wanyu/IteraTeR_v2 huggingface dataset:
Original:  The goal of the problem is to preprocess n strings of length d  to quickly answer queries q of the form: if there is a database string within edit distance r of q, return a database string within edit distance cr of q. A data structure solving this problem is analyzed using two criteria: the amount of extra space used in preprocessing, and the expected time to answer a query.
Revised:  The goal of the problem is to preprocess n strings of length d , to quickly answer queries q of the form: if there is a database string within edit distance r of q, return a database string within edit distance cr of q. A data structure solving this problem is analyzed using two criteria: the amount of extra space used in preprocessing, and the expected time to answer a query.
Edit intent: fluency
Original:  The goal of the problem is to preprocess n strings of length d  to quickly answer queries q of the form: if there is a database string within edit distance r of q, return a database string within edit distance cr of q. A data structure solving this problem is analyzed using two criteria: the amount of extra space used in preprocessing, and the expected time to answer a query.
Revised:  The goal of the problem is to preprocess n strings of length d to quickly answer queries q of the form: if there is a database string within edit distance r of q, return a database string within edit distance cr of q. 
Edit intent: coherence
Original:  Previous approaches to this problem have either used trie-based methods, which give exact solutions at the cost of expensive queries, or embeddings, which only work for large (superconstant) values of c.
Revised:  Previous approaches to this problem either rely on very large (superconstant) values of c.
Edit intent: clarity
Original:  In this work we achieve the first bounds for any approximation factor c, via a simple and easy-to-implement hash function .
Revised:  In this work give a simple and easy-to-implement hash function .
Edit intent: clarity
Original:  This gives a running time of O(d3^rn^{1/c}) , with space (3^rn^{1 + 1/c show how to apply these ideas to the closely-related Approximate Nearest Neighbor problem for edit distance, obtaining similar time bounds.
Revised:  This gives a running time of O(d3^rn^{1/c}) (3^rn^{1 + 1/c show how to apply these ideas to the closely-related Approximate Nearest Neighbor problem for edit distance, obtaining similar time bounds.
Edit intent: coherence
Original:  This gives a running time of O(d3^rn^{1/c}) , with space (3^rn^{1 + 1/c show how to apply these ideas to the closely-related Approximate Nearest Neighbor problem for edit distance, obtaining similar time bounds.
Revised:  This gives a running time of O(d3^rn^{1/c}) , with space (3^rn^{1 + 1/c show how to apply our ideas to the closely-related Approximate Nearest Neighbor problem for edit distance, obtaining similar time bounds.
Edit intent: fluency
Original:  Our astrocytic module may serve as a natural extension for neuromorphic hardware by mimicking the distinct computational roles of its biological counterpart .
Revised:  Our astrocytic module may serve as an extension for neuromorphic hardware by mimicking the distinct computational roles of its biological counterpart .
Edit intent: clarity
Original:  Our astrocytic module may serve as a natural extension for neuromorphic hardware by mimicking the distinct computational roles of its biological counterpart .
Revised:  Our astrocytic module may serve as a natural extension for neuromorphic hardware , by either replicating or exploring the distinct computational roles of its biological counterpart .
Edit intent: clarity
Original:  Our astrocytic module may serve as a natural extension for neuromorphic hardware by mimicking the distinct computational roles of its biological counterpart .
Revised:  Our astrocytic module may serve as a natural extension for neuromorphic hardware by mimicking the distinct computational roles that astrocytes have in forming biological intelligence .
Edit intent: clarity
Original:  In particular, we show for all problems in NC^1---many practically relevant ones , such as integer multiplication and division and the parity function,  being among these---how to solve them in a constant number of deterministic MapReduce rounds.
Revised:  In particular, we show for all problems in NC^1---many practically relevant ones such as integer multiplication and division and the parity function, being among these---how to solve them in a constant number of deterministic MapReduce rounds.
