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The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.

Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.

Background/Aims: Inflammation plays a vital role in the etiology and pathogenesis of chronic noncommunicable diseases (NCDs), which are the leading health issues throughout the world. Our previous studies verified the satisfactory therapeutic effects of Coccomyxa gloeobotrydiformis (CGD) polysaccharide on several NCDs. In this study, we aimed to investigate the anti-inflammatory effects of CGD polysaccharide, and the corresponding molecular mechanisms, on lipopolysaccharide (LPS)-induced inflammation in RAW264.7 cells. Methods: A viability assay and a lactate dehydrogenase (LDH) assay were used to measure the cytotoxic effects of CGD polysaccharide on LPS-stimulated RAW264.7 cells. To investigate the potential anti-inflammatory mechanisms of CGD polysaccharide in LPS-stimulated RAW264.7 cells, nitric oxide (NO) production was determined using a NO assay and the expression of inflammatory mediators (PGE 2 , iNOS and COX-2), inflammatory cytokines (TNF-α, IL-6, IL-1β and IL-10) and inflammation-related signaling pathways (the MAPK/NF-κB, PI3K/AKT/JNK, JAK/STAT and Nrf2/HO-1pathways) were observed by western blotting. The translocation of NF-κB p65 was also observed using an immunofluorescent assay. Results: CGD polysaccharide significantly inhibited LPS-induced NO production and PGE 2 expression by reducing the expression of iNOS and COX-2. It also suppressed the expression of the pro-inflammatory cytokines TNF-α, IL-6 and IL-1β, and up-regulated the expression of the anti-inflammatory cytokine IL-10. Further experiments demonstrated that CGD polysaccharide could inhibit inflammatory signaling pathways (the MAPK/NF-κB, PI3K/AKT/JNK and JAK/STAT pathways). At the same time, it enhanced the anti-inflammatory pathway Nrf2/HO-1. In addition, CGD polysaccharide did not display any cytotoxic effects, even at a high concentration. Conclusion: Taken together, the results suggest that CGD polysaccharide significantly inhibits LPS-induced inflammation in RAW264.7 cells. This effect lies in its regulatory effects on the signaling pathways MAPK/ NF-κB, PI3K/AKT/JNK, JAK/STAT and Nrf2/HO-1.Our findings reveal that CGD polysaccharide has the potential to be used as a relatively safe and effective drug as part of the treatment of NCDs.

While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision: (1) treating images as 1D sequences neglects their 2D structures; (2) the quadratic complexity is too expensive for high-resolution images; (3) it only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings. Furthermore, we present a neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN achieves comparable results with similar size convolutional neural networks (CNNs) and vision transformers (ViTs) in various tasks, including image classification, object detection, semantic segmentation, panoptic segmentation, pose estimation, etc. For example, VAN-B6 achieves 87.8% accuracy on ImageNet benchmark, and sets new state-of-the-art performance (58.2 PQ) for panoptic segmentation. Besides, VAN-B2 surpasses Swin-T 4 mIoU (50.1 vs. 46.1) for semantic segmentation on ADE20K benchmark, 2.6 AP (48.8 vs. 46.2) for object detection on COCO dataset. It provides a novel method and a simple yet strong baseline for the community. The code is available at https://github.com/Visual-Attention-Network .

Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server and participants are only limited to model parameters. However, this setting is vulnerable to model poisoning attack, since the participants have permission to modify the model parameters. In this paper, we perform systematic investigation for such threats in federated learning and propose a novel optimization-based model poisoning attack. Different from existing methods, we primarily focus on the effectiveness, persistence and stealth of attacks. Numerical experiments demonstrate that the proposed method can not only achieve high attack success rate, but it is also stealthy enough to bypass two existing defense methods.

A highlight of the knowledge derived in large part from structural work on physical motions and chemical interactions involved in voltage sensing, pore opening, ion conductance and selectivity, and voltage-dependent inactivation mechanisms of the voltage-gated channels Na V and Ca V . Electrical signals generated by minute currents of ions moving across cell membranes are central to all rapid processes in biology. Initiation and propagation of electrical signals requires voltage-gated sodium (Na V ) and calcium (Ca V ) channels. These channels contain a tetramer of membrane-bound subunits or domains comprising a voltage sensor and a pore module. Voltage-dependent activation occurs as membrane depolarization drives outward movements of positive gating changes in the voltage sensor via a sliding-helix mechanism, which leads to a conformational change in the pore module that opens its intracellular activation gate. A unique negatively charged site in the selectivity filter conducts hydrated Na + or Ca 2+ rapidly and selectively. Ion conductance is terminated by voltage-dependent inactivation, which causes asymmetric pore collapse. This Review focuses on recent advances in structure and function of Na V and Ca V channels that expand our current understanding of the chemical basis for electrical signaling mechanisms conserved from bacteria to humans.

