Parallel Numerical Simulation of Complex Unsteady Multi-Component Three-Dimensional Flow Field of Nonequilibrium Chemical Reaction

In this paper, the gridless method, which is known for its complete independence of grids, is combined with parallel method to obtain a dynamic parallel multi-component three-dimensional (3D) gridless method to compute the complex unsteady multi-component 3D flow field of nonequilibrium chemical reaction (NCR). Specifically, the flow field was described with a multi-component arbitrary Lagrangian-Eulerian (ALE) control equation, which contains the source term of the chemical reaction. The flow term was decoupled from the chemical reaction term, and the stiff problem of the latter term was solved by time splitting. To control the convective flux in the control equation, the multi-component artificially upstream flux vector splitting (AUFS) scheme was derived for the 3D space. In addition, 3D local point cloud reconstruction was carried out to reconstruct the abnormal point cloud near the large moving boundary in real time. Besides, geometrical zoning was adopted for the parallel part to dynamically balance the computing load across different zones. The message passing interface (MPI) was selected to realize the communication between the zones. After that, the proposed multi-component gridless algorithm was proven accurate through two examples: hydrogen combustion reaction in a vessel, and shock-induced combustion with blunt projectile. Finally, the proposed dynamic parallel multi-component 3D gridless method was applied to compute the 3D muzzle flow field of prefilled serial-connected projectiles. The evolution of the complex flow field was obtained for projectile 2. The parallel efficiency of our method surpassed 79%

An Effective Strategy to Build Up a Balanced Test Suite for Spectrum-Based Fault Localization

During past decades, many automated software faults diagnosis techniques including Spectrum-Based Fault Localization (SBFL) have been proposed to improve the efficiency of software debugging activity. In the field of SBFL, suspiciousness calculation is closely related to the number of failed and passed test cases. Studies have shown that the ratio of the number of failed and passed test case has more significant impact on the accuracy of SBFL than the total number of test cases, and a balanced test suite is more beneficial to improving the accuracy of SBFL. Based on theoretical analysis, we proposed an PNF (Passed test cases, Not execute Faulty statement) strategy to reduce test suite and build up a more balanced one for SBFL, which can be used in regression testing. We evaluated the strategy making experiments using the Siemens program and Space program. Experiments indicated that our PNF strategy can be used to construct a new test suite effectively. Compared with the original test suite, the new one has smaller size (average 90% test case was reduced in experiments) and more balanced ratio of failed test cases to passed test cases, while it has the same statement coverage and fault localization accuracy

Robustness of Half-Integer Quantized Hall Conductivity against Disorder in an Anisotropic Dirac Semimetal with Parity Anomaly

Two-dimensional Dirac semimetals with a single massless Dirac cone exhibit the parity anomaly. Usually, such a kind of anomalous topological semimetallic phase in real materials is unstable where any amount of disorder can drive it into a diffusive metal and destroy the half-integer quantized Hall conductivity as an indicator of parity anomaly. Here, based on low-energy effective model, we propose an anisotropic Dirac semimetal which explicitly breaks time-reversal symmetry and carries a half-integer quantized Hall conductivity. This topological semimetallic phase can be realized on a deformed honeycomb lattice subjected to a magnetic flux. Moreover, we perceptively investigate the disorder correction to the Hall conductivity. The results show that the effects of disorder can be strongly suppressed and thereby the nearly half-integer quantization of Hall conductivity can exist in a wide region of disorder, indicating that our proposed anisotropic Dirac semimetal is an exciting platform to investigate the parity anomaly phenomena.Comment: 7 pages, 4 figure

The One-dimensional Chiral Anomaly and its Disorder Response

The condensed-matter realization of chiral anomaly has attracted tremendous interest in exploring unexpected phenomena of quantum field theory. Here, we show that one-dimensional (1D) chiral anomaly (i.e., 1D nonconservational chiral current under a background electromagnetic field) can be realized in a generalized Su-Schrieffer-Heeger model where a single gapless Dirac cone occurs. Based on the topological Thouless pump and anomalous dynamics of chiral displacement, we elucidate that such a system possesses the half-integer quantization of winding number. Moreover, we investigate the evolution of 1D chiral anomaly with respect to two typical types of disorder, i.e., on-site disorder and bond disorder. The results show that the on-site disorder tends to smear the gapless Dirac cone. However, we propose a strategy to stabilize the half-integer quantization, facilitating its experimental detection. Furthermore, we demonstrate that the bond disorder causes a unique crossover with disorder-enhanced topological charge pumping, driving the system into a topological Anderson insulator phase

Large Language Models can be Guided to Evade AI-Generated Text Detection

Large Language Models (LLMs) have demonstrated exceptional performance in a variety of tasks, including essay writing and question answering. However, it is crucial to address the potential misuse of these models, which can lead to detrimental outcomes such as plagiarism and spamming. Recently, several detectors have been proposed, including fine-tuned classifiers and various statistical methods. In this study, we reveal that with the aid of carefully crafted prompts, LLMs can effectively evade these detection systems. We propose a novel Substitution-based In-Context example Optimization method (SICO) to automatically generate such prompts. On three real-world tasks where LLMs can be misused, SICO successfully enables ChatGPT to evade six existing detectors, causing a significant 0.54 AUC drop on average. Surprisingly, in most cases these detectors perform even worse than random classifiers. These results firmly reveal the vulnerability of existing detectors. Finally, the strong performance of SICO suggests itself as a reliable evaluation protocol for any new detector in this field

DeepMag+ : Sniffing Mobile Apps in Magnetic Field Through Deep Learning

This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only 15% and at the same time has a negligible effect on benign Apps. ©2019 Published by Elsevier B.V

Photoinduced High-Chern-Number Quantum Anomalous Hall Effect from Higher-Order Topological Insulators

Quantum anomalous Hall (QAH) insulators with high Chern number host multiple dissipationless chiral edge channels, which are of fundamental interest and promising for applications in spintronics and quantum computing. However, only a limited number of high-Chern-number QAH insulators have been reported to date. Here, we propose a dynamic approach for achieving high-Chern-number QAH phases in periodically driven two-dimensional higher-order topological insulators (HOTIs).In particular, we consider two representative kinds of HOTIs which are characterized by a quantized quadruple moment and the second Stiefel-Whitney number, respectively. Using the Floquet formalism for periodically driven systems, we demonstrate that QAH insulators with tunable Chern number up to four can be achieved. Moreover, we show by first-principles calculations that the monolayer graphdiyne, a realistic HOTI, is an ideal material candidate. Our work not only establishes a strategy for designing high-Chern-number QAH insulators in periodically driven HOTIs, but also provides a powerful approach to investigate exotic topological states in nonequilibrium cases.Comment: 6 pages, 3 figure