wanyu/IteraTeR_v2 huggingface dataset:
Original:  The combination of the different layers of information allowed us to extract from the multiplex indications on the regulatory pattern and the functional role of both the already known oncogenes and the new candidate driver genes.
Revised:  The combination of the different layers of information allowed us to extract from the multiplex indications on the regulatory pattern and the functional role of both the already known and the new candidate driver genes.
Edit intent: clarity
Original: This paper presents a study on gossip protocols employed to spread data in unstructured Peer-to-Peer (P2P) network overlays.
Revised: This paper presents a study on data dissemination in unstructured Peer-to-Peer (P2P) network overlays.
Edit intent: clarity
Original:  We consider two classic protocols (fixed probability and probabilistic broadcast) and compare them with two new degree dependent gossip protocols that change the probability of dissemination based on the degree of peers.
Revised:  The absence of a structure in unstructured overlays eases the network management, at the cost of non-optimal mechanisms to spread messages in the network. Thus, dissemination schemes must be employed that allow covering a large portion of the network with a high probability (e.g.~gossip based approaches).
Edit intent: coherence
Original:  We identify principal metrics, provide a theoretical model and perform the assessment evaluation using  parallel and distributed simulation .
Revised:  We identify principal metrics, provide a theoretical model and perform the assessment evaluation using parallel and distributed architecture .
Edit intent: clarity
Original:  A main point of this study is that our large-scale simulations consider implementation technical details, such as the use of caching and Time To Live (TTL) in message dissemination, that are usually neglected in simulation , due to the additional overhead they cause.
Revised:  A main point of this study is that our simulation model considers implementation technical details, such as the use of caching and Time To Live (TTL) in message dissemination, that are usually neglected in simulation , due to the additional overhead they cause.
Edit intent: clarity
Original:  A main point of this study is that our large-scale simulations consider implementation technical details, such as the use of caching and Time To Live (TTL) in message dissemination, that are usually neglected in simulation , due to the additional overhead they cause.
Revised:  A main point of this study is that our large-scale simulations consider implementation technical details, such as the use of caching and Time To Live (TTL) in message dissemination, that are usually neglected in simulations , due to the additional overhead they cause.
Edit intent: fluency
Original:  Outcomes confirm that these technical strategies have an important influence on the performance of gossip schemes .
Revised:  Outcomes confirm that these technical details have an important influence on the performance of gossip schemes .
Edit intent: clarity
Original:  By tuning a novel exploration parameter, our algorithms could adaptively find the optimal channel access strategies and achieve the almost optimal learning performance of EECC  provided with the vanishing long-term power budget violations.
Revised:  By tuning a novel exploration parameter, our algorithms could adaptively find the optimal channel access strategies and achieve the almost optimal learning performance of EECC in different scenarios provided with the vanishing long-term power budget violations.
Edit intent: clarity
Original:  However, being responsive to multiple sensory modalities does not guarantee that a neuron has actually engaged in integrating its multiple sensory inputs  rather than simply responding to the most salient stimulus.
Revised:  However, being responsive to multiple sensory modalities does not guarantee that a neuron has actually engaged in integrating its multiple sensory inputs , rather than simply responding to the most salient stimulus.
Edit intent: fluency
Original:  The traditional criterion for identifying "  multisensory enhancement " is a statistically significant increase of the response measure elicited by a crossmodal stimulus compared to the value of the response measure to the most effective of the individual components.
Revised:  The traditional criterion for identifying " multisensory " is a statistically significant increase of the response measure elicited by a crossmodal stimulus compared to the value of the response measure to the most effective of the individual components.
Edit intent: clarity
Original:  However, as different authors use different definitions of the Shapley Value, the specific degree of relatedness depends on the specific version of the Shapley Value - it ranges from a high correlation index to equality of the indices.
Revised:  However, as different authors use different definitions of the Shapley Value, the specific degree of relatedness depends on the specific version of the Shapley Value -- it ranges from a high correlation index to equality of the indices.
Edit intent: fluency
Original:  We  show that even though the chance of two rankings being exactly identical (when obtained from different versions of the Shapley Value) decreases with an increasing number of taxa, the distance between the two rankings converges to zero, i.e.  the rankings are becoming more and more alike.