Edit intent: fluency
Original:  In particular, we show for all problems in NC^1---many practically relevant ones , such as integer multiplication and division and the parity function,  being among these---how to solve them in a constant number of deterministic MapReduce rounds.
Revised:  In particular, we show for all problems in NC^1---many practically relevant ones , such as integer multiplication and division , the parity function, being among these---how to solve them in a constant number of deterministic MapReduce rounds.
Edit intent: coherence
Original: State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures.
Revised: State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures.
Edit intent: fluency
Original:  In this work, we propose a hybrid deep learning structure , called "SeER", that uses collaborative filtering (CF) and deep learning sequence models on the MIDI content of songs for recommendation in order to provide more accurate personalized recommendations;
Revised:  In this work, we propose a hybrid deep learning model , called "SeER", that uses collaborative filtering (CF) and deep learning sequence models on the MIDI content of songs for recommendation in order to provide more accurate personalized recommendations;
Edit intent: clarity
Original:  With an end-to-end workflow and integration with commonly used modeling software, the system takes a 3D model and material properties as input and allows user-designers to (1)
Revised:  By taking a 3D model and material properties as input and allows user-designers to (1)
Edit intent: clarity
Original:  With an end-to-end workflow and integration with commonly used modeling software, the system takes a 3D model and material properties as input and allows user-designers to (1)
Revised:  With an end-to-end workflow and integration with commonly used modeling software, the system takes a 3D building model with material properties as input and allows user-designers to (1)
Edit intent: clarity
Original:  With an end-to-end workflow and integration with commonly used modeling software, the system takes a 3D model and material properties as input and allows user-designers to (1)
Revised:  With an end-to-end workflow and integration with commonly used modeling software, the system takes a 3D model and material properties as input , RadVR allows users to (1)
Edit intent: coherence
Original:  Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with the maximum correlation.
Revised:  Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with maximum correlation.
Edit intent: fluency
Original:  The traditional CCA can only be used to calculate the linear correlation between two views.
Revised:  Traditional CCA can only be used to calculate the linear correlation between two views.
Edit intent: coherence
Original:  The traditional CCA can only be used to calculate the linear correlation between two views.
Revised:  The traditional CCA can only be used to calculate the linear correlation of two views.
Edit intent: clarity
Original:  Moreover , it is unsupervised , and the label information is wasted in supervised learning tasks .
Revised:  Besides , it is unsupervised , and the label information is wasted in supervised learning tasks .
Edit intent: fluency
Original:  Moreover , it is unsupervised , and the label information is wasted in supervised learning tasks .
Revised:  Moreover , it is unsupervised and the label information is wasted in supervised learning tasks .
Edit intent: fluency
Original:  Moreover , it is unsupervised , and the label information is wasted in supervised learning tasks .
Revised:  Moreover , it is unsupervised , and the label information is wasted .
Edit intent: clarity
Original:  However, to our knowledge, there is no up-to-date overview of these approaches.
Revised:  However, to our knowledge, there is no overview for these approaches.
Edit intent: clarity
Original:  This paper fills this gap, by providing a comprehensive overview of many classical and latest CCA approaches, and describing their typical applications in pattern recognition, multi-modal retrieval and classification, and multi-view embedding .
Revised:  This paper provides an overview of many classical and latest CCA approaches, and describing their typical applications in pattern recognition, multi-modal retrieval and classification, and multi-view embedding .
Edit intent: clarity
Original:  We demonstrate appealing properties of the proposed algorithm on both numerical simulations and in real hardware experiments.
Revised:  We demonstrate appealing properties of the proposed algorithm in both numerical simulations and in real hardware experiments.
Edit intent: clarity
Original:  We demonstrate appealing properties of the proposed algorithm on both numerical simulations and in real hardware experiments.
Revised:  We demonstrate appealing properties of the proposed algorithm on both numerical simulations and real hardware experiments.
Edit intent: clarity
Original:  Not only does our algorithm accurately recover the missing phase, but it mitigates the effects of quantization and the sensitivity threshold, thus also improving the measured magnitudes.
