wanyu/IteraTeR_v2 huggingface dataset:
Original: Object-oriented programming has long been regarded as too inefficient for SIMD high-performance computing, despite the fact that many important applications in HPC have an inherent object structure.
Revised: Object-oriented programming has long been regarded as too inefficient for SIMD high-performance computing, despite the fact that many important HPC applications have an inherent object structure.
Edit intent: clarity
Original:  On SIMD accelerators  including GPUs, this is mainly due to performance problems with memory allocation  : There are a few libraries that support parallel memory allocation directly on accelerator devices, but all of them suffer from uncoalesed memory accesses.
Revised:  On SIMD accelerators , including GPUs, this is mainly due to performance problems with memory allocation : There are a few libraries that support parallel memory allocation directly on accelerator devices, but all of them suffer from uncoalesed memory accesses.
Edit intent: fluency
Original:  On SIMD accelerators  including GPUs, this is mainly due to performance problems with memory allocation  : There are a few libraries that support parallel memory allocation directly on accelerator devices, but all of them suffer from uncoalesed memory accesses.
Revised:  On SIMD accelerators including GPUs, this is mainly due to performance problems with memory allocation and memory access : There are a few libraries that support parallel memory allocation directly on accelerator devices, but all of them suffer from uncoalesed memory accesses.
Edit intent: clarity
Original:  DynaSOAr was designed for a class of object-oriented programs that we call Single-Method Multiple Objects (SMMO), in which parallelism is expressed over a set of objects . DynaSOAr is the first GPU object allocatorthat provides a parallel do-all operation , which is the foundation of SMMO applications.
Revised:  DynaSOAr was designed for a class of object-oriented programs that we call Single-Method Multiple-Objects (SMMO), in which parallelism is expressed over a set of objects . DynaSOAr is the first GPU object allocatorthat provides a parallel do-all operation , which is the foundation of SMMO applications.
Edit intent: fluency
Original:  DynaSOAr was designed for a class of object-oriented programs that we call Single-Method Multiple Objects (SMMO), in which parallelism is expressed over a set of objects . DynaSOAr is the first GPU object allocatorthat provides a parallel do-all operation , which is the foundation of SMMO applications.
Revised:  DynaSOAr was designed for a class of object-oriented programs that we call Single-Method Multiple Objects (SMMO), because parallelism is expressed over a set of objects . DynaSOAr is the first GPU object allocatorthat provides a parallel do-all operation , which is the foundation of SMMO applications.
Edit intent: coherence
Original:  DynaSOAr improves the usage of allocated memory with a Structure of Arrays (SOA) data layout and achieves low memory fragmentation through efficient management of free and allocated memory blocks with lock-free, hierarchical bitmaps.
Revised:  DynaSOAr improves the usage of allocated memory with a Structure of Arrays data layout and achieves low memory fragmentation through efficient management of free and allocated memory blocks with lock-free, hierarchical bitmaps.
Edit intent: clarity
Original:   In our benchmarks, DynaSOAr achieves a significant speedup of application code of up to 3x over state-of-the-art allocators.
Revised:  In our benchmarks, DynaSOAr achieves a speedup of application code of up to 3x over state-of-the-art allocators.
Edit intent: clarity
Original: To understand and predict the performance of parallel and distributed programs , several analytical and machine learning approaches have been proposed, each having its advantages and disadvantages.
Revised: To understand and predict the performance of scientific applications , several analytical and machine learning approaches have been proposed, each having its advantages and disadvantages.
Edit intent: clarity
Original: We introduce the Visual Data Management System (VDMS), a data management solution that enables efficient access of big-visual-data to support visual analytics.
Revised: We introduce the Visual Data Management System (VDMS), which enables faster access to big-visual-data and adds support to visual analytics.
Edit intent: clarity
Original:  This is achieved by searching for relevant visual data via metadata stored as a graph, as well as enabling faster access to visual data through new machine-friendly storage formats.
Revised:  This is achieved by searching for relevant visual data via metadata stored as a graph, and enabling faster access to visual data through new machine-friendly storage formats.
Edit intent: coherence
Original:  VDMS differs from existing large scale photo serving, video streaming, and textual big-data data management systems due to its primary focus on supporting machine learning and data analytics pipelines that use visual data , and in its treatment of visual data such as images, videos, and feature vectors  as first class entities.
Revised:  VDMS differs from existing large scale photo serving, video streaming, and textual big-data management systems due to its primary focus on supporting machine learning and data analytics pipelines that use visual data , and in its treatment of visual data such as images, videos, and feature vectors as first class entities.
Edit intent: fluency
Original:  VDMS differs from existing large scale photo serving, video streaming, and textual big-data data management systems due to its primary focus on supporting machine learning and data analytics pipelines that use visual data , and in its treatment of visual data such as images, videos, and feature vectors  as first class entities.
