wanyu/IteraTeR_v2 huggingface dataset:
Original:  Here, we study a generic Model In which N motors are elastically coupled to a cargo, which itself is subject to thermal noise in the cytoplasm and  an additional external applied force.
Revised:  Here, we study a generic Model In which N motors are elastically coupled to a cargo, which itself is subject to thermal noise in the cytoplasm and to an additional external applied force.
Edit intent: fluency
Original:  The expansion procedure also allows us to quantify load-sharing features among the cargo-bound motors.
Revised:  The expansion procedure also allows us to quantify load-sharing among the cargo-bound motors.
Edit intent: clarity
Original: This document formulates string buffer overflow analysis for C programs.
Revised: Buffer overflow detection and mitigation for C programs has been an important concern for a long time. This paper defines a string buffer overflow analysis for C programs.
Edit intent: clarity
Original:  The key ideas of our formulation are (a) separating buffers from the pointers that point to them, (b) modelling buffers in terms of sizes and sets of positions of null characters, and (c) defining stateless functions to compute the sets of null positions and mappings between buffers and pointer .
Revised:  The key ideas of our formulation are (a) separating buffers from the pointers that point to them, (b) modelling buffers in terms of sizes and sets of positions of null characters, and (c) defining stateless functions to compute the sets of null positions and mappings between buffers and pointers .
Edit intent: fluency
Original:  Given the above motivation, the focus of this paper is not on showing good static approximations for buffer overflow analysis but to show how a given set of static approximations could be formalized in terms of stateless formulations so that they become amenable to automatic construction of analysers.
Revised:  Given the above motivation, the focus of this paper is not on showing good static approximations for buffer overflow analysis but to show how given static approximations could be formalized in terms of stateless formulations so that they become amenable to automatic construction of analysers.
Edit intent: clarity
Original:  Given the above motivation, the focus of this paper is not on showing good static approximations for buffer overflow analysis but to show how given static approximations could be formalized in terms of stateless formulations so that they become amenable to automatic construction of analysers.
Revised:  Given the above motivation, the focus of this paper is not to build good static approximations for buffer overflow analysis but to show how given static approximations could be formalized in terms of stateless formulations so that they become amenable to automatic construction of analysers.
Edit intent: clarity
Original:  The model builds on the decomposition of systemic risk into a number of interconnected segments, for which the level of vulnerability is measured.
Revised:  We decompose systemic risk into a number of interconnected segments, for which the level of vulnerability is measured.
Edit intent: clarity
Original:  For this purpose , the Choquet integral is employed for aggregating expert evaluations of measures, as it allows for the integration of interrelations among factors in the aggregation process.
Revised:  To this end , the Choquet integral is employed for aggregating expert evaluations of measures, as it allows for the integration of interrelations among factors in the aggregation process.
Edit intent: clarity
Original:  In addition we explore two generalizations of self-testing to multiple variables - the case of multilinear functions and homomorphisms on a multidimensional vector space.
Revised:  In addition we explore a generalization of self-testing to multiple variables - the case of multilinear functions and homomorphisms on a multidimensional vector space.
Edit intent: clarity
Original:  In addition we explore two generalizations of self-testing to multiple variables - the case of multilinear functions and homomorphisms on a multidimensional vector space.
Revised:  In addition we explore two generalizations of self-testing to homomorphisms on a multidimensional vector space.
Edit intent: coherence
Original:  The number of queries made by our algorithms are independent of domain size.
Revised:  The number of queries made by our algorithms is independent of domain size.
Edit intent: fluency
Original:  This merging affects the H-index computed by Google Scholar .
Revised:  This merging affects the H-index .
Edit intent: clarity
Original:  We analyze the parameterized complexity of maximizing the H-index using article merges.
Revised:  We analyze the (parameterized) computational complexity of maximizing the H-index using article merges.
Edit intent: clarity
Original:  In contrast, if we are allowed to merge arbitrary articles , then already increasing the H-index by one is NP-hard  .
Revised:  In contrast, if we allow to merge arbitrary articles , then already increasing the H-index by one is NP-hard .
Edit intent: clarity
Original:  The author is allowed to merge articles , which may affect the H-index.
