wanyu/IteraTeR_v2 huggingface dataset:
Original:  Here, we introduce an approach that  composes a set of interconnected memristive networks into a larger reservoir utilizing signal amplification and restoration to reduce reservoir state correlation which improves feature extraction from the input signals.
Revised:  Here, we introduce an approach that composes a set of interconnected memristive networks into a larger reservoir utilizing signal amplification and restoration to reduce reservoir state correlation , which improves the feature extraction from the input signals.
Edit intent: fluency
Original:  Using the same number of output signals, such an architecture of heterogeneous small networks outperforms single memristive networks by at least 20 %DIFDELCMD < \\%%% %DIF < on waveform  generation tasks.
Revised:  Using the same number of output signals, such a hierarchical composition of heterogeneous small networks outperforms single memristive networks by at least 20 %DIFDELCMD < \\%%% %DIF < on waveform generation tasks.
Edit intent: clarity
Original:  Using the same number of output signals, such an architecture of heterogeneous small networks outperforms single memristive networks by at least 20 %DIFDELCMD < \\%%% %DIF < on waveform  generation tasks.
Revised:  Using the same number of output signals, such an architecture of heterogeneous small networks outperforms monolithic memristive networks by at least 20 %DIFDELCMD < \\%%% %DIF < on waveform generation tasks.
Edit intent: clarity
Original:  On the NARMA-10 task, we reduce the error by up to a factor of 2 compared with homogeneous reservoirs with sigmoidal neurons, whereas single memristive networks are unable to produce the correct result.
Revised:  On the NARMA-10 task, we reduce the error by up to a factor of 2 compared to homogeneous reservoirs with sigmoidal neurons, whereas single memristive networks are unable to produce the correct result.
Edit intent: fluency
Original:  Our approach presents another step towards the realization of computationally powerful nano-scale hardware implementations of unconventional computing architectures .
Revised:  Hierarchical composition is key for solving more complex tasks with such novel nano-scale hardware implementations of unconventional computing architectures .
Edit intent: clarity
Original:  Our approach presents another step towards the realization of computationally powerful nano-scale hardware implementations of unconventional computing architectures .
Revised:  Our approach presents another step towards the realization of computationally powerful nano-scale hardware .
Edit intent: clarity
Original:  At the core of the method is the D-relation defined in the lattice theory framework, and the hypergraph dualization algorithm that allows us to effectively produce the set of transversals for a given Sperner hypergraph.
Revised:  At the core of the method are the D-relation defined in the lattice theory framework, and the hypergraph dualization algorithm that allows us to effectively produce the set of transversals for a given Sperner hypergraph.
Edit intent: fluency
Original:  The latter algorithm, first developed by specialits from Rutgers Center for Operations Research, has already found numerous applications in solving the optimization problems in data base theory, artificial intelligence and game theory.
Revised:  The latter algorithm, first developed by specialists from Rutgers Center for Operations Research, has already found numerous applications in solving the optimization problems in data base theory, artificial intelligence and game theory.
Edit intent: fluency
Original:  The latter algorithm, first developed by specialits from Rutgers Center for Operations Research, has already found numerous applications in solving the optimization problems in data base theory, artificial intelligence and game theory.
Revised:  The latter algorithm, first developed by specialits from Rutgers Center for Operations Research, has already found numerous applications in solving optimization problems in data base theory, artificial intelligence and game theory.
Edit intent: fluency
Original:  One application of the method is in analysis of gene expression data related to a particular phenotypic variable, and some initial testing is done for the data provided by the University of Hawaii Cancer Center.
Revised:  One application of the method is for analysis of gene expression data related to a particular phenotypic variable, and some initial testing is done for the data provided by the University of Hawaii Cancer Center.
Edit intent: fluency
Original: We study a classical iterative algorithm for the problem of balancing matrices in the L_\infty norm via a scaling transformation.
Revised: We study a classical iterative algorithm for balancing matrices in the L_\infty norm via a scaling transformation.
Edit intent: clarity
Original:  This algorithm, which goes back to Osborne , and Parlett and  Reinsch in the 1960s, is implemented as a standard preconditioner in many numerical linear algebra packages.
