wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Whereas in the case of commitment, subject to the buyer's truthtelling and participation constraints, the seller's problem is a decision problem, in the case of limited commitment, the seller's problem corresponds to an intrapersonal game, where different "incarnations" of the seller represent the different beliefs he may have about the buyer's valuation .
Revised:  Whereas in the case of commitment, subject to the buyer's valuation is binary and fully persistent. We show that posted prices implement all equilibrium outcomes of an infinite-horizon, mechanism selection game. Despite being able to choose mechanisms, the seller can do no better and no worse than if he chose prices in each period, so that he is subject to Coase's conjecture. Our analysis marries insights from information and mechanism design with those from the literature on durable goods. We do so by relying on the revelation principle in Doval and Skreta (2020) .
Edit intent: meaning-changed
Original:  With simulations, we illustrate the influence of variations in order flow and valuation distributions on price/volume, noting the emergence of resistance levels, as well as the distinction between high- and low-volume auction price variance.
Revised:  By means of simulations, we illustrate the influence of variations in order flow and valuation distributions on price/volume, noting the emergence of resistance levels, as well as the distinction between high- and low-volume auction price variance.
Edit intent: clarity
Original:  In this work, we provide a general framework of summarizing diagrams that we call Persistence Curves (PC). The main idea is so-called Fundamental Lemma of Persistent Homology, which is derived from the classic elder rule. Under this framework, certain  well-known summaries, such as persistent Betti numbers, and persistence landscape, are special cases of the PC . Moreover, we prove a rigorous bound for a general families of PCs.
Revised:  In this work, we provide a general framework of summarizing diagrams that we call the well-known summaries, such as persistent Betti numbers, and persistence landscape, are special cases of the PC . Moreover, we prove a rigorous bound for a general families of PCs.
Edit intent: coherence
Original:  Under this framework, certain  well-known summaries, such as persistent Betti numbers, and persistence landscape, are special cases of the PC . Moreover, we prove a rigorous bound for a general families of PCs.
Revised:  Under this framework, certainPersistence Curves well-known summaries, such as persistent Betti numbers, and persistence landscape, are special cases of the PC . Moreover, we prove a rigorous bound for a general families of PCs.
Edit intent: meaning-changed
Original:  Under this framework, certain  well-known summaries, such as persistent Betti numbers, and persistence landscape, are special cases of the PC . Moreover, we prove a rigorous bound for a general families of PCs.
Revised:  Under this framework, certain (PCs). We show that some well-known summaries, such as persistent Betti numbers, and persistence landscape, are special cases of the PC . Moreover, we prove a rigorous bound for a general families of PCs.
Edit intent: coherence
Original:  Under this framework, certain  well-known summaries, such as persistent Betti numbers, and persistence landscape, are special cases of the PC . Moreover, we prove a rigorous bound for a general families of PCs.
Revised:  Under this framework, certain well-known summaries, such as Betti number curves, the Euler Characteristic Curve, and Persistence Landscapes fall under the PC framework or are easily derived from it. Second, we provide a theoretical foundation for the stability analysis of PCs.
Edit intent: meaning-changed
Original:  First, we develop a general  framework of vectorizing diagrams that we call the Persistence Curves (PCs) . We show that some well-known summaries, such as Betti number curves, the Euler Characteristic Curve, and Persistence Landscapes fall under the PC framework or are easily derived from it . Second, we provide a theoretical foundation for the stability analysis of PCs.
Revised:  First, we develop a general framework of vectorizing diagrams that we call the Persistence Curves (PCs) , and show that several well-known summaries, such as Betti number curves, the Euler Characteristic Curve, and Persistence Landscapes fall under the PC framework or are easily derived from it . Second, we provide a theoretical foundation for the stability analysis of PCs.
Edit intent: coherence
Original:  We show that some well-known summaries, such as Betti number curves, the Euler Characteristic Curve, and Persistence Landscapes fall under the PC framework or are easily derived from it . Second, we provide a theoretical foundation for the stability analysis of PCs.
