wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This represents an improvement when s < \log_2 (d-1), which is satisfied by many classes of RBMs with "few latent variables'' . Furthermore, we give a version of this learning algorithm that recovers a model with small prediction error and whose sample complexity is independent of the minimum potential in the Markov Random Field of the observed variables.
Revised:  This is an improvement when s < \log_2 (d-1), which is satisfied by many classes of RBMs with "few latent variables'' . Furthermore, we give a version of this learning algorithm that recovers a model with small prediction error and whose sample complexity is independent of the minimum potential in the Markov Random Field of the observed variables.
Edit intent: clarity
Original:  This represents an improvement when s < \log_2 (d-1), which is satisfied by many classes of RBMs with "few latent variables'' . Furthermore, we give a version of this learning algorithm that recovers a model with small prediction error and whose sample complexity is independent of the minimum potential in the Markov Random Field of the observed variables.
Revised:  This represents an improvement when s < \log_2 (d-1), which corresponds to RBMs with sparse latent variables . Furthermore, we give a version of this learning algorithm that recovers a model with small prediction error and whose sample complexity is independent of the minimum potential in the Markov Random Field of the observed variables.
Edit intent: clarity
Original: We present a Systems and a Network Medicine view of Crohn's Disease (CD) and Ulcerative Colitis (UC). We identify distinctly different genes and molecular processes involved in each disease , which are used to find potential drug targets, as well as novel candidate drugs. CD and UC , both under the umbrella of Inflammatory Bowel Diseases (IBD), involve many distinct molecular processes.
Revised: We present a Systems and a Network Medicine view of Crohn's disease and ulcerative colitis , both under the umbrella of Inflammatory Bowel Diseases (IBD), involve many distinct molecular processes.
Edit intent: coherence
Original:  We have created a manually curated one-stop-shop informatics database (IBD-Omics) of genes , SNPs, drugs, biomarkers and microarray studies involved in CD and UC. This curated database was created by mining published literature , published studies such as the Genome Wide Association Studies, GWAS . The identified genes were then subject to informatics analytics software such as IPA, to reveal their molecular processes .A network pharmacology approach has identified newer targets and drugs that can be repurposed.
Revised:  Methods: The initial set of genes was obtained by mining published literature , published studies such as the Genome Wide Association Studies, GWAS . The identified genes were then subject to informatics analytics software such as IPA, to reveal their molecular processes .A network pharmacology approach has identified newer targets and drugs that can be repurposed.
Edit intent: clarity
Original:  This curated database was created by mining published literature , published studies such as the Genome Wide Association Studies, GWAS . The identified genes were then subject to informatics analytics software such as IPA, to reveal their molecular processes .A network pharmacology approach has identified newer targets and drugs that can be repurposed.
Revised:  This curated database was created by mining published literature and several curated databases . The identified genes were then subject to informatics analytics software such as IPA, to reveal their molecular processes .A network pharmacology approach has identified newer targets and drugs that can be repurposed.
Edit intent: clarity
Original:  This curated database was created by mining published literature , published studies such as the Genome Wide Association Studies, GWAS . The identified genes were then subject to informatics analytics software such as IPA, to reveal their molecular processes .A network pharmacology approach has identified newer targets and drugs that can be repurposed.
Revised:  This curated database was created by mining published literature , published studies such as the Genome Wide Association Studies, GWAS . The identified genes were then subject to Systems and Network analysis to reveal their molecular processes .A network pharmacology approach has identified newer targets and drugs that can be repurposed.
Edit intent: meaning-changed
Original:  This curated database was created by mining published literature , published studies such as the Genome Wide Association Studies, GWAS . The identified genes were then subject to informatics analytics software such as IPA, to reveal their molecular processes .A network pharmacology approach has identified newer targets and drugs that can be repurposed.
Revised:  This curated database was created by mining published literature , published studies such as the Genome Wide Association Studies, GWAS . The identified genes were then subject to informatics analytics software such as IPA, to reveal their molecular processes and shed some light on their pathogenesis. Such methodologies have identified newer targets and drugs that can be repurposed.
Edit intent: clarity
Original:  We found that CD involved the Th1 pathway, while UC involved the Th2 pathway.Further analysis on gene networks demonstrated that dyslipidemia, epigenetic modulations and dysbiosis play a key role in CD, while inflammation, angiogenesis and potential carcinogenesis processes are involved in UC. Further, this dysbiosis and dyslipidemia in CD is associated with microbiome population change, suggestive of altered levels of micronutrients. Finally, using extended networks based on metabolomics and protein-protein interactions, we were able to suggest a role for synergistic approach in guiding treatment for CD and UC .
