wanyu/IteraTeR_full_sent huggingface dataset:
Original:  By a technical result on explicit expressions for the comparison theorem, uniqueness theorem and viability on a rectangle of solutions to multidimensional backward stochastic differential equations, some necessary and sufficient conditions are given for the constancy, monotonicity, positivity, homogeneity and translatability properties of multidimensional conditional g-expectation and multidimensional dynamic risk measure ;
Revised:  By a technical result on explicit expressions for the comparison theorem, uniqueness theorem and viability on a rectangle of solutions to multidimensional backward stochastic differential equations, some necessary and sufficient conditions are given for the constancy, monotonicity, positivity, homogeneity and translatability properties of multidimensional conditional g-expectation and multidimensional dynamic risk measures ;
Edit intent: fluency
Original:  Several examples are presented to show how this multidimensional approach is applied to a class of agent-based model and to the problem of risk allocation .
Revised:  Similarly to the one dimensional case, a sufficient condition for a multidimensional dynamic risk measure to be a g-expectation is also explored. As to applications, we show how this multidimensional approach is applied to a class of agent-based model and to the problem of risk allocation .
Edit intent: meaning-changed
Original:  As to applications, we show how this multidimensional approach can be applied to measure the insolvency risk of a firm with interacted subsidiaries \protect .
Revised:  As to applications, we show how this multidimensional approach can be applied to measure the insolvency risk of a firm with interacted subsidiaries \protect\gamma -tolerant g-risk measures is investigated. Insurance g-risk measure and other ways to induce g-risk measures are also studied at the end of the paper .
Edit intent: meaning-changed
Original:  It is shown that a chaotic (deterministic) order, rather than a stochastic randomness, controls the energy minima positions of the staking interactions in the DNA sequences .
Revised: Different numerical mappings of the DNA sequences have been studied using a new cluster-scaling method and the well known spectral methods. It is shown that a chaotic (deterministic) order, rather than a stochastic randomness, controls the energy minima positions of the staking interactions in the DNA sequences .
Edit intent: meaning-changed
Original:  It is shown that a chaotic (deterministic) order, rather than a stochastic randomness, controls the energy minima positions of the staking interactions in the DNA sequences .
Revised:  It is shown , in particular, that the nucleotide sequences in DNA molecules have robust cluster-scaling properties. These properties are relevant to both types of nucleotide pair-bases interactions: hydrogen bonds and stacking interactions. It is shown that taking into account the cluster-scaling properties can help to improve heterogeneous models of the DNA dynamics. It is also shown that a chaotic (deterministic) order, rather than a stochastic randomness, controls the energy minima positions of the staking interactions in the DNA sequences .
Edit intent: meaning-changed
Original:  It is shown that a chaotic (deterministic) order, rather than a stochastic randomness, controls the energy minima positions of the staking interactions in the DNA sequences . The Kaplan-Yorke map and the Arabidopsis genome have been considered as relevant simple examples. The chaotic order results in a large-scale chaotic coherence between the two complimentary DNA-duplex's sequences.
Revised:  It is shown that a chaotic (deterministic) order, rather than a stochastic randomness, controls the energy minima positions of the staking interactions in the DNA sequences on large scales. The chaotic order results in a large-scale chaotic coherence between the two complimentary DNA-duplex's sequences.
Edit intent: meaning-changed
Original:  A competition between this broad-band chaotic coherence and the resonance coherence produced by genetic code has been briefly discussed  .
Revised:  A competition between this broad-band chaotic coherence and the resonance coherence produced by genetic code has been briefly discussed . The Arabidopsis plant genome (which is a model plant for genome analysis) and two human genes: BRCA2 and NRXN1, have been considered as examples .
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Original: There are many different mathematical setups to model the dynamics of gene regulatory networks (GRNs), most based on Boolean networks or chemical kinetics ideas. Here we present an approach to model GRNs through a class of irreversible interacting particle systems called type-dependent stochastic spin models that depart from these traditions.
Revised: We describe an approach to model GRNs through a class of irreversible interacting particle systems called type-dependent stochastic spin models that depart from these traditions.
Edit intent: clarity
Original:  Here we present an approach to model GRNs through a class of irreversible interacting particle systems called type-dependent stochastic spin models that depart from these traditions.