Revised:  This algorithm, which goes back to Osborne and Reinsch in the 1960s, is implemented as a standard preconditioner in many numerical linear algebra packages.
Edit intent: coherence
Original:  In this paper we prove that, for a large class of irreducible n\times n (real or complex) input matrices A, a natural variant of the algorithm converges in O(n^3\log(n\rho/\varepsilon)) elementary balancing operations, where \rho measures the initial imbalance of  A and \varepsilon is the target imbalance of the output matrix. (The imbalance of  A is \max_i |\log(a_i^{out}/a_i^{in})|, where a_i^{out},a_i^{in} are the maximum entries in magnitude in the ith row and column respectively.)
Revised:  In this paper we prove that, for any irreducible n\times n (real or complex) input matrices A, a natural variant of the algorithm converges in O(n^3\log(n\rho/\varepsilon)) elementary balancing operations, where \rho measures the initial imbalance of A and \varepsilon is the target imbalance of the output matrix. (The imbalance of A is \max_i |\log(a_i^{out}/a_i^{in})|, where a_i^{out},a_i^{in} are the maximum entries in magnitude in the ith row and column respectively.)
Edit intent: clarity
Original:  In this paper we prove that, for a large class of irreducible n\times n (real or complex) input matrices A, a natural variant of the algorithm converges in O(n^3\log(n\rho/\varepsilon)) elementary balancing operations, where \rho measures the initial imbalance of  A and \varepsilon is the target imbalance of the output matrix. (The imbalance of  A is \max_i |\log(a_i^{out}/a_i^{in})|, where a_i^{out},a_i^{in} are the maximum entries in magnitude in the ith row and column respectively.)
Revised:  In this paper we prove that, for a large class of irreducible n\times n (real or complex) input matrix~ A, a natural variant of the algorithm converges in O(n^3\log(n\rho/\varepsilon)) elementary balancing operations, where \rho measures the initial imbalance of A and \varepsilon is the target imbalance of the output matrix. (The imbalance of A is \max_i |\log(a_i^{out}/a_i^{in})|, where a_i^{out},a_i^{in} are the maximum entries in magnitude in the ith row and column respectively.)
Edit intent: fluency
Original:  In this paper we prove that, for a large class of irreducible n\times n (real or complex) input matrices A, a natural variant of the algorithm converges in O(n^3\log(n\rho/\varepsilon)) elementary balancing operations, where \rho measures the initial imbalance of  A and \varepsilon is the target imbalance of the output matrix. (The imbalance of  A is \max_i |\log(a_i^{out}/a_i^{in})|, where a_i^{out},a_i^{in} are the maximum entries in magnitude in the ith row and column respectively.)
Revised:  In this paper we prove that, for a large class of irreducible n\times n (real or complex) input matrices A, a natural variant of the algorithm converges in O(n^3\log(n\rho/\varepsilon)) elementary balancing operations, where \rho measures the initial imbalance of ~ A and \varepsilon is the target imbalance of the output matrix. (The imbalance of A is \max_i |\log(a_i^{out}/a_i^{in})|, where a_i^{out},a_i^{in} are the maximum entries in magnitude in the ith row and column respectively.)
Edit intent: fluency
Original:  In this paper we prove that, for a large class of irreducible n\times n (real or complex) input matrices A, a natural variant of the algorithm converges in O(n^3\log(n\rho/\varepsilon)) elementary balancing operations, where \rho measures the initial imbalance of  A and \varepsilon is the target imbalance of the output matrix. (The imbalance of  A is \max_i |\log(a_i^{out}/a_i^{in})|, where a_i^{out},a_i^{in} are the maximum entries in magnitude in the ith row and column respectively.)
Revised:  In this paper we prove that, for a large class of irreducible n\times n (real or complex) input matrices A, a natural variant of the algorithm converges in O(n^3\log(n\rho/\varepsilon)) elementary balancing operations, where \rho measures the initial imbalance of A and \varepsilon is the target imbalance of the output matrix. (The imbalance of ~ A is \max_i |\log(a_i^{out}/a_i^{in})|, where a_i^{out},a_i^{in} are the maximum entries in magnitude in the ith row and column respectively.)
Edit intent: fluency
Original:  A balancing operation scales the ith row and column so that their maximum entries are equal, and requires O(m/n) arithmetic operations on average, where m is the number of non-zero elements in  A.
