Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm

Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods

A Novel Color Parameter As A Luminosity Calibrator for Type Ia Supernovae

Type Ia supernovae (SNe Ia) provide us with a unique tool for measuring extragalactic distances and determining cosmological parameters. As a result, the precise and effective calibration for peak luminosities of SNe Ia becomes extremely crucial and thus is critically scrutinized for cosmological explorations. In this Letter, we reveal clear evidence for a tight linear correlation between peak luminosities of SNe Ia and their $B-V$ colors $\sim 12$ days after the $B$ maximum denoted by $\Delta C_{12}$. By introducing such a novel color parameter, $\Delta C_{12}$, this empirical correlation allows us to uniformly standardize SNe Ia with decline rates $\Delta m_{15}$ in the range of $0.8<\Delta m_{15}<2.0$ and to reduce scatters in estimating their peak luminosities from $\sim 0.5$ mag to the levels of 0.18 and 0.12 mag in the $V$ and $I$ bands, respectively. For a sample of SNe Ia with insignificant reddenings of host galaxies [e.g., E(B-V)_{host}\lsim 0.06 mag], the scatter drops further to only 0.07 mag (or 3-4% in distance), which is comparable to observational accuracies and is better than other calibrations for SNe Ia. This would impact observational and theoretical studies of SNe Ia and cosmological scales and parameters.Comment: 13 pages, including 3 figures. To appear in ApJL (2005 Feb issue

The Progenitor of Supernova 2004dj in a Star Cluster

The progenitor of type II-plateau supernova (SN) 2004dj is identified with a supergiant in a compact star cluster known as "Sandage Star 96" (S96) in the nearby spiral galaxy NGC 2403, which was fortuitously imaged as part of the Beijing-Arizona-Taiwan-Connecticut (BATC) Multicolor Sky Survey from Feb 1995 to Dec 2003 prior to SN 2004dj. The superior photometry of BATC images for S96, taken with 14 intermediate-band filters covering 3000-10000\AA, unambiguously establishes the star cluster nature of S96 with an age of $\sim 20$Myr, a reddening of $\hbox{E}(B-V)\sim 0.35$ mag and a total mass of $\sim 96,000$M$_{\odot}$. The compact star cluster nature of S96 is also consistent with the lack of light variations in the past decade. The SN progenitor is estimated to have a main-sequence mass of $\sim$12M$_{\odot}$. The comparison of our intermediate-band data of S96 with the post-outburst photometry obtained as the SN has significantly dimmed, may hopefully conclusively establish the nature of the progenitor.Comment: 4 pages; 3 figures. To accept for Publications in ApJ Letters, but slightly longer in this perprin

SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised Learning

Self-supervised learning (SSL) is a prevalent approach for encoding data representations. Using a pre-trained SSL image encoder and subsequently training a downstream classifier, impressive performance can be achieved on various tasks with very little labeled data. The growing adoption of SSL has led to an increase in security research on SSL encoders and associated Trojan attacks. Trojan attacks embedded in SSL encoders can operate covertly, spreading across multiple users and devices. The presence of backdoor behavior in Trojaned encoders can inadvertently be inherited by downstream classifiers, making it even more difficult to detect and mitigate the threat. Although current Trojan detection methods in supervised learning can potentially safeguard SSL downstream classifiers, identifying and addressing triggers in the SSL encoder before its widespread dissemination is a challenging task. This challenge arises because downstream tasks might be unknown, dataset labels may be unavailable, and the original unlbeled training dataset might be inaccessible during Trojan detection in SSL encoders. We introduce SSL-Cleanse as a solution to identify and mitigate backdoor threats in SSL encoders. We evaluated SSL-Cleanse on various datasets using 1200 encoders, achieving an average detection success rate of 82.2% on ImageNet-100. After mitigating backdoors, on average, backdoored encoders achieve 0.3% attack success rate without great accuracy loss, proving the effectiveness of SSL-Cleanse.Comment: 9 pages, 6 figures, 4 table

New Algorithms for Secure Outsourcing of Modular Exponentiations

With the rapid development in availability of cloud services, the techniques for securely outsourcing the prohibitively expensive computations to untrusted servers are getting more and more attentions in the scientific community. Exponentiations modulo a large prime have been considered the most expensive operation in discrete-logarithm based cryptographic protocols, and the computationally limited devices such as RFID tags or smartcard may be incapable to accomplish these operations. Therefore, it is meaningful to present an efficient method to securely outsource most of this work-load to (untrusted) cloud servers. In this paper, we propose a new secure outsourcing algorithm for (variable-exponent, variable-base) exponentiation modular a prime in the two untrusted program model. Compared with the state-of-the-art algorithm \cite{HL05}, the proposed algorithm is superior in both efficiency and checkability. We then utilize this algorithm as a subroutine to achieve outsource-secure Cramer-Shoup encryptions and Schnorr signatures. Besides, we propose the first outsource-secure and efficient algorithm for simultaneous modular exponentiations. Moreover, we formally prove that both the algorithms can achieve the desired security notions. We also provide the experimental evaluation that demonstrates the efficiency and effectiveness of the proposed outsourcing algorithms and schemes

A nonalcoholic fatty liver disease cirrhosis model in gerbil:the dynamic relationship between hepatic lipid metabolism and cirrhosis

Nonalcoholic fatty liver disease (NAFLD) usually takes decades to develop into cirrhosis, which limits the longitudinal study of NAFLD. This work aims at developing a NAFLD-caused cirrhosis model in gerbil and examining the dynamic relationship between hepatic lipid metabolism and cirrhosis. We fed gerbil a high-fat and high-cholesterol diet (HFHCD) for 24 weeks, and recorded the gerbil's phenotype at 3, 6, 9, 12, 15, 18, 21, 24 weeks. The model's pathological process, lipid metabolism, oxidative stress, liver collagen deposition and presence of relevant cytokines were tested and evaluated during the full-time frame of disease onset. The gerbil model can induce nonalcoholic steatohepatitis (NASH) within 9 weeks, and can develop cirrhosis after 21 weeks induction. The model's lipids metabolism disorder is accompanied with the liver damage development. During the NAFLD progression, triglycerides (TG) and free fatty acids (FFA) have presented distinct rise and fall tendency, and the turning points are at the fibrosis stage. Besides that, the ratios of total cholesterol (CHO) to high-density lipoprotein cholesterol (HDL-C) exhibited constant growth tendency, and have a good linear relationship with hepatic stellate cells (HSC) (R-2 = 0.802, P <0.001). The gerbil NAFLD cirrhosis model has been developed and possesses positive correlation between lipids metabolism and cirrhosis. The compelling rise and fall tendency of TG and FFA indicated that the fibrosis progression can lead to impairment in lipoprotein synthesis and engender decreased TG level. CHO/HDL-C ratios can imply the fibrosis progress and be used as a blood indicator for disease prediction and prevention