and is deployed for real-time metrics monitoring on high-resolution, high-ingest rate data.
Edit intent: fluency
Original: Collecting large-scale annotated satellite imagery datasets is essential for deep-learning-based global building change surveillance.
Revised: Collecting large-scale annotated satellite imagery datasets is therefore essential for global building change surveillance.
Edit intent: clarity
Original:  In this paper, we introduce S2Looking, a building change detection dataset that contains large-scale side-looking satellite images captured at varying off-nadir angles.
Revised:  In this paper, we introduce S2Looking, a building change detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles.
Edit intent: clarity
Original:  Our S2Looking dataset consists of 5000 registered bitemporal image pairs (size of 1024*1024, 0.5 ~ 0.8 m/pixel) of rural areas throughout the world and more than 65,920 annotated change instances .
Revised:  The dataset consists of 5000 registered bitemporal image pairs (size of 1024*1024, 0.5 ~ 0.8 m/pixel) of rural areas throughout the world and more than 65,920 annotated change instances .
Edit intent: clarity
Original:  Our S2Looking dataset consists of 5000 registered bitemporal image pairs (size of 1024*1024, 0.5 ~ 0.8 m/pixel) of rural areas throughout the world and more than 65,920 annotated change instances .
Revised:  Our S2Looking dataset consists of 5000 bitemporal image pairs (size of 1024*1024, 0.5 ~ 0.8 m/pixel) of rural areas throughout the world and more than 65,920 annotated change instances .
Edit intent: clarity
Original:  Our S2Looking dataset consists of 5000 registered bitemporal image pairs (size of 1024*1024, 0.5 ~ 0.8 m/pixel) of rural areas throughout the world and more than 65,920 annotated change instances .
Revised:  Our S2Looking dataset consists of 5000 registered bitemporal image pairs of rural areas and more than 65,920 annotated change instances .
Edit intent: clarity
Original:  The proposed S2Looking dataset presents three main challenges : 1) large viewing angle changes, 2) large illumination variances  and 3) various complex scene characteristics encountered in rural areas.
Revised:  The proposed S2Looking dataset presents three main challenges : 1) larger viewing angles; 2) large illumination variances and 3) various complex scene characteristics encountered in rural areas.
Edit intent: clarity
Original:  The proposed S2Looking dataset presents three main challenges : 1) large viewing angle changes, 2) large illumination variances  and 3) various complex scene characteristics encountered in rural areas.
Revised:  The proposed S2Looking dataset presents three main challenges : 1) large viewing angle changes, 2) large illumination variances ; and 3) various complex scene characteristics encountered in rural areas.
Edit intent: fluency
Original: Existing long-tailed recognition methods, aiming to train class-balance models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution.
Revised: Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution.
Edit intent: fluency
Original:  However, the practical test class distribution often violates such an assumption (e.g., being long-tailed or even inversely long-tailed), which would lead existing methods to fail in real-world applications.
Revised:  However, practical test class distribution often violates such an assumption (e.g., being long-tailed or even inversely long-tailed), which would lead existing methods to fail in real-world applications.
Edit intent: fluency
Original:  However, the practical test class distribution often violates such an assumption (e.g., being long-tailed or even inversely long-tailed), which would lead existing methods to fail in real-world applications.
Revised:  However, the practical test class distributions often violate this assumption (e.g., being long-tailed or even inversely long-tailed), which would lead existing methods to fail in real-world applications.
