wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show that the recursive autoconvolutional operator, adopted from physics, boosts existing unsupervised methods to learn more powerful filters.
Revised:  We show that the recursive autoconvolution operator, adopted from physics, boosts existing unsupervised methods to learn more powerful filters.
Edit intent: fluency
Original:  We show that the recursive autoconvolutional operator, adopted from physics, boosts existing unsupervised methods to learn more powerful filters.
Revised:  We show that the recursive autoconvolutional operator, adopted from physics, boosts existing unsupervised methods by learning more discriminative filters.
Edit intent: clarity
Original:  We use a well established multilayer convolutional network and train filters layer-wise.
Revised:  We take well established convolutional neural networks and train their filters layer-wise.
Edit intent: clarity
Original:  The total number of trainable parameters is also greatly reduced by using shared filters in higher layers.
Revised:  The total number of trainable parameters greatly reduced by using shared filters in higher layers.
Edit intent: clarity
Original:  The total number of trainable parameters is also greatly reduced by using shared filters in higher layers.
Revised:  The total number of trainable parameters is also greatly reduced by introducing shared filters in higher layers.
Edit intent: clarity
Original:  Pseudo-relevance feedback (PRF) is known as a technique for updating query language models .
Revised:  Pseudo-relevance feedback (PRF) is a well-known technique for updating query language models .
Edit intent: clarity
Original:  The proposed embedded coefficient divergence minimization model (ECDMM) takes the top-ranked documents retrieved by the query and obtains a couple of positive and negative samples ;
Revised:  The proposed embedded coefficient divergence minimization model (ECDMM) takes top-ranked documents retrieved by the query and obtains a couple of positive and negative samples ;
Edit intent: fluency
Original:  The proposed embedded coefficient divergence minimization model (ECDMM) takes the top-ranked documents retrieved by the query and obtains a couple of positive and negative samples ;
Revised:  The proposed embedded coefficient divergence minimization model (ECDMM) takes the top-ranked documents retrieved by the query and obtains a couple of positive and negative sample sets ;
Edit intent: clarity
Original:  these samples are used for learning the coefficient matrix which will be used for  updating the query language model  . Experimental results on several TREC and CLEF data sets  demonstrate effectiveness of ECDMM compared to state-of-the-art PRF techniques in language modeling framework .
Revised:  these samples are used for learning the coefficient matrix which will be used for updating the query language model . Experimental results on several TREC and CLEF data sets demonstrate effectiveness of ECDMM . The experimental results reveal that the new formulation for the query works as well as state-of-the-art PRF techniques in language modeling framework .
Edit intent: coherence
Original:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%%  , (d_1(v_n), d_2(v_n))  the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%%  (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Revised:  the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%% , (d_1(v_n), d_2(v_n)) the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%% (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Edit intent: coherence
Original:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%%  , (d_1(v_n), d_2(v_n))  the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%%  (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Revised:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) is the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%% , (d_1(v_n), d_2(v_n)) the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%% (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Edit intent: clarity
Original:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%%  , (d_1(v_n), d_2(v_n))  the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%%  (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Revised:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. The vector d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%% , (d_1(v_n), d_2(v_n)) the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%% (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Edit intent: coherence
Original:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%%  , (d_1(v_n), d_2(v_n))  the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%%  (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Revised:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%% , (d_1(v_n), d_2(v_n)) is the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%% (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Edit intent: fluency
Original:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%%  , (d_1(v_n), d_2(v_n))  the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%%  (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Revised:  , is defined as the number of vertices at distance 2 from v. Then d_1(v) denotes the usual degree of vertex v. Denote \mathbf{d ^2(G)=( (d_1(v_1), d_2(v_1)), %DIFDELCMD < \dots%%% , (d_1(v_n), d_2(v_n)) the second order degree sequence of the graph G. In this note we show that the problem to decide whether a double-sequence of natural numbers ((i_1,j_1), %DIFDELCMD < \dots %%% \ldots (i_n,j_n)) is a second order degree sequence of a simple undirected graph G is strongly NP-complete.
