wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We build on these findings to deploy three network-based drug repurposing strategies, relying on network proximity, diffusion, and AI-based metrics, allowing to rank all approved drugs based on their likely efficacy for COVID-19 patients, aggregate all predictions, and, thereby to arrive at 81 promising repurposing candidates. We validate the accuracy of our predictions using drugs currently in clinical trials, and an expression-based validation of selected candidates suggests that these drugs, with known toxicities and side effects, could be moved to clinical trials rapidly .
Revised:  We build on these findings to deploy three network-based drug repurposing strategies, relying on network proximity, diffusion, and AI-based metrics, allowing to rank all approved drugs based on their likely efficacy for COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development .
Edit intent: meaning-changed
Original: Given a compact E \subset R^n and s > 0, the maximum distance problem asks to find a compact and connected subset of R^n of smallest one dimensional Hausdorff measure , whose s-neighborhood covers E.
Revised: Given a compact E \subset R^n and s > 0, the maximum distance problem seeks a compact and connected subset of R^n of smallest one dimensional Hausdorff measure , whose s-neighborhood covers E.
Edit intent: clarity
Original: Given a compact E \subset R^n and s > 0, the maximum distance problem asks to find a compact and connected subset of R^n of smallest one dimensional Hausdorff measure , whose s-neighborhood covers E.
Revised: Given a compact E \subset R^n and s > 0, the maximum distance problem asks to find a compact and connected subset of R^n of smallest one dimensional Hausdorff measure whose s-neighborhood covers E.
Edit intent: fluency
Original:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve  in R^2 , with a finite number  balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Revised:  The main difficulty in proving this result is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve in R^2 , with a finite number balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Edit intent: fluency
Original:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve  in R^2 , with a finite number  balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Revised:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve \Gamma in R^2 , with a finite number balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Edit intent: fluency
Original:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve  in R^2 , with a finite number  balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Revised:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve in R^2 with a finite number balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Edit intent: fluency
Original:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve  in R^2 , with a finite number  balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Revised:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve in R^2 , with a finite number of balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Edit intent: fluency
Original:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve  in R^2 , with a finite number  balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Revised:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve in R^2 , with a finite number balls of radius s, and connect their centers with another Lipschitz curve \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Edit intent: fluency
Original:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve  in R^2 , with a finite number  balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). 
Revised:  The main difficulty in proving this result , is overcome by the proof of Lemma 3.5, which states that one is able to cover the s-neighborhood of a Lipschitz curve in R^2 , with a finite number balls of radius s, and connect their centers with another Lipschitz curve , \Gamma_\ast, where H^1(\Gamma_\ast) is arbitrarily close to H^1(\Gamma). We also present an open source package for computational exploration of the maximum distance problem using minimum spanning trees, available at URL
Edit intent: meaning-changed
Original:  As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 'Immunity Passport' has been mooted as a way to enable individuals , especially those in healthcare and other key industries, to return safely back to work at 'the right moment'.
Revised: Goal: As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 'Immunity Passport' has been mooted as a way to enable individuals , especially those in healthcare and other key industries, to return safely back to work at 'the right moment'.
Edit intent: clarity
Original:  As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 'Immunity Passport' has been mooted as a way to enable individuals , especially those in healthcare and other key industries, to return safely back to work at 'the right moment'.
Revised:  As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 'Immunity Passport' has been mooted as a way to enable individuals to return back to work at 'the right moment'.
Edit intent: clarity
Original:  As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 'Immunity Passport' has been mooted as a way to enable individuals , especially those in healthcare and other key industries, to return safely back to work at 'the right moment'. Despite a number of ethical, social, legal, and biological dilemmas (e.g. 'COVID-19 antibody testing ' is itself unproven), verifiable test results could well become highly desirable, at least in certain key sectors. But a 'paper certificate' has a number of dilemmas of its own, including weak tamper-proofness and rather arcane and clumsy verifiability. To address many of the underlying issues involved in certification, and as a proof of concept for future pandemics, we have developed a prototype mobile phone app  that facilitates instant verification of tamper-proof test results.
