wanyu/IteraTeR_v2 huggingface dataset:
Original:  Results: Simulations show that MAGORINO reduces bias in both PDFF and R2* measurements compared to Gaussian fitting, through two main mechanisms: (i) a greater chance of selecting the true (non-swapped optimum) , and (ii) a shift in the position of the optima such that the estimates are closer to ground truth solutions, as a result of the correct noise model.
Revised:  Results: Simulations show that MAGORINO reduces bias in both PDFF and R2* measurements compared to Gaussian fitting, through two main mechanisms: (i) a greater chance of selecting the true (non-swapped ) optimum , and (ii) a shift in the position of the optima such that the estimates are closer to ground truth solutions, as a result of the correct noise model.
Edit intent: fluency
Original:  Conclusion: MAGORINO reduces fat-water swaps and noise bias in PDFF and R2* estimation, and improves fitting performance compared to traditional Gaussian noise-based magnitude-only algorithms.
Revised:  Conclusion: MAGORINO reduces fat-water swaps and noise bias in PDFF and R2* estimation, thus addressing key limitations of traditional Gaussian noise-based magnitude-only algorithms.
Edit intent: clarity
Original: We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for initial non-covalent ligands to high nM for the final covalent compound (IC50=830 +/ - 50 nM).
Revised: We report the fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for initial non-covalent ligands to high nM for the final covalent compound (IC50=830 +/ - 50 nM).
Edit intent: fluency
Original: We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for initial non-covalent ligands to high nM for the final covalent compound (IC50=830 +/ - 50 nM).
Revised: We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for initial non-covalent ligands to high nM for the final covalent compound (IC50=830 +/ -50 nM).
Edit intent: fluency
Original:  The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligands binding poses through the explicit reconstruction of the ligand-protein conformation spaces.
Revised:  The study is complemented by extensive adaptive sampling simulations used to rationalize the different ligands binding poses through the explicit reconstruction of the ligand-protein conformation spaces.
Edit intent: coherence
Original:  The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligands binding poses through the explicit reconstruction of the ligand-protein conformation spaces.
Revised:  The study is complemented by extensive adaptive sampling simulations that are used to rationalize different ligands binding poses through the explicit reconstruction of the ligand-protein conformation spaces.
Edit intent: fluency
Original:  Machine Learning predictions are also performed to predict selected compound properties.
Revised:  Machine Learning predictions are also utilized to predict selected compound properties.
Edit intent: clarity
Original:  Computations were performed on GPU-accelerated supercomputers and high performance cloud infrastructures to exponentially reduce time-to-solution  and were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and in vitro characterization of compounds.
Revised:  Computations were performed on GPU-accelerated supercomputers and high-performance cloud infrastructures to exponentially reduce time-to-solution and were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and in vitro characterization of compounds.
Edit intent: fluency
Original:  Computations were performed on GPU-accelerated supercomputers and high performance cloud infrastructures to exponentially reduce time-to-solution  and were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and in vitro characterization of compounds.
Revised:  Computations were performed on GPU-accelerated supercomputers and high performance cloud infrastructures to exponentially reduce time-to-solution , and were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and in vitro characterization of compounds.
Edit intent: fluency
Original:  Computations were performed on GPU-accelerated supercomputers and high performance cloud infrastructures to exponentially reduce time-to-solution  and were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and in vitro characterization of compounds.
Revised:  Computations were performed on GPU-accelerated supercomputers and high performance cloud infrastructures to exponentially reduce time-to-solution and were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and in vitro characterization of compounds.
Edit intent: fluency
Original:  Such study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows to address the protein conformational multiplicity problem .
Revised:  The study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows to address the protein conformational multiplicity problem .
Edit intent: fluency
Original:  Such study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows to address the protein conformational multiplicity problem .
Revised:  Such study highlights the power of in silico strategies that rely on structure-based approaches for drug design and address protein conformational heterogeneity .
Edit intent: clarity
Original:  The proposed innovative scaffolds open routes for further optimization of Mpro inhibitors towards low nM affinities.
Revised:  The proposed scaffolds open a path toward further optimization of Mpro inhibitors towards low nM affinities.
Edit intent: clarity
Original:  The proposed innovative scaffolds open routes for further optimization of Mpro inhibitors towards low nM affinities.
