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Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority.

Efficiently capturing multidimensional signals containing spectral and temporal information is crucial for intelligent machine vision. Although in-sensor computing shows promise for efficient visual processing by reducing data transfer, its capability to compress temporal/spectral data is rarely reported. Here we demonstrate a programmable two-dimensional (2D) heterostructure-based optoelectronic sensor integrating sensing, memory, and computation for in-sensor data compression. Our 2D sensor captured and memorized/encoded optical signals, leading to in-device snapshot compression of dynamic videos and three-dimensional spectral data with a compression ratio of 8:1. The reconstruction quality, indicated by a peak signal-to-noise ratio value of 15.81 dB, is comparable to the 16.21 dB achieved through software. Meanwhile, the compressed action videos (in the form of 2D images) preserve all semantic information and can be accurately classified using in-sensor convolution without decompression, achieving accuracy on par with uncompressed videos (93.18% vs 83.43%). Our 2D optoelectronic sensors promote the development of efficient intelligent vision systems at the edge. In-sensor computing offers a promising solution for image processing with reduced data transfer. Here, the authors report programmable and multifunctional van der Waals optoelectronic sensors, showing their application for snapshot compression and recognition of dynamic videos and 3D spectral data.

Eight novel fluorescent antifolates were designed and docked with folate receptors FRα and FRβ. The structures of the complexes were further calculated by molecular dynamic (MD) simulations. The binding energies were calculated by molecular docking and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) studies. The binding energy differences between FRα and FRβ (|E bα |–|E bβ |) values for compounds 3 and 8 were 1.3 and 1.1 kcal/mol calculated by molecular docking, and 13.9 and 10.4 kcal/mol by MM-PBSA simulation, respectively. The results indicated that compounds 3 and 8 may be the best candidates for targeted drug delivery to FRα. The binding structures, interaction residues, negatively charged pocket volume, and surface area were analyzed for all the complexes. We further calculated the root mean square displacement and secondary structural elements of the bound complexes using molecular dynamics simulations. The purpose of this study is to design novel antifolates targeted to FRα and FRβ, and to further distinguish between cancer cells and inflammation. Graphical abstract

With the rapid development of generative artificial intelligence (AI), AI agents are evolving into “intelligent partners” integrated into various consumer scenarios, posing new challenges to conventional consumer decision-making processes and perceptions. However, the mechanisms through which consumers develop trust and adopt AI agents in common scenarios remain unclear. Therefore, this article develops a framework based on the heuristic–systematic model to explain the behavioral decision-making mechanisms of future consumers. This model is validated through PLS-SEM with data from 632 participants in China. The results show that trust can link individuals’ dual decision paths to further drive user behavior. Additionally, we identify the key drivers of consumer behavior from two dimensions. These findings provide practical guidance for businesses and policymakers to optimize the design and development of AI agents and promote the widespread acceptance and adoption of AI technologies.

Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In‐memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first‐order dynamics. Here, a second‐order memristor is experimentally demonstrated using yttria‐stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second‐order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL‐based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second‐order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high‐efficiency, compact footprint, and hardware‐encoded plasticity. Spontaneous threshold lowering (STL) originates from the intrinsic neuronal plasticity observed in biological neurons, which plays an important role in a number of learning protocols like spatial attention. Here for the first time artificial STL neurons using second‐order yttria‐stabilized zirconia with Ag doping (YSZ:Ag) memristors at a small hardware overhead are realized, which mimic neural intrinsic plasticity and boost the performance of spiking neural networks.

Let $\\mathbb {F}_q$ be the finite field of q elements. In this paper, we study the vanishing behavior of multizeta values over $\\mathbb {F}_q[t]$ at negative integers. These values are analogs of the classical multizeta values. At negative integers, they are series of products of power sums $S_d(k)$ which are polynomials in t . By studying the t -valuation of $S_d(s)$ for $s < 0$ , we show that multizeta values at negative integers vanish only at trivial zeros. The proof is inspired by the idea of Sheats in the proof of a statement of “greedy element” by Carlitz.

Let $\\mathbb {F}_q$ be the finite field of q elements. In this paper, we study the vanishing behavior of multizeta values over $\\mathbb {F}_q[t]$ at negative integers. These values are analogs of the classical multizeta values. At negative integers, they are series of products of power sums $S_d(k)$ which are polynomials in t. By studying the t-valuation of $S_d(s)$ for $s < 0$ , we show that multizeta values at negative integers vanish only at trivial zeros. The proof is inspired by the idea of Sheats in the proof of a statement of “greedy element” by Carlitz.

