wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our results revealed that human experts using actual specimens yield the lowest classification error . However, our proposed, much faster, automated approach using deep Convolutional Neural Nets comes very close to human accuracy. Contrary to previous findings in the literature, we find that machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts. Finally, we publicly share our unique dataset to serve as a public benchmark dataset in this field.
Revised:  Our results revealed that human experts using actual specimens yield the lowest classification error (
Edit intent: coherence
Original:  In a novel approach , we explicitly relate the discovery of relevant new information sources to jumps in the conditional expectation process of a partially observed signal. We derive the resulting endogenous jump-diffusion , and show that it is a solution to the stochastic filtering problem associated with the Markov-modulated multivariate information process .
Revised:  In a novel approach , we explicitly relate the discovery of relevant new information sources to jumps in conditional expectation processes of partially observed signals. We show that the underlying measure-valued process follows jump-diffusion , and show that it is a solution to the stochastic filtering problem associated with the Markov-modulated multivariate information process .
Edit intent: meaning-changed
Original:  We derive the resulting endogenous jump-diffusion , and show that it is a solution to the stochastic filtering problem associated with the Markov-modulated multivariate information process . We give a Feynman-Ka\'c representation for the endogenous jump-diffusion, and produce an explicit expression for the size of the jumps . The jump-size distribution is a function of a weighted sum of those information processes that are activated at the jump time.
Revised:  We derive the resulting endogenous jump-diffusion dynamics, where the jumps are governed by information source switches. The dynamic representation gives rise to a set of martingales on random time intervals that capture evolving information states as well as to a state-dependent stochastic volatility evolution with jumps. The nature of information flows usually exhibits complex behaviour, however, we succeed in maintaining analytic tractability by introducing what we term the effective and complementary information processes. The effective information captures in a univariate process all information provided by the active sources, and the complementary information is a weighted sum of those information processes that are activated at the jump time.
Edit intent: meaning-changed
Original:  The jump-size distribution is a function of a weighted sum of those information processes that are activated at the jump time. The proposed approach can be applied broadly in signal processing, and an example in Mathematical Finance is provided. We consider a vanilla option and find that its Merton-type price can be expressed by a weighted sum of vanilla prices based on the possible combinations of active information processes at option maturity.
Revised:  The jump-size distribution is a function of a function-valued stochastic process which is constant between changes of states. One of the many potential applications is in finance: we associate the signal to a random payoff and price a financial derivative within the modulated information approach. It turns out that the price of a vanilla option can be expressed by a weighted sum of vanilla prices based on the possible combinations of active information processes at option maturity.
Edit intent: meaning-changed
Original:  We consider a vanilla option and find that its Merton-type price can be expressed by a weighted sum of vanilla prices based on the possible combinations of active information processes at option maturity. The constructed information-flow models lend themselves to the quantification of informational advantage in a competition where agents have asymmetric information flows.
Revised:  We consider a vanilla option and find that its Merton-type price can be expressed by a weighted sum of vanilla prices based on the possible state configurations at option expiry. This result may be viewed as an information-based analogue of Merton's option price, but where jump-diffusion arises endogenously. The proposed information flow models also lend themselves to the quantification of informational advantage in a competition where agents have asymmetric information flows.
Edit intent: meaning-changed
Original:  By modulating a multi-dimensional information process over a random point field, we explicitly relate the discovery of relevant new information sources to jumps in conditional expectation processes of partially observed signals. We show that the underlying measure-valued process follows jump-diffusion dynamics, where the jumps are governed by information source switches.
Revised:  By modulating a multi-dimensional information process over a random point field, we explicitly relate the discovery of relevant new information sources to jumps in conditional expectation martingales. In the canonical Brownian random bridge case, we show that the underlying measure-valued process follows jump-diffusion dynamics, where the jumps are governed by information source switches.
Edit intent: meaning-changed
Original:  The dynamic representation gives rise to a set of martingales on random time intervals that capture evolving information states  as well as to a state-dependent stochastic volatility evolution with jumps.
