wanyu/IteraTeR_v2 huggingface dataset:
Original:  There have also been advanced in biologically inspired cognitive architectures of which we will cite a few.
Revised:  There have also been advances in biologically inspired cognitive architectures of which we will cite a few.
Edit intent: fluency
Original: In the study of static networks, numerous "centrality " measures have been developed to quantify the importances of nodes in  networks, and one can express many of these measures in terms of the leading eigenvector of a matrix.
Revised: Numerous centrality measures have been developed to quantify the importances of nodes in networks, and one can express many of these measures in terms of the leading eigenvector of a matrix.
Edit intent: clarity
Original: In the study of static networks, numerous "centrality " measures have been developed to quantify the importances of nodes in  networks, and one can express many of these measures in terms of the leading eigenvector of a matrix.
Revised: In the study of static networks, numerous "centrality " measures have been developed to quantify the importances of nodes in static networks, and one can express many of these measures in terms of the leading eigenvector of a matrix.
Edit intent: fluency
Original: In the study of static networks, numerous "centrality " measures have been developed to quantify the importances of nodes in  networks, and one can express many of these measures in terms of the leading eigenvector of a matrix.
Revised: In the study of static networks, numerous "centrality " measures have been developed to quantify the importances of nodes in networks, and many of them can be expressed as the leading eigenvector of a matrix.
Edit intent: clarity
Original: In the study of static networks, numerous "centrality " measures have been developed to quantify the importances of nodes in  networks, and one can express many of these measures in terms of the leading eigenvector of a matrix.
Revised: In the study of static networks, numerous "centrality " measures have been developed to quantify the importances of nodes in networks, and one can express many of these measures in terms of the leading eigenvector of some matrix.
Edit intent: clarity
Original:  By studying the limit of strong coupling between layers , we derive expressions for " time-averaged centralities, " which are given by the zeroth-order terms of a singular perturbation expansion.
Revised:  We find that the strength of coupling between layers , we derive expressions for " time-averaged centralities, " which are given by the zeroth-order terms of a singular perturbation expansion.
Edit intent: clarity
Original:  By studying the limit of strong coupling between layers , we derive expressions for " time-averaged centralities, " which are given by the zeroth-order terms of a singular perturbation expansion.
Revised:  By studying the limit of strong coupling between layers , we derive expressions for time-averaged centralities, " which are given by the zeroth-order terms of a singular perturbation expansion.
Edit intent: fluency
Original:  By studying the limit of strong coupling between layers , we derive expressions for " time-averaged centralities, " which are given by the zeroth-order terms of a singular perturbation expansion.
Revised:  By studying the limit of strong coupling between layers , we derive expressions for " time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion.
Edit intent: fluency
Original:  We also study first-order terms to obtain " first-order-mover scores, " which concisely describe the magnitude of nodes' centrality changes over time.
Revised:  We also study first-order terms to obtain first-order-mover scores, " which concisely describe the magnitude of nodes' centrality changes over time.
Edit intent: fluency
Original:  We also study first-order terms to obtain " first-order-mover scores, " which concisely describe the magnitude of nodes' centrality changes over time.
Revised:  We also study first-order terms to obtain " first-order-mover scores, which concisely describe the magnitude of nodes' centrality changes over time.
Edit intent: fluency
Original:  As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the Supreme Court of the United States .
Revised:  As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court .
Edit intent: clarity
Original:  With the increasing availability of network data that changes in time, it is important to extend  eigenvector-based centrality measures to time-dependent networks.
Revised:  With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks.
Edit intent: clarity
Original:  We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT \times NT whose dominant eigenvector gives the centrality of each node i at each time t.
Revised:  We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NTxNT whose dominant eigenvector gives the centrality of each node i at each time t.
Edit intent: clarity
Original:  In the regime of strong coupling , we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion.
Revised:  In the strong-coupling regime , we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion.
Edit intent: fluency
Original: In  a patent technology network map, almost all pairs of technology classes are connected, whereas most of the connections  are extremely weak.
Revised: In a patent technology map, almost all pairs of technology classes are connected, whereas most of the connections are extremely weak.
Edit intent: clarity
Original: In  a patent technology network map, almost all pairs of technology classes are connected, whereas most of the connections  are extremely weak.
Revised: In a patent technology network map, almost all pairs of technology classes are connected, whereas most of the connections between them are extremely weak.
