wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The intervention is in a software is used to classify imagined right and left limb movement into binary results, and then make use of the magnetic field produced by the amplified pulses to reconstruct the activity into ferrofluid droplets movement- these moved due rotation of a  desk according to the data received .
Revised:  The intervention is in a software is used to classify imagined right and left limb movement into binary results, and then make use of the magnetic field produced by the amplified pulses to reconstruct the activity into ferrofluid droplets movement- these moved due rotation of a glass surface desk according to the data received .
Edit intent: clarity
Original:  The intervention is in a software is used to classify imagined right and left limb movement into binary results, and then make use of the magnetic field produced by the amplified pulses to reconstruct the activity into ferrofluid droplets movement- these moved due rotation of a  desk according to the data received . Objectives of the project is to address the challenges of the inaccurate performance and cost of user-training which are yet the main issues preventing BCI from being upgraded into more immersive and effective technology.
Revised:  The intervention is in a software is used to classify imagined right and left limb movement into binary results, and then make use of the magnetic field produced by the amplified pulses to reconstruct the activity into ferrofluid droplets movement- these moved due rotation of a desk according to the data received from the brain. The goal of this project is to address the challenges of the inaccurate performance and cost of user-training which are yet the main issues preventing BCI from being upgraded into more immersive and effective technology.
Edit intent: clarity
Original:  Objectives of the project is to address the challenges of the inaccurate performance and cost of user-training which are yet the main issues preventing BCI from being upgraded into more immersive and effective technology.
Revised:  Objectives of the project is to address the challenges of the inaccurate performance and cost of user-training which is yet the main issues preventing BCI from being upgraded into more immersive and effective technology.
Edit intent: fluency
Original:  Objectives of the project is to address the challenges of the inaccurate performance and cost of user-training which are yet the main issues preventing BCI from being upgraded into more immersive and effective technology. The design requirements addressed were the communication speed of the droplet movement and the accuracy of hitting fixed targets.
Revised:  Objectives of the project is to address the challenges of the inaccurate performance and cost of user-training which are yet the main issues preventing BCI from being upgraded into more applicable technology. Tests were performed based on Open ViBE software after uploading recorded files of Motor Imagery MI tasks and the design requirements tested were the motion speed of the droplet movement and the accuracy of hitting fixed targets.
Edit intent: meaning-changed
Original:  If the function satisfies constraint such as continuity and monotonicity, neural network  would always go to one unique fixed point. A biological image classifier is proposed to utilize this fixed point .
Revised:  If the function satisfies constraint such as continuity and monotonicity, neural network would always go to fixed point, and there could be one-one-one mapping between external stimulus and synaptic strength at fixed point. In other words, neural network "memorizes" external stimulus in its synapses. A biological classifier is proposed to utilize this fixed point .
Edit intent: meaning-changed
Original:  If the function satisfies constraints such as continuity and monotonicity, neural network under external stimulus would always go to fixed point, and there could be one-one-one mapping between external stimulus and synaptic strength at fixed point.
Revised:  If the function satisfies constraints such as continuity and monotonicity, neural network under external stimulus will always go to fixed point, and there could be one-one-one mapping between external stimulus and synaptic strength at fixed point.
Edit intent: fluency
Original:  If the function satisfies constraints such as continuity and monotonicity, neural network under external stimulus would always go to fixed point, and there could be one-one-one mapping between external stimulus and synaptic strength at fixed point.
Revised:  If the function satisfies constraints such as continuity and monotonicity, neural network under external stimulus would always go to fixed point, and there could be one-to-one mapping between external stimulus and synaptic strength at fixed point.
Edit intent: fluency
Original:  We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories , generalise structural knowledge , and also exhibit neuronal representations mirroring those found in the brain .
Revised:  We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories and generalise structural knowledge , and also exhibit neuronal representations mirroring those found in the brain .
Edit intent: fluency
Original:  We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories , generalise structural knowledge , and also exhibit neuronal representations mirroring those found in the brain .
Revised:  We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories , generalise structural knowledge . Spatial neuronal representations mirroring those found in the brain .
Edit intent: meaning-changed
Original:  We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories , generalise structural knowledge , and also exhibit neuronal representations mirroring those found in the brain . We experimentally support model assumptions, showing a preserved relationship between grid and place cells across environments.
