wanyu/IteraTeR_v2 huggingface dataset:
Original:  In addition, semantic image repre- sentation is also exploited to compensate for the usage of patch proposals.
Revised:  In addition, semantic image representation is also exploited to compensate for the usage of patch proposals.
Edit intent: fluency
Original:  At the same time, our proposed PCT shows great generalization to most coordinate- based 3D detection frameworks.
Revised:  At the same time, our proposed PCT shows great generalization to most coordinate-based 3D detection frameworks.
Edit intent: fluency
Original:  The code is available at: URL amazon-research/progressive-coordinate-transforms .
Revised:  The code is available at: URL .
Edit intent: coherence
Original:  It learns a model with general language features on the vast text and then fine-tunes the model using a task-specific dataset .
Revised:  It can learn general language features on the vast text and then fine-tunes the model using a task-specific dataset .
Edit intent: clarity
Original:  It learns a model with general language features on the vast text and then fine-tunes the model using a task-specific dataset .
Revised:  It learns a model with general language features on massive data and then be fine-tuned on task-specific dataset .
Edit intent: clarity
Original:  It learns a model with general language features on the vast text and then fine-tunes the model using a task-specific dataset .
Revised:  It learns a model with general language features on the vast text and then fine-tunes the model using a task-specific data .
Edit intent: fluency
Original:  Unfortunately, PTM training requires prohibitively expensivecomputing devices, especially fine-tuning, which is still a game for a small proportion of people in the AI community.
Revised:  Unfortunately, the computing hardware requirement of PTM training is prohibitively expensive, which makes it a game for a small proportion of people in the AI community.
Edit intent: clarity
Original:  Enabling PTMs training on low-quality devices, PatrickStar now makes PTM accessible to everyone.
Revised:  Therefore, we proposed a system called PatrickStar to lower the hardware requirements of PTMs and make them accessible to everyone.
Edit intent: clarity
Original:  PatrickStar reduces memory requirements of computing platforms by using the CPU-GPU heterogeneous memory space to store model data, consisting of parameters, gradients, and optimizer states.
Revised:  PatrickStar uses the CPU-GPU heterogeneous memory space to store model data, consisting of parameters, gradients, and optimizer states.
Edit intent: clarity
Original:  Symbiosis with the Zero Redundancy Optimizer, PatrickStar scales to multiple GPUs using data parallelism, with the lowest communication bandwidth requirements and more efficient bandwidth utilization.
Revised:  Symbiosis with the Zero Redundancy Optimizer, PatrickStar scales to multiple GPUs using data parallelism, with lower communication bandwidth requirements and more efficient bandwidth utilization.
Edit intent: fluency
Original:   Experimental results show  PatrickStar trains a 12 billion parameters GPT model, 2x larger than the STOA work , on an 8-V100 and 240GB CPU memory node, and is also more efficient on the same modelsize .
Revised:  Experimental results show that PatrickStar trains a 12 billion parameters GPT model, 2x larger than the STOA work , on an 8-V100 and 240GB CPU memory node, and is also more efficient on the same modelsize .
Edit intent: fluency
Original:   Experimental results show  PatrickStar trains a 12 billion parameters GPT model, 2x larger than the STOA work , on an 8-V100 and 240GB CPU memory node, and is also more efficient on the same modelsize .
Revised:  Experimental results show PatrickStar trains a 12 billion parameters GPT model, 2x larger than the STOA work , on an 8xV100 and 240GB CPU memory node, and is also more efficient on the same modelsize .
Edit intent: fluency
Original: Originally studied for their suitability to store information compactly, memristive arrays are now being analysed as implementations of neuromorphic systems.
Revised: Originally studied for their suitability to store information compactly, memristive networks are now being analysed as implementations of neuromorphic systems.
Edit intent: clarity
Original:  By comparing the response of uniform and homogeneous arrays (ideal) with non-uniform and heterogeneous ones (distorted) , we delineate differences that might be experimentally observed when the number of memristive units is scaled up and disorder arises as an inevitable feature.
