wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper we present a salience-affected neural network (SANN) model which, at the same time as local network processing of task-specific information, includes non-local salience (significance) effects responding to an input salience signal.
Revised:  In this paper we present a bio-inspired ANN architecture which we call a Salience-Affected Neural Network (SANN) model which, at the same time as local network processing of task-specific information, includes non-local salience (significance) effects responding to an input salience signal.
Edit intent: meaning-changed
Original:  In this paper we present a salience-affected neural network (SANN) model which, at the same time as local network processing of task-specific information, includes non-local salience (significance) effects responding to an input salience signal.
Revised:  In this paper we present a salience-affected neural network (SANN) , which, at the same time as local network processing of task-specific information, includes non-local salience (significance) effects responding to an input salience signal.
Edit intent: clarity
Original:  In addition, training with salience affects the weights of connections between nodes, and improves the overall accuracy of a classification of images similar to the salience-tagged input after just a single iteration of training  .
Revised:  In addition, training with salience affects the weights of connections between nodes, and improves the overall accuracy of a classification of images similar to the salience-tagged input after just a single iteration of training . Note that we are not aiming to present an accurate model of the biological salience system; rather we present an artificial neural network inspired by those biological systems in the human brain, that has unique strengths .
Edit intent: meaning-changed
Original: Standard artificial neural networks (ANNs), loosely based on the structure of columns in the cortex, model key cognitive aspects of brain function such as learning and classification, but do not model the affective (emotional) aspects of brain function. However these are a key feature of the brain (the associated `ascending systems' have been hard-wired into the brain by evolutionary processes) . These emotions are associated with memories when neuromodulators such as dopamine and noradrenaline affect  entire patterns of synaptically activated neurons.
Revised: In this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way neuromodulators such as dopamine and noradrenaline affect entire patterns of synaptically activated neurons.
Edit intent: coherence
Original:  These emotions are associated with memories when neuromodulators such as dopamine and noradrenaline affect  entire patterns of synaptically activated neurons.
Revised:  These emotions are associated with memories when neuromodulators such as dopamine and noradrenaline affect neural dynamics in the human brain by being distributed diffusely through neocortical regions. This allows one-time learning to take place through strengthening entire patterns of synaptically activated neurons.
Edit intent: meaning-changed
Original:  These emotions are associated with memories when neuromodulators such as dopamine and noradrenaline affect  entire patterns of synaptically activated neurons. Here we present a bio-inspired ANN architecture which we call a Salience-Affected Neural Network (SANN), which, at the same time as local network processing of task-specific information, includes non-local salience (significance) effects responding to an input salience signal. During pattern recognition, inputs similar to the salience-affected inputs in the training data will produce reverse salience signals corresponding to those experienced when the memories were laid down. In addition, training with salience affects the weights of connections between nodes, and improves the overall accuracy of a classification of images similar to the salience-tagged input after just a single iteration of training. Note that we are not aiming to present an accurate model of the biological salience system; rather we present an artificial neural network inspired by those biological systems in the human brain, that has unique strengths .
Revised:  These emotions are associated with memories when neuromodulators such as dopamine and noradrenaline affect entire patterns of activation at one go. We present a model that accepts a .
Edit intent: clarity
Original:  rather we present an artificial neural network inspired by those biological systems in the human brain, that has unique strengths .
Revised:  rather we present an artificial neural network inspired by those biological systems in the human brain, that has unique strengths. We demonstrate that we can tag an image with salience with only a single training iteration, and that the same image will then produces the highest reverse salience signal during classification. We explore the effects of salience on learning via its effect on the activation functions of each node, as well as on the strength of weights in the network. We demonstrate that a salience signal improves classification accuracy of the specific image that was tagged with salience, as well as all images in the same class, while penalizing images in other classes. Results are validated using 5-fold validation testing on MNIST and Fashion MNIST datasets. This research serves as a proof of concept, and could be the first step towards introducing salience tagging into Deep Learning Networks and robotics .
Edit intent: meaning-changed
Original: In this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way neuromodulators such as dopamine and noradrenaline affect neural dynamics in the human brain by being distributed diffusely through neocortical regions . This allows one-time learning to take place through strengthening entire patterns of activation at one go.