Revised:  We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories , generalise structural knowledge , and also exhibit neuronal representations mirroring those found in the brain emerge, suggesting spatial cognition is an instance of more URLanising principles. We further unify many entorhinal cell types as basis functions for constructing transition graphs, and show these representations effectively utilise memories. We experimentally support model assumptions, showing a preserved relationship between grid and place cells across environments.
Edit intent: meaning-changed
Original:  This result is contrary to the situation where the insurer maximizes the utility of the total wealth of the company (without distinguishing between equity and policy holders ), in which case a VaR constraint may induce the insurer to take excessive risk leading to higher losses than in the case of no regulation, see Basak and Shapiro (2001).
Revised:  This result is contrary to the situation where the insurer maximizes the utility of the total wealth of the company (without distinguishing between contributions of equity holders and policyholders ), in which case a VaR constraint may induce the insurer to take excessive risk leading to higher losses than in the case of no regulation, see Basak and Shapiro (2001).
Edit intent: clarity
Original:  This result is contrary to the situation where the insurer maximizes the utility of the total wealth of the company (without distinguishing between equity and policy holders ), in which case a VaR constraint may induce the insurer to take excessive risk leading to higher losses than in the case of no regulation, see Basak and Shapiro (2001).
Revised:  This result is contrary to the situation where the insurer maximizes the utility of the total wealth of the company (without distinguishing between equity and policy holders ), in which case a VaR constraint may induce the insurer to take excessive risks leading to higher losses than in the case of no regulation, see Basak and Shapiro (2001).
Edit intent: fluency
Original:  This result is contrary to the situation where the insurer maximizes the utility of the total wealth of the company (without distinguishing between contributions of equity holders and policyholders), in which case a VaR constraint may induce the insurer to take excessive risks leading to higher losses than in the case of no regulation , see Basak and Shapiro (2001). Furthermore, importantly for regulators we observe that for participating insurance contracts both relatively small or relatively large policyholder contributions yield rather risky and volatile strategies. Finally, we also discuss the regulatory effect of a portfolio insurance (PI) , and analyze different choices for the parameters of the participating contract numerically .
Revised:  This result is contrary to the situation where the insurer maximizes the utility of the total wealth of the company (without distinguishing between contributions of equity holders and policyholders), in which case a VaR constraint may induce the insurer to take excessive risks leading to higher losses than in the case of no regulation . Compared to the unregulated problem, the VaR-constrained strategy leads to a higher expected utility for the policyholders, highlighting the potential usefulness of a VaR-regulation in the context of insurance. The prudent investment behavior is more significant if a VaR-type regulation is replaced by a portfolio insurance (PI) , and analyze different choices for the parameters of the participating contract numerically .
Edit intent: meaning-changed
Original:  Finally, we also discuss the regulatory effect of a portfolio insurance (PI) , and analyze different choices for the parameters of the participating contract numerically .
Revised:  Finally, we also discuss the regulatory effect of a portfolio insurance (PI) -type regulation. Furthermore, a stricter regulation (a smaller allowed default probability in the VaR problem or a higher minimum guarantee level in the PI problem) enhances the benefit of the policyholder but deteriorates that of the insurer. For both types of regulation, the gains in terms of expected utility are greater for higher participation rates, while being smaller for higher bonus rates. We also extend our analysis to frameworks where dividend and premature death benefit payments are made at an intermediate time date .
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Original: Trained recurrent neural networksare commonly used both in neuroscience and in machine learning. Recently, several attempts were made to understand the dynamics of such trained networks. Understanding how these dynamics arise through learning , however, is an uncharted territory. Here, we study this process in a simple setting -- learning to memorize analog values, leading to the formation of a line attractor  .
Revised: Artificial neural networks, trained to perform cognitive tasks, have recently been used as models for neural recordings from animals performing these tasks. While some progress has been made in performing such comparisons, the evolution of network dynamics throughout learning remains unexplored. This is paralleled by an experimental focus on recording from trained animals, with few studies following neural activity throughout training. In this work, we address this gap in the realm of artificial networks by analyzing networks that are trained to perform memory and pattern generation tasks. The functional aspect of these tasks corresponds to dynamical objects in the fully trained network - a line attractor .
Edit intent: meaning-changed
Original:  Here, we study this process in a simple setting -- learning to memorize analog values, leading to the formation of a line attractor  .
