Fruit Classification Based on Improved YOLOv7 Algorithm

With the rapid development of technology and advancements, unmanned vending machines have emerged as the primary contactless retail method. The efficient and accurate implementation of automated identification technology for agricultural products in their distribution and sales has become an urgent problem that needs to be addressed. This article presents an improved YOLOv7 (You Only Look Once) algorithm for fruit detection in complex environments. By replacing the 3×3 convolutions in the backbone of YOLOv7 with Deformable ConvNet v2(DCNv2), the recognition accuracy and efficiency of fruit classification in YOLOv7 are significantly enhanced. The results indicate that the overall recognition accuracy of this system for ten types of fruits is 98.3%, showcasing its high precision and stability

Wood-Veneer-Reinforced Mycelium Composites for Sustainable Building Components

The demand for building materials has been constantly increasing, which leads to excessive energy consumption for their provision. The looming environmental consequences have triggered the search for sustainable alternatives. Mycelium, as a rapidly renewable, low-carbon natural material that can withstand compressive forces and has inherent acoustic and fire-resistance properties, could be a potential solution to this problem. However, due to its low tensile, flexural and shear strength, mycelium is not currently widely used commercially in the construction industry. Therefore, this research focuses on improving the structural performance of mycelium composites for interior use through custom robotic additive manufacturing processes that integrate continuous wood fibers into the mycelial matrix as reinforcement. This creates a novel, 100% bio-based, wood-veneer-reinforced mycelium composite. As base materials, Ganoderma lucidum and hemp hurds for mycelium growth and maple veneer for reinforcement were pre-selected for this study. Compression, pull-out, and three-point bending tests comparing the unreinforced samples to the veneer-reinforced samples were performed, revealing improvements on the bending resistance of the reinforced samples. Additionally, the tensile strength of the reinforcement joints was examined and proved to be stronger than the material itself. The paper presents preliminary experiment results showing the effect of veneer reinforcements on increasing bending resistance, discusses the potential benefits of combining wood veneer and mycelium’s distinct material properties, and highlights methods for the design and production of architectural components

3-Hydroxyphthalic Anhydride- Modified Rabbit Anti-PAP IgG as a Potential Bifunctional HIV-1 Entry Inhibitor

Several studies have reported that amyloid fibrils in human semen formed from a naturally occurring peptide fragment of prostatic acidic phosphatase (PAP248-286), known as semen-derived enhancer of viral infection (SEVI), could dramatically enhance human immunodeficiency virus type 1 (HIV-1) infection. Accordingly, SEVI might serve as a novel target for new antiviral drugs or microbicide candidates for the prevention of sexually transmitted HIV. Theoretically, a special anti-PAP or anti-SEVI antibody could reduce the enhancement of viral infection by blocking the binding of HIV and SEVI fibrils. Here, 3-hydroxyphthalic anhydride modified anti-PAP248-286 antibody, named HP-API, exhibited broad-spectrum and highly effective anti-HIV-1 activities on different subtypes and tropism. By using time-of-addition, cell–cell fusion and a single-cycle HIV-1 infection assays, we demonstrated that HP-API is an HIV-1 entry/fusion inhibitor. Mechanism studies suggest that HP-API inhibited HIV-1 entry/fusion by targeting both HIV-1 gp120 envelop and CD4 receptor on the host cell specifically. It is noteworthy that HP-API abrogated the formation of SEVI fibrils and partially interfered with SEVI-mediated enhancement of HIV-1 infection. Based on these findings, HP-API could be considered a bifunctional HIV-1 entry/fusion inhibitor with high potential

Mastering Surface Reconstruction of Metastable Spinel Oxides for Better Water Oxidation

International audienceDeveloping highly active electrocatalysts for oxygen evolution reaction (OER) is critical for the commercial effectiveness of water splitting to produce hydrogen fuels. Low-cost spinel oxides have attracted increasing interest as alternatives to noble-metal-based OER catalysts. A rational design of spinel catalysts can be guided by studying the structural/elemental properties which determine the reaction mechanism and activity. Here, using densit

Experimental quantum adversarial learning with programmable superconducting qubits

Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks. Yet, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from the vulnerability problem: adding tiny carefully-crafted perturbations to the legitimate original data samples would facilitate incorrect predictions at a notably high confidence level. This will pose serious problems for future quantum machine learning applications in safety and security-critical scenarios. Here, we report the first experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built upon variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 $\mu$s, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4% respectively, with both real-life images (e.g., medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would significantly enhance their robustness to such perturbations. Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.Comment: 26 pages, 17 figures, 8 algorithm

PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and the reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques. Our design goal for PyPose is to make it user-friendly, efficient, and interpretable with a tidy and well-organized architecture. Using an imperative style interface, it can be easily integrated into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves 3-20$\times$ speedup in computation compared to state-of-the-art libraries. To boost future research, we provide concrete examples across several fields of robotics, including SLAM, inertial navigation, planning, and control

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$

A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T

We report a search on a sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne-year exposure collected by PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and electrons. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such dark matter into photon final states. In particular, we present the first direct detection limits for an axial-vector (vector) interaction which are the strongest in the mass range from 25 to 45 (35 to 50) keV/c$^2$