wanyu/IteraTeR_v2 huggingface dataset:
Original:  However, in this case there exists an approximation O(n^3) -time algorithm with performance ratio less than 4/3.
Revised:  However, in this case there exists an approximation O(n^3) time.
Edit intent: clarity
Original:  These models are in widespread use by the medical community, but are difficult to learn from data because they need to be accurate and sparse, have coprime integer coefficients, and accommodate operational constraints.
Revised:  These models are in widespread use by the medical community, but are difficult to learn from data because they need to be accurate and sparse, have coprime integer coefficients, and satisfy multiple operational constraints.
Edit intent: clarity
Original:  We present a new method for creating data-driven scoring systems called  Supersparse Linear Integer Models (SLIM).
Revised:  We present a new method for creating data-driven scoring systems called a Supersparse Linear Integer Models (SLIM).
Edit intent: fluency
Original:  We present a new method for creating data-driven scoring systems called  Supersparse Linear Integer Models (SLIM).
Revised:  We present a new method for creating data-driven scoring systems called Supersparse Linear Integer Model (SLIM).
Edit intent: fluency
Original:  SLIM scoring systems are built by solving a discrete optimization problem that directly encodes measures of accuracy (the 0--1 loss) and sparsity (the \ell_0-seminorm ) while restricting coefficients to coprime integers.
Revised:  SLIM scoring systems are built by using an integer programming problem that directly encodes measures of accuracy (the 0--1 loss) and sparsity (the \ell_0-seminorm ) while restricting coefficients to coprime integers.
Edit intent: clarity
Original:  SLIM scoring systems are built by solving a discrete optimization problem that directly encodes measures of accuracy (the 0--1 loss) and sparsity (the \ell_0-seminorm ) while restricting coefficients to coprime integers.
Revised:  SLIM scoring systems are built by solving a discrete optimization problem that directly encodes measures of accuracy (the 0-1 loss) and sparsity (the \ell_0-seminorm ) while restricting coefficients to coprime integers.
Edit intent: fluency
Original:  SLIM scoring systems are built by solving a discrete optimization problem that directly encodes measures of accuracy (the 0--1 loss) and sparsity (the \ell_0-seminorm ) while restricting coefficients to coprime integers.
Revised:  SLIM scoring systems are built by solving a discrete optimization problem that directly encodes measures of accuracy (the 0--1 loss) and sparsity (the L0-norm ) while restricting coefficients to coprime integers.
Edit intent: fluency
Original:  We provide bounds on the testing and training accuracy of SLIM scoring systems, as well as a new data reduction technique that can improve scalability by discarding a portion of the training data .
Revised:  We provide bounds on the testing and training accuracy of SLIM scoring systems, and present a new data reduction technique that can improve scalability by discarding a portion of the training data .
Edit intent: clarity
Original:  We provide bounds on the testing and training accuracy of SLIM scoring systems, as well as a new data reduction technique that can improve scalability by discarding a portion of the training data .
Revised:  We provide bounds on the testing and training accuracy of SLIM scoring systems, as well as a new data reduction technique that can improve scalability by eliminating a portion of the training data .
Edit intent: clarity
Original:  We present results from an ongoing collaboration with the Massachusetts General Hospital Sleep Apnea Laboratory, where SLIM is being used to construct a highly tailored scoring system for sleep apnea screening.
Revised:  We present results from an ongoing collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM is being used to construct a highly tailored scoring system for sleep apnea screening.
Edit intent: fluency
Original:  We present results from an ongoing collaboration with the Massachusetts General Hospital Sleep Apnea Laboratory, where SLIM is being used to construct a highly tailored scoring system for sleep apnea screening.
Revised:  We present results from an ongoing collaboration with the Massachusetts General Hospital Sleep Apnea Laboratory, where SLIM is being used to create a highly tailored scoring system for sleep apnea screening.
Edit intent: fluency
Original:  SLIM scoring systems are built by using an integer programming problem that directly encodes measures of accuracy (the 0-1 loss) and sparsity (the L0-norm ) while restricting coefficients to coprime integers.
Revised:  SLIM scoring systems are built by solving an integer program that directly encodes measures of accuracy (the 0-1 loss) and sparsity (the L0-norm ) while restricting coefficients to coprime integers.
Edit intent: clarity
Original:  SLIM can seamlessly incorporate a wide range of operational constraints related to accuracy and sparsity, and can produce acceptable models without parameter tuning because of the direct control provided over these quantities .
Revised:  SLIM can seamlessly incorporate a wide range of operational constraints related to accuracy and sparsity, and can produce highly tailored models without parameter tuning because of the direct control provided over these quantities .