Revised:  Here, we study this process in a simple setting -- learning to memorize analog values, leading to the formation of a line attractor or a set of limit cycles for the two respective tasks. We use these dynamical objects as anchors to study the effect of learning on their emergence. We find that the sequential nature of learning has major consequences for the learning trajectory and its final outcome .
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Original:  In contrast, we show that the FORCE algorithm avoids this issue, and analyze the underlying causes. Using these insights, we suggest steps towards a more biologically plausible version of FORCE, as well as an approach to design optimal training protocols .
Revised:  In contrast, we show that Least Mean Squares (LMS), a simple gradient descent suggested as a biologically plausible version of FORCE, as well as an approach to design optimal training protocols .
Edit intent: meaning-changed
Original:  Using these insights, we suggest steps towards a more biologically plausible version of FORCE, as well as an approach to design optimal training protocols .
Revised:  Using these insights, we suggest steps towards a more biologically plausible version of the FORCE algorithm, is constantly obstructed by URLetting, which is manifested as the destruction of dynamical objects from previous trials. The degree of interference is determined by the correlation between different trials. We show which specific ingredients of FORCE avoid this phenomenon. Overall, this difference results in convergence that is orders of magnitude slower for LMS. Learning implies accumulating information across multiple trials to form the overall concept of the task. Our results show that interference between trials can greatly affect learning, in a learning rule dependent manner. These insights can help design experimental protocols that minimize such interference, and possibly infer underlying learning rules by observing behavior and neural activity throughout learning .
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Original: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{\mathbb{R .
Revised: Persistent homology has been recently studied with the tools of sheaf theory in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{\mathbb{R .
Edit intent: clarity
Original: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{\mathbb{R .
Revised: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting by Kashiwara and Schapira, after J. Curry has made the first link between persistent homology and sheaves .
Edit intent: meaning-changed
Original:  We prove an isometry theorem in this derived setting,  expressing the convolution distance  as a matching distance , mirroring the situation of persistence with one parameter.
Revised:  We prove an isometry theorem in this derived setting, expressing the convolution distance as a matching distance between combinatorial objects associated to them that we call graded barcodes. This allows to consider sheaf-theoretical constructions as combinatorial, stable topological descriptors of data, and generalizes the situation of persistence with one parameter.
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Original:  As a byproduct of our isometry theorem, we prove that the convolution distance is closed, and also give c}(}\textbf{  some explicit examples of computation of the convolution distance.
Revised:  As a byproduct of our isometry theorem, we prove that the convolution distance is closed, give a precise description of connected components of D^b_{\mathbb{R c}(}\textbf{ some explicit examples of computation of the convolution distance.
Edit intent: meaning-changed
Original:  As a byproduct of our isometry theorem, we prove that the convolution distance is closed, and also give c}(}\textbf{  some explicit examples of computation of the convolution distance.
Revised:  As a byproduct of our isometry theorem, we prove that the convolution distance is closed, and also give c}(}\textbf{k some explicit examples of computation of the convolution distance.
Edit intent: meaning-changed
Original:  As a byproduct of our isometry theorem, we prove that the convolution distance is closed, and also give c}(}\textbf{  some explicit examples of computation of the convolution distance.
Revised:  As a byproduct of our isometry theorem, we prove that the convolution distance is closed, and also give c}(}\textbf{ _\mathbb{R some explicit examples of computation of the convolution distance.
Edit intent: meaning-changed
Original:  Our first contribution is to identify distinct generalizations of the notion of%DIFDELCMD < {\em %%% margin from hyperplanes to polytopes and to understand how they relate geometrically; this result may be of interest beyond the learning setting. Our novel learning algorithm constructs a consistent polytope as an intersection of about t \log t halfspaces  in time polynomial in t (where t is the number of halfspaces forming an optimal polytope).
Revised:  Our first contribution is to identify distinct generalizations of the notion of%DIFDELCMD < {\em %%% margin learning algorithm constructs a consistent polytope as an intersection of about t \log t halfspaces in time polynomial in t (where t is the number of halfspaces forming an optimal polytope).
Edit intent: clarity
Original:  Our novel learning algorithm constructs a consistent polytope as an intersection of about t \log t halfspaces  in time polynomial in t (where t is the number of halfspaces forming an optimal polytope).
