在科学计算的许多领域越来越流行的人工神经网络（ANN）的大量使用迅速增加了现代高性能计算系统的能源消耗。新型的神经形态范式提供了一种吸引人的替代方案，它直接在硬件中实施了ANN。但是，对于科学计算中用例使用ANN在神经形态硬件上运行ANN的实际好处知之甚少。在这里，我们提出了一种方法，用于测量使用常规硬件的ANN来计算推理任务的时间。此外，我们为这些任务设计了一个体系结构，并根据最先进的模拟内存计算（AIMC）平台估算了相同的指标，这是神经形态计算中的关键范例之一。在二维凝结物质系统中的量子多体物理学中的用例比较两种方法，并在粒子物理学中大型强子对撞机上以40 MHz的速率以40 MHz的速率进行异常检测。我们发现，与传统硬件相比，AIMC最多可以达到一个较短的计算时间，最高三个数量级的能源成本。这表明使用神经形态硬件进行更快，更可持续的科学计算的潜力。

In recent years, reinforcement learning (RL) has become increasingly successful in its application to science and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent's learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent's policy.

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign with the purpose of enabling a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

Our goal is to reconstruct tomographic images with few measurements and a low signal-to-noise ratio. In clinical imaging, this helps to improve patient comfort and reduce radiation exposure. As quantum computing advances, we propose to use an adiabatic quantum computer and associated hybrid methods to solve the reconstruction problem. Tomographic reconstruction is an ill-posed inverse problem. We test our reconstruction technique for image size, noise content, and underdetermination of the measured projection data. We then present the reconstructed binary and integer-valued images of up to 32 by 32 pixels. The demonstrated method competes with traditional reconstruction algorithms and is superior in terms of robustness to noise and reconstructions from few projections. We postulate that hybrid quantum computing will soon reach maturity for real applications in tomographic reconstruction. Finally, we point out the current limitations regarding the problem size and interpretability of the algorithm.

Optimization equips engineers and scientists in a variety of fields with the ability to transcribe their problems into a generic formulation and receive optimal solutions with relative ease. Industries ranging from aerospace to robotics continue to benefit from advancements in optimization theory and the associated algorithmic developments. Nowadays, optimization is used in real time on autonomous systems acting in safety critical situations, such as self-driving vehicles. It has become increasingly more important to produce robust solutions by incorporating uncertainty into optimization programs. This paper provides a short survey about the state of the art in optimization under uncertainty. The paper begins with a brief overview of the main classes of optimization without uncertainty. The rest of the paper focuses on the different methods for handling both aleatoric and epistemic uncertainty. Many of the applications discussed in this paper are within the domain of control. The goal of this survey paper is to briefly touch upon the state of the art in a variety of different methods and refer the reader to other literature for more in-depth treatments of the topics discussed here.

We introduce an architecture for processing signals supported on hypergraphs via graph neural networks (GNNs), which we call a Hyper-graph Expansion Neural Network (HENN), and provide the first bounds on the stability and transferability error of a hypergraph signal processing model. To do so, we provide a framework for bounding the stability and transferability error of GNNs across arbitrary graphs via spectral similarity. By bounding the difference between two graph shift operators (GSOs) in the positive semi-definite sense via their eigenvalue spectrum, we show that this error depends only on the properties of the GNN and the magnitude of spectral similarity of the GSOs. Moreover, we show that existing transferability results that assume the graphs are small perturbations of one another, or that the graphs are random and drawn from the same distribution or sampled from the same graphon can be recovered using our approach. Thus, both GNNs and our HENNs (trained using normalized Laplacians as graph shift operators) will be increasingly stable and transferable as the graphs become larger. Experimental results illustrate the importance of considering multiple graph representations in HENN, and show its superior performance when transferability is desired.

Unhealthy dietary habits are considered as the primary cause of multiple chronic diseases such as obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoF) of people with dietary related diseases through dietary assessment. In this work, we propose a novel contact-less radar-based food intake monitoring approach. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movement from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network (3D-TCN) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions. We create a public dataset that contains 48 meal sessions (3121 eating gestures and 608 drinking gestures) from 48 participants with a total duration of 783 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, 8-fold cross validation method is applied. Experimental results show that our proposed 3D-TCN outperforms the model that combines a convolutional neural network and a long-short-term-memory network (CNN-LSTM), and also the CNN-Bidirectional LSTM model (CNN-BiLSTM) in eating and drinking gesture detection. The 3D-TCN model achieves a segmental F1-score of 0.887 and 0.844 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

研究随机噪声的特性以优化复杂的非凸函数一直是机器学习领域的活跃研究领域。先前的工作表明，随机梯度下降的噪声通过克服景观中的不良障碍来改善优化。此外，注射人造高斯噪音已成为快速逃脱鞍点的流行想法。确实，在没有可靠的梯度信息的情况下，噪声用于探索景观，但目前尚不清楚哪种类型的噪声在探索能力方面是最佳的。为了在我们的知识上缩小这一差距，我们基于布朗尼运动的一般类型的连续时间非马克维亚过程，该过程允许该过程的相关性增加。这将基于布朗运动（例如Ornstein-Uhlenbeck过程）进行概括。我们演示了如何离散此类过程，从而导致新算法FPGD。该方法是已知算法PGD和抗PGD的概括。我们在理论上和经验上都研究了FPGD的特性，表明它具有勘探能力，在某些情况下，它比PGD和抗PGD有利。这些结果为利用噪声用于训练机器学习模型的新颖方式开辟了领域。

数字取证是在数字设备中提取，保存和记录证据的过程。数字取证中的一种常用方法是从数字设备的主要内存中提取数据。但是，主要的挑战是确定要提取的重要数据。几个关键信息都存在于主内存中，例如用户名，密码和加密密钥，例如SSH会话键。在本文中，我们提出了SmartKex，SmartKex是一种机器学习辅助方法，以从OpenSSH进程的Heap Memory快照中提取会话键。此外，我们发布了一个公开可用的数据集和用于创建其他数据的相应工具链。最后，我们将SmartKex与幼稚的蛮力方法进行比较，并从经验上表明，SmartKex可以以高精度和高吞吐量提取会话键。有了提供的资源，我们打算加强有关数字取证，网络安全和机器学习之间交集的研究。