Revised:  Here we present an approach to model biochemical reaction networks at the level of promotion-inhibition circuitry through a class of irreversible interacting particle systems called type-dependent stochastic spin models that depart from these traditions.
Edit intent: meaning-changed
Original:  Here we present an approach to model GRNs through a class of irreversible interacting particle systems called type-dependent stochastic spin models that depart from these traditions. The approach allows for a straightforward modeling of biochemical pathways at the level of promotion--inhibition circuitry, and a particular but otherwise generally applicable choice for the microscopic transition rates of the models also makes them of independent interest.
Revised:  Here we present an approach to model GRNs through a class of irreversible interacting particle systems called type-dependent stochastic spin models that depart from the usual chemical kinetics setup and includes spatial and temporal density fluctuations in a most natural way. A particular but otherwise generally applicable choice for the microscopic transition rates of the models also makes them of independent interest.
Edit intent: meaning-changed
Original: We describe an approach to model biochemical reaction networks at the level of promotion-inhibition circuitry through a class of stochastic spin models that depart from the usual chemical kinetics setup and includes spatial and temporal density fluctuations in a most natural way.
Revised: We describe an approach to model genetic regulatory networks at the level of promotion-inhibition circuitry through a class of stochastic spin models that depart from the usual chemical kinetics setup and includes spatial and temporal density fluctuations in a most natural way.
Edit intent: clarity
Original: We describe an approach to model biochemical reaction networks at the level of promotion-inhibition circuitry through a class of stochastic spin models that depart from the usual chemical kinetics setup and includes spatial and temporal density fluctuations in a most natural way.
Revised: We describe an approach to model biochemical reaction networks at the level of promotion-inhibition circuitry through a class of stochastic spin models that includes spatial and temporal density fluctuations in a most natural way.
Edit intent: coherence
Original: We describe an approach to model biochemical reaction networks at the level of promotion-inhibition circuitry through a class of stochastic spin models that depart from the usual chemical kinetics setup and includes spatial and temporal density fluctuations in a most natural way.
Revised: We describe an approach to model biochemical reaction networks at the level of promotion-inhibition circuitry through a class of stochastic spin models that depart from the usual chemical kinetics setup and includes spatial and temporal density fluctuations in a natural way.
Edit intent: clarity
Original:   A particular but otherwise generally applicable choice for the microscopic transition rates of the models also makes them of independent interest.
Revised:  The formalism can be viewed as an agent-based model formalism with agent behavior ruled by a classical spin-like pseudo-Hamiltonian playing the role of a local, individual objective function. A particular but otherwise generally applicable choice for the microscopic transition rates of the models also makes them of independent interest.
Edit intent: meaning-changed
Original:  To illustrate the formalism, we investigate  some stationary state properties of the repressilator, a synthetic three-gene network of transcriptional regulators that possesses a rich dynamical behaviour .
Revised:  To illustrate the formalism, we investigate (by Monte Carlo simulations) some stationary state properties of the repressilator, a synthetic three-gene network of transcriptional regulators that possesses a rich dynamical behaviour .
Edit intent: meaning-changed
Original: We consider a multi-channel wireless network with user-level CSMA, which consists in running one instance of the CSMA algorithm per active user at each MAC interface . Specifically, each such instance attempts to access a randomly chosen radio channel after some random time and transmits a packet of the corresponding user if the channel is sensed idle .
Revised: We analyze the performance of CSMA in multi-channel wireless network with user-level CSMA, which consists in running one instance of the CSMA algorithm per active user at each MAC interface . Specifically, each such instance attempts to access a randomly chosen radio channel after some random time and transmits a packet of the corresponding user if the channel is sensed idle .
Edit intent: meaning-changed
Original: We consider a multi-channel wireless network with user-level CSMA, which consists in running one instance of the CSMA algorithm per active user at each MAC interface . Specifically, each such instance attempts to access a randomly chosen radio channel after some random time and transmits a packet of the corresponding user if the channel is sensed idle .
Revised: We consider a multi-channel wireless networks, accounting for the random nature of traffic . Specifically, each such instance attempts to access a randomly chosen radio channel after some random time and transmits a packet of the corresponding user if the channel is sensed idle .
