812 research outputs found
"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, 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 , and increasing
towards a few . 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) in 1D
tangential velocity, which is 0.1-1. Further studies are
required to characterize the internal star cluster kinematics using Gaia proper
motions
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