Revised:  VDMS differs from existing large scale photo serving, video streaming, and textual big-data data management systems due to its primary focus on supporting machine learning and data analytics pipelines that use visual data ( images, videos, and feature vectors as first class entities.
Edit intent: coherence
Original:  VDMS differs from existing large scale photo serving, video streaming, and textual big-data data management systems due to its primary focus on supporting machine learning and data analytics pipelines that use visual data , and in its treatment of visual data such as images, videos, and feature vectors  as first class entities.
Revised:  VDMS differs from existing large scale photo serving, video streaming, and textual big-data data management systems due to its primary focus on supporting machine learning and data analytics pipelines that use visual data , and in its treatment of visual data such as images, videos, and feature vectors ), treating these as first class entities.
Edit intent: clarity
Original:  We describe how to use VDMS via its user friendly interface as well as how it enables rich and efficient vision analytics through a machine learning pipeline for processing medical images.
Revised:  We describe how to use VDMS via its user friendly interface and how it enables rich and efficient vision analytics through a machine learning pipeline for processing medical images.
Edit intent: coherence
Original:  Our indexing schemes can be used to improve the decoding complexity of state-of-the-art error correcting codes for insertion and deletions.
Revised:  Our indexing schemes can be used to improve the decoding complexity of state-of-the-art error correcting codes for insertions and deletions.
Edit intent: fluency
Original:  In addition  we use performance models to offer predictions about the expected performance on upcoming exascale system configurations based on current technology trends.
Revised:  In addition , we use performance models to offer predictions about the expected performance on upcoming exascale system configurations based on current technology trends.
Edit intent: fluency
Original: We characterize the effectiveness of a natural and classic algorithm for recovering the Markov graph of a general discrete pairwise graphical model from i.i.d. samples.
Revised: We characterize the effectiveness of a classical algorithm for recovering the Markov graph of a general discrete pairwise graphical model from i.i.d. samples.
Edit intent: clarity
Original:  for Ising models this is \ell_1-constrained logistic regression, while for more  alphabets an 2,1 group-norm constraint needs to be used.
Revised:  for Ising models this is \ell_1-constrained logistic regression, while for more general alphabets an 2,1 group-norm constraint needs to be used.
Edit intent: clarity
Original:  We also show that the proposed convex programs can be efficiently optimized in O(n^2) running time (where n is the number of variables) under the same statistical guarantee.
Revised:  We also show that the proposed convex programs can be efficiently solved in O(n^2) running time (where n is the number of variables) under the same statistical guarantee.
Edit intent: clarity
Original: Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking  an ad or an item, is critical to many online applications such as online advertising and recommender systems.
Revised: Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertising and recommender systems.
Edit intent: fluency
Original:  The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on high-order combinatorial features ( a.k.a. cross features), which are very time-consuming to hand-craft by domain experts and are impossible to be enumerated.
Revised:  The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on high-order combinatorial features ( a.k.a. cross features), which are very time-consuming to hand-craft by domain experts and are impossible to be enumerated.
Edit intent: fluency
Original:  In this paper, we propose an effective and efficient algorithm to automatically learn the high-order feature combinations of input features.
Revised:  In this paper, we propose an effective and efficient method called the to automatically learn the high-order feature combinations of input features.
Edit intent: clarity
Original:  In this paper, we propose an effective and efficient algorithm to automatically learn the high-order feature combinations of input features.
Revised:  In this paper, we propose an effective and efficient algorithmAutoInt to automatically learn the high-order feature combinations of input features.
Edit intent: clarity
Original:  In this paper, we propose an effective and efficient algorithm to automatically learn the high-order feature combinations of input features.
Revised:  In this paper, we propose an effective and efficient algorithm to automatically learn the high-order feature interactions of input features.
Edit intent: clarity
Original:  Afterward , a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.
Revised:  Afterwards , a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.
Edit intent: fluency
Original:  In case A, stochastic variation is introduced in the two non-dimensional parameters (Rayleigh and Prandtl numbers) with an assumption that the temperature boundary condition is uniform.
Revised:  In case A, stochastic variation is introduced in the two non-dimensional parameters (Rayleigh and Prandtl numbers) with an assumption that the boundary temperature is uniform.
Edit intent: clarity
Original:  For a high stochastic dimension like 32, it is computationally expensive to fit the PCE.
Revised:  For a high stochastic dimension such as 32, it is computationally expensive to fit the PCE.
Edit intent: fluency
Original:  This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method of uncertainty quantification with the number simulations much lesser than that required for fitting the PCE  thus, saving the computational cost.
Revised:  This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method for uncertainty quantification with the number simulations much lesser than that required for fitting the PCE thus, saving the computational cost.
