本文研究了黑盒安全测试配置中基于方案的安全测试算法。对于与不同采样分布共享相同州行动集覆盖的算法，通常认为优先考虑探索高风险状态现象会提高采样效率。我们的提案通过引入不可能的定理来对上述直觉提出异议，该定理可证明显示上述差异的所有安全测试算法，同样具有相同的预期采样效率。此外，对于涵盖不同状态活动集的测试算法，采样效率标准不再适用，因为不同的算法不一定会收敛到相同的终止条件。然后，我们提出了基于几乎安全集合概念的测试攻击性定义，以及一种无偏和有效的算法，比较了测试算法之间的侵略性。还提出了来自两足球运动控制器和车辆决策模块的安全测试的经验观察，以支持提出的理论意义和方法。
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Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras. Despite recent advances in deep convolutional neural networks (DCNNs), this remains challenging. Images or videos collected by long-range cameras often suffer from atmospheric turbulence, blur, low-resolution, unconstrained poses, and poor illumination. In this paper, we provide a brief survey of recent advances in person recognition at a distance. In particular, we review recent work in multi-spectral face verification, person re-identification, and gait-based analysis techniques. Furthermore, we discuss the merits and drawbacks of existing approaches and identify important, yet under explored challenges for deploying remote person recognition systems in-the-wild.
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Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state-of-the-art accuracy on motion estimation using events, may suffer from an overfitting problem called event collapse. Prior works are computationally expensive or cannot alleviate the overfitting, which undermines the benefits of the CMax framework. We propose a novel, computationally efficient regularizer based on geometric principles to mitigate event collapse. The experiments show that the proposed regularizer achieves state-of-the-art accuracy results, while its reduced computational complexity makes it two to four times faster than previous approaches. To the best of our knowledge, our regularizer is the only effective solution for event collapse without trading off runtime. We hope our work opens the door for future applications that unlocks the advantages of event cameras.
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Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adversary may be interested not only in decreasing the reward, but also in modifying specific temporal logic properties of the policy. This paper presents a metric that measures the exact impact of adversarial attacks against such properties. We use this metric to craft optimal adversarial attacks. Furthermore, we introduce a model checking method that allows us to verify the robustness of RL policies against adversarial attacks. Our empirical analysis confirms (1) the quality of our metric to craft adversarial attacks against temporal logic properties, and (2) that we are able to concisely assess a system's robustness against attacks.
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Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
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成功培训端到端的深网进行真实运动去缩合，需要尖锐/模糊的图像对数据集，这些数据集现实且多样化，足以实现概括以实现真实的图像。获得此类数据集仍然是一项具有挑战性的任务。在本文中，我们首先回顾了现有的Deblurring基准数据集的局限性，从泛化到野外模糊图像的角度。其次，我们提出了一种有效的程序方法，以基于一个简单而有效的图像形成模型来生成清晰/模糊的图像对。这允许生成几乎无限的现实和多样化的培训对。我们通过在模拟对上训练现有的DeBlurring架构，并在四个真实模糊图像的标准数据集中对其进行评估，从而证明了所提出的数据集的有效性。我们观察到使用建议方法训练时动态场景的真实运动毛线照片的最终任务的出色概括性能。
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本文介绍了Cool-MC，这是一种集成了最先进的加固学习（RL）和模型检查的工具。具体而言，该工具建立在OpenAI健身房和概率模型检查器风暴上。COOL-MC提供以下功能：（1）模拟器在OpenAI体育馆训练RL政策，用于Markov决策过程（MDPS），这些模拟器定义为暴风雨的输入，（2）使用“ SORM”的新型号构建器，用于使用回调功能要验证（神经网络）RL策略，（3）与OpenAI Gym或Storm中指定的模型和政策相关的正式抽象，以及（4）算法以获得有关所谓允许政策的性能的界限。我们描述了Cool-MC的组件和体系结构，并在多个基准环境中演示了其功能。
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蛋白质 - 蛋白质相互作用（PPI）网络由生物体的蛋白质之间的物理和/或功能相互作用组成。由于用于形成PPI网络的生物物理和高通量方法是昂贵的，耗时的，而且通常包含不准确性，因此最终的网络通常不完整。为了推断这些网络中缺少的相互作用，我们提出了基于连续的经典和量子随机步行的新型链接预测方法。在量子步行的情况下，我们检查了网络邻接和拉普拉斯矩阵的用法来控制步行动力学。我们根据相应的过渡概率定义得分函数，并在四个现实世界PPI数据集上执行测试。我们的结果表明，使用网络邻接矩阵的连续时间经典随机步行和量子步行可以成功预测缺失的蛋白质 - 蛋白质相互作用，并且性能与艺术的状态媲美。
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近年来，深度学习（DL）算法的使用改善了基于视觉的空间应用的性能。但是，生成大量的注释数据来培训这些DL算法已被证明具有挑战性。虽然可以使用合成生成的图像，但在实际环境中测试时，经过合成数据训练的DL模型通常容易受到性能降解。在这种情况下，卢森堡大学的安全，可靠性和信任（SNT）跨学科中心开发了“ SNT Zero-G Lab”，用于在模拟现实世界太空环境的条件下培训和验证基于视觉的空间算法。 SNT Zero-G实验室开发的一个重要方面是设备选择。从实验室开发过程中学到的经验教训，本文提出了一种系统的方法，将市场调查和设备选择的实验分析结合在一起。特别是，本文专注于太空实验室中的图像采集设备：背景材料，相机和照明灯。实验分析的结果表明，在太空实验室开发项目中选择有效的设备选择需要通过实验分析来称赞的市场调查。
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一些研究人员专注于研究驾驶时驾驶员的认知行为和精神负荷。随着心理和感知负荷水平而变化的自适应界面可能有助于减少事故并增强驾驶员体验。在本文中，我们分析了心理工作量和感知负荷对心理生理维度的影响，并在双车间互动的双重任务方案中为精神和感知负荷估算提供了基于机器学习的框架（https://github.com/ Amrgomaaelhady/mwl-pl-估计器）。我们使用现成的非侵入传感器，可以轻松地集成到车辆系统中。我们的统计分析表明，尽管心理工作负载影响了一些心理生理方面，但感知负荷几乎没有影响。此外，我们通过融合这些测量值对心理和感知负载水平进行了分类，朝着实时自适应的车载界面迈进，该界面是个性化的，该界面是个性化的用户行为和驾驶条件。我们报告多达89％的心理工作负载分类准确性，并提供实时最低侵入的解决方案。
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