Revised:  Not only does our algorithm accurately recover the missing phase, but it mitigates the effects of quantization and the sensitivity threshold, thus improving the measured magnitudes.
Edit intent: clarity
Original: In this paper  the design and analysis of a class of second order accurate IMEX finite volume schemes for the compressible Euler equations in the zero Mach number limit is presented.
Revised: In this paper , the design and analysis of a class of second order accurate IMEX finite volume schemes for the compressible Euler equations in the zero Mach number limit is presented.
Edit intent: fluency
Original:  It is well known that in the zero Mach number limit fast acoustic waves are filtered out, which results in the incompressible Euler equations containing only slow advection waves.
Edit intent: coherence
Original:  Both the time semi-discrete and space-time fully-discrete schemes are shown to be asymptotic preserving , i. e.\ both these schemes are consistent with the incompressible system in the zero Mach number limit, and their stability characteristics are independent of the Mach number.
Revised:  Both the time semi-discrete and space-time fully-discrete schemes are shown to be asymptotic preserving .
Edit intent: coherence
Original: Generative Adversarial Networks (GANs) are the emerging machine learning technology that can learn to automatically create labeled datasets in massive application domains such as speech, image, video and texts.
Revised: Generative Adversarial Networks (GANs) are the emerging machine learning technology that can learn to automatically create labeled datasets in application domains such as speech, image, video and texts.
Edit intent: clarity
Original:  Both convolutional and deconvolutional layers are the major source of performance overhead for GANs and directly impacts the efficiency of GAN-based systems.
Revised:  Both convolutional and deconvolutional layers are the major sources of performance overhead for GANs and directly impacts the efficiency of GAN-based systems.
Edit intent: fluency
Original:  Both convolutional and deconvolutional layers are the major source of performance overhead for GANs and directly impacts the efficiency of GAN-based systems.
Revised:  Both convolutional and deconvolutional layers are the major source of performance overhead for GANs and directly impact the efficiency of GAN-based systems.
Edit intent: fluency
Original:  In contrast, this work proposes a novel deconvolution layer implementation with a software approach and enables fast and efficient generative network inference on the legacy Convolutional Neural Networks (CNNs) accelerators .
Revised:  In contrast, this work proposes a novel deconvolution layer implementation with a software approach and enables fast and efficient generative network inference on the legacy deep learning processors .
Edit intent: clarity
Original:  Our proposed method reorganizes the computation of deconvolutional layer and allows the CNN accelerators to treat it as the standard convolutional layer after we split the original deconvolutional filters into multiple small filters.
Revised:  Our proposed method reorganizes the computation of deconvolutional layer and allows the deep learning processor to treat it as the standard convolutional layer after we split the original deconvolutional filters into multiple small filters.
Edit intent: clarity
Original:  The proposed data flow is implemented on representative CNN accelerators including dot-production array and  regular 2D PE array architectures.
Revised:  The proposed data flow is implemented on representative deep learning processors including the dot-production array and regular 2D PE array architectures.
Edit intent: clarity
Original:  The proposed data flow is implemented on representative CNN accelerators including dot-production array and  regular 2D PE array architectures.
Revised:  The proposed data flow is implemented on representative CNN accelerators including dot-production array and the regular 2D PE array architectures.
Edit intent: fluency
Original:  Compared to the prior baseline acceleration scheme , the implemented acceleration scheme achieves 2.4 X - 4.3 X performance speedup and reduces the energy consumption by 27.7\% - 54.5\% on a set of realistic benchmarks  .
Revised:  Compared to prior acceleration schemes , the implemented acceleration scheme achieves 2.4 X - 4.3 X performance speedup and reduces the energy consumption by 27.7\% - 54.5\% on a set of realistic benchmarks .
