wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed  .
Revised:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed . In our experimental evaluation, we have tested the ability of the proposed algorithm to identify samples of abnormal behavior in induction-loop data from Dublin, Ireland .
Edit intent: coherence
Original: The method of moments has been proposed as a potential means to reduce the dimensionality of the chemical master equation (CME) appearing in stochastic chemical kinetics . However, attempts to apply the method of moments to the CME usually result in the so-called closure problem.
Revised: The method of moments has been proposed as a potential means to reduce the dimensionality of the chemical master equation (CME) appearing in stochastic chemical kinetics often leads to the so-called closure problem.
Edit intent: clarity
Original:  Recently, a method was proposed for calculating rigorous bounds on quantities of interest for stochastic chemical kinetic systems at steady state.
Revised:  Recently, several authors showed that this problem can be partially overcome using moment-based semidefinite programs (SDPs). In particular, they showed that moment-based SDPs can be used to calculate rigorous bounds on quantities of interest for stochastic chemical kinetic systems at steady state.
Edit intent: meaning-changed
Original:  Recently, a method was proposed for calculating rigorous bounds on quantities of interest for stochastic chemical kinetic systems at steady state. In particular, the method used semidefinite programming to calculate bounds on mean molecular counts, variances in these counts, and probability histograms .
Revised:  Recently, a method was proposed for calculating rigorous bounds on quantities of interest for stochastic chemical kinetic system's stationary distribution(s) -- for example, mean molecular counts, variances in these counts, and probability histograms .
Edit intent: meaning-changed
Original: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture  (Hi-C) or genome architecture mapping  (GAM) experiments.
Revised: It is widely believed that the folding of the chromosome in the nucleus has a major effect on genetic expression. For example co-regulated genes in several species have been shown to colocalize in space despite being far away on the DNA sequence. In this manuscript , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture (Hi-C) or genome architecture mapping (GAM) experiments.
Edit intent: meaning-changed
Original: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture  (Hi-C) or genome architecture mapping  (GAM) experiments.
Revised: In this article , we present a new method to model the three-dimensional structure of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture (Hi-C) or genome architecture mapping (GAM) experiments.
Edit intent: clarity
Original: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture  (Hi-C) or genome architecture mapping  (GAM) experiments.
Revised: In this article , we present a polymer model of the chromosome in live cells, based on DNA-DNA interactions measured in high-throughput chromosome conformation capture (Hi-C) or genome architecture mapping (GAM) experiments.
Edit intent: meaning-changed
Original: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture  (Hi-C) or genome architecture mapping  (GAM) experiments.
Revised: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture experiments (Hi-C) or genome architecture mapping (GAM) experiments.
Edit intent: fluency
Original: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture  (Hi-C) or genome architecture mapping  (GAM) experiments.
Revised: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture (Hi-C) and genome architecture mapping (GAM) experiments.
Edit intent: fluency
Original: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture  (Hi-C) or genome architecture mapping  (GAM) experiments.
Revised: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture (Hi-C) or genome architecture mapping experiments (GAM) experiments.
Edit intent: fluency
Original: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture  (Hi-C) or genome architecture mapping  (GAM) experiments. Specifically, our Gaussian effective model (GEM) introduces harmonic potentials to represent interactions detected during such experiments. As a central property, we derive an exact relation between the couplings of these potentials and the resulting contact probabilities .
Revised: In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture (Hi-C) or genome architecture mapping (GAM) .
Edit intent: clarity
Original:  This relation is used here to solve the inverse problem of constructing a GEM which best reproduces the contact probabilities measured experimentally. For that purpose, we present a minimization scheme that searches for the GEM that has contact probabilities as close as possible to the experimental ones. We apply this method to several data sets generated from experiments using the Hi-C or GAM techniques.
Revised:  This relation is used here to solve the inverse problem of constructing a GEM which best reproduces the contact probabilities measured in Hi-C or GAM techniques.
Edit intent: coherence
Original:  We apply this method to several data sets generated from experiments using the Hi-C or GAM techniques. To illustrate potential applications of our method , we show how the reconstructed couplings can be used to investigate the URLanizationby Brownian Dynamics .
Revised:  We apply this method to several data sets generated from experiments using the Hi-C and GAM experiments rather than estimates of average distances between genomic loci. Specifically, we model the chromosome as a Gaussian polymer with harmonic interactions and extract the coupling coefficients best reproducing the experimental contact probabilities. In contrast to existing methods, we give an exact expression of the contact probabilities at thermodynamic equilibrium. The Gaussian effective model (GEM) reconstructed with our method reproduces experimental contacts with high accuracy. We also show how Brownian Dynamics simulations of our reconstructed GEM can be used to investigate the URLanizationby Brownian Dynamics .
Edit intent: meaning-changed
Original: We provide an epistemic foundation for the core of a cooperative game by proof theory . Given a cooperative game, we first transform each payoff vector into a decision problem ( accept or reject ) for each player .