Revised: In this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way neuromodulators such as dopamine and noradrenaline affect neural dynamics in the human brain by being distributed diffusely through neocortical regions , allowing both salience signals to modulate cognition immediately, and one time learning to take place through strengthening entire patterns of activation at one go.
Edit intent: meaning-changed
Original:  We demonstrate that a salience signal improves classification accuracy of the specific image that was tagged with salience, as well as all images in the same class , while penalizing images in other classes. Results are validated using 5-fold validation testing on MNIST and Fashion MNIST datasets .
Revised:  We demonstrate that a salience signal improves classification accuracy of the specific image that was tagged with salience, as well as the class of images it belongs to. We also show that the computation impact of producing a salience response is minimal .
Edit intent: clarity
Original:  In this paper, we give an overview of the advances in machine learning based automated personality detection with an emphasis on deep learning techniques. We compare various popular approaches in this field based on input modality, the computational datasetsavailable and discuss potential industrial applications. We also discuss the state-of-the-art machine learning models for different modalities of input such as text, audio, visual and multimodal . Personality detection is a very broad topic and this literature survey focuses mainly on machine learning techniques rather than the psychological aspect of personality detection.
Revised:  In this paper, we give an overview of the advances in machine learning based automated personality detection with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for different modalities of input such as text, audio, visual and multimodal . Personality detection is a very broad topic and this literature survey focuses mainly on machine learning techniques rather than the psychological aspect of personality detection.
Edit intent: clarity
Original:  We also discuss the state-of-the-art machine learning models for different modalities of input such as text, audio, visual and multimodal . Personality detection is a very broad topic and this literature survey focuses mainly on machine learning techniques rather than the psychological aspect of personality detection.
Revised:  We also discuss the state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches . Personality detection is a very broad topic and this literature survey focuses mainly on machine learning techniques rather than the psychological aspect of personality detection.
Edit intent: clarity
Original:  We also discuss the state-of-the-art machine learning models for different modalities of input such as text, audio, visual and multimodal . Personality detection is a very broad topic and this literature survey focuses mainly on machine learning techniques rather than the psychological aspect of personality detection.
Revised:  We also discuss the state-of-the-art machine learning models for different modalities of input such as text, audio, visual and multimodal . Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.
Edit intent: clarity
Original:  Under weak selection, meaning a small perturbation of the neutral model, we give a general first-order expansion of a mutant's fixation probability for an arbitrary initial configuration of mutant and resident types.
Revised:  This fixation probability is difficult to compute in general, as it depends on the mutation's effect on URLanism as well as the population's spatial structure, mating patterns, and other factors. Here we consider weak selection, meaning a small perturbation of the neutral model, we give a general first-order expansion of a mutant's fixation probability for an arbitrary initial configuration of mutant and resident types.
Edit intent: meaning-changed
Original:  Under weak selection, meaning a small perturbation of the neutral model, we give a general first-order expansion of a mutant's fixation probability for an arbitrary initial configuration of mutant and resident types.
Revised:  Under weak selection, meaning the mutation's effect on URLanism is small. We obtain a weak-selection perturbation expansion of a mutant's fixation probability for an arbitrary initial configuration of mutant and resident types.
Edit intent: meaning-changed
Original:  Evolving datasets arise in energy systems (e.g., real-time smart meter measurements), transportation (e.g., real-time traces of individual movements), and retail industry (e.g., customer interactions and purchases from online stores). We first define privacy loss as a measure of the amount of information leaked by the reports at a certain fixed time and relate privacy loss to differential privacy .
Revised:  We use privacy loss as a measure of the amount of information leaked by the reports at a certain fixed time and relate privacy loss to differential privacy .
Edit intent: coherence
Original:  Using an agent-based model, we show that cells with memory navigating rugged chemoattractant landscapes can enhance their drift speed by extracting information from the correlationsin their environment beyond local gradients. The improvement is maximal when the cellular memory is comparable to the timescale of the fluctuations perceived during swimming.
Revised:  Using an agent-based model, we show that cells with memory navigating rugged chemoattractant landscapes can enhance their drift speed by extracting information from environmental correlations. Maximal advantage is achieved when the memory is comparable to the timescale of the fluctuations perceived during swimming.
