wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Due to the gigantic number of 10-Q filings prevailing in the market for each quarter and diverse variations in the implementation of formatgiven company-specific nature , it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information .
Revised:  Due to the gigantic number of 10-Q filings and the enormous variations in the implementation of formatgiven company-specific nature , it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information .
Edit intent: clarity
Original:  Due to the gigantic number of 10-Q filings prevailing in the market for each quarter and diverse variations in the implementation of formatgiven company-specific nature , it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information .
Revised:  Due to the gigantic number of 10-Q filings prevailing in the market for each quarter and diverse variations in the reporting format , it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information .
Edit intent: clarity
Original:  Due to the gigantic number of 10-Q filings prevailing in the market for each quarter and diverse variations in the implementation of formatgiven company-specific nature , it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information . In this paper , we create a tool to itemize 10-Q filings using multi-stage processes, blending a rule-based algorithm with a CNN deep learning model.
Revised:  Due to the gigantic number of 10-Q filings prevailing in the market for each quarter and diverse variations in the implementation of formatgiven company-specific nature , it has been a long-standing challenge to retrieve item-specific information from 10-Q filings using multi-stage processes, blending a rule-based algorithm with a CNN deep learning model.
Edit intent: clarity
Original:  In this paper , we create a tool to itemize 10-Q filings using multi-stage processes, blending a rule-based algorithm with a CNN deep learning model.
Revised:  In this paper , we create a tool to itemize 10-Q filings that lack machine-readable hierarchy. This paper presents a solution for itemizing 10-Q files by complementing a rule-based algorithm with a CNN deep learning model.
Edit intent: meaning-changed
Original:  In this paper , we create a tool to itemize 10-Q filings using multi-stage processes, blending a rule-based algorithm with a CNN deep learning model. The implementation is an integrated pipeline which provides a solution to the item retrieval on a large scale. This would enable cross sectional and longitudinal textual analysis on massive number of companies .
Revised:  In this paper , we create a tool to itemize 10-Q filings using multi-stage processes, blending a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier. This solution demonstrates a pipeline that can be generalized to a rapid data retrieval solution among a large volume of textual data using only typographic items. The extracted textual data can be used as unlabeled content-specific data to train transformer models (e.g., BERT) or fit into various field-focus natural language processing (NLP) applications .
Edit intent: meaning-changed
Original:  In this paper, we build a Vietnamese dataset (UIT-ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites.
Revised:  In this paper, we build a Open-domain Complaint Detection dataset (UIT-ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites.
Edit intent: meaning-changed
Original: Spike-timing-dependent plasticity(STDP) is a biological process in which the precise order and timing of neuronal spikes affect the degree of synaptic modification . While there has been numerous research focusing on the role of STDP in neural coding, the functional implications of STDP at the macroscopic level in the brain have not been fully explored yet. In this work, we propose that STDP in an ensemble of spiking neurons renders storing high dimensional information in the form of a `memory plane'.
Revised: Spike-timing-dependent plasticity(STDP) is a biological process of synaptic modification caused by the difference of firing order and timing between neurons. In our separate work, we propose that STDP in an ensemble of spiking neurons renders storing high dimensional information in the form of a `memory plane'.
Edit intent: clarity
Original:  In this work, we propose that STDP in an ensemble of spiking neurons renders storing high dimensional information in the form of a `memory plane'. Neural activity based on STDP transforms periodic spatio-temporal input patterns into the corresponding memory plane, where the stored information can be dynamically revived with a proper cue .
Revised:  In this work, we propose that STDP in a network of neurons transforms periodic input patterns into the corresponding memory plane, where the stored information can be dynamically revived with a proper cue .
Edit intent: clarity
Original:  Neural activity based on STDP transforms periodic spatio-temporal input patterns into the corresponding memory plane, where the stored information can be dynamically revived with a proper cue .
Revised:  Neural activity based on STDP transforms periodic spatio-temporal input patterns into the corresponding memory plane, and a proper memory cue near such structure dynamically revives the stored information. Using these results, in this paper, we demonstrate the following two encoding/decoding algorithms handling practical data .
Edit intent: meaning-changed
Original:  Using the dynamical systems theory that shows the analytic relation between the input and the memory plane, we were able to demonstrate a specific memory process for high-dimensional associative datasets. In the auto-associative memory task , a group of images that were continuously streamed to the system can be retrieved from the oscillating neural state.
Revised:  First, we perform an auto-associative memory task , a group of images that were continuously streamed to the system can be retrieved from the oscillating neural state.
Edit intent: clarity
Original:  In the auto-associative memory task , a group of images that were continuously streamed to the system can be retrieved from the oscillating neural state. The second application deals with the process of semantic memory components that are embedded from sentences.