Revised:  We show that even though the chance of two rankings being exactly identical (when obtained from different versions of the Shapley Value) decreases with an increasing number of taxa, the distance between the two rankings converges to zero, i.e. , the rankings are becoming more and more alike.
Edit intent: fluency
Original:  However, as different authors use different definitions of the Shapley Value, the specific degree of relatedness depends on the specific version of the Shapley Value -- it ranges from a high correlation index to equality of the indices.
Revised:  However, as different authors use different definitions of the Shapley Value, the specific degree of relatedness depends on the specific version of the Shapley Value - it ranges from a high correlation index to equality of the indices.
Edit intent: fluency
Original:  Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average case sense, for a uniformly random choice of target node.
Revised:  Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average-case sense, for a uniformly random choice of target node.
Edit intent: fluency
Original:  We also  modify our methods to estimate random-walk probabilities for any length distribution, thereby obtaining fast algorithms for estimating general graph diffusions, including the heat kernel, on undirected networks .
Revised:  We also discuss how to modify our methods to estimate random-walk probabilities for any length distribution, thereby obtaining fast algorithms for estimating general graph diffusions, including the heat kernel, on undirected networks .
Edit intent: clarity
Original:  The question still unanswered is: why,what is the  phenomena that underlies the individual differences in color reporting?.
Revised:  The question still unanswered is: why,what is the phenomena that underlie the individual differences in color reporting?.
Edit intent: fluency
Original: We propose a constructive algorithm, called the Tensor-based Kronecker Product (KP) Singular Value Decomposition (TKPSVD), that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors, namely A = j=1^R \sigma_j\, A^{dj} \otimes \cdots \otimes A^{1j}.
Revised: We propose a constructive algorithm, called the tensor-based Kronecker product (KP) Singular Value Decomposition (TKPSVD), that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors, namely A = j=1^R \sigma_j\, A^{dj} \otimes \cdots \otimes A^{1j}.
Edit intent: clarity
Original: We propose a constructive algorithm, called the Tensor-based Kronecker Product (KP) Singular Value Decomposition (TKPSVD), that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors, namely A = j=1^R \sigma_j\, A^{dj} \otimes \cdots \otimes A^{1j}.
Revised: We propose a constructive algorithm, called the Tensor-based Kronecker Product (KP) singular value decomposition (TKPSVD), that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors, namely A = j=1^R \sigma_j\, A^{dj} \otimes \cdots \otimes A^{1j}.
Edit intent: fluency
Original:  The TTr1 decomposition exhibits a singular value profile as  the SVD, allowing for a low-rank truncated series whenever the singular value decay is prominent.
Revised:  The TTr1 decomposition exhibits a singular value profile as with the SVD, allowing for a low-rank truncated series whenever the singular value decay is prominent.
Edit intent: coherence
Original:  It also permits a straightforward way to compute the relative approximation error without the need to explicitly compute the approximant.
Revised:  It also permits a straightforward relative approximation error without the need to explicitly compute the approximant.
Edit intent: clarity
Original:  It also permits a straightforward way to compute the relative approximation error without the need to explicitly compute the approximant.
Revised:  It also permits a straightforward way to compute the relative approximation error measure without explicitly computing the approximant.
Edit intent: clarity
Original:  In providing these proofs we generalize the notion of symmetric matrices into general symmetric matrices  .
Revised:  In providing these proofs we generalize the notion of symmetric matrices to general symmetric matrices .
Edit intent: fluency
Original: We propose a constructive algorithm, called the tensor-based Kronecker product (KP)
Revised: We propose the tensor Kronecker product
Edit intent: clarity
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition ~ (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj } \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Edit intent: fluency
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) that decomposes a real k-way tensor \mathcal{A with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj } \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Edit intent: clarity
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors \mathcal{A = j=1^R \sigma_j\, A ^{ dj } \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Edit intent: clarity
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ (d) } \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Edit intent: clarity
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj } _j \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Edit intent: clarity
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj } \otimes \cdots \otimes A ^{ %DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Edit intent: clarity
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj } \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% (1) The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Edit intent: clarity
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj } \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original tensor into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Edit intent: fluency
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj } \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which a polyadic decomposition with orthogonal rank-1 (TTr1) decomposition is computed.