Revised: We provide an epistemic foundation for the core of a cooperative game by proof theory by studying the knowledge needed for players to unanimously accept only core payoffs. We first transform each payoff vector into a decision problem ( accept or reject ) for each player .
Edit intent: meaning-changed
Original:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process . Especially , we define  C-acceptability to capture the criterion for  a core payoff vector.
Revised:  Then we use a modified KD-system in epistemic logic to describe a player's knowledge, decision-making criterion, and reasoning process . Especially , we define C-acceptability to capture the criterion for a core payoff vector.
Edit intent: clarity
Original:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process . Especially , we define  C-acceptability to capture the criterion for  a core payoff vector.
Revised:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process ; especially , we define C-acceptability to capture the criterion for a core payoff vector.
Edit intent: fluency
Original:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process . Especially , we define  C-acceptability to capture the criterion for  a core payoff vector.
Revised:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process . Especially , we define a formula called C-acceptability to capture the criterion for a core payoff vector.
Edit intent: clarity
Original:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process . Especially , we define  C-acceptability to capture the criterion for  a core payoff vector.
Revised:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process . Especially , we define C-acceptability to describe the criterion for a core payoff vector.
Edit intent: clarity
Original:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process . Especially , we define  C-acceptability to capture the criterion for  a core payoff vector.
Revised:  Then we use a modified KD-system in epistemic logic to describe a player's belief/ knowledge, decision-making criterion, and reasoning process . Especially , we define C-acceptability to capture the criterion for accepting a core payoff vector.
Edit intent: clarity
Original:  Based on this result, we discuss  epistemic inconsistency behind Debreu-Scarf Theorem  .
Revised:  Based on this result, we discuss epistemic inconsistency behind Debreu-Scarf Theorem , that is, the increase of the number of replicas, on one hand, has constant require- ment on each participator's knowledge in the competitive market, while, on the other hand, requires unbounded epistemic ability for at least one player in the cooperative situation .
Edit intent: meaning-changed
Original: The paper is concerned with an in-body system gathering data for medical purposes. It is focused on communication between the following two components of the system: liposomes gathering the data inside human veins and a detector collecting the data from liposomes. Foerster Resonance Energy Transfer (FRET) is considered as a mechanism for communication between the system components.
Revised: The paper is concerned with Forster Resonance Energy Transfer (FRET) is considered as a mechanism for communication between the system components.
Edit intent: clarity
Original:  Foerster Resonance Energy Transfer (FRET) is considered as a mechanism for communication between the system components. The usage of bioluminescent molecules as an energy source for generating FRET signals is suggested and the performance evaluation of this approach is given. FRET transmission may be initiated without an aid of an external laser, which is crucial in case of communication taking place inside of human body. It is also shown how to solve the problem of FRET signals recording. The usage of channelrhodopsin molecules, able to receive FRET signals and convert them into voltage, is proposed.
Revised:  Foerster Resonance Energy Transfer (FRET) is considered as a mechanism for communication between nanodevices. Two solved issues are reported in the paper, namely: signal generation and signal storage in FRET-based nanonetworks. First, luciferase molecules as FRET transmitters which are able to generate FRET signals themselves, taking energy from chemical reactions without any external light exposure, are proposed. Second, channelrhodopsins as FRET receivers, as they can convert FRET signals into voltage, is proposed.
Edit intent: coherence
Original:  The usage of channelrhodopsin molecules, able to receive FRET signals and convert them into voltage, is proposed. The communication system is modelled with molecular structures and spectral characteristics of the proposed molecules and further validatedby using Monte Carlo computer simulations, calculating the data throughputand the bit error rate  .
Revised:  The usage of channelrhodopsin molecules, able to receive FRET signals and convert them into voltage, are suggested. Further, medical in-body systems where both molecule types might be successfully applied, are discussed. Luciferase-channelrhodopsin communication is modeled and its performance is numerically validated, reporting on its throughput, bit error rate .
Edit intent: meaning-changed
Original:  The communication system is modelled with molecular structures and spectral characteristics of the proposed molecules and further validatedby using Monte Carlo computer simulations, calculating the data throughputand the bit error rate  .
Revised:  The communication system is modelled with molecular structures and spectral characteristics of the proposed molecules and further validatedby using Monte Carlo computer simulations, calculating the data throughputand the bit error rate , propagation delay and energy consumption .
Edit intent: meaning-changed
Original: Predicting how a proposed cancer treatment will affect a given tumor can be cast as a machine learning problem, but the complexity of biological systems, the number of potentially relevant genomic and clinical features , and the lack of very large scale patient data repositories make this a unique challenge. "Pure data" approaches to this problem are underpowered to detect combinatorially complex interactions and are bound to uncover false correlations despite statistical precautions taken (1). To investigate this setting, we propose a method to integrate simulations , a strong form of prior knowledge, into machine learning , a combination which to date has been largely unexplored .
Revised: Motivation: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning , a combination which to date has been largely unexplored .
