Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective

Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original training samples. In this study, we delve into the theoretical aspects of backdoor attacks and dataset distillation based on kernel methods. We introduce two new theory-driven trigger pattern generation methods specialized for dataset distillation. Following a comprehensive set of analyses and experiments, we show that our optimization-based trigger design framework informs effective backdoor attacks on dataset distillation. Notably, datasets poisoned by our designed trigger prove resilient against conventional backdoor attack detection and mitigation methods. Our empirical results validate that the triggers developed using our approaches are proficient at executing resilient backdoor attacks.Comment: 19 pages, 4 figure

Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

Recently, quantum classifiers have been known to be vulnerable to adversarial attacks, where quantum classifiers are fooled by imperceptible noises to have misclassification. In this paper, we propose one first theoretical study that utilizing the added quantum random rotation noise can improve the robustness of quantum classifiers against adversarial attacks. We connect the definition of differential privacy and demonstrate the quantum classifier trained with the natural presence of additive noise is differentially private. Lastly, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples supported by experimental results.Comment: Submitted to IEEE ICASSP 202

Acquiring Authentic Data in Unattended Wireless Sensor Networks

An Unattended Wireless Sensor Network (UWSN) can be used in many applications to collect valuable data. Nevertheless, due to the unattended nature, the sensors could be compromised and the sensor readings would be maliciously altered so that the sink accepts the falsified sensor readings. Unfortunately, few attentions have been given to this authentication problem. Moreover, existing methods suffer from different kinds of DoS attacks such as Path-Based DoS (PDoS) and False Endorsement-based DoS (FEDoS) attacks. In this paper, a scheme, called AAD, is proposed to Acquire Authentic Data in UWSNs. We exploit the collaboration among sensors to address the authentication problem. With the proper design of the collaboration mechanism, AAD has superior resilience against sensor compromises, PDoS attack, and FEDoS attack. In addition, compared with prior works, AAD also has relatively low energy consumption. In particular, according to our simulation, in a network with 1,000 sensors, the energy consumed by AAD is lower than 30% of that consumed by the existing method, ExCo. The analysis and simulation are also conducted to demonstrate the superiority of the proposed AAD scheme over the existing methods

Targeted gene therapy of nasopharyngeal cancer in vitro and in vivo by enhanced thymidine kinase expression driven by human TERT promoter and CMV enhancer

<p>Abstract</p> <p>Background/Aim</p> <p>To explore the therapeutic effects of thymidine kinase (TK) expressed by enhanced vector pGL3-basic- hTERTp-TK-EGFP-CMV driven by human telomerase reverse transcriptase promoter (hTERTp) as well as cytomegalovirus immediate early promoter enhancer (CMV).</p> <p>Materials/Methods</p> <p>Enhanced TK-EGFP expression was confirmed by fluorescent microscopy, real time PCR and telomerase activity. Its effects were examined by survival of tumor cells NPC 5-8F and MCF-7, index of xenograft implanted in nude mice and histology.</p> <p>Results</p> <p>Compared with non-enhanced vector pGL3-basic-TK-hTERTp-EGFP, TK expressed by the enhanced vector significantly decreased NPC 5-8F and MCF-7 cell survival rates after ganciclovir (GCV) treatment (p < 0.001) and tumor progress in nude mice with NPC xenograft and treated with GCV, without obvious toxicity to mouse liver and kidney.</p> <p>Conclusion</p> <p>The enhanced TK expression vector driven by hTERTp with CMV enhancer has brighter clinical potentials in nasopharyngeal carcinoma therapy than the non-enhanced vector.</p

Silicon Encapsulated Carbon Nanotubes

A dual stage process of depositing bamboo-like carbon nanotubes (BCNTs) by hot filament chemical vapor deposition (HFCVD) and coating Si using Radio frequency sputtering (RFS) technique. The films were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and electron field emission studies (EFE). SEM results suggest a dense network of homogeneous silicon-coated BCNTs. From the comprehensive analysis of the results provided by these techniques emerges the picture of Si encapsulated BCNTs

Localising Sovereign Debt: The Rise of Local Currency Bond Markets in Sub-Saharan Africa

This paper analyses the development of local currency sovereign bond markets (LCBMs), a potentially important but often overlooked source of longer-term public finance, in Sub-Saharan Africa (SSA). We construct a novel dataset comprising 28 SSA countries for the period 2000-2014 to identify the main correlates of LCBM capitalization, of local currency bond (LCB) tenors and of LCB issue yields. Our econometric analysis is complemented by case studies of Ghana, Kenya and Nigeria, where we further investigate the drivers of LCBM development and place LCBMs in a broader public debt context. We find that LCBMs have become important sources of financing in SSA but that new vulnerabilities emerge from the costs of domestic borrowing, short bond tenors and the composition of the investor base

Intrinsic Determinants of Aβ12–24 pH-Dependent Self-Assembly Revealed by Combined Computational and Experimental Studies

The propensity of amyloid- (A) peptide to self-assemble into highly ordered amyloid structures lies at the core of their accumulation in the brain during Alzheimer's disease. By using all-atom explicit solvent replica exchange molecular dynamics simulations, we elucidated at the atomic level the intrinsic determinants of the pH-dependent dimerization of the central hydrophobic segment A and related these with the propensity to form amyloid fibrils measured by experimental