Edit intent: clarity
Original:  There are many prior works investigating specialized hardware architectures that can accelerate convolution and deconvolution simultaneously, but they entail intensive hardware modifications to the existing deep learning accelerators like Google TPU and Diannao that focus on convolutionacceleration .
Revised:  There are many prior works investigating specialized hardware architectures through intensive hardware modifications to the existing deep learning accelerators like Google TPU and Diannao that focus on convolutionacceleration .
Edit intent: clarity
Original:  There are many prior works investigating specialized hardware architectures that can accelerate convolution and deconvolution simultaneously, but they entail intensive hardware modifications to the existing deep learning accelerators like Google TPU and Diannao that focus on convolutionacceleration .
Revised:  There are many prior works investigating specialized hardware architectures that can accelerate convolution and deconvolution simultaneously, but they entail intensive hardware modifications to the existing deep learning processors to accelerate deconvolution together with the convolution .
Edit intent: clarity
Original:  In contrast, this work proposes a novel deconvolution layer implementation with a software approach and enables fast and efficient generative network inference on the legacy deep learning processors.
Revised:  In contrast, this work proposes a novel deconvolution implementation with a software approach and enables fast and efficient generative network inference on the legacy deep learning processors.
Edit intent: clarity
Original:  In contrast, this work proposes a novel deconvolution layer implementation with a software approach and enables fast and efficient generative network inference on the legacy deep learning processors.
Revised:  In contrast, this work proposes a novel deconvolution layer implementation with a software approach and enables fast and efficient deconvolution execution on the legacy deep learning processors.
Edit intent: clarity
Original:  Our proposed method reorganizes the computation of deconvolutional layer and allows the deep learning processor to treat it as the standard convolutional layer after we split the original deconvolutional filters into multiple small filters.
Revised:  Our proposed method reorganizes the computation of deconvolution and allows the deep learning processor to treat it as the standard convolutional layer after we split the original deconvolutional filters into multiple small filters.
Edit intent: fluency
Original:  Our proposed method reorganizes the computation of deconvolutional layer and allows the deep learning processor to treat it as the standard convolutional layer after we split the original deconvolutional filters into multiple small filters.
Revised:  Our proposed method reorganizes the computation of deconvolutional layer and allows the deep learning processors to treat it as the standard convolutional layer after we split the original deconvolutional filters into multiple small filters.
Edit intent: clarity
Original:  Our proposed method reorganizes the computation of deconvolutional layer and allows the deep learning processor to treat it as the standard convolutional layer after we split the original deconvolutional filters into multiple small filters.
Revised:  Our proposed method reorganizes the computation of deconvolutional layer and allows the deep learning processor to treat it as the standard convolution by splitting the original deconvolution filters into multiple small filters.
Edit intent: coherence
Original:  The proposed data flow is implemented on representative deep learning processors including the dot-production array and the regular 2D PE array architectures.
Edit intent: coherence
Original:  Compared to prior acceleration schemes, the implemented acceleration scheme achieves 2.41 X - 4.34 X performance speedup and reduces the energy consumption by 27.7\% - 54.5\% on a set of realistic benchmarks.
Revised:  Compared to prior acceleration schemes, the implemented acceleration scheme achieves 2.41 x - 4.34 X performance speedup and reduces the energy consumption by 27.7\% - 54.5\% on a set of realistic benchmarks.
Edit intent: fluency
Original:  Compared to prior acceleration schemes, the implemented acceleration scheme achieves 2.41 X - 4.34 X performance speedup and reduces the energy consumption by 27.7\% - 54.5\% on a set of realistic benchmarks.
Revised:  Compared to prior acceleration schemes, the implemented acceleration scheme achieves 2.41 X - 4.34 x performance speedup and reduces the energy consumption by 27.7\% - 54.5\% on a set of realistic benchmarks.
Edit intent: fluency
Original:  In addition, we also applied the deconvolution computing approach to  off-the-shelf commodity deep learning processor chips .
Revised:  In addition, we also applied the deconvolution computing approach to the off-the-shelf commodity deep learning processor chips .