Revised:  A balancing operation scales the ith row and column so that their maximum entries are equal, and requires O(m/n) arithmetic operations on average, where m is the number of non-zero elements in ~ A.
Edit intent: fluency
Original:  The class of matrices for which the above analysis holds are those which satisfy a condition we call Unique Balance, meaning that the limit of the iterative balancing process does not depend on the order in which balancing operations are performed .
Revised:  The class of matrices for which the limit of the iterative balancing process does not depend on the order in which balancing operations are performed .
Edit intent: clarity
Original:  The class of matrices for which the above analysis holds are those which satisfy a condition we call Unique Balance, meaning that the limit of the iterative balancing process does not depend on the order in which balancing operations are performed .
Revised:  The class of matrices for which the above analysis holds are those which satisfy a condition we call Unique Balance, meaning that the limit of the balancing process is independent of the order in which balancing operations are performed .
Edit intent: clarity
Original:  They both depend on the investors initial preferences and the dynamically changing investment opportunities.
Revised:  They both depend on the investor's initial preferences and the dynamically changing investment opportunities.
Edit intent: fluency
Original: In this article, we explore a class of tractable interest rate models that have the property that the prices of zero-coupon bonds can be expressed as polynomials of a state diffusion process.
Revised: In this article, we explore a class of tractable interest rate models that have the property that the price of a zero-coupon bonds can be expressed as polynomials of a state diffusion process.
Edit intent: fluency
Original: In this article, we explore a class of tractable interest rate models that have the property that the prices of zero-coupon bonds can be expressed as polynomials of a state diffusion process.
Revised: In this article, we explore a class of tractable interest rate models that have the property that the prices of zero-coupon bond can be expressed as polynomials of a state diffusion process.
Edit intent: fluency
Original: In this article, we explore a class of tractable interest rate models that have the property that the prices of zero-coupon bonds can be expressed as polynomials of a state diffusion process.
Revised: In this article, we explore a class of tractable interest rate models that have the property that the prices of zero-coupon bonds can be expressed as a polynomial of a state diffusion process.
Edit intent: fluency
Original:  The PHSP captures applicationsranging from monitoring new contents on the web, blogosphere, and Twitterverse, to analyzing influence properties in social networks , and detecting failure propagation on large electronic circuits .
Revised:  The PHSP captures applicationsranging from monitoring new contents on the web, blogosphere, and Twitterverse, to analyzing influence properties in network , and detecting failure propagation on large electronic circuits .
Edit intent: clarity
Original:  The Probabilistic Hitting Set Paradigm (PHSP) defines an infinite time generating process that places new items in subsets of nodes , according to an unknown probability distribution that may change in time.
Revised:  The Probabilistic Hitting Set Paradigm (PHSP) defines an infinite time generating process that places new items in subsets of nodes according to an unknown probability distribution that may change in time.
Edit intent: fluency
Original:  Finally we apply our method to real social networks , demonstrating the practicality and optimality of our solution .
Revised:  Finally we apply our method to real social networks demonstrate the practicality of our approach .
Edit intent: clarity
Original:  Simulation results demonstrate the improvement on network utility and load balancing with the proposed algorithm .
Revised:  Simulation results demonstrate the improvement on network utility , load balancing and user fairness .
Edit intent: clarity
Original:  Controlled toggles  compute, in parallel, parameters of critical importance for each of several complex images recalled from associative long term memory.
Revised:  Controlled toggles can be made to compute, in parallel, parameters of critical importance for each of several complex images recalled from associative long term memory.
Edit intent: clarity
Original:   We show that maximization of the{\it   total Granger causality to a given target, over all the possible partitions of the set of driving variables , puts in evidence redundant multiplets of variables influencing the target, provided that an%DIFDELCMD < {\it %%% unnormalized definition of Granger causality is adopted.
Revised:  We show that adopting an{\it total Granger causality to a given target, over all the possible partitions of the set of driving variables , puts in evidence redundant multiplets of variables influencing the target, provided that an%DIFDELCMD < {\it %%% unnormalized definition of Granger causality is adopted.