Edit intent: clarity
Original:  SLIM can seamlessly incorporate a wide range of operational constraints related to accuracy and sparsity, and can produce acceptable models without parameter tuning because of the direct control provided over these quantities .
Revised:  SLIM can seamlessly incorporate a wide range of operational constraints related to accuracy and sparsity, and can produce acceptable models without parameter tuning .
Edit intent: clarity
Original:  Our paper includes results from a collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM is being used to create a highly tailored scoring system for sleep apnea screening .
Revised:  Our paper includes results from a collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM was used to create a highly tailored scoring system for sleep apnea screening .
Edit intent: clarity
Original:  Our paper includes results from a collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM is being used to create a highly tailored scoring system for sleep apnea screening .
Revised:  Our paper includes results from a collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM is being used to create a highly tailored scoring system for sleep apnea screening 
Edit intent: fluency
Original: We introduce a method for computing probabilities for spontaneous activity and propagation failure of the action potential in spatially extended, conductance-based neuronal models subject to channel noise, based on statistical properties of the membrane potential.
Revised: We introduce a method for computing probabilities for spontaneous activity and propagation fail- ure of the action potential in spatially extended, conductance-based neuronal models subject to channel noise, based on statistical properties of the membrane potential.
Edit intent: fluency
Original:  The quality of the approximation with respect to the noise amplitude is discussed and illustrated with numerical results for the spatially extended Hodgkin-Huxley and the simpler FitzHugh-Nagumo model .
Revised:  The quality of the approximation with respect to the noise amplitude is discussed and illustrated with numerical results for the spatially extended Hodgkin-Huxley .
Edit intent: clarity
Original: Although it has long been suggested that accumulation of errors in transcription and translation is one of the most important molecular mechanisms leading to ageing , a quantitative link between the error accumulation dynamics and mortality of species has so far remained elusive.
Revised: Although accumulation of molecular damage is suggested to be an important molecular mechanism of aging , a quantitative link between the error accumulation dynamics and mortality of species has so far remained elusive.
Edit intent: clarity
Original:  We analyze age-dependent microarray datasets and metabolic profiles forDrosophila melanogaster and explicitly show that the resulting GRNs are nearly critical and inherently unstable.
Revised:  We analyze age-dependent changes in gene-expression and metabolic profiles forDrosophila melanogaster and explicitly show that the resulting GRNs are nearly critical and inherently unstable.
Edit intent: clarity
Original:  We analyze age-dependent microarray datasets and metabolic profiles forDrosophila melanogaster and explicitly show that the resulting GRNs are nearly critical and inherently unstable.
Revised:  We analyze age-dependent microarray datasets and metabolic profiles Drosophila melanogaster and explicitly show that the resulting GRNs are nearly critical and inherently unstable.
Edit intent: fluency
Original:  We analyze age-dependent microarray datasets and metabolic profiles forDrosophila melanogaster and explicitly show that the resulting GRNs are nearly critical and inherently unstable.
Revised:  We analyze age-dependent microarray datasets and metabolic profiles for and explicitly show that the resulting GRNs are nearly critical and inherently unstable.
Edit intent: clarity
Original:  We analyze age-dependent microarray datasets and metabolic profiles forDrosophila melanogaster and explicitly show that the resulting GRNs are nearly critical and inherently unstable.
Revised:  We analyze age-dependent microarray datasets and metabolic profiles forDrosophila melanogaster and explicitly show that the underlying GRNs are nearly critical and inherently unstable.
Edit intent: clarity
Original:  On top of that we explain the plateau in mortality rates observed at very late ages for large populations.
Revised:  In addition, we explain late-life mortality deceleration observed at very late ages for large populations.
Edit intent: clarity
Original:  Since mortality in humans, where it is characterized best, is mostly associated with age related diseases, we support the idea of ageing being the driving force behind the development of major diseases.
Revised:  Since mortality in humans, where it is characterized best, is strongly associated with the incidence of age-related diseases, our findings support the idea of ageing being the driving force behind the development of major diseases.
Edit intent: clarity
Original:  Since mortality in humans, where it is characterized best, is mostly associated with age related diseases, we support the idea of ageing being the driving force behind the development of major diseases.
Revised:  Since mortality in humans, where it is characterized best, is mostly associated with age related diseases, we support the idea that aging is the driving force behind the development of major diseases.
Edit intent: fluency
Original:  Since mortality in humans, where it is characterized best, is mostly associated with age related diseases, we support the idea of ageing being the driving force behind the development of major diseases.
Revised:  Since mortality in humans, where it is characterized best, is mostly associated with age related diseases, we support the idea of ageing being the driving force behind the development of chronic human diseases.
Edit intent: clarity
Original:  Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes , i. e., diagonal preconditioners.