Revised:  By comparing the response of homogeneous and heterogeneous networks , we delineate differences that might be experimentally observed when the number of memristive units is scaled up and disorder arises as an inevitable feature.
Edit intent: clarity
Original: Detecting online hate is a complex task, and low-performing detection models have harmful consequences when used for sensitive applications such as content moderation.
Revised: Detecting online hate is a complex task, and low-performing models have harmful consequences when used for sensitive applications such as content moderation.
Edit intent: clarity
Original:  Emoji-based hate is a key emerging challenge for online hate detection.
Revised:  Emoji-based hate is a key emerging challenge for automated detection.
Edit intent: clarity
Original:  We present HatemojiCheck, a test suite of 3,930 short-form statements that allows us to evaluate how detection models perform on hateful language expressed with emoji.
Revised:  We present HatemojiCheck, a test suite of 3,930 short-form statements that allows us to evaluate performance on hateful language expressed with emoji.
Edit intent: clarity
Original:  To address these weaknesses, we create the HatemojiTrain dataset using an innovative human-and-model-in-the-loop approach.
Revised:  To address these weaknesses, we create the HatemojiTrain dataset using a human-and-model-in-the-loop approach.
Edit intent: clarity
Original:  To that end, we outline six key impact areas in which these challenges will arise--energy , environmental justice, transportation, infrastructure, agriculture, and environmental monitoring and forecasting--then identify specific ways in which computing research can help address the associated problems.
Revised:  To that end, we outline six key impact areas in which these challenges will arise -- energy , environmental justice, transportation, infrastructure, agriculture, and environmental monitoring and forecasting--then identify specific ways in which computing research can help address the associated problems.
Edit intent: fluency
Original:  To that end, we outline six key impact areas in which these challenges will arise--energy , environmental justice, transportation, infrastructure, agriculture, and environmental monitoring and forecasting--then identify specific ways in which computing research can help address the associated problems.
Revised:  To that end, we outline six key impact areas in which these challenges will arise--energy , environmental justice, transportation, infrastructure, agriculture, and environmental monitoring and forecasting -- then identify specific ways in which computing research can help address the associated problems.
Edit intent: fluency
Original: Ergonomics and human comfort are essential concerns in physical human-robot interaction applications, and common practical methods either fail in estimating the correct posture due to occlusion or suffer from less accurate ergonomics models in their postural optimizationmethods.
Revised: Ergonomics and human comfort are essential concerns in physical human-robot interaction . Common practical methods in the area either fail in estimating the correct posture due to occlusion or suffer from less accurate ergonomics models in their postural optimizationmethods.
Edit intent: clarity
Original: Ergonomics and human comfort are essential concerns in physical human-robot interaction applications, and common practical methods either fail in estimating the correct posture due to occlusion or suffer from less accurate ergonomics models in their postural optimizationmethods.
Revised: Ergonomics and human comfort are essential concerns in physical human-robot interaction applications, and common practical methods either fail in estimating the correct posture due to occlusion or suffer from inaccurate ergonomics models in their postural optimizationmethods.
Edit intent: clarity
Original: Real-time detection and tracking of fast-moving objects have achieved great success in various fields.
Revised: Detection and tracking of fast-moving objects have achieved great success in various fields.
Edit intent: clarity
Original: Real-time detection and tracking of fast-moving objects have achieved great success in various fields.
Revised: Real-time detection and tracking of fast-moving objects have widespread utility in many fields.
Edit intent: clarity
Original:  We experimentally demonstrate that our approach can achieve a temporal resolution of 105 frames per second at a 1.28\% sampling rate by using a 22,000 Hz digital micro-mirror device .
Revised:  We experimentally demonstrate that the proposed method, using a 22kHz digital micro-mirror device .
Edit intent: clarity
Original: In this work, ] we study the limitations of the Quantum Approximate Optimization Algorithm (QAOA) through the lens of statistical physics and show that there exists \epsilon > 0, such that \epsilon\log(n) depth QAOA cannot arbitrarily-well approximate the ground state energy of random diluted k-spin glasses when k%DIFDELCMD < \geq4 %%% is even.