Edit intent: coherence
Original:  To address this task, we propose a new method, called Test-time Aggregating Diverse Experts (TADE) , that presents two solution strategies: (1) a novel skill-diverse expert learning strategy that trains diverse experts to excel at handling different test distributions from a single long-tailed training distribution;
Revised:  To handle this task, we propose a new method, called Test-time Aggregating Diverse Experts (TADE) , that presents two solution strategies: (1) a novel skill-diverse expert learning strategy that trains diverse experts to excel at handling different test distributions from a single long-tailed training distribution;
Edit intent: clarity
Original:  To address this task, we propose a new method, called Test-time Aggregating Diverse Experts (TADE) , that presents two solution strategies: (1) a novel skill-diverse expert learning strategy that trains diverse experts to excel at handling different test distributions from a single long-tailed training distribution;
Revised:  To address this task, we propose a new method, called Test-time Aggregating Diverse Experts , that presents two solution strategies: (1) a novel skill-diverse expert learning strategy that trains diverse experts to excel at handling different test distributions from a single long-tailed training distribution;
Edit intent: clarity
Original:  To address this task, we propose a new method, called Test-time Aggregating Diverse Experts (TADE) , that presents two solution strategies: (1) a novel skill-diverse expert learning strategy that trains diverse experts to excel at handling different test distributions from a single long-tailed training distribution;
Revised:  To address this task, we propose a new method, called Test-time Aggregating Diverse Experts (TADE) , that presents two solution strategies: (1) a new skill-diverse expert learning strategy that trains diverse experts to excel at handling different test distributions from a single long-tailed training distribution;
Edit intent: clarity
Original:  To address this task, we propose a new method, called Test-time Aggregating Diverse Experts (TADE) , that presents two solution strategies: (1) a novel skill-diverse expert learning strategy that trains diverse experts to excel at handling different test distributions from a single long-tailed training distribution;
Revised:  To address this task, we propose a new method, called Test-time Aggregating Diverse Experts (TADE) , that presents two solution strategies: (1) a novel skill-diverse expert learning strategy that trains diverse experts to excel at handling different class distributions from a single long-tailed training distribution;
Edit intent: clarity
Original:  Moreover, we theoretically show that our method has  provable ability to simulate unknown test class distributions.
Revised:  We theoretically show that our method has provable ability to simulate unknown test class distributions.
Edit intent: coherence
Original:  Moreover, we theoretically show that our method has  provable ability to simulate unknown test class distributions.
Revised:  Moreover, we theoretically show that our method has a provable ability to simulate unknown test class distributions.
Edit intent: fluency
Original: Objects could often have multiple probable poses in single-shot measurements due to symmetry, occlusion or perceptual failures.
Revised: Objects could often have multiple probable poses when viewed from a single vantage point, due to symmetry, occlusion or perceptual failures.
Edit intent: clarity
Original:  A robust object-level simultaneous localization and mapping (object SLAM) algorithm needs to be aware of the pose ambiguity.
Revised:  A robust object-level simultaneous localization and mapping (object SLAM) algorithm needs to be aware of this pose ambiguity.
Edit intent: fluency
Original:  We propose to maintain and subsequently dis-ambiguate the multiple pose interpretations to gradually recover a globally consistent world representation.
Revised:  We propose to maintain and subsequently disambiguate the multiple pose interpretations to gradually recover a globally consistent world representation.
Edit intent: fluency
Original:  The temporally consistent hypotheses are extracted to guide the optimization solution into the global optimum.
Revised:  The temporally consistent hypotheses are extracted , guiding the optimization into the global optimum.
Edit intent: clarity
Original:  This consensus-informed inference method is implemented on top of the incremental SLAM framework  iSAM2, via landmark variable re-initialization .
Revised:  This consensus-informed inference method is applied online via landmark variable re-initialization within an incremental SLAM framework iSAM2, via landmark variable re-initialization .
Edit intent: clarity
Original:  This consensus-informed inference method is implemented on top of the incremental SLAM framework  iSAM2, via landmark variable re-initialization .
Revised:  This consensus-informed inference method is implemented on top of the incremental SLAM framework , iSAM2, via landmark variable re-initialization .
Edit intent: fluency
Original:  To prevent any type of attacks, state of the art fraud detection systems are now embedding Machine Learning (ML) modules.
Revised:  To prevent any type of attacks, state-of-the-art fraud detection systems are now embedding Machine Learning (ML) modules.