Edit intent: clarity
Original:  We then extend coarse-grained population models and derive an analytical approximation for the drift velocity in rugged landscapes that includes the relevant time and length scales. Our model explains the improved velocity, and recovers standard Keller-Segel gradient-sensing results in the limits of no cellular memory, no ruggedness of the landscape, and when memory and fluctuation timescales are well separated.
Revised:  We then extend coarse-grained population models and derive an analytical approximation for the drift velocity in rugged landscapes that explains the enhanced velocity, and recovers standard Keller-Segel gradient-sensing results in the limits of no cellular memory, no ruggedness of the landscape, and when memory and fluctuation timescales are well separated.
Edit intent: clarity
Original:  Our model explains the improved velocity, and recovers standard Keller-Segel gradient-sensing results in the limits of no cellular memory, no ruggedness of the landscape, and when memory and fluctuation timescales are well separated.
Revised:  Our model explains the improved velocity, and recovers standard Keller-Segel gradient-sensing results in the limits when memory and fluctuation timescales are well separated.
Edit intent: clarity
Original:  Our model explains the improved velocity, and recovers standard Keller-Segel gradient-sensing results in the limits of no cellular memory, no ruggedness of the landscape, and when memory and fluctuation timescales are well separated.
Revised:  Our model explains the improved velocity, and recovers standard Keller-Segel gradient-sensing results in the limits of no cellular memory, no ruggedness of the landscape, and when memory and fluctuation time scales are well separated.
Edit intent: fluency
Original:  Fairness is usually understood as the absence of envy, but when agents differ in endowments it is impossible to rule out envy without violating property rights. Instead we seek to rule out {\em justified envy}, defined as envy for which the remedy would not violate any agent's property rights .
Revised:  Fairness is usually understood as the absence of envy, but this can be incompatible with reservation utilities. It is possible that Alice's envy of Bob's assignment cannot be remedied without violating Bob's participation constraint. Instead, we seek to rule out {\em justified envy}, defined as envy for which the remedy would not violate any agent's property rights .
Edit intent: meaning-changed
Original:  We show that fairness, meaning the absence of justified envy, can be achieved together with efficiency and individual rationality (respect for property rights). Our approach requires standard assumptions on agents' preferences, and is compatible with quantity constraints on allocations. The main application of our results is to school choice, where we can simultaneously achieve fairness, efficiency, and diversity-motivated quantity constraints .
Revised:  We show that fairness, meaning the absence of justified envy, can be achieved together with efficiency and individual rationality . We introduce a competitive equilibrium approach with price-dependent incomes obtaining the desired properties .
Edit intent: meaning-changed
Original:  Forecasting and forecast evaluation techniques for the ES are rapidly gaining attention through the recently introduced Basel III Accords, which stipulate the use of the ES as primary market risk measure for the international banking regulations. Encompassing tests generally rely on the existence of strictly consistent loss functions for the functionals under consideration, which do not exist for the ES . However, our encompassing tests are based on recently introduced loss functions and an associated regression framework which considers the ES jointly with the corresponding Value at Risk (VaR).
Revised:  Forecasting and forecast evaluation techniques for the ES are rapidly gaining attention through the recently introduced Basel III Accords, which stipulate the use of the ES as primary market risk measure for the international banking regulation. We utilize joint loss functions for the functionals under consideration, which do not exist for the ES . However, our encompassing tests are based on recently introduced loss functions and an associated regression framework which considers the ES jointly with the corresponding Value at Risk (VaR).
Edit intent: clarity
Original:  Encompassing tests generally rely on the existence of strictly consistent loss functions for the functionals under consideration, which do not exist for the ES . However, our encompassing tests are based on recently introduced loss functions and an associated regression framework which considers the ES jointly with the corresponding Value at Risk (VaR).
Revised:  Encompassing tests generally rely on the existence of strictly consistent loss functions for the pair ES and Value at Risk (VaR).
Edit intent: clarity
Original:  Encompassing tests generally rely on the existence of strictly consistent loss functions for the functionals under consideration, which do not exist for the ES . However, our encompassing tests are based on recently introduced loss functions and an associated regression framework which considers the ES jointly with the corresponding Value at Risk (VaR). This setup facilitates several testing specifications which allow for both, joint tests for the ES and VaR and stand-alone tests for the ES. We present asymptotic theory for our encompassing tests and verify their finite sample properties through various simulation setups.
