亚洲男人的天堂2018av,欧美草比,久久久久久免费视频精选,国色天香在线看免费,久久久久亚洲av成人片仓井空

3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands high-resolution 3D feature grids that are computationally expensive to process. As a result, most manipulation policies operate directly in 2D, foregoing 3D inductive biases. In this paper, we introduce Act3D, a manipulation policy transformer that represents the robot's workspace using a 3D feature field with adaptive resolutions dependent on the task at hand. The model lifts 2D pre-trained features to 3D using sensed depth, and attends to them to compute features for sampled 3D points. It samples 3D point grids in a coarse to fine manner, featurizes them using relative-position attention, and selects where to focus the next round of point sampling. In this way, it efficiently computes 3D action maps of high spatial resolution. Act3D sets a new state-of-the-art in RL-Bench, an established manipulation benchmark, where it achieves 10% absolute improvement over the previous SOTA 2D multi-view policy on 74 RLBench tasks and 22% absolute improvement with 3x less compute over the previous SOTA 3D policy. We quantify the importance of relative spatial attention, large-scale vision-language pre-trained 2D backbones, and weight tying across coarse-to-fine attentions in ablative experiments. Code and videos are available on our project website: //act3d.github.io/.

Accident of struck-by machines is one of the leading causes of casualties on construction sites. Monitoring workers' proximities to avoid human-machine collisions has aroused great concern in construction safety management. Existing methods are either too laborious and costly to apply extensively, or lacking spatial perception for accurate monitoring. Therefore, this study proposes a novel framework for proximity monitoring using only an ordinary 2D camera to realize real-time human-machine collision warning, which is designed to integrate a monocular 3D object detection model to perceive spatial information from 2D images and a post-processing classification module to identify the proximity as four predefined categories: Dangerous, Potentially Dangerous, Concerned, and Safe. A virtual dataset containing 22000 images with 3D annotations is constructed and publicly released to facilitate the system development and evaluation. Experimental results show that the trained 3D object detection model achieves 75% loose AP within 20 meters. Besides, the implemented system is real-time and camera carrier-independent, achieving an F1 of roughly 0.8 within 50 meters under specified settings for machines of different sizes. This study preliminarily reveals the potential and feasibility of proximity monitoring using only a 2D camera, providing a new promising and economical way for early warning of human-machine collisions.

Due to the popularity of Artificial Intelligence (AI) technology, numerous backdoor attacks are designed by adversaries to mislead deep neural network predictions by manipulating training samples and training processes. Although backdoor attacks are effective in various real scenarios, they still suffer from the problems of both low fidelity of poisoned samples and non-negligible transfer in latent space, which make them easily detectable by existing backdoor detection algorithms. To overcome the weakness, this paper proposes a novel frequency-based backdoor attack method named WaveAttack, which obtains image high-frequency features through Discrete Wavelet Transform (DWT) to generate backdoor triggers. Furthermore, we introduce an asymmetric frequency obfuscation method, which can add an adaptive residual in the training and inference stage to improve the impact of triggers and further enhance the effectiveness of WaveAttack. Comprehensive experimental results show that WaveAttack not only achieves higher stealthiness and effectiveness, but also outperforms state-of-the-art (SOTA) backdoor attack methods in the fidelity of images by up to 28.27\% improvement in PSNR, 1.61\% improvement in SSIM, and 70.59\% reduction in IS.

Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting. However, the current landscape of DA in TSC is plagued with fragmented literature reviews, nebulous methodological taxonomies, inadequate evaluative measures, and a dearth of accessible, user-oriented tools. In light of these challenges, this study embarks on an exhaustive dissection of DA methodologies within the TSC realm. Our initial approach involved an extensive literature review spanning a decade, revealing that contemporary surveys scarcely capture the breadth of advancements in DA for TSC, prompting us to meticulously analyze over 100 scholarly articles to distill more than 60 unique DA techniques. This rigorous analysis precipitated the formulation of a novel taxonomy, purpose-built for the intricacies of DA in TSC, categorizing techniques into five principal echelons: Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and Automated Data Augmentation. Our taxonomy promises to serve as a robust navigational aid for scholars, offering clarity and direction in method selection. Addressing the conspicuous absence of holistic evaluations for prevalent DA techniques, we executed an all-encompassing empirical assessment, wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR time-series datasets, employing ResNet and a multi-faceted evaluation paradigm encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the inconsistent efficacies of DA techniques, with...