Edit intent: fluency
Original:  This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method of uncertainty quantification with the number simulations much lesser than that required for fitting the PCE  thus, saving the computational cost.
Revised:  This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method of uncertainty quantification with the number of simulations much fewer than that required for fitting the PCE thus, saving the computational cost.
Edit intent: fluency
Original:  This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method of uncertainty quantification with the number simulations much lesser than that required for fitting the PCE  thus, saving the computational cost.
Revised:  This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method of uncertainty quantification with the number simulations much lesser than that required for fitting the PCE , thus, saving the computational cost.
Edit intent: fluency
Original: We study how shocks to the forward-looking expectations of investors buying call and put options transmit across the financial system.
Revised: This paper introduces forward-looking expectations of investors buying call and put options transmit across the financial system.
Edit intent: clarity
Original:  Second, the optimal configuration of hyper-parameters for one DL framework (e.g., TensorFlow) often does not work well for another DL framework (e.g., Caffe or Torch) under the same CPU-GPU runtime environment .
Revised:  Second, the optimal configuration of hyper-parameters .
Edit intent: coherence
Original:  In this paper, we propose a new abstraction called splitability annotations (SAs) that enables key data movement optimizations on black-box library functions.
Revised:  In this paper, we propose a new technique called split annotations (SAs) that enables key data movement optimizations on black-box library functions.
Edit intent: clarity
Original:  In this paper, we propose a new abstraction called splitability annotations (SAs) that enables key data movement optimizations on black-box library functions.
Revised:  In this paper, we propose a new abstraction called splitability annotations (SAs) that enables key data movement optimizations over unmodified library functions.
Edit intent: clarity
Original:  SAs only require that users add an annotation for existing, unmodified functions and implement a small API to split data values in the library.
Revised:  SAs only require developers to annotate functions and implement a small API to split data values in the library.
Edit intent: clarity
Original:  SAs only require that users add an annotation for existing, unmodified functions and implement a small API to split data values in the library.
Revised:  SAs only require that users add an annotation for existing, unmodified functions and implement an API that specifies how to partition data in the library.
Edit intent: clarity
Original:  Together, this interface describes how to partition values that are passed among functions to enable data pipelining and automatic parallelization while respecting each library 's correctness constraints.
Revised:  The annotation and API describe how to enable cross-function data pipelining and automatic parallelization while respecting each library 's correctness constraints.
Edit intent: clarity
Original:  Together, this interface describes how to partition values that are passed among functions to enable data pipelining and automatic parallelization while respecting each library 's correctness constraints.
Revised:  Together, this interface describes how to partition values that are passed among functions to enable data pipelining and parallelization, while respecting each library 's correctness constraints.
Edit intent: clarity
Original:  Together, this interface describes how to partition values that are passed among functions to enable data pipelining and automatic parallelization while respecting each library 's correctness constraints.
Revised:  Together, this interface describes how to partition values that are passed among functions to enable data pipelining and automatic parallelization while respecting each function 's correctness constraints.
Edit intent: clarity
Original:  Unmanned Aerial Vehicle Base Stations ( UAV-BSs ) are envisioned to be an integral component of NGCNs and they create opportunities to enhance the capacity of the network by dynamically moving the supply towards the demand while facilitating services that cannot be provided via other means efficiently.
Revised:  Unmanned Aerial Vehicle Base Stations ( UAVBSs ) are envisioned to be an integral component of NGCNs and they create opportunities to enhance the capacity of the network by dynamically moving the supply towards the demand while facilitating services that cannot be provided via other means efficiently.
Edit intent: fluency
Original:  Unmanned Aerial Vehicle Base Stations ( UAV-BSs ) are envisioned to be an integral component of NGCNs and they create opportunities to enhance the capacity of the network by dynamically moving the supply towards the demand while facilitating services that cannot be provided via other means efficiently.
Revised:  Unmanned Aerial Vehicle Base Stations ( UAV-BSs ) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) by dynamically moving the supply towards the demand while facilitating services that cannot be provided via other means efficiently.
Edit intent: clarity
Original: We present an analysis of the impact of automated accounts , or bots, on opinions in a social network.
Revised: Online social networks are often subject to influence campaigns by malicious actors through the use of automated accounts , or bots, on opinions in a social network.
Edit intent: clarity
Original:  Here, we consider the existing literature on how driver steering in slalom tasks is affected by  down-scaling of vestibular cues, and propose a driver model that can explain the empirically observed effects :
Revised:  Here, we consider the existing literature on how driver steering in slalom tasks is affected by the down-scaling of vestibular cues, and propose a driver model that can explain the empirically observed effects :
Edit intent: fluency
Original:  However, (3) in the slalom task, limited such underestimation brings a path-tracking benefit .
