wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper we propose a novel approach based on periodic SARS-CoV 2 antibody testing to reduce the risk of contagious within the working space, and evaluate it using stochastic simulations of the health evolution of the workforce. Our simulations indicate that the proper use of testing and quarantine of workers suspected of being infected can greatly reduce the number of infections while improving the productivity of the companyin the long run.
Revised:  In this paper , we present a novel safety protocol based on rapid antibody testing (ABT). Using discrete event simulation, we evaluated its performance on the cumulative number of infected workers, effective reproductive number (R_e), and active work force within a company. Using a synthetic experiment, we showed that ABT twice a week (ABT 3) performed the best, detecting 5.7
Edit intent: meaning-changed
Original:  Instead of off-the-shelf search and alignment algorithms, which failed to discover the orthologs, we used a special alignment scoring that does not penalize C/T and A/G mismatches  . RNA bases C and U both can bind to G;
Revised:  Instead of off-the-shelf search and alignment algorithms, which failed to discover the orthologs, we used a special alignment scoring that does not penalize C/T and A/G mismatches as much as the other mutations . RNA bases C and U both can bind to G;
Edit intent: meaning-changed
Original:   To validate our results, we confirmed the discovered orthologs are fully conserved in all the  publicly available genomes of various strains of SARS-CoV-2;
Revised:  We also validate this hypothesis using a novel, independent computational experiment. To validate our results, we confirmed the discovered orthologs are fully conserved in all the publicly available genomes of various strains of SARS-CoV-2;
Edit intent: meaning-changed
Original: Higher life expectancy and rapidly aging populations in developing countries, especially in the last three decades, have created the need for policymakers to introduce pension programs in developing countries. China launched the New Rural Pension Scheme (NRPS) in 2009 to ease internal demographic pressures and concerns about old-age poverty .
Revised: Historically, economists have mainly focused on human capital accumulation and considerably less so on the causes and consequences of human capital depreciation in late adulthood. Studying human capital depreciation over the life cycle has powerful economic consequences for decision-making in old age .
Edit intent: meaning-changed
Original:  Using data from the introduction of the NRPS in China, we estimate the effects of pension benefits, due to participation in the NRPS, on individual cognition, measured by episodic memory and intact mental status, among individuals aged 60 and above .
Revised:  Using data from the introduction of a retirement program in China, we estimate the effects of pension benefits, due to participation in the NRPS, on individual cognition, measured by episodic memory and intact mental status, among individuals aged 60 and above .
Edit intent: clarity
Original: As the world braces itself for a pandemic-induced surge in automation and a consequent (accelerated) shift in the nature of jobs, it is essential now more than ever to understand how people's creative performances are impacted by their interactions with peers in a social network. However, when it comes to creative ideation, it is unclear how the demographic cues of one' s peers can influence the network dynamics and the associated performance outcomes of people. In this paper, we ask: (1) Given the task of creative idea generation, how do social network connectivities adapt to people's demographic cues? (2) How are creative outcomes influenced by such demography-informed network dynamics? We find that link formations in creativity-centric networks are primarily guided by the creative performances of one's peers .
Revised: The characteristics of social partners have long been hypothesized as influential in guiding group interactions. Understanding how demographic cues impact networks of creative collaborators is critical for elevating creative performances therein. We conducted a randomized experiment to investigate how the knowledge of peers' gender and racial identities distorts people's connection patterns and the resulting creative outcomes in a dynamic social network. Consistent with prior work, we found that creative inspiration links are primarily formed with top idea-generators .
Edit intent: clarity
Original:  However, in the presence of demographic information, the odds of same-gender links to persist increase by 82.03\%, after controlling for merit-based link persistence.
Revised:  However, when gender and racial identities are known, not only is there (1) an increase of 82.03\% in the odds of same-gender links to persist increase by 82.03\%, after controlling for merit-based link persistence.
Edit intent: meaning-changed
Original:  However, in the presence of demographic information, the odds of same-gender links to persist increase by 82.03\%, after controlling for merit-based link persistence. In essence, homophily-guided link persistence takes place when demographic cues are available. We further find that the semantic similarities between socially stimulated idea-sets increase significantly in the presence of demographic cues (P<1e-4), which is counter-productive for the purposes of divergent creativity.
Revised:  However, in the presence of demographic information, the odds of same-gender connections (but not for same-race connections), but (2) the semantic similarity of idea-sets increase significantly in the presence of demographic cues (P<1e-4), which is counter-productive for the purposes of divergent creativity.
Edit intent: meaning-changed
Original:  We further find that the semantic similarities between socially stimulated idea-sets increase significantly in the presence of demographic cues (P<1e-4), which is counter-productive for the purposes of divergent creativity.
Revised:  We further find that the semantic similarities between socially stimulated idea-sets stimulated by these connections also increase significantly compared to demography-agnostic networks, negatively impacting the outcomes of divergent creativity.