Edit intent: clarity
Original:   We show that maximization of the{\it   total Granger causality to a given target, over all the possible partitions of the set of driving variables , puts in evidence redundant multiplets of variables influencing the target, provided that an%DIFDELCMD < {\it %%% unnormalized definition of Granger causality is adopted.
Revised:  We show that maximization of the{\it total Granger causality to a given target, over all the possible partitions of the set of driving variables %DIFDELCMD < {\it %%% unnormalized definition of Granger causality is adopted.
Edit intent: clarity
Original:  Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target , differently from previous definitions of synergy  .
Revised:  Along the same lines we also introduce a pairwise index of synergy which is zero when two independent sources additively influence a common target , differently from previous definitions of synergy .
Edit intent: clarity
Original:  Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target , differently from previous definitions of synergy  .
Revised:  Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence the future state of the system , differently from previous definitions of synergy .
Edit intent: clarity
Original: We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network.
Revised: Objectives: We develop a framework for the analysis of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network.
Edit intent: clarity
Original: We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network.
Revised: We analyze by means of Granger causality the effect of synergy and redundancy in the pattern of information flow between subsystems of a complex network.
Edit intent: clarity
Original:  We show that adopting an %DIFDELCMD < {\it %%% unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables.
Revised:  We show that adopting an %DIFDELCMD < {\it %%% unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables.
Edit intent: fluency
Original:  Moreover we also introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy ;
Revised:  Consequently we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy ;
Edit intent: coherence
Original:  We show the application of the proposed approach to resting state fMRI data from the Human Connectome Project, and show that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, whilst synergy occurs mainly between non-homologous pairs of regions in opposite hemispheres  .
Revised:  We show the application of the proposed approach to resting state fMRI data from the Human Connectome Project, showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, whilst synergy occurs mainly between non-homologous pairs of regions in opposite hemispheres .
Edit intent: coherence
Original:  To minimize a potential energy function, about 95 \% efforts are spent on the \\%DIF > efforts are spent on the Lennard-Jones potential of van der Waals interactions;
Revised:  To minimize a potential energy function, about 95 \\%DIF > efforts are spent on the Lennard-Jones potential of van der Waals interactions;
Edit intent: fluency
Original:  we also give a detailed review on some effective computational optimization methods listed in the Cambridge Cluster Database to solve the problem of Lennard-Jones clusters.
Revised:  we also give a detailed review on some effective computational optimization methods in the Cambridge Cluster Database to solve the problem of Lennard-Jones clusters.
Edit intent: clarity
Original:  interesting results (e. g.\beta2\alpha2  (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2  .
Revised:  interesting results (e. g.\beta2-\alpha2 (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2 .
Edit intent: fluency
Original:  interesting results (e. g.\beta2\alpha2  (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2  .
Revised:  interesting results (e. g.\beta2\alpha2 loop of prion protein structures, and we found (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2 .
Edit intent: coherence
Original:  interesting results (e. g.\beta2\alpha2  (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2  .
Revised:  interesting results (e. g.\beta2\alpha2 (i) the species that has the clearly and highly ordered \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2 .
Edit intent: fluency
Original:  interesting results (e. g.\beta2\alpha2  (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2  .
Revised:  interesting results (e. g.\beta2\alpha2 (i) the species that has the clearly and highly ordered S2-H2 \beta2-\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2 .
Edit intent: fluency
Original:  interesting results (e. g.\beta2\alpha2  (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2  .
Revised:  interesting results (e. g.\beta2\alpha2 (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3_{10 in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2 .
Edit intent: clarity
Original:  interesting results (e. g.\beta2\alpha2  (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2  .
Revised:  interesting results (e. g.\beta2\alpha2 (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " \pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2 .
Edit intent: fluency
Original:  interesting results (e. g.\beta2\alpha2  (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128-F175-Y218-Y163-F175-Y169-R164-Y128(-Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2  .
Revised:  interesting results (e. g.\beta2\alpha2 (i) the species that has the clearly and highly ordered S2-H2 \beta2\alpha2 loop usually owns a 3-10-helix in this loop, (ii) a " pi-circle " Y128--F175--Y218--Y163--F175--Y169--R164--Y128(--Y162 ) is around the S2-H2 loopof prion protein structures) were found \beta2\alpha2 .
Edit intent: clarity