wanyu/IteraTeR_full_sent huggingface dataset:
Original:  After being trained on the large database of scPDB, DeepSurf demonstrates superior performance on two diverse testing datasets, by surpassing all its main deep learning-based competitors  .
Revised:  After being trained on the large database of scPDB, DeepSurf demonstrates superior performance on two diverse testing datasets, by surpassing all its main deep learning-based competitors , while attaining competitive performance to a set of traditional non-data-driven approaches .
Edit intent: meaning-changed
Original: We develop an analytical framework to study experimental design in two-sided platforms. In the settings we consider, customers rent listings; rented listings are occupied for some amount of time, then become available. Platforms typically use two common designs to study interventions in such settings: customer-side randomization (CR), and listing-side randomization (LR), along with associated estimators .
Revised: We develop an analytical framework to study experimental design in two-sided marketplaces. Many of these experiments exhibit interference, where an intervention applied to one market participant influences the behavior of another participant. This interference leads to biased estimates of the treatment effect of the intervention .
Edit intent: meaning-changed
Original:  We develop a stochastic  model and associated mean field limit to capture dynamics in such systems , and use our model to investigate how performance of these estimators is affected by interference effects between listings and between customers.
Revised:  We develop a stochastic market model and associated mean field limit to capture dynamics in such systems , and use our model to investigate how performance of these estimators is affected by interference effects between listings and between customers.
Edit intent: meaning-changed
Original:  We develop a stochastic  model and associated mean field limit to capture dynamics in such systems , and use our model to investigate how performance of these estimators is affected by interference effects between listings and between customers.
Revised:  We develop a stochastic model and associated mean field limit to capture dynamics in such systems , and use our model to investigate how the performance of different designs and estimators is affected by interference effects between listings and between customers.
Edit intent: meaning-changed
Original:  We develop a stochastic  model and associated mean field limit to capture dynamics in such systems , and use our model to investigate how performance of these estimators is affected by interference effects between listings and between customers. Good experimental design depends on market balance: we show that in highly demand-constrained markets, CR is unbiased, while LR is biased;
Revised:  We develop a stochastic model and associated mean field limit to capture dynamics in such systems , and use our model to investigate how performance of these estimators is affected by marketplace interference effects. Platforms typically use two common experimental designs: demand-side ("customer") randomization (CR) and supply-side ("listing") randomization (LR), along with their associated estimators. We show that good experimental design depends on market balance: we show that in highly demand-constrained markets, CR is unbiased, while LR is biased;
Edit intent: meaning-changed
Original:  We also study a design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control , and show that appropriate choices of such designs can be unbiased in both extremes of market balance, and also yield low bias in intermediate regimes of market balance.
Revised:  We also introduce and study a novel experimental design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control , and show that appropriate choices of such designs can be unbiased in both extremes of market balance, and also yield low bias in intermediate regimes of market balance.
Edit intent: clarity
Original:  We also study a design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control , and show that appropriate choices of such designs can be unbiased in both extremes of market balance, and also yield low bias in intermediate regimes of market balance.
Revised:  We also study a design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control . We show that appropriate choices of such designs can be unbiased in both extremes of market balance, and also yield low bias in intermediate regimes of market balance.
Edit intent: fluency
Original:  We also study a design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control , and show that appropriate choices of such designs can be unbiased in both extremes of market balance, and also yield low bias in intermediate regimes of market balance.
Revised:  We also study a design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control , and show that appropriate choices of TSR designs can be unbiased in both extremes of market balance, and also yield low bias in intermediate regimes of market balance.
Edit intent: clarity
Original:  We also study a design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control , and show that appropriate choices of such designs can be unbiased in both extremes of market balance, and also yield low bias in intermediate regimes of market balance.
Revised:  We also study a design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control , and show that appropriate choices of such designs can be unbiased in both extremes of market balance, while yielding relatively low bias in intermediate regimes of market balance.
