wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Searchers are uncertain about search poolqualities of different periods but infer these fundamentals from search outcomes of the previous generation. Searchers' stopping decisions impose a censoring effect on the data of their successors, as the values they would have found in later periods had they kept searching remain unobserved. While innocuous for rational agents , this censoring effect interacts with the gambler' s fallacy and creates a feedback loopbetween distorted stopping rules and pessimistic beliefs about search pool qualities of later periods. In general settings, the stopping rules used by different generations monotonically converge to a steady-state rule that stops searching earlier than optimal. In settings where true pool qualities increase over time - so there is option value in rejecting above-average early draws - learning is monotonically harmful and welfare strictly decreases across generations .
Revised:  Searchers are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and must learn its parameters from previous agents' histories. Agents stop when early draws are deemed "good enough," so predecessors' histories contain negative streaks but not positive streaks. Since biased learners understate the likelihood of consecutive below-average draws, histories induce pessimistic beliefs about the distribution's mean. When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop. Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring. When payoffs are convex in the draws (e.g. managers can hire previously rejected interviewees), variance uncertainty provides another channel of positive feedback between past and future thresholds .
Edit intent: meaning-changed
Original:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and must learn its parameters from previous agents' histories . Agents stop when early draws are deemed "good enough," so predecessors' histories contain negative streaks but not positive streaks.
Revised:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and learn its parameters from previous agents' histories . Agents stop when early draws are deemed "good enough," so predecessors' histories contain negative streaks but not positive streaks.
Edit intent: clarity
Original:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and must learn its parameters from previous agents' histories . Agents stop when early draws are deemed "good enough," so predecessors' histories contain negative streaks but not positive streaks.
Revised:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and must learn its parameters from their predecesors . Agents stop when early draws are deemed "good enough," so predecessors' histories contain negative streaks but not positive streaks.
Edit intent: clarity
Original:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and must learn its parameters from previous agents' histories . Agents stop when early draws are deemed "good enough," so predecessors' histories contain negative streaks but not positive streaks.
Revised:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and must learn its parameters from previous agents' histories . Agents stop when early draws are deemed "good enough," so predecessors' experience contain negative streaks but not positive streaks.
Edit intent: clarity
Original:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Revised:  When early agents decrease their acceptance thresholds due to pessimism, later agents will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Edit intent: clarity
Original:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Revised:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Edit intent: fluency
Original:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Revised:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Edit intent: fluency
Original:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Revised:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback cycle . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Edit intent: clarity
Original:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring . When payoffs are convex in the draws (e.g. managers can hire previously rejected interviewees), variance uncertainty provides another channel of positive feedback between past and future thresholds .
Revised:  When early agents decrease their acceptance thresholds due to pessimism, later learners will become more surprised by the lack of positive reversals in their predecessors' histories, leading to even more pessimistic inferences and even lower acceptance thresholds -- a positive-feedback loop . Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Edit intent: coherence
Original:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and learn its parameters from their predecesors . Agents stop when early draws are deemed "good enough," so predecessors' experience contain negative streaks but not positive streaks.
Revised:  They are uncertain about the underlying distribution and learn its parameters from their predecesors . Agents stop when early draws are deemed "good enough," so predecessors' experience contain negative streaks but not positive streaks.
Edit intent: clarity
Original:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and learn its parameters from their predecesors . Agents stop when early draws are deemed "good enough," so predecessors' experience contain negative streaks but not positive streaks.
Revised:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and learn its parameters from predecessors . Agents stop when early draws are deemed "good enough," so predecessors' experience contain negative streaks but not positive streaks.
Edit intent: fluency
Original:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and learn its parameters from their predecesors . Agents stop when early draws are deemed "good enough," so predecessors' experience contain negative streaks but not positive streaks.
Revised:  They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and learn its parameters from their predecesors . Agents stop when early draws are "good enough," so predecessors' experience contain negative streaks but not positive streaks.