Edit intent: clarity
Original:  To investigate this setting, we propose a method to integrate simulations , a strong form of prior knowledge, into machine learning , a combination which to date has been largely unexplored .
Revised:  To investigate this setting, we propose a method to integrate simulations , a strong form of prior knowledge, into machine learning , which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines .
Edit intent: meaning-changed
Original:  These similarity values, rather than the original high dimensional feature data, are used to train kernelized machine learning algorithms such as support vector machines, thus handling the curse-of-dimensionality that typically affects genomic machine learning. Using four synthetic datasets of complex systems--three biological models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.
Revised:  These similarity values, rather than the original high dimensional feature data, are used to build the kernel. Results: We demonstrate and explore the simulation based kernel (SimKern) concept using four synthetic complex systems--three biological models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.
Edit intent: meaning-changed
Original:  Using four synthetic datasets of complex systems--three biological models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.
Revised:  Using four synthetic datasets of complex systems--three biologically inspired models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.
Edit intent: meaning-changed
Original:  Using four synthetic datasets of complex systems--three biological models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.
Revised:  Using four synthetic datasets of complex systems--three biological models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.
Edit intent: coherence
Original:  Using four synthetic datasets of complex systems--three biological models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.
Revised:  Using four synthetic datasets of complex systems--three biological models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods.
Edit intent: meaning-changed
Original:  However , by revisiting the polar clasps, we cannot confirm this very important intersheet hydrogen bond; instead we found another hydrogen bond ( Asn171 :H-Gln172: OE1  between the strand  of one sheet and the opposing strand  of the mating sheet ) to replace it .
Revised:  However , by revisiting the polar clasps, we found another two hydrogen bonds (B. Asn171 :H-Gln172: OE1 between the strand of one sheet and the opposing strand of the mating sheet ) to replace it .
Edit intent: meaning-changed
Original:  instead we found another hydrogen bond ( Asn171 :H-Gln172: OE1  between the strand  of one sheet and the opposing strand  of the mating sheet ) to replace it .
Revised:  instead we found another hydrogen bond ( Asn171 @H-A.Gln172 OE1 between the strand of one sheet and the opposing strand of the mating sheet ) to replace it .
Edit intent: meaning-changed
Original:  instead we found another hydrogen bond ( Asn171 :H-Gln172: OE1  between the strand  of one sheet and the opposing strand  of the mating sheet ) to replace it .
Revised:  instead we found another hydrogen bond ( Asn171 :H-Gln172: OE1 , B.Tyr169@OH-A.Gln172@N) between the strand of one sheet and the opposing strand of the mating sheet ) to replace it .
Edit intent: meaning-changed
Original:  instead we found another hydrogen bond ( Asn171 :H-Gln172: OE1  between the strand  of one sheet and the opposing strand  of the mating sheet ) to replace it .
Revised:  instead we found another hydrogen bond ( Asn171 :H-Gln172: OE1 between the strand A of one sheet and the opposing strand of the mating sheet ) to replace it .
Edit intent: meaning-changed
Original:  instead we found another hydrogen bond ( Asn171 :H-Gln172: OE1  between the strand  of one sheet and the opposing strand  of the mating sheet ) to replace it .
Revised:  instead we found another hydrogen bond ( Asn171 :H-Gln172: OE1 between the strand of one sheet and the opposing strand B of the mating sheet ) to replace it .
Edit intent: meaning-changed
Original:   In addition, by revisiting the polar clasps, we found another two hydrogen bonds (B.Asn171@H-A.Gln172@OE1, B.Tyr169@OH-A.
Revised:  - this hydrogen bond is not between the neighbouring Chain B and Chain A directly. In addition, by revisiting the polar clasps, we found another two hydrogen bonds (B.Asn171@H-A.Gln172@OE1, B.Tyr169@OH-A.
Edit intent: meaning-changed
Original: Gln172@N) between the strand A of one sheet and the opposing strand B of the mating sheet   .
Revised: Gln172@N) between the strand A of one sheet and the opposing strand B of the mating sheet . For the neighbouring two single \beta-sheets AB, the two new hydrogen bonds are completely different from the experimental one (an intersheet hydrogen bond between Tyr169 and the backbone carbonyl of Asn171 on an opposing strand) in .
Edit intent: meaning-changed
Original:  If only insertions are considered, our algorithm is also able to achieve optimal packing density as defined in our paper , with an amortized reallocation cost of O(c\log 1{c}) for squares, and O(c(1+s^2)1+s^21{c}) for right angled triangles, where s is the ratio of the lengths of the second shortest side to the shortest, when inserting a circle of area c.
Revised:  If only insertions are considered, our algorithm is also able to achieve optimal packing density , with an amortized reallocation cost of O(c\log 1{c}) for squares, and O(c(1+s^2)1+s^21{c}) for right angled triangles, where s is the ratio of the lengths of the second shortest side to the shortest, when inserting a circle of area c.
Edit intent: clarity