"Love for China:" an ethnography of US-based Chinese international students' patriotism

While the COVID-19 pandemic has wrecked havoc around the world, it has also caused upheavals to many Chinese international students studying in the US. Facing exorbitant prices of US-China airline tickets and stringent quarantine regulations, some of these previously mobile Chinese find themselves stranded in the US. Yet, they nevertheless declare their “love for China” after spending a majority of their adolescent and adult life abroad. Despite radical cultural and political differences between China and the US and the Chinese government’s unwelcoming gesture, they show an enduring bond to their motherland. Therefore, the following research questions are raised: In the contemporary context of China and the US, how do these international students negotiate patriotism—their “love for China”—while navigating educational and social terrains across the two nations? And more specifically, how is their institutional/lived experience in China and in the US able to reshape their understanding of and relation to these countries, and how do these well-educated youth reject, reconnect, and redefine Chinese patriotism over time as they mature academically in the US? This dissertation explores these questions by drawing on my ethnographic engagement with a dozen of young Chinese students, who have been pursuing their education in the US for at least a quarter of their lives. Through ethnographic interviews and observations, I show that the development of these Chinese international students’ patriotism is by no means a smooth transplant from China to the US but rather represents a fragmented, contingent process fraught with paradoxes and liminality. Defying simplification and dualism, these migrant students’ patriotism are under continuous negotiation. By dovetailing theoretical understanding with corporeal lived experience, contrasting privilege in China with challenges in the US, conjoining Chinese political resistance and US racial awakening, and combining Confucian epistemology with Enlightenment thinking, these US-based Chinese students approach their love for China in their unique, diasporic way. This ethnography of transnational patriotism attempts to capture the complex “love for China” of Chinese international students in the US

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers. Machine learning-based (ML-based) Android malware detection (AMD) methods are crucial in addressing this problem; however, their vulnerability to adversarial examples raises concerns. Current attacks against ML-based AMD methods demonstrate remarkable performance but rely on strong assumptions that may not be realistic in real-world scenarios, e.g., the knowledge requirements about feature space, model parameters, and training dataset. To address this limitation, we introduce AdvDroidZero, an efficient query-based attack framework against ML-based AMD methods that operates under the zero knowledge setting. Our extensive evaluation shows that AdvDroidZero is effective against various mainstream ML-based AMD methods, in particular, state-of-the-art such methods and real-world antivirus solutions.Comment: To Appear in the ACM Conference on Computer and Communications Security, November, 202

Static Semantics Reconstruction for Enhancing JavaScript-WebAssembly Multilingual Malware Detection

The emergence of WebAssembly allows attackers to hide the malicious functionalities of JavaScript malware in cross-language interoperations, termed JavaScript-WebAssembly multilingual malware (JWMM). However, existing anti-virus solutions based on static program analysis are still limited to monolingual code. As a result, their detection effectiveness decreases significantly against JWMM. The detection of JWMM is challenging due to the complex interoperations and semantic diversity between JavaScript and WebAssembly. To bridge this gap, we present JWBinder, the first technique aimed at enhancing the static detection of JWMM. JWBinder performs a language-specific data-flow analysis to capture the cross-language interoperations and then characterizes the functionalities of JWMM through a unified high-level structure called Inter-language Program Dependency Graph. The extensive evaluation on one of the most representative real-world anti-virus platforms, VirusTotal, shows that \system effectively enhances anti-virus systems from various vendors and increases the overall successful detection rate against JWMM from 49.1\% to 86.2\%. Additionally, we assess the side effects and runtime overhead of JWBinder, corroborating its practical viability in real-world applications.Comment: Accepted to ESORICS 202

Active and intelligent control onto thermal behaviors of a motorized spindle unit

Motorized spindle unit is the core component of a precision CNC machine tool. Its thermal errors perform generally serious disturbance onto the accuracy and accuracy stability of precision machining. Traditionally, the effectiveness of the compensation method for spindle thermal errors is restricted by machine freedom degrees. For this problem, this paper presents an active, differentiated, and intelligent control method onto spindle thermal behaviors, to realize comprehensive and accurate suppressions onto spindle thermal errors. Firstly, the mechanism of spindle heat generation/dissipation-structural temperature-thermal deformation error is analyzed. This modeling conveys that the constantly least spindle thermal errors can be realized by differentiated and active controls onto its structural thermal behaviors. Based on this principle, besides, the active control method is developed by a combination of extreme learning machine (ELM) and genetic algorithm (GA). The aim is to realize the general applicability of this active and intelligent control algorithm, for the spindle time-varying thermal behaviors. Consequently, the contrasting experiments clarify that the proposed active and intelligent control method can suppress accurately and synchronously all kinds of spindle thermal errors. It is significantly beneficial for the improvements of the accuracy and accuracy stability of motorized spindle units

