wanyu/IteraTeR_v2 huggingface dataset:
Original: In this work we obtain results related to the approximation of h-dimensional dominant subspaces and low rank approximations of matrices \mathbf A\in\mathbb K^{m\times n} (where \mathbb K=\mathbb R or \mathbb C) in case there is no singular gap  , i.e. if \sigma_h=h+1 (where \sigma_1\geq \ldots\geq \sigma_p\geq 0 denote the singular values of \mathbf A, and p=\min\{m,n\}).
Revised: In this work we obtain results related to the approximation of h-dimensional dominant subspaces and low rank approximations of matrices \mathbf A\in\mathbb K^{m\times n} (where \mathbb K=\mathbb R or \mathbb C) in case there is no singular gap , i.e. if \sigma_h=h+1 (where \sigma_1\geq \ldots\geq \sigma_p\geq 0 denote the singular values of A, and p=\min\{m,n\}).
Edit intent: fluency
Original:  Then, we show that starting with a matrix \mathbf X\in\mathbb K^{n\times r} with r\geq h satisfying a compatibility assumption with some h-dimensional right dominant subspace,  block Krylov methods produce arbitrarily good approximations for both problems mentioned above.
Revised:  Then, we show that starting with a matrix X\in\mathbb K^{n\times r} with r\geq h satisfying a compatibility assumption with some h-dimensional right dominant subspace, block Krylov methods produce arbitrarily good approximations for both problems mentioned above.
Edit intent: clarity
Original:  Then, we show that starting with a matrix \mathbf X\in\mathbb K^{n\times r} with r\geq h satisfying a compatibility assumption with some h-dimensional right dominant subspace,  block Krylov methods produce arbitrarily good approximations for both problems mentioned above.
Revised:  Then, we show that starting with a matrix \mathbf X\in\mathbb K^{n\times r} with r\geq h satisfying a compatibility assumption with some h-dimensional right dominant subspace, we show that block Krylov methods produce arbitrarily good approximations for both problems mentioned above.
Edit intent: fluency
Original:  To better promote the research in CTR field, we will release our code as well as reference implementation of those baseline models in the final version.
Revised:  To better promote the research in CTR field, we released our code as well as reference implementation of those baseline models in the final version.
Edit intent: fluency
Original:  To better promote the research in CTR field, we will release our code as well as reference implementation of those baseline models in the final version.
Revised:  To better promote the research in CTR field, we will release our code as well as reference implementation at: URL
Edit intent: clarity
Original:  We show that DeepFake faces have fewer feature points than real ones, especially in certain facial regions.
Revised:  For the first time, we show that DeepFake faces have fewer feature points than real ones, especially in certain facial regions.
Edit intent: clarity
Original:  Therefore , detecting deepfakes has attracted increasing attentions from both academia and industry.
Revised:  Thus , detecting deepfakes has attracted increasing attentions from both academia and industry.
Edit intent: fluency
Original:  Our approach follows the continuous selective decay framework  ,   which enables dissipating an otherwise conserved quantity while conserving the total energy.
Revised:  Our approach follows the continuous selective decay framework , , which enables dissipating an otherwise conserved quantity while conserving the total energy.
Edit intent: fluency
Original:  However, it can be quite a challenging undertaking to introduce and fully leverage security tools without affecting the speed or frequency of deployments in the DevOps paradigm.
Revised:  However, it can be quite challenging to introduce and fully leverage security tools without affecting the speed or frequency of deployments in the DevOps paradigm.
Edit intent: clarity
Original:  Aims: We aim to empirically investigate the key challenges practitioners face when integrating security tools into a DevOps workflow in order to provide recommendations for overcoming the challenges .
Revised:  Aims: We aim to empirically investigate the key challenges practitioners face when integrating security tools into a DevOps workflow in order to provide recommendations to overcome them .
Edit intent: fluency
Original:  Method: We conducted a study involving 31 systematically selected webinars on integrating security  in DevOps.
Revised:  Method: We conducted a study involving 31 systematically selected webinars on integrating security tools in DevOps.
Edit intent: clarity
Original:  We used a qualitative data analysis method, i.e., thematic analysis, to identify and understand the challenges of integrating security tools in DevOps . Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is developing and adopting new generations of security tools that have started focusing on the needs of DevOps .
Revised:  We used a qualitative data analysis method, i.e., thematic analysis, to identify and understand the challenges of integrating security tools in DevOps . Results: We find that while traditional security tools are unable to cater for the needs of DevOps, the industry is developing and adopting new generations of security tools that have started focusing on the needs of DevOps .