Edit intent: clarity
Original:   To answer the question "What would a human operator like to ask the ML model?" we propose a conversational system explaining decisions of the predictive model.
Revised:  The analysis of needs, if done, takes the form of an A/B test rather than a study of open questions. To answer the question "What would a human operator like to ask the ML model?" we propose a conversational system explaining decisions of the predictive model.
Edit intent: meaning-changed
Original:  Firstly, I show that in the context of a single-species model, neglecting  intraspecific competition leads to an overestimation of a patterned ecosystem's resilience to increases in aridity.
Revised:  Firstly, I show that in the context of a single-species model, neglecting local intraspecific competition leads to an overestimation of a patterned ecosystem's resilience to increases in aridity.
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Original:  Secondly, in the context of a multispecies model, I argue that  intraspecific competition is a key element in the successful capture of species coexistence in model solutions representing a vegetation pattern.
Revised:  Secondly, in the context of a multispecies model, I argue that local intraspecific competition is a key element in the successful capture of species coexistence in model solutions representing a vegetation pattern.
Edit intent: meaning-changed
Original:  In this paper, we present ACCORDION (Automated Clustering Conditional On Relating Data of Interactions tO a Network), a novel tool and a methodology to enable efficient answering of biological questions by automatically assembling new  or expanding existing models using published literature.
Revised:  In this paper, we present ACCORDION (Automated Clustering Conditional On Relating Data of Interactions tO a Network), a novel tool and a methodology to enable efficient answering of biological questions by automatically assembling new , or expanding existing models using published literature.
Edit intent: fluency
Original: AI has long pursued the goal of having systems reason over *explicitly provided* knowledge, but building suitable representations has proved challenging. Here we explore whether transformers can similarly learn to reason (or emulate reasoning) , but using rules expressed in language , thus bypassing a formal representation.
Revised: AI has long pursued the goal of providing a system with explicit, general knowledge and having the system reason over that knowledge. However, expressing the knowledge in a formal (logical or probabilistic) representation has been a major obstacle to this research. This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences , thus bypassing a formal representation.
Edit intent: meaning-changed
Original:  We  provide the first  demonstration that this is possible, and characterize the extent of this capability. To do this, we use a collection of synthetic datasets that test increasing levels of reasoning complexity (number of rules, presence of negation, and depth of chaining). We find transformers appear to learn rule-based reasoning with high (99\%) accuracy on these datasets, and in a way that generalizes to test data requiring substantially deeper chaining than in the training data (95\%+ scores).
Revised:  We provide the first demonstration that this kind of soft reasoning over language is learnable, can achieve high (99\%) accuracy on these datasets, and in a way that generalizes to test data requiring substantially deeper chaining than in the training data (95\%+ scores).
Edit intent: clarity
Original:  We find transformers appear to learn rule-based reasoning with high (99\%) accuracy on these datasets, and in a way that generalizes to test data requiring substantially deeper chaining than in the training data (95\%+ scores).
Revised:  We find transformers appear to learn rule-based reasoning with high (99\%) accuracy , and generalizes to test data requiring substantially deeper chaining than in the training data (95\%+ scores).
Edit intent: clarity
Original:  We find transformers appear to learn rule-based reasoning with high (99\%) accuracy on these datasets, and in a way that generalizes to test data requiring substantially deeper chaining than in the training data (95\%+ scores).
Revised:  We find transformers appear to learn rule-based reasoning with high (99\%) accuracy on these datasets, and in a way that generalizes to test data requiring substantially deeper chaining than seen during training (95\%+ scores).
Edit intent: clarity
Original:  We first apply our technique to a variety of synthetic and real-world datasets with known strange attractors, and we use established and novel measures of attractor fidelity to show that our method successfully reconstructs attractors better than existing techniques .
Revised:  We show that our technique reconstructs the strange attractors of synthetic and real-world datasets with known strange attractors, and we use established and novel measures of attractor fidelity to show that our method successfully reconstructs attractors better than existing techniques .
