wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We provide a set of computational examples in 2D and 3D  that can be used to form a better understanding on how, and up to which extent, the deformations of the porous structure dictate the generation and suppression of spatial patterning dynamics, also related to the onset of mechanochemical waves.
Revised:  We provide a set of computational examples in 2D and 3D (related to brain mechanobiology) that can be used to form a better understanding on how, and up to which extent, the deformations of the porous structure dictate the generation and suppression of spatial patterning dynamics, also related to the onset of mechanochemical waves.
Edit intent: meaning-changed
Original:  We provide a set of computational examples in 2D and 3D  that can be used to form a better understanding on how, and up to which extent, the deformations of the porous structure dictate the generation and suppression of spatial patterning dynamics, also related to the onset of mechanochemical waves.
Revised:  We provide a set of computational examples in 2D and 3D that can be used to form a better understanding on how, and up to which extent, the deformations of the porous structure dictate the generation and suppression of spatial patterning dynamics, also related to the onset of mechano-chemical waves.
Edit intent: clarity
Original: The constraints on a continuous growth in a finite environment are formally analyzed, adding the effect of the necessary dynamics of costs.
Revised: It is still common wisdom amongst economists, politicians and lay people that economic growth is a necessity of our social systems, at least to avoid distributional conflicts. This paper challenges such belief moving from a purely physical theoretical perspective. It formally considers the constraints imposed by a finite environment on the prospect of continuous growth, including the dynamics of costs.
Edit intent: meaning-changed
Original:  The unavoidable global collapse is deduced. The effect of competition in a growing economic system on the evolution of  unequal share of wealth is also analyzed and discussed, showing the necessity of increase of inequalities under the premisesof growth and competition .
Revised:  The unavoidable global collapse . Then, analyzing and discussing the evolution of unequal share of wealth is also analyzed and discussed, showing the necessity of increase of inequalities under the premisesof growth and competition .
Edit intent: coherence
Original:  The effect of competition in a growing economic system on the evolution of  unequal share of wealth is also analyzed and discussed, showing the necessity of increase of inequalities under the premisesof growth and competition .
Revised:  The effect of competition in a growing economic system on the evolution of the unequal share of wealth is also analyzed and discussed, showing the necessity of increase of inequalities under the premisesof growth and competition .
Edit intent: fluency
Original:  The effect of competition in a growing economic system on the evolution of  unequal share of wealth is also analyzed and discussed, showing the necessity of increase of inequalities under the premisesof growth and competition .
Revised:  The effect of competition in a growing economic system on the evolution of unequal share of wealth under the premises of growth and competition, it is shown that the increase of inequalities under the premisesof growth and competition .
Edit intent: coherence
Original:  Based on this identification strategy we provide consistent estimators of the network formation model under NTU. Finite-sample performance of our method is analyzed in a simulationstudy, and an empirical illustration using the risk-sharing network data from Nyakatoke demonstrates that our proposed method is able to obtain economically intuitive estimates .
Revised:  We provide a consistent estimator and analyze its performance via simulation, and apply our method to the Nyakatoke risk-sharing network data from Nyakatoke demonstrates that our proposed method is able to obtain economically intuitive estimates .
Edit intent: clarity
Original:  We first leverage the lead bias in news articles to pretrain the model on large-scale corpora. Then , we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of  summaries.
Revised:  We first leverage the lead bias in news articles to pretrain the model on millions of unlabeled corpora. Next , we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of summaries.
Edit intent: clarity
Original:  Surprisingly, for d=4,5,6 the stretched grid yields better bounds than the stretched diagonal (unlike for all cases k=0 and for the case (d,k)=(3,1), in which both point sets yield the same bound).
Revised:  We provide strong evidence that, surprisingly, for d=4,5,6 the stretched grid yields better bounds than the stretched diagonal (unlike for all cases k=0 and for the case (d,k)=(3,1), in which both point sets yield the same bound).
Edit intent: meaning-changed
Original:  The stretched grid yields c_{4,1}\leq 0.00457936, c_{5,1}\leq 0.000405335, and c_{6,1}\leq 0.0000291323.
Revised:  Our experiments indicate that the stretched grid yields c_{4,1}\leq 0.00457936, c_{5,1}\leq 0.000405335, and c_{6,1}\leq 0.0000291323.
Edit intent: coherence
Original: We consider a generalization of the nonautonomous model in Xingge Niu, Tailei Zhang, Zhidong Teng, The asymptotic behavior of a nonautonomous eco-epidemic model with disease in the prey, Applied Mathematical Modelling 35, 457-470 (2011)%DIFDELCMD < ] %%% by adding a general function corresponding to predation on uninfected preys as well as general functions associated to the vital dynamics of the susceptible prey and predator populations.