Edit intent: fluency
Original:  We used a qualitative data analysis method, i.e., thematic analysis, to identify and understand the challenges of integrating security tools in DevOps . Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is developing and adopting new generations of security tools that have started focusing on the needs of DevOps .
Revised:  We used a qualitative data analysis method, i.e., thematic analysis, to identify and understand the challenges of integrating security tools in DevOps . Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is moving towards new generations of security tools that have started focusing on the needs of DevOps .
Edit intent: clarity
Original:  We used a qualitative data analysis method, i.e., thematic analysis, to identify and understand the challenges of integrating security tools in DevOps . Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is developing and adopting new generations of security tools that have started focusing on the needs of DevOps .
Revised:  We used a qualitative data analysis method, i.e., thematic analysis, to identify and understand the challenges of integrating security tools in DevOps . Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is developing and adopting new generations of tools that have started focusing on the needs of DevOps .
Edit intent: fluency
Original:  We used a qualitative data analysis method, i.e., thematic analysis, to identify and understand the challenges of integrating security tools in DevOps . Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is developing and adopting new generations of security tools that have started focusing on the needs of DevOps .
Revised:  We used a qualitative data analysis method, i.e., thematic analysis, to identify and understand the challenges of integrating security tools in DevOps . Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is developing and adopting new generations of security tools that have started focusing on these requirements .
Edit intent: clarity
Original:  Conclusion: Whilst the latest security tools are addressing some of the requirements of DevOps, there are many tool-related drawbacks yet to be adequately addressed.
Revised:  Conclusion: While the latest security tools are addressing some of the requirements of DevOps, there are many tool-related drawbacks yet to be adequately addressed.
Edit intent: fluency
Original:  First we find that for low resolution people their keypoint detection performance improved once SR was applied.
Revised:  First we find that for low resolution people their keypoint detection performance would improve once SR was applied.
Edit intent: clarity
Original:  Second, the keypoint detection performance gained is dependent on the persons initial resolution (segmentation area in pixels) in the original image  ;
Revised:  Second, the keypoint detection performance gained is dependent on that persons pixel count in the original image ;
Edit intent: clarity
Original:  As a consequence, the literature has fragmented too, and now the novel approaches proposed by the community are usually specialised to fit only one specific setup.
Revised:  As a consequence, the literature has fragmented too, and now novel approaches proposed by the community are usually specialised to fit only one specific setup.
Edit intent: fluency
Original:  To understand to what extent this specialisation is actually necessary, in this work we present UniTrack, a unified tracking solution to address five different tasks within the same framework.
Revised:  To understand to what extent this specialisation is necessary, in this work we present UniTrack, a unified tracking solution to address five different tasks within the same framework.
Edit intent: clarity
Original:  To understand to what extent this specialisation is actually necessary, in this work we present UniTrack, a unified tracking solution to address five different tasks within the same framework.
Revised:  To understand to what extent this specialisation is actually necessary, in this work we present UniTrack, a solution to address five different tasks within the same framework.
Edit intent: clarity
Original:  UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple "heads" to address individual tasks and that do not require training.
Revised:  UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple ``heads'' that address individual tasks and that do not require training.
Edit intent: fluency
Original:  UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple "heads" to address individual tasks and that do not require training.
Revised:  UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple "heads" to address individual tasks and do not require training.
Edit intent: fluency
Original:  We show how most tracking tasks can be solved within this framework, and that the same appearance model can be  used to obtain performance that is competitive against specialised methods for all the five tasks considered.
Revised:  We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain performance that is competitive against specialised methods for all the five tasks considered.
Edit intent: clarity
Original:  We show how most tracking tasks can be solved within this framework, and that the same appearance model can be  used to obtain performance that is competitive against specialised methods for all the five tasks considered.
Revised:  We show how most tracking tasks can be solved within this framework, and that the same appearance model can be used to obtain results that are competitive against specialised methods for all the five tasks considered.
Edit intent: clarity
Original:  We show how most tracking tasks can be solved within this framework, and that the same appearance model can be  used to obtain performance that is competitive against specialised methods for all the five tasks considered.
Revised:  We show how most tracking tasks can be solved within this framework, and that the same appearance model can be used to obtain performance that is competitive against specialised methods for most of the tasks considered.
Edit intent: clarity
Original:  The framework also allows us to analyse appearance models obtained with the most recent self-supervised methods, thus significantly extending their evaluation and comparison to a larger variety of important problems.
Revised:  The framework also allows us to analyse appearance models obtained with the most recent self-supervised methods, thus extending their evaluation and comparison to a larger variety of important problems.
Edit intent: clarity
Original: Recent studies show that hierarchical Vision Transformer with  interleaved non-overlapped intra window self-attention \& shifted window self-attention is able to achieve state-of-the-art performance in various visual recognition tasks and challenges CNN's dense sliding window paradigm .
