wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls . Drawing upon  a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%.
Revised:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from major depressive disorder (MDD) and healthy controls . Drawing upon a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%.
Edit intent: fluency
Original:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls . Drawing upon  a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%.
Revised:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy control (HC) based on neuroimaging data . Drawing upon a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%.
Edit intent: meaning-changed
Original:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls . Drawing upon  a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%.
Revised:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls . Drawing upon structural magnetic resonance imaging (MRI) data from a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%.
Edit intent: meaning-changed
Original:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls . Drawing upon  a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%.
Revised:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls . Drawing upon a balanced sample of N = 1,868 MDD patients and HC from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%.
Edit intent: clarity
Original:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls . Drawing upon  a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \%. Next, we mimicked the process by which researchers would draw samples of various sizes (N=4 to N=150) from the population and showed a strong risk of overestimation. Specifically, for small sample sizes (N=20), we observe accuracies of up to 95\%. For medium sample sizes (N=100) accuracies up to 75\% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance overestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets.
Revised:  Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls . Drawing upon a balanced sample of N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61 \,
Edit intent: meaning-changed
Original:  In this short paper, we build an experimental system AutoAIViz that aims to visualize AutoAI's model generation process to increase users' level of understanding and trust in AutoAI systems.
Revised:  In this short paper, we provide a first user evaluation by 10 data scientists of an experimental system AutoAIViz that aims to visualize AutoAI's model generation process to increase users' level of understanding and trust in AutoAI systems.
Edit intent: meaning-changed
Original:  In this short paper, we build an experimental system AutoAIViz that aims to visualize AutoAI's model generation process to increase users' level of understanding and trust in AutoAI systems. Through a user study with 10 professional data scientists, we find that the proposed system helps participants to complete the data science tasks, and increases their perceptions of understandingand trust in the AutoAI system.
Revised:  In this short paper, we build an experimental system AutoAIViz that aims to visualize AutoAI's model generation process . We find that the proposed system helps participants to complete the data science tasks, and increases their perceptions of understandingand trust in the AutoAI system.
Edit intent: coherence
Original:  Through a user study with 10 professional data scientists, we find that the proposed system helps participants to complete the data science tasks, and increases their perceptions of understandingand trust in the AutoAI system.
Revised:  Through a user study with 10 professional data scientists, we find that the proposed system helps users to complete the data science tasks, and increases their perceptions of understandingand trust in the AutoAI system.
Edit intent: clarity
Original:  Through a user study with 10 professional data scientists, we find that the proposed system helps participants to complete the data science tasks, and increases their perceptions of understandingand trust in the AutoAI system.
Revised:  Through a user study with 10 professional data scientists, we find that the proposed system helps participants to complete the data science tasks, and increases their understanding, toward the goal of increasing trust in the AutoAI system.
Edit intent: clarity
Original:   There are two possible extensions for scalar backward stochastic differential equations for the set-valued framework: (1) backward stochastic differential inclusions  ;
Revised:  In the multivariate setting, dynamic risk measures have been defined and studied as families of set-valued functionals in the recent literature. There are two possible extensions for scalar backward stochastic differential equations for the set-valued framework: (1) backward stochastic differential inclusions ;
Edit intent: meaning-changed
Original:  In this paper, we consider developing new universal features for churn predictions for long-term players based on player in-game time spending. In particular, we measure player time spending regularity using the notion of entropy and cross-entropy from information theory as the metric to characterize variance and change in the in-game time spent by a player, from data sets of six free online games of different types. We leverage information from players' in-game time spending regularity in the form of universal features for churn prediction. Experiments show that our developed features are better at predicting churners compared to baseline features. Thus, the experiment results imply that our proposed features could utilize the information extracted from players' in-game time spending more effectively than related baseline time spending features .
Revised:  In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime .
Edit intent: coherence
Original:  Our  construction begins by noting that the statistical uncertainty of the SC prediction is governed by two distinct sources of randomness: one coming from the construction of the (likely misspecified) SC weights in the pre-treatment period, and the other coming from the unobservable stochastic error in the post-treatment period when the treatment effect is analyzed.
Revised:  Our method allows for covariate adjustment and non-stationary data, among other practically relevant features. The construction begins by noting that the statistical uncertainty of the SC prediction is governed by two distinct sources of randomness: one coming from the construction of the (likely misspecified) SC weights in the pre-treatment period, and the other coming from the unobservable stochastic error in the post-treatment period when the treatment effect is analyzed.
Edit intent: meaning-changed
Original: In recent decades, in developed economies, slack on the product and labor markets has fluctuated a lot over the business cycle, while inflation has been very stable. At the same time, these economies have been prone to enter long-lasting liquidity traps with stable positive inflation and high unemployment. Motivated by these observations, this paper develops a simple policy-oriented business-cycle model in which (1) fluctuations in aggregate demand and supply lead to fluctuations in slack but not in inflation;
Revised: This paper develops a new model of business cycles. The model is economical in that it is solved with an aggregate demand-aggregate supply diagram, and the effects of shocks and policies are obtained by comparative statics. The model builds on two unconventional assumptions. First, producers and consumers meet through a matching function. Thus, the model features unemployment, which fluctuates in response to aggregate demand and supply lead to fluctuations in slack but not in inflation;
Edit intent: meaning-changed
Original:  Motivated by these observations, this paper develops a simple policy-oriented business-cycle model in which (1) fluctuations in aggregate demand and supply lead to fluctuations in slack but not in inflation; and (2) the aggregate demand structure is consistent with permanent liquidity traps. The model extends the money-in-the-utility-function model by introducing matching frictions and including real wealth into the utility function .