Edit intent: clarity
Original:  singular value decomposition  (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj }  \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% .  The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 (TTr1) decomposition is computed.
Revised:  singular value decomposition (TKPSVD) , that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors , namely A = j=1^R \sigma_j\, A ^{ dj } \otimes \cdots \otimes A ^{ 1j%DIFDELCMD < \MBLOCKRIGHTBRACE%%% . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train rank-1 terms is computed.
Edit intent: clarity
Original: Vector-space word representations based on neural network models can include linguistic regularities, enabling semantic operations based on vector arithmetic.
Revised: Vector-space word representations obtained from neural network models can include linguistic regularities, enabling semantic operations based on vector arithmetic.
Edit intent: clarity
Original: Vector-space word representations based on neural network models can include linguistic regularities, enabling semantic operations based on vector arithmetic.
Revised: Vector-space word representations based on neural network models have been shown to enable semantic operations based on vector arithmetic.
Edit intent: clarity
Original:  We define a methodology to obtain large and sparse vectors from individual images and image classes , by using a pre-trained model of the GoogLeNet architecture .
Revised:  We define a methodology to obtain large , sparse vector representations of individual images and image classes , by using a pre-trained model of the GoogLeNet architecture .
Edit intent: clarity
Original:  Further exploration of image representations shows how semantically similar elements are clustered in that space , regardless of large visual variances (e.g., 118 kinds of dogs ), and how the space distinguishes abstract classes of objects without supervision (e.g., living things from non-living things ).
Revised:  Further exploration of image representations shows how semantically similar elements are clustered in that space , regardless of significant visual variances (e.g., 118 kinds of dogs ), and how the space distinguishes abstract classes of objects without supervision (e.g., living things from non-living things ).
Edit intent: clarity
Original:  Further exploration of image representations shows how semantically similar elements are clustered in that space , regardless of large visual variances (e.g., 118 kinds of dogs ), and how the space distinguishes abstract classes of objects without supervision (e.g., living things from non-living things ).
Revised:  Further exploration of image representations shows how semantically similar elements are clustered in that space , regardless of large visual variances (e.g., 118 dogs types). More surprisingly, we find that the space unsupervisedly separates abstract classes without prior knowledge (e.g., living things from non-living things ).
Edit intent: clarity
Original:  Further exploration of image representations shows how semantically similar elements are clustered in that space , regardless of large visual variances (e.g., 118 kinds of dogs ), and how the space distinguishes abstract classes of objects without supervision (e.g., living things from non-living things ).
Revised:  Further exploration of image representations shows how semantically similar elements are clustered in that space , regardless of large visual variances (e.g., 118 kinds of dogs ), and how the space distinguishes abstract classes of objects without supervision (e.g., living things ).
Edit intent: clarity
Original:  Finally, we consider vector arithmetic , and find them to be related with image concatenation (e.g., "horse cart - horse = rickshaw"), image overlap ("Panda - Brown bear = Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger").
Revised:  Finally, we consider vector arithmetics , and find them to be related with image concatenation (e.g., "horse cart - horse = rickshaw"), image overlap ("Panda - Brown bear = Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger").
Edit intent: clarity
Original:  All these results indicate that visual semantics contain a large amount of general information, and that those semanticscan be extracted as vector representations from neural network models, making them available for further learning and reasoning  .
Revised:  These results indicate that visual semantics contain a large amount of general information, and that those semanticscan be extracted as vector representations from neural network models, making them available for further learning and reasoning .
Edit intent: coherence
Original:  All these results indicate that visual semantics contain a large amount of general information, and that those semanticscan be extracted as vector representations from neural network models, making them available for further learning and reasoning  .
Revised:  All these results indicate that image vector embeddings may contain diverse and rich visual semantics, usable for learning and reasoning .
Edit intent: clarity
Original:  All these results indicate that visual semantics contain a large amount of general information, and that those semanticscan be extracted as vector representations from neural network models, making them available for further learning and reasoning  .
Revised:  All these results indicate that visual semantics contain a large amount of general information, and that those semanticscan be extracted as vector representations from neural network models, making them available for further learning and reasoning purposes .
Edit intent: clarity
Original:  In this paper, we explore the existence of similar information on vector-space representations of images.
Revised:  In this paper, we explore the existence of similar information on vector representations of images.
Edit intent: clarity