Revised: Recent studies indicate that hierarchical Vision Transformer with interleaved non-overlapped intra window self-attention \& shifted window self-attention is able to achieve state-of-the-art performance in various visual recognition tasks and challenges CNN's dense sliding window paradigm .
Edit intent: clarity
Original: Recent studies show that hierarchical Vision Transformer with  interleaved non-overlapped intra window self-attention \& shifted window self-attention is able to achieve state-of-the-art performance in various visual recognition tasks and challenges CNN's dense sliding window paradigm .
Revised: Recent studies show that hierarchical Vision Transformer with interleaved non-overlapped intra window self-attention \& shifted-window operation is able to achieve state-of-the-art performance in various visual recognition tasks and challenges CNN's dense sliding window paradigm .
Edit intent: clarity
Original: Recent studies show that hierarchical Vision Transformer with  interleaved non-overlapped intra window self-attention \& shifted window self-attention is able to achieve state-of-the-art performance in various visual recognition tasks and challenges CNN's dense sliding window paradigm .
Revised: Recent studies show that hierarchical Vision Transformer with interleaved non-overlapped intra window self-attention \& shifted window self-attention is able to achieve state-of-the-art performance in various visual recognition tasks , and challenges the ubiquitous convolutional neural networks (CNNs) using densely slid kernels .
Edit intent: clarity
Original:  Most follow-up works try to replace shifted window operation with other kinds of cross window communication while treating self-attention as the de-facto standard for intra window information aggregation.
Revised:  Most follow-up works attempt to replace the shifted-window operation with other kinds of cross window communication while treating self-attention as the de-facto standard for intra window information aggregation.
Edit intent: fluency
Original:  Most follow-up works try to replace shifted window operation with other kinds of cross window communication while treating self-attention as the de-facto standard for intra window information aggregation.
Revised:  Most follow-up works try to replace shifted window operation with other kinds of cross-window communication paradigms, while treating self-attention as the de-facto standard for intra window information aggregation.
Edit intent: clarity
Original:  Most follow-up works try to replace shifted window operation with other kinds of cross window communication while treating self-attention as the de-facto standard for intra window information aggregation.
Revised:  Most follow-up works try to replace shifted window operation with other kinds of cross window communication while treating self-attention as the de-facto standard for window-based information aggregation.
Edit intent: clarity
Original:  In this short preprint , we question whether self-attention is the only choice for hierarchical Vision Transformer to attain strong performance, and what makes for hierarchical Vision Transformer?
Revised:  In this manuscript , we question whether self-attention is the only choice for hierarchical Vision Transformer to attain strong performance, and what makes for hierarchical Vision Transformer?
Edit intent: clarity
Original:  We replace self-attention layers in Swin Transformer and Shuffle Transformer with simple linear mapping and keep other components unchanged.
Revised:  We replace self-attention layers with embarrassingly simple linear mapping and keep other components unchanged.
Edit intent: clarity
Original: 4M parameters and 4.2G FLOPs achieves 80.5%DIFDELCMD < \\%%% %DIF < Top-1 accuracy, compared to 81.3%DIFDELCMD < \\%%% %DIF < for Swin Transformer with 28.
Revised: 4M parameters and 4.2G FLOPs achieves 80.5%DIFDELCMD < \\%%% %DIF < Top-1 %DIFDELCMD < \\%%% %DIF < for Swin Transformer with 28.
Edit intent: coherence
Original:  We also experiment with other alternatives to self-attention for context aggregation inside each non-overlapped window  , which all give similar competitive results under the same architecture .
Revised:  We also experiment with other alternatives to self-attention for content aggregation inside each non-overlapped window , which all give similar competitive results under the same architecture .
Edit intent: clarity
Original:  We also experiment with other alternatives to self-attention for context aggregation inside each non-overlapped window  , which all give similar competitive results under the same architecture .
Revised:  We also experiment with other alternatives to self-attention for context aggregation inside each non-overlapped window , which all give similar competitive results .
Edit intent: clarity
Original:  Our study reveals that the \textbf{macro architecture} of Swin model families (i.e., interleaved intra window \& cross window communications), other than specific aggregation layers or specific means of cross window communication, may be more responsible for its strong performance and is the real challenger to  CNN's dense sliding window paradigm  .
Revised:  Our study reveals that the \textbf{macro architecture} of Swin model families , other than specific aggregation layers or specific means of cross window communication, may be more responsible for its strong performance and is the real challenger to CNN's dense sliding window paradigm .
