wanyu/IteraTeR_v2 huggingface dataset:
Original:  Appropriate networks can be constructed with sparse random connections combined with glomerular nodesand a local associative learning rule, using low dynamic-range weights.
Revised:  Appropriate networks can be constructed with sparse random connections , glomerular nodes, and associative learning using low dynamic-range weights.
Edit intent: clarity
Original:  Thus, sparse quasi-random constraint structures -- characteristic of an important class of modern error-correcting codes -- may provide for high-performance computation in artificial neural networks and the brain.
Revised:  Thus, sparse quasi-random structures---characteristic of important error-correcting codes -- may provide for high-performance computation in artificial neural networks and the brain.
Edit intent: clarity
Original:  Thus, sparse quasi-random constraint structures -- characteristic of an important class of modern error-correcting codes -- may provide for high-performance computation in artificial neural networks and the brain.
Revised:  Thus, sparse quasi-random constraint structures -- characteristic of an important class of modern error-correcting codes---may provide for high-performance computation in artificial neural networks and the brain.
Edit intent: fluency
Original:  An %DIFDELCMD < {\em %DIFDELCMD < %%% unrooted phylogenetic network on a  finite set X of taxa, or %DIFDELCMD < {\em %%% network , is a connected graph in which every vertex has degree 1 or 3 and whose leaf-set is X.
Revised:  An %DIFDELCMD < {\em %DIFDELCMD < %%% unrooted phylogenetic network on a finite set X of taxa, or %DIFDELCMD < {\em %%% network , is a connected graph in which every vertex has degree 1 or 3 and whose leaf-set is X.
Edit intent: fluency
Original:  It is called a %DIFDELCMD < {\em %%% phylogenetic tree if the underlying graph is a tree.
Revised:  It is called a %DIFDELCMD < {\em %%% phylogenetic tree if the underlying graph is a tree.
Edit intent: fluency
Original:  In this paper we consider properties of %DIFDELCMD < {\em %%% tree-based networks , that is, networks that can be constructed by adding edges into a phylogenetic tree.
Revised:  In this paper we consider properties of %DIFDELCMD < {\em %%% tree-based networks , that is, networks that can be constructed by adding edges into a phylogenetic tree.
Edit intent: fluency
Original:  We explore several solution strategies for these models, and numerically compare them to the well-known Wavefront algorithm for zero forcing developed by Grout et al .
Revised:  We explore several solution strategies for these models, test them on various types of graphs, and show that they are competitive with the state-of-the-art algorithm for zero forcing developed by Grout et al .
Edit intent: clarity
Original:  We explore several solution strategies for these models, and numerically compare them to the well-known Wavefront algorithm for zero forcing developed by Grout et al .
Revised:  We explore several solution strategies for these models, and numerically compare them to the well-known Wavefront algorithm for zero forcing .
Edit intent: clarity
Original:  Our proposed algorithms for connected zero forcing and for controlling the propagation time are the first general-purpose computational methods for these problems ;
Revised:  Our proposed algorithms for connected zero forcing and for controlling the number of zero forcing timesteps are the first general-purpose computational methods for these problems ;
Edit intent: clarity
Original: This paper is devoted to understanding the shortest augmenting path approach for computing a maximum matching .
Revised: The shortest augmenting path approach for computing a maximum matching .
Edit intent: clarity
Original:  In INFOCOM 2009.
Revised:  In INFOCOM 2009
Edit intent: fluency
Original:  Recently a bound of O(n \log^2 n) has been proven given that the underlying graph is a tree [B. Bosek, D. Leniowski, P. Sankowski, and A. Zych.
Revised:  Recently a bound of O(n \log^2 n) bound [B. Bosek, D. Leniowski, P. Sankowski, and A. Zych.
Edit intent: clarity
Original: Cross-modal retrieval has become a highlighted research topic , which can provide flexible retrieval experience across multimedia data such as image , video, text and audio.
Revised: Cross-modal retrieval has become a highlighted research topic for retrieval across multimedia data such as image , video, text and audio.
Edit intent: clarity
Original:  Recently, researchers explore it via DNN, and a two-stage learning framework is  adopted by most existing methods  : The first learning stage is to generate separate representation for each modality, and the second learning stage is to get  cross-modal common representation.
Revised:  Recently, researchers explore it via DNN, and a two-stage learning framework is widely adopted by most existing methods : The first learning stage is to generate separate representation for each modality, and the second learning stage is to get cross-modal common representation.
Edit intent: clarity
Original:  Recently, researchers explore it via DNN, and a two-stage learning framework is  adopted by most existing methods  : The first learning stage is to generate separate representation for each modality, and the second learning stage is to get  cross-modal common representation.