Edit intent: clarity
Original: We consider a multi-channel wireless network with user-level CSMA, which consists in running one instance of the CSMA algorithm per active user at each MAC interface . Specifically, each such instance attempts to access a randomly chosen radio channel after some random time and transmits a packet of the corresponding user if the channel is sensed idle .
Revised: We consider a multi-channel wireless network with user-level CSMA, which consists in running one instance of the CSMA algorithm per active user at each MAC interface . Specifically, we assess the ability of CSMA to fully utilize the radio resources and in turn to stabilize the network in a dynamic setting with flow arrivals and departures .
Edit intent: meaning-changed
Original:  We prove that , unlike the standard CSMA algorithm, this simple distributed access scheme is optimal in the sense that the network is stable for all traffic intensities in the capacity region of the network. Simulations show that capacity (in terms of maximum sustainable traffic) increases by a factor ranging from 1.5 to 2.5 compared to the standard CSMAalgorithm, depending on the network topology and the number of available radio channels .
Revised:  We prove that , unlike the standard CSMA algorithm, this simple distributed access scheme is optimal in ad-hoc mode but not in infrastructure mode, when all data flows originate from or are destined to some access points, due to the inherent bias of CSMA against downlink traffic. We propose a slight modification of CSMA, that we refer to as flow-aware CSMA, which corrects this bias and makes the algorithm optimal in all cases. The analysis is based on some time-scale separation assumption which is proved valid in the limit of large flow sizes .
Edit intent: meaning-changed
Original:  The RBF technique is demonstrated by solving the partial integro-differential equation (PIDE) in one-dimension for the American put and the European vanilla call/put options on dividend-paying stocks in the Merton and Kou Jump-diffusion models.
Revised:  The RBF technique is demonstrated by solving the partial integro-differential equation (PIDE) in one-dimension for the American put and the European vanilla call/put options on dividend-paying stocks in the Merton and Kou jump-diffusion models.
Edit intent: fluency
Original: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't  touch, to  choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once .
Revised: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to respond to objects it doesn't touch, to choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once .
Edit intent: clarity
Original: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't  touch, to  choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once .
Revised: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't physically touch, to choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once .
Edit intent: clarity
Original: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't  touch, to  choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once .
Revised: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't touch, to take all paths to a destination, to choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once .
Edit intent: fluency
Original: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't  touch, to  choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once .
Revised: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't touch, to choose a route after it arrives, and to spin in any direction both ways at once .
Edit intent: clarity
Original: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't  touch, to  choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once .
Revised: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't touch, to choose a route after it arrives, to take all paths to a destination and to spin both ways at once .
Edit intent: clarity
Original: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't  touch, to  choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once . In this model, a photon is a processing wave front instantiated from an entity program class, and quantum collapse is when one class stops to merge with another, which restarts its processing .
Revised: This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to detect objects it doesn't touch, to choose a route after it arrives, to take all paths to a destination and to spin in any direction both ways at once as it moves. Here a photon is a processing wave front instantiated from an entity program class, and quantum collapse is when one class stops to merge with another, which restarts its processing .
Edit intent: clarity
Original:  In this model, a photon is a processing wave front instantiated from an entity program class, and quantum collapse is when one class stops to merge with another, which restarts its processing . This processing approach to physics also gives insights into  entanglement, superposition, counterfactuals, the holographic principle and the measurement problem.
Revised:  In this model, a photon is a processing wave front instantiated from an entity program spreading as a processing wave of instances. It becomes a "particle" if any part of it overloads the grid network that runs it, causing the photon program to reboot and restart at a new node. The "collapse of the wave function" is how quantum processing creates what we call a physical photon. This informational approach gives insights into entanglement, superposition, counterfactuals, the holographic principle and the measurement problem.
Edit intent: meaning-changed
Original:  However, because of the heavy-tailedness of this distribution, estimation of its tail quantiles is not a trivial task, which becomes even more difficult when there are outliers in the data, or data is pooled among several sources.
Revised:  However, because of the heavy-tailedness of this distribution, estimation of its tail quantiles is not a trivial task, in particular if one takes into account the heavy tails of this distribution, the possibility of singular outliers, and, moreover, the fact that data is usually pooled among several sources.
