Clinical language processing has received a lot of attention in recent years, resulting in new models or methods for disease phenotyping, mortality prediction, and other tasks. Unfortunately, many of these approaches are tested under different experimental settings (e.g., data sources, training and testing splits, metrics, evaluation criteria, etc.) making it difficult to compare approaches and determine state-of-the-art. To address these issues and facilitate reproducibility and comparison, we present the Clinical Language Understanding Evaluation (CLUE) benchmark with a set of four clinical language understanding tasks, standard training, development, validation and testing sets derived from MIMIC data, as well as a software toolkit. It is our hope that these data will enable direct comparison between approaches, improve reproducibility, and reduce the barrier-to-entry for developing novel models or methods for these clinical language understanding tasks.

Runtime verification (RV) has the potential to enable the safe operation of safety-critical systems that are too complex to formally verify, such as Robot Operating System 2 (ROS2) applications. Writing correct monitors can itself be complex, and errors in the monitoring subsystem threaten the mission as a whole. This paper provides an overview of a formal approach to generating runtime monitors for autonomous robots from requirements written in a structured natural language. Our approach integrates the Formal Requirement Elicitation Tool (FRET) with Copilot, a runtime verification framework, through the Ogma integration tool. FRET is used to specify requirements with unambiguous semantics, which are then automatically translated into temporal logic formulae. Ogma generates monitor specifications from the FRET output, which are compiled into hard-real time C99. To facilitate integration of the monitors in ROS2, we have extended Ogma to generate ROS2 packages defining monitoring nodes, which run the monitors when new data becomes available, and publish the results of any violations. The goal of our approach is to treat the generated ROS2 packages as black boxes and integrate them into larger ROS2 systems with minimal effort.

The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system. In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical data, by bringing together techniques from gradient-based optimization from machine learning with efficient quantum state representations in terms of tensor networks. Our approach is highly practical, experimentally friendly, and intrinsically scalable to allow for system sizes of above 100 spins. In particular, we demonstrate on synthetic data that the algorithm works even if one is restricted to one simple initial state, a small number of single-qubit observables, and time evolution up to relatively short times. For the concrete example of the one-dimensional Heisenberg model our algorithm exhibits an error constant in the system size and scaling as the inverse square root of the size of the data set.

We propose using a two-layered deployment of machine learning Artificial Intelligence (AI) solutions have penetrated the Industry models to prevent adversarial attacks. The first layer determines 4.0 domain by revolutionizing the rigid production lines enabling whether the data was tampered, while the second layer solves a innovative functionalities like mass customization, predictive maintenance, domain-specific problem. We explore three sets of features and zero defect manufacturing, and digital twins. However, three dataset variations to train machine learning models. Our results AI-fuelled manufacturing floors involve many interactions between show clustering algorithms achieved promising results. In the AI systems and other legacy Information and Communications particular, we consider the best results were obtained by applying Technology (ICT) systems, generating a new territory for malevolent the DBSCAN algorithm to the structured structural similarity index actors to conquer. Hence, the threat landscape of Industry 4.0 is measure computed between the images and a white reference expanded unpredictably if we also consider the emergence of adversary image.

We present a formal tasK AllocatioN and scheduling apprOAch for multi-robot missions (KANOA). KANOA supports two important types of task constraints: task ordering, which requires the execution of several tasks in a specified order; and joint tasks, which indicates tasks that must be performed by more than one robot. To mitigate the complexity of robotic mission planning, KANOA handles the allocation of the mission tasks to robots, and the scheduling of the allocated tasks separately. To that end, the task allocation problem is formalised in first-order logic and resolved using the Alloy model analyzer, and the task scheduling problem is encoded as a Markov decision process and resolved using the PRISM probabilistic model checker. We illustrate the application of KANOA through a case study in which a heterogeneous robotic team is assigned a hospital maintenance mission.

We consider the problem of learning the optimal threshold policy for control problems. Threshold policies make control decisions by evaluating whether an element of the system state exceeds a certain threshold, whose value is determined by other elements of the system state. By leveraging the monotone property of threshold policies, we prove that their policy gradients have a surprisingly simple expression. We use this simple expression to build an off-policy actor-critic algorithm for learning the optimal threshold policy. Simulation results show that our policy significantly outperforms other reinforcement learning algorithms due to its ability to exploit the monotone property. In addition, we show that the Whittle index, a powerful tool for restless multi-armed bandit problems, is equivalent to the optimal threshold policy for an alternative problem. This observation leads to a simple algorithm that finds the Whittle index by learning the optimal threshold policy in the alternative problem. Simulation results show that our algorithm learns the Whittle index much faster than several recent studies that learn the Whittle index through indirect means.

Over the course of the last several years, a significant amount of scholarly attention has been drawn to the issue of feature selection. At a high level, feature selection can be considered as a branch of reducing data dimensionality of which the two primary methods are feature learning and feature selection. The problem of feature learning involves the creation of new features from the original data. In contrast, the feature selection problem does not change the original representation of the data variables, so the physical meaning of each variable is preserved. To be more specific, the feature selection problem can be subdivided into two scenarios: supervised and unsupervised. Since we do not have target variables, selecting unsupervised features is more challenging. Typically, the unsupervised feature selection relies on matrix decomposition (Cheng et al., 2005; Liberty et al., 2007; Martinsson et al., 2011; Lu, 2022a), filter (Dash et al., 2002), and embeddings (Dy & Brodley, 2004; Hou et al., 2011). On the other hand, matrix decomposition algorithms such as QR decomposition, and singular value decomposition have been used extensively over the years to reveal hidden structures of data matrices in scientific and engineering areas such as collaborative filtering (Marlin, 2003; Lim & Teh, 2007; Mnih & Salakhutdinov, 2007; Lu, 2022c;a), recommendation systems (Lu, 2022c), clustering and classification (Li et al., 2009; Wang et al., 2013).

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. Army test-fired Javelin anti-tank missiles at a recent exhibition in Fort Hood, Texas to demonstrate technological advancement in its fighting capabilities. During a series of weapons drills and exercises, soldiers fired Javelins and .50-caliber A Javelin missile fired by soldiers with the 2nd Stryker Brigade Combat Team, separate from the exhibition in Texas.

Patrick St-Amant is the CTO and cofounder of Zetane Systems with advanced education in mathematics. He is the inventor of Zetane's technology and leads the development of Zetane Protector (ML models robustness testing and evaluation) and Zetane Insight Engine (models introspection 3D engine). He has successfully led several end-to-end ML projects with industrial clients and partners in the fields of Security, Defense, Aerospace, Construction, Aviation, Simulation and Manufacturing. This included project scoping, ML solution design, planning, data engineering, implementation, robustness testing and client's interactions. He has spent years as a researcher in number theory, set theory and fundamentals of mathematics.

Three distinct classes of aircraft piloted by artificial intelligence have emerged as options to fly alongside current and future U.S. Air Force fighters. The candidates range from expendable to exquisite systems, with a potential middle tier of attritable aircraft that leverage modular design features inspired by the automotive industry. All of these concepts were on full display inside the exhibit hall of the Air Force Association's annual Air, Space and Cyber Conference, which celebrated the 75th anniversary of the founding of the Air Force as an independent branch of the military. On the high end, Northrop Grumman's booth featured a concept model of the SG-101, the latest example of the company's long line of advanced flying-wing aircraft. Lockheed Martin, meanwhile, showed off the Skunk Works' concept for the Speed Racer, an expendably cheap uncrewed aircraft system (UAS) that will soon be teamed with F-35s for a demonstration called Project Carrera.