Verification and Validation of Common Derivative Terms Approximation in Meshfree Numerical Scheme

In order to improve the approximation of spatial derivatives without meshes, a set of meshfree numerical schemes for derivative terms is developed, which is compatible with the coordinates of Cartesian, cylindrical, and spherical. Based on the comparisons between numerical and theoretical solutions, errors and convergences are assessed by a posteriori method, which shows that the approximations for functions and derivatives are of the second accuracy order, and the scale of the support domain has some influences on numerical errors but not on accuracy orders. With a discrete scale h=0.01, the relative errors of the numerical simulation for the selected functions and their derivatives are within 0.65%

A Two-Stage Resilience Enhancement for Distribution Systems Under Hurricane Attacks

Hurricane events can cause severe consequences to the secure supply of electricity systems. This article designs a novel two-stage approach to minimize hurricane impact on distribution networks by automatic system operation. A dynamic hurricane model is developed, which has a variational wind intensity and moving path. The article then presents a two-stage resilience enhancement scheme that considers predisaster strengthening and postcatastrophe system reconfiguration. The pre-disaster stage evaluates load importance by an improved PageRank algorithm to help deploy the strengthening scheme precisely. Then, a combined soft open point and networked microgrid strategy is applied to enhance system resilience. Load curtailment is quantified considering both power unbalancing and the impact of line overloading. To promote computational efficiency, particle swarm optimization is applied to solve the designed model. A 33-bus electricity system is employed to demonstrate the effectiveness of the proposed method. The results clearly illustrate that the impact of hurricanes on load curtailment, which can be significantly reduced by appropriate network reconfiguration strategies. This model provides system operators a powerful tool to enhance the resilience of distribution systems against extreme hurricane events, reducing load curtailment

Decompiling x86 Deep Neural Network Executables

Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitives. This approach allows DL computations to be undertaken at low cost across a variety of computing platforms, including CPUs, GPUs, and various hardware accelerators. We present BTD (Bin to DNN), a decompiler for deep neural network (DNN) executables. BTD takes DNN executables and outputs full model specifications, including types of DNN operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. BTD delivers a practical framework to process DNN executables compiled by different DL compilers and with full optimizations enabled on x86 platforms. It employs learning-based techniques to infer DNN operators, dynamic analysis to reveal network architectures, and symbolic execution to facilitate inferring dimensions and parameters of DNN operators. Our evaluation reveals that BTD enables accurate recovery of full specifications of complex DNNs with millions of parameters (e.g., ResNet). The recovered DNN specifications can be re-compiled into a new DNN executable exhibiting identical behavior to the input executable. We show that BTD can boost two representative attacks, adversarial example generation and knowledge stealing, against DNN executables. We also demonstrate cross-architecture legacy code reuse using BTD, and envision BTD being used for other critical downstream tasks like DNN security hardening and patching.Comment: The extended version of a paper to appear in the Proceedings of the 32nd USENIX Security Symposium, 2023, (USENIX Security '23), 25 page

Farnesoid X Receptor (FXR) Aggravates Amyloid-β-Triggered Apoptosis by Modulating the cAMP-Response Element-Binding Protein (CREB)/Brain-Derived Neurotrophic Factor (BDNF) Pathway In Vitro

BACKGROUND: Alzheimer’s disease (AD), which results in cognitive deficits, usually occurs in older people and is mainly caused by amyloid beta (Aß) deposits and neurofibrillary tangles. The bile acid receptor, farnesoid X receptor (FXR), has been extensively studied in cardiovascular diseases and digestive diseases. However, the role of FXR in AD is not yet understood. The purpose of the present study was to investigate the mechanism of FXR function in AD. MATERIAL AND METHODS: Lentivirus infection, flow cytometry, real-time PCR, and western blotting were used to detect the gain or loss of FXR in cell apoptosis induced by Aß. Co-immunoprecipitation was used to analyze the molecular partners involved in Aß-induced apoptosis. RESULTS: We found that the mRNA and protein expression of FXR was enhanced in Ab-triggered neuronal apoptosis in differentiated SH-SY5Y cells and in mouse hippocampal neurons. Overexpression of FXR aggravated Aß-triggered neuronal apoptosis in differentiated SH-SY5Y cells, and this effect was further increased by treatment with the FXR agonist 6ECDCA. Molecular mechanism analysis by co-immunoprecipitation and immunoblotting revealed that FXR interacted with the cAMP-response element-binding protein (CREB), leading to decreased CREB and brain-derived neurotrophic factor (BDNF) protein levels. Low expression of FXR mostly reversed the Aß-triggered neuronal apoptosis effect and prevented the reduction in CREB and BDNF. CONCLUSIONS: These data suggest that FXR regulates Aß-induced neuronal apoptosis, which may be dependent on the CREB/BDNF signaling pathway in vitro

An Adaptive Fuzzy Min-Max Neural Network Classifier Based on Principle Component Analysis and Adaptive Genetic Algorithm

A novel adaptive fuzzy min-max neural network classifier called AFMN is proposed in this paper. Combined with principle component analysis and adaptive genetic algorithm, this integrated system can serve as a supervised and real-time classification technique. Considering the loophole in the expansion-contraction process of FMNN and GFMN and the overcomplex network architecture of FMCN, AFMN maintains the simple architecture of FMNN for fast learning and testing while rewriting the membership function, the expansion and contraction rules for hyperbox generation to solve the confusion problems in the hyperbox overlap region. Meanwhile, principle component analysis is adopted to finish dataset dimensionality reduction for increasing learning efficiency. After training, the confidence coefficient of each hyperbox is calculated based on the distribution of samples. During classifying procedure, utilizing adaptive genetic algorithm to complete parameter optimization for AFMN can also fasten the entire procedure than traversal method. For conditions where training samples are insufficient, data core weight updating is indispensible to enhance the robustness of classifier and the modified membership function can adjust itself according to the input varieties. The paper demonstrates the performance of AFMN through substantial examples in terms of classification accuracy and operating speed by comparing it with FMNN, GFMN, and FMCN

Experimental cyclic inter-conversion between Coherence and Quantum Correlations

Quantum resource theories seek to quantify sources of non-classicality that bestow quantum technologies their operational advantage. Chief among these are studies of quantum correlations and quantum coherence. The former to isolate non-classicality in the correlations between systems, the latter to capture non-classicality of quantum superpositions within a single physical system. Here we present a scheme that cyclically inter-converts between these resources without loss. The first stage converts coherence present in an input system into correlations with an ancilla. The second stage harnesses these correlations to restore coherence on the input system by measurement of the ancilla. We experimentally demonstrate this inter-conversion process using linear optics. Our experiment highlights the connection between non-classicality of correlations and non-classicality within local quantum systems, and provides potential flexibilities in exploiting one resource to perform tasks normally associated with the other.Comment: 8 pages, 4 figures, comments welcom