Revised:  In the auto-associative memory task , a group of images . The results show that any relevant image to the group can be used as a cue in order to reconstruct the original images. The next one deals with the process of semantic memory components that are embedded from sentences.
Edit intent: coherence
Original: In the NK modelgiven by Kauffman , myopic local search involves flipping one randomly-chosen bit of an N-bit decision string in every time step and accepting the new configuration if that has higher fitness.
Revised: In Kauffman-s NK model , myopic local search involves flipping one randomly-chosen bit of an N-bit decision string in every time step and accepting the new configuration if that has higher fitness.
Edit intent: clarity
Original:  The proofs use tools from differential geometry and geometric measure theory, which may be of independent interest. The practical performance of our method is first demonstrated in a high-dimensional simulation resembling decision-making by machine learning algorithms. Our estimator has smaller mean squared errors compared to alternative estimators. We finally apply our estimator to evaluate the effect of  Coronavirus Aid, Relief, and Economic Security (CARES) Act, where more than %DIFDELCMD < \10 %%% \175 billion worth of relief funding is allocated to hospitals via an algorithmic rule.
Revised:  We apply our estimator to evaluate the effect of Coronavirus Aid, Relief, and Economic Security (CARES) Act, where more than %DIFDELCMD < \10 %%% \175 billion worth of relief funding is allocated to hospitals via an algorithmic rule.
Edit intent: coherence
Original:  We finally apply our estimator to evaluate the effect of  Coronavirus Aid, Relief, and Economic Security (CARES) Act, where more than %DIFDELCMD < \10 %%% \175 billion worth of relief funding is allocated to hospitals via an algorithmic rule.
Revised:  We finally apply our estimator to evaluate the effect of the Coronavirus Aid, Relief, and Economic Security (CARES) Act, where more than %DIFDELCMD < \10 %%% \175 billion worth of relief funding is allocated to hospitals via an algorithmic rule.
Edit intent: fluency
Original:  Numerical experiments with synthetic problems and problems from the Maros-M\'ezr\'aros QP collection show that our preconditioned inexact interior point  are effective at improving conditioning and reducing cost.
Revised:  Numerical experiments with synthetic problems and problems from the Maros-M\'esz\'aros QP collection show that our preconditioned inexact interior point are effective at improving conditioning and reducing cost.
Edit intent: fluency
Original:  Numerical experiments with synthetic problems and problems from the Maros-M\'ezr\'aros QP collection show that our preconditioned inexact interior point  are effective at improving conditioning and reducing cost.
Revised:  Numerical experiments with synthetic problems and problems from the Maros-M\'ezr\'aros QP collection show that our preconditioned inexact interior point solvers are effective at improving conditioning and reducing cost.
Edit intent: clarity
Original: We release synth1B1, a multi-modal audio corpus consisting of 1 billion 4-second synthesized sounds, which is 100x larger than any audio dataset in the literature.
Revised: We release synth1B1, a multi-modal audio corpus consisting of 1 billion 4-second synthesized sounds, paired with the synthesis parameters used to generate them. The dataset is 100x larger than any audio dataset in the literature.
Edit intent: meaning-changed
Original:  Each sound is paired with the corresponding latent parameters used to generate it. synth1B1 samples are deterministically generated on-the-fly  16200x faster than real-time (714MHz) on a single GPU using torchsynth URL an open-source modular synthesizer we release.
Revised:  We also introduce torchsynth, an open source modular synthesizer that generates the synth1B1 samples are deterministically generated on-the-fly 16200x faster than real-time (714MHz) on a single GPU using torchsynth URL an open-source modular synthesizer we release.
Edit intent: meaning-changed
Original:  Each sound is paired with the corresponding latent parameters used to generate it. synth1B1 samples are deterministically generated on-the-fly  16200x faster than real-time (714MHz) on a single GPU using torchsynth URL an open-source modular synthesizer we release.
Revised:  Each sound is paired with the corresponding latent parameters used to generate it. synth1B1 samples on-the-fly 16200x faster than real-time (714MHz) on a single GPU using torchsynth URL an open-source modular synthesizer we release.
Edit intent: clarity
Original:  Each sound is paired with the corresponding latent parameters used to generate it. synth1B1 samples are deterministically generated on-the-fly  16200x faster than real-time (714MHz) on a single GPU using torchsynth URL an open-source modular synthesizer we release.
Revised:  Each sound is paired with the corresponding latent parameters used to generate it. synth1B1 samples are deterministically generated on-the-fly at 16200x faster than real-time (714MHz) on a single GPU using torchsynth URL an open-source modular synthesizer we release.
Edit intent: fluency
Original:  Each sound is paired with the corresponding latent parameters used to generate it. synth1B1 samples are deterministically generated on-the-fly  16200x faster than real-time (714MHz) on a single GPU using torchsynth URL an open-source modular synthesizer we release. Additionally , we release two new audio datasets: FM synth timbre URL and subtractive synth pitch URL Using these datasets, we demonstrate new rank-based synthesizer-motivated evaluation criteria for existing audio representations.
