wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This  document describes the format  of the SPHERE dataset , as well as the known and potential data quality problems  , their workarounds, and other information important to people working with the SPHERE data, software, and hardware  .
Revised:  This document describes the format of the SPHERE dataset , as well as the known and potential data quality problems , their workarounds, and other information important to people working with the SPHERE data, software, and hardware . This document does not aim to be an exhaustive descriptor of the SPHERE dataset(s); it also does not aim to discuss or validate the potential scientific uses of the SPHERE data .
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Original:  In this research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE), to predict the stock trend of major critical metal producers. Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors , " each advisor dealing with a particular economic sector.
Revised:  In this research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE), for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to group the features into different "advisors , " each advisor dealing with a particular economic sector.
Edit intent: meaning-changed
Original:  Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors , " each advisor dealing with a particular economic sector.Then through ensembles of weak classifiers, each advisorproduces a prediction result, and all the advisors are combined again in a biased online update fashion to dynamically make the final prediction. Based on a misclassification error of 32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an excess return of 130\% within one year .
Revised:  Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors , " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy. We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an excess return of 130\% within one year .
Edit intent: meaning-changed
Original:  Based on a misclassification error of 32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an excess return of 130\% within one year .
Revised:  Based on a misclassification error of 31.12\% and excess return of 130\% within one year .
Edit intent: meaning-changed
Original:  Based on a misclassification error of 32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an excess return of 130\% within one year .
Revised:  Based on a misclassification error of 32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an excess return of 477\% compared to the stock itself in a year and a half .
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Original:  In addition , the feature set selected by dynABE also suggests potentially influential factors to metal criticality, because stock prices of major producers influence metal production. Therefore, not only does this research propose a novel framework for specialized stock trend prediction, it also provides domain insights into dynamic features that potentially influence metal criticality .
Revised:  In addition to presenting an effective stock prediction model with decent profitabilities, this research further analyzes dynABE to visualize how it works in practice, which also yields discoveries of its interesting behaviors when processing time-series data .
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Original: The demand for metals by modern technology has been shifting from common base metals to a variety of minor metals, such as cobalt or indium. The industrial importance and limited geological availability of some minor metals have led to them being considered more "critical, " and there is a growing investment interest in such critical metals and their producing companies .
Revised: Stock trend prediction is a challenging task due to the market's noise, and machine learning techniques have recently been successful in coping with this challenge .
Edit intent: meaning-changed
Original:  In this research, we create a novel framework  , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Revised:  In this research, we create a novel framework for stock prediction , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Edit intent: fluency
Original:  In this research, we create a novel framework  , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Revised:  In this research, we create a novel framework , Dynamic Advisor-Based Ensemble (dynABE) . dynABE explores domain-specific areas based on the companies of interest, diversifies the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Edit intent: meaning-changed
Original:  In this research, we create a novel framework  , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Revised:  In this research, we create a novel framework , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by creating different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Edit intent: coherence
Original:  In this research, we create a novel framework  , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Revised:  In this research, we create a novel framework , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Edit intent: fluency
Original:  In this research, we create a novel framework  , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Revised:  In this research, we create a novel framework , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " that each handles a different area, follows an effective model ensemble procedure for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Edit intent: meaning-changed
Original:  In this research, we create a novel framework  , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Revised:  In this research, we create a novel framework , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together in a second-level ensemble through an online update strategy .
Edit intent: clarity
Original:  In this research, we create a novel framework  , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
Revised:  In this research, we create a novel framework , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy we developed. dynABE is able to adapt to price pattern changes of the market during the active trading period robustly, without needing to retrain the entire model.
Edit intent: meaning-changed
Original:  We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12\% and excess return of 477\% compared to the stock itself in a year and a half. In addition to presenting an effective stock prediction model with decent profitabilities, this research further analyzes dynABE to visualize how it works in practice, which also yields discoveries of its interesting behaviors when processing time-series data .
Revised:  We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12\% and an annualized absolute return of 359.55\% with zero maximum drawdown. dynABE also consistently outperforms the baseline models of support vector machine, neural network, and random forest in all case studies .
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Original: New technologies such as single-cell RNA sequencing and mass cytometry are measuring cellular populations in high dimensions, offering unparalleled insights into cellular behavior and enabling new scientific discoveries. However, when these measurements are applied to multiple samples or experimental conditions, the resulting systematic variations, or batch effects, confound biological variation and create a vexing problem in comparing cellular populations. Moreover, these batch effects, which arise as a result of changed environmental condition, instrument variation, machine calibration, or human handling differences, can be complex and highly non-linear transformations. Despite their ubiquity, there are few computational tools designed to correct generally for such effects while maintaining biological differences. The ones that exist hold strong assumptions (such as linear shifts between batches). Here, we propose an entirely novel approach to disentangling biological from batch variation where we take a specific subpopulation of cells as a control between the batches. This subpopulation can be an unchanged population (known via prior biology) or a repeatedly measured spike-in.
Revised: While neural networks can be trained to map from one specific dataset to another, they usually do not learn a generalized transformation that can extrapolate accurately outside the space of training. For instance, a generative adversarial network (GAN) exclusively trained to transform images of cars from light to dark might not have the same effect on images of horses. This is because neural networks are good at generation within the manifold of the data that they are trained on. However, generating new samples outside of the manifold or extrapolating "out-of-sample" is a much harder problem that has been less well studied. To address this, we introduce a technique called neuron editing that learns how neurons encode an edit for a particular transformation in a latent space.
