wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This study URLanized as a guide to implementing multiple-server dispatching policies , from the specification of  cost functions towards the computation of interval bounds for the value functions and the  implementation of the policy improvement step.
Revised:  This study URLanized as a guide to implementing multiple-server dispatching policies , from the specification of general cost functions towards the computation of interval bounds for the value functions and the implementation of the policy improvement step.
Edit intent: meaning-changed
Original:  This study URLanized as a guide to implementing multiple-server dispatching policies , from the specification of  cost functions towards the computation of interval bounds for the value functions and the  implementation of the policy improvement step.
Revised:  This study URLanized as a guide to implementing multiple-server dispatching policies , from the specification of cost functions towards the computation of interval bounds for the value functions and the exact implementation of the policy improvement step.
Edit intent: meaning-changed
Original:  This study URLanized as a guide to implementing multiple-server dispatching policies , from the specification of  cost functions towards the computation of interval bounds for the value functions and the  implementation of the policy improvement step.
Revised:  This study URLanized as a guide to implementing multiple-server dispatching policies , from the specification of cost functions towards the computation of interval bounds for the value functions and the implementation of the first-policy improvement step.
Edit intent: meaning-changed
Original:  Since inference about the change-point dates is a nonstandard statistical problem, the original insight of Laplace to interpret a certain transformation of a least-squares criterion function as a statistical belief over the parameter space provides a better approximation about the uncertainty in the data about the change-points relative to existing methods.
Revised:  This approach provides a better approximation about the uncertainty in the data about the change-points relative to existing methods.
Edit intent: coherence
Original:  Since inference about the change-point dates is a nonstandard statistical problem, the original insight of Laplace to interpret a certain transformation of a least-squares criterion function as a statistical belief over the parameter space provides a better approximation about the uncertainty in the data about the change-points relative to existing methods.
Revised:  Since inference about the change-point dates is a nonstandard statistical problem, the original insight of Laplace to interpret a certain transformation of a least-squares criterion function as a statistical belief over the parameter space provides a better approximation about the uncertainty in the data of the change-points relative to existing methods.
Edit intent: clarity
Original:  namely, the classical shrinkage asymptotic distribution of Bai and Perron (1998) , or a Bayes-type asymptotic distribution  .
Revised:  namely, the classical shrinkage asymptotic distribution of Bai and Perron (1998) , or a Bayes-type asymptotic distribution . We propose an inference method based on Highest Density Regions using the latter distribution. We show that it has attractive theoretical properties not shared by the other popular alternatives, i.e., it is bet-proof. Simulations con?rm that these theoretical properties translate to better ?nite-sample performance .
Edit intent: meaning-changed
Original: Energy-use is a key concern when migrating current deep learning applications onto low power heterogeneous devices such as a mobile device. This is because deep neural networks are typically designed and trained on high-end GPUs or servers and require additional processing steps to deploy them on low powerdevices.
Revised: There is a huge demand for on-device execution of deep learning algorithms on mobile and embedded platforms. These devices present constraints on the application due to limited resources and power. Hence, developing energy-efficient solutions to address this issue will require innovation in algorithmic design, software and hardware. Such innovation requires benchmarking and characterization of Deep Neural Networks based on performance and energy-consumption alongside accuracy. However, current benchmarks studies in existing deep learning frameworks (for example, Caffe, Tensorflow, Torch and others) are based on performance of these applications on high-end GPUs or servers and require additional processing steps to deploy them on low powerdevices.
Edit intent: clarity
Original:  This is because deep neural networks are typically designed and trained on high-end GPUs or servers and require additional processing steps to deploy them on low powerdevices. Such steps include the use of compression techniques to scale down the network size or the provision of efficient device-specific software implementations. Migration is further aggravated by the lack of tools and the inability to measure power and performance accurately and consistently across devices. We present a novel evaluation framework for measuring energy and performance for deep neural networks using ARMs Streamline Performance Analyser integrated with standard deep learning frameworks such as Caffe and CuDNNv5 .
Revised:  This is because deep neural networks are typically designed and trained on high-end CPUs and GPUs. In this work, we introduce a benchmarking framework called "SyNERGY" to measure the energy and time of 11 representative Deep Convolutional Neural Networks on embedded platforms such as NVidia Jetson TX1. We integrate ARM's Streamline Performance Analyser integrated with standard deep learning frameworks such as Caffe and CuDNNv5 .
Edit intent: meaning-changed
Original:  We present a novel evaluation framework for measuring energy and performance for deep neural networks using ARMs Streamline Performance Analyser integrated with standard deep learning frameworks such as Caffe and CuDNNv5 .