Edit intent: clarity
Original:  Since biased agents understate the likelihood of consecutive below-average draws, society converges to over-pessimistic beliefs about the distribution's mean . When early agents decrease their acceptance thresholds due to pessimism, later agents will become more surprised by the lack of positive reversals in their predecessors' histories, leading to more pessimistic inferences and lower acceptance thresholds -- a positive-feedback cycle. Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Revised:  Since biased agents understate the likelihood of consecutive below-average draws, society converges to over-pessimistic beliefs about the distribution's mean and stops too early. Agents uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Edit intent: clarity
Original:  Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent depending on the severity of data censoring .
Revised:  Agents who are additionally uncertain about the distribution's variance believe in fictitious variation (exaggerated variance) to an extent that depends on predecessors' stopping thresholds. Subsidizing search partially mitigates long-run belief distortions .
Edit intent: meaning-changed
Original:  Hand labeling sufficient numbers of particles can take months of effort and current computationally based approaches are often ineffective. Here, we frame particle picking as a positive-unlabeled classification problem in which we seek to learn a convolutional neural network (CNN) to classify micrograph regions as particle or background from a small number of labeled positive examples and many unlabeled examples. However, model fitting with very few labeled data points is a challenging machine learning problem .
Revised:  Hand labeling sufficient numbers of particles can take months of manual effort. Current computational approaches are limited by high false positive rates and require significant ad-hoc post-processing, especially for unusually shaped particles .
Edit intent: clarity
Original:  To address this  , we develop a novel objective function, GE-binomial, for learning model parameters in this context. This objective uses a newly-formulated generalized expectation criteria to learn effectively from unlabeled data when using minibatched stochastic gradient descent optimizers. On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples }  . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.
Revised:  To address this , we develop Topaz, an efficient and accurate particle picking pipeline using neural networks trained with few labeled particles by newly leveraging the remaining unlabeled particles through the framework of positive-unlabeled (PU) learning. Remarkably, despite using minimal labeled particles, Topaz allows us to improve reconstruction resolution by up to 0.15 } . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.
Edit intent: meaning-changed
Original:  On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples }  . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.
Revised:  On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples \AA} . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.
Edit intent: coherence
Original:  On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples }  . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.
Revised:  On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples } over published particles on three public cryoEM datasets without any post-processing . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.
Edit intent: meaning-changed
Original:  On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples }  . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available. We also compare our GE-binomial method with other positive-unlabeled learning methods never before applied to particle picking .
Revised:  On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples } . Furthermore, we show that our novel generalized-expectation criteria approach to PU learning outperforms existing general PU learning approaches when applied to particle picking .
Edit intent: meaning-changed
Original:  We expect our particle picking tool, Topaz , based on CNNs trained with GE-binomial, to be an essential component of single particle cryoEM analysisand our GE-binomial objective function to be widely applicable to positive-unlabeled classification problems .
Revised:  We expect Topaz to be an essential component of single particle cryoEM analysisand our GE-binomial objective function to be widely applicable to positive-unlabeled classification problems .
Edit intent: clarity
Original:  We expect our particle picking tool, Topaz , based on CNNs trained with GE-binomial, to be an essential component of single particle cryoEM analysisand our GE-binomial objective function to be widely applicable to positive-unlabeled classification problems .
Revised:  We expect our particle picking tool, Topaz , based on CNNs trained with GE-binomial, to be an essential component of cryoEM analysis .
Edit intent: clarity
Original: This paper presents a continuous-time equilibrium model of TWAP tradingand liquidity provision in a market with multiple strategic investors with heterogeneous intraday trading targets. We solve the model in closed-form and show there are infinitely many equilibria.
Revised: This paper presents a continuous-time model of intraday trading, pricing, and liquidity with dynamic TWAP and VWAP benchmarks. The model is solved in closed-form and show there are infinitely many equilibria.
Edit intent: meaning-changed
Original:  In addition, we show intraday TWAP benchmarking reduces market liquidity relative to just terminal trading targets alone.