Revised:  Motivated by these observations, this paper develops a simple policy-oriented business-cycle model in which (1) fluctuations in aggregate demand and supply shocks. Second, wealth enters the utility function .
Edit intent: clarity
Original:  The model extends the money-in-the-utility-function model by introducing matching frictions and including real wealth into the utility function . Matching frictions allow us to represent slack and to consider a general equilibrium with constant inflation. Wealth in the utility function enriches the aggregate demand structure to be consistent with permanent liquidity traps. We use the modelto study the effects of various aggregate demand and supply shocks, and to analyze several stabilization policies---such as conventional monetary policy , helicopter drop of money, tax on wealth , and government spending .
Revised:  The model extends the money-in-the-utility-function model by introducing matching frictions and including real wealth into the utility function , so the model allows for permanent zero-lower-bound episodes. In the model, the optimal monetary policy is to set the interest rate at the level that eliminates the unemployment gap. This optimal interest rate is computed from the prevailing unemployment gap and monetary multiplier (the effect of the nominal interest rate on the unemployment rate). If the unemployment gap is exceedingly large, monetary policy cannot eliminate it before reaching the zero lower bound, but a wealth tax can .
Edit intent: coherence
Original: We provide existence, uniqueness and stability results for affine stochastic Volterra equations with L^1-kernels  . Such equations arise as scaling limits of branching processes in population genetics and self-exciting Hawkes processes in mathematical finance.
Revised: We provide existence, uniqueness and stability results for affine stochastic Volterra equations with L^1-kernels and jumps . Such equations arise as scaling limits of branching processes in population genetics and self-exciting Hawkes processes in mathematical finance.
Edit intent: meaning-changed
Original:  The strategy we adopt for the existence part is based on approximations using stochastic Volterra equations with L^2-kernels  . Most importantly, we establish weak uniqueness using a duality argument on the Fourier-Laplace transform via a deterministic Riccati-Volterra integral equation.
Revised:  The strategy we adopt for the existence part is based on approximations using stochastic Volterra equations with L^2-kernels combined with a general stability result . Most importantly, we establish weak uniqueness using a duality argument on the Fourier-Laplace transform via a deterministic Riccati-Volterra integral equation.
Edit intent: meaning-changed
Original:  The strategy we adopt for the existence part is based on approximations using stochastic Volterra equations with L^2-kernels  . Most importantly, we establish weak uniqueness using a duality argument on the Fourier-Laplace transform via a deterministic Riccati-Volterra integral equation.
Revised:  The strategy we adopt for the existence part is based on approximations using stochastic Volterra equations with L^2-kernels . Most importantly, we establish weak uniqueness using a duality argument on the Fourier--Laplace transform via a deterministic Riccati-Volterra integral equation.
Edit intent: fluency
Original:  The strategy we adopt for the existence part is based on approximations using stochastic Volterra equations with L^2-kernels  . Most importantly, we establish weak uniqueness using a duality argument on the Fourier-Laplace transform via a deterministic Riccati-Volterra integral equation.
Revised:  The strategy we adopt for the existence part is based on approximations using stochastic Volterra equations with L^2-kernels . Most importantly, we establish weak uniqueness using a duality argument on the Fourier-Laplace transform via a deterministic Riccati--Volterra integral equation.
Edit intent: fluency
Original:  We illustrate the applicability of our results on  a class of hyper-rough Volterra Heston models with a Hurst index H \in (-1/2,1/2].
Revised:  We illustrate the applicability of our results on Hawkes processes and a class of hyper-rough Volterra Heston models with a Hurst index H \in (-1/2,1/2].
Edit intent: meaning-changed
Original: In many scenarios, detecting keywords from natural language queries is sufficient to understand the intent of the user. In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, allowing a user to include a  voice interface to its device without having to retrain a model on task-specific data.
Revised: In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, allowing a user to include a voice interface to its device without having to retrain a model on task-specific data.
Edit intent: coherence
Original:  In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, allowing a user to include a  voice interface to its device without having to retrain a model on task-specific data.
Revised:  In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, that allows the users to include a voice interface to its device without having to retrain a model on task-specific data.
Edit intent: coherence
Original:  In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, allowing a user to include a  voice interface to its device without having to retrain a model on task-specific data.
Revised:  In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, allowing a user to include a customizable voice interface to its device without having to retrain a model on task-specific data.
Edit intent: meaning-changed
Original:  We present a keyword detection neural network weighing less than 550KB , in which the topmost layer performing keyword detection is predicted by an auxiliary network, that may be run offline to generate a detector for any keyword  .