Revised:  We show that some well-known summaries, such as Persistence Landscapes, fall under the PC framework or are easily derived from it . Second, we provide a theoretical foundation for the stability analysis of PCs.
Edit intent: meaning-changed
Original:  We show that some well-known summaries, such as Betti number curves, the Euler Characteristic Curve, and Persistence Landscapes fall under the PC framework or are easily derived from it . Second, we provide a theoretical foundation for the stability analysis of PCs.
Revised:  We show that some well-known summaries, such as Betti number curves, the Euler Characteristic Curve, and Persistence Landscapes fall under the PC framework . Second, we provide a theoretical foundation for the stability analysis of PCs.
Edit intent: coherence
Original:  We show that some well-known summaries, such as Betti number curves, the Euler Characteristic Curve, and Persistence Landscapes fall under the PC framework or are easily derived from it . Second, we provide a theoretical foundation for the stability analysis of PCs. In addition, we propose several new summaries based on PC framework and investigate their stability .
Revised:  We show that some well-known summaries, such as Betti number curves, the Euler Characteristic Curve, and Persistence Landscapes fall under the PC framework or are easily derived from it . Second, we propose several new summaries based on PC framework and investigate their stability .
Edit intent: coherence
Original: We introduce DeepNovoV2, the state-of-the-art neural networks based model for de novo peptide sequencing.
Revised: Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy. The key step in developing personalized therapeutic cancer vaccines is to identify tumor-specific neoantigens that are on the surface of tumor cells. A promising method for this is through de novo peptide sequencing from mass spectrometry data. In this paper we introduce DeepNovoV2, the state-of-the-art neural networks based model for de novo peptide sequencing.
Edit intent: coherence
Original:  Contrary to existing models like DeepNovo or DeepMatch which represents each spectrum as a long sparse vector, in DeepNovoV2, we propose to directly represent a spectrum as a set of (m/z, intensity) pairs .
Revised:  Contrary to existing models like DeepNovo or DeepMatch which represents each spectrum as a long sparse vector, in DeepNovoV2, a spectrum is directly represented as a set of (m/z, intensity) pairs .
Edit intent: clarity
Original:  Contrary to existing models like DeepNovo or DeepMatch which represents each spectrum as a long sparse vector, in DeepNovoV2, we propose to directly represent a spectrum as a set of (m/z, intensity) pairs . Then we use an order invariant network structure (T-Net) to extract features from the spectrum.
Revised:  Contrary to existing models like DeepNovo or DeepMatch which represents each spectrum as a long sparse vector, in DeepNovoV2, we propose to directly represent a spectrum as a set of (m/z, intensity) pairs , therefore it does not suffer from the accuracy-speed/memory trade-off problem. The model combines an order invariant network structure (T-Net) to extract features from the spectrum.
Edit intent: meaning-changed
Original:  Then we use an order invariant network structure (T-Net) to extract features from the spectrum. By representing spectrums as sets of peaks, we argue that our method is more straightforward and do not have the accuracy-speed/memory trade-off problem .
Revised:  Then we use an order invariant network structure (T-Net) and recurrent neural networks and provides a complete end-to-end training and prediction framework to sequence patterns of peptides .
Edit intent: meaning-changed
Original:  Current literature has either characterized asymptotic behavior in general settings under the assumption that the agent's action converges (which sometimes does not) or has established convergence of the action in specific applications. By noting that the key element to predict the agent's behavior is the frequency of her past actions, we are able to characterize asymptotic behavior in general settings in terms of the solutions of a generalization of a differential equation that describes the evolution of the frequency of actions.
Revised:  Current literature has either characterized asymptotic behavior under fairly specific assumptions. By first showing that the key element to predict the agent's behavior is the frequency of her past actions, we are able to characterize asymptotic behavior in general settings in terms of the solutions of a generalization of a differential equation that describes the evolution of the frequency of actions.
Edit intent: clarity
Original:  This is also known as the conflict-free chromatic guarding problem with vertex guards (which is quite different from point guards considered in some other papers).We study the problem in two scenarios of a set of viewers. In the first one, we assume that the viewers are all points of the polygon  .