Decades of progress in energy-efficient and low-power design have successfully reduced the operational carbon footprint in the semiconductor industry. However, this has led to an increase in embodied emissions, encompassing carbon emissions arising from design, manufacturing, packaging, and other infrastructural activities. While existing research has developed tools to analyze embodied carbon at the computer architecture level for traditional monolithic systems, these tools do not apply to near-mainstream heterogeneous integration (HI) technologies. HI systems offer significant potential for sustainable computing by minimizing carbon emissions through two key strategies: ``reducing" computation by reusing pre-designed chiplet IP blocks and adopting hierarchical approaches to system design. The reuse of chiplets across multiple designs, even spanning multiple generations of integrated circuits (ICs), can substantially reduce embodied carbon emissions throughout the operational lifespan. This paper introduces a carbon analysis tool specifically designed to assess the potential of HI systems in facilitating greener VLSI system design and manufacturing approaches. The tool takes into account scaling, chiplet and packaging yields, design complexity, and even carbon overheads associated with advanced packaging techniques employed in heterogeneous systems. Experimental results demonstrate that HI can achieve a reduction of embodied carbon emissions up to 70\% compared to traditional large monolithic systems. These findings suggest that HI can pave the way for sustainable computing practices, contributing to a more environmentally conscious semiconductor industry.

Content recommendation tasks increasingly use Graph Neural Networks, but it remains challenging for machine learning experts to assess the quality of their outputs. Visualization systems for GNNs that could support this interrogation are few. Moreover, those that do exist focus primarily on exposing GNN architectures for tuning and prediction tasks and do not address the challenges of recommendation tasks. We developed RekomGNN, a visual analytics system that supports ML experts in exploring GNN recommendations across several dimensions and making annotations about their quality. RekomGNN straddles the design space between Neural Network and recommender system visualization to arrive at a set of encoding and interaction choices for recommendation tasks. We found that RekomGNN helps experts make qualitative assessments of the GNN's results, which they can use for model refinement. Overall, our contributions and findings add to the growing understanding of visualizing GNNs for increasingly complex tasks.

Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generate low-dimensional embeddings of entities and relations, and use these to make inferences. ConvE, a recently proposed approach, applies convolutional filters on 2D reshapings of entity and relation embeddings in order to capture rich interactions between their components. However, the number of interactions that ConvE can capture is limited. In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE. InteractE is based on three key ideas -- feature permutation, a novel feature reshaping, and circular convolution. Through extensive experiments, we find that InteractE outperforms state-of-the-art convolutional link prediction baselines on FB15k-237. Further, InteractE achieves an MRR score that is 9%, 7.5%, and 23% better than ConvE on the FB15k-237, WN18RR and YAGO3-10 datasets respectively. The results validate our central hypothesis -- that increasing feature interaction is beneficial to link prediction performance. We make the source code of InteractE available to encourage reproducible research.

Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations. We argue that the explanation for an answer is of the same or even more importance compared with the answer itself, since it makes the question and answering process more understandable and traceable. To this end, we propose a new task of VQA-E (VQA with Explanation), where the computational models are required to generate an explanation with the predicted answer. We first construct a new dataset, and then frame the VQA-E problem in a multi-task learning architecture. Our VQA-E dataset is automatically derived from the VQA v2 dataset by intelligently exploiting the available captions. We have conducted a user study to validate the quality of explanations synthesized by our method. We quantitatively show that the additional supervision from explanations can not only produce insightful textual sentences to justify the answers, but also improve the performance of answer prediction. Our model outperforms the state-of-the-art methods by a clear margin on the VQA v2 dataset.