Defending Against Local Adversarial Attacks through Empirical Gradient Optimization

Deep neural networks (DNNs) are susceptible to adversarial attacks, including the recently introduced locally visible adversarial patch attack, which achieves a success rate exceeding 96%. These attacks pose significant challenges to DNN security. Various defense methods, such as adversarial training, robust attention modules, watermarking, and gradient smoothing, have been proposed to enhance empirical robustness against patch attacks. However, these methods often have limitations concerning patch location requirements, randomness, and their impact on recognition accuracy for clean images.To address these challenges, we propose a novel defense algorithm called Local Adversarial Attack Empirical Defense using Gradient Optimization (LAAGO). The algorithm incorporates a low-pass filter before noise suppression to effectively mitigate the interference of high-frequency noise on the classifier while preserving the low-frequency areas of the images. Additionally, it emphasizes the original target features by enhancing the image gradients. Extensive experimental results demonstrate that the proposed method improves defense performance by 3.69% for 80 × 80 noise patches (representing approximately 4% of the images), while experiencing only a negligible 0.3% accuracy drop on clean images. The LAAGO algorithm provides a robust defense mechanism against local adversarial attacks, overcoming the limitations of previous methods. Our approach leverages gradient optimization, noise suppression, and feature enhancement, resulting in significant improvements in defense performance while maintaining high accuracy for clean images. This work contributes to the advancement of defense strategies against emerging adversarial attacks, thereby enhancing the security and reliability of deep neural networks

meso-5,5′-Bis[(4-fluoro­phen­yl)diazen­yl]-2,2′-(pentane-3,3-di­yl)di-1H-pyrrole

There are two independent molecules in the asymmetric unit of the title compound, C25H24F2N6, in which the N=N bonds adopt a trans configuration with distances in the range 1.262 (2)–1.269 (3) Å. The dihedral angles between heterocycles are 86.7 (2) and 85.6 (2)° in the two molecules while the dihedral angles between the heterocylic rings and the adjacent benzene rings are 13.4 (2) and 13.4 (2)° in one molecule and 5.3 (2) and 6.5 (2)° in the other. In the crystal, pairs of independent mol­ecules are held together by four N—H⋯N hydrogen bonds, forming inter­locked dimers

Credit Risk Assessment of Banks' Loan Enterprise Customer Based on State-Constraint

Commercial banks are facing increasingly complex enterprise loan customers and businesses. It is important for banks' enterprise loan business to efficiently assess credit risks. Our study builds an enterprise credit risk assessment model based on the state and constraint of bank and customer, and get empirical researches with RF, SVM and DT algorithms. The results show that our model has excellent performance with accuracy 99 % and great characteristic importance in the evaluation of enterprise credit risk. The study can provide important decision-making reference for bank loan business and enrich the theoretical system of bank credit risk research

SIVALHIPPUS PTYCHODUS AND SIVALHIPPUS PLATYODUS (PERISSODACTYLA, MAMMALIA) FROM THE LATE MIOCENE OF CHINA

  Herein, the authors report on skulls, mandibles and postcranial specimens of two species of Chinese Sivalhippus, S. ptychodus and S. platyodus. We frame our description and analyses within the context of newly described characters of the cheek teeth of Hippotherium from the Pannonian C of the Vienna Basin, the oldest and most primitive Old World hipparions. Our report includes original skull, mandibular and limited postcranial material of Sivalhippus ptychodus and skulls and dentitions of Sivalhippus platyodus from the Paleontological Museum of Uppsala (PMU, Uppsala, Sweden), the American Museum of Natural History (AMNH, New York, USA) and the Licent Collection in Tianjin Natural History Museum (Tianjin, China). The skull, maxillary and mandibular material we attribute to Sivalhippus ptychodus and Sivalhippus platyodus exhibit some primitive features for Old World hipparions and synapamorphies of the face and dentition that unite it with the Sivalhippus clade. Our analysis shows that S. ptychodus and S. platyodus differ significantly from the Cormohipparion occidentale – Hippotherium primigenium clade. Species belonging to the Sivalhippus clade are found in IndoPakistan (S. nagriensis, S. theobaldi, S. perimensis and S. anwari), Libya and Kenya (S. turkanensis) and Uganda (S. macrodon). We hypothesize that the Sivalhippus clade originated in South Asia where it is earliest represented by Sivalhipus nagriensis, ca. 10.4 Ma and underwent range extension into Africa and China circa 9-7 Ma

Robust Secure Wireless Powered MISO Cognitive Mobile Edge Computing

Wireless power transfer (WPT) and cognitive radio (CR) are two promising techniques in designing mobile-edge computing (MEC) systems. In this paper, we study a robust secure wireless powered multiple-input single-output (MISO) cognitive MEC system, which integrates several techniques: physical-layer security, WPT, CR, underlay spectrum sharing and MEC. Three optimization problems are formulated to minimize the total transmission power (TTP) of the primary transmitter (PT) and the secondary base station (SBS) under perfect channel state information (CSI) model, bounded CSI error model and the probabilistic CSI error model, respectively. The formulated problems are nonconvex and hard to solve. Three two-phase iterative optimization algorithms combined with Lagrangian dual, semidefinite relaxation (SDR), S-Procedure and Bernstein-type inequalities are proposed to jointly optimize the beamforming vectors of the PT and the SBS, the central processing unit (CPU) frequency and the transmit power of the MD. Simulation results are provided to verify the effectiveness of the proposed algorithms

Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations

Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt’s lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.Koch Institute for Integrative Cancer Research (Support (core) Grant P30-CA14051)National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967)National Institute of General Medical Sciences (U.S.) (Interdepartmental Biotechnology Training Program 5T32GM008334