Learning Efficient Convolutional Networks through Irregular Convolutional Kernels

As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma becomes apparent: the trend is to grow models to absorb increasing data that gives rise to memory intensive; however low-power devices are designed with very limited memory that can not store large models. Parameters pruning is critical for deep model deployment on low-power devices. Existing efforts mainly focus on designing highly efficient structures or pruning redundant connections for networks. They are usually sensitive to the tasks or relay on dedicated and expensive hashing storage strategies. In this work, we introduce a novel approach for achieving a lightweight model from the views of reconstructing the structure of convolutional kernels and efficient storage. Our approach transforms a traditional square convolution kernel to line segments, and automatically learn a proper strategy for equipping these line segments to model diverse features. The experimental results indicate that our approach can massively reduce the number of parameters (pruned 69% on DenseNet-40) and calculations (pruned 59% on DenseNet-40) while maintaining acceptable performance (only lose less than 2% accuracy)

Predicting implied volatility in the commodity futures options markets

Both academics and practitioners have a substantial interest in understanding patterns in implied volatility that are recoverable from commodity futures options. Such knowledge enhances their ability to accurately forecast volatility embedded in these high risk options. This paper examines option-implied volatility contained in the heavily traded September corn futures option contracts for the ten-year period, 1991-2000. We also test whether a "weekend effect" exists in the market for these contracts. We evalu- ate the performance of various measures widely employed in the literature to estimate historical volatility. We further report the nature of profits from a short straddle strategy which seeks to exploit differences between option-implied and historical volatilit

Modeling and assessing load redistribution attacks considering cyber vulnerabilities in power systems

Introduction: Load Redistribution (LR) attacks, as a common form of false data injection attack, have emerged as a significant cybersecurity threat to power system operations by manipulating load buses’ measurements at substations. Existing LR attack methods typically assume that any substation can be equally attacked, contributing to the analysis of LR attacks in power systems. However, the diversity of cyber vulnerabilities in substation communication links implies varying costs associated with falsifying load buses’ measurements. Thus, quantitatively evaluating these costs and analyzing the impact of LR attacks on power systems within cost constraints holds practical significance.Methods: In this paper, we employ a Bayesian attack graph model to characterize the intrusion process through cyber vulnerabilities. The costs of falsifying load buses’ measurements at substations are quantitatively evaluated using the mean time-to-compromise model. Subsequently, from the attacker’s perspective, we propose a bi-level optimization model for LR attacks, considering the mean time to compromise in conjunction with limited attack resources and power flow constraints.Results: Simulations conducted on the IEEE 14-bus system illustrate the influence of cyber vulnerabilities on LR attacks within power systems. Furthermore, we verify that the attack scenario of the existing LR attack model aligns with a case of the proposed bi-level LR attack model when there is sufficient attack time to compromise all communication links.Discussion: The findings of this research demonstrate that the impact of cyber vulnerabilities on LR attacks can be quantified by assessing the attack costs. Effective management of LR attacks can be achieved under cost constraints through optimization methods. These insights contribute to enhancing network security strategies for power systems, mitigating potential threats posed by LR attacks in power system operations

Secondary infection of Fasciola gigantica in buffaloes shows a similar pattern of serum cytokine secretion as in primary infection