Revised: In this work, ], we study the limitations of the Quantum Approximate Optimization Algorithm (QAOA) through the lens of statistical physics and show that there exists \epsilon > 0, such that \epsilon\log(n) depth QAOA cannot arbitrarily-well approximate the ground state energy of random diluted k-spin glasses when k%DIFDELCMD < \geq4 %%% is even.
Edit intent: fluency
Original: In this work, ] we study the limitations of the Quantum Approximate Optimization Algorithm (QAOA) through the lens of statistical physics and show that there exists \epsilon > 0, such that \epsilon\log(n) depth QAOA cannot arbitrarily-well approximate the ground state energy of random diluted k-spin glasses when k%DIFDELCMD < \geq4 %%% is even.
Revised: In this work, ] we study the limitations of the Quantum Approximate Optimization Algorithm (QAOA) and show that there exists \epsilon > 0, such that \epsilon\log(n) depth QAOA cannot arbitrarily-well approximate the ground state energy of random diluted k-spin glasses when k%DIFDELCMD < \geq4 %%% is even.
Edit intent: clarity
Original:  [arXiv:1812.04170, 2018] asserting that the landscape independence of QAOA extends to logarithmic depth---in other words, for every fixed choice of QAOA angle parameters, the algorithm at logarithmic depth performs almost equally well on almost all instances.
Revised:  [arXiv:1812.04170, 2018] asserting that the landscape independence of QAOA extends to logarithmic depth -- in other words, for every fixed choice of QAOA angle parameters, the algorithm at logarithmic depth performs almost equally well on almost all instances.
Edit intent: fluency
Original:  Additionally, to further reduce the cost of MSA computation  , we design a cyclic shifted boxing scheme which brings greater efficiency by limiting the MSA computation to non-overlapping local boxes while also preserving cross-box connection.
Revised:  Additionally, to further reduce the cost of MSA computation , we design a cyclic shifted boxing scheme by limiting the MSA computation to non-overlapping local boxes while also preserving cross-box connection.
Edit intent: coherence
Original:  Additionally, to further reduce the cost of MSA computation  , we design a cyclic shifted boxing scheme which brings greater efficiency by limiting the MSA computation to non-overlapping local boxes while also preserving cross-box connection.
Revised:  Additionally, to further reduce the cost of MSA computation , we design a cyclic shifted boxing scheme which brings greater efficiency by limiting the MSA computation to non-overlapping local box and also preserving cross-box connection.
Edit intent: coherence
Original:  Our method fully exploits the potentials of Transformer architecture, paving the road to efficient and accurate recognition results.
Edit intent: coherence
Original:  Evaluated on classification and segmentation benchmarks, our PVT not only achieves strong accuracy but outperforms previous state-of-the-art Transformer-based models with 9x measured speedup on average.
Revised:  Evaluated on classification benchmark, our method not only achieves strong accuracy but outperforms previous state-of-the-art Transformer-based models with 9x measured speedup on average.
Edit intent: clarity
Original:  Evaluated on classification and segmentation benchmarks, our PVT not only achieves strong accuracy but outperforms previous state-of-the-art Transformer-based models with 9x measured speedup on average.
Revised:  Evaluated on classification and segmentation benchmarks, our PVT not only achieves strong accuracy but outperforms previous Transformer-based models with 9x measured speedup on average.
Edit intent: clarity
Original:   For 3D object detection task, we replace the primitives in Frustrum PointNet with PVT layer and achieve the improvement of 8.6\%  .
Revised:  For 3D object detection task, we replace the primitives in Frustrum PointNet with PVT layer and achieve the improvement of 8.6\% AP .
Edit intent: fluency
Original:  To overcome these two challenges , we present a study of a curriculum learning based approach, which helps improves the pre-training convergence speed of autoregressive models.
