Broadband phase shifter design for phased array radar systems

Ph.DDOCTOR OF PHILOSOPH

ChatGPT is a Potential Zero-Shot Dependency Parser

Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance. However, it remains an understudied question whether pre-trained language models can spontaneously exhibit the ability of dependency parsing without introducing additional parser structure in the zero-shot scenario. In this paper, we propose to explore the dependency parsing ability of large language models such as ChatGPT and conduct linguistic analysis. The experimental results demonstrate that ChatGPT is a potential zero-shot dependency parser, and the linguistic analysis also shows some unique preferences in parsing outputs.Comment: 10 page

Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. To address this problem, this paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model. Specifically, the CE-LIF model incorporates a meticulously designed contextual embedding component into the adaptive neuronal firing threshold, thereby enhancing the memory storage of spiking neurons and facilitating effective sequential modeling. Additionally, theoretical analysis is provided to elucidate how the CE-LIF model enables long-term temporal credit assignment. Remarkably, when compared to state-of-the-art recurrent SNNs, feedforward SNNs comprising the proposed CE-LIF neurons demonstrate superior performance across extensive sequential modeling tasks in terms of classification accuracy, network convergence speed, and memory capacity

A Fast Near-Infrared Image Colorization Deep Learning Mode

Near-infrared(NIR) image colorization is the main research content in the field of current near-infrared image application. It has a wide range of application value. For the problem of image colorization, such as diffuse color and even color error, and can not be automated, A fast near-infrared image colorization model consisting of a lightweight image recognition network module and an image colorization CNN module with a fusion layer, firstly using a lightweight image recognition network for image recognition of near-infrared images, and then selecting from the IamgeNet image library The image of the same class as the scene is used as the training set of the colorized network. After training with the colored CNN module with the fusion layer, the near-infrared image is input as the testing set for colorization. The experimental results show that the color is colored by the algorithm. The image details are clear, the color transfer effect is good and the running speed is fast

Metallic elements combine with herbal compounds upload in microneedles to promote wound healing: a review

Wound healing is a dynamic and complex restorative process, and traditional dressings reduce their therapeutic effectiveness due to the accumulation of drugs in the cuticle. As a novel drug delivery system, microneedles (MNs) can overcome the defect and deliver drugs to the deeper layers of the skin. As the core of the microneedle system, loaded drugs exert a significant influence on the therapeutic efficacy of MNs. Metallic elements and herbal compounds have been widely used in wound treatment for their ability to accelerate the healing process. Metallic elements primarily serve as antimicrobial agents and facilitate the enhancement of cell proliferation. Whereas various herbal compounds act on different targets in the inflammatory, proliferative, and remodeling phases of wound healing. The interaction between the two drugs forms nanoparticles (NPs) and metal-organic frameworks (MOFs), reducing the toxicity of the metallic elements and increasing the therapeutic effect. This article summarizes recent trends in the development of MNs made of metallic elements and herbal compounds for wound healing, describes their advantages in wound treatment, and provides a reference for the development of future MNs

Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration

Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while satisfying cost constraints. When learning from scratch, safe RL agents tend to be overly conservative, which impedes exploration and restrains the overall performance. In many realistic tasks, e.g. autonomous driving, large-scale expert demonstration data are available. We argue that extracting expert policy from offline data to guide online exploration is a promising solution to mitigate the conserveness issue. Large-capacity models, e.g. decision transformers (DT), have been proven to be competent in offline policy learning. However, data collected in real-world scenarios rarely contain dangerous cases (e.g., collisions), which makes it prohibitive for the policies to learn safety concepts. Besides, these bulk policy networks cannot meet the computation speed requirements at inference time on real-world tasks such as autonomous driving. To this end, we propose Guided Online Distillation (GOLD), an offline-to-online safe RL framework. GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms. Experiments in both benchmark safe RL tasks and real-world driving tasks based on the Waymo Open Motion Dataset (WOMD) demonstrate that GOLD can successfully distill lightweight policies and solve decision-making problems in challenging safety-critical scenarios

Towards Understanding the Adoption and Social Experience of Digital Wallet Systems

For millions around the globe, digital wallets are replacing cash and credit cards. These services support user-to-user payments, and add a social component to transactions. However, there is little understanding of the key factors behind digital wallets’ rapid growth in US (Venmo) and China (WeChat Pay). What are the factors that led to their success? How social relationships play a role in their adoption? We conduct a mixed methods study, using a comprehensive survey (N=879) and semi-structured interviews (N=41) to explore the interplay of the two roles of these digital wallets, i.e., a payment system and a social platform. Our analysis suggests that the network effect does benefit their adoption and retention, but through different mechanisms. In return, transaction activities performed in digital wallets help strengthen existing social ties. We also present design implications for future social payment services

Surface-exposed loops L7 and L8 of Haemophilus (Glaesserella) parasuis OmpP2 contribute to the expression of proinflammatory cytokines in porcine alveolar macrophages

International audienceOuter membrane protein P2 (OmpP2) of the virulent Haemophilus (Glaesserella) parasuis has been shown to induce the release of proinflammatory cytokines. The OmpP2 protein is composed of eight or nine surface-exposed loops, but it is unclear which of them participates in the OmpP2-induced inflammatory response. In this study, we synthesized linear peptides corresponding to surface-exposed loops L1–L8 of OmpP2 from the virulent H. parasuis SC096 strain to stimulate porcine alveolar macrophages (PAMs) in vitro. We found that both L7 and L8 significantly upregulated the mRNA expression of interleukin (IL)-1α, IL-1β, IL-6, IL-8, IL-17, and IL-23 and the chemokines CCL-4 and CCL-5 in a time- and dose-dependent manner. Additionally, we constructed ompP2ΔLoop7 and ompP2ΔLoop8 mutant SC096 strains and extracted their native OmpP2 proteins to stimulate PAMs. These mutant proteins induced significantly less mRNA expression of inflammatory cytokines than SC096 OmpP2. Next, the amino acid sequences of L7 and L8 from 15 serovars of H. parasuis OmpP2 were aligned. These sequences were relatively conserved among the most virulent reference strains, suggesting that L7 and L8 are the most active peptides of the OmpP2 protein. Furthermore, L7 and L8 significantly upregulated the NF-κB and AP-1 activity levels based on luciferase reporter assays in a dose-dependent manner. Therefore, our results demonstrated that both surface-exposed loops L7 and L8 of H. parasuis OmpP2 induced the expression of proinflammatory cytokines possibly by activating the NF-κB and MAPK signalling pathways in cells infected by H. parasuis