Revised:  The dynamic representation gives rise to a set of stochastically-linked Brownian motions on random time intervals that capture evolving information states as well as to a state-dependent stochastic volatility evolution with jumps.
Edit intent: meaning-changed
Original:  The nature of information flows usually exhibits complex behaviour, however, we succeed in maintaining analytic tractability by introducing what we term the effective and complementary information processes . The effective information captures in a univariate process all information provided by the active sources, and the complementary informationis a function-valued stochastic process which is constant between changes of states. One of the many potential applications is in finance: we associate the signal to a random payoff and price a financial derivative within the modulated information approach.
Revised:  The nature of information flows usually exhibits complex behaviour, however, we succeed in maintaining analytic tractability by introducing what we term the effective and complementary information processes , which dynamically incorporate active and inactive information, respectively. As an application, we price a financial derivative within the modulated information approach.
Edit intent: clarity
Original:  One of the many potential applications is in finance: we associate the signal to a random payoff and price a financial derivative within the modulated information approach. It turns out that the price of a vanilla optioncan be expressed by a weighted sum of option values based on the possible state configurations at option expiry.
Revised:  One of the many potential applications is in finance: we associate the signal to a random payoff and price a financial vanilla option, which we prove is expressed by a weighted sum of option values based on the possible state configurations at option expiry.
Edit intent: clarity
Original:  In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves even further the forecasting accuracy . Finally, we present a third model to predict the probability of price spikes; then, we use it as an input in the other two forecasters to detect spikes.
Revised:  In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves even further the forecasting accuracy even further.
Edit intent: coherence
Original:  We propose a deep learning algorithm similar in spirit to Galerkin methods, using a deep neural network instead of linear combinations of basis functions. The PDE is approximated with a deep neural network , which is trained on random batches of spatial points to satisfy the differential operator  and boundary conditions.
Revised:  We propose to solve high-dimensional PDEs by approximating the solution with a deep neural network , which is trained on random batches of spatial points to satisfy the differential operator and boundary conditions.
Edit intent: clarity
Original:   We call the algorithm a "Deep Galerkin Method (DGM)" since it is similar in spirit to Galerkin methods, with the solution approximated by a neural network instead of a linear combination of basis functions  .
Revised:  The algorithm is also tested on a high-dimensional Hamilton-Jacobi-Bellman PDE and Burgers' equation. The deep learning algorithm approximates the general solution to the Burgers' equation for a continuum of different boundary conditions and physical conditions (which can be viewed as a high-dimensional space). We call the algorithm a "Deep Galerkin Method (DGM)" since it is similar in spirit to Galerkin methods, with the solution approximated by a neural network instead of a linear combination of basis functions .
Edit intent: meaning-changed
Original:   We call the algorithm a "Deep Galerkin Method (DGM)" since it is similar in spirit to Galerkin methods, with the solution approximated by a neural network instead of a linear combination of basis functions  .
Revised:  We call the algorithm a "Deep Galerkin Method (DGM)" since it is similar in spirit to Galerkin methods, with the solution approximated by a neural network instead of a linear combination of basis functions . In addition, we prove a theorem regarding the approximation power of neural networks for a class of quasilinear parabolic PDEs .
Edit intent: meaning-changed
Original:  By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions in addition to variable-length insertions  , we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families  .
Revised:  By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions as well as variable-length insertions , we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families .
Edit intent: coherence
Original:  By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions in addition to variable-length insertions  , we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families  .
Revised:  By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions in addition to variable-length insertions to reproduce the statistics of the multiple sequence alignment of a given protein family , we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families .
Edit intent: meaning-changed
Original:  By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions in addition to variable-length insertions  , we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families  .
Revised:  By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions in addition to variable-length insertions , we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families of various folds .