Revised:  However, (3) in the slalom task, a certain degree of such yaw rate underestimation is beneficial to path tracking performance .
Edit intent: clarity
Original:  This minimal number is called the rank of the polynomial/tensor.
Revised:  This minimal number is called the rank of the polynomial/tensor.
Edit intent: fluency
Original:  We introduce a superfast algorithm that improves the previous approaches with results from structured linear algebra .
Revised:  We introduce a superfast algorithm that improves the previous approaches with results from structured linear algebra .
Edit intent: fluency
Original:  It achieves a softly linear arithmetic complexity bound.
Revised:  It achieves a softly linear arithmetic complexity bound.
Edit intent: fluency
Original:  Our algorithm computes a symbolic decomposition in O( \mathtt{M (D) \log (D)) arithmetic operations, where \mathtt{M (D) is the complexity of multiplying two polynomials of degree D.
Revised:  Our algorithm computes a symbolic decomposition in O( M (D) \log (D)) arithmetic operations, where \mathtt{M (D) is the complexity of multiplying two polynomials of degree D.
Edit intent: fluency
Original:  Our algorithm computes a symbolic decomposition in O( \mathtt{M (D) \log (D)) arithmetic operations, where \mathtt{M (D) is the complexity of multiplying two polynomials of degree D.
Revised:  Our algorithm computes a symbolic decomposition in O( \mathtt{M (D) log (D)) arithmetic operations, where \mathtt{M (D) is the complexity of multiplying two polynomials of degree D.
Edit intent: coherence
Original:  Our algorithm computes a symbolic decomposition in O( \mathtt{M (D) \log (D)) arithmetic operations, where \mathtt{M (D) is the complexity of multiplying two polynomials of degree D.
Revised:  Our algorithm computes a symbolic decomposition in O( \mathtt{M (D) \log (D)) arithmetic operations, where M (D) is the complexity of multiplying two polynomials of degree D.
Edit intent: fluency
Original:  From the symbolic decomposition, we approximate the terms of the decomposition with an error of 2^{ -\varepsilon } , in O %DIFDELCMD < \big(%%% D \log ^2(D) %DIFDELCMD < \big(%%% \log ^2(D) + \log(\varepsilon)%DIFDELCMD < \big)\big) %%%  arithmetic operations.
Revised:  From the symbolic decomposition, we approximate the terms of the decomposition with an error of 2^{ -\varepsilon } , in O %DIFDELCMD < \big(%%% (D log ^2(D) %DIFDELCMD < \big(%%% \log ^2(D) + \log(\varepsilon)%DIFDELCMD < \big)\big) %%% arithmetic operations.
Edit intent: fluency
Original:  From the symbolic decomposition, we approximate the terms of the decomposition with an error of 2^{ -\varepsilon } , in O %DIFDELCMD < \big(%%% D \log ^2(D) %DIFDELCMD < \big(%%% \log ^2(D) + \log(\varepsilon)%DIFDELCMD < \big)\big) %%%  arithmetic operations.
Revised:  From the symbolic decomposition, we approximate the terms of the decomposition with an error of 2^{ -\varepsilon } , in O %DIFDELCMD < \big(%%% D \log ^2(D) %DIFDELCMD < \big(%%% (log ^2(D) + \log(\varepsilon)%DIFDELCMD < \big)\big) %%% arithmetic operations.
Edit intent: fluency
Original:  We use results from \mbox{ \citet{kaltofen1989improved }\hspace{0pt} }  to bound the size of the representation of the coefficients involved in the decomposition and we bound the algebraic degree of the problem by \min (rank, D-rank + 1).
Revised:  We use results from }\hspace{0pt} } to bound the size of the representation of the coefficients involved in the decomposition and we bound the algebraic degree of the problem by \min (rank, D-rank + 1).
Edit intent: clarity
Original:  We use results from \mbox{ \citet{kaltofen1989improved }\hspace{0pt} }  to bound the size of the representation of the coefficients involved in the decomposition and we bound the algebraic degree of the problem by \min (rank, D-rank + 1).
Revised:  We use results from \mbox{ \citet{kaltofen1989improved }\hspace{0pt} } to bound the size of the representation of the coefficients involved in the decomposition and we bound the algebraic degree of the problem by min (rank, D-rank + 1).
Edit intent: fluency
Original:  We use results from \mbox{ \citet{kaltofen1989improved }\hspace{0pt} }  to bound the size of the representation of the coefficients involved in the decomposition and we bound the algebraic degree of the problem by \min (rank, D-rank + 1).
Revised:  We use results from \mbox{ \citet{kaltofen1989improved }\hspace{0pt} } to bound the size of the representation of the coefficients involved in the decomposition and we bound the algebraic degree of the problem by \min (rank, D -- rank + 1).
Edit intent: fluency