Edit intent: clarity
Original:  This observation suggests the need and also the possibility to filter the network map by removing the negligible and noisy links.
Revised:  This observation suggests the possibility of filtering the patent network map by removing the negligible and noisy links.
Edit intent: clarity
Original:  But link removal may reduce the  power of the network for predicting the cross-field patent portfolio diversification  of inventors and inventing organizations.
Revised:  But link removal may reduce the explanatory power of the network for predicting the cross-field patent portfolio diversification of inventors and inventing organizations.
Edit intent: clarity
Original:  But link removal may reduce the  power of the network for predicting the cross-field patent portfolio diversification  of inventors and inventing organizations.
Revised:  But link removal may reduce the power of the network on inventor or organization diversification. The network links may explain the patent portfolio diversification of inventors and inventing organizations.
Edit intent: clarity
Original:  But link removal may reduce the  power of the network for predicting the cross-field patent portfolio diversification  of inventors and inventing organizations.
Revised:  But link removal may reduce the power of the network for predicting the cross-field patent portfolio diversification paths of inventors and inventing organizations.
Edit intent: clarity
Original:  This paper proposes a metric for such predictive power of a patent network , and a method that allows one to objectively choose a best tradeoff between predictive power and the removal of weak links.
Revised:  This paper proposes a metric for such predictive power of a patent network , and a method that allows one to objectively choose an optimal trade-off between explanatory power and removing weak links.
Edit intent: clarity
Original:  We show the results that identify filtered networks below the optimal tradeoff, and also remove a degree of arbitrariness compared with other filtering treatments from the literature.
Revised:  We show that this method can remove a degree of arbitrariness compared with other filtering treatments from the literature.
Edit intent: clarity
Original:  On that basis, we further demonstrate the use of filtered technology maps to visualize and analyze the main paths of patent portfolio diversification of a prolific inventor (Leonard Forbes) and a technology company (Google Inc.) .
Revised:  On that basis, we further demonstrate the use of filtered technology maps to visualize and analyze the main paths of patent portfolio diversification of a prolific inventor (Leonard Forbes) and company (Google Inc.) .
Edit intent: clarity
Original:  On that basis, we further demonstrate the use of filtered technology maps to visualize and analyze the main paths of patent portfolio diversification of a prolific inventor (Leonard Forbes) and a technology company (Google Inc.) .
Revised:  On that basis, we further demonstrate the use of filtered technology maps to visualize and analyze the main paths of patent portfolio diversification of a prolific inventor (Leonard Forbes) and a technology company (Google ) are demonstrated .
Edit intent: clarity
Original:  Universality of reciprocity is confirmed with an analysis of simple models, and biological significance is discussed.
Revised:  Universality of reciprocity is confirmed with theoretical analysis of simple models, and biological significance is discussed.
Edit intent: clarity
Original:  Universality of reciprocity is confirmed with an analysis of simple models, and biological significance is discussed.
Revised:  Universality of reciprocity is confirmed with an analysis of simple models, while biological significance is discussed.
Edit intent: coherence
Original: Finite automata working bitwise on the local coordinates of points in the plane are constructed and shown to lead to self-affine surfaces ('patchwork quilts') under general circumstances.
Revised: Fractal surfaces ('patchwork quilts') under general circumstances.
Edit intent: clarity
Original:  We prove that these models give rise to a roughness exponent  that shapes the resulting spatial patterns : Larger values of the exponent lead to coarser surfaces .
Revised:  We prove that these models give rise to a roughness exponent that shapes the resulting spatial patterns , larger values of the exponent lead to coarser surfaces .
Edit intent: fluency
Original:  We prove that these models give rise to a roughness exponent  that shapes the resulting spatial patterns : Larger values of the exponent lead to coarser surfaces .
Revised:  We prove that these models give rise to a roughness exponent that shapes the resulting spatial patterns : Larger values of the exponent leading to coarser surfaces .
Edit intent: fluency
Original:  A theory is presented for all  bitwise operations on a finite alphabet which are not governed by chance .
Revised:  A theory is presented for all bitwise operations on a finite alphabet .
Edit intent: clarity
Original:  On a fundamental level, an understanding of the network dynamics can be used to inform the process of measuring social networks.
Revised:  On a fundamental level, understanding of the network dynamics can be used to inform the process of measuring social networks.