Edit intent: meaning-changed
Original:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and  a variety of computational problems in \mathbb{R unknot or 3-sphere recognition  which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Revised:  Our construction also shows that deciding whether a 3-manifold with boundary tori admits an \mathbb{S stands in contrast with the lower dimensional cases which can be solved in linear time,and a variety of computational problems in \mathbb{R unknot or 3-sphere recognition which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Edit intent: meaning-changed
Original:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and  a variety of computational problems in \mathbb{R unknot or 3-sphere recognition  which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Revised:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and the latter with a variety of computational problems in \mathbb{R unknot or 3-sphere recognition which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Edit intent: clarity
Original:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and  a variety of computational problems in \mathbb{R unknot or 3-sphere recognition  which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Revised:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and a variety of computational problems in 3-manifold topology (for example, unknot or 3-sphere recognition which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Edit intent: meaning-changed
Original:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and  a variety of computational problems in \mathbb{R unknot or 3-sphere recognition  which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Revised:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and a variety of computational problems in \mathbb{R unknot or 3-sphere recognition , which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Edit intent: fluency
Original:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and  a variety of computational problems in \mathbb{R unknot or 3-sphere recognition  which are in NP and co-NP (assuming the generalized Riemann hypothesis ).
Revised:  This stands in contrast with the lower dimensional cases which can be solved in linear time,and a variety of computational problems in \mathbb{R unknot or 3-sphere recognition which are in NP and co-NP assuming the Generalized Riemann Hypothesis ).
Edit intent: fluency
Original:  Here we study a theoretical morphogenetic model using Kauffman's NK random Boolean network (RBN) as a GRN and spring-mass-damper kinetics for cellular movements , and reveal the relationship between criticality of GRNs and morphogenetic pattern formation.
Revised:  Here we propose a theoretical morphogenetic model using Kauffman's NK random Boolean network (RBN) as a GRN and spring-mass-damper kinetics for cellular movements , and reveal the relationship between criticality of GRNs and morphogenetic pattern formation.
Edit intent: clarity
Original:  Here we study a theoretical morphogenetic model using Kauffman's NK random Boolean network (RBN) as a GRN and spring-mass-damper kinetics for cellular movements , and reveal the relationship between criticality of GRNs and morphogenetic pattern formation.
Revised:  Here we study a theoretical morphogenetic model representing an aggregation of cells , and reveal the relationship between criticality of GRNs and morphogenetic pattern formation.
Edit intent: meaning-changed
Original:  Our modelrepresents an aggregation of cells, where all the cells have identical GRNs in it. The properties of GRNs were varied from ordered, through critical, to chaotic by  node in-degree K.
Revised:  In our model, the positions of the cells are determined by spring-mass-damper kinetics. Each cell has an identical Kauffman's NK random Boolean network (RBN) as its GRN. We varied the properties of GRNs were varied from ordered, through critical, to chaotic by node in-degree K.
Edit intent: meaning-changed
Original:  For cellular behaviors, we randomly assigned four cell fates to the attractors of RBNs  . By comparing diverse morphologies generated in our morphogenetic systems, we investigated what the role of the criticality of GRNs is in forming morphologies.
Revised:  We randomly assigned four cell fates to the attractors of RBNs . By comparing diverse morphologies generated in our morphogenetic systems, we investigated what the role of the criticality of GRNs is in forming morphologies.
Edit intent: coherence
Original:  For cellular behaviors, we randomly assigned four cell fates to the attractors of RBNs  . By comparing diverse morphologies generated in our morphogenetic systems, we investigated what the role of the criticality of GRNs is in forming morphologies.
Revised:  For cellular behaviors, we randomly assigned four cell fates to the attractors of RBNs for cellular behaviors . By comparing diverse morphologies generated in our morphogenetic systems, we investigated what the role of the criticality of GRNs is in forming morphologies.
Edit intent: meaning-changed
Original:  We illustrate our method with simulations on a known utilisation distribution, and show theoretically and empirically that locations simulated from the local Gibbs sampler arise from the correct resource selection function  .
Revised:  We illustrate our method with simulations on a known utilisation distribution, and show theoretically and empirically that locations simulated from the local Gibbs sampler arise from the correct resource selection function . Using simulated data, we demonstrate how this framework can be used to estimate resource selection and movement parameters .
Edit intent: meaning-changed
Original:  Finally, we  demonstrate via experiments the preservation of the up Laplacian during sparsification, as well as the utility of sparsification with respect to spectral clustering .