Revised:  To overcome these two challenges , we present a study of a curriculum learning-based approach, which helps improves the pre-training convergence speed of autoregressive models.
Edit intent: fluency
Original:  Our evaluations demonstrate that curriculum learning enables training GPT-2 models (with up to 1.5B parameters) with 8x larger batch size and 4x larger learning rate, whereas the baseline approach struggles with training divergence.
Revised:  Our evaluations demonstrate that curriculum learning enables training GPT-2 models with 8x larger batch size and 4x larger learning rate, whereas the baseline approach struggles with training divergence.
Edit intent: clarity
Original: To overcome the burdens on frequent model uploads and downloads during federated learning (FL), we propose a communication-efficient re-parameterization, FedPara .
Revised: In this work, we propose a communication-efficient parameterization, FedPara, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads during federated learning (FL), we propose a communication-efficient re-parameterization, FedPara .
Edit intent: clarity
Original: To overcome the burdens on frequent model uploads and downloads during federated learning (FL), we propose a communication-efficient re-parameterization, FedPara .
Revised: To overcome the burdens on frequent model uploads and downloads .
Edit intent: clarity
Original:  Our method re-parameterizes the model's layers using low-rank matrices or tensors followed by the Hadamard product.
Revised:  Our method re-parameterizes weight parameters of layers using low-rank matrices or tensors followed by the Hadamard product.
Edit intent: clarity
Original:  Our method re-parameterizes the model's layers using low-rank matrices or tensors followed by the Hadamard product.
Revised:  Our method re-parameterizes the model's layers using low-rank weights followed by the Hadamard product.
Edit intent: clarity
Original:  Different from the conventional low-rank parameterization, our  method is not limited to low-rank constraints .
Revised:  Compared to the conventional low-rank parameterization, our method is not limited to low-rank constraints .
Edit intent: coherence
Original:  Different from the conventional low-rank parameterization, our  method is not limited to low-rank constraints .
Revised:  Different from the conventional low-rank parameterization, our method is not restricted to low-rank constraints .
Edit intent: fluency
Original:  It can achieve comparable performance to the original models while requiring 2.8 to 10.1 times lower communication costs than the original models , which is not achievable by the traditional low-rank parameterization.
Revised:  It can achieve comparable performance to the original models while requiring 2.8 to 10.1 times lower communication costs than the model with the original layers , which is not achievable by the traditional low-rank parameterization.
Edit intent: clarity
Original:  Moreover, the efficiency can be further improved by combining our method and other efficient FL techniques because our method is compatible with others.
Revised:  Moreover, the efficiency can be further improved by combining with other efficient FL techniques because our method is compatible with others.
Edit intent: fluency
Original: Semantic change detection (SCD) extends the  change detection ( CD ) task to provide not only the change locations but also the detailed semantic categories ( before and after the observation intervals ) . This fine-grained  change information is more useful in land-cover/land-use (LC/LU) applications.
Revised: Semantic change detection (SCD) extends the change detection ( CD ) task to provide not only the change locations but also the detailed land-cover/land-use (LC/LU) categories before and after the observation intervals ) . This fine-grained change information is more useful in land-cover/land-use (LC/LU) applications.
Edit intent: clarity
Original: Semantic change detection (SCD) extends the  change detection ( CD ) task to provide not only the change locations but also the detailed semantic categories ( before and after the observation intervals ) . This fine-grained  change information is more useful in land-cover/land-use (LC/LU) applications.
Revised: Semantic change detection (SCD) extends the change detection ( CD ) task to provide not only the change locations but also the detailed semantic categories ( before and after the observation intervals . This fine-grained change information is more useful in land-cover/land-use (LC/LU) applications.
Edit intent: fluency
Original: Semantic change detection (SCD) extends the  change detection ( CD ) task to provide not only the change locations but also the detailed semantic categories ( before and after the observation intervals ) . This fine-grained  change information is more useful in land-cover/land-use (LC/LU) applications.