Revised:  Each sound is paired with the corresponding latent parameters used to generate it. synth1B1 samples are deterministically generated on-the-fly 16200x faster than real-time (714MHz) on a single GPU . Finally , we release two new audio datasets: FM synth timbre URL and subtractive synth pitch URL Using these datasets, we demonstrate new rank-based synthesizer-motivated evaluation criteria for existing audio representations.
Edit intent: coherence
Original:  Additionally , we release two new audio datasets: FM synth timbre URL and subtractive synth pitch URL Using these datasets, we demonstrate new rank-based synthesizer-motivated evaluation criteria for existing audio representations.
Revised:  Additionally , we release two new audio datasets: FM synth timbre and subtractive synth pitch URL Using these datasets, we demonstrate new rank-based synthesizer-motivated evaluation criteria for existing audio representations.
Edit intent: fluency
Original:  Additionally , we release two new audio datasets: FM synth timbre URL and subtractive synth pitch URL Using these datasets, we demonstrate new rank-based synthesizer-motivated evaluation criteria for existing audio representations.
Revised:  Additionally , we release two new audio datasets: FM synth timbre URL and subtractive synth pitch . Using these datasets, we demonstrate new rank-based synthesizer-motivated evaluation criteria for existing audio representations.
Edit intent: fluency
Original: The distributions of random matrix theory have seen an explosion of interest in recent years, and have been found in various applied fields including physics, high-dimensional statistics, wireless communications, finance, etc. The Tracy-Widom distribution is one of the most important distributions in random matrix theory, and its numerical evaluation is a subject of great practical importance.
Revised: The distributions of the k-th largest level at the soft edge scaling limit of Gaussian ensembles are some of the most important distributions in random matrix theory, and its numerical evaluation is a subject of great practical importance.
Edit intent: meaning-changed
Original:  With our algorithm, the Tracy-Widom distribution can be evaluated to full absolute precision everywhere rapidly , and, furthermore, its right tailcan be computed to full relative precision .
Revised:  This allows us to rapidly evaluate the distributions of the k-th largest level to full relative precision rapidly everywhere except in the left tail, where they are computed to absolute precision everywhere rapidly , and, furthermore, its right tailcan be computed to full relative precision .
Edit intent: meaning-changed
Original:  With our algorithm, the Tracy-Widom distribution can be evaluated to full absolute precision everywhere rapidly , and, furthermore, its right tailcan be computed to full relative precision .
Revised:  With our algorithm, the Tracy-Widom distribution can be evaluated to full absolute precision . In addition, we characterize the eigenfunctions of the Airy integral operator, and describe their extremal properties in relation to an uncertainty principle involving the Airy transform. We observe that the Airy integral operator is fairly universal, and we describe a separate application to Airy beams in optics. Using the eigenfunctions, we compute a finite-energy Airy beam that is optimal, in the sense that the beam is both maximally concentrated, and maximally non-diffracting and self-accelerating .
Edit intent: meaning-changed
Original: We show that Poncelet 3-periodics in a confocal pair (elliptic billiard) conserve the same sum of cosines as their affine image with a fixed incircle.
Revised: Poncelet N-periodics in a confocal pair (elliptic billiard) conserve the same sum of cosines as their affine image with a fixed incircle.
Edit intent: meaning-changed
Original: We show that Poncelet 3-periodics in a confocal pair (elliptic billiard) conserve the same sum of cosines as their affine image with a fixed incircle.
Revised: We show that Poncelet 3-periodics in a confocal pair (elliptic billiard) conserve the same sum of cosines as their affine image with fixed incircle.
Edit intent: fluency
Original: The Ensemble Kalman inversion ( EnKI ), proposed by Iglesias et al. for the solution of Bayesian inverse problems of type y= Au +\varepsilon, with u  being an unknown parameter and y a given datum, is a powerful tool usually derived from a sequential Monte Carlo point of view.
Revised: The Ensemble Kalman inversion ( EKI ), proposed by Iglesias et al. for the solution of Bayesian inverse problems of type y= Au +\varepsilon, with u being an unknown parameter and y a given datum, is a powerful tool usually derived from a sequential Monte Carlo point of view.
Edit intent: fluency
Original: The Ensemble Kalman inversion ( EnKI ), proposed by Iglesias et al. for the solution of Bayesian inverse problems of type y= Au +\varepsilon, with u  being an unknown parameter and y a given datum, is a powerful tool usually derived from a sequential Monte Carlo point of view.