Revised:  Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes .
Edit intent: coherence
Original:  ESN performance has been analyzed based on the connectivity pattern in the network structure and the input bias.
Revised:  ESN performance has been analyzed with respect to the connectivity pattern in the network structure and the input bias.
Edit intent: clarity
Original:  Here, we use an approach based on the Taylor expansion of a frequently used transfer function, the hyperbolic tangent function, to systematically study the effect of increasing nonlinearity of the transfer function on the memory, nonlinear capacity, and signal processing performance of the ESN.
Revised:  Here, we use an approach tanh on the Taylor expansion of a frequently used transfer function, the hyperbolic tangent function, to systematically study the effect of increasing nonlinearity of the transfer function on the memory, nonlinear capacity, and signal processing performance of the ESN.
Edit intent: fluency
Original:  Here, we use an approach based on the Taylor expansion of a frequently used transfer function, the hyperbolic tangent function, to systematically study the effect of increasing nonlinearity of the transfer function on the memory, nonlinear capacity, and signal processing performance of the ESN.
Revised:  Here, we use an approach based on the Taylor expansion of a frequently used transfer function, the hyperbolic tangent function, to systematically study the effect of increasing nonlinearity of the transfer function on the memory, nonlinear capacity, and signal processing performance of ESN.
Edit intent: fluency
Original:  The results of this study can have application to both software and hardware implementation of ESN.
Revised:  The results of this study apply to both software and hardware implementation of ESN.
Edit intent: clarity
Original:  We propose an extension of the backpropagation algorithm that incorporates this information in the learning process, so that the neural network predictions are robust to variations and noise in the feature vector.
Revised:  We propose an extension of the backpropagation algorithm that trains a neural network to be robust to variations and noise in the feature vector.
Edit intent: clarity
Original:  We apply our algorithm to a collection of datasets for image classification, confirm its theoretically established properties and demonstrate an improvement of the classification accuracy with respect to the standard backpropagation algorithm in the majority of cases.
Revised:  We apply our algorithm to a collection of datasets for image classification, confirm its theoretically established properties and demonstrate an improvement of the classification accuracy with respect to the competing algorithms in the majority of cases.
Edit intent: clarity
Original: Robustness to particular transformations is a desired property in many classification tasks.
Revised: In many classification problems a classifier should be robust to small variations in the input vector. This is a desired property in many classification tasks.
Edit intent: clarity
Original:  We propose an extension of the backpropagation algorithm that trains a neural network to be robust to variations and noise in the feature vector.
Revised:  We propose an extension of the backpropagation algorithm that train a neural network to be robust to variations and noise in the feature vector.
Edit intent: fluency
Original:  We propose an extension of the backpropagation algorithm that trains a neural network to be robust to variations and noise in the feature vector.
Revised:  We propose an extension of the backpropagation algorithm that trains a neural network to be robust to variations in the feature vector.
Edit intent: clarity
Original: We study the issues of testing and designing (nearly cost-optimal) temporal networks that are temporally connected.
Revised: We study the design and verification of temporal graphs that are temporally connected.
Edit intent: clarity
Original:  We follow the temporal (di)graphs abstraction , where each edge e of a (di)graph G is associated with a set L_e of discrete time instances of availability (labels).
Revised:  We follow the temporal (di)graphs abstraction , where each edge has an associated set of discrete availability instances (labels).
Edit intent: clarity
Original:  A temporally connected temporal (di)graph G(L) is a temporal (di)graph in which,}  for any pair of vertices u,v \in V(G), u%DIFDELCMD < \not%%% = v, there exists both a (u,v)-journey and a (v,u)-journey.
Revised:  A temporally connected temporal (di)graph G(L) is a temporal (di)graph in which,} for any pair of vertices u,v %DIFDELCMD < \not%%% = v, there exists both a (u,v)-journey and a (v,u)-journey.
Edit intent: clarity
Original: We define the removal cost r(G,L)of a given labeling L on G,with respect to REACH,as the  maximum number of labels that can be removed from L without violating REACH and show that computing it is APX-hard .
Revised: We define the removal cost r(G,L)of a given labeling L on G,with respect to REACH,as the maximum number of labels that are redundant is APX-hard .
Edit intent: clarity
Original:  We mainly consider undirected graphs of n vertices and follow the model of \mbox{ kempe , where each edge has an associated set of discrete availability instances (labels).
Revised:  We mainly consider undirected graphs of n vertices , where each edge has an associated set of discrete availability instances (labels).
Edit intent: coherence
Original:  We then consider the case in which a designer  can freely choose availability instances for all edges and aims for temporal connectivity with a very small cost;
Revised:  We then consider the case in which a designer can freely choose availability instances for all edges and aims for temporal connectivity with very small cost;
Edit intent: fluency
Original:  the cost here is the total number of availability instances used.