Edit intent: fluency
Original:  Similarly, denote D_2(v) the sum of the degrees of the neighbors of v. (This is always bigger than d_2(v) and several vertices may occur in it multiple ties .) We also show that the decision  whether a pair ( \mathbf{d (G), \mathbf{D _2(v)) is graphical is NP-complete.
Revised:  Similarly, denote D_2(v) the sum of the degrees of the neighbors of v. (This is always bigger than d_2(v) and several vertices may occur in it multiple times .) We also show that the decision whether a pair ( \mathbf{d (G), \mathbf{D _2(v)) is graphical is NP-complete.
Edit intent: clarity
Original:  Similarly, denote D_2(v) the sum of the degrees of the neighbors of v. (This is always bigger than d_2(v) and several vertices may occur in it multiple ties .) We also show that the decision  whether a pair ( \mathbf{d (G), \mathbf{D _2(v)) is graphical is NP-complete.
Revised:  Similarly, denote D_2(v) the sum of the degrees of the neighbors of v. (This is always bigger than d_2(v) and several vertices may occur in it multiple ties .) We also show that the decision problem whether a pair ( \mathbf{d (G), \mathbf{D _2(v)) is graphical is NP-complete.
Edit intent: clarity
Original:  Similarly, denote D_2(v) the sum of the degrees of the neighbors of v. (This is always bigger than d_2(v) and several vertices may occur in it multiple ties .) We also show that the decision  whether a pair ( \mathbf{d (G), \mathbf{D _2(v)) is graphical is NP-complete.
Revised:  Similarly, denote D_2(v) the sum of the degrees of the neighbors of v. (This is always bigger than d_2(v) and several vertices may occur in it multiple ties .) We also show that the decision whether a pair ( d (G), \mathbf{D _2(v)) is graphical is NP-complete.
Edit intent: fluency
Original:  Similarly, denote D_2(v) the sum of the degrees of the neighbors of v. (This is always bigger than d_2(v) and several vertices may occur in it multiple ties .) We also show that the decision  whether a pair ( \mathbf{d (G), \mathbf{D _2(v)) is graphical is NP-complete.
Revised:  Similarly, denote D_2(v) the sum of the degrees of the neighbors of v. (This is always bigger than d_2(v) and several vertices may occur in it multiple ties .) We also show that the decision whether a pair ( \mathbf{d (G), D _2(v)) is graphical is NP-complete.
Edit intent: fluency
Original: The structure of communication networks is an important determinant of the capacity of teams, organizations , and societies to solve policy, business , and science problems.
Revised: The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business , and science problems.
Edit intent: fluency
Original: The structure of communication networks is an important determinant of the capacity of teams, organizations , and societies to solve policy, business , and science problems.
Revised: The structure of communication networks is an important determinant of the capacity of teams, organizations , and societies to solve policy, business and science problems.
Edit intent: fluency
Original:  Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both efficient and inefficient network structures.
Revised:  Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures.
Edit intent: clarity
Original:  Here we argue that understanding how communication networks affect collective performance requires taking into consideration the social learning strategies of individual team members.
Revised:  Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members.
Edit intent: clarity
Original:  Specifically, efficient networks outperform the inefficient networks when individuals rely on the conformity strategy .
Revised:  Specifically, efficient networks outperform inefficient networks when individuals rely on the conformity strategy .
Edit intent: clarity
Original:  Specifically, efficient networks outperform the inefficient networks when individuals rely on the conformity strategy .
Revised:  Specifically, efficient networks outperform the inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts .
Edit intent: clarity
Original:  However, inefficient networks are superior when team members rely on the best member strategy .
Revised:  However, inefficient networks are superior when individuals follow the best member strategy .
Edit intent: clarity
Original:  However, inefficient networks are superior when team members rely on the best member strategy .
Revised:  However, inefficient networks are superior when team members rely on the best member by copying the group member with the highest payoff .
Edit intent: clarity
Original:  In addition, we find that collectives relying on conformity based on a small sample of other individuals excel on complex tasks, while collectives following the best member achieve best performance for simple tasks.