Revised:  The intraday trajectories of TWAP trading targets cause predictable intraday patterns of price pressure, and randomness in VWAP target trajectories induces additional randomness in intraday price-pressure patterns. TWAP and VWAP trading both reduce market liquidity and increase price volatility relative to just terminal trading targets alone.
Edit intent: clarity
Original:  All of our data structures are work-efficient to build in theory (all need O(n log n) work) and achieve a low parallel depth (poly-logarithmic for the multi-level tree structures, and O(n?) for sweepline algorithms) .
Revised:  All of our data structures are work-efficient to build in theory and achieve a low parallel depth (poly-logarithmic for the multi-level tree structures, and O(n?) for sweepline algorithms) .
Edit intent: clarity
Original:  All of our data structures are work-efficient to build in theory (all need O(n log n) work) and achieve a low parallel depth (poly-logarithmic for the multi-level tree structures, and O(n?) for sweepline algorithms) .
Revised:  All of our data structures are work-efficient to build in theory (all need O(n log n) work) and achieve a low parallel depth . The query time is almost linear to the output size .
Edit intent: meaning-changed
Original:  Our sequential range query implementations outperforms the CGAL implementation by more than a factor of 2, and gets an additional 63-fold speedup on all cores .
Revised:  Our sequential range query implementations outperforms the CGAL library by at least 2x in both construction and queries. Our sequential implementation can be slightly slower than the R-tree in the Boost library in some cases (0.6-2.5x), but has significantly better query performance (1.6-1400x) than Boost .
Edit intent: meaning-changed
Original:  The features inferred by the RBM can be biologically interpreted in terms of structural modes, including residue-residue tertiary contacts and extended secondary motifs (\alpha-helix and \beta-sheet) , of functional modes controlling activity and ligand specificity , or of phylogenetic identity.
Revised:  The features inferred by the RBM are biologically interpretable: they are related to structure (such as residue-residue tertiary contacts and extended secondary motifs (\alpha-helix and \beta-sheet) , of functional modes controlling activity and ligand specificity , or of phylogenetic identity.
Edit intent: clarity
Original:  The features inferred by the RBM can be biologically interpreted in terms of structural modes, including residue-residue tertiary contacts and extended secondary motifs (\alpha-helix and \beta-sheet) , of functional modes controlling activity and ligand specificity , or of phylogenetic identity.
Revised:  The features inferred by the RBM can be biologically interpreted in terms of structural modes, including residue-residue tertiary contacts , extended secondary motifs (\alpha-helix and \beta-sheet) , of functional modes controlling activity and ligand specificity , or of phylogenetic identity.
Edit intent: fluency
Original:  The features inferred by the RBM can be biologically interpreted in terms of structural modes, including residue-residue tertiary contacts and extended secondary motifs (\alpha-helix and \beta-sheet) , of functional modes controlling activity and ligand specificity , or of phylogenetic identity.
Revised:  The features inferred by the RBM can be biologically interpreted in terms of structural modes, including residue-residue tertiary contacts and extended secondary motifs (\alpha-helix and \beta-sheet) and intrinsically disordered regions), to function (such as activity and ligand specificity , or of phylogenetic identity.
Edit intent: meaning-changed
Original:  The features inferred by the RBM can be biologically interpreted in terms of structural modes, including residue-residue tertiary contacts and extended secondary motifs (\alpha-helix and \beta-sheet) , of functional modes controlling activity and ligand specificity , or of phylogenetic identity.
Revised:  The features inferred by the RBM can be biologically interpreted in terms of structural modes, including residue-residue tertiary contacts and extended secondary motifs (\alpha-helix and \beta-sheet) , of functional modes controlling activity and ligand specificity ), or to phylogenetic identity.
Edit intent: fluency
Original:  We show that under two alternative sets of assumptions, such regressions identify weighted sums of  average treatment effects in each group and period, where some weights may be negative.
Revised:  We study two commonly used two-way fixed effects regressions. We start by showing that without assuming constant treatment effects, those regressions identify weighted sums of average treatment effects in each group and period, where some weights may be negative.
Edit intent: meaning-changed