Edit intent: fluency
Original:  In addition, we also applied the deconvolution computing approach to  off-the-shelf commodity deep learning processor chips .
Revised:  In addition, we also applied the deconvolution computing approach to off-the-shelf commodity deep learning processors .
Edit intent: clarity
Original:  The performance of GANs on the Google TPU chip and Intel NCS2 exhibits 1.67X - 3.04X speedup on average over prior deconvolution implementations.
Revised:  The performance of deconvolution also exhibits significant performance speedup over prior deconvolution implementations.
Edit intent: clarity
Original: We consider the geometrical problem of the passive transport of a hypersurface by a prescribed velocity field in the special case where the hypersurface intersects the domain boundary .
Revised: We consider the interface advection problem by a prescribed velocity field in the special case where the hypersurface intersects the domain boundary .
Edit intent: clarity
Original: We consider the geometrical problem of the passive transport of a hypersurface by a prescribed velocity field in the special case where the hypersurface intersects the domain boundary .
Revised: We consider the geometrical problem of the passive transport of a hypersurface by a prescribed velocity field in the special case when the interface intersects the domain boundary .
Edit intent: clarity
Original:  In the present study, it serves as a reference to verify the numerical transport of the contact angle  .
Revised:  The goal of the present work is to develop an interface advection method that is consistent with the fundamental kinematics and transports the contact angle .
Edit intent: clarity
Original:  In many real-world applications, any cyclic shift of P is a relevant pattern, and thus one is interested in computing the minimal distance of every length-m substring of T and any cyclic shift of P.
Revised:  In many real-world applications, any cyclic shift of P is a relevant pattern, and thus one is interested in computing the minimal distance of every length-m substring of T and any cyclic rotation of P.
Edit intent: clarity
Original: On-demand ride-sharing is rapidly growing due to its benefits of convenience and low price .
Revised: On-demand ride-sharing is rapidly growing .
Edit intent: clarity
Original:  Generally, an approach that matches requests with vehicles first identifies those vehicles that could be matched through a pruning step, and then selects among these the optimal one(s) in a selection step .
Revised:  Generally, an approach that matches requests with vehicles first identifies those vehicles that could be matched through a pruning step, and then selects among these the optimal one(s) from the feasible vehicles .
Edit intent: clarity
Original:  The pruning step is crucial to reduce the complexity of the selection step and to achieve a highly efficient matchingprocess.
Revised:  The pruning stage is crucial to reduce the complexity of the selection step and to achieve a highly efficient matchingprocess.
Edit intent: fluency
Original:  The pruning step is crucial to reduce the complexity of the selection step and to achieve a highly efficient matchingprocess.
Revised:  The pruning step is crucial to reduce the complexity of the selection stage and to achieve a highly efficient matchingprocess.
Edit intent: clarity
Original:  GeoPrune exploits the geometric properties of the waiting time constraints and detour time constraints of the trip requests , which can be computed and updated efficiently.
Revised:  GeoPrune represents the time constraints of trip requests using circles and ellipses , which can be computed and updated efficiently.
Edit intent: clarity
Original:  Experiments on real-world datasets show that GeoPrune reduces the number of potential vehicles by more than a factor of ten and the update cost by two to three orders of magnitude compared to  state-of-the-art algorithms .
Revised:  Experiments on real-world datasets show that GeoPrune reduces the number of potential vehicles by more than a factor of ten and the update cost by two to three orders of magnitude compared to the state-of-the-art algorithms .
Edit intent: fluency
Original:  Experiments on real-world datasets show that GeoPrune reduces the number of potential vehicles by more than a factor of ten and the update cost by two to three orders of magnitude compared to  state-of-the-art algorithms .
Revised:  Experiments on real-world datasets show that GeoPrune reduces the number of potential vehicles by more than a factor of ten and the update cost by two to three orders of magnitude compared to state-of-the-art .
Edit intent: clarity