统计模型检查是一类顺序算法，可以验证网络物理系统集合中感兴趣的规格（例如，来自批处理的99％的汽车是否符合其能源效率的要求）。这些算法通过绘制足够数量的独立和相同分布的样本来推断具有可证明的统计保证的系统满足给定规范的概率。在统计模型检查过程中，可能会推断出样品的值（例如，用户的汽车能源效率），从而在消费者级别的应用程序（例如自闭症和医疗设备）中引起隐私问题。本文从差异隐私的角度介绍了统计模型检查算法的隐私。这些算法是顺序的，绘制样品直到满足其值的条件。我们表明，揭示绘制的样品数量可能侵犯隐私。我们还表明，在顺序算法的背景下，将算法的输出随机输出的标准指数机制无法实现。取而代之的是，我们放宽了差异隐私的保守要求，即该算法的输出的灵敏度应与任何数据集的任何扰动界定。我们提出了一个新的差异隐私概念，我们称之为预期的差异隐私。然后，我们提出了对顺序算法的新型预期灵敏度分析，并提出了一种相应的指数机制，该机制将终止时间随机，以实现预期的差异隐私。我们将提出的机制应用于统计模型检查算法，以保留其绘制样品的隐私。在案例研究中证明了所提出算法的效用。

计算机视觉和机器学习的进步使机器人能够以强大的新方式感知其周围环境，但是这些感知模块具有众所周知的脆弱性。我们考虑了合成尽管有知觉错误的安全控制器的问题。所提出的方法基于具有输入依赖性噪声的高斯过程构建状态估计器。该估计器为给定状态计算实际状态的高信心集。然后，合成了可证明可以处理状态不确定性的强大神经网络控制器。此外，提出了一种自适应采样算法来共同改善估计器和控制器。模拟实验，包括Carla中基于逼真的巷道示例，说明了提出方法在与基于深度学习的感知合成强大控制器中提出的方法的希望。

许多现有的景点（ROA）分析工具难以解决具有大规模神经网络（NN）政策和/或高维感测模式的反馈系统，如相机。在本文中，我们定制了在对冲学习界中开发的预计梯度下降（PGD）攻击方法作为大型非线性系统的通用ROA分析工具和基于端到端的感知的控制。我们表明ROA分析可以近似为约束的最大化问题，其目标是找到最坏情况的最坏情况初始条件最多。然后我们提出了两个基于PGD的迭代方法，可用于解决所得的受限最大化问题。我们的分析不是基于Lyapunov理论，因此需要问题结构的最低信息。在基于模型的设置中，我们示出了可以使用反向传播有效地执行PGD更新。在无模型设置（与基于感知的控制的ROA分析更相关）中，我们提出了一个有限差异的PGD估计，这是一般的，只需要一个黑盒模拟器来产生闭环系统的轨迹给予任何初始状态。我们在具有大规模NN政策和高维图像观测的几个数字示例下展示了我们分析工具的可扩展性和一般性。我们认为，我们所提出的分析是进一步了解大规模非线性系统的闭环稳定性和基于感知的控制的有意义的初步步骤。

基于政策的强化学习（RL）最近的经验成功，有一项研究趋势，研究了基于政策的RL方法对标准控制基准问题的研究。在本文中，我们研究了基于政策的RL方法的有效性在重要的强大控制问题上，即$ \ mu $综合。我们在强大的对策RL和$ \ mu $综合之间建立连接，并开发出众所周知的$ DK $ antication的无模型版本，用于解决静态$ d $-scaling的状态反馈$ \ mu $ synthesis。在所提出的算法中，$ k $步骤通过将最近开发的双循环对冲RL方法作为子程序来模仿经典的中央路径算法，$ D $步骤基于无模型有限差分近似。还提出了广泛的数值研究以展示我们提出的无模型算法的效用。我们的研究揭示了对抗对抗和鲁棒控制之间的联系。

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.

Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.

Spectrum coexistence is essential for next generation (NextG) systems to share the spectrum with incumbent (primary) users and meet the growing demand for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service (CBRS) band, where the 5G and beyond communication systems need to sense the spectrum and then access the channel in an opportunistic manner when the incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity classifier based on a deep neural network is needed for low misdetection (to protect incumbent users) and low false alarm (to achieve high throughput for NextG). In a dynamic wireless environment, the classifier can only be used for a limited period of time, i.e., coherence time. A portion of this period is used for learning to collect sensing results and train a classifier, and the rest is used for transmissions. In spectrum sharing systems, there is a well-known tradeoff between the sensing time and the transmission time. While increasing the sensing time can increase the spectrum sensing accuracy, there is less time left for data transmissions. In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier. We consider both additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this GAN-based approach can significantly improve both the protection of the high-priority user and the throughput of the NextG user (more in Rayleigh channels than AWGN channels).