A Fast Order-Based Approach for Core Maintenance

Graphs have been widely used in many applications such as social networks, collaboration networks, and biological networks. One important graph analytics is to explore cohesive subgraphs in a large graph. Among several cohesive subgraphs studied, k-core is one that can be computed in linear time for a static graph. Since graphs are evolving in real applications, in this paper, we study core maintenance which is to reduce the computational cost to compute k-cores for a graph when graphs are updated from time to time dynamically. We identify drawbacks of the existing efficient algorithm, which needs a large search space to find the vertices that need to be updated, and has high overhead to maintain the index built, when a graph is updated. We propose a new order-based approach to maintain an order, called k-order, among vertices, while a graph is updated. Our new algorithm can significantly outperform the state-of-the-art algorithm up to 3 orders of magnitude for the 11 large real graphs tested. We report our findings in this paper

Extension of Process Limits in High‐Strength Aluminum Forming by Local Contact Heating

The aluminum alloy EN AW‐7075 T6 is used in the automotive sector for its favorable strength‐to‐weight ratio. However, the limited cold formability is currently addressed by energy‐ and time‐consuming temperature‐assisted processes. In order to limit the effort to critical forming areas only, the state‐of‐the‐art shows promising results for increasing the blank temperature in the range of warm forming. The design of new processes in an industrial context requires appropriate numerical simulation with inherent complexity due to time‐ and temperature‐dependent effects. Herein, the potential of a newly developed tool setup and process chain with integrated local contact heating of the EN AW‐7075 T6 blank is investigated on the basis of a curved hat profile. A thermomechanically coupled FE model of the process is developed and validated. The influence of the local heating layout is analyzed in experimental forming tests and a corresponding process window is derived. The influence of local heating on the occurring failure mechanisms is discussed based on simulation results. The equivalent plastic strain evolution is successfully used to evaluate the local heating dependent failure behavior. A significant increase in the overall formability of the part is achieved by the proposed process chain

Fault line selection in cooperation with multi-mode grounding control for the floating nuclear power plant grid

The Floating nuclear power plant grid is composed of power generation, in-station power supply and external power delivery. To ensure the safety of the nuclear island, the in-station system adopts a special power supply mode, while the external power supply needs to be adapted to different types of external systems. Because of frequent single phase-ground faults and various fault forms, the fault line selection protection should be accurate, sensitive and adaptive. This paper presents a fault line selection method in cooperation with multi-mode grounding control. Based on the maximum united energy entropy ratio (MUEER), the optimal wavelet basis function and decomposition scale are adaptively chosen, while the fault line is selected by wavelet transform modulus maxima (WTMM). For high-impedance faults (HIFs), to enlarge the fault feature, the system grounding mode can be switched by the multi-mode grounding control. Based on the characteristic of HIFs, the fault line can be selected by comparing phase differences of zero-sequence current mutation and fault phase voltage mutation before and after the fault. Simulation results using MATLAB/Simulink show the effectiveness of the proposed method in solving the protection problems

Detecting GPC3-Expressing Hepatocellular Carcinoma with L5 Peptide-Guided Pretargeting Approach: An In Vitro MRI Experiment

Background and Aim: Glypican-3 (GPC3) is a novel molecular target for hepatocellular carcinoma (HCC). This study investigated the potential of an L5 peptide-guided pretargeting approach to identify GPC3-expressing HCC cells using ultra-small super-paramagnetic iron oxide (USPIO) as the MRI probe.Methods: Immunofluorescence with carboxyfluorescein (FAM)-labeled L5 peptide was performed in HepG2 and HL-7702 cells. Polyethylene glycol-modified ultrasmall superparamagnetic iron oxide (PEG-USPIO) and its conjugates with streptavidin (SA-PEG-USPIO) were synthesized, and hydrodynamic diameters, zeta potential, T2 relaxivity, and cytotoxicity were measured. MR T2-weighted imaging of HepG2 was performed to observe signal changes in the pretargeting group, which was first incubated with biotinylated L5 peptide and then with SA-PEG-USPIO. Prussian blue staining of cells was used to assess iron deposition.Results: Immunofluorescence assays showed high specificity of L5 peptide for GPC3. SA-PEG-USPIO nanoparticles had ≈36 nm hydrodynamic diameter, low toxicity, negative charge and high T2 relaxivity. MR imaging revealed that a significant negative enhancement was only observed in HepG2 cells from the pretargeting group, which also showed significant iron deposition with Prussian blue staining.Conclusion: MR imaging with USPIO as the probe has potential to identify GPC3-expressing HCC through L5 peptide-guided pretargeting approach

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of face recognition systems and evaluate their robustness before deployed. However, most existing physical attacks are either detectable readily or ineffective against commercial recognition systems. The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems. It requires that this technique can simultaneously deceive black-box recognition models and evade defensive mechanisms. To fulfill this, we design adversarial textured 3D meshes (AT3D) with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses. However, the mesh-based optimization regime calculates gradients in high-dimensional mesh space, and can be trapped into local optima with unsatisfactory transferability. To deviate from the mesh-based space, we propose to perturb the low-dimensional coefficient space based on 3D Morphable Model, which significantly improves black-box transferability meanwhile enjoying faster search efficiency and better visual quality. Extensive experiments in digital and physical scenarios show that our method effectively explores the security vulnerabilities of multiple popular commercial services, including three recognition APIs, four anti-spoofing APIs, two prevailing mobile phones and two automated access control systems

Spatial Disassociation of Disrupted Functional Connectivity for the Default Mode Network in Patients with End-Stage Renal Disease

To investigate the aberrant functional connectivity of the default mode network (DMN) in patients with end-stage renal disease (ESRD) and their clinical relevance