Revised:  We present a novel evaluation framework for measuring energy and performance for deep neural networks using ARMs Streamline Performance Analyser with standard deep learning frameworks such as Caffe and CuDNNv5 .
Edit intent: coherence
Original:  We present a novel evaluation framework for measuring energy and performance for deep neural networks using ARMs Streamline Performance Analyser integrated with standard deep learning frameworks such as Caffe and CuDNNv5 . We apply the framework to study the execution behaviour of SqueezeNet on the Maxwell GPU of the NVidia Jetson TX1 , on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network .
Revised:  We present a novel evaluation framework for measuring energy and performance for deep neural networks using ARMs Streamline Performance Analyser integrated with standard deep learning frameworks such as Caffe and CuDNNv5 , to study the execution behaviour of SqueezeNet on the Maxwell GPU of the NVidia Jetson TX1 , on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network .
Edit intent: clarity
Original:  We apply the framework to study the execution behaviour of SqueezeNet on the Maxwell GPU of the NVidia Jetson TX1 , on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network .
Revised:  We apply the framework to study the execution behaviour of current deep learning models at a fine-grained level (or specific layers) on image processing tasks. In addition, we build an initial multi-variable linear regression model to predict energy consumption of unseen neural network models based on the number of SIMD instructions executed and main memory accesses of the CPU cores of the TX1 , on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network .
Edit intent: meaning-changed
Original:  We apply the framework to study the execution behaviour of SqueezeNet on the Maxwell GPU of the NVidia Jetson TX1 , on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network .
Revised:  We apply the framework to study the execution behaviour of SqueezeNet on the Maxwell GPU of the NVidia Jetson TX1 with an average relative test error rate of 8.04 +/- 5.96 \%. Surprisingly, we find that it is possible to refine the model to predict the number of SIMD instructions and main memory accesses solely from the application's Multiply-Accumulate (MAC) counts, hence, eliminating the need for actual measurements. Our predicted results demonstrate 7.08 +/- 6.0 \% average relative error over actual energy measurements of all 11 networks tested, except MobileNet. By including MobileNet the average relative test error increases to 17.33 +/- 12.2 \% .
Edit intent: meaning-changed
Original:  We propose Pathway Induced Multiple Kernel Learning (PIMKL), a novel methodology to classify samples reliably that can , at the same time, provide a pathway-based molecular fingerprint of the signature that underlies the classification.
Revised:  We propose Pathway Induced Multiple Kernel Learning (PIMKL), a novel methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification.
Edit intent: clarity
Original:  PIMKL exploits prior knowledge in the form of molecular interaction networks and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning algorithm (MKL)  , an approach that has demonstrated excellent performance in different machine learning applications.
Revised:  PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning algorithm (MKL) , an approach that has demonstrated excellent performance in different machine learning applications.
Edit intent: fluency
Original:  PIMKL exploits prior knowledge in the form of molecular interaction networks and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning algorithm (MKL)  , an approach that has demonstrated excellent performance in different machine learning applications.
Revised:  PIMKL exploits prior knowledge in the form of molecular interaction networks and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) , an approach that has demonstrated excellent performance in different machine learning applications.
Edit intent: clarity
Original:  PIMKL exploits prior knowledge in the form of molecular interaction networks and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning algorithm (MKL)  , an approach that has demonstrated excellent performance in different machine learning applications.
Revised:  PIMKL exploits prior knowledge in the form of molecular interaction networks and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning algorithm (MKL) algorithm , an approach that has demonstrated excellent performance in different machine learning applications.
Edit intent: coherence
Original: The least squares Monte Carlo algorithm has become a popular tool for solving stochastic control problems, mainly due to its ability to handle multiple stochastic state variables. However, a configuration that remains challenging is when dealing with high dimensional controls, due to the exponential complexity of grid search methods or the mathematical difficulty of deriving and solving high-dimensional first-order optimality conditions. This paper proposes an efficient least squares Monte Carlo algorithm that is suitable for handling high-dimensional controls. In particular, we first approximate backward recursive dynamic programs on a coarse grid of controls , then use a combined technique of local control regression and adaptive refinement grids to improve the optimal control estimates.
Revised: The least squares Monte Carlo algorithm has become popular for solving portfolio optimization problems. A simple approach is to approximate the value functions on a discrete grid of portfolio weights , then use a combined technique of local control regression and adaptive refinement grids to improve the optimal control estimates.