Edit intent: fluency
Original:  We show on CIFAR-10 and STL-10 datasets that the general robustness of our method on unseen occlusion  and Gaussian perturbations improves, while even improving the performance on clean images slightly without performing any data augmentation.
Revised:  We show on CIFAR-10 and STL-10 datasets that the general robustness of our method on unseen occlusion and Gaussian perturbations improves, while even improving the performance on clean images without any data augmentation.
Edit intent: clarity
Original:  Our CANITA achieves the first accelerated rate O\bigg(\Big(1+\sqrt{\frac{\omega^3{n}}\Big)L{\epsilon}} + \omega\big(1{\epsilon}\big)^{1{3}}\bigg), which improves upon the state-of-the-art non-accelerated rate O\left((1+\omega{n})L{\epsilon} + \frac{\omega^2+n{\omega+n}} {\omega+n}} 1{\epsilon}\right)
Revised:  Our CANITA achieves the first accelerated rate O\bigg(\Big(1+\sqrt{\frac{\omega^3{n}}\Big)L{\epsilon}} + \omega\big(1{\epsilon}\big)^{1{3}}\bigg), which improves upon the state-of-the-art non-accelerated rate O\left((1+\omega{n})L{\epsilon} + \frac{\omega^2+n{\omega+n}} \frac{\omega^2+\omega{\omega+n}} 1{\epsilon}\right)
Edit intent: fluency
Original: In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still a challenging task to design a calibration-free system, since there exists a significant variability of EEG signals among different subjects and recording sessions.
Revised: In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since there exists a significant variability of EEG signals among different subjects and recording sessions.
Edit intent: clarity
Original: In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still a challenging task to design a calibration-free system, since there exists a significant variability of EEG signals among different subjects and recording sessions.
Revised: In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still a challenging task to design a calibration-free system, since EEG signals vary significantly among different subjects and recording sessions.
Edit intent: clarity
Original:  As deep learning has received much research attention in recent years, many efforts have been made to use deep learning methods for EEG signal recognition .
Revised:  Many efforts have been made to use deep learning methods for EEG signal recognition .
Edit intent: clarity
Original:  As deep learning has received much research attention in recent years, many efforts have been made to use deep learning methods for EEG signal recognition .
Revised:  As deep learning has received much research attention in recent years, many efforts have been made to use deep learning methods for mental state recognition from EEG signals .
Edit intent: clarity
Original:  However, existing works mostly treat deep learning models as blackbox classifiers, while what have been learned by the models and to which extent they are affected by the noise from EEG data are still underexplored.
Revised:  However, existing work mostly treats deep learning models as blackbox classifiers, while what have been learned by the models and to which extent they are affected by the noise from EEG data are still underexplored.
Edit intent: fluency
Original:  However, existing works mostly treat deep learning models as blackbox classifiers, while what have been learned by the models and to which extent they are affected by the noise from EEG data are still underexplored.
Revised:  However, existing works mostly treat deep learning models as black-box classifiers, while what have been learned by the models and to which extent they are affected by the noise from EEG data are still underexplored.
Edit intent: fluency
Original:  However, existing works mostly treat deep learning models as blackbox classifiers, while what have been learned by the models and to which extent they are affected by the noise from EEG data are still underexplored.
Revised:  However, existing works mostly treat deep learning models as blackbox classifiers, while what have been learned by the models and to which extent they are affected by the noise in EEG data are still underexplored.
Edit intent: fluency
Original:  In this paper, we develop a novel convolutional neural network that can explain its decision by highlighting the local areas of the input sample that contain important information for the classification.
Revised:  In this paper, we develop a novel convolutional neural network that can explain its decision by highlighting the local areas of the input sample that contain important information for classification.
Edit intent: fluency
Original:  The network has a compact structure for ease of interpretation and takes advantage of separable convolutions to process the EEG signals in a spatial-temporal sequence.