Revised:  The proposed innovative scaffolds open routes for further optimization of Mpro inhibitors with nM affinities.
Edit intent: clarity
Original: We introduce Disease Informed Neural Networks (DINNs) -- neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).
Revised: In this work, we present an approach called Disease Informed Neural Networks (DINNs) -- neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).
Edit intent: clarity
Original:  Lastly, we show the ease at which one can use DINN to effectively learn COVID's spread dynamics and forecast its progression a month into the future from real-life data.
Revised:  Lastly, we show the ease at which one can use DINN to effectively learn COVID's spread dynamics and forecast its progression into the future from real-life data.
Edit intent: clarity
Original:  These networks are simple, yet powerful neural architectures that -- exhibiting memory over multiple time-scales and non-linear high-dimensional representations -- can discover temporal relations in the data while still maintaining linearity in the learning.
Revised:  These networks are simple, yet powerful neural architectures that - exhibiting memory over multiple time-scales and non-linear high-dimensional representations -- can discover temporal relations in the data while still maintaining linearity in the learning.
Edit intent: fluency
Original:  These networks are simple, yet powerful neural architectures that -- exhibiting memory over multiple time-scales and non-linear high-dimensional representations -- can discover temporal relations in the data while still maintaining linearity in the learning.
Revised:  These networks are simple, yet powerful neural architectures that -- exhibiting memory over multiple time-scales and non-linear high-dimensional representations - can discover temporal relations in the data while still maintaining linearity in the learning.
Edit intent: fluency
Original: Given the popularity of social media and the notion of it being a platform encouraging free speech, it has become an open playground for user (bot ) accountstrying to manipulate other users using these platforms . Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as scammers, manipulate stock markets, etc .
Revised: As our reliance on social media platforms and web services increase day by day, exploiters view these platforms as an opportunity to manipulate our thoughts ad actions. These platforms have become an open playground for user (bot ) accountstrying to manipulate other users using these platforms . Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as scammers, manipulate stock markets, etc .
Edit intent: clarity
Original: Given the popularity of social media and the notion of it being a platform encouraging free speech, it has become an open playground for user (bot ) accountstrying to manipulate other users using these platforms . Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as scammers, manipulate stock markets, etc .
Revised: Given the popularity of social media and the notion of it being a platform encouraging free speech, it has become an open playground for social bot accounts . Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as scammers, manipulate stock markets, etc .
Edit intent: clarity
Original: Given the popularity of social media and the notion of it being a platform encouraging free speech, it has become an open playground for user (bot ) accountstrying to manipulate other users using these platforms . Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as scammers, manipulate stock markets, etc .
Revised: Given the popularity of social media and the notion of it being a platform encouraging free speech, it has become an open playground for user (bot ) accountstrying to manipulate other users using these platforms . Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as scammers, manipulate stock markets, and so on .
Edit intent: fluency
Original:  So identification and prevention of such campaigns that release or create the bots have become critical to tackling it at its source of origin .
Revised:  So identification and prevention of such campaigns that release or create these bots have become critical to tackling it at its source of origin .
Edit intent: fluency
Original:  So identification and prevention of such campaigns that release or create the bots have become critical to tackling it at its source of origin .
Revised:  So identification and prevention of such campaigns that release or create the bots have become critical .
Edit intent: clarity
Original:  Our goal  is to leverage semantic web mining techniques to identify fake bots or accounts involved in these activities .
Revised:  Our goal in this paper is to leverage semantic web mining techniques to identify fake bots or accounts involved in these activities .
Edit intent: clarity
Original:  Our goal  is to leverage semantic web mining techniques to identify fake bots or accounts involved in these activities .
Revised:  Our goal is to leverage web mining techniques to identify fake bots or accounts involved in these activities .
Edit intent: clarity
Original:  Our goal  is to leverage semantic web mining techniques to identify fake bots or accounts involved in these activities .
Revised:  Our goal is to leverage semantic web mining techniques to help detect fake bots on social media platforms such as Twitter, thereby mitigating the spread of disinformation .
Edit intent: clarity
Original: Automatic speaker verification (ASV) with ad-hoc microphone arrays has received attention.
Revised: Ad-hoc microphone arrays has received attention.