Revised:  In addition, groups relying on conformity based on a small sample of other individuals excel on complex tasks, while collectives following the best member achieve best performance for simple tasks.
Edit intent: clarity
Original:  In addition, we find that collectives relying on conformity based on a small sample of other individuals excel on complex tasks, while collectives following the best member achieve best performance for simple tasks.
Revised:  In addition, we find that collectives relying on conformity based on a small sample of others excel at complex tasks, while collectives following the best member achieve best performance for simple tasks.
Edit intent: clarity
Original:  In addition, we find that collectives relying on conformity based on a small sample of other individuals excel on complex tasks, while collectives following the best member achieve best performance for simple tasks.
Revised:  In addition, we find that collectives relying on conformity based on a small sample of other individuals excel on complex tasks, while groups following the best member achieve best performance for simple tasks.
Edit intent: clarity
Original:  In addition, we find that collectives relying on conformity based on a small sample of other individuals excel on complex tasks, while collectives following the best member achieve best performance for simple tasks.
Revised:  In addition, we find that collectives relying on conformity based on a small sample of other individuals excel on complex tasks, while collectives following the best member achieve greatest performance for simple tasks.
Edit intent: clarity
Original:  Our findings reconcile contradictory results in the literature , and have broad implications for the study of social learning across disciplines.
Revised:  Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.
Edit intent: fluency
Original:  In contrast to the standard log-likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions.
Revised:  In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions.
Edit intent: fluency
Original:  We generalize the Treelet Transform to the Multi-View Treelet Transform (MVTT) to allow for the capture of hierarchical structure when multiple views are avilable .
Revised:  We generalize the Treelet Transform to the Multi-View Treelet Transform (MVTT) to allow for the capture of hierarchical structure when multiple views are available .
Edit intent: fluency
Original:  The signature is distinguishable from signatures for other speech signals , representing different words, thereby enabling digit recognition and discrimination in devices that use only spiking neurons.
Revised:  The signature is distinguishable from signatures for other speech signals representing different words, thereby enabling digit recognition and discrimination in devices that use only spiking neurons.
Edit intent: fluency
Original:  This approach introduces an efficient , fast, and multi-speaker strategy without error-feedback training, although it does require supervised training.
Revised:  This approach introduces an efficient and fast network without error-feedback training, although it does require supervised training.
Edit intent: clarity
Original:  However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance.
Edit intent: coherence
Original:  We demonstrate that our model, DeepSurv, not only works as well as the standard linear Cox proportional hazards model but actually outperforms it in predictive ability on survival data with linear and nonlinear risk functions.
Revised:  We demonstrate that our model, DeepSurv, not only works as well as the standard linear Cox proportional hazards model but actually outperforms in predictive ability on survival data with linear and nonlinear risk functions.
Edit intent: clarity
Original: Previous research has shown that neural networks can model survival data in situations in which some patients' death times are unknown, e.g. right-censored.
Revised: Medical practitioners use survival models to explore and understand the relationships between patients' covariates ( e.g. right-censored.
Edit intent: clarity
Original:  In this paper, we run simulated experiments and use real survival data to build upon the risk-regression architecture proposed by Faraggi and Simon .
Revised:  In this paper, we run simulated experiments and use real survival data .
Edit intent: coherence
Original:  We demonstrate that our model, DeepSurv , not only works as well as other survival models but actually outperforms in predictive ability on survival data with linear and nonlinear risk functions .
Revised:  We demonstrate that DeepSurv performs as well as other survival models but actually outperforms in predictive ability on survival data with linear and nonlinear risk functions .
Edit intent: clarity
Original: Dynamic textures are spatial-temporal processes that exhibit statistical stationarity or stochastic repetitiveness in the temporal dimension.
Revised: Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal dimension.
Edit intent: clarity
Original:  In this paper, we study the problem of modeling and synthesizing dynamic textures using a generative version of the convolution neural network (ConvNet or CNN) that consists of multiple layers of spatial-temporal filters to capture the spatial-temporal patterns in the dynamic textures.