Revised:  Recently, researchers explore it via DNN, and a two-stage learning framework is adopted by most existing methods : The first learning stage is to generate separate representation for each modality, and the second learning stage is to get the cross-modal common representation.
Edit intent: coherence
Original:  But existing methods have three limitations:  In the first learning stage, they only model intra-modality correlation, but ignore inter-modality one which can provide complementary contextfor learning better separate representation;
Revised:  However, the existing methods have three limitations: In the first learning stage, they only model intra-modality correlation, but ignore inter-modality one which can provide complementary contextfor learning better separate representation;
Edit intent: coherence
Original:  in the second learning stage, they only adopt shallow network structures with single-loss regularization, which ignore intrinsic relevance of intra-modality and inter-modality correlation , so cannot effectively exploit and balance them to improve generalization performance;
Revised:  in the second learning stage, they only adopt shallow networks with single-loss regularization, which ignore intrinsic relevance of intra-modality and inter-modality correlation , so cannot effectively exploit and balance them to improve generalization performance;
Edit intent: clarity
Original:  in the second learning stage, they only adopt shallow network structures with single-loss regularization, which ignore intrinsic relevance of intra-modality and inter-modality correlation , so cannot effectively exploit and balance them to improve generalization performance;
Revised:  in the second learning stage, they only adopt shallow network structures with single-loss regularization, but ignore the intrinsic relevance of intra-modality and inter-modality correlation , so cannot effectively exploit and balance them to improve generalization performance;
Edit intent: coherence
Original:  besides, only original instances are considered while  complementary fine-grained clues provided by their patches are ignored.
Revised:  besides, only original instances are considered while the complementary fine-grained clues provided by their patches are ignored.
Edit intent: fluency
Original:  For addressing  above problems, this paper proposes a cross-modal correlation learning (CCL) approach with multi-grained fusion by hierarchical network, with following contributions : (1) In the first learning stage, CCL exploits multi-level association with joint optimization to preserve  complementary context from intra-modality and inter-modality correlation simultaneously.
Revised:  For addressing the above problems, this paper proposes a cross-modal correlation learning (CCL) approach with multi-grained fusion by hierarchical network, with following contributions : (1) In the first learning stage, CCL exploits multi-level association with joint optimization to preserve complementary context from intra-modality and inter-modality correlation simultaneously.
Edit intent: fluency
Original:  For addressing  above problems, this paper proposes a cross-modal correlation learning (CCL) approach with multi-grained fusion by hierarchical network, with following contributions : (1) In the first learning stage, CCL exploits multi-level association with joint optimization to preserve  complementary context from intra-modality and inter-modality correlation simultaneously.
Revised:  For addressing above problems, this paper proposes a cross-modal correlation learning (CCL) approach with multi-grained fusion by hierarchical network, and the contributions are as follows : (1) In the first learning stage, CCL exploits multi-level association with joint optimization to preserve complementary context from intra-modality and inter-modality correlation simultaneously.
Edit intent: coherence
Original:  For addressing  above problems, this paper proposes a cross-modal correlation learning (CCL) approach with multi-grained fusion by hierarchical network, with following contributions : (1) In the first learning stage, CCL exploits multi-level association with joint optimization to preserve  complementary context from intra-modality and inter-modality correlation simultaneously.
Revised:  For addressing above problems, this paper proposes a cross-modal correlation learning (CCL) approach with multi-grained fusion by hierarchical network, with following contributions : (1) In the first learning stage, CCL exploits multi-level association with joint optimization to preserve the complementary context from intra-modality and inter-modality correlation simultaneously.
Edit intent: fluency
Original:  (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance  intra-modality semantic category constraint and inter-modality pairwise similarity constraint . (3) CCL adopts multi-grained modeling that fuses coarse-grained instances and fine-grained patches to make cross-modal correlation more precise.
Revised:  (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance the intra-modality semantic category constraint and inter-modality pairwise similarity constraint . (3) CCL adopts multi-grained modeling that fuses coarse-grained instances and fine-grained patches to make cross-modal correlation more precise.
Edit intent: fluency
Original:  (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance  intra-modality semantic category constraint and inter-modality pairwise similarity constraint . (3) CCL adopts multi-grained modeling that fuses coarse-grained instances and fine-grained patches to make cross-modal correlation more precise.
Revised:  (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance intra-modality semantic category constraints and inter-modality pairwise similarity constraint . (3) CCL adopts multi-grained modeling that fuses coarse-grained instances and fine-grained patches to make cross-modal correlation more precise.
Edit intent: fluency
Original:  (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance  intra-modality semantic category constraint and inter-modality pairwise similarity constraint . (3) CCL adopts multi-grained modeling that fuses coarse-grained instances and fine-grained patches to make cross-modal correlation more precise.