BackgroundAs a natural host of Fasciola gigantica, buffalo is widely infected by F. gigantica. Its impact on buffalo production has caused great losses to the husbandry sector, and repeat infection is non-negligible. In buffaloes experimentally infected with F. gigantica, primary and secondary infection have yielded the same rate of fluke recovery, indicating a high susceptibility of buffalo to F. gigantica, which contributes to the high infection rate. Determining the immunological mechanism of susceptibility will deepen the understanding of the interaction between F. gigantica and buffalo. Here, we explored the immune response of buffaloes against primary and secondary F. gigantica infection, with a focus on cytokines’ dynamics explored through serum cytokine detection.MethodsBuffaloes were assigned to three groups: group A (noninfected, n = 4), group B (primary infection, n = 3), and group C (secondary infection, n = 3). Group B was infected via oral gavage with 250 viable F. gigantica metacercariae, and group C was infected twice with 250 metacercariae at an interval of 4 weeks. The second infection of group C was performed simultaneously with that of group B. Whole blood samples were collected pre-infection (0 weeks) and at 1–6, 10, and 12  weeks after that. The serum levels of seven cytokines (IFN-γ, IL-4, IL-5, IL-10, IL-13, TGF-β, and IL-17) were simultaneously determined using ELISA and further analyzed.ResultsIn the present study, no significant changes in Th1-type cytokines production were detected in early infection, both in primary and secondary infections, while the Th2-type response was strongly induced. A comparison of primary and secondary infection showed no significant difference in the cytokine secretion, which may indicate that the re-infection at 4 weeks after primary infection could not induce a robust adaptive immune response. The full extent of interaction between buffalo and F. gigantica in re-infection requires further study

Strong [O III] {\lambda}5007 Compact Galaxies Identified from SDSS DR16 and Their Scaling Relations

Green pea galaxies are a special class of star-forming compact galaxies with strong [O III]{\lambda}5007 and considered as analogs of high-redshift Ly{\alpha}-emitting galaxies and potential sources for cosmic reionization. In this paper, we identify 76 strong [O III]{\lambda}5007 compact galaxies at z < 0.35 from DR1613 of the Sloan Digital Sky Survey. These galaxies present relatively low stellar mass, high star formation rate, and low metallicity. Both star-forming main sequence relation (SFMS) and mass-metallicity relation (MZR) are investigated and compared with green pea and blueberry galaxies collected from literature. It is found that our strong [O III] {\lambda}5007 compact galaxies share common properties with those compact galaxies with extreme star formation and show distinct scaling relations in respect to those of normal star-forming galaxies at the same redshift. The slope of SFMS is higher, indicates that strong [O III]{\lambda}5007 compact galaxies might grow faster in stellar mass. The lower MZR implies that they may be less chemically evolved and hence on the early stage of star formation. A further environmental investigation confirms that they inhabit relatively low-density regions. Future largescale spectroscopic surveys will provide more details on their physical origin and evolution.Comment: 12 pages, 8 figures, 1 table. Published in A

Palmitoleate Induces Hepatic Steatosis but Suppresses Liver Inflammatory Response in Mice

The interaction between fat deposition and inflammation during obesity contributes to the development of non-alcoholic fatty liver disease (NAFLD). The present study examined the effects of palmitoleate, a monounsaturated fatty acid (16∶1n7), on liver metabolic and inflammatory responses, and investigated the mechanisms by which palmitoleate increases hepatocyte fatty acid synthase (FAS) expression. Male wild-type C57BL/6J mice were supplemented with palmitoleate and subjected to the assays to analyze hepatic steatosis and liver inflammatory response. Additionally, mouse primary hepatocytes were treated with palmitoleate and used to analyze fat deposition, the inflammatory response, and sterol regulatory element-binding protein 1c (SREBP1c) activation. Compared with controls, palmitoleate supplementation increased the circulating levels of palmitoleate and improved systemic insulin sensitivity. Locally, hepatic fat deposition and SREBP1c and FAS expression were significantly increased in palmitoleate-supplemented mice. These pro-lipogenic events were accompanied by improvement of liver insulin signaling. In addition, palmitoleate supplementation reduced the numbers of macrophages/Kupffer cells in livers of the treated mice. Consistently, supplementation of palmitoleate decreased the phosphorylation of nuclear factor kappa B (NF-κB, p65) and the expression of proinflammatory cytokines. These results were recapitulated in primary mouse hepatocytes. In terms of regulating FAS expression, treatment of palmitoleate increased the transcription activity of SREBP1c and enhanced the binding of SREBP1c to FAS promoter. Palmitoleate also decreased the phosphorylation of NF-κB p65 and the expression of proinflammatory cytokines in cultured macrophages. Together, these results suggest that palmitoleate acts through dissociating liver inflammatory response from hepatic steatosis to play a unique role in NAFLD