Revised:  Our novel learning algorithm constructs a consistent polytope as an intersection of about t \log t halfspaces with margins in time polynomial in t (where t is the number of halfspaces forming an optimal polytope).
Edit intent: meaning-changed
Original:  This is an exponential improvement over the state of the art Arriaga and Vempala, 2006%DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio 2008%DIFDELCMD < ]%%% , while achieving a better sample complexity.
Revised:  Arriaga and Vempala, 2006%DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio 2008%DIFDELCMD < ]%%% , while achieving a better sample complexity.
Edit intent: coherence
Original:  This is an exponential improvement over the state of the art Arriaga and Vempala, 2006%DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio 2008%DIFDELCMD < ]%%% , while achieving a better sample complexity.
Revised:  This is an exponential improvement over the state of the art %DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio 2008%DIFDELCMD < ]%%% , while achieving a better sample complexity.
Edit intent: coherence
Original:  This is an exponential improvement over the state of the art Arriaga and Vempala, 2006%DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio 2008%DIFDELCMD < ]%%% , while achieving a better sample complexity.
Revised:  This is an exponential improvement over the state of the art Arriaga and Vempala, 2006%DIFDELCMD < ]%%% 2008%DIFDELCMD < ]%%% , while achieving a better sample complexity.
Edit intent: coherence
Original:  This is an exponential improvement over the state of the art Arriaga and Vempala, 2006%DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio 2008%DIFDELCMD < ]%%% , while achieving a better sample complexity.
Revised:  This is an exponential improvement over the state of the art Arriaga and Vempala, 2006%DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio %DIFDELCMD < ]%%% , while achieving a better sample complexity.
Edit intent: coherence
Original:  This is an exponential improvement over the state of the art Arriaga and Vempala, 2006%DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio 2008%DIFDELCMD < ]%%% , while achieving a better sample complexity. Finally, we provide the first nearly matching hardness-of-approximation lower bound, whence our claim of near optimality .
Revised:  This is an exponential improvement over the state of the art Arriaga and Vempala, 2006%DIFDELCMD < ]%%% . We also improve over the super-polynomial-in-t algorithm of Klivans and Servedio 2008%DIFDELCMD < ]%%% We also identify distinct generalizations of the notion of margin from hyperplanes to polytopes and investigate how they relate geometrically; this result may be of interest beyond the learning setting .
Edit intent: meaning-changed
Original: Consumer markets are quickly growing, creating the need to design new sales mechanisms. Here we introduce a new auction model for selling products in real time and without production limitations. Interested buyers continuously offer bids and if the price is 'right', the bid is accepted .
Revised: In today's economy, selling a new zero-marginal cost product is a real challenge, as it is difficult to determine a product's "correct" sales price based on its profit and dissemination. As an example, think of the price of a new app or video game. New sales mechanisms for selling this type of product need to be designed, in particular ones that consider consumer preferences and reality. Current auction mechanisms establish a time deadline for the auction to take place. This deadline is set to increase the number of bidders and thus the final offering price. Consumers want to obtain the product as quickly as possible from the moment they become interested in it, and this time does not always coincide with the seller's deadline. Naturally, consumers also want to pay a price they consider "fair". Here we introduce an auction model where buyers continuously place bids and the challenge is to decide quickly whether or not to accept them .
Edit intent: coherence
Original:  In this paper, we focus on brain imaging data, including both calcium imaging and functional MRI data. Standard vector-autoregressive models are limited by their linearity assumptions, while nonlinear general-purpose, large-scale temporal models, such as LSTM networks, typically require large amounts of training data, not always readily available in biological applications; furthermore, such models have limited interpretability. We introduce here a novel approach for learning a nonlinear differential equation model aimed at capturing brain dynamics . Specifically, we propose a variable-projection optimization approach to estimate the parameters of the multivariate (coupled ) van der Pol oscillator, and demonstrate that such a model can accurately represent nonlinear dynamics of the brain data.
Revised:  In this paper, we focus on brain calcium imaging (CaI) of URLanisms (zebrafish and rat), aiming to build a model of joint activation dynamics in large neuronal populations, including the whole brain of zebrafish. We propose a new approach for capturing dynamics of temporal SVD components that uses the coupled (multivariate ) van der Pol oscillator, and demonstrate that such a model can accurately represent nonlinear dynamics of the brain data.
Edit intent: meaning-changed