Edit intent: fluency
Original:  We show that in order to accurately model spreading processes on the network, the dynamic processes that occur on the order of minutes are essential and must be included in the analysis.
Revised:  We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order of minutes are essential and must be included in the analysis.
Edit intent: clarity
Original:  It therefore provides a unified way of using LSTM for both deep and sequential computation.
Revised:  The network provides a unified way of using LSTM for both deep and sequential computation.
Edit intent: clarity
Original:  We also observe that a two-dimensional translation model based on Grid LSTM outperforms a phrase-based reference system on a Chinese-to-English translation task , and that 3D Grid LSTM yields a near state-of-the-art error rate of 0.32\% on MNIST .
Revised:  We also observe that a two-dimensional translation model based on Grid LSTM outperforms a phrase-based reference system on a Chinese-to-English translation task .
Edit intent: coherence
Original: Low rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis.
Revised: Low-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis.
Edit intent: fluency
Original:  However, the performance of existing LRMA-based compression methods are still limited .
Revised:  However, its potential for data compression has not yet been fully investigated in the literature .
Edit intent: coherence
Original:  In this paper, we propose compact low rank matrix approximation ( CLRMA), a very effective tool for data compression , which extends the LRMA by exploring both the intra- and inter-coherence of data samples simultaneously.
Revised:  In this paper, we propose compact low rank matrix approximation ( SLRMA), an effective computational tool for data compression , which extends the LRMA by exploring both the intra- and inter-coherence of data samples simultaneously.
Edit intent: clarity
Original:  Technically, under the assistance of prescribed orthogonal transforms ( such as such as discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction.
Revised:  With the aid of prescribed orthogonal transforms ( such as such as discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction.
Edit intent: coherence
Original:  Technically, under the assistance of prescribed orthogonal transforms ( such as such as discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction.
Revised:  Technically, under the assistance of prescribed orthogonal transforms ( e.g., discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction.
Edit intent: clarity
Original:  Technically, under the assistance of prescribed orthogonal transforms ( such as such as discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction.
Revised:  Technically, under the assistance of prescribed orthogonal transforms ( such as such as discrete cosine/wavelet transform and graph transform), SLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction.
Edit intent: clarity
Original:  Technically, under the assistance of prescribed orthogonal transforms ( such as such as discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction.
Revised:  Technically, under the assistance of prescribed orthogonal transforms ( such as such as discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices , where one matrix is made of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction.
Edit intent: clarity
Original:  Numerically , we formulate the CLRMA problemby minimizing the \ell_0-norm and orthogonality regularized approximation (or reconstruction) error , and solve it by the inexact augmented Lagrangian multiplier method.
Revised:  Numerically , we formulate the CLRMA problemby minimizing the \ell_0-norm and orthogonality , and solve it by the inexact augmented Lagrangian multiplier method.
Edit intent: coherence
Original:  Numerically , we formulate the CLRMA problemby minimizing the \ell_0-norm and orthogonality regularized approximation (or reconstruction) error , and solve it by the inexact augmented Lagrangian multiplier method.
Revised:  Numerically , we formulate the CLRMA problemby minimizing the \ell_0-norm and orthogonality regularized approximation (or reconstruction) error , and solve it using the inexact augmented Lagrangian multiplier method.
Edit intent: fluency
Original:  We demonstrate the efficacy of CLMRA on various types of real-world data, including 3D meshes, image datasets, videos as well as human motion capture data, that is, the proposed CLRMA, in a much more compact form, can produce comparable approximation (or reconstruction) error as LRMA . Moreover, we present a CLRMA-based compression scheme for 3D dynamic meshes, and experimental results show that it outperforms the state-of-the-art scheme to a large extend in terms of compression performance.
Revised:  Through extensive tests on real-world data, including 3D meshes, image datasets, videos as well as human motion capture data, that is, the proposed CLRMA, in a much more compact form, can produce comparable approximation (or reconstruction) error as LRMA . Moreover, we present a CLRMA-based compression scheme for 3D dynamic meshes, and experimental results show that it outperforms the state-of-the-art scheme to a large extend in terms of compression performance.