Quantitative Proteomic and Transcriptomic Analyses of Metabolic Regulation of Adult Reproductive Diapause in Drosophila suzukii (Diptera: Drosophilidae) Females

Diapause is a form of dormancy used by many insects to survive adverse environmental conditions, which can occur in specific developmental stages in different species. Drosophila suzukii is a serious economic pest and we determined the conditions for adult reproductive diapause by the females in our previous studies. In this study, we combined RNA-Seq transcriptomic and quantitative proteomic analyses to identify adult reproductive diapause-related genes and proteins. According to the transcriptomic analysis, among 242 annotated differentially expressed genes in non-diapause and diapause females, 129 and 113 genes were up- and down-regulated, respectively. In addition, among the 2,375 proteins quantified, 39 and 23 proteins were up- and down-regulated, respectively. The gene expression patterns in diapause- and non-diapause were confirmed by qRT-PCR or western blot analysis. The overall analysis of robustly regulated genes at the protein and mRNA levels found four genes that overlapped in the up-regulated group and six genes in the down-regulated group, and thus these proteins/genes may regulate adult reproductive diapause. These differentially expressed proteins/genes act in the citrate cycle, insulin signaling pathway, PI3K-Akt signaling pathway, and amino acid biosynthesis pathways. These results provide the basis for further studies of the molecular regulation of reproductive diapause in this species

Limits to two-spin-qubit gate fidelity from thermal and vacuum fluctuations

High-fidelity quantum gate operations are essential for achieving scalable quantum circuits. In spin qubit quantum computing systems, metallic gates and antennas which are necessary for qubit operation, initialization, and readout, also cause detriments by enhancing fluctuations of electromagnetic fields. Therefore evanescent wave Johnson noise (EWJN) caused by thermal and vacuum fluctuations becomes an important unmitigated noise, which induces the decay of spin qubits and limits the quantum gate operation fidelity. Here, we first develop a quantum electrodynamics theory of EWJN. Then we propose a numerical technique based on volume integral equations to quantify EWJN strength in the vicinity of nanofabricated metallic gates with arbitrary geometry. We study the limits to two spin-qubit gate fidelity from EWJN-induced relaxation processes in two experimentally relevant quantum computing platforms: (a) silicon quantum dot system and (b) NV centers in diamond. Finally, we introduce the Lindbladian engineering method to optimize the control pulse sequence design and show its enhanced performance over Hamiltonian engineering in mitigating the influence of thermal and vacuum fluctuations. Our work leverages advances in computational electromagnetics, fluctuational electrodynamics and open quantum systems to suppress the effects of thermal and vacuum fluctuations and reach the limits of two-spin-qubit gate fidelity.Comment: 16 pages, 8 figure

Binary Star Evolution in Different Environments: Filamentary, Fractal, Halo and Tidal-tail Clusters

Using membership of 85 open clusters from previous studies (Pang et al. 2021a,b, 2022b; Li et al. 2021) based on Gaia DR3 data, we identify binary candidates in the color-magnitude diagram, for systems with mass ratio q > 0.4. The binary fraction is corrected for incompleteness at different distances due to the Gaia angular resolution limit. We find a decreasing binary fraction with increasing cluster age, with substantial scatter. For clusters with a total mass > 200$M_\odot$, the binary fraction is independent of cluster mass. The binary fraction depends strongly on stellar density. Among four types of cluster environments, the lowest-density filamentary and fractal stellar groups have the highest mean binary fraction: 23.6% and 23.2%, respectively. The mean binary fraction in tidal-tail clusters is 20.8%, and is lowest in the densest halo-type clusters: 14.8%. We find clear evidence of early disruptions of binary stars in the cluster sample. The radial binary fraction depends strongly on the cluster-centric distance across all four types of environments, with the smallest binary fraction within the half-mass radius $r_h$, and increasing towards a few $r_h$. Only hints of mass segregation is found in the target clusters. The observed amount of mass segregation is not significant to generate a global effect inside the target clusters. We evaluate the bias of unresolved binary systems (assuming a primary mass of 1$M_\odot$) in 1D tangential velocity, which is 0.1-1$\,\rm km\,s^{-1}$. Further studies are required to characterize the internal star cluster kinematics using Gaia proper motions