Revised:  As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 'Immunity Passport' has been mooted as a way to enable individuals , especially those in healthcare and other key industries, to return safely back to work or be admitted to current off-limits locations. While the quality of antibody testing and the likelihood of even attaining COVID-19 immunity continue to be researched, we address the issues involved in certification, and as a proof of concept for future pandemics, we have developed a prototype mobile phone app that facilitates instant verification of tamper-proof test results.
Edit intent: meaning-changed
Original:  To address many of the underlying issues involved in certification, and as a proof of concept for future pandemics, we have developed a prototype mobile phone app  that facilitates instant verification of tamper-proof test results.
Revised:  To address many of the underlying issues involved in providing tamper-proof and privacy-preserving certification, both for COVID-19 immunity and in general. Methods: We developed a prototype mobile phone app that facilitates instant verification of tamper-proof test results.
Edit intent: meaning-changed
Original:  To address many of the underlying issues involved in certification, and as a proof of concept for future pandemics, we have developed a prototype mobile phone app  that facilitates instant verification of tamper-proof test results.
Revised:  To address many of the underlying issues involved in certification, and as a proof of concept for future pandemics, we have developed a prototype mobile phone app and requisite distributed server architecture that facilitates instant verification of tamper-proof test results.
Edit intent: meaning-changed
Original:  Behind the scenes it relies upon (a) the 2019 World Wide Web Consortium standard called 'Verifiable Credentials', (b) Tim Berners-Lee's decentralised personal data platform 'Solid', and (c) a consortium Ethereum-based blockchain.
Revised:  Behind the scenes it relies upon (a) the 2019 World Wide Web Consortium standard called 'Verifiable Credentials', (b) Tim Berners-Lee's decentralized personal data platform 'Solid', and (c) a consortium Ethereum-based blockchain.
Edit intent: fluency
Original:  These enable the aforementioned mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalised to avoid restrictive ownership of sensitive digital keys and/or data.
Revised:  Results: Our mobile phone app and distributed server architecture enable the mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalised to avoid restrictive ownership of sensitive digital keys and/or data.
Edit intent: meaning-changed
Original:  We describe the underlying principles, ethical considerations, and a 9-step usecase scenario .
Revised:  For the test certificate Holder, Issuer (e.g. doctor, pharmacy) and Verifier (e.g. employer), it is 'just another app' which takes only minutes to use. Conclusions: The app and distributed server architecture offer a prototype proof of concept that is readily scalable, applicable generically, and in effect 'waiting in the wings' for the biological issues, plus key ethical issues raised in the discussion section, to be resolved .
Edit intent: meaning-changed
Original: Goal: As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 'Immunity Passport' has been mooted as a way to enable individuals to return back to work or be admitted to current off-limits locations .
Revised: Goal: As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 'Immunity Passport' has been mooted as a way to enable individuals to return back to work .
Edit intent: coherence
Original:  While the quality of antibody testing  and the likelihood of even attaining COVID-19 immunity continue to be researched, we address the issues involved in providing tamper-proof and privacy-preserving certification , both for COVID-19 immunity and in general .
Revised:  While the quality of antibody testing , the availability of vaccines, and the likelihood of even attaining COVID-19 immunity continue to be researched, we address the issues involved in providing tamper-proof and privacy-preserving certification , both for COVID-19 immunity and in general .
Edit intent: meaning-changed
Original:  While the quality of antibody testing  and the likelihood of even attaining COVID-19 immunity continue to be researched, we address the issues involved in providing tamper-proof and privacy-preserving certification , both for COVID-19 immunity and in general .
Revised:  While the quality of antibody testing and the likelihood of even attaining COVID-19 immunity continue to be researched, we address the issues involved in providing tamper-proof and privacy-preserving certification for test results and vaccinations .
Edit intent: clarity
Original:  Behind the scenes it relies upon (a) the 2019 World Wide Web Consortium standard called 'Verifiable Credentials', (b) Tim Berners-Lee's decentralized personal data platform 'Solid', and (c) a consortium Ethereum-based blockchain.
Revised:  Behind the scenes it relies upon (a) the 2019 World Wide Web Consortium standard called 'Verifiable Credentials', (b) Tim Berners-Lee's decentralized personal data platform 'Solid', and (c) a Consortium Ethereum-based blockchain.