Revised:  In this paper, we study the problem of modeling and synthesizing dynamic textures using a generative version of the convolution neural network (ConvNet or CNN) that consists of multiple layers of spatial-temporal filters to capture spatial-temporal patterns in the dynamic textures.
Edit intent: fluency
Original:  We also apply the temporal generative ConvNet to the one-dimensional sound data, and show that the model can synthesize realistic natural and man-made sounds.
Revised:  We also apply the temporal generative ConvNet to the one-dimensional sound data, and show that the learning algorithm can synthesize realistic natural and man-made sounds.
Edit intent: clarity
Original:  According to this approach, different OFDM subcarriers are used to coordinate nodes access to the shared medium.
Revised:  According to this approach, different OFDM subcarriers are used to coordinate how nodes access the shared medium.
Edit intent: clarity
Original:  The algorithm is deterministic and runs in polynomial time if one is allowed to call oracles for factoring integers and polynomials over finite fields .
Revised:  The algorithm is randomized and runs in polynomial time if one is allowed to call oracles for factoring integers and polynomials over finite fields .
Edit intent: clarity
Original:  The algorithm is deterministic and runs in polynomial time if one is allowed to call oracles for factoring integers and polynomials over finite fields .
Revised:  The algorithm is deterministic and runs in polynomial time if one is allowed to call oracles for factoring integers .
Edit intent: clarity
Original:  Our flow model is new and differs substantially from the " Flows over time" model, also called "dynamic flows" in the literature.
Revised:  Our flow model is new and differs from the " Flows over time" model, also called "dynamic flows" in the literature.
Edit intent: clarity
Original:  Our flow model is new and differs substantially from the " Flows over time" model, also called "dynamic flows" in the literature.
Revised:  Our flow model is new and differs substantially from the " flows over time" model, also called "dynamic flows" in the literature.
Edit intent: fluency
Original:  We provide a (simplified ) static Time-Extended network  , which is of polynomial size to the input and whose static flow rates are equivalent to the respective temporal flow of the temporal network ;
Revised:  We provide a simplified static Time-Extended network , which is of polynomial size to the input and whose static flow rates are equivalent to the respective temporal flow of the temporal network ;
Edit intent: fluency
Original:  We further show that temporal flows can always be decomposed into flows, each of which is "pushed" only through a journey, i.e., a directed path whose successive edges have strictly increasing moments of existence.
Revised:  We further show that temporal flows can always be decomposed into flows, each of which moves only through a journey, i.e., a directed path whose successive edges have strictly increasing moments of existence.
Edit intent: clarity
Original:  Using the latter, we provide linear expected time algorithms for the maximum s-t temporal flow problem in networks of bounded node degrees with uniform, random , unique availabilities of edges .
Revised:  Using the latter, we provide linear expected time algorithms for the maximum s-t temporal flow .
Edit intent: coherence
Original:  We then consider mixed temporal networks, which have some edges with specified availabilities and some edges with random availabilities ;
Revised:  We then consider mixed temporal networks, which have some edges with specified availabilities and some edges with random availabilities ,
Edit intent: fluency
Original:  The generalized theta graph \Theta { \iota } 1,...,{ \iota }p consists in two end-vertices joined by p \ge 2 internally vertex-disjoint paths with respective lengths 1 \le { \iota } 1 \le . . . \le { \iota } p. We prove that the packing chromatic number of any undirected generalized theta graph lies between 3 and max{5, n 3 + 2}, where n 3 = |{i / 1 \le i \le p, { \iota } i = 3}|, and that both these bounds are tight.
Revised:  The generalized theta graph \Theta { \ell } 1,...,{ \iota }p consists in two end-vertices joined by p \ge 2 internally vertex-disjoint paths with respective lengths 1 \le { \iota } 1 \le . . . \le { \iota } p. We prove that the packing chromatic number of any undirected generalized theta graph lies between 3 and max{5, n 3 + 2}, where n 3 = |{i / 1 \le i \le p, { \iota } i = 3}|, and that both these bounds are tight.
Edit intent: clarity