Revised:  This is also known as the conflict-free chromatic guarding problem with vertex guards , and is motivated, e.g., by the problem of radio frequency assignment to sensors placed at the polygon vertices. We first study the scenario in which viewers can be all points of the polygon .
Edit intent: meaning-changed
Original:  We efficiently solve the related problem of minimizing the number of guards and approximate (up to only an additive error) the number of colours  in the special case of funnels. We also give an upper bound of O(log^2 n) colours on weak-visibility polygonswhich generalizes to all simple polygons.
Revised:  We efficiently solve the related problem of minimizing the number of guards and approximate (up to only an additive error) the number of colours in the special case of polygons called funnels. Then we give an upper bound of O(log^2 n) colours on weak-visibility polygonswhich generalizes to all simple polygons.
Edit intent: clarity
Original:  We devise a fast  shortest path algorithm on arbitrary induced subgraphs of terrain visibility graphs and a polynomial-time algorithm for Dominating Set on special terrain visibility graphs (called funnel visibility graphs).
Revised:  We devise a fast output-sensitive shortest path algorithm on arbitrary induced subgraphs of terrain visibility graphs and a polynomial-time algorithm for Dominating Set on special terrain visibility graphs (called funnel visibility graphs).
Edit intent: meaning-changed
Original: We investigate different notions of arbitrage in a multiple-priors setting in discrete time . We revisit the so-called quasi-sure no-arbitrage condition and prove a geometric and a quantitative version of it .
Revised: We investigate different notions of arbitrage in a multiple-priors setting, we propose a new characterisation of the condition of quasi-sure no-arbitrage condition and prove a geometric and a quantitative version of it .
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Original:  We revisit the so-called quasi-sure no-arbitrage condition and prove a geometric and a quantitative version of it .
Revised:  We revisit the so-called quasi-sure no-arbitrage which has become a standard assumption. This characterisation shows that it is indeed a well-chosen condition being equivalent to several previously used alternative notions of no-arbitrage and allowing the proof of important results in mathematical finance .
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Original: It has been known for nearly a decade that deterministically modeled reaction networks that are weakly reversible and consist of a single linkage class have bounded, persistent trajectories (i.e., for each trajectory x(t)\in%DIFDELCMD < \R%%% ^d_{\ge 0, the expression i=1^d |\ln(x_i(t))| is uniformly bounded in time) \mbox{%DIFAUXCMD AndBounded_ONE2011,AndGAC_ONE2011}0pt%DIFAUXCMD }  . It is conjectured that the stochastically modeled analogs of these systems are positive recurrent.
Revised: It has been known for nearly a decade that deterministically modeled reaction networks that are weakly reversible and consist of a single linkage class have %DIFDELCMD < \R%%% ^d_{\ge 0, the expression i=1^d |\ln(x_i(t))| is uniformly bounded in time) \mbox{%DIFAUXCMD AndBounded_ONE2011,AndGAC_ONE2011}0pt%DIFAUXCMD } . It is conjectured that the stochastically modeled analogs of these systems are positive recurrent.
Edit intent: clarity
Original: It has been known for nearly a decade that deterministically modeled reaction networks that are weakly reversible and consist of a single linkage class have bounded, persistent trajectories (i.e., for each trajectory x(t)\in%DIFDELCMD < \R%%% ^d_{\ge 0, the expression i=1^d |\ln(x_i(t))| is uniformly bounded in time) \mbox{%DIFAUXCMD AndBounded_ONE2011,AndGAC_ONE2011}0pt%DIFAUXCMD }  . It is conjectured that the stochastically modeled analogs of these systems are positive recurrent.
Revised: It has been known for nearly a decade that deterministically modeled reaction networks that are weakly reversible and consist of a single linkage class have bounded, persistent trajectories (i.e., for each trajectory x(t)\in%DIFDELCMD < \R%%% , the expression i=1^d |\ln(x_i(t))| is uniformly bounded in time) \mbox{%DIFAUXCMD AndBounded_ONE2011,AndGAC_ONE2011}0pt%DIFAUXCMD } . It is conjectured that the stochastically modeled analogs of these systems are positive recurrent.