The unprecedented development of Internet of Things results in the explosion of spatiotemporal signals generated by smart edge devices, leading to a surge of interest in real‐time learning of such data. This imposes a big challenge to conventional digital hardware because of physically separated memory and processing units and the transistor scaling limit. Memristors are deemed a solution for efficient and portable deep learning. However, their ionic resistive switching incurs large programming stochasticity and energy, compromising their advantages in real‐time learning spatiotemporal signals. To address the aforementioned issues, we propose a novel hardware–software codesign. Hardware‐wise, the stochasticity in memristor programming is leveraged to produce random matrices for efficient in‐memory computing. Software‐wise, random convolutional‐pooling architectures are integrated with echo‐state networks that compute with the hardware random matrices and make real‐time learning affordable. The synergy of the hardware and software not only improves the performance over conventional echo‐state networks, that is, 90.94% and 91.67% (compared to baselines 88.33% and 62.50%), but also retains 187.79× and 93.66× improvement of energy efficiency compared to the digital alternatives on the representative Human Activity Recognition Using Smartphones (HAR) and CRICKET datasets, respectively. These advantages make random convolutional echo‐state network (RCESN) a promising solution for the future smart edge hardware. Herein, a novel hardware–software codesign, the random memristor‐based convolutional echo‐state neural network (RCESN), is developed for edge spatiotemporal signal learning. Hardware‐wise, the stochasticity in memristor programming is leveraged to produce random matrices for efficient in‐memory computing. Software‐wise, random convolutional‐pooling architectures are paired with echo‐state networks that compute with the hardware random matrices and make real‐time learning affordable.

Autonomous one‐shot on‐the‐fly learning copes with the high privacy, small dataset, and in‐stream data at the edge. Implementing such learning on digital hardware suffers from the well‐known von‐Neumann and scaling bottlenecks. The optical neural networks featuring large parallelism, low latency, and high efficiency offer a promising solution. However, ex‐situ training of conventional optical networks, where optical path configuration and deep learning model optimization are separated, incurs hardware, energy and time overheads, and defeats the advantages in edge learning. Here, we introduced a bio‐inspired material‐algorithm co‐design to construct a hydrogel‐based optical Willshaw model (HOWM), manifesting Hebbian‐rule‐based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto‐chemical reactions. We first employed the HOWM as an all optical in‐sensor AI processor for one‐shot pattern classification, association and denoising. We then leveraged HOWM to function as a ternary content addressable memory (TCAM) of an optical memory augmented neural network (MANN) for one‐shot learning the Omniglot dataset. The HOWM empowered one‐shot on‐the‐fly edge learning leads to 1000× boost of energy efficiency and 10× boost of speed, which paves the way for the next‐generation autonomous, efficient, and affordable smart edge systems. Biological synapses of human brain undergo a period of overproduction after birth, which is followed by consolidating part of the synapses and pruning the rest, bearing great significance to the development of intelligence as shown in Figure A. Such structural plasticity inspires a material‐algorithm co‐design, a hydrogel‐based optical Willshaw model (HOWM) in Figure B, thanks to the underlying opto‐chemical reactions of hydrogel shown in Figure C. The HOWM empowers one‐shot on‐the‐fly learning and leads to 1000× boost of energy efficiency and 10× boost of speed, which may pave the way for the next‐generation autonomous, efficient and affordable smart edge systems.

The electric conductivity is an important factor for reducing the switching voltage of polymer-dispersed liquid crystals (PDLC) films. The electric conductivity of polymer matrix is changed by doped nanographite which is uniform dispersed in polymer matrix in acid condition. The influence of doped nanographite to switching electric field is studied. With increasing of doped nanographite, the switching voltage is dramatically reduced. The effect of nanographite on the polymerization and electro-optic are discussed. The kinetic polymerization of the PDLCs is monitored in lights scattering by UV/VIS spectrometer. The polymerization speed is compared by the max scattering point in different samples which doped by nanographite. The electro-optic of PDLCs films is measured by Polarimeter (PerkinElmer Model 341) to determine the threshold voltage. Information gained from polarizing optical microscope and Fourier transform infrared image depict the morphology of the liquid crystal droplets dispersed in polymer matrix.