Edit intent: clarity
Original:  In particular, we first approximate backward recursive dynamic programs on a coarse grid of controls , then use a combined technique of local control regression and adaptive refinement grids to improve the optimal control estimates. We numerically show that the local control regression is more accurate and more efficient than global control regression , and that the overall computational runtime scales polynomially with respect to the dimension of the control.
Revised:  In particular, we first approximate backward recursive dynamic programs on a coarse grid of controls , then use control regression to generalize the discrete estimates. However, the classical global control regression , and that the overall computational runtime scales polynomially with respect to the dimension of the control.
Edit intent: meaning-changed
Original:  We numerically show that the local control regression is more accurate and more efficient than global control regression , and that the overall computational runtime scales polynomially with respect to the dimension of the control. Finally, we further validate our method by solving a portfolio optimization problem with twelve risky assets with transaction cost, liquidity cost and market impact, which involves twelve exogenous risk factors (asset returns) and thirteen endogenous risk factors (portfolio value and asset prices subject to market impact) .
Revised:  We numerically show that the local control regression is more accurate and more efficient than global control regression can be expensive and inaccurate. To overcome this difficulty, we introduce a local control regression technique, combined with adaptive grids. We show that choosing a coarse grid for local regression can produce sufficiently accurate results .
Edit intent: meaning-changed
Original:  This approach led to an accuracy of 97.2 \% on MNIST, without requiring an external classifier. In addition, we demonstrated that R-STDP extracts features that are diagnostic for the task at hand, and discards the other ones, whereas STDP extracts any feature that repeats. Finally, our approach is biologically plausible, hardware friendly, and energy-efficient.
Revised:  This approach led to an accuracy of 97.2 
Edit intent: coherence
Original: Building upon the continuous record asymptotic framework recently introduced by Casini and Perron ( 2017a ) for inference in structural change models, we propose a Laplace-based (Quasi-Bayes) procedure for the construction of the estimate and confidence set for the date of a structural change.
Revised: Building upon the continuous record asymptotic framework recently introduced by Casini and Perron ( 2018a ) for inference in structural change models, we propose a Laplace-based (Quasi-Bayes) procedure for the construction of the estimate and confidence set for the date of a structural change.
Edit intent: meaning-changed
Original:  Besides providing an alternative estimate that is more precise---lower mean absolute error (MAE) and lower root-mean squared error (RMSE) ---than the usual least-squares one, the Quasi-posterior distribution can be used to construct asymptotically valid inference using the concept of Highest Density Region.
Revised:  Besides providing an alternative estimate that is more precise|lower mean absolute error (MAE) and lower root-mean squared error (RMSE) ---than the usual least-squares one, the Quasi-posterior distribution can be used to construct asymptotically valid inference using the concept of Highest Density Region.
Edit intent: fluency
Original:  Besides providing an alternative estimate that is more precise---lower mean absolute error (MAE) and lower root-mean squared error (RMSE) ---than the usual least-squares one, the Quasi-posterior distribution can be used to construct asymptotically valid inference using the concept of Highest Density Region.
Revised:  Besides providing an alternative estimate that is more precise---lower mean absolute error (MAE) and lower root-mean squared error (RMSE) |than the usual least-squares one, the Quasi-posterior distribution can be used to construct asymptotically valid inference using the concept of Highest Density Region.
Edit intent: fluency
Original:  The resulting Laplace-based inferential procedure proposed is shown to have lower MAE and RMSE, and the confidence sets strike the best balance between empirical coverage rates and average lengths of the confidence sets relative to traditional long-span methods, whether the break size is small or large.
Revised:  The resulting Laplace-based inferential procedure is shown to have lower MAE and RMSE, and the confidence sets strike the best balance between empirical coverage rates and average lengths of the confidence sets relative to traditional long-span methods, whether the break size is small or large.
Edit intent: clarity
Original:  However,  physical basis of metal individuality or psychiatric symptoms has not been delineated . We found geometric fingerprints of schizophrenia and control individuals by analyzing three-dimensional structures of cerebral tissues of the anterior cingulate cortex .
Revised:  However, physical basis of metal individuality or psychiatric symptoms has not been delineated at the cellular level. Here we report nanometer-scale three-dimensional analysis of brain tissues of schizophrenia and control individuals by analyzing three-dimensional structures of cerebral tissues of the anterior cingulate cortex .
Edit intent: meaning-changed
Original:  We found geometric fingerprints of schizophrenia and control individuals by analyzing three-dimensional structures of cerebral tissues of the anterior cingulate cortex . Tissue images were obtained with nanotomography and used to build Cartesian coordinate models of tissue structures.