Edit intent: coherence
Original: It has been known for nearly a decade that deterministically modeled reaction networks that are weakly reversible and consist of a single linkage class have bounded, persistent trajectories (i.e., for each trajectory x(t)\in%DIFDELCMD < \R%%% ^d_{\ge 0, the expression i=1^d |\ln(x_i(t))| is uniformly bounded in time) \mbox{%DIFAUXCMD AndBounded_ONE2011,AndGAC_ONE2011}0pt%DIFAUXCMD }  . It is conjectured that the stochastically modeled analogs of these systems are positive recurrent.
Revised: It has been known for nearly a decade that deterministically modeled reaction networks that are weakly reversible and consist of a single linkage class have bounded, persistent trajectories (i.e., for each trajectory x(t)\in%DIFDELCMD < \R%%% ^d_{\ge 0, the expression i=1^d |\ln(x_i(t))| is uniformly bounded in time) \mbox{%DIFAUXCMD AndBounded_ONE2011,AndGAC_ONE2011}0pt%DIFAUXCMD } trajectories that are bounded from both above and below by positive constants (so long as the initial condition has strictly positive components) . It is conjectured that the stochastically modeled analogs of these systems are positive recurrent.
Edit intent: meaning-changed
Original: While there are well-developed tools for maximizing a  submodular function subject to a matroid constraint , there is much less work on the corresponding supermodular maximization problems. We develop new techniques for attacking these problemsinspired by the continuous greedy method{2}i,j\in AA_{ij}:S\inM, where M is a matroid.
Revised: While there are well-developed tools for maximizing a submodular function subject to a matroid constraint . This framework has a continuous optimization part and a rounding part. We extend both parts to a wider array of problems. In particular, we make a conceptual contribution by identifying a family of parameterized functions. As a running example we focus on solving diversity problems \max f(S)=\frac{1{2}i,j\in AA_{ij}:S\inM, where M is a matroid.
Edit intent: meaning-changed
Original:  The multilinear framework cannot be directly } applied to the multi-linear extension of a submodular function. We first adapt the continuous greedy algorithm to work for general twice-continuously differentiable functions . The performance of the adapted algorithm depends on a new smoothness parameter. If F: 0,^2 which measures the approximability of the associated problem \max\{F(x):x\in P\}, for solvable downwards-closed polytopes P.
Revised:  The multilinear framework cannot be directly } applied to the 0,^2 which measures the approximability of the associated problem \max\{F(x):x\in P\}, for solvable downwards-closed polytopes P.
Edit intent: clarity
Original:  If F: 0,^2 which measures the approximability of the associated problem \max\{F(x):x\in P\}, for solvable downwards-closed polytopes P.
Revised:  If F: multilinear extension of such functions. We introduce a new parameter for functions F\in{\bf C^2 which measures the approximability of the associated problem \max\{F(x):x\in P\}, for solvable downwards-closed polytopes P.
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Original:  We give an \Omega(} 1 ^n\rightarrow\mathbb{R one-sided \sigma-smooth , then the approximation factor only depends on \sigma.
Revised:  We give an \Omega(} 1 /\sigma)-approximation for the maximization problem of monotone, normalized one-sided \sigma-smooth , then the approximation factor only depends on \sigma.
Edit intent: meaning-changed
Original:  We give an \Omega(} 1 ^n\rightarrow\mathbb{R one-sided \sigma-smooth , then the approximation factor only depends on \sigma. We apply the new algorithm to a broad class of quadratic supermodular functions arising in diversity maximization. This captures metric diversity maximization ( \sigma =2) and negative-type diversity (\sigma=4) .
Revised:  We give an \Omega(} 1 ^n\rightarrow\mathbb{R one-sided \sigma-smooth F with an additional property: non-positive third order partial derivatives. Using the multilinear framework and new matroid rounding techniques for quadratic objectives, we give an \Omega(1/ \sigma =2) and negative-type diversity (\sigma=4) .