Revised:  Results: We use a Systems and Network Medicine approach to understand the mechanism of actions of genes involved in IBD. From an initial set of genes mined from literature and curated databases, we used the Multi-Steiner Tree algorithm implemented within the CoVex systems medicine platform to expand each disease module by incorporating candidate genes with significant connections to the disease-related seed genes. Such expanded disease modules will identify a larger set of potential targets and drugs. We used the Closeness Centrality algorithm implemented within CoVex to search for newer targets and repurposable drugs. Through a network medicine approach, we provide a mechanistic view of the diseases and point to newer drugs and targets. Conclusion: We demonstrate that the Systems and Network Medicine approach is a powerful way to understand diseases and understand their mechanisms of action .
Edit intent: meaning-changed
Original:  In this paper, we propose a novel cause-effect look at the language bias , where the bias is formulated as the direct  effect of question on answer from the view of causal inference.
Revised:  In this paper, we investigate how to capture and mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framework, which enables us to capture the language bias , where the bias is formulated as the direct effect of question on answer from the view of causal inference.
Edit intent: meaning-changed
Original:  In this paper, we propose a novel cause-effect look at the language bias , where the bias is formulated as the direct  effect of question on answer from the view of causal inference.
Revised:  In this paper, we propose a novel cause-effect look at the language bias , where the bias is formulated as the direct causal effect of questions on answers and reduce the language bias by subtracting the direct language effect from the total causal effect of question on answer from the view of causal inference.
Edit intent: meaning-changed
Original:  In this paper, we propose a novel cause-effect look at the language bias , where the bias is formulated as the direct  effect of question on answer from the view of causal inference. The effectcan be captured by counterfactual VQA, where the image had not existed in an imagined scenario. Our proposed cause-effect look 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
Revised:  In this paper, we propose a novel cause-effect look at the language bias , where the bias is formulated as the direct effect . Experiments demonstrate that our proposed counterfactual inference framework 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
Edit intent: coherence
Original:  Our proposed cause-effect look 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
Revised:  Our proposed cause-effect look 1) is general to various VQA backbones and fusion strategies , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
Edit intent: meaning-changed
Original:  Our proposed cause-effect look 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
Revised:  Our proposed cause-effect look 1) is general to any baseline VQA architecture , 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
Edit intent: clarity
Original:  Our proposed cause-effect look 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
Revised:  Our proposed cause-effect look 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset .
Edit intent: meaning-changed
Original:  Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset .
Revised:  Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset without any augmented data. The code is available at URL
Edit intent: meaning-changed
Original:  Lastly , we provided a workable web-based recognition system which interactively handles the cold start problem of new user's addition by transferring representations of old arms to new ones with an extendable contextual bandit.
Revised:  Additionally , we provided a workable web-based recognition system which interactively handles the cold start problem of new user's addition by transferring representations of old arms to new ones with an extendable contextual bandit.
Edit intent: coherence
Original:  We demonstrate its capabilities by providing a new probabilistic complexity analysis for the Plantinga-Vegter algorithm , which covers both random sparse (alas a restricted sparseness structure) polynomials and random Gaussian polynomials.
Revised:  We demonstrate its capabilities in two problems, under very mild probabilistic assumptions. On the one hand, we show that the average run-time of the Plantinga-Vegter algorithm , which covers both random sparse (alas a restricted sparseness structure) polynomials and random Gaussian polynomials.
Edit intent: meaning-changed
Original:  We demonstrate its capabilities by providing a new probabilistic complexity analysis for the Plantinga-Vegter algorithm , which covers both random sparse (alas a restricted sparseness structure) polynomials and random Gaussian polynomials.
Revised:  We demonstrate its capabilities by providing a new probabilistic complexity analysis for the Plantinga-Vegter algorithm is polynomial in the degree for random sparse (alas a restricted sparseness structure) polynomials and random Gaussian polynomials.
Edit intent: meaning-changed
Original:  We present explicit results with structured random polynomials for problems with two or more dimensions. Additionally, we provide some estimates of the separation bound of a univariate polynomial in our current framework .
Revised:  On the other hand, we study the size of the subdivision tree for Descartes' solver and run-time of the solver by Jindal and Sagraloff (arXiv:1704.06979). In both cases, we provide a bound that is polynomial in the size of the input (size of the support plus logarithm of the degree) for not only on the average, but all higher moments .
Edit intent: meaning-changed
Original:  However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated  , 2) the duration extracted from the teacher model is not accurate enough, and the target mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality.
Revised:  However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated and time-consuming , 2) the duration extracted from the teacher model is not accurate enough, and the target mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality.