Edit intent: clarity
Original:  We first apply our technique to a variety of synthetic and real-world datasets with known strange attractors, and we use established and novel measures of attractor fidelity to show that our method successfully reconstructs attractors better than existing techniques . We then use our technique to discover dynamical attractors in datasets ranging from patient electrocardiograms, to household electricity usage, to eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Revised:  We first apply our technique to a variety of synthetic and real-world datasets with known strange attractors, and we use established and novel measures of attractor fidelity to show that our method successfully reconstructs attractors better than existing techniques , and that it creates consistent, predictive representations of even stochastic systems. We conclude by using our technique to discover dynamical attractors in datasets ranging from patient electrocardiograms, to household electricity usage, to eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Edit intent: meaning-changed
Original:  We then use our technique to discover dynamical attractors in datasets ranging from patient electrocardiograms, to household electricity usage, to eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Revised:  We then use our technique to discover dynamical attractors in diverse systems such as patient electrocardiograms, to household electricity usage, to eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Edit intent: clarity
Original:  We then use our technique to discover dynamical attractors in datasets ranging from patient electrocardiograms, to household electricity usage, to eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Revised:  We then use our technique to discover dynamical attractors in datasets ranging from patient electrocardiograms, household electricity usage, to eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Edit intent: fluency
Original:  We then use our technique to discover dynamical attractors in datasets ranging from patient electrocardiograms, to household electricity usage, to eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Revised:  We then use our technique to discover dynamical attractors in datasets ranging from patient electrocardiograms, to household electricity usage, and eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Edit intent: fluency
Original:  Inspired by classical techniques for studying the strange attractors of chaotic systems , we introduce a general embedding technique for  time series, consisting of an autoencoder trained with a novel latent-space loss function.
Revised:  Inspired by classical techniques for studying the strange attractors of chaotic systems , we introduce a general embedding technique for univariate and multivariate time series, consisting of an autoencoder trained with a novel latent-space loss function.
Edit intent: meaning-changed
Original:  We conclude by using our technique to discover dynamical attractors in diverse systems such as patient electrocardiograms, household electricity usage  , and eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Revised:  We conclude by using our technique to discover dynamical attractors in diverse systems such as patient electrocardiograms, household electricity usage , neural spiking , and eruptions of the Old Faithful geyser---demonstrating diverse applications of our technique for exploratory data analysis.
Edit intent: meaning-changed
Original:  We use  predictive information maximization  as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes in the robot's morphology and environment.
Revised:  We use an information theoretic quantity - predictive information maximization as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes in the robot's morphology and environment.
Edit intent: meaning-changed
Original:  We use  predictive information maximization  as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes in the robot's morphology and environment.
Revised:  We use predictive information maximization - as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes in the robot's morphology and environment.
Edit intent: fluency
Original:  We use  predictive information maximization  as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes in the robot's morphology and environment.
Revised:  We use predictive information maximization as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes to the robot's morphology and environment.
Edit intent: fluency
Original:  We use  predictive information maximization  as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes in the robot's morphology and environment. To the authors' knowledge there are no previous HRI studies that evaluate the effect of intrinsically motivated behavior in robots on the human perception of those robots.
Revised:  We use predictive information maximization as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes in the robot's morphology and to its environment.
Edit intent: coherence
Original:  We conducted a within-subjects study (N=24) were participants interacted with a fully autonomous Sphero BB8 robot with different behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive.
Revised:  We conducted a within-subjects study (N=24) where participants interacted with a fully autonomous Sphero BB8 robot with different behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive.
Edit intent: fluency
Original:  Here we evaluate if a physical robot that generates its behavior based on its intrinsic motivations could address this challenge. We use an information theoretic quantity - predictive information maximization - as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes to the robot 's morphology and to its environment. We present a game-like study design, which allows us to focus on the interplay between the robot and the human participant. In contrast to a study design where participants order or control a robot to do a specific task, the robot andthe human participants in our study design explore their behaviors without any specific goals .