Edit intent: clarity
Original:  Unlike traditional microphone arrays, the number of microphones and their spatial arrangement in an ad-hoc microphone array is unknown, which makes conventional multi-channel ASV techniques ineffective in ad-hoc microphone array settings.
Revised:  Unlike traditional microphone arrays, the number of microphones and their spatial arrangement in an ad-hoc microphone array is unknown, which makes conventional multi-channel processing methods can not be directly used in ad-hoc microphone array settings.
Edit intent: clarity
Original:  Our system achieves 2.4 \%/ 5.3 \% on test-clean/test-other subsets of LibriSpeech, which is competitive with the supervised baseline's performance.
Revised:  Our system achieves 2.4 \%/ 5.3 \% on test-clean/test-other subsets of LibriSpeech, which closely matches with the supervised baseline's performance.
Edit intent: clarity
Original:  To this end, we propose a universal generalized memory AMP (GMAMP) framework including the existing orthogonal AMP/VAMP, GVAMP, and MAMP as special instances.
Revised:  To this end, we propose a universal generalized memory AMP (GMAMP) framework including the existing orthogonal AMP/VAMP, GVAMP, and memory AMP (MAMP) as special instances.
Edit intent: clarity
Original:  The GMAMP framework provides a new principle toward building new advanced AMP-type algorithms.
Revised:  The GMAMP framework provides a principle toward building new advanced AMP-type algorithms.
Edit intent: clarity
Original:  Extremophiles gain equal value by being part of a framework for high level characterization of the evolutionary mechanisms that must necessarily restrict or promote their emergence and presence on solar system bodies.
Revised:  Extremophiles gain equal value by being part of a framework for high-level characterisation of the evolutionary mechanisms that must necessarily restrict or promote their emergence and presence on solar system bodies.
Edit intent: fluency
Original:  Thus, extremophiles exist in extreme environments, and therein lies the paradox: extremophiles can only live in extreme environments , yet are not able to originate in such environments.
Revised:  Thus, extremophiles exist in extreme environments, and therein lies the paradox: extremophiles can only live in extreme environments but yet are not able to originate in such environments.
Edit intent: fluency
Original:  So even though the range of extremophile capabilities in extreme environments is wider than  mesophiles, the range of their emergence possibilities is still equally restricted with these.
Revised:  Therefore, even though the range of extremophile capabilities in extreme environments is wider than mesophiles, the range of their emergence possibilities is still equally restricted with these.
Edit intent: fluency
Original:  So even though the range of extremophile capabilities in extreme environments is wider than  mesophiles, the range of their emergence possibilities is still equally restricted with these.
Revised:  So even though the range of extremophile capabilities in extreme environments is wider than that in mesophiles, the range of their emergence possibilities is still equally restricted with these.
Edit intent: clarity
Original:  Thus, life may exist on an extraterrestrial extreme world (if imported there),  chemical evolution may be present on that world.
Revised:  Thus, life may exist on an extraterrestrial extreme world (if imported there), and chemical evolution may be present on that world.
Edit intent: coherence
Original:  Yet , it can be predicted that there is no native life anyway.
Revised:  However , it can be predicted that there is no native life anyway.
Edit intent: coherence
Original:  This situation can be predicted to function as a chemosignature , and eventually as a biosignature.
Revised:  This situation can be predicted to function as a chemosignature and eventually as a biosignature.
Edit intent: fluency
Original:  Codes and models will be made available at URL
Revised:  Codes and models are available at URL
Edit intent: clarity
Original:  We demonstrate experimentally on 2D images of roads and 3D image stacks of neuronal processes that networks trained in this manner are better at recovering the topology of the curvilinear structures they extract .
Revised:  We demonstrate experimentally on 2D and 3D image stacks of neuronal processes that networks trained in this manner are better at recovering the topology of the curvilinear structures they extract .
Edit intent: clarity
Original: The article is aimed to address a combinative use of asymptotic analysis and machine learning, for fast stiffness design of configurations infilled with smoothly-varying graded microstructures.
Revised: The article is aimed to address a mutually boosting use of asymptotic analysis and machine learning, for fast stiffness design of configurations infilled with smoothly-varying graded microstructures.