Ten years ago, three teams experimentally demonstrated the first spasers, or plasmonic nanolasers, after the spaser concept was first proposed theoretically in 2003. An overview of the significant progress achieved over the last 10 years is presented here, together with the original context of and motivations for this research. After a general introduction, we first summarize the fundamental properties of spasers and discuss the major motivations that led to the first demonstrations of spasers and nanolasers. This is followed by an overview of crucial technological progress, including lasing threshold reduction, dynamic modulation, room-temperature operation, electrical injection, the control and improvement of spasers, the array operation of spasers, and selected applications of single-particle spasers. Research prospects are presented in relation to several directions of development, including further miniaturization, the relationship with Bose–Einstein condensation, novel spaser-based interconnects, and other features of spasers and plasmonic lasers that have yet to be realized or challenges that are still to be overcome.Plasmonic nanolasers and Spasers: Their evolution, properties, and future applicationsA review of plasmonic nanolasers charts breakthroughs in the technology over the past decade and points towards future research pathways and potential new applications. Plasmonic nanolasers, or spasers, are the counterparts of lasers, but instead of emitting photons, spasers emit composite particles made of photons and plasmons on the surfaces of metal nanoparticles. Their applications range from spectroscopic detection, on-chip light sources, and microscopy to optical sensors and probes. Now, an international team of researchers, led by Cun-Zheng Ning from Tsinghua University, has conducted a comprehensive review of the evolution spasers, from their first experimental demonstrations through to technological advances in the field and future research and new applications. After showing how the drive for miniaturization led to their creation, the review then summarises their properties and crucial progress made, and offers perspectives on unresolved issues and future challenges in the field.

Nanomaterials-based photoluminescence thermometry (PLT) is a new contact-free photonic approach for temperature sensing, important for applications ranging from quantum technology to biomedical imaging and diagnostics. Even though numerous new materials have been explored, great challenges and deficiencies remain that hamper many applications. In contrast to most of the existing approaches that use large ensembles of rare-earth-doped nanomaterials with large volumes and unavoidable inhomogeneity, we demonstrate the ultimate size reduction and simplicity of PLT by using only a single erbium-chloride-silicate (ECS) nanowire. Importantly, we propose and demonstrate a novel strategy that contains a self-optimization or “smart” procedure to automatically identify the best PL intensity ratio for temperature sensing. The automated procedure is used to self-optimize key sensing metrics, such as sensitivity, precision, or resolution to achieve an all-around superior PLT including several record-setting metrics including the first sensitivity exceeding 100% K −1 (~138% K −1 ), the highest resolution of 0.01 K, and the largest range of sensible temperatures 4–500 K operating completely within 1500–1800 nm (an important biological window). The high-quality ECS nanowire enables the use of well-resolved Stark-sublevels to construct a series of PL intensity ratios for optimization in infrared, allowing the completely Boltzmann-based sensing at cryogenic temperature for the first time. Our single-nanowire PLT and the proposed optimization strategy overcome many existing challenges and could fundamentally impact PL nano-thermometry and related applications such as single-cell thermometry. A self-optimized single-nanowire photoluminescence thermometer allows the construction of multiple criteria for temperature measurement and chooses the best one by an automated routine based on specified optimization targets like sensitivity.

Bioresorbable electronic stimulators are of rapidly growing interest as unusual therapeutic platforms, i.e., bioelectronic medicines, for treating disease states, accelerating wound healing processes and eliminating infections. Here, we present advanced materials that support operation in these systems over clinically relevant timeframes, ultimately bioresorbing harmlessly to benign products without residues, to eliminate the need for surgical extraction. Our findings overcome key challenges of bioresorbable electronic devices by realizing lifetimes that match clinical needs. The devices exploit a bioresorbable dynamic covalent polymer that facilitates tight bonding to itself and other surfaces, as a soft, elastic substrate and encapsulation coating for wireless electronic components. We describe the underlying features and chemical design considerations for this polymer, and the biocompatibility of its constituent materials. In devices with optimized, wireless designs, these polymers enable stable, long-lived operation as distal stimulators in a rat model of peripheral nerve injuries, thereby demonstrating the potential of programmable long-term electrical stimulation for maintaining muscle receptivity and enhancing functional recovery. Bioresorbable electronic stimulators can deliver electrical stimulation in rodents to enhance functional muscle recovery after nerve injury. Here, the authors present a bioresorbable dynamic covalent polymer that enables reliable, long-lived operation of soft, stretchable devices of this type.

Triboelectric nanogenerator (TENG) provides a new solution to the energy supply by harvesting high entropy energy. However, wearable electronic devices have high requirements for flexible, humidity-resistant, and low-cost TENG. Here, environment-friendly and multi-functional wheat starch TENG (S-TENG) was made by a simple and green method. The open-circuit voltage and short-circuit current of S-TENG are 151.4 V and 47.1 µA, respectively. S-TENG can be used not only to drive and intelligently control electronic equipment, but also to effectively harvest energy from body movements and wind. In addition, the output of S-TENG was not negatively affected with the increase in environmental humidity, but increased abnormally. In the range of 20% RH–80% RH, S-TENG can be potentially used as a sensitive self-powered humidity sensor. The S-TENG paves the way for large-scale preparation of multi-functional biomaterials-based TENG, and practical application of self-powered sensing and wearable devices.