Edit intent: clarity
Original:  We demonstrate the efficacy of CLMRA on various types of real-world data, including 3D meshes, image datasets, videos as well as human motion capture data, that is, the proposed CLRMA, in a much more compact form, can produce comparable approximation (or reconstruction) error as LRMA . Moreover, we present a CLRMA-based compression scheme for 3D dynamic meshes, and experimental results show that it outperforms the state-of-the-art scheme to a large extend in terms of compression performance.
Revised:  We demonstrate the efficacy of CLMRA on various types of real-world data, including 3D dynamic meshes, we observe that (i) SLRMA empirically converges well; (ii) SLRMA can produce approximation error comparable to LRMA but in a much sparse form; (iii) SLRMA-based compression schemes significantly outperform the state-of-the-art scheme to a large extend in terms of compression performance.
Edit intent: coherence
Original:  We demonstrate the efficacy of CLMRA on various types of real-world data, including 3D meshes, image datasets, videos as well as human motion capture data, that is, the proposed CLRMA, in a much more compact form, can produce comparable approximation (or reconstruction) error as LRMA . Moreover, we present a CLRMA-based compression scheme for 3D dynamic meshes, and experimental results show that it outperforms the state-of-the-art scheme to a large extend in terms of compression performance.
Revised:  We demonstrate the efficacy of CLMRA on various types of real-world data, including 3D meshes, image datasets, videos as well as human motion capture data, that is, the proposed CLRMA, in a much more compact form, can produce comparable approximation (or reconstruction) error as LRMA . Moreover, we present a CLRMA-based compression scheme for 3D dynamic meshes, and experimental results show that it outperforms the state-of-the-art in terms of compression performance.
Edit intent: clarity
Original:  Contrary to existing algorithms, a key feature of the proposed algorithms is that they do not require to know the network size, which can be estimated by every node, and that they are clock-free.
Revised:  A key feature of the proposed algorithms is that they do not require to know the network size, which can be estimated by every node, and that they are clock-free.
Edit intent: clarity
Original:  Contrary to existing algorithms, a key feature of the proposed algorithms is that they do not require to know the network size, which can be estimated by every node, and that they are clock-free.
Revised:  Contrary to existing algorithms, a key feature of the proposed algorithms is that they do not require to know the network size, which can be simultaneously estimated at every node, and that they are clock-free.
Edit intent: clarity
Original:  To address the PageRank problem in time-varying graphs, we introduce the novel concept of a persistent graph, which eliminates the effect of spamming nodes.
Revised:  To address the PageRank problem of time-varying graphs, we introduce the novel concept of a persistent graph, which eliminates the effect of spamming nodes.
Edit intent: clarity
Original:  To address the PageRank problem in time-varying graphs, we introduce the novel concept of a persistent graph, which eliminates the effect of spamming nodes.
Revised:  To address the PageRank problem in time-varying graphs, we introduce the novel concept of persistent graph, which eliminates the effect of spamming nodes.
Edit intent: fluency
Original:  The main result of the paper is to prove that these distributed algorithms converge almost surely and in the sense of L^p.
Revised:  Moreover, we prove that these distributed algorithms converge almost surely and in the sense of L^p.
Edit intent: coherence
Original:  The main result of the paper is to prove that these distributed algorithms converge almost surely and in the sense of L^p.
Revised:  The main result of the paper is to prove that these algorithms converge almost surely and in the sense of L^p.
Edit intent: clarity
Original:  Finally, the paper includes simulations, based on random graphs, which verify the performance of the proposed distributed algorithms .
Revised:  Finally, the effectiveness of the proposed distributed algorithms .
Edit intent: clarity
Original:  For any fixed k \ge 2, we construct a family of codes over alphabet of size k with positive rate, which allow efficient recovery from a worst-case deletion fraction approaching 1- \frac{2{k+\sqrt k}} . In particular, for binary codes, we are able to recover a fraction of deletions approaching 1/ 3. Previously, even non-constructively the largest deletion fraction known to be correctable with positive rate was 1-\Theta(1/k), and around 0.17 for the binary case.
Revised:  For any fixed k \ge 2, we construct a family of codes over alphabet of size k with positive rate, which allow efficient recovery from a worst-case deletion fraction approaching 1- \frac{2{k+\sqrt k}} . In particular, for binary codes, we are able to recover a fraction of deletions approaching 1/ 3. Previously, even non-constructively the largest deletion fraction known to be correctable with positive rate was 1-\Theta(1/k), and around 0.17 for the binary case.
Edit intent: fluency