Edit intent: clarity
Original:  The discussion is conducted in the context of an improved asymptotic-homogenisation topology optimisation (AHTO plus) framework (Zhu et al. , 2019).
Revised:  The discussion is conducted in the context of an improved asymptotic-homogenisation topology optimisation (AHTO plus) framework .
Edit intent: clarity
Original:  It is demonstrated that  machine learning can be employed to represent the key but implicit inter-relationships between formulations obtained at different orders from asymptotic analysis .
Revised:  It is demonstrated that machine learning can be employed to represent the key but implicit inter-relationships revealed from asymptotic analysis .
Edit intent: clarity
Original:  Firstly, asymptotic analysis identifies a computational routine for data acquisition, thus the training data are sufficient in theory.
Revised:  Firstly, asymptotic analysis identifies a computational routine for data acquisition, thus the training data here are inexhaustible in theory.
Edit intent: clarity
Original:  Other issues on incorporating machine learning into the AHTO plus framework, such as ensuring the positive definiteness of the homogenised elasticity tensor and the speeding-up of the associated sensitivity analysis , are also discussed here.
Revised:  Other issues on incorporating machine learning into the AHTO plus framework, such as ensuring the positive definiteness of the homogenised elasticity tensor represented with neural networks , are also discussed here.
Edit intent: clarity
Original:  The author wishes this book to be available to students and engineers free of charge, subject to the copyright policy on page 3.
Revised:  The author wishes this book to be available free of charge, subject to the copyright policy on page 3.
Edit intent: coherence
Original:  Existing transfer learning methods mostly focus on how to improve the performance of the target task through a specific source model, but assume that the source model is given.
Revised:  Existing transfer learning methods mostly focus on how to improve the performance of the target task through a specific source model, and assume that the source model is given.
Edit intent: coherence
Original:  As many source models are available, it is difficult for data scientists to select the best source model for the target task manually.
Revised:  Although many source models are available, it is difficult for data scientists to select the best source model for the target task manually.
Edit intent: fluency
Original:  For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit the clusters of soft labels, and finally measures its distinguishing ability using Gaussian mixture-based metric.
Revised:  For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures its distinguishing ability using Gaussian mixture-based metric.
Edit intent: clarity
Original:  Thus, model reuse has received attention in the ML community, where it is called transfer learning.
Revised:  Thus, reusing an already trained model has received attention in the ML community, where it is called transfer learning.
Edit intent: clarity
Original:  Thus, model reuse has received attention in the ML community, where it is called transfer learning.
Revised:  Thus, model reuse has received attention , which is called transfer learning.
Edit intent: clarity
Original:  Hence, how to efficiently select a suitable source model  for model reuse is still an unsolved problem.
Revised:  Hence, how to efficiently select a suitable source model for model reuse is an interesting but unsolved problem.
Edit intent: coherence
Original:  In this paper, we propose SMS, an effective, efficient  and flexible source model selection framework.
Revised:  In this paper, we propose SMS, an effective, efficient , and flexible source model selection framework.
Edit intent: fluency
Original:  SMS is effective even when  source and target datasets have significantly different data labels,  is flexible to support source models with any type of structure, and is efficient to avoid any training process.
Revised:  SMS is effective even when the source and target datasets have significantly different data labels, is flexible to support source models with any type of structure, and is efficient to avoid any training process.
Edit intent: fluency
Original:  SMS is effective even when  source and target datasets have significantly different data labels,  is flexible to support source models with any type of structure, and is efficient to avoid any training process.
Revised:  SMS is effective even when source and target datasets have significantly different data labels, and is flexible to support source models with any type of structure, and is efficient to avoid any training process.
Edit intent: fluency
Original:  For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures its distinguishing ability using Gaussian mixture-based metric.
Revised:  For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures the distinguishing ability of the source model using Gaussian mixture-based metric.
Edit intent: clarity
Original:  Moreover, we present an improved SMS (I-SMS), which decreases the output number of  source model.
Revised:  Moreover, we present an improved SMS (I-SMS), which decreases the output number of the source model.
Edit intent: fluency
Original: Human intelligence has the remarkable ability to quickly adapt to new tasks and environments  . Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Revised: Human intelligence has the remarkable ability to adapt to new tasks and environments . Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Edit intent: clarity