Revised:  Finally, we introduce higher-order generalizations of spectral clustering and label propagation for simplicial complexes and demonstrate via experiments the preservation of the up Laplacian during sparsification, as well as the utility of sparsification with respect to spectral clustering .
Edit intent: meaning-changed
Original:  In particular, we introduce a generalized effective resistance for simplexes , provide an algorithm for sparsifying simplicial complexes at a fixed dimension, and give a specific version of the generalized Cheeger inequality for weighted simplicial complexes.
Revised:  In particular, we introduce a generalized effective resistance for simplices , provide an algorithm for sparsifying simplicial complexes at a fixed dimension, and give a specific version of the generalized Cheeger inequality for weighted simplicial complexes.
Edit intent: clarity
Original:  We restrict attention to physical systems that can be modeled as a time-inhomogeneous continuous-time Markov chain (CTMC) over a finite state space, which includes many of the systems considered in stochastic thermodynamics.
Revised:  We restrict attention to physical systems whose dynamics can be modeled as a time-inhomogeneous continuous-time Markov chain (CTMC) over a finite state space, which includes many of the systems considered in stochastic thermodynamics.
Edit intent: clarity
Original:  We restrict attention to physical systems that can be modeled as a time-inhomogeneous continuous-time Markov chain (CTMC) over a finite state space, which includes many of the systems considered in stochastic thermodynamics.
Revised:  We restrict attention to physical systems that can be modeled with a time-inhomogeneous continuous-time Markov chain (CTMC) over a finite state space, which includes many of the systems considered in stochastic thermodynamics.
Edit intent: fluency
Original:  We restrict attention to physical systems that can be modeled as a time-inhomogeneous continuous-time Markov chain (CTMC) over a finite state space, which includes many of the systems considered in stochastic thermodynamics.
Revised:  We restrict attention to physical systems that can be modeled as a time-inhomogeneous master equation over a finite state space, which includes many of the systems considered in stochastic thermodynamics.
Edit intent: meaning-changed
Original:  Any f over a set of "visible" states X can be implemented --- if the system has access to some additional "hidden" states not in X.
Revised:  Examples range from logical gates in digital circuits to the (partial) function evaluated by an entire digital computer. As we show, any function f over a set of "visible" states X can be implemented --- if the system has access to some additional "hidden" states not in X.
Edit intent: meaning-changed
Original:  Motivated by engineering considerations, we  consider a natural decomposition of any such CTMC into a sequence of "hidden" timesteps, demarcated by changes in the set of allowed transitions between states.
Revised:  Motivated by engineering considerations, we consider a natural decomposition of any master equation that implements a given function into a sequence of "hidden" timesteps, demarcated by changes in the set of allowed transitions between states.
Edit intent: meaning-changed
Original:  We demonstrate a  tradeoff between the number of hidden states and the number of hidden timesteps needed to implement any given f using a CTMC, analogous to space / time tradeoffs in theoretical computer science  .
Revised:  We demonstrate a fundamental tradeoff between the number of hidden states and the number of hidden timesteps needed to implement any given f using a CTMC, analogous to space / time tradeoffs in theoretical computer science .
Edit intent: clarity
Original:  We demonstrate a  tradeoff between the number of hidden states and the number of hidden timesteps needed to implement any given f using a CTMC, analogous to space / time tradeoffs in theoretical computer science  .
Revised:  We demonstrate a tradeoff between the number of hidden states and the number of hidden timesteps needed to implement any given f using master equation dynamics. These tradeoffs are analogous to space / time tradeoffs in theoretical computer science .
Edit intent: meaning-changed
Original:  We demonstrate a  tradeoff between the number of hidden states and the number of hidden timesteps needed to implement any given f using a CTMC, analogous to space / time tradeoffs in theoretical computer science  .
Revised:  We demonstrate a tradeoff between the number of hidden states and the number of hidden timesteps needed to implement any given f using a CTMC, analogous to space / time tradeoffs in theoretical computer science . They can also be viewed as a type of resource theory, applying to master equation models of physical systems rather than quantum computation .
Edit intent: meaning-changed