Revised: We consider a Xingge Niu, Tailei Zhang, Zhidong Teng, The asymptotic behavior of a nonautonomous eco-epidemic model with disease in the prey, Applied Mathematical Modelling 35, 457-470 (2011)%DIFDELCMD < ] %%% by adding a general function corresponding to predation on uninfected preys as well as general functions associated to the vital dynamics of the susceptible prey and predator populations.
Edit intent: clarity
Original: We consider a generalization of the nonautonomous model in Xingge Niu, Tailei Zhang, Zhidong Teng, The asymptotic behavior of a nonautonomous eco-epidemic model with disease in the prey, Applied Mathematical Modelling 35, 457-470 (2011)%DIFDELCMD < ] %%% by adding a general function corresponding to predation on uninfected preys as well as general functions associated to the vital dynamics of the susceptible prey and predator populations.
Revised: We consider a generalization of the nonautonomous model in %DIFDELCMD < ] %%% by adding a general function corresponding to predation on uninfected preys as well as general functions associated to the vital dynamics of the susceptible prey and predator populations.
Edit intent: coherence
Original: We consider a generalization of the nonautonomous model in Xingge Niu, Tailei Zhang, Zhidong Teng, The asymptotic behavior of a nonautonomous eco-epidemic model with disease in the prey, Applied Mathematical Modelling 35, 457-470 (2011)%DIFDELCMD < ] %%% by adding a general function corresponding to predation on uninfected preys as well as general functions associated to the vital dynamics of the susceptible prey and predator populations.
Revised: We consider a generalization of the nonautonomous model in Xingge Niu, Tailei Zhang, Zhidong Teng, The asymptotic behavior of a nonautonomous eco-epidemic model with disease in the prey, Applied Mathematical Modelling 35, 457-470 (2011)%DIFDELCMD < ] %%% nonautonomous eco-epidemiological model with general functions for predation on infected and uninfected preys as well as general functions associated to the vital dynamics of the susceptible prey and predator populations.
Edit intent: clarity
Original:  We develop a co-authorship network to determine the degree to which homophily in researchers' social networks explains gendered citation practices and we find that men tend to overcite other men even when their social networks are representative of the field .
Revised:  We develop a co-authorship network to assess homophily in researchers' social networks explains gendered citation practices and we find that men tend to overcite other men even when their social networks are representative of the field .
Edit intent: clarity
Original:  We develop a co-authorship network to determine the degree to which homophily in researchers' social networks explains gendered citation practices and we find that men tend to overcite other men even when their social networks are representative of the field .
Revised:  We develop a co-authorship network to determine the degree to which homophily in researchers' social networks , and we find that men tend to overcite other men even when their social networks are representative of the field .
Edit intent: clarity
Original:  We develop a co-authorship network to determine the degree to which homophily in researchers' social networks explains gendered citation practices and we find that men tend to overcite other men even when their social networks are representative of the field .
Revised:  We develop a co-authorship network to determine the degree to which homophily in researchers' social networks explains gendered citation practices and we find that men tend to overcite men even when their social networks are representative of the field .
Edit intent: clarity
Original:  We develop a co-authorship network to determine the degree to which homophily in researchers' social networks explains gendered citation practices and we find that men tend to overcite other men even when their social networks are representative of the field .
Revised:  We develop a co-authorship network to determine the degree to which homophily in researchers' social networks explains gendered citation practices and we find that men tend to overcite other men even when their social networks are representative .
Edit intent: clarity
Original:  In this paper, we develop variants of layer-wise relevance backpropagation (LRP) and gradient backpropagation, tailored to image captioning with attention. The result provides simultaneously pixel-wise image explanation and linguistic explanation for each word in the captions.
Revised:  In this paper, we develop variants of layer-wise relevance backpropagation (LRP) and gradient backpropagation, tailored to image captioning models with attention mechanisms. The explanations provide simultaneously pixel-wise image explanation and linguistic explanation for each word in the captions.
Edit intent: clarity
Original:  We show that explanation methods, firstly, correlate to object locations with higher precision than attention, secondly, are able to identify object words that are unsupported by image content, and thirdly, provide guidance to debias and improve the model.
Revised:  We show that explanation methods, firstly, correlate to object locations with higher precision than attention, secondly, are able to identify object words that are unsupported by image content, and thirdly, provide guidance to improve and de-bias the model.
Edit intent: clarity
Original:  The explanations provide simultaneously pixel-wise image explanation and linguistic explanation for each word in the captions. We show that given a word in the caption to be explained, explanation methods such as LRP reveal supporting and opposing pixels as well as preceding words. We compare the properties of attention heatmaps systematically against those computed with explanation methods such as LRP, Grad-CAM  and Guided Grad-CAM.