Revised:  Encompassing tests generally rely on the existence of strictly consistent loss functions for the functionals under consideration, which do not exist for the ES . However, our encompassing tests are based on recently introduced loss functions and an associated regression framework which considers the ES jointly with the corresponding Value at Risk to set up three ES encompassing test variants. The tests are built on misspecification robust asymptotic theory and we verify the finite sample properties through various simulation setups.
Edit intent: meaning-changed
Original:  We present asymptotic theory for our encompassing tests and verify their finite sample properties through various simulation setups. In an empirical application, we utilize the encompassing tests in order to demonstrate the superiority of forecast combination methods for the ES for the IBM stock .
Revised:  We present asymptotic theory for our encompassing tests and verify their finite sample properties of the tests in an extensive simulation study. We use the encompassing tests in order to demonstrate the superiority of forecast combination methods for the ES for the IBM stock .
Edit intent: clarity
Original:  The evaluation results show that our proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively , across a diverse set of real-world deep learning models .
Revised:  Across a diverse set of real-world deep learning models, the evaluation results show that our proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively , across a diverse set of real-world deep learning models .
Edit intent: clarity
Original:  The evaluation results show that our proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively , across a diverse set of real-world deep learning models .
Revised:  The evaluation results show that the proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively , across a diverse set of real-world deep learning models .
Edit intent: clarity
Original:  The evaluation results show that our proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively , across a diverse set of real-world deep learning models .
Revised:  The evaluation results show that our proposed performance tuning guidelines outperform the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively , across a diverse set of real-world deep learning models .
Edit intent: clarity
Original:  The evaluation results show that our proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively , across a diverse set of real-world deep learning models .
Revised:  The evaluation results show that our proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively .
Edit intent: clarity
Original: Analyzing the force that a walking individual applies to the ground has been proposed for identification purpose . Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty.
Revised: The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion . Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty.
Edit intent: clarity
Original:  To solve this system, we first fix the optimal exercise boundary for each regime resulting in multi-variable fixed domains. We further eliminate the first-order derivatives associated with the regime-switching model by taking derivatives to obtain a system of coupled partial differential equations which we called the asset-delta-gamma-speed option equations.
Revised:  To solve this system, we first fix the optimal exercise boundary for each regime to multi-fixed intervals and then eliminate the first-order derivatives associated with the regime-switching model by taking derivatives to obtain a system of coupled partial differential equations which we called the asset-delta-gamma-speed option equations.
Edit intent: clarity
Original:  We further eliminate the first-order derivatives associated with the regime-switching model by taking derivatives to obtain a system of coupled partial differential equations which we called the asset-delta-gamma-speed option equations. The fourth-order compact finite difference scheme is then employed in each regime for solving the systemof the equations.
Revised:  We further eliminate the first-order derivatives associated with the regime-switching model by taking derivatives to obtain a system of coupled partial differential equations which we called the asset-delta-gamma-speed equations. As such, the fourth-order compact finite difference scheme is then employed in each regime for solving the systemof the equations.
Edit intent: coherence
Original:  The fourth-order compact finite difference scheme is then employed in each regime for solving the systemof the equations. In particular, the performance of cubic and quintic Hermite and cubic spline interpolation is explored in estimating the coupled asset, delta, gamma  and speed options in the set of equations.
Revised:  The fourth-order compact finite difference scheme can be used for solving this system. The influence of other asset, delta, gamma and speed options in the set of equations.
Edit intent: clarity
Original:  In particular, the performance of cubic and quintic Hermite and cubic spline interpolation is explored in estimating the coupled asset, delta, gamma  and speed options in the set of equations. The numerical method is finally tested with several examples.
Revised:  In particular, the performance of cubic and quintic Hermite and cubic spline interpolation is explored in estimating the coupled asset, delta, gamma and speed options in the present regime is estimated based on Hermite interpolations. Finally, the numerical method is finally tested with several examples.
Edit intent: meaning-changed