Edit intent: fluency
Original:  Results: Our mobile phone app and distributed server architecture enable the mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalized to avoid restrictive ownership of sensitive digital keys and/or data.
Revised:  Results: Our mobile phone app and decentralized server architecture enable the mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalized to avoid restrictive ownership of sensitive digital keys and/or data.
Edit intent: clarity
Original:  For the test certificate Holder, Issuer (e.g. doctor , pharmacy) and Verifier (e.g. employer), it is 'just another app' which takes only minutes to use.
Revised:  For the test certificate Holder, Issuer (e.g. healthcare staff , pharmacy) and Verifier (e.g. employer), it is 'just another app' which takes only minutes to use.
Edit intent: meaning-changed
Original:  Conclusions: The app and distributed server architecture offer a prototype proof of concept that is readily scalable, applicable generically, and in effect 'waiting in the wings' for the biological issues, plus key ethical issues raised in the discussion section, to be resolved.
Revised:  Conclusions: The app and decentralized server architecture offer a prototype proof of concept that is readily scalable, applicable generically, and in effect 'waiting in the wings' for the biological issues, plus key ethical issues raised in the discussion section, to be resolved.
Edit intent: clarity
Original:  Behind the scenes it relies upon (a) the 2019 World Wide Web Consortium standard called 'Verifiable Credentials', (b) Tim Berners-Lee's decentralized personal data platform 'Solid', and (c) a Consortium Ethereum-based blockchain.
Revised:  The architecture, designed for scalability, relies upon (a) the 2019 World Wide Web Consortium standard called 'Verifiable Credentials', (b) Tim Berners-Lee's decentralized personal data platform 'Solid', and (c) a Consortium Ethereum-based blockchain.
Edit intent: meaning-changed
Original:  Results: Our mobile phone app and decentralized server architecture enable the mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalized to avoid restrictive ownership of sensitive digital keys and/or data  .
Revised:  Results: Our mobile phone app and decentralized server architecture enable the mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalized to avoid restrictive ownership of sensitive digital keys and/or data . Benchmark performance tests show it to scale linearly in the worst case, as significant processing is done locally on each app .
Edit intent: meaning-changed
Original:  In the absence of a safe and effective vaccine or antivirals , strategies for mitigating the burden of the pandemic are focused on non-pharmaceutical interventions, such as social-distancing, contact-tracing, quarantine, isolation and the use of face-masks in public.
Revised:  In the absence of a safe and effective vaccine or antiviral , strategies for mitigating the burden of the pandemic are focused on non-pharmaceutical interventions, such as social-distancing, contact-tracing, quarantine, isolation and the use of face-masks in public.
Edit intent: fluency
Original:  The worst-case scenario projections for cumulative mortality are 105, 100 for New York state and 164, 000 for the entire US by the end of the pandemic.
Revised:  The worst-case scenario projections for cumulative mortality (based on the baseline levels of anti-COVID non-pharmaceutical interventions considered in the study) in New York State and the entire US by the end of the pandemic.
Edit intent: meaning-changed
Original:  The worst-case scenario projections for cumulative mortality are 105, 100 for New York state and 164, 000 for the entire US by the end of the pandemic. These numbers decrease by 80\% and 64\%, respectively, if adherence to strict social-distancing measures is improved and maintained until the end of May or June .
Revised:  The worst-case scenario projections for cumulative mortality are 105, 100 for New York state and 164, 000 for the entire US decrease dramatically by 80\% and 64\%, respectively, if adherence to strict social-distancing measures is improved and maintained until the end of May or June .
Edit intent: clarity
Original:  In particular, we show how the affinity of ligand-receptor binding can be largely varied by various oligomer formations in the low concentration range of G-protein stimulus . More broadly, our work provides concrete modeling principles to explore general relations between receptor aggregation and various signaling properties .
Revised:  In particular, we show how the affinity of ligand-receptor binding can be largely varied by various oligomer formations in the low concentration range of G-protein stimulus .
