二次无约束的二进制优化（QUBO）求解器可以应用于设计最佳结构以避免共振。在经典或量子设备上使用的QUBO算法在某些工业应用中取得了成功。但是，由于难以从原始优化问题转变为QUBO，它们的应用仍受到限制。最近，已经提出了黑盒优化（BBO）方法，可以使用机器学习技术和贝叶斯治疗来解决此问题，以进行组合优化。我们采用了BBO方法来设计印刷电路板以避免共振。该设计问题是为了最大程度地提高固有频率并同时最大程度地减少安装点的数量。固有频率是QUBO公式的瓶颈，在BBO方法中近似于二次模型。我们证明，使用分解机的BBO在计算时间和找到最佳解决方案的成功概率中都表现出良好的性能。我们的结果可以打开Qubo求解器在结构设计中的其他应用的潜力。

机器学习方法最近被用作替代品或用于动态系统的物理/数学建模方法的帮助。为了开发一种用于建模和预测多尺度动力学的有效机器学习方法，我们通过使用异质性泄漏积分器（LI）神经元的复发网络提出了具有不同时间尺度的储层计算（RC）模型。我们在两个时间序列的预测任务中评估了所提出模型的计算性能，该任务与四个混乱的快速动力学系统有关。在仅从快速子系统提供输入数据的一步预测任务中，我们表明，所提出的模型比具有相同LI神经元的标准RC模型产生的性能更好。我们的分析表明，通过模型训练，适当，灵活地从储层动力学中选择了产生目标多尺度动力学的每个组件所需的时间尺度。在长期的预测任务中，我们证明了所提出的模型的闭环版本可以实现长期的预测，而与与参数相同的LI神经元相比，它可以实现长期预测。

Vision-based tactile sensors have gained extensive attention in the robotics community. The sensors are highly expected to be capable of extracting contact information i.e. haptic information during in-hand manipulation. This nature of tactile sensors makes them a perfect match for haptic feedback applications. In this paper, we propose a contact force estimation method using the vision-based tactile sensor DIGIT, and apply it to a position-force teleoperation architecture for force feedback. The force estimation is done by building a depth map for DIGIT gel surface deformation measurement and applying a regression algorithm on estimated depth data and ground truth force data to get the depth-force relationship. The experiment is performed by constructing a grasping force feedback system with a haptic device as a leader robot and a parallel robot gripper as a follower robot, where the DIGIT sensor is attached to the tip of the robot gripper to estimate the contact force. The preliminary results show the capability of using the low-cost vision-based sensor for force feedback applications.

Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers. Perceptual hashing algorithms help determine whether two images are visually similar while preserving the privacy of the input images. Several efforts from industry and academia propose to conduct content scanning on client devices such as smartphones due to the impending roll out of end-to-end encryption that will make server-side content scanning difficult. However, these proposals have met with strong criticism because of the potential for the technology to be misused and re-purposed. Our work informs this conversation by experimentally characterizing the potential for one type of misuse -- attackers manipulating the content scanning system to perform physical surveillance on target locations. Our contributions are threefold: (1) we offer a definition of physical surveillance in the context of client-side image scanning systems; (2) we experimentally characterize this risk and create a surveillance algorithm that achieves physical surveillance rates of >40% by poisoning 5% of the perceptual hash database; (3) we experimentally study the trade-off between the robustness of client-side image scanning systems and surveillance, showing that more robust detection of illegal material leads to increased potential for physical surveillance.

文本引导的图像操纵任务最近在视觉和语言社区中获得了关注。虽然大多数事先研究专注于单拐操纵，但我们本文的目标是解决更具挑战性的多转映像操纵（MTIM）任务。考虑到一系列指令和先前生成的图像，此任务的先前模型成功生成了图像。然而，这种方法遭受了发布的遭受，并且缺乏指令中描述的物体的产生质量，从而降低了整体性能。为了克服这些问题，我们提出了一种称为视觉引导语言的新建筑，GaN（Lattegan）。在这里，我们通过引入视觉引导的语言注意（拿铁）模块来解决先前方法的局限性，该语言模块提取生成器的细粒度文本表示，以及识别全局和全局的文本条件的U-Net鉴别器架构。假冒或真实图像的本地代表。在两个不同的MTIM数据集，CodraW和I-CLEVR上进行广泛的实验，证明了所提出的模型的最先进的性能。