Edit intent: meaning-changed
Original:  This captures metric diversity maximization ( \sigma =2) and negative-type diversity (\sigma=4) . We also develop new methods (inspired by swap rounding and approximate integer decomposition)for rounding quadratics over a matroid polytope. Together with the adapted continuous greedy this leads to a O(\sigma ^{3/2})-approximation .
Revised:  This captures metric diversity maximization ( \sigma ^{3/2})-approximation .
Edit intent: coherence
Original:  Together with the adapted continuous greedy this leads to a O(\sigma ^{3/2})-approximation . This is the best asymptotic approximation known for this class of quadratics and the evidence suggests that it may be tight.
Revised:  Together with the adapted continuous greedy this leads to a O(\sigma ^{3/2})-approximation for maximizing a \sigma-semi-metric diversity function subject to matroid constraint. This improves upon the previous best bound of \Omega(1/\sigma^2) and we give evidence that it may be tight.
Edit intent: meaning-changed
Original:  We then consider general (non-quadratic) functions. We give a broad parameterized family of monotone functions which include submodular functions and the just-discussed supermodular family of discrete quadratics. Such set functions are called \gamma-meta-submodular. We develop local search algorithms with approximation factors that depend only on \gamma. We show that the \gamma-meta-submodular families include well-known function classes including meta-submodular functions (\gamma=0), proportionally submodular (\gamma= 1 ), and diversity functions based on negative-type distances or Jensen-Shannon divergence (both \gamma=2) and (semi-)metric diversity)-approximation, independent of n.
Revised:  For general one-sided smooth functions, we show the continuous process gives an \Omega( 1 ), and diversity functions based on negative-type distances or Jensen-Shannon divergence (both \gamma=2) and (semi-)metric diversity)-approximation, independent of n.
Edit intent: meaning-changed
Original:  We show that the \gamma-meta-submodular families include well-known function classes including meta-submodular functions (\gamma=0), proportionally submodular (\gamma= 1 ), and diversity functions based on negative-type distances or Jensen-Shannon divergence (both \gamma=2) and (semi-)metric diversity)-approximation, independent of n.
Revised:  We show that the \gamma-meta-submodular families include well-known function classes including meta-submodular functions (\gamma=0), proportionally submodular (\gamma= 1 /3^{2\sigma)-approximation, independent of n.
Edit intent: meaning-changed
Original:  Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood.
Revised:  Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood.
Edit intent: fluency
Original:  We investigated jellyfish locomotion by conducting in silico comparative studies and explored swimming performance in different fluids (e.g., changing viscosities) at different fluid scales (e.g., Reynolds Number), bell contraction frequencies,  contraction phase kinematics , as well as bell morphologies and contraction amplitude .
Revised:  We investigated jellyfish locomotion by conducting in silico comparative studies and explored swimming performance across different fluid scales (e.g., Reynolds Number), bell contraction frequencies, contraction phase kinematics , as well as bell morphologies and contraction amplitude .
Edit intent: clarity
Original:  We investigated jellyfish locomotion by conducting in silico comparative studies and explored swimming performance in different fluids (e.g., changing viscosities) at different fluid scales (e.g., Reynolds Number), bell contraction frequencies,  contraction phase kinematics , as well as bell morphologies and contraction amplitude .
Revised:  We investigated jellyfish locomotion by conducting in silico comparative studies and explored swimming performance in different fluids (e.g., changing viscosities) at different fluid scales (e.g., Reynolds Number), bell contraction frequencies, and contraction phase kinematics , as well as bell morphologies and contraction amplitude .
Edit intent: coherence
Original:  We investigated jellyfish locomotion by conducting in silico comparative studies and explored swimming performance in different fluids (e.g., changing viscosities) at different fluid scales (e.g., Reynolds Number), bell contraction frequencies,  contraction phase kinematics , as well as bell morphologies and contraction amplitude .
Revised:  We investigated jellyfish locomotion by conducting in silico comparative studies and explored swimming performance in different fluids (e.g., changing viscosities) at different fluid scales (e.g., Reynolds Number), bell contraction frequencies, contraction phase kinematics for a jellyfish with a fineness ratio of 1 (ratio of bell height to bell diameter) .
Edit intent: meaning-changed