Revised:  We found geometric fingerprints of schizophrenia and control individuals by analyzing three-dimensional structures of cerebral tissues of the anterior cingulate cortex were visualized with synchrotron radiation nanotomography. Tissue constituents observed in the three-dimensional images were traced to build Cartesian coordinate models of tissue structures.
Edit intent: meaning-changed
Original:  Tissue images were obtained with nanotomography and used to build Cartesian coordinate models of tissue structures. Structures of neuronal processes were evaluated by calculating curvature and torsion from the model coordinates.
Revised:  Tissue images were obtained with nanotomography and used to build Cartesian coordinate models of neurons. The obtained Cartesian coordinates were used for calculating curvature and torsion from the model coordinates.
Edit intent: clarity
Original:  Structures of neuronal processes were evaluated by calculating curvature and torsion from the model coordinates. Mean curvature of schizophrenia cases was approximately 1.5 -times as high as that of controls .
Revised:  Structures of neuronal processes were evaluated by calculating curvature and torsion of neurites. The geometric analyses indicated that the curvature of neurites is significantly different between schizophrenia and control cases. The mean curvature of distal neurites of the schizophrenia cases was approximately 1.5 -times as high as that of controls .
Edit intent: meaning-changed
Original:  Mean curvature of schizophrenia cases was approximately 1.5 -times as high as that of controls .
Revised:  Mean curvature of schizophrenia cases was approximately 1.5 times higher than that of the controls. A schizophrenia case carrying a frame shift mutation in the GLO1 gene showed the highest neurite curvature, suggesting that oxidative stress due to the GLO1 mutation caused the structural alteration of the neurites. The differences in the neurite curvature result in differences in the spatial trajectory and hence alter neuronal circuits .
Edit intent: meaning-changed
Original:  Structures of cerebral tissues of the anterior cingulate cortex were visualized with synchrotron radiation nanotomography. Tissue constituents observed in the three-dimensional images were traced to build Cartesian coordinate models of neurons. The obtained Cartesian coordinates were used for calculating curvature and torsion of neurites. The geometric analyses indicated thatthe curvature of neurites is significantly different between schizophrenia and control cases. The mean curvature of distal neurites of the schizophrenia cases was approximately 1.5 times higher than that of the controls .
Revised:  The analysis revealed that: (1) neuronal structures vary between individuals, (2) the mean curvature of distal neurites of the schizophrenia cases was approximately 1.5 times higher than that of the controls .
Edit intent: clarity
Original:  The mean curvature of distal neurites of the schizophrenia cases was approximately 1.5 times higher than that of the controls . A schizophrenia case carrying a frame shift mutation in the GLO1 gene showed the highest neurite curvature, suggesting that oxidative stress due to the GLO1 mutation caused the structural alteration of the neurites .
Revised:  The mean curvature of distal neurites of the schizophrenia cases was approximately 1.5 times higher than that of the controls , and (3) dendritic spines were categorized into two geometrically distinctive groups, though no structural differences were observed between the disease and control cases .
Edit intent: meaning-changed
Original: We prove that the Reeb space of a proper definable map  in an arbitrary o-minimal expansion of the reals is realizable as a proper definable quotient which can be seen as the definable analog of Stein factorization of proper morphisms in algebraic geometry.
Revised: We prove that the Reeb space of a proper definable map f:X \rightarrow Y in an arbitrary o-minimal expansion of the reals is realizable as a proper definable quotient which can be seen as the definable analog of Stein factorization of proper morphisms in algebraic geometry.
Edit intent: meaning-changed
Original: We prove that the Reeb space of a proper definable map  in an arbitrary o-minimal expansion of the reals is realizable as a proper definable quotient which can be seen as the definable analog of Stein factorization of proper morphisms in algebraic geometry.
Revised: We prove that the Reeb space of a proper definable map in an arbitrary o-minimal expansion of a real closed field is realizable as a proper definable quotient which can be seen as the definable analog of Stein factorization of proper morphisms in algebraic geometry.
Edit intent: meaning-changed
Original: We prove that the Reeb space of a proper definable map  in an arbitrary o-minimal expansion of the reals is realizable as a proper definable quotient which can be seen as the definable analog of Stein factorization of proper morphisms in algebraic geometry.
Revised: We prove that the Reeb space of a proper definable map in an arbitrary o-minimal expansion of the reals is realizable as a proper definable quotient . This result can be seen as the definable analog of Stein factorization of proper morphisms in algebraic geometry.
Edit intent: clarity