Revised:  (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance intra-modality semantic category constraint and inter-modality pairwise similarity constraints . (3) CCL adopts multi-grained modeling that fuses coarse-grained instances and fine-grained patches to make cross-modal correlation more precise.
Edit intent: fluency
Original:  (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance  intra-modality semantic category constraint and inter-modality pairwise similarity constraint . (3) CCL adopts multi-grained modeling that fuses coarse-grained instances and fine-grained patches to make cross-modal correlation more precise.
Revised:  (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance intra-modality semantic category constraint and inter-modality pairwise similarity constraint . (3) CCL adopts multi-grained modeling , which fuses the coarse-grained instances and fine-grained patches to make cross-modal correlation more precise.
Edit intent: coherence
Original:  Compared with 9 methods on 3 cross-modal datasets, the experimental results show the effectiveness of our CCL approach  .
Revised:  Compared with 9 methods on 3 cross-modal datasets, the experimental results show our CCL approach .
Edit intent: clarity
Original:  And a two-stage learning framework is widely adopted by most existing methods based on Deep Neural Network (DNN): The first learning stage is to generate separate representation for each modality, and the second learning stage is to get the cross-modal common representation.
Revised:  A two-stage learning framework is widely adopted by most existing methods based on Deep Neural Network (DNN): The first learning stage is to generate separate representation for each modality, and the second learning stage is to get the cross-modal common representation.
Edit intent: coherence
Original:  We prove that this dynamic version of the (1+\lambda) ~ EA finds the optimum in an expected optimization time (number of fitness evaluations) of O(n\lambda/ \! \log\lambda+n\log n).
Revised:  We prove that this dynamic version of the (1+\lambda) EA finds the optimum in an expected optimization time (number of fitness evaluations) of O(n\lambda/ \! \log\lambda+n\log n).
Edit intent: fluency
Original:  We prove that this dynamic version of the (1+\lambda) ~ EA finds the optimum in an expected optimization time (number of fitness evaluations) of O(n\lambda/ \! \log\lambda+n\log n).
Revised:  We prove that this dynamic version of the (1+\lambda) ~ EA finds the optimum in an expected optimization time (number of fitness evaluations) of O(n\lambda/ \log\lambda+n\log n).
Edit intent: coherence
Original: Recently, two new indicators (Equalized Mean-based Normalized Proportion Cited, EMNPC, the Mean-based Normalized Proportion Cited, MNPC) were proposed which are intended for sparse data.
Revised: Recently, two new indicators (Equalized Mean-based Normalized Proportion Cited, EMNPC, and Mean-based Normalized Proportion Cited, MNPC) were proposed which are intended for sparse data.
Edit intent: coherence
Original:  Past efforts mainly focused on single-media retrieval , such as image retrieval, video retrieval and audio retrieval .
Revised:  Past efforts mainly focused on single-media retrieval .
Edit intent: clarity
Original:  However, users' demands could be flexible, such as using an image to retrieve its relevant audio clips  . So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, are attracting increasing attention.
Revised:  However, the requirements of users are highly flexible, such as using an image to retrieve its relevant audio clips . So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, are attracting increasing attention.
Edit intent: clarity
Original:  However, users' demands could be flexible, such as using an image to retrieve its relevant audio clips  . So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, are attracting increasing attention.
Revised:  However, users' demands could be flexible, such as retrieving the relevant audio clips . So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, are attracting increasing attention.
Edit intent: clarity
Original:  However, users' demands could be flexible, such as using an image to retrieve its relevant audio clips  . So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, are attracting increasing attention.
Revised:  However, users' demands could be flexible, such as using an image to retrieve its relevant audio clips . So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, have attracted increasing attention.
Edit intent: fluency
Original:  Cross-media retrieval is designed for the scenario where users' queries and retrieval results are of different media types.
Revised:  Cross-media retrieval is designed for the scenarios where the queries and retrieval results are of different media types.
Edit intent: clarity
Original:  However, as a relatively new research topic, its concepts, methodologies and benchmarks are still not clear in the literatures.
Revised:  As a relatively new research topic, its concepts, methodologies and benchmarks are still not clear in the literatures.
Edit intent: coherence
Original:  To address these issues, this paper reviews more than 100 references, gives an overview including the concepts, methodologies, major challenges and open issues, and also builds up the benchmarks including datasets and experiment results.
Revised:  To address these issues, we review more than 100 references, gives an overview including the concepts, methodologies, major challenges and open issues, and also builds up the benchmarks including datasets and experiment results.