Revised:  The author is allowed to merge articles ; this may affect the H-index.
Edit intent: fluency
Original:  We implement our indifference price , implied volatility and implied Sharpe ratio approximations in two examples.
Revised:  We implement our indifference price and implied volatility approximations in two examples.
Edit intent: clarity
Original:  Our system, called DeepSpeech , outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.5 \% error on the full test set.
Revised:  Our system, called Deep Speech , outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.5 \% error on the full test set.
Edit intent: fluency
Original:  DeepSpeech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.
Revised:  Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.
Edit intent: clarity
Original:  This new deep learning object detection diagram has innovations in multiple aspects.
Revised:  This new deep learning object detection framework has innovations in multiple aspects.
Edit intent: clarity
Original: Motivated by viral marketing, the problem of influence maximization in social networks has attracted much attention in recent years and several studies have been done on that.
Revised: While influence maximization in social networks has attracted much attention in recent years and several studies have been done on that.
Edit intent: clarity
Original:  However, almost all of these studies are focused on the progressive influence models, such as independent cascade (IC) and Linear threshold (LT) models, which cannot capture the reversibility in actions .
Revised:  However, almost all of these studies are focused on the progressive influence models, such as independent cascade (IC) and Linear threshold (LT) models, which cannot capture the reversibility of choices .
Edit intent: clarity
Original:  In this paper, we present the Heat Conduction (HC) model which is a non-progressive influence model and has favorable real world interpretations.
Revised:  In this paper, we present the Heat Conduction (HC) model which is a non-progressive influence model with real-world interpretations.
Edit intent: coherence
Original:  We also show that HC unifies, generalizes, and extends the existing nonprogressive models, such as Voter model  and non-progressive LT [ 1 ].
Revised:  We show that HC unifies, generalizes, and extends the existing nonprogressive models, such as Voter model and non-progressive LT [ 1 ].
Edit intent: clarity
Original:  We also show that HC unifies, generalizes, and extends the existing nonprogressive models, such as Voter model  and non-progressive LT [ 1 ].
Revised:  We also show that HC unifies, generalizes, and extends the existing nonprogressive models, such as the Voter model and non-progressive LT [ 1 ].
Edit intent: fluency
Original:  In addition, we tackle the influence maximization problem for HC, which is proved to be NP-hard, with a scalable and provably near-optimal solution;
Revised:  In addition, we tackle the influence maximization problem with a scalable and provably near-optimal solution;
Edit intent: coherence
Original:  To the best of our knowledge, we are the first to present a scalable solution for influence maximization under non-progressive LT model, as a special case of  HC model.
Revised:  To the best of our knowledge, we are the first to present a scalable solution for influence maximization under nonprogressive LT model, as a special case of HC model.
Edit intent: fluency
Original:  To the best of our knowledge, we are the first to present a scalable solution for influence maximization under non-progressive LT model, as a special case of  HC model.
Revised:  To the best of our knowledge, we are the first to present a scalable solution for influence maximization under non-progressive LT model, as a special case of the HC model.
Edit intent: fluency
Original:  Our fast and efficient  algorithm benefits from two key properties of the proposed HC framework, where we establish closed-form expressions for the influence function computation and the greedy seed selection.
Revised:  Our fast and efficient algorithm benefits from two analytically computable steps: closed-form expressions for the influence function computation and the greedy seed selection.
Edit intent: clarity
Original:  Our fast and efficient  algorithm benefits from two key properties of the proposed HC framework, where we establish closed-form expressions for the influence function computation and the greedy seed selection.
Revised:  Our fast and efficient algorithm benefits from two key properties of the proposed HC framework, where we establish closed-form expressions for the greedy seed selection.
Edit intent: clarity
Original:  Through extensive experiments on several  real and synthetic networks, we validate the efficacy of our algorithm and demonstrate that it outperforms the state-of-the-art methods  in terms of both influence spread and scalability.
Revised:  Through extensive experiments on several real and synthetic networks, we validate the efficacy of our algorithm and demonstrate that it outperforms the state-of-the-art methods , in terms of both influence spread and scalability.
