wanyu/IteraTeR_full_sent huggingface dataset:
Original: The recent COVID-19 pandemic has highlighted the need for rapid therapeutic development for infectious diseases. To accelerate this process, we present a deep learning based generative modeling framework, CogMol, to design drug candidates specific to a given target protein sequence with high off-target selectivity.
Revised: The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and off-target selectivity.
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Original:  We augment this generative framework with an in silico screening process that accounts for toxicity, to lower the failure rate of the generated drug candidates in later stages of the drug development pipeline. We apply this framework to three relevant proteins of the SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain (RBD) of the S protein.
Revised:  CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme that uses guidance from attribute predictors trained on latent features. To generate novel and optimal drug-like molecules for unseen viral targets, CogMol leverages a protein-molecule binding affinity predictor that is trained using SMILES VAE embeddings and protein sequence embeddings learned unsupervised from a large corpus. CogMol framework is applied to three SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain (RBD) of the S protein.
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Original:  We apply this framework to three relevant proteins of the SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain (RBD) of the S protein.
Revised:  We apply this framework to three relevant proteins of the SARS-CoV-2 target proteins: main protease, and the receptor-binding domain (RBD) of the S protein.
Edit intent: meaning-changed
Original:  We apply this framework to three relevant proteins of the SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain (RBD) of the S protein.
Revised:  We apply this framework to three relevant proteins of the SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, receptor-binding domain (RBD) of the S protein.
Edit intent: fluency
Original:  We apply this framework to three relevant proteins of the SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain (RBD) of the S protein. Docking to the target proteins demonstrate the potential of these generated molecules as ligands. Structural similarity analyses further imply novelty of the generated molecules with respect to the training dataset as well as possible biological association of a number of generated molecules that might be of relevance to COVID-19 therapeutic design. While the validation of these molecules is underway, we release ~ 3000 novel COVID-19 drug candidates generated using our framework . URL : URL
Revised:  We apply this framework to three relevant proteins of the SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain of the spike protein, and non-structural protein 9 replicase. The generated candidates are novel at both molecular and chemical scaffold levels when compared to the training data. CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations. Docking reveals favorable binding of generated molecules to the target protein structure, where 87-95 \% of high affinity molecules showed docking free energy < -6 kcal/mol. When compared to approved drugs, the majority of designed compounds show low parent molecule and metabolite toxicity and high synthetic feasibility. In summary, CogMol handles multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity, and does not need target-dependent fine-tuning of the framework or target structure information.
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Original:  We also use more recent data to compute prediction errors given our models, and find that in many cities, exponential growth models trained on data before March 26 are poor predictor for case numbers in this more recent period (March 26-30), likely indicating a reduction in the number of new cases facilitated through social distancing.
Revised:  We also use more recent data to compute prediction errors given our models, and find that in many cities, exponential growth models trained on data before March 26 are poor predictors for case numbers in this more recent period (March 26-30), likely indicating a reduction in the number of new cases facilitated through social distancing.
Edit intent: fluency
Original:  We perform a rigorous runtime analysis of a basic non-elitist evolutionary algorithm (EA), the (\mu,\lambda) EA, on the most basic benchmark function with a local optimum, the jump function.
Revised:  To improve our understanding of this mechanism, we perform a rigorous runtime analysis of a basic non-elitist evolutionary algorithm (EA), the (\mu,\lambda) EA, on the most basic benchmark function with a local optimum, the jump function.
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Original:  We prove that for all reasonable values of the parameters and the problem, the expected runtime of the (\mu,\lambda)  EA is, apart from lower order terms, at least as large as the expected runtime of its elitist counterpart, the (\mu+\lambda)~EA (for which we conduct the first runtime analysis  to allow this comparison).
Revised:  We prove that for all reasonable values of the parameters and the problem, the expected runtime of the (\mu,\lambda) ~ EA is, apart from lower order terms, at least as large as the expected runtime of its elitist counterpart, the (\mu+\lambda)~EA (for which we conduct the first runtime analysis to allow this comparison).
Edit intent: fluency
Original:  We prove that for all reasonable values of the parameters and the problem, the expected runtime of the (\mu,\lambda)  EA is, apart from lower order terms, at least as large as the expected runtime of its elitist counterpart, the (\mu+\lambda)~EA (for which we conduct the first runtime analysis  to allow this comparison).