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Original:  We use an autoencoder to model the variation in the control, and then interfere with neuron activations on inference to correct for these differences on the entire sample. This technique , which we term neuron interference, is unique in its ability to generalize a batch effect learned on a subpopulation to the entire population .
Revised:  We use an autoencoder to decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons. By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations. We showcase our technique on image domain/style transfer and two biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs .
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Original:  The shrinkage priors are complemented by heavy tailed state innovations that cater for potential large swings in the latent states even if the amount of shrinkage introduced is high . Moreover, we allow for leptokurtic stochastic volatility in the observation equation.
Revised:  The shrinkage priors are complemented by heavy tailed state innovations that cater for potential large breaks in the latent states even if the amount of shrinkage introduced is high . Moreover, we allow for leptokurtic stochastic volatility in the observation equation.
Edit intent: clarity
Original:  The shrinkage priors are complemented by heavy tailed state innovations that cater for potential large swings in the latent states even if the amount of shrinkage introduced is high . Moreover, we allow for leptokurtic stochastic volatility in the observation equation.
Revised:  The shrinkage priors are complemented by heavy tailed state innovations that cater for potential large swings in the latent states . Moreover, we allow for leptokurtic stochastic volatility in the observation equation.
Edit intent: clarity
Original: The understanding of variations in genome sequences assists us in identifying people who are predisposed to common diseases , solving rare diseases, and finding the corresponding population group of the individuals from a larger population group. Although classical machine learning techniques allow researchers to identify groups (i. e. clusters) of related variables, the accuracy, and effectiveness of these methods diminish for large and high-dimensional datasets such as the whole human genome. On the other hand , deep neural network architectures (the core of deep learning) can better exploit large-scale datasets to build complex models .
Revised: The understanding of variations in genome sequences assists us in identifying people who are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning algorithms are increasingly being applied to identify interacting GVs to understand their complex phenotypic traits. Since the performance of a learning algorithm not only depends on the size and nature of the data but also on the quality of underlying representation , deep neural network architectures (the core of deep learning) can better exploit large-scale datasets to build complex models .
Edit intent: coherence
Original:  In this paper, we use the K-means clustering approach for scalable genomic data analysis aiming towards clustering genotypic variants at the population scale. Finally, we train a deep belief network (DBN) for predicting the geographic ethnicity . We used the genotype data from the 1000 Genomes Project, which covers the result of genome sequencing for 2504 individuals from 26 different ethnic origins and comprises 84 million variants.
Revised:  In this paper, we proposed convolutional embedded networks in which we combine two DNN architectures called convolutional embedded clustering and convolutional autoencoder classifier for clustering individuals and predicting geographic ethnicity based on GVs, respectively. We employed CAE-based representation learning on 95 million GVs from the 1000 Genomes Project, which covers the result of genome sequencing for 2504 individuals from 26 different ethnic origins and comprises 84 million variants.
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Original:  We used the genotype data from the 1000 Genomes Project, which covers the result of genome sequencing for 2504 individuals from 26 different ethnic origins and comprises 84 million variants. Our experimental results, with a focus on accuracy and scalability , show the effectiveness and superiority compared to the state-of-the-art  .
Revised:  We used the genotype data from the 1000 genomes and Simons genome diversity projects. Quantitative and qualitative analyses with a focus on accuracy and scalability , show the effectiveness and superiority compared to the state-of-the-art .
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Original:  Our experimental results, with a focus on accuracy and scalability , show the effectiveness and superiority compared to the state-of-the-art  .
Revised:  Our experimental results, with a focus on accuracy and scalability , show the effectiveness and superiority compared to the state-of-the-art approaches such as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted population groups in 22 hours with an adjusted rand index of 0.915, the normalized mutual information of 0.92, and the clustering accuracy of 89\%. Contrarily, the CAE classifier can predict the geographic ethnicity of unknown samples with an F1 and Mathews correlation coefficient(MCC) score of 0.9004 and 0.8245, respectively. To provide interpretations of the predictions, we identify significant biomarkers using gradient boosted trees(GBT) and SHAP. Overall, our approach is transparent and faster than the baseline methods, and scalable for 5\% to 100\% of the full human genome .
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Original:  MLM run time is an order of magnitude faster than eGFRD when diffusing macromolecules with typical concentrations reported in the cell.abstract 
Revised:  MLM run time is an order of magnitude faster than eGFRD when diffusing macromolecules with typical concentrations reported in the cell.abstract \% larger than the molecule. MLM is validated by numerical simulations, showing good agreement with the off-lattice particle-based method, eGFRD. MLM run time is more than an order of magnitude faster than eGFRD when diffusing macromolecules with typical concentrations in the cell. MLM also showed good agreements with eGFRD and mean-field models in case studies of two basic motifs of intracellular signaling, the protein production-degradation process and the dual phosphorylation cycle. Moreover, when a reaction compartment is populated with volume-excluding obstacles, MLM captures the non-classical reaction kinetics caused by anomalous diffusion of reacting molecules.
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Original:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning  . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
Revised:  Here we propose an alternative perspective: that higher-order associations actually arise from natural errors in learning . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
Edit intent: clarity
Original:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning  . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
Revised:  Here we propose a competing perspective: that higher-order associations instead arise from natural errors in learning . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
Edit intent: clarity
Original:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning  . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
Revised:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning and memory . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
Edit intent: meaning-changed
Original:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning  . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
Revised:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning . Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) model of people's internal expectations about the transition structures underlying sequences of ordered events .
Edit intent: meaning-changed