Edit intent: fluency
Original: Terrestrial world is generally green with a small part (<10\%) of plant matter consumed by herbivore annually,but the reason has been unclear with the lack of the food web model that predicts the absolute biomass density of herbivores accompanied by realistic physical units.
Revised: Terrestrial ecosystems are generally green and only a small part (<10\%) of plant matter consumed by herbivore annually,but the reason has been unclear with the lack of the food web model that predicts the absolute biomass density of herbivores accompanied by realistic physical units.
Edit intent: clarity
Original: Terrestrial world is generally green with a small part (<10\%) of plant matter consumed by herbivore annually,but the reason has been unclear with the lack of the food web model that predicts the absolute biomass density of herbivores accompanied by realistic physical units.
Revised: Terrestrial world is generally green with a small part (<10\%) of the plant matter is consumed by herbivores annually,but the reason has been unclear with the lack of the food web model that predicts the absolute biomass density of herbivores accompanied by realistic physical units.
Edit intent: clarity
Original: Terrestrial world is generally green with a small part (<10\%) of plant matter consumed by herbivore annually,but the reason has been unclear with the lack of the food web model that predicts the absolute biomass density of herbivores accompanied by realistic physical units.
Revised: Terrestrial world is generally green with a small part (<10\%) of plant matter consumed by herbivore annually,but the reason has been unclear due to the lack of the food web model that predicts the absolute biomass density of herbivores accompanied by realistic physical units.
Edit intent: clarity
Original: Terrestrial world is generally green with a small part (<10\%) of plant matter consumed by herbivore annually,but the reason has been unclear with the lack of the food web model that predicts the absolute biomass density of herbivores accompanied by realistic physical units.
Revised: Terrestrial world is generally green with a small part (<10\%) of plant matter consumed by herbivore annually,but the reason has been unclear with the lack of food web models for predicting the absolute herbivore biomass in physical units.
Edit intent: clarity
Original:  Here, I present a simple  mathematical food web model that can predict the biomass density of herbivore (primary consumer)
Revised:  Here, I present a simple mathematical food web model that can predict the biomass density of herbivores
Edit intent: clarity
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivores c from ecological factors such as nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: clarity
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as the nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: fluency
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as nutritive values of plants np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: fluency
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as nutritive values of plant np (kg protein/m3), herbivores nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: fluency
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as nutritive values of plant np (kg protein/m3), herbivore nh, and carnivores nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: fluency
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivores S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: fluency
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency (speed) of herbivores eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: clarity
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivores ec, respiratory decrease in herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: fluency
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomasses , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Edit intent: clarity
Original:  and carnivore (secondary consumer) c from ecological factors such as  nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio)  , and preying probability of carnivore on herbivore or carnivore, Phc (ratio) and Pcc.
Revised:  and carnivore (secondary consumer) c from ecological factors such as nutritive values of plant np (kg protein/m3), herbivore nh, and carnivore nc, searching efficiency (volume) of carnivore S (/day), eating efficiency of herbivore eh (/day) and carnivore ec, daily respiratory decrease of herbivore and carnivore biomass , dh (/day) and dc, absorption efficiency of herbivore and carnivore (ratio) , Phc (ratio) and Pcc.
Edit intent: clarity
Original: The model predicts a stable equilibrium with low herbivore biomass h enough to keep the world green if 1. np<nh,nc , 2. S>>eh, and 3.
Revised: The model predicts a stable equilibrium with low herbivore biomass h sufficient to keep the world green if 1. np<nh,nc , 2. S>>eh, and 3.
Edit intent: clarity
Original: The model predicts a stable equilibrium with low herbivore biomass h enough to keep the world green if 1. np<nh,nc , 2. S>>eh, and 3.
Revised: The model predicts a stable equilibrium with low herbivore biomass h enough to keep the world green if 1. np<nh,nc ; S>>eh, and 3.
Edit intent: coherence
Original: The model predicts a stable equilibrium with low herbivore biomass h enough to keep the world green if 1. np<nh,nc , 2. S>>eh, and 3.
Revised: The model predicts a stable equilibrium with low herbivore biomass h enough to keep the world green if 1. np<nh,nc , 2. S>>eh, and 
Edit intent: coherence