Revised:  Our idea is to imbue the robot with intrinsic motivation (IM) so that it can handle new situations and appears as a genuine social other to humans and thus be of more interest to a human interaction partner. Human-robot interaction (HRI) experiments mainly focus on scripted or teleoperated robots, that mimic characteristics such as IM to control isolated behavior factors. This article presents a "robotologist" study design that allows comparing autonomously generated behaviors with each other, and, for the first time, evaluates the human perception of IM-based generated behavior in robots .
Edit intent: coherence
Original:  We conducted a within-subjects  study (N=24) where participants interacted with a fully autonomous Sphero BB8 robot with different behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive.
Revised:  We conducted a within-subjects user study (N=24) where participants interacted with a fully autonomous Sphero BB8 robot with different behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive.
Edit intent: meaning-changed
Original:  For any (randomized) algorithm, there are input graphs with n nodes and maximum degree \Delta on which \Omega(\min\{\log n/\log \log n,\log \Delta/\log \log \Delta\}) (expected) communication rounds are required to obtain polylogarithmic approximations to a minimum vertex cover, minimum dominating set, or maximum matching , respectively . Via reduction, this hardness extends to symmetry breaking tasks like finding maximal independent sets or maximal matchings.
Revised:  For any (randomized) algorithm, there are input graphs with n nodes and maximum degree \Delta on which \Omega(\min\{\log n/\log \log n,\log \Delta/\log \log \Delta\}) (expected) communication rounds are required to obtain polylogarithmic approximations to a minimum vertex cover, minimum dominating set, or maximum matching . Via reduction, this hardness extends to symmetry breaking tasks like finding maximal independent sets or maximal matchings.
Edit intent: coherence
Original:  Modelling a target variable using the correct set of causal variables yields a model that generalises across environments or subjects (if these environments leave the causal mechanisms intact ). Conversely , if a candidate model does not generalise, then either it includes non-causes of the target variable  or the underlying variables are wrongly defined .
Revised:  Modelling a target variable using the correct set of causal variables yields a model that generalises across environments or subjects (if these environments leave the causal mechanisms intact . Consequently , if a candidate model does not generalise, then either it includes non-causes of the target variable or the underlying variables are wrongly defined .
Edit intent: coherence
Original:  FQuAD is  French Native Reading Comprehension dataset that consists of 25,000+ questions on a set of Wikipedia articles . A baseline model is trained which achieves an F1 score of 88.0\% and an exact match ratio of 77.9\% on the test set.
Revised:  FQuAD is French Native Reading Comprehension dataset that consists of 25,000+ questions on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 88.0\% and an exact match ratio of 77.9\% on the test set.
Edit intent: meaning-changed
Original:  A notable result was a series of feedback items pointing to the importance of facilitating educational experiences through learning resources to support individuals with different abilities, age, gender expression, to better adopt off-the-shelf IoT-based solutions.
Revised:  A notable result was a series of feedback items pointing to the importance of creating learning resources to support individuals with different abilities, age, gender expression, to better adopt off-the-shelf IoT-based solutions.
Edit intent: clarity
Original: We propose UniViLM: a Unified Video and Language pre-training Model for multimodal understanding and generation. Motivated by the recent success of BERT based pre-training technique for NLP and image-language tasks, VideoBERT and CBT are proposed to exploit BERT model for video and language pre-training using narrated instructional videos.
Revised: With the recent success of BERT based pre-training technique for NLP and image-language tasks, VideoBERT and CBT are proposed to exploit BERT model for video and language pre-training using narrated instructional videos.
Edit intent: coherence
Original:  Motivated by the recent success of BERT based pre-training technique for NLP and image-language tasks, VideoBERT and CBT are proposed to exploit BERT model for video and language pre-training using narrated instructional videos.
Revised:  Motivated by the recent success of pre-training technique for NLP and image-language tasks, VideoBERT and CBT are proposed to exploit BERT model for video and language pre-training using narrated instructional videos.
Edit intent: coherence