Edit intent: meaning-changed
Original:  We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end training and even faster inferencethan FastSpeech . Experimental results show that 1) FastSpeech 2 and 2s outperform FastSpeech in voice quality with much simplified training pipeline and reduced training time ;
Revised:  We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of fully end-to-end training and even faster inferencethan FastSpeech . Experimental results show that 1) FastSpeech 2 and 2s outperform FastSpeech in voice quality with much simplified training pipeline and reduced training time ;
Edit intent: fluency
Original:  We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end training and even faster inferencethan FastSpeech . Experimental results show that 1) FastSpeech 2 and 2s outperform FastSpeech in voice quality with much simplified training pipeline and reduced training time ;
Revised:  We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end inference . Experimental results show that 1) FastSpeech 2 and 2s outperform FastSpeech in voice quality with much simplified training pipeline and reduced training time ;
Edit intent: clarity
Original:  We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end training and even faster inferencethan FastSpeech . Experimental results show that 1) FastSpeech 2 and 2s outperform FastSpeech in voice quality with much simplified training pipeline and reduced training time ;
Revised:  We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end training and even faster inferencethan FastSpeech . Experimental results show that 1) FastSpeech 2 achieves a 3x training speed-up over FastSpeech, and FastSpeech 2s outperform FastSpeech in voice quality with much simplified training pipeline and reduced training time ;
Edit intent: meaning-changed
Original:  We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end training and even faster inferencethan FastSpeech . Experimental results show that 1) FastSpeech 2 and 2s outperform FastSpeech in voice quality with much simplified training pipeline and reduced training time ;
Revised:  We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end training and even faster inferencethan FastSpeech . Experimental results show that 1) FastSpeech 2 and 2s enjoys even faster inference speed ;
Edit intent: clarity
Original:  Empirically, we characterize the short-term and long-term risks from a large-scale dynamic network on all S P500 constituents' return volatilities. Consistent with theory, we show that stocks with high sensitivities to dynamic network risk earn lower returns.
Revised:  Empirically, we characterize the short-term and long-term risks from a large-scale dynamic network on all S Stocks with high sensitivities to dynamic network risk earn lower returns.
Edit intent: clarity
Original:  A two-standard deviation increase in long-term (short-term) network risk loadings associate with a 14.73\% (12.96 \%) drop in annualized expected returns.
Revised:  We rationalize our finding with economic theory that allows the stochastic discount factor to load on network risk through the precautionary savings channel. A one-standard deviation increase in long-term (short-term) network risk loadings associate with a 14.73\% (12.96 \%) drop in annualized expected returns.
Edit intent: meaning-changed
Original:  A two-standard deviation increase in long-term (short-term) network risk loadings associate with a 14.73\% (12.96 \%) drop in annualized expected returns.
Revised:  A two-standard deviation increase in long-term (short-term) network risk loadings associate with a 7.66\% (6.71 \%) drop in annualized expected returns.
Edit intent: meaning-changed
Original: We use an elementary argument building on group actions to prove that the selection-free steady state genetic algorithm analyzed by Sutton and Witt (GECCO 2019) takes an expected number of \Omega(2^n / \sqrt n) iterations to find any particular target search point , regardless of the population size \mu. This improves over the previous best lower bound of \Omega(\exp(n^{\delta/2})) valid for population sizes \mu = O(n^{1/2 - \delta}), 0 < \delta < 1/2.
Revised: We use an elementary argument building on group actions to prove that the selection-free steady state genetic algorithm analyzed by Sutton and Witt (GECCO 2019) takes an expected number of \Omega(2^n / \sqrt n) iterations to find any particular target search point . This bound is valid for all population sizes \mu. Our result improves over the previous best lower bound of \Omega(\exp(n^{\delta/2})) valid for population sizes \mu = O(n^{1/2 - \delta}), 0 < \delta < 1/2.
Edit intent: clarity
Original:  This improves over the previous best lower bound of \Omega(\exp(n^{\delta/2})) valid for population sizes \mu = O(n^{1/2 - \delta}), 0 < \delta < 1/2.
Revised:  This improves over the previous lower bound of \Omega(\exp(n^{\delta/2})) valid for population sizes \mu = O(n^{1/2 - \delta}), 0 < \delta < 1/2.
Edit intent: clarity
Original:  Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG dataset , which is far fewer than the millions of data for other tasks such as machine translation.
Revised:  Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG ~2017 dataset after preprocessing , which is far fewer than the millions of data for other tasks such as machine translation.
Edit intent: meaning-changed
Original:  We present CycleGT, an unsupervised training framework that can bootstrap from fully non-parallel graph and text datasets, iteratively back translate between the two forms , and use a novel pretraining strategy .
Revised:  We present CycleGT, an unsupervised training method that can bootstrap from fully non-parallel graph and text datasets, iteratively back translate between the two forms , and use a novel pretraining strategy .
Edit intent: clarity
Original:  We present CycleGT, an unsupervised training framework that can bootstrap from fully non-parallel graph and text datasets, iteratively back translate between the two forms , and use a novel pretraining strategy .
Revised:  We present CycleGT, an unsupervised training framework that can bootstrap from fully non-parallel graph and text data, and iteratively back translate between the two forms , and use a novel pretraining strategy .
Edit intent: fluency
Original:  However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Revised:  However, protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Edit intent: clarity
Original:  However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Revised:  However, the information about protected attributes in datasets may be inaccurate due to noise in the data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Edit intent: clarity
Original:  However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Revised:  However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or if the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Edit intent: coherence
Original:  However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Revised:  However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes are imputed either in whole or in part . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Edit intent: clarity