Revised: The Ensemble Kalman inversion ( EnKI ), proposed by Iglesias et al. for the solution of Bayesian inverse problems of type y= A u^\dagger +\varepsilon, with u being an unknown parameter and y a given datum, is a powerful tool usually derived from a sequential Monte Carlo point of view.
Edit intent: meaning-changed
Original: The Ensemble Kalman inversion ( EnKI ), proposed by Iglesias et al. for the solution of Bayesian inverse problems of type y= Au +\varepsilon, with u  being an unknown parameter and y a given datum, is a powerful tool usually derived from a sequential Monte Carlo point of view.
Revised: The Ensemble Kalman inversion ( EnKI ), proposed by Iglesias et al. for the solution of Bayesian inverse problems of type y= Au +\varepsilon, with u ^\dagger being an unknown parameter and y a given datum, is a powerful tool usually derived from a sequential Monte Carlo point of view.
Edit intent: meaning-changed
Original:  In particular, we debunk a common working assumption in the field, saying that in the linear Gaussian regime, the empirical measure at time t=1 is a surrogate for the posterior , i.e., the correct Bayesian answer to the inverse problem .
Revised:  In particular, we analyze dynamics of deterministic EKI, averaged quantities of stochastic EKI, and mean-field EKI. We show that in the linear Gaussian regime, the empirical measure at time t=1 is a surrogate for the posterior , i.e., the correct Bayesian answer to the inverse problem .
Edit intent: meaning-changed
Original:  In particular, we debunk a common working assumption in the field, saying that in the linear Gaussian regime, the empirical measure at time t=1 is a surrogate for the posterior , i.e., the correct Bayesian answer to the inverse problem .
Revised:  In particular, we debunk a common working assumption in the field, saying that in the linear Gaussian regime, the Bayesian posterior can only be recovered with the mean-field limit and not with finite sample sizes or deterministic EKI .
Edit intent: meaning-changed
Original:  Furthermore, we show that  residuals in parameter space do not decrease monotonously in the Euclidean norm and suggest a problem-adapted norm, where monotonicity can be proved.
Revised:  Furthermore, we show that -- even in the deterministic case -- residuals in parameter space do not decrease monotonously in the Euclidean norm and suggest a problem-adapted norm, where monotonicity can be proved.
Edit intent: meaning-changed
Original:  Vector Bin Packing is a natural generalization of the classic bin packing problem where given a set of d-dimensional vectors from (0,1]^d, and the aim is to partition the set into  bins such that for each bin B, we have \left\|v\in Bv\right\|_\infty\leq 1.
Revised:  Vector Bin Packing we are given a set of d-dimensional vectors from (0,1]^d, and the aim is to partition the set into bins such that for each bin B, we have \left\|v\in Bv\right\|_\infty\leq 1.
Edit intent: clarity
Original:  Vector Bin Packing is a natural generalization of the classic bin packing problem where given a set of d-dimensional vectors from (0,1]^d, and the aim is to partition the set into  bins such that for each bin B, we have \left\|v\in Bv\right\|_\infty\leq 1.
Revised:  Vector Bin Packing is a natural generalization of the classic bin packing problem where given a set of d-dimensional vectors from (0,1]^d, and the aim is to partition the set into the minimum number of bins such that for each bin B, we have \left\|v\in Bv\right\|_\infty\leq 1.
Edit intent: meaning-changed
Original:  In fact, we show that it is NP-hard to get an asymptotic approximation ratio better than 600{599}  v\geq \mathbf 1. Using ideas similar to our vector bin packing result, we show that for vector bin covering there is no APTAS for dimensions greater than or equal to 2.
Revised:  In fact, we show that it is NP-hard to get an asymptotic approximation ratio better than 600{599} , for d=2. In the vector bin covering problem given a set of d-dimensional vectors from (0,1 v\geq \mathbf 1. Using ideas similar to our vector bin packing result, we show that for vector bin covering there is no APTAS for dimensions greater than or equal to 2.
Edit intent: meaning-changed
Original:  In fact, we show that it is NP-hard to get an asymptotic approximation ratio better than 600{599}  v\geq \mathbf 1. Using ideas similar to our vector bin packing result, we show that for vector bin covering there is no APTAS for dimensions greater than or equal to 2.
Revised:  In fact, we show that it is NP-hard to get an asymptotic approximation ratio better than 600{599} ^d, the aim is to obtain a family of disjoint subsets (called bins) with the maximum cardinality such that for each bin B, we have \sum_{v\in Bv\geq \mathbf 1. Using ideas similar to our vector bin packing result, we show that for vector bin covering there is no APTAS for dimensions greater than or equal to 2.
Edit intent: meaning-changed
Original: Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS. In this work, we have developed an Intent Bulk Labeling system for SDS developers .
Revised: Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS. In this work, we showcase an Intent Bulk Labeling system for SDS developers .
Edit intent: clarity