Revised:  We prove that for all reasonable values of the parameters and the problem, the expected runtime of the (\mu,\lambda) EA is, apart from lower order terms, at least as large as the expected runtime of its elitist counterpart, the (\mu+\lambda)~EA (for which we conduct the first runtime analysis on jump functions to allow this comparison).
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Original:  An agile, scalable and cost-effective testing, deployable at a global scale, can act as a game changerin this war. To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI) -based test for COVID-19 preliminary diagnosis.
Revised:  A scalable screening tool would be a game changer. Building on the prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI) -based test for COVID-19 preliminary diagnosis.
Edit intent: clarity
Original:  To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI) -based test for COVID-19 preliminary diagnosis.
Revised:  To address this challenge, building on the promising results of our prior work on cough-based diagnosis of respiratory diseases, we develop an Artificial Intelligence (AI) -based test for COVID-19 preliminary diagnosis.
Edit intent: clarity
Original:  To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI) -based test for COVID-19 preliminary diagnosis.
Revised:  To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI) -based test for COVID-19 preliminary diagnosis.
Edit intent: meaning-changed
Original:  To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI) -based test for COVID-19 preliminary diagnosis.
Revised:  To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI) -powered screening solution for COVID-19 preliminary diagnosis.
Edit intent: clarity
Original:  To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI) -based test for COVID-19 preliminary diagnosis. The test is deployable at scale through a mobile appnamed AI4COVID-19. The AI4COVID-19 apprequires 2-second cough recordings of the subject. By analyzing the cough samples through an AI engine running in the cloud, the app returns a preliminary diagnosis within a minute.
Revised:  To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI) -based test for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-19 records and sends three 3-second cough sounds to an AI engine running in the cloud, the app returns a preliminary diagnosis within a minute.
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Original:  By analyzing the cough samples through an AI engine running in the cloud, the app returns a preliminary diagnosis within a minute. Unfortunately, cough is common symptom of over two dozen non-COVID-19 related medical conditions.
Revised:  By analyzing the cough samples through an AI engine running in the cloud, and returns a result within two minutes. Methods: Cough is a symptom of over two dozen non-COVID-19 related medical conditions.
Edit intent: clarity
Original:  We solve this problem by developing a novel multi-pronged mediator centered risk-averse AI architecture that minimizes misdiagnosis.
Revised:  We solve this problem by investigating the distinctness of pathomorphological alterations in the respiratory system induced by COVID-19 infection when compared to other respiratory infections. To overcome the COVID-19 cough training data shortage we exploit transfer learning. To reduce the misdiagnosis risk stemming from the complex dimensionality of the problem, we leverage a multi-pronged mediator centered risk-averse AI architecture that minimizes misdiagnosis.
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Original:  We solve this problem by developing a novel multi-pronged mediator centered risk-averse AI architecture that minimizes misdiagnosis. At the time of writing, our AI engine can distinguish between COVID-19 patient coughs and several types of non-COVID-19 coughs with over 90\% accuracy.
Revised:  We solve this problem by developing a novel multi-pronged mediator centered risk-averse AI architecture . Results: Results show AI4COVID-19 can distinguish among COVID-19 patient coughs and several types of non-COVID-19 coughs with over 90\% accuracy.
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Original:  At the time of writing, our AI engine can distinguish between COVID-19 patient coughs and several types of non-COVID-19 coughs with over 90\% accuracy. AI4COVID-19's performance is likely to improve as more and better data becomes available. This paper presents a proof of concept to encourage controlled clinical trials and serves as a call for labeled cough data  . AI4COVID-19 is not designed to compete with clinical testing .
Revised:  At the time of writing, our AI engine can distinguish between COVID-19 patient coughs and several types of non-COVID-19 coughs . The accuracy is promising enough to encourage a large-scale collection of labeled cough data . AI4COVID-19 is not designed to compete with clinical testing .
Edit intent: clarity
Original:  This paper presents a proof of concept to encourage controlled clinical trials and serves as a call for labeled cough data  . AI4COVID-19 is not designed to compete with clinical testing .