Edit intent: coherence
Original:  To address these issues, this paper reviews more than 100 references, gives an overview including the concepts, methodologies, major challenges and open issues, and also builds up the benchmarks including datasets and experiment results.
Revised:  To address these issues, this paper reviews more than 100 references, give an overview including the concepts, methodologies, major challenges and open issues, and also builds up the benchmarks including datasets and experiment results.
Edit intent: fluency
Original:  To address these issues, this paper reviews more than 100 references, gives an overview including the concepts, methodologies, major challenges and open issues, and also builds up the benchmarks including datasets and experiment results.
Revised:  To address these issues, this paper reviews more than 100 references, gives an overview including the concepts, methodologies, major challenges and open issues, as well as build up the benchmarks including datasets and experiment results.
Edit intent: fluency
Original:  To address these issues, this paper reviews more than 100 references, gives an overview including the concepts, methodologies, major challenges and open issues, and also builds up the benchmarks including datasets and experiment results.
Revised:  To address these issues, this paper reviews more than 100 references, gives an overview including the concepts, methodologies, major challenges and open issues, and also builds up the benchmarks including datasets and experimental results.
Edit intent: fluency
Original:  Researchers can directly adopt the datasets and experiment results to promptly evaluate their proposed methods.
Revised:  Researchers can directly adopt the benchmarks to promptly evaluate their proposed methods.
Edit intent: clarity
Original:  This will help them to focus on algorithm design, rather than the time-consuming compared methods and their results.
Revised:  This will help them to focus on algorithm design, rather than the time-consuming compared methods and results.
Edit intent: clarity
Original:  It should be noted that we have constructed a new dataset XMedia, which is the first publicly available dataset with up to 5 media types (text, image, video, audio and 3D model) to the best of our knowledge .
Revised:  It is noted that we have constructed a new dataset XMedia, which is the first publicly available dataset with up to 5 media types (text, image, video, audio and 3D model) to the best of our knowledge .
Edit intent: clarity
Original:  It should be noted that we have constructed a new dataset XMedia, which is the first publicly available dataset with up to 5 media types (text, image, video, audio and 3D model) to the best of our knowledge .
Revised:  It should be noted that we have constructed a new dataset XMedia, which is the first publicly available dataset with up to five media types (text, image, video, audio and 3D model) to the best of our knowledge .
Edit intent: fluency
Original:  It should be noted that we have constructed a new dataset XMedia, which is the first publicly available dataset with up to 5 media types (text, image, video, audio and 3D model) to the best of our knowledge .
Revised:  It should be noted that we have constructed a new dataset XMedia, which is the first publicly available dataset with up to 5 media types (text, image, video, audio and 3D model) .
Edit intent: coherence
Original:  We believe that this overview will attract more researchers to focus on cross-media retrieval and be helpful to them.
Revised:  We believe this overview will attract more researchers to focus on cross-media retrieval and be helpful to them.
Edit intent: fluency
Original:  Specifically, W^*[P_4]=3 for P_4 being the path graph on 4 vertices.  ]
Revised:  Specifically, W^*[P_4]=3 for P_4 being the path graph on 4 vertices. F]
Edit intent: coherence
Original: BLASFEO is a dense linear algebra library providing high-performance implementation of BLAS- and LAPACK-like routines for use in embedded optimization.
Revised: BLASFEO is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization.
Edit intent: fluency
Original:  A key difference with respect to existing high-performance implementations of BLAS is that the computational performance is optimized for small to medium scale matrices, i.e.  for sizes up to a few hundreds . BLASFEO comes with three different implementations:
Revised:  A key difference with respect to existing high-performance implementations of BLAS is that the computational performance is optimized for small to medium scale matrices, i.e. , for sizes up to a few hundreds . BLASFEO comes with three different implementations:
Edit intent: fluency
Original:  A key difference with respect to existing high-performance implementations of BLAS is that the computational performance is optimized for small to medium scale matrices, i.e.  for sizes up to a few hundreds . BLASFEO comes with three different implementations:
Revised:  A key difference with respect to existing high-performance implementations of BLAS is that the computational performance is optimized for small to medium scale matrices, i.e. for sizes up to a few hundred . BLASFEO comes with three different implementations:
Edit intent: fluency
Original:  a high-performance implementation ( aiming at providing the highest performance for matrices fitting in cache ) , a reference implementation ( providing portability and embeddability , and optimized for very small matrices ) , and a wrapper to standard BLAS and LAPACK (for high-performance on large matrices ) .
Revised:  a high-performance implementation aiming at providing the highest performance for matrices fitting in cache ) , a reference implementation ( providing portability and embeddability , and optimized for very small matrices ) , and a wrapper to standard BLAS and LAPACK (for high-performance on large matrices ) .
Edit intent: fluency