Edit intent: clarity
Original:  This result can partly be explained by the observation that people's ideas tend to be more homogeneous within demographic groups than between demographic groups (P<1e-7). Therefore, choosing to maintain connections based on demographic similarity can negatively impact one's creative inspiration sources by taking away potential diversity bonuses. Our results can inform intelligent intervention possibilities towards maximizing a social system's creative outcomes .
Revised:  This result can partly be explained by the observation that people's ideas tend to be more homogeneous within demographic groups than between , taking away diversity-bonuses from similarity-based links and partly explaining the results. These insights can inform intelligent intervention possibilities towards maximizing a social system's creative outcomes .
Edit intent: coherence
Original: The model of streaming algorithms is motivated by the increasingly common situation in which the sheer amount of available data limits the ways in which the data can be accessed. Streaming algorithms are typically allowed a single pass over the data and can only store a sublinear fraction of the data at any time. We initiate the study of classic online selection problems\dots in a streaming model where the data stream consists of two parts: historical data points that an algorithm can use to learn something about the input;
Revised: A prophet inequality states, for some \alpha\in[0,1], that the expected value achievable by a gambler who sequentially observes random variables X_1,\dots in a streaming model where the data stream consists of two parts: historical data points that an algorithm can use to learn something about the input;
Edit intent: meaning-changed
Original:  We initiate the study of classic online selection problems\dots in a streaming model where the data stream consists of two parts: historical data points that an algorithm can use to learn something about the input;
Revised:  We initiate the study of classic online selection problems\dots,X_n and selects one of them is at least an \alpha fraction of the maximum value in the sequence. We obtain three distinct improvements for a setting that was first studied by Correa et al. (EC, 2019) and is particularly relevant to modern applications in algorithmic pricing. In this setting, the random variables are i.i.d. from an unknown distribution and the gambler has access to an additional \beta n samples for some \beta\geq 0. We first give improved lower bounds on \alpha for a wide range of values of \beta; specifically, \alpha\geq(1+\beta)/e when \beta\leq 1/(e-1), which is tight, and \alpha\geq 0.648 when \beta=1, which improves on a bound of around 0.635 due to Correa et al. (SODA, 2020). Adding to their practical appeal, specifically in the context of algorithmic pricing, we then show that the new bounds can be obtained even in a streaming model where the data stream consists of two parts: historical data points that an algorithm can use to learn something about the input;
Edit intent: meaning-changed
Original:  We initiate the study of classic online selection problems\dots in a streaming model where the data stream consists of two parts: historical data points that an algorithm can use to learn something about the input;
Revised:  We initiate the study of classic online selection problems\dots in a streaming model of computation and thus in situations where the use of relevant data is complicated by the sheer amount of data stream consists of two parts: historical data points that an algorithm can use to learn something about the input;
Edit intent: meaning-changed
Original:  We initiate the study of classic online selection problems\dots in a streaming model where the data stream consists of two parts: historical data points that an algorithm can use to learn something about the input; and data points from which a selection can be made. Both types of data points are i.i.d. draws from an unknown distribution .
Revised:  We initiate the study of classic online selection problems\dots in a streaming model where the data available. We finally establish that the upper bound of 1/e for the case without samples is robust to additional information about the distribution, and applies also to sequences of i.i.d. draws from an unknown distribution .
Edit intent: meaning-changed
Original:  Both types of data points are i.i.d. draws from an unknown distribution . We consider the two canonical objectives for online selection---maximizing the probability of selecting the maximum and maximizing the expected value of the selection---and provide the first performance guarantees for both these objectives in the streaming model .
Revised:  Both types of data points are i.i.d. random variables whose distribution is itself drawn, according to a known distribution, from a finite set of known candidate distributions. This implies a tight prophet inequality for exchangeable sequences of random variables, answering a question of Hill and Kertz (Contemporary Mathematics, 1992), but leaves open the possibility of better guarantees when the number of candidate distributions is small, a setting we believe is of strong interest to applications .
Edit intent: meaning-changed
Original: Many pairwise additive force fields parameterized to reproduce folded protein structures are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), most of which are found to disagree with solution experiments for the disordered states.
Revised: Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), most of which are found to disagree with solution experiments for the disordered states.
Edit intent: clarity
Original: Many pairwise additive force fields parameterized to reproduce folded protein structures are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), most of which are found to disagree with solution experiments for the disordered states. Recent modifications of protein-water and water-water energetic interactions in these standard force fields have sought to increase agreement with solution experiments of IDPsand IDRswhile also maintaining the ability to describe folded protein structures. Many-body potentials that include polarization should provide a better physical model for capturing proteins with and without intrinsic disorder, but in this case testing has been more limited for proteins of either class .
Revised: Many pairwise additive force fields parameterized to reproduce folded protein structures are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve description of IDPs/IDRs, but are largely in disagreement with solution experiments for the disordered states .
Edit intent: clarity
Original:  We have evaluated representative non-polarizable and polarizable protein and water force fields against experimental data on the histatin 5 intrinsically disordered protein, the disorder-to-order temperature transitionfor the (AAQAA)3 repeat peptide, the TSR4 domain of F-spondin which has both folded and intrinsically disordered regions, and seven globular folded proteins ranging in size from 130-266 amino acids.