Revised:  We compare the interpretability of attention heatmaps systematically against those computed with explanation methods such as LRP, Grad-CAM and Guided Grad-CAM.
Edit intent: clarity
Original:  We show that explanation methods , firstly, correlate to object locations with higher precisionthan attention, secondly, are able to identify object wordsthat are unsupported by image content, and thirdly, provide guidance to improve and de-bias the model.
Revised:  We show that explanation methods provide simultaneously pixel-wise image explanation (supporting and opposing pixels of the input image) and linguistic explanation (supporting and opposing words of the preceding sequence) for each word in the predicted captions. We demonstrate with extensive experiments that explanation methods can 1) reveal more related evidence used by the model to make decisions than attention; 2) correlate to object locations with higher precisionthan attention, secondly, are able to identify object wordsthat are unsupported by image content, and thirdly, provide guidance to improve and de-bias the model.
Edit intent: meaning-changed
Original:  We show that explanation methods , firstly, correlate to object locations with higher precisionthan attention, secondly, are able to identify object wordsthat are unsupported by image content, and thirdly, provide guidance to improve and de-bias the model. Results are reported using two different image captioning attention models trained with Flickr30K and MSCOCO2017 datasets. Experimental analyses show the strength of explanation methods for understanding image captioning attention models .
Revised:  We show that explanation methods , firstly, correlate to object locations with high precision; 3) is helpful to `debug' the model such as analyzing the reasons for hallucinated object words. With the observed properties of explanations, we further design an LRP-inference fine-tuning strategy that can alleviate the object hallucination of image captioning models, meanwhile, maintain the sentence fluency. We conduct experiments with two widely used attention mechanisms: the adaptive attention mechanism calculated with the additive attention and the multi-head attention calculated with the scaled dot product .
Edit intent: meaning-changed
Original:  Motivated by a working Laplace likelihood approach in Bayesian quantile regression, BayesMAR adopts a parametric model bearing the same structure of autoregressive (AR) models by altering the Gaussian error to Laplace, leading to a simple, robust, and interpretable modeling strategy for time series forecasting.
Revised:  Motivated by a working Laplace likelihood approach in Bayesian quantile regression, BayesMAR adopts a parametric model bearing the same structure as autoregressive models by altering the Gaussian error to Laplace, leading to a simple, robust, and interpretable modeling strategy for time series forecasting.
Edit intent: clarity
Original:  An application to U.S. macroeconomic data forecasting shows that BayesMAR leads to favorable and often superior predictive performances than the selected mean-based alternatives under various loss functions  .
Revised:  An application to U.S. macroeconomic data forecasting shows that BayesMAR leads to favorable and often superior predictive performances than the selected mean-based alternatives under various loss functions that encompass both point and probabilistic forecasts .
Edit intent: meaning-changed
Original: The aim of this work is to present an inexpensive, light-weight and easily controlled robotic arm based on Arduino Uno. The peculiarity of the arm is it fully voice operated, in other words it responds to the instructions given by the human operator. Although there are many approaches to make the robot work without controlling it manually but unlike other approaches the voice based approach renders more accuracy and efficiency to the robot to perform delicate tasks. The robotic arm is made up of three modules: The arm, the arduino microcontroller, and the voice module.
Revised: Controlling a robot still requires traditional user interfaces. A more intuitive approach is using verbal or gesture commands. In this paper, we propose a robotic arm that can recognize the human voice commands. The Speech recognition is an essential asset for the robot, enhancing its ability to interact with human beings using their natural form of communication. The approach is verified by deploying the robotic arm into different environments with low, high and medium noise perturbations, where it is tested to perform a set of tasks. With this approach, we have successfully reduced the response time, enhanced the accuracy of the robotic arm to grasp the voice commands and reduced the sentence overlapping by a significant amount. The entire system is divided into three modules: The arm, the arduino microcontroller, and the voice module.
Edit intent: meaning-changed
Original:  The robotic arm is made up of three modules: The arm, the arduino microcontroller, and the voice module. The arm contains three rotary joints and an end effector, where the rotary motion is provided using the servomotor. Moreover, the robot can be operated in two modes, manually using potentiometer shafts and automatically using instructions. Arduino is programmed in such a way that selecting one mode will disable the other .
Revised:  The robotic arm is made up of three modules: the manipulator, the voice recognition module and the microcontroller. The robotic arm is programmed to orient to the direction where the signal to noise ratio is maximum .