Revised:  This paper presents a proof of concept to encourage controlled clinical trials and serves as a call for labeled cough data to gauge the generalization capability of AI4COVID-19 . AI4COVID-19 is not designed to compete with clinical testing .
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Original:  Instead, it offers a complementing tele-testing tool deployable anytime, anywhere, by anyone , so clinical-testing and treatment can be channeled to those who need it the most, thereby saving more lives.
Revised:  Instead, it offers a complementing tele-testing tool deployable anytime, anywhere, by anyone . It can also be a clinical decision assistance tool used to channel clinical-testing and treatment can be channeled to those who need it the most, thereby saving more lives.
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Original:  We provide comprehensive experimental results, demonstrating (i) the scalability of our approach, (ii) advantages of higher-order clustering on synthetic examples, and (iii) the effectiveness of our techniques on a variety of real world data sets  .
Revised:  We provide comprehensive experimental results, demonstrating (i) the scalability of our approach, (ii) advantages of higher-order clustering on synthetic examples, and (iii) the effectiveness of our techniques on a variety of real world data sets ; and compare against several techniques from the literature .
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Original:   We also demonstrate `perfect' stability results in idealized settings and discuss mechanisms which could bring realistic settings closer to the idealized stable settings.
Revised:  These deleveraging spirals, which resemble short squeezes, lead to faster collateral drawdown (and potential shortfalls) and are accompanied by higher price variance, as experienced on Black Thursday. We also demonstrate `perfect' stability results in idealized settings and discuss mechanisms which could bring realistic settings closer to the idealized stable settings.
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Original: This research develops a Machine Learning approach able to predict labor shortagesfor occupations .
Revised: Skill shortages are a drain on society. They hamper economic opportunities for individuals, slow growth for firms, and impede labor productivity in aggregate. Therefore, the ability to understand and predict skill shortages in advance is critical for policy-makers and educators to help alleviate their adverse effects. This research implements a high-performing Machine Learning approach able to predict labor shortagesfor occupations .
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Original: This research develops a Machine Learning approach able to predict labor shortagesfor occupations . We compile a unique dataset that incorporates both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018.
Revised: This research develops a Machine Learning approach to predict occupational skill shortages. In addition, we demonstrate methods to analyze the underlying skill demands of occupations in shortage and the most important features for predicting skill shortages. For this work, we compile a unique dataset that incorporates both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018.
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Original: A neutral model for the evolution of abundances in a vegetal metacommunity is introduced.
Revised: This article partakes of the PEGASE project the goal of which is a better understanding of the mechanisms explaining the behaviour of species living in a network of forest patches linked by ecological corridors (hedges for instance). Actually we plan to study the effect of the fragmentation of the habitat on biodiversity. A simple neutral model for the evolution of abundances in a vegetal metacommunity is introduced.
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Original:  The hydrodynamic limit  is proved to be the solution of a partial differential equation with a deterministic part coming from the migration process and a diffusion part due to the Wright-Fisher process.
Revised:  The hydrodynamic limit of this split-step method is proved to be the solution of a partial differential equation with a deterministic part coming from the migration process and a diffusion part due to the Wright-Fisher process.
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Original:  Two immersive displays were tested , a curved screen (CS) and a head-mounted display (HMD) , combined with three visual conditions (audio-only, virtual characters and video recordings) .
Revised:  Two immersive displays were tested and compared: a curved screen (CS) and a head-mounted display (HMD) , combined with three visual conditions (audio-only, virtual characters and video recordings) .
Edit intent: coherence
Original:  Two immersive displays were tested , a curved screen (CS) and a head-mounted display (HMD) , combined with three visual conditions (audio-only, virtual characters and video recordings) . Three groups of participants were tested: seventeen young normal-hearing, eleven older normal-hearing and ten older hearing-impaired  with hearing aids.
Revised:  Two immersive displays were tested , a curved screen (CS) and a head-mounted display (HMD) , combined with three visual conditions (audio-only, virtual characters and video recordings) , three groups of participants were tested: seventeen young normal-hearing, eleven older normal-hearing and ten older hearing-impaired with hearing aids.
Edit intent: fluency
Original:  How then do we isolate theseinvisible pre-symptomatic spreaders? Here we propose an alternating quarantine strategy, in which at every instance, half of the population remains under lock-down while the other half continues to be active, maintaining a routine of weekly succession between activity and quarantine.