Revised:  the cost is the total number of availability instances used.
Edit intent: clarity
Original:  Then our aim is to decrease the cost by removing some  labels without destroying temporal connectivity (redundant labels).
Revised:  Then our aim is to decrease the cost by removing somegiven labels without destroying temporal connectivity (redundant labels).
Edit intent: fluency
Original:  Our main technical result is that computing the maximum number of labels that are redundant is APX-hard, i.e., there is no PTAS unless P=NP.
Revised:  Our main technical result is that computing the maximum number of redundant labels is APX-hard, i.e., there is no PTAS unless P=NP.
Edit intent: clarity
Original:  On the other hand, a temporal design may be "minimal"\emph{  i.e. all its labels may be needed for temporal connectivity.
Revised:  On the other hand, a temporal design may , however, be\emph{ i.e. all its labels may be needed for temporal connectivity.
Edit intent: coherence
Original:  On the other hand, a temporal design may be "minimal"\emph{  i.e. all its labels may be needed for temporal connectivity.
Revised:  On the other hand, a temporal design may be "minimal"\emph{ , i.e. all its labels may be needed for temporal connectivity.
Edit intent: fluency
Original: We present a typepseudo-normal form that any type built from arrows, products and sums can be isomorphically mapped to and that systematically minimizes the number of premises of sum typeused.
Revised: We present a typepseudo-normal form that any type built from arrows, products , and sums, can be isomorphically mapped to and that systematically minimizes the number of premises of sum typeused.
Edit intent: fluency
Original: We present a typepseudo-normal form that any type built from arrows, products and sums can be isomorphically mapped to and that systematically minimizes the number of premises of sum typeused.
Revised: We present a typepseudo-normal form that any type built from arrows, products and sums can be isomorphically mapped to , but that systematically minimizes the number of premises of sum typeused.
Edit intent: coherence
Original: Lambda calculi with algebraic data types appear at the core of functional programming languages , but still pose theoretical challenges today: for instance, even in the presence of the simplest non-trivial data type, the sum type  , we do not know how to assign a unique canonical normal form to a class of beta-eta-equal programs .
Revised: Lambda calculi with algebraic data types lie at the core of functional programming languages , but still pose theoretical challenges today: for instance, even in the presence of the simplest non-trivial data type, the sum type , we do not know how to assign a unique canonical normal form to a class of beta-eta-equal programs .
Edit intent: clarity
Original: Lambda calculi with algebraic data types appear at the core of functional programming languages , but still pose theoretical challenges today: for instance, even in the presence of the simplest non-trivial data type, the sum type  , we do not know how to assign a unique canonical normal form to a class of beta-eta-equal programs .
Revised: Lambda calculi with algebraic data types appear at the core of functional programming languages , but still pose theoretical challenges today: for instance, even in the presence of the simplest non-trivial data type, the sum type . First , we do not know how to assign a unique canonical normal form to a class of beta-eta-equal programs .
Edit intent: coherence
Original:  Finally, we describe an alternative , more canonical, representation of terms of the lambda calculus with sums, together with a Coq-implemented type-directed partial evaluator into/from our new term calculus.
Revised:  Finally, we describe an alternative , more canonical, representation of terms of the lambda calculus with sums, together with a Coq-implemented converter into/from our new term calculus.
Edit intent: clarity
Original:  and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for a filmstar , it should show movies and marriages and awards, not just movies).
Revised:  and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor , it should show movies and marriages and awards, not just movies).
Edit intent: style
Original:  We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web- co-occurrence statistics with provable performance guarantees.
Revised:  We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees.
Edit intent: fluency
Original: Time anticipation is the ability of a system under a periodic stimulation to produce transient responses with a time scale similar to that of the stimulation after the stimulation is removed.
Revised: Many biological systems can sense periodical variations in a stimulus input and produce well-timed, anticipatory responses after the input is removed.
Edit intent: clarity
Original: Belief Propagation (BP) allows to approximate exact probabilistic inference in graphical models, such as Markov networks (also called Markov random fields, or undirected graphical models).
Revised: Belief Propagation (BP) is a widely used approximation for exact probabilistic inference in graphical models, such as Markov networks (also called Markov random fields, or undirected graphical models).
Edit intent: coherence
Original:  Recent work has proposed to approximate BP by linearizing the update equations around default values for the special case when all edges in the Markov network carry the same symmetric, doubly stochastic potential .
Revised:  For the case when all edges in the Markov network carry the same symmetric, doubly stochastic potential .
Edit intent: clarity