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Diagnosing Alzheimer's Disease (AD) early and cost-effectively is crucial. Recent advancements in Large Language Models (LLMs) like ChatGPT have made accurate, affordable AD detection feasible. Yet, HIPAA compliance and the challenge of integrating these models into hospital systems limit their use. Addressing these constraints, we introduce ADetectoLocum, an open-source LLM equipped model designed for AD risk detection within hospital environments. This model evaluates AD risk through spontaneous patient speech, enhancing diagnostic processes without external data exchange. Our approach secures local deployment and significantly surpasses previous models in predictive accuracy for AD detection, especially in early-stage identification. ADetectoLocum therefore offers a reliable solution for AD diagnostics in healthcare institutions. 

We conduct three-dimensional direct numerical simulations to investigate the mixing, entrainment and energy budgets of gravity currents emerging from two-layer stratified locks. Depending on the density and layer thickness ratios, we find that either the upper layer or lower layer fluid can propagate faster, and that the density structure of the overall gravity current can range from strongly stratified to near-complete mixing. We furthermore observe that intermediate values of the density ratio can maximise mixing between the gravity current layers. Based on the vorticity budget, we propose a theoretical model for predicting the overall gravity current height, along with the front velocity of the two layers, for situations in which the lower layer moves faster than the upper layer. The model identifies the role of the height and thickness ratios in determining the velocity structure of the current, and it clarifies the dynamics of the ambient counter-current. A detailed analysis of the energy budget quantifies the conversion of potential into kinetic energy as a function of the governing parameters, along with the energy transfer between the different layers of the gravity current and the ambient fluid. Depending on the values of the density and layer thickness ratios, we find that the lower lock layer can gain or lose energy, whereas the upper layer always loses energy. 

Elastic surface waves pumping is realized by a smart pillar-type metasurface. 

Abstract Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human cognition, e.g., as it relates to navigation, as well as in work on robotics and artificial intelligence. Although previous work has mainly focused on various spatial and temporal calculi, more recently representation learning techniques such as embedding have been applied to reasoning and inference tasks such as query answering and knowledge base completion. These subsymbolic and learnable representations are well suited for handling noise and efficiency problems that plagued prior work. However, applying embedding techniques to spatial and temporal reasoning has received little attention to date. In this paper, we explore two research questions: (1) How do embedding-based methods perform empirically compared to traditional reasoning methods on QSR/QTR problems? (2) If the embedding-based methods are better, what causes this superiority? In order to answer these questions, we first propose a hyperbolic embedding model, called HyperQuaternionE, to capture varying properties of relations (such as symmetry and anti-symmetry), to learn inversion relations and relation compositions (i.e., composition tables), and to model hierarchical structures over entities induced by transitive relations. We conduct various experiments on two synthetic datasets to demonstrate the advantages of our proposed embedding-based method against existing embedding models as well as traditional reasoners with respect to entity inference and relation inference. Additionally, our qualitative analysis reveals that our method is able to learn conceptual neighborhoods implicitly. We conclude that the success of our method is attributed to its ability to model composition tables and learn conceptual neighbors, which are among the core building blocks of QSR/QTR. 

Abstract The application of hardware‐based neural networks can be enhanced by integrating sensory neurons and synapses that enable direct input from external stimuli. This work reports direct optical control of an oscillatory neuron based on volatile threshold switching in V₃O₅. The devices exhibit electroforming‐free operation with switching parameters that can be tuned by optical illumination. Using temperature‐dependent electrical measurements, conductive atomic force microscopy (C‐AFM), in situ thermal imaging, and lumped element modelling, it is shown that the changes in switching parameters, including threshold and hold voltages, arise from overall conductivity increase of the oxide film due to the contribution of both photoconductive and bolometric characteristics of V₃O₅, which eventually affects the oscillation dynamics. Furthermore, V₃O₅is identified as a new bolometric material with a temperature coefficient of resistance (TCR) as high as −4.6% K⁻¹at 423 K. The utility of these devices is illustrated by demonstrating in‐sensor reservoir computing with reduced computational effort and an optical encoding layer for spiking neural network (SNN), respectively, using a simulated array of devices. 

We investigate the submerged collapse of weakly polydisperse, loosely packed cohesive granular columns, as a function of aspect ratio and cohesive force strength, via grain-resolving direct numerical simulations. The cohesive forces act to prevent the detachment of individual particles from the main body of the collapsing column, reduce its front velocity, and yield a shorter and thicker final deposit. All of these effects can be captured accurately across a broad range of parameters by piecewise power-law relationships. The cohesive forces reduce significantly the amount of available potential energy released by the particles. For shallow columns, the particle and fluid kinetic energy decreases for stronger cohesion. For tall columns, on the other hand, moderate cohesive forces increase the maximum particle kinetic energy, since they accelerate the initial free-fall of the upper column section. Only for larger cohesive forces does the peak kinetic energy of the particles decrease. Computational particle tracking indicates that the cohesive forces reduce the mixing of particles within the collapsing column, and it identifies the regions of origin of those particles that travel the farthest. The simulations demonstrate that cohesion promotes aggregation and the formation of aggregates. Furthermore, they provide complete information on the temporally and spatially evolving network of cohesive and direct contact force bonds. While the normal contact forces are aligned primarily in the vertical direction, the cohesive bonds adjust their preferred spatial orientation throughout the collapse. They result in a net macroscopic stress that counteracts deformation and slows the spreading of the advancing particle front.