Edit intent: clarity
Original:  Our study reveals that the \textbf{macro architecture} of Swin model families (i.e., interleaved intra window \& cross window communications), other than specific aggregation layers or specific means of cross window communication, may be more responsible for its strong performance and is the real challenger to  CNN's dense sliding window paradigm  .
Revised:  Our study reveals that the \textbf{macro architecture} of Swin model families (i.e., interleaved intra window \& cross window communications), other than specific aggregation layers or specific means of cross-window communication, may be more responsible for its strong performance and is the real challenger to CNN's dense sliding window paradigm .
Edit intent: fluency
Original:  Our study reveals that the \textbf{macro architecture} of Swin model families (i.e., interleaved intra window \& cross window communications), other than specific aggregation layers or specific means of cross window communication, may be more responsible for its strong performance and is the real challenger to  CNN's dense sliding window paradigm  .
Revised:  Our study reveals that the \textbf{macro architecture} of Swin model families (i.e., interleaved intra window \& cross window communications), other than specific aggregation layers or specific means of cross window communication, may be more responsible for its strong performance and is the real challenger to the ubiquitous CNN's dense sliding window paradigm .
Edit intent: clarity
Original:  For instance, Transformer-LS achieves 0.97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3\times as long sequences compared to its full-attention version on the same hardware.
Revised:  For instance, Transformer-LS achieves 0.97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3x as long sequences compared to its full-attention version on the same hardware.
Edit intent: fluency
Original:  On ImageNet, it can obtain the state-of-the-art results ~ (e.g.,  Top-1 accuracy 84.1\% trained on 224%DIFDELCMD < \times224 %DIFDELCMD < %%% ImageNet-1K only ), while being more scalable on high-resolution images.
Revised:  On ImageNet, it can obtain the state-of-the-art results (e.g., Top-1 accuracy 84.1\% trained on 224%DIFDELCMD < \times224 %DIFDELCMD < %%% ImageNet-1K only ), while being more scalable on high-resolution images.
Edit intent: fluency
Original:  On ImageNet, it can obtain the state-of-the-art results ~ (e.g.,  Top-1 accuracy 84.1\% trained on 224%DIFDELCMD < \times224 %DIFDELCMD < %%% ImageNet-1K only ), while being more scalable on high-resolution images.
Revised:  On ImageNet, it can obtain the state-of-the-art results ~ (e.g., Top-1 accuracy 84.1\% %DIFDELCMD < \times224 %DIFDELCMD < %%% ImageNet-1K only ), while being more scalable on high-resolution images.
Edit intent: clarity
Original:  On ImageNet, it can obtain the state-of-the-art results ~ (e.g.,  Top-1 accuracy 84.1\% trained on 224%DIFDELCMD < \times224 %DIFDELCMD < %%% ImageNet-1K only ), while being more scalable on high-resolution images.
Revised:  On ImageNet, it can obtain the state-of-the-art results ~ (e.g., Top-1 accuracy 84.1\% trained on 224%DIFDELCMD < \times224 %DIFDELCMD < %%% ), while being more scalable on high-resolution images.
Edit intent: coherence
Original: Context: Software crowdsourcing platforms typically employ extrinsic rewards such as rating or ranking systems to motivate workers.
Revised:  Software crowdsourcing platforms typically employ extrinsic rewards such as rating or ranking systems to motivate workers.
Edit intent: coherence
Original: Context: Software crowdsourcing platforms typically employ extrinsic rewards such as rating or ranking systems to motivate workers.
Revised: Context: Software crowdsourcing platforms employ extrinsic rewards such as rating or ranking systems to motivate workers.
Edit intent: clarity
Original:  Such rating systems are noisy and only provide limited knowledge about worker' s preferences and performance.
Revised:  Such rating systems are noisy and provide limited knowledge about worker' s preferences and performance.
Edit intent: clarity
Original:  Such rating systems are noisy and only provide limited knowledge about worker' s preferences and performance.
Revised:  Such rating systems are noisy and only provide limited knowledge about workers' preferences and performance.
Edit intent: fluency
Original:  Method: First, we create the bipartite network of active workers based on common registration for tasks .
Revised:  Method: First, we create the bipartite network of active workers based on common task registrations .
Edit intent: clarity
Original:  Finally, we conduct an empirical evaluation to measure and analyze workers' behavior per identified cluster in the platform by workers' ranking .
Revised:  Finally, we conduct an empirical evaluation to measure and analyze workers' behavior per identified community in the platform by workers' ranking .
Edit intent: clarity
Original:  Finally, we conduct an empirical evaluation to measure and analyze workers' behavior per identified cluster in the platform by workers' ranking .
Revised:  Finally, we conduct an empirical evaluation to measure and analyze workers' behavior per identified cluster in the platform by workers' rating .
Edit intent: clarity