Edit intent: clarity
Original:  In this paper, we propose a robotic arm that can recognize the human voice commands. The Speech recognition is an essential asset for the robot, enhancing its ability to interact with human beings using their natural form of communication. The approach is verified by deploying the robotic arm into different environments with low, high and medium noise perturbations, where it is tested to perform a set of tasks. With this approach, we have successfully reduced the response time, enhanced the accuracy of the robotic arm to grasp the voice commands and reduced the sentence overlapping by a significant amount. The entire system is divided into three modules: the manipulator, the voice recognition moduleand the microcontroller.
Revised:  In this paper, we propose a phoneme based speech recognition system to control industrial robots. Speech recognition has become one of the popular interfaces when it comes to reducing robot operator's efforts to control and command the robot. This paper intends to investigate the potential of Linear Predictive coding technique to develop a stable and robust phoneme speech recognition system for robotics applications. Our system is divided into three modules: the manipulator, the voice recognition moduleand the microcontroller.
Edit intent: coherence
Original:  The entire system is divided into three modules: the manipulator, the voice recognition moduleand the microcontroller. The robotic armis programmed to orient to the direction where the signal to noise ratio is maximum .
Revised:  The entire system is divided into three segments: a microphone array, a voice module, and a 3-DOF robotic arm. To validate our approach, we have performed tests with simple and complex sentences for various robotics activities like manipulating a cube and pick and place tasks. Moreover, we also analyzed the test result to rectify the problems and limitations in our approach. The paper presents all the test results which we have achieved through conducting experiments on our project .
Edit intent: meaning-changed
Original: In the "gold rush" for higher performance numbers, a lot of confusion was introduced in supercomputing. The present paper attempts to clear up the terms through scrutinizing the basic terms, contributions, measurement methods. It is shown that using extremely large number of processing elements in computing systems leads to unexpected phenomena,  that cannot be explained in the frame of the classical computing paradigm.
Revised: Using extremely large number of processing elements in computing systems leads to unexpected phenomena, that cannot be explained in the frame of the classical computing paradigm.
Edit intent: coherence
Original:  The  model also enables to derive predictions of supercomputer performance for the near future as well as provides hints for enhancing supercomputer components .
Revised:  The paper shows that the degradation of the efficiency of the parallelized sequential system is a natural consequence of the computing paradigm, rather than an engineering imperfectness. The workload is greatly responsible for wasting the energy as well as limiting the size and the type of tasks the supercomputers can run. Case studies provide insight how the different contributions compete for dominating the resulting payload performance of the computing system, and how enhancing the technology made the computing+communication the dominating contribution in defining the efficiency of supercomputers. The model also enables to derive predictions of supercomputer performance for the near future as well as provides hints for enhancing supercomputer components .
Edit intent: meaning-changed
Original:  The  model also enables to derive predictions of supercomputer performance for the near future as well as provides hints for enhancing supercomputer components .
Revised:  The model also enables to derive predictions of supercomputer performance for the near future as well as provides hints for enhancing the supercomputer components. The phenomena show interesting parallels with the phenomena experienced in science more than a century ago and through their studying a modern science was developed .
Edit intent: meaning-changed
Original: Spiking Neural Network (SNN), as a brain-inspired machine learning algorithm, is closer to the computing mechanism of human brain and more suitable to reveal the essence of intelligence compared with Artificial Neural Networks (ANN), attracting more and more attention in recent years. In addition, the information processed by SNN is in the form of discrete spikes, which makes SNN have low power consumption characteristics .
Revised: Spiking Neural Network (SNN), as a brain-inspired machine learning algorithm, is attracting attention due to its data-driven, event-based computing style and its potential to produce ultra-high-energy-efficient hardware. Most unsupervised SNNs are trained through competitive learning with Spike-Timing-Dependent Plasticity (STDP) .
Edit intent: coherence
Original:   In this paper, we propose an efficient and strong unsupervised SNN named BioSNet with high biological plausibility to handle image classification tasks.
Revised:  But the previous SNNs trained through this approach are limited by slow learning speed or unsatisfactory learning capability. In this paper, we propose an efficient and strong unsupervised SNN named BioSNet with high biological plausibility to handle image classification tasks.
Edit intent: meaning-changed
Original:   In this paper, we propose an efficient and strong unsupervised SNN named BioSNet with high biological plausibility to handle image classification tasks. In BioSNet, we propose a new biomimetic spiking neuron model named MRON inspired by 'recognition memory' in the human brain, design an efficient and robust network architecture corresponding to biological characteristics of the human brain as well, and extend the traditional voting mechanism to the Vote-for-All (VFA) decoding layer so as to reduce information loss during decoding.
Revised:  In this paper, to ease these limitations: 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature. 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer so as to reduce information loss during decoding.
Edit intent: coherence