Revised:  How then do we isolate theseinvisible As an alternative, we propose here an alternating quarantine strategy, in which at every instance, half of the population remains under lock-down while the other half continues to be active, maintaining a routine of weekly succession between activity and quarantine.
Edit intent: clarity
Original:  Under this regime , if an individual was exposed during their active week, by the time they complete their quarantine they will, in most cases, begin to exhibit symptoms. Hence this strategy isolates the majority of pre-symptomatic individuals during their infectious phase, leading to a rapid decline in the viral spread - all whilesustaining a continuously active economy at 50\\%DIF < capacity.abstract %DIF > capacity.
Revised:  This regime affords a dual partition:\ half of the population interacts for only half of the time, resulting in a dramatic reduction in transmission, comparable to that achieved by a population-wide lock-down. All the while, it enables socioeconomic continuity at 50\\%DIF < capacity.abstract %DIF > capacity.
Edit intent: clarity
Original:  Although the model is relatively simple, the comparison with the public data show that the analytical solution of the model (with the correctly-specified parameters) leads to the results, which are in good agreement with the measured data  .
Revised:  Although the model is relatively simple, the comparison with the public data shows that an exact solution solution of the model (with the correctly-specified parameters) leads to the results, which are in good agreement with the measured data .
Edit intent: clarity
Original:  Although the model is relatively simple, the comparison with the public data show that the analytical solution of the model (with the correctly-specified parameters) leads to the results, which are in good agreement with the measured data  .
Revised:  Although the model is relatively simple, the comparison with the public data show that the analytical solution of the model (with the correctly-specified parameters) leads to the results, which are in good agreement with the measured data . Prediction of the total number of the COVID-19 cases is discussed and an example is presented using the measured data in Austria .
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Original: Lcp-values, lcp-intervals, and maximal repeatsare powerful tools in various string processing tasks and have a wide variety of applications . Although many researchers have focused on developing enumeration algorithms for them, those algorithms are inefficient in that the space usage is proportional to the length of the input string.
Revised: Lcp-values, lcp-intervals, and maximal repeatsare powerful tools in various string processing tasks and have a wide variety of applications in various areas such as string processing and computational biology. Although several enumeration algorithms for them, those algorithms are inefficient in that the space usage is proportional to the length of the input string.
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Original:  Recently, the run-length-encoded Burrows-Wheeler transform (RLBWT) has attracted increased attention in string processing, and various algorithms on the RLBWT have been developed.
Revised:  Recently, the run-length encoded Burrows-Wheeler transform (RLBWT) has attracted increased attention in string processing, and various algorithms on the RLBWT have been developed.
Edit intent: fluency
Original:  Recently, the run-length-encoded Burrows-Wheeler transform (RLBWT) has attracted increased attention in string processing, and various algorithms on the RLBWT have been developed.
Revised:  Recently, the run-length-encoded Burrows-Wheeler transform (RLBWT) has attracted increased attention in string processing, and various algorithms for the RLBWT have been developed.
Edit intent: clarity
Original:  In this paper, we present the first such enumeration algorithms with space usage not proportional to the string length. The complexities of our enumeration algorithms are O(n \log \log (n/r)) time and  O(r ) words of working space for string length n and RLBWT size r .
Revised:  In this paper, we present r-enum (RLBWT-based enumeration), the first enumeration algorithm for characteristic substrings based on RLBWT. R-enum runs in O(n \log \log (n/r)) time and O(r ) words of working space for string length n and RLBWT size r .
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Original:  The complexities of our enumeration algorithms are O(n \log \log (n/r)) time and  O(r ) words of working space for string length n and RLBWT size r .
Revised:  The complexities of our enumeration algorithms are O(n \log \log (n/r)) time and with O(r ) words of working space for string length n and RLBWT size r .
Edit intent: coherence
Original:  The complexities of our enumeration algorithms are O(n \log \log (n/r)) time and  O(r ) words of working space for string length n and RLBWT size r .
Revised:  The complexities of our enumeration algorithms are O(n \log \log (n/r)) time and O(r \log n) bits of working space for string length n and RLBWT size r .
Edit intent: meaning-changed