Edit intent: meaning-changed
Original:  In such situations where the origin and representativeness of the available data is not clear , robust methods provide a remedy which can provide reliable estimates when classical methods already fail.
Revised:  Moreover, if, as is frequently the case, operational losses are pooled anonymously, relevance of the fitting data for the respective bank is not self-evident. In such situations , robust methods provide a remedy which can provide reliable estimates when classical methods already fail.
Edit intent: meaning-changed
Original:  We illustrate this , applying such robust methods for parameter estimation of a GPD - including some recently developed methods - to data from Algorithmics Inc.
Revised:  In this paper, optimally-robust procedures MBRE, OMSE, RMXE are introduced to the application domain of operational risk. We apply these procedures to parameter estimation of a GPD - including some recently developed methods - to data from Algorithmics Inc.
Edit intent: meaning-changed
Original: To date , there is little structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins  , although many experimental studies have shown that this region has amyloid fibril forming properties .
Revised: X-ray crystallography is a powerful tool to determine the protein 3D structure. However, it is time-consuming and expensive, and not all proteins can be successfully crystallized, particularly for membrane proteins. Although nuclear magnetic resonance (NMR) spectroscopy is indeed a very powerful tool in determining the 3D structures of membrane proteins, it is also time-consuming and costly. To the best of the authors' knowledge , there is little structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins , although many experimental studies have shown that this region has amyloid fibril forming properties .
Edit intent: meaning-changed
Original: To date , there is little structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins  , although many experimental studies have shown that this region has amyloid fibril forming properties .
Revised: To date , there is little structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins due to the noncrystalline and insoluble nature of the amyloid fibril , although many experimental studies have shown that this region has amyloid fibril forming properties .
Edit intent: meaning-changed
Original: To date , there is little structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins  , although many experimental studies have shown that this region has amyloid fibril forming properties . This region belongs to the N-terminal unstructured region (1-123) of prions, the structure of which has proved hard to determine using NMR or X-ray crystallography. Computational optimization approaches , however, allow us to obtain a description of prion 113-120 peptide at a microscopic level. Zhang (J. Mol. Model., 2010, DOI: 10.1007/s00894-010-0691-y) using the traditional local optimization search steepest descent and conjugate gradient methods hybridized with the standard global optimization search simulated annealingmethod successfully constructed three atomic-resolution structures of prion AGAAAAGA amyloid fibrils.
Revised: To date , there is little structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins , although many experimental studies have shown that this region has amyloid fibril forming properties and plays an important role in prion diseases. In view of this, the present study is devoted to address this problem from computational approaches such as global energy optimization, simulated annealing, and structural bioinformatics. The optimal atomic-resolution structures of prion AGAAAAGA amyloid fibrils.
Edit intent: coherence
Original:  Zhang (J. Mol. Model., 2010, DOI: 10.1007/s00894-010-0691-y) using the traditional local optimization search steepest descent and conjugate gradient methods hybridized with the standard global optimization search simulated annealingmethod successfully constructed three atomic-resolution structures of prion AGAAAAGA amyloid fibrils. Zhang pointed out, basing on the NNQNTF peptide of elk prion 173--178 (3FVA.pdb released on 30-JUN-2009 in the Protein Data Bank), new models for prion AGAAAAGA amyloid fibrils might also be able to be constructed. In this paper , using the hybrid local and global optimization search methods, the author successfully constructs another two optimal atomic-resolution amyloid fibril models for the prion AGAAAAGA palindrome. According to Brown (Mol. Cell. Neurosci., 2000, Vol. 15, pp. 66--78), AGAAAAGA is an inhibitor of infectious prions. These atomic-resolution structures of the AGAAAAGA amyloid fibrils constructed might be useful in furthering the goals of medicinal chemistry for controlling prion diseases.
Revised:  Zhang (J. Mol. Model., 2010, DOI: 10.1007/s00894-010-0691-y) using the traditional local optimization search steepest descent and conjugate gradient methods hybridized with the standard global optimization search simulated annealingmethod successfully constructed three atomic-resolution structures of prion AGAAAAGA amyloid fibils reported in this paper have a value to the scientific community in its drive to find treatments for prion diseases.
Edit intent: meaning-changed