An SDN-Based Authentication Mechanism for Securing Neighbor Discovery Protocol in IPv6

The Neighbor Discovery Protocol (NDP) is one of the main protocols in the Internet Protocol version 6 (IPv6) suite, and it provides many basic functions for the normal operation of IPv6 in a local area network (LAN), such as address autoconfiguration and address resolution. However, it has many vulnerabilities that can be used by malicious nodes to launch attacks, because the NDP messages are easily spoofed without protection. Surrounding this problem, many solutions have been proposed for securing NDP, but these solutions either proposed new protocols that need to be supported by all nodes or built mechanisms that require the cooperation of all nodes, which is inevitable in the traditional distributed networks. Nevertheless, Software-Defined Networking (SDN) provides a new perspective to think about protecting NDP. In this paper, we proposed an SDN-based authentication mechanism to verify the identity of NDP packets transmitted in a LAN. Using the centralized control and programmability of SDN, it can effectively prevent the spoofing attacks and other derived attacks based on spoofing. In addition, this mechanism needs no additional protocol supporting or configuration at hosts and routers and does not introduce any dedicated devices

One is More: Diverse Perspectives within a Single Network for Efficient DRL

Deep reinforcement learning has achieved remarkable performance in various domains by leveraging deep neural networks for approximating value functions and policies. However, using neural networks to approximate value functions or policy functions still faces challenges, including low sample efficiency and overfitting. In this paper, we introduce OMNet, a novel learning paradigm utilizing multiple subnetworks within a single network, offering diverse outputs efficiently. We provide a systematic pipeline, including initialization, training, and sampling with OMNet. OMNet can be easily applied to various deep reinforcement learning algorithms with minimal additional overhead. Through comprehensive evaluations conducted on MuJoCo benchmark, our findings highlight OMNet's ability to strike an effective balance between performance and computational cost.Comment: Preprin

Evaluating Object and Text Detectors under the Binary Classification Scenario: A Review

With the explosively increasing volume of hateful speech presented with images on the Internet, it is necessary to detect hateful speech automatically. Due to the intense demand for computation from the hateful meme detection pipeline, it is vital to classify the text and non-text images for accelerating the speed of the multimodal hateful speech system. This study reviews the recent development of object and text detection architectures and categorizes them into one-stage or two-stage detectors to better compare accuracy and efficiency. Additionally, this study proposes two datasets as the benchmarks for the binary classification scenario to evaluate two representative object detectors and two state-of-art text detectors on the customized datasets with two types of texts embedded in images. The results indicate that one-stage detectors may not necessarily achieve higher throughputs than two-stage detectors, and the performance of detectors varies depending on the type of image texts. This thesis can contribute to further evaluation of detectors in binary detection tasks

Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference. Project Page: https://latent-consistency-models.github.io

Nonnegative Matrix Factorization Numerical Method for Integrated Photonic Cavity Based Spectroscopy

Nonnegative matrix factorization numerical method has been used to improve the spectral resolution of integrated photonic cavity based spectroscopy. Based on the experimental results for integrated photonic cavity device on Optics Letters 32, 632 (2007), the theoretical results show that the spectral resolution can be improved more than 3 times from 5.5 nm to 1.8 nm. It is a promising way to release the difficulty of fabricating high-resolution devices

Energy and Economic Analysis of Life Cycle Zero Energy Building in the Temperate Region

Life cycle zero energy buildings (LCZEBs) can present energy use more accurately than net zero energy buildings (NZEBs). Economic benefits are crucial for residents to accept LCZEBs. However, few relevant case studies have been conducted. A comparative analysis between a NZEB and a LCZEB with a multi-story apartment in a temperate region that meets the requirements of local building energy codes as the reference building was conducted in this study to ascertain economic feasibility of LCZEB. First, a building model and an energy model were established on the basis of site test, survey, and monitoring data. Then, the energy balances of the NZEB and LCZEB were calculated on the basis of the results of energy simulation and the foregoing data. Finally, the LCZEB and NZEB were realized on the condition that high thermal performance materials and high energy efficiency building equipment were adopted in accordance with the principle of maximizing net present value (NPV) and solar energy was fully utilized. Results demonstrate that solar hot water and photovoltaic systems are critical to the NZEB and LCZEB. Annual net energy (ANE) and annual NPV per square meter of thermal collector are −571.11 kWh and $455.5, respectively, and ANE and annual NPV per square meter of photovoltaic panel are −115.62 kWh and$13.2. The NZEB and LCZEB are economically feasible in the temperate region although the NZEB is superior to the LCZEB in terms of economic benefits. Their NPVs for the calculation period (20 years) are $15369.64 and$4718.77, and their payback periods are 11 and 16 years. This study can provide references for energy and economic optimization of NZEBs and LCZEBs

High blood galectin-3 level associated with risk of frailty in aging

BackgroundFrailty is one of the most problematic expressions of population aging, but its underlying mechanism has not been fully elucidated. Circulating galectin-3 (Gal-3) is involved in the pathogenesis of many age-related diseases. This study aims to explore the influence of circulating Gal-3 on the regulation of frailty and aging and to identify the potential mechanism further.MethodsIn this cross-sectional analysis, the Fried frailty phenotype (FP) was assessed among 149 community elderly residents in Shanghai. Peripheral blood mononuclear cells (PBMCs) were isolated by the Ficoll-Paque density gradient method, and differentially expressed genes (DEGs) encoding transcription factors in frailty were detected by Illumina and bioinformatics analyzed with R software. Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the functional roles of these DEGs and the target genes related to frailty phenotypes. The serum Gal-3 concentration was tested by enzyme-linked immunosorbent assay (ELISA). Mouse frailty phenotype was used to construct an in vivo model of frailty, after which the serum levels of circulating Gal-3 and its gene expression levels in mouse tissues were determined.ResultsParticipants’ mean age was 72.04 ± 7.05 years. In total, 21.48% were frail and 36.91% were pre-frail. The mean serum Gal-3 concentration was 46.34 ± 17.99 ng/mL in frail participants, 32.30 ± 8.14 ng/mL in pre-frail participants, and 26.00 ± 5.87 ng/mL in non-frail individuals (p < 0.001). Significant positive correlations between serum Gal-3 level and FP score, SARC-F score, C-reactive protein (CRP), interleukin-6, etc., were observed. In addition, the KEGG pathway and GO enrichment analyses showed that 265 DEGs in PBMCs of frail participants were mainly related to inflammatory response, translation, RNA binding, protein binding, ribosome, and primary immunodeficiency. LGALS3 was identified as the overlapping gene between frailty-related DEGs and aging-related DEGs. The elevated serum Gal-3 concentration in the in vivo model of frailty was consistent with the results in participants.ConclusionIn both community-dwelling older adults and aged mice, serum Gal-3 concentration was positively correlated with frailty. This circulating mediator may be a promising indicator of frailty.Clinical trial registrationChinese Clinical Trial Registry identifier, ChiCTR2000036399