Revised:  We present a keyword detection neural network weighing less than 250KB , in which the topmost layer performing keyword detection is predicted by an auxiliary network, that may be run offline to generate a detector for any keyword .
Edit intent: meaning-changed
Original:  We present a keyword detection neural network weighing less than 550KB , in which the topmost layer performing keyword detection is predicted by an auxiliary network, that may be run offline to generate a detector for any keyword  .
Revised:  We present a keyword detection neural network weighing less than 550KB , in which the topmost layer performing keyword detection is predicted by an auxiliary network, that may be run offline to generate a detector for any keyword . We show that the proposed model outperforms acoustic keyword spotting baselines by a large margin on two tasks of detecting keywords in utterances and three tasks of detecting isolated speech commands. We also propose a method to fine-tune the model when specific training data is available for some keywords, which yields a performance similar to a standard speech command neural network while keeping the ability of the model to be applied to new keywords .
Edit intent: meaning-changed
Original: A safe transition between autonomous and manual control requires sustained visual attentionof the driver for the perception and assessment of hazards in dynamic driving environments. Thus, drivers must retain a certain level of situation awareness to safely takeover. Understanding the visual attention allocation of drivers can pave the way for inferring their dynamic state of situational awareness.
Revised: Inspired by human visual attention, we introduce a Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework for modeling the visual attention allocation of drivers can pave the way for inferring their dynamic state of situational awareness.
Edit intent: meaning-changed
Original:  Understanding the visual attention allocation of drivers can pave the way for inferring their dynamic state of situational awareness. We propose a reinforcement and inverse-reinforcement learning framework for modeling passive drivers' visual attention allocation in semi-autonomous vehicles. The proposed approach measures the eye-movement of passive drivers to evaluate their responses to real-world rear-end collisions.
Revised:  Understanding the visual attention allocation of drivers in imminent rear-end collisions.
Edit intent: clarity
Original:  The proposed approach measures the eye-movement of passive drivers to evaluate their responses to real-world rear-end collisions. The results show substantial individual differences in the eye fixation patterns by driving experience, even among fully attentive drivers.
Revised:  The proposed approach measures the eye-movement of passive drivers to evaluate their responses to real-world rear-end collisions. MEDIRL is composed of visual, driving, and attention modules. Given a front-view driving video and corresponding eye fixations from humans, the visual and driving modules extract generic and driving-specific visual features, respectively. Finally, the attention module learns the intrinsic task-sensitive reward functions induced by eye fixation policies recorded from attentive drivers.
Edit intent: meaning-changed
Original:  Experienced drivers were more attentive to the situational dynamics and were able to identify potentially hazardous objects before any collisions occurred. These models of visual attention could potentially be integrated into autonomous systems to continuously monitor and guide effective intervention. Keywords: Visual attention allocation ; Situation awareness; Eye movements; Eye fixation; Eye-Tracking; Reinforcement Learning; Inverse Reinforcement Learning
Revised:  MEDIRL uses the learned policies to predict visual attention allocation of drivers. We also introduce EyeCar, a new driver visual attention dataset during accident-prone situations. We conduct comprehensive experiments and show that MEDIRL outperforms previous state-of-the-art methods on driving task-related visual attention allocation on the following large-scale driving attention benchmark datasets: DR(eye)VE, BDD-A, and DADA-2000. The code and dataset are provided for reproducibility.
Edit intent: meaning-changed
Original:  The genotype space is taken to be a Cartesian power graph A ^L, where L is the number of genetic loci and the allele graph A encodes the possible allelic states and mutational transitions on one locus.
Revised:  The genotype space is taken to be a Cartesian power graph \mathcal{A ^L, where L is the number of genetic loci and the allele graph A encodes the possible allelic states and mutational transitions on one locus.
Edit intent: meaning-changed
Original:  The genotype space is taken to be a Cartesian power graph A ^L, where L is the number of genetic loci and the allele graph A encodes the possible allelic states and mutational transitions on one locus.
Revised:  The genotype space is taken to be a Cartesian power graph A ^L, where L is the number of genetic loci and the allele graph \mathcal{A encodes the possible allelic states and mutational transitions on one locus.
Edit intent: meaning-changed
Original:  The probability of existence of accessible paths between two genotypes at a distance linear in L displays a sharp transition from 0 to 1 at a threshold value \beta^\ast of the fitness difference between the initial and final genotype.
Revised:  The probability of existence of accessible paths between two genotypes at a distance linear in L displays a transition from 0 to 1 at a threshold value \beta^\ast of the fitness difference between the initial and final genotype.
Edit intent: clarity
Original:  The probability of existence of accessible paths between two genotypes at a distance linear in L displays a sharp transition from 0 to 1 at a threshold value \beta^\ast of the fitness difference between the initial and final genotype.
Revised:  The probability of existence of accessible paths between two genotypes at a distance linear in L displays a sharp transition from 0 to a positive value at a threshold value \beta^\ast of the fitness difference between the initial and final genotype.
Edit intent: meaning-changed