Revised: Semantic change detection (SCD) extends the change detection ( CD ) task to provide not only the change locations but also the detailed semantic categories ( before and after the observation intervals ) . This fine-grained semantic change information is more useful in land-cover/land-use (LC/LU) applications.
Edit intent: fluency
Original: Semantic change detection (SCD) extends the  change detection ( CD ) task to provide not only the change locations but also the detailed semantic categories ( before and after the observation intervals ) . This fine-grained  change information is more useful in land-cover/land-use (LC/LU) applications.
Revised: Semantic change detection (SCD) extends the change detection ( CD ) task to provide not only the change locations but also the detailed semantic categories ( before and after the observation intervals ) . This fine-grained change information is very useful in many applications.
Edit intent: clarity
Original:  However, in this architecture, the connections between the temporal branches and the change branch are weak .
Revised:  However, in this architecture, the communications between the temporal branches and the change branch are weak .
Edit intent: clarity
Original:  However, in this architecture, the connections between the temporal branches and the change branch are weak .
Revised:  However, in this architecture, the connections between the temporal branches and the change branch are in-sufficient .
Edit intent: clarity
Original:  To overcome these limitations , we propose a novel CNN architecture for the SCD, where the  temporal features are re-used and are deeply merged in the temporal branch .
Revised:  To overcome the limitations in existing methods , we propose a novel CNN architecture for the SCD, where the temporal features are re-used and are deeply merged in the temporal branch .
Edit intent: clarity
Original:  To overcome these limitations , we propose a novel CNN architecture for the SCD, where the  temporal features are re-used and are deeply merged in the temporal branch .
Revised:  To overcome these limitations , we propose a novel CNN architecture for the SCD, where the temporal features are merged in a deep CD unit .
Edit intent: clarity
Original:  Furthermore, we elaborate on this architecture to model the bi-temporal semantic correlations.
Revised:  Furthermore, we elaborate on this architecture to reason the bi-temporal semantic correlations.
Edit intent: clarity
Original: Semantic change detection (SCD) extends the multi-class change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use ( LC/LU ) categories before and after the observation intervals.
Revised: Semantic change detection (SCD) extends the multi-class change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use ( LCLU ) categories before and after the observation intervals.
Edit intent: clarity
Original:  However, in this architecture, the communications between the temporal branches and the change branch are in-sufficient .
Revised:  However, in this architecture, the communications between the temporal branches and the change branch are insufficient .
Edit intent: clarity
Original:  This task relies on different model compression techniques, such as network pruning, quantization  and the recent advancement of XNOR-Net.
Revised:  This task relies on different model compression techniques, such as network pruning, quantization , and the recent advancement of XNOR-Net.
Edit intent: fluency
Original:  This paper examines the suitability of these techniques for audio classification in microcontrollers.
Revised:  This paper examines the suitability of these techniques for audio classification on microcontrollers.
Edit intent: fluency
Original:  However, as the number of classes increases significantly, performance degrades  and pruning-and-quantization based compression techniques take over as the preferred technique being able to satisfy the same space constraints but requiring about 8x more computation.
Revised:  However, as the number of classes increases significantly, performance degrades , and pruning-and-quantization based compression techniques take over as the preferred technique being able to satisfy the same space constraints but requiring about 8x more computation.
Edit intent: fluency
Original: There has been growing concern about public distrust in science (1-3).
Edit intent: clarity
Original:  Opposition and hesitancy to vaccination have been one of the major threats to global health (4-6) .
Revised:  Opposition and hesitancy to vaccination have been one of the major threats to global health .
Edit intent: clarity
Original:  Social media sites have been suspected as a breeding ground of misleading and unsubstantiated narratives about vaccines (5, 7-10), but little is known about how pervasive anti-vaccine views  on the world's largest social media, Facebook , and how online groups produce their false narratives .
Revised:  Social media sites have been suspected as a breeding ground of misleading narratives about vaccines (5, 7-10), but little is known about how pervasive anti-vaccine views on the world's largest social media, Facebook , and how online groups produce their false narratives .
Edit intent: clarity