The resilience of interdependent transportation networks under targeted attack

Modern world builds on the resilience of interdependent infrastructures characterized as complex networks. Recently, a framework for analysis of interdependent networks has been developed to explain the mechanism of resilience in interdependent networks. Here we extend this interdependent network model by considering flows in the networks and study the system's resilience under different attack strategies. In our model, nodes may fail due to either overload or loss of interdependency. Under the interaction between these two failure mechanisms, it is shown that interdependent scale-free networks show extreme vulnerability. The resilience of interdependent SF networks is found in our simulation much smaller than single SF network or interdependent SF networks without flows.Comment: 5 pages, 4 figure

Cyclohexadione-aniline conjugate inhibits proliferation of melanoma cells via upregulation of Mek 1/2 kinase activity

Purpose: To investigate the antiproliferative effect of cyclohexadione-aniline conjugate (CHAC) on melanoma cells, and the mechanism of action involved. Methods: Human melanoma cell lines (B16 F1 and A375) were used in this study. The cells were cultured in RPMI 1640 medium supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin/streptomycin at 37 °C in a humidified atmosphere of 5 % CO2 and 95 % air. After attaining 70 - 80 % confluency, the cells were treated with serum-free medium and graded concentrations of CHAC (10 – 60 μM) for 24 h. Normal cell culture without CHAC served as control group. B16 F1 and A375 cells were used in logarithmic growth phase in this study. Cell viability and apoptosis were assessed using 3-(4, 5-dimethylthiazol-2-yl) 2, 5-diphe¬nyltetrazolium bromide (MTT) and flow cytometric assays, respectively. Western blotting was used to assess the levels of protein expression of X linked inhibitor of apoptosis (XIAP), survivin, p-Erk 1/2, and p-Mek 1/2. Results: Treatment of B16 F1 and A375 cells with CHAC led to significant and concentrationdependent reductions in their viability (p < 0.05). The proliferation of B16 F1 cells decreased from 93.41 to 32.87 %, while that of A375 cells was reduced from 95.23 to 36.50 %. Treatment of B16 F1 cells with CHAC significantly and concentration-dependently increased the population of cells in G0/G1 phase, and significantly reduced cell proportion in S and G2/M phases (p < 0.05). It also significantly and concentration-dependently promoted apoptosis in B16 F1 cells (p < 0.05). CHAC treatment significantly and concentration-dependently down-regulated the expressions of XIAP and survivin proteins (p < 0.05). Exposure of B16 F1 cells to CHAC significantly and concentration-dependently upregulated the expression of p-Mek 1/2, but down-regulated p-Erk 1/2 protein expression (p < 0.05). Densitometric analysis revealed that the expression of p-Mek 1/2 was increased from 12 to 91 %. Conclusion: The results of this study indicate that CHAC inhibits the proliferation of melanoma cells via upregulation of Mek 1/2 kinase activity, and therefore may find application in the management of melanoma

Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation

Explainability poses a major challenge to artificial intelligence (AI) techniques. Current studies on explainable AI (XAI) lack the efficiency of extracting global knowledge about the learning task, thus suffer deficiencies such as imprecise saliency, context-aware absence and vague meaning. In this paper, we propose the class association embedding (CAE) approach to address these issues. We employ an encoder-decoder architecture to embed sample features and separate them into class-related and individual-related style vectors simultaneously. Recombining the individual-style code of a given sample with the class-style code of another leads to a synthetic sample with preserved individual characters but changed class assignment, following a cyclic adversarial learning strategy. Class association embedding distills the global class-related features of all instances into a unified domain with well separation between classes. The transition rules between different classes can be then extracted and further employed to individual instances. We then propose an active XAI framework which manipulates the class-style vector of a certain sample along guided paths towards the counter-classes, resulting in a series of counter-example synthetic samples with identical individual characters. Comparing these counterfactual samples with the original ones provides a global, intuitive illustration to the nature of the classification tasks. We adopt the framework on medical image classification tasks, which show that more precise saliency maps with powerful context-aware representation can be achieved compared with existing methods. Moreover, the disease pathology can be directly visualized via traversing the paths in the class-style space

END-TO-END ENCRYPTION AND DECRYPTION WITHIN A HIERARCHICAL SD-WAN WITH AN IPV6 TRANSPORT

Techniques are presented herein that address a singular pain point in a hierarchical software-defined wide area network (SD-WAN) deployment comprising an Internet Protocol (IP) version 6 (IPv6) transport – end-to-end encryption and decryption. Aspects of the presented techniques leverage the IPv6 address schema to support a new concept that may be referred to herein as a micro-Transport Locator (TLOC) or uTLOC. Under the presented techniques, when an Overlay Management Protocol (OMP) virtual private network (VPN) route is published a next hop may be set to the combination of all of the uTLOCs along a path. Within such a context, each router (along the path) may program a customized action (such as, for example, the shifting of a destination, an insertion into a source, etc.) into a routing table for a uTLOC prefix and then forward a packet to a destination edge without the need for decryption and re-encryption operations in an intermediate border router