Revised:  We have evaluated representative pairwise and many-body protein and water force fields against experimental data on the histatin 5 intrinsically disordered protein, the disorder-to-order temperature transitionfor the (AAQAA)3 repeat peptide, the TSR4 domain of F-spondin which has both folded and intrinsically disordered regions, and seven globular folded proteins ranging in size from 130-266 amino acids.
Edit intent: meaning-changed
Original:  We have evaluated representative non-polarizable and polarizable protein and water force fields against experimental data on the histatin 5 intrinsically disordered protein, the disorder-to-order temperature transitionfor the (AAQAA)3 repeat peptide, the TSR4 domain of F-spondin which has both folded and intrinsically disordered regions, and seven globular folded proteins ranging in size from 130-266 amino acids.
Revised:  We have evaluated representative non-polarizable and polarizable protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order temperature transitionfor the (AAQAA)3 repeat peptide, the TSR4 domain of F-spondin which has both folded and intrinsically disordered regions, and seven globular folded proteins ranging in size from 130-266 amino acids.
Edit intent: meaning-changed
Original:  We have evaluated representative non-polarizable and polarizable protein and water force fields against experimental data on the histatin 5 intrinsically disordered protein, the disorder-to-order temperature transitionfor the (AAQAA)3 repeat peptide, the TSR4 domain of F-spondin which has both folded and intrinsically disordered regions, and seven globular folded proteins ranging in size from 130-266 amino acids.
Revised:  We have evaluated representative non-polarizable and polarizable protein and water force fields against experimental data on the histatin 5 intrinsically disordered protein, the disorder-to-order transition, and for seven globular proteins ranging in size from 130-266 amino acids.
Edit intent: coherence
Original:  We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states are simultaneously valuable for describing IDPs and IDRs and disorder to order transitions.
Revised:  We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder to order transitions.
Edit intent: clarity
Original: Motivation: NLP continues improving substantially through auto-regressive and auto-encoding Language Models . These LMsrequire expensive computing resources for self-supervised or un-supervised learning from huge unlabelled text corpora. The information learned is transferred through so-called embeddings to downstream prediction tasks. Bioinformatics provide vast gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost .
Revised: Computational biology and bioinformatics provide vast data gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost .
Edit intent: clarity
Original:  Bioinformatics provide vast gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost .
Revised:  Bioinformatics provide vast gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference cost .
Edit intent: clarity
Original:  Bioinformatics provide vast gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost .
Revised:  Bioinformatics provide vast gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference costs .
Edit intent: fluency
Original:  Here, we addressed two questions: (1) To which extent can HPC up-scale protein LMs to larger databases and larger models? (2) To which extent can LMs extract features from single proteins to get closer to the performance of methods using evolutionary information? Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids  from 2.1 billion protein sequences ( BFD ).
Revised:  Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids from 2.1 billion protein sequences ( BFD ).
Edit intent: clarity
Original:  Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids  from 2.1 billion protein sequences ( BFD ).
Revised:  Methodology: Here, we trained two auto-regressive language models (Transformer-XL , XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids from 2.1 billion protein sequences ( BFD ).
Edit intent: coherence
Original:  Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids  from 2.1 billion protein sequences ( BFD ).
Revised:  Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids from 2.1 billion protein sequences ( BFD ).
Edit intent: clarity
Original:  Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids  from 2.1 billion protein sequences ( BFD ).
Revised:  Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids (words) from 2.1 billion protein sequences ( BFD ).
Edit intent: clarity
Original:  Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids  from 2.1 billion protein sequences ( BFD ).
Revised:  Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids from 2.1 billion protein sequences ( 22- and 112-times the entire English Wikipedia ).
Edit intent: meaning-changed
Original:  The LMs were trained on the Summit supercomputer , using 5616 GPUs  and one TPU Pod , using V3-512 cores. Results: The results of training these LMs on proteins was assessed by predicting secondary structure in three- and eight-states ( Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).
Revised:  The LMs were trained on the Summit supercomputer , using 5616 GPUs and one TPU Pod , using V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure in three- and eight-states ( Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).
Edit intent: clarity
Original:  Results: The results of training these LMs on proteins was assessed by predicting secondary structure in three- and eight-states ( Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).
Revised:  Results: The results of training these LMs on proteins was assessed by predicting secondary structure (3-states: Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).
Edit intent: clarity
Original:  Results: The results of training these LMs on proteins was assessed by predicting secondary structure in three- and eight-states ( Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).
Revised:  Results: The results of training these LMs on proteins was assessed by predicting secondary structure in three- and eight-states ( Q3= 76-84, 8-states: Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).
Edit intent: meaning-changed
Original:  Results: The results of training these LMs on proteins was assessed by predicting secondary structure in three- and eight-states ( Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).
Revised:  Results: The results of training these LMs on proteins was assessed by predicting secondary structure in three- and eight-states ( Q3= 75-83, Q8= 65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).
Edit intent: meaning-changed