Revised:  The network has a compact structure and takes advantage of separable convolutions to process the EEG signals in a spatial-temporal sequence.
Edit intent: clarity
Original:  Results show that the model achieves an average accuracy of 78.35\% on 11 subjects for leave-one-out cross-subject drowsiness recognition, which is higher than the conventional baseline methods of 53.4\%-72.68\% and state-of-art deep learning methods of 63.90\% -65.61 \%.
Revised:  Results show that the model achieves an average accuracy of 78.35\% on 11 subjects for leave-one-out cross-subject drowsiness recognition, which is higher than the conventional baseline methods of 53.4\%-72.68\% and state-of-the-art deep learning methods of 63.90\% -65.61 \%.
Edit intent: fluency
Original:  Our work illustrates a promising direction on using interpretable deep learning models to discover meaning patterns related to different mental states from complex EEG signals.
Revised:  Our work illustrates a promising direction on using interpretable deep learning models to discover meaningful patterns related to different mental states from complex EEG signals.
Edit intent: clarity
Original:  In this paper, we develop a novel convolutional neural network that can explain its decision by highlighting the local areas of the input sample that contain important information for classification.
Revised:  In this paper, we develop a novel convolutional neural network combined with an interpretation technique that allows sample-wise analysis of important features for classification.
Edit intent: clarity
Original:  Visualization results show that the model has learned to recognize biologically explainable features from EEG signals, e.g., Alpha spindles, as strong indicators of drowsiness across different subjects.
Revised:  Visualization results show that the model has learned to recognize biologically meaningful features from EEG signals, e.g., Alpha spindles, as strong indicators of drowsiness across different subjects.
Edit intent: clarity
Original:  Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff especially in handling MAA based lookups. between the routing table compression rate and the chance of Unstructured discovery routing approaches, such as 4%DIFDELCMD < ] [%%% 5 , causing misleading routing.
Revised:  Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff between the routing table compression rate and the chance of Unstructured discovery routing approaches, such as 4%DIFDELCMD < ] [%%% 5 , causing misleading routing.
Edit intent: coherence
Original:  Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff especially in handling MAA based lookups. between the routing table compression rate and the chance of Unstructured discovery routing approaches, such as 4%DIFDELCMD < ] [%%% 5 , causing misleading routing.
Revised:  Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff especially in handling MAA based lookups. between the routing table compression rate and the chance of 4%DIFDELCMD < ] [%%% 5 , causing misleading routing.
Edit intent: clarity
Original:  Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff especially in handling MAA based lookups. between the routing table compression rate and the chance of Unstructured discovery routing approaches, such as 4%DIFDELCMD < ] [%%% 5 , causing misleading routing.
Revised:  Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff especially in handling MAA based lookups. between the routing table compression rate and the chance of Unstructured discovery routing approaches, such as %DIFDELCMD < ] [%%% 5 , causing misleading routing.
Edit intent: fluency
Original:  Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff especially in handling MAA based lookups. between the routing table compression rate and the chance of Unstructured discovery routing approaches, such as 4%DIFDELCMD < ] [%%% 5 , causing misleading routing.
Revised:  Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff especially in handling MAA based lookups. between the routing table compression rate and the chance of Unstructured discovery routing approaches, such as 4%DIFDELCMD < ] [%%% 5 causing misleading routing.
Edit intent: fluency
Original:  In active touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy.
Revised:  Inactive touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy.
Edit intent: fluency
Original:  To address this issue, we present the Query-based Video Highlights ( QVHighlights ) dataset.
Revised:  To address this issue, we present the Query-based Video Highlights ( QVHIGHLIGHTS ) dataset.
Edit intent: clarity
Original:  With weakly supervised pretraining using ASR captions, Moment-DETR substantially outperforms previous methods.
Revised:  With weakly supervised pretraining using ASR captions, MomentDETR substantially outperforms previous methods.
Edit intent: fluency