Edit intent: clarity
Original:  We devise a significantly improved algorithm for this problem in the entire range of parameter r, in both the classical centralized and in the parallel (PRAM) models of computation  . Specifically, our centralized algorithm for this problem requires time O(|E| \cdot n^{o(1)} + n^{\omega(r)}), where n^{\omega(r)} is the time required to multiply an n^r \times n matrix by an n \times n one.
Revised:  We devise a significantly improved algorithm for this problem in the entire range of parameter r, in both the classical centralized and the parallel (PRAM) models of computation . Specifically, our centralized algorithm for this problem requires time O(|E| \cdot n^{o(1)} + n^{\omega(r)}), where n^{\omega(r)} is the time required to multiply an n^r \times n matrix by an n \times n one.
Edit intent: fluency
Original:  We devise a significantly improved algorithm for this problem in the entire range of parameter r, in both the classical centralized and in the parallel (PRAM) models of computation  . Specifically, our centralized algorithm for this problem requires time O(|E| \cdot n^{o(1)} + n^{\omega(r)}), where n^{\omega(r)} is the time required to multiply an n^r \times n matrix by an n \times n one.
Revised:  We devise a significantly improved algorithm for this problem in the entire range of parameter r, in both the classical centralized and in the parallel (PRAM) models of computation , and in a wide range of r in the distributed (Congested Clique) model . Specifically, our centralized algorithm for this problem requires time O(|E| \cdot n^{o(1)} + n^{\omega(r)}), where n^{\omega(r)} is the time required to multiply an n^r \times n matrix by an n \times n one.
Edit intent: meaning-changed
Original:  We also devise efficient algorithms for computing (\le \le 1 +\epsilon)-approximate shortest paths from each vertex to its k nearest neighbors in%DIFDELCMD < {\em %%% directed graphs.
Revised:  In the Congested Clique model, our algorithm solves the problem in polylogarithmic time for |S| = n^r sources, for r\le \le 1 +\epsilon)-approximate shortest paths from each vertex to its k nearest neighbors in%DIFDELCMD < {\em %%% directed graphs.
Edit intent: meaning-changed
Original:  We also devise efficient algorithms for computing (\le \le 1 +\epsilon)-approximate shortest paths from each vertex to its k nearest neighbors in%DIFDELCMD < {\em %%% directed graphs.
Revised:  We also devise efficient algorithms for computing (\le 0.655, while previous state-of-the-art algorithms did so only for r\le 1 +\epsilon)-approximate shortest paths from each vertex to its k nearest neighbors in%DIFDELCMD < {\em %%% directed graphs.
Edit intent: meaning-changed
Original:  We also devise efficient algorithms for computing (\le \le 1 +\epsilon)-approximate shortest paths from each vertex to its k nearest neighbors in%DIFDELCMD < {\em %%% directed graphs.
Revised:  We also devise efficient algorithms for computing (\le \le 1 +\epsilon)-approximate shortest paths from each vertex to its k nearest neighbors in%DIFDELCMD < {\em %%% graphs.
Edit intent: clarity
Original:  We also devise efficient algorithms for computing (\le \le 1 +\epsilon)-approximate shortest paths from each vertex to its k nearest neighbors in%DIFDELCMD < {\em %%% directed graphs. Here too the running time is only O(n^{2+o(1))even for polynomially large k}%DIFDELCMD < \le %%% n^{0.168.
Revised:  We also devise efficient algorithms for computing (\le \le 1 +\epsilon)-approximate shortest paths from each vertex to its k nearest neighbors in%DIFDELCMD < {\em %%% directed /2. Moreover, it improves previous bounds for all r > 1/2. For unweighted graphs, the running time is only O(n^{2+o(1))even for polynomially large k}%DIFDELCMD < \le %%% n^{0.168.
Edit intent: meaning-changed
Original:  In this work we remedy this gap by studying the diffraction limit as a statistical inverse problem and, based on connections to provable algorithms for learning mixture models, we rigorously prove upper and lower bounds on how many photons we need (and how precisely we need to record their locations) to resolve closely-spaced point sources.
Revised:  In this work we remedy this gap by studying the diffraction limit as a statistical inverse problem and, based on connections to provable algorithms for learning mixture models, we rigorously prove upper and lower bounds on the statistical and algorithmic complexity needed to resolve closely spaced point sources.
Edit intent: clarity