wanyu/IteraTeR_v2 huggingface dataset:
Original:   The AlphaFold framework is a mixture of two types of workloads: 1) MSA construction based on CPUs and 2) model inference on GPUs.
Revised:  The AlphaFold framework is a mixture of two types of workloads: MSA construction based on CPUs and 2) model inference on GPUs.
Edit intent: fluency
Original:   The AlphaFold framework is a mixture of two types of workloads: 1) MSA construction based on CPUs and 2) model inference on GPUs.
Revised:  The AlphaFold framework is a mixture of two types of workloads: 1) MSA construction based on CPUs and model inference on GPUs.
Edit intent: coherence
Original:  The first CPU stage dominates the overall runtime, taking up to hours for a single protein due to the large database sizes and I/O bottlenecks.
Revised:  The first CPU stage dominates the overall runtime, taking hours for a single protein due to the large database sizes and I/O bottlenecks.
Edit intent: clarity
Original:  ParaFold separates the CPU and GPU parts to enable large-scale structure predictions and to improve GPU utilization . ParaFold also effectively reduces the CPU and GPU runtime with two optimizations without compromising the quality of prediction results: using multi-threaded parallelism on CPUs and using optimized JAX compilation on GPUs.
Revised:  ParaFold separates the CPU and GPU parts to enable large-scale structure predictions . ParaFold also effectively reduces the CPU and GPU runtime with two optimizations without compromising the quality of prediction results: using multi-threaded parallelism on CPUs and using optimized JAX compilation on GPUs.
Edit intent: clarity
Original:  For the small dataset, we evaluated the accuracy and efficiency of optimizations on CPUs and GPUs ;
Revised:  We evaluated the accuracy and efficiency of optimizations on CPUs and GPUs ;
Edit intent: clarity
Original:  For the large dataset, we showed the large-scale prediction capability by running model 1 inferences of%DIFDELCMD < \sim20%%% ,000 small proteins in five hours on one NVIDIA DGX-2. ParaFold offers a rapid and effective approach for high-throughput structure predictions, leveraging the predictive power by running on supercomputers, with shorter time, and at a lower cost  .
Revised:  For the large dataset, we showed the large-scale prediction capability by running %DIFDELCMD < \sim20%%% ,000 small proteins in five hours on one NVIDIA DGX-2. ParaFold offers a rapid and effective approach for high-throughput structure predictions, leveraging the predictive power by running on supercomputers, with shorter time, and at a lower cost .
Edit intent: clarity
Original:  For the large dataset, we showed the large-scale prediction capability by running model 1 inferences of%DIFDELCMD < \sim20%%% ,000 small proteins in five hours on one NVIDIA DGX-2. ParaFold offers a rapid and effective approach for high-throughput structure predictions, leveraging the predictive power by running on supercomputers, with shorter time, and at a lower cost  .
Revised:  For the large dataset, we showed the large-scale prediction capability by running model 1 inferences of%DIFDELCMD < \sim20%%% ,000 small proteins in five hours on one NVIDIA DGX-2. ParaFold offers a rapid and effective approach for high-throughput structure predictions, leveraging the predictive power by running on supercomputers, with shorter time, and at a lower cost . The development of ParaFold will greatly speed up high-throughput studies and render the protein "structure-omics" feasible .
Edit intent: coherence
Original: The Non-Uniform k-Center (NUkC) is a generalization of the famous k-center clustering problem, where we want to cover the given set of points in a metric space , using the specified number of balls of the specified radii.
Revised: In the Non-Uniform k-Center (NUkC) is a generalization of the famous k-center clustering problem, where we want to cover the given set of points in a metric space , using the specified number of balls of the specified radii.
Edit intent: coherence
Original: The Non-Uniform k-Center (NUkC) is a generalization of the famous k-center clustering problem, where we want to cover the given set of points in a metric space , using the specified number of balls of the specified radii.
Revised: The Non-Uniform k-Center problem, a generalization of the famous k-center clustering problem, where we want to cover the given set of points in a metric space , using the specified number of balls of the specified radii.
Edit intent: clarity
Original: The Non-Uniform k-Center (NUkC) is a generalization of the famous k-center clustering problem, where we want to cover the given set of points in a metric space , using the specified number of balls of the specified radii.
Revised: The Non-Uniform k-Center (NUkC) is a generalization of the famous k-center clustering problem, we want to cover the given set of points in a metric space , using the specified number of balls of the specified radii.
Edit intent: coherence
Original: The Non-Uniform k-Center (NUkC) is a generalization of the famous k-center clustering problem, where we want to cover the given set of points in a metric space , using the specified number of balls of the specified radii.
Revised: The Non-Uniform k-Center (NUkC) is a generalization of the famous k-center clustering problem, where we want to cover the given set of points in a metric space by finding a placement of balls with specified radii.
Edit intent: clarity
Original:  In the t-NUkC  , we assume that the number of distinct radii is equal to t, and we are allowed to use k_i balls of radius r_i, for 1 \le i \le t.
Revised:  In t-NUkC , we assume that the number of distinct radii is equal to t, and we are allowed to use k_i balls of radius r_i, for 1 \le i \le t.
Edit intent: fluency
Original:  In the t-NUkC  , we assume that the number of distinct radii is equal to t, and we are allowed to use k_i balls of radius r_i, for 1 \le i \le t.
Revised:  In the t-NUkC Problem , we assume that the number of distinct radii is equal to t, and we are allowed to use k_i balls of radius r_i, for 1 \le i \le t.
Edit intent: clarity
Original:  \ Alg. \ 16(4):46:1-46:19], who showed that a constant approximation for t-NUkC is not possible if t is unbounded.
Revised:  Alg. 16(4):46:1-46:19], who showed that a constant approximation for t-NUkC is not possible if t is unbounded.
Edit intent: fluency
Original:  We introduce the concept of equilibrium inequality , and devise an equilibrium separation oracle to separate non-equilibrium strategies from PNEs.
Revised:  We introduce the concept of equilibrium inequality and devise an equilibrium separation oracle to separate non-equilibrium strategies from PNEs.
Edit intent: fluency
Original:  Specifically, we divide the wireless link transmission systems into three scenarios according to the decay rate of the queue-length tail distribution with the finite average power consumption .
Revised:  We find that, under the finite average power consumption .
Edit intent: coherence
Original:  Based on this heuristic policy , we generalize the analysis to Nakagami-m fading channels . Numerical results with approximation validate our analysis.
Revised:  Based on this heuristic policy , we generalize the analysis to Nakagami-m fading channel . Numerical results with approximation validate our analysis.
Edit intent: fluency
Original:  \alpha-entmax of  arXiv:1905.05702  solves this problem, but is considerably slower than softmax.
Revised:  \alpha-entmax of arXiv:1905.05702 ) solves this problem, but is considerably slower than softmax.
Edit intent: fluency
Original:  While neural networks (NNs) have already been applied to a range of three-dimensional problems with success, the backward energy transfer of two-dimensional flows still remains a stability issue for trained models .
Revised:  While neural networks (NNs) have already been applied to a range of three-dimensional flows with success, the backward energy transfer of two-dimensional flows still remains a stability issue for trained models .
Edit intent: clarity
Original:  While neural networks (NNs) have already been applied to a range of three-dimensional problems with success, the backward energy transfer of two-dimensional flows still remains a stability issue for trained models .
Revised:  While neural networks (NNs) have already been applied to a range of three-dimensional problems with success, two dimensional flows are more challenging because of the backscatter of energy from small to large scales .
Edit intent: clarity
Original:  In this paper we show that whenever an eigenvector %DIFDELCMD < {\em %%% is a generator of the symmetric decomposition of a symmetric tensor, then (if the order of the tensor is sufficiently high) this eigenvector is %DIFDELCMD < {\em %%% robust , i.e., it is an attracting fixed point of the tensor power method.
Revised:  In this paper we show that whenever an eigenvector %DIFDELCMD < {\em %%% is a generator of the symmetric decomposition of a symmetric tensor, then (if the order of the tensor is sufficiently high) this eigenvector is %DIFDELCMD < {\em %%% robust , i.e., it is an attracting fixed point of the tensor power method.
Edit intent: fluency
Original:  Generalizing orthogonally decomposable tensors, we consider %DIFDELCMD < {\em %%% equiangular tight frame decomposable and%DIFDELCMD < {\em %%%  equiangular set decomposable tensors.
Revised:  Generalizing orthogonally decomposable tensors, we consider %DIFDELCMD < {\em %%% equiangular tight frame decomposable %DIFDELCMD < {\em %%% equiangular set decomposable tensors.
Edit intent: fluency
Original:  Generalizing orthogonally decomposable tensors, we consider %DIFDELCMD < {\em %%% equiangular tight frame decomposable and%DIFDELCMD < {\em %%%  equiangular set decomposable tensors.
Revised:  Generalizing orthogonally decomposable tensors, we consider %DIFDELCMD < {\em %%% equiangular tight frame decomposable and%DIFDELCMD < {\em %%% and equiangular set decomposable tensors.
Edit intent: fluency
Original:  We demonstrate, that these two contributions can lead to the detection and tracking of moving people through densely occluding forest 
Revised:  We demonstrate, that these two contributions can lead to the detection and tracking of moving people through densely occluding forest .
Edit intent: fluency
Original: Mitigating nonlinear weight update characteristics is known as one of the main challenges in the design of neural networks based on synaptic devices.
Revised: Mitigating nonlinear weight update characteristics is one of the main challenges in the design of neural networks based on synaptic devices.
Edit intent: coherence
Original: Mitigating nonlinear weight update characteristics is known as one of the main challenges in the design of neural networks based on synaptic devices.
Revised: Mitigating nonlinear weight update characteristics is known as one of the main challenges in designing neural networks based on synaptic devices.
Edit intent: fluency
Original:  In this paper , we present a novel weight update scheme named conditional reverse update scheme for HNN (Hardware Neural Network ) consisting of synaptic devices with highly nonlinear or abrupt conductance update characteristics.
Revised:  This paper presents a novel weight update scheme named conditional reverse update scheme for HNN (Hardware Neural Network ) consisting of synaptic devices with highly nonlinear or abrupt conductance update characteristics.
Edit intent: coherence
Original:  In this paper , we present a novel weight update scheme named conditional reverse update scheme for HNN (Hardware Neural Network ) consisting of synaptic devices with highly nonlinear or abrupt conductance update characteristics.
Revised:  In this paper , we present a novel weight update method named conditional reverse update scheme for HNN (Hardware Neural Network ) consisting of synaptic devices with highly nonlinear or abrupt conductance update characteristics.
Edit intent: clarity
Original:  We formulate a linear optimization method of conductance in synaptic devices to reduce the average deviation of weight changes in synaptic devices from the weight changes calculated by the Stochastic Gradient Rule (SGD) algorithm.
Revised:  We formulate a linear optimization method of conductance in synaptic devices to reduce the average deviation of weight changes from those calculated by the Stochastic Gradient Rule (SGD) algorithm.
Edit intent: clarity
Original:  To this end, we introduce a metric called Update Noise (UN) to analyze the update noise dynamics during the trainingprocess .
Revised:  We introduce a metric called Update Noise (UN) to analyze the update noise dynamics during the trainingprocess .
Edit intent: coherence
Original:  To this end, we introduce a metric called Update Noise (UN) to analyze the update noise dynamics during the trainingprocess .
Revised:  To this end, we introduce a metric called update noise (UN) to analyze the update noise dynamics during the trainingprocess .
Edit intent: fluency
Original:  To this end, we introduce a metric called Update Noise (UN) to analyze the update noise dynamics during the trainingprocess .
Revised:  To this end, we introduce a metric called Update Noise (UN) to analyze the training dynamics during training .
Edit intent: fluency
Original:  We then design a weight update rule that effectively reduces the UN averaged over the whole training process.
Revised:  We then design a weight update rule that reduces the UN averaged over the whole training process.
Edit intent: clarity
Original:  We then design a weight update rule that effectively reduces the UN averaged over the whole training process.
Revised:  We then design a weight update rule that effectively reduces the UN averaged over the training process.
Edit intent: clarity
Original:  The optimized network in terms of UN under the conditional reverse update scheme achieved >90\% accuracy on  MNIST dataset under highly nonlinear LTP and LTD conditions while using inaccurate and infrequent conductance sensing.
Revised:  The optimized network achieves >90\% accuracy on MNIST dataset under highly nonlinear LTP and LTD conditions while using inaccurate and infrequent conductance sensing.
Edit intent: clarity
Original:  The optimized network in terms of UN under the conditional reverse update scheme achieved >90\% accuracy on  MNIST dataset under highly nonlinear LTP and LTD conditions while using inaccurate and infrequent conductance sensing.
Revised:  The optimized network in terms of UN under the conditional reverse update scheme achieved >90\% accuracy on the MNIST dataset under highly nonlinear LTP and LTD conditions while using inaccurate and infrequent conductance sensing.
Edit intent: fluency
Original:  Furthermore, HNN trained with the proposed method showed better accuracy compared to previously reported nonlinear weight update mitigation techniques under the same hardware specifications and device conditions.
Revised:  Furthermore, the proposed method showed better accuracy compared to previously reported nonlinear weight update mitigation techniques under the same hardware specifications and device conditions.
Edit intent: clarity
Original:  Furthermore, HNN trained with the proposed method showed better accuracy compared to previously reported nonlinear weight update mitigation techniques under the same hardware specifications and device conditions.
Revised:  Furthermore, HNN trained with the proposed method shows better accuracy than previously reported nonlinear weight update mitigation techniques under the same hardware specifications and device conditions.
Edit intent: fluency
Original:  In addition, the proposed method exhibited robustness to temporal variations in conductance updates.
Revised:  It also exhibits robustness to temporal variations in conductance updates.
Edit intent: clarity
Original:  We expect that the conditional reverse update scheme can relieve design requirements in device and circuit engineering and serve as a practical technique that can be applied to future hardware for HNNs.
Revised:  We expect our scheme to relieve design requirements in device and circuit engineering and serve as a practical technique that can be applied to future hardware for HNNs.
Edit intent: clarity
Original:  We expect that the conditional reverse update scheme can relieve design requirements in device and circuit engineering and serve as a practical technique that can be applied to future hardware for HNNs.
Revised:  We expect that the conditional reverse update scheme can relieve design requirements in device and circuit engineering and serve as a practical technique that can be applied to future HNNs.
Edit intent: clarity
Original:  With LHC-Net we managed to achieve a new state of the art in the famous FER2013 dataset with a significantly lower complexity and impact on the ``host'' architecture in terms of computational cost when compared with the previous SOTA.
Revised:  With LHC-Net we managed to achieve a new state of the art in the famous FER2013 dataset with a significantly lower complexity and impact on the "host" architecture in terms of computational cost when compared with the previous SOTA.
Edit intent: fluency
Original:  However, it usually takes a lot of effort to fully exploit the potential of auxiliary losses, since improperly weighted auxiliary losses would distract the model from the inpainting task, and the effectiveness of an auxiliary loss might vary during the training process.
Revised:  However, it usually requires a lot of effort to fully exploit the potential of auxiliary losses, since improperly weighted auxiliary losses would distract the model from the inpainting task, and the effectiveness of an auxiliary loss might vary during the training process.
Edit intent: fluency
Original:  However, it usually takes a lot of effort to fully exploit the potential of auxiliary losses, since improperly weighted auxiliary losses would distract the model from the inpainting task, and the effectiveness of an auxiliary loss might vary during the training process.
Revised:  However, it usually takes a lot of effort to fully exploit the potential of auxiliary losses, or otherwise, improperly weighted auxiliary losses would distract the model from the inpainting task, and the effectiveness of an auxiliary loss might vary during the training process.
Edit intent: coherence
Original:  Furthermore, the design of auxiliary losses takes  domain expertise.
Revised:  Hence the design of auxiliary losses takes domain expertise.
Edit intent: coherence
Original:  In this work, we introduce the Auxiliary Loss Adaption (Adaption ) algorithm to dynamically adjust the parameters of the auxiliary loss , to better assist the primary task.
Revised:  To mitigate the problem, in this work, we introduce the Auxiliary Loss Adaption (Adaption ) algorithm to dynamically adjust the parameters of the auxiliary loss , to better assist the primary task.
Edit intent: coherence
Original:  Our algorithm is based on the principle that better auxiliary loss is the one that helps increase the performance of the main loss  through several steps of gradient descent.
Revised:  Our algorithm is based on the principle that the best auxiliary loss is the one that helps increase the performance of the main loss through several steps of gradient descent.
Edit intent: fluency
Original: Computer vision tasks can benefit from the estimation of the salient object regions and interactions between those object regions.
Revised: Video-based computer vision tasks can benefit from the estimation of the salient object regions and interactions between those object regions.
Edit intent: clarity
Original: Computer vision tasks can benefit from the estimation of the salient object regions and interactions between those object regions.
Revised: Computer vision tasks can benefit from the estimation of the salient regions and interactions between those object regions.
Edit intent: fluency
Original:  Identifying the object regions involves utilizing pretrained models to perform object detection, object segmentation  and/or object pose estimation.
Revised:  Identifying the object regions in the images by utilizing pre-trained models to perform object detection, object segmentation and/or object pose estimation.
Edit intent: clarity
Original:  Identifying the object regions involves utilizing pretrained models to perform object detection, object segmentation  and/or object pose estimation.
Revised:  Identifying the object regions involves utilizing pretrained models to perform object detection, object segmentation , and/or object pose estimation.
Edit intent: fluency
Original:  However , it is infeasible in practice due to the following reasons: 1) The object categories of pretrained models' training dataset may not cover all the object categoriesexhaustively needed for general computer vision tasks, 2) The domain gap between pretrained models' training dataset and target task's dataset may differ and negatively impact the performance, 3) The bias and variance present in pretrained modelsmay leak into target task leading to an inadvertently biased target model .
Revised:  However , it is infeasible in practice due to the need for exhaustive annotation of object categories, domain gap between pretrained models' training dataset and target task's dataset may differ and negatively impact the performance, 3) The bias and variance present in pretrained modelsmay leak into target task leading to an inadvertently biased target model .
Edit intent: clarity
Original:  However , it is infeasible in practice due to the following reasons: 1) The object categories of pretrained models' training dataset may not cover all the object categoriesexhaustively needed for general computer vision tasks, 2) The domain gap between pretrained models' training dataset and target task's dataset may differ and negatively impact the performance, 3) The bias and variance present in pretrained modelsmay leak into target task leading to an inadvertently biased target model .
Revised:  However , it is infeasible in practice due to the following reasons: 1) The object categories of pretrained models' training dataset may not cover all the object categoriesexhaustively needed for general computer vision tasks, 2) The domain gap between datasets, and bias present in pre-trained models .
Edit intent: clarity
Original:  In this regard, we propose a generic module called "Co-Segmentation Activation Module" (COSAM) that can be plugged-in to any CNN to promote the notion of co-segmentation based attention among a sequence of video frame features.
Revised:  In this regard, we propose a generic module called "Co-Segmentation Activation Module" (COSAM) that can be plugged into any CNN to promote the notion of co-segmentation based attention among a sequence of video frame features.
Edit intent: fluency
Original:  We show the application of COSAM in three video based tasks namely 1) Video-based person re-ID, 2) Video captioning, & 3) Video action classification  and demonstrate that COSAM is able to capture salient regions in the video frames, thus leading to notable performance improvements along with interpretable attention maps.
Revised:  We show the application of COSAM in three video-based tasks namely: 1) Video-based person re-ID, 2) Video captioning, & 3) Video action classification and demonstrate that COSAM is able to capture salient regions in the video frames, thus leading to notable performance improvements along with interpretable attention maps.
Edit intent: fluency
Original:  We show the application of COSAM in three video based tasks namely 1) Video-based person re-ID, 2) Video captioning, & 3) Video action classification  and demonstrate that COSAM is able to capture salient regions in the video frames, thus leading to notable performance improvements along with interpretable attention maps.
Revised:  We show the application of COSAM in three video based tasks namely 1) Video-based person re-ID, 2) Video captioning, & 3) Video action classification , and demonstrate that COSAM is able to capture salient regions in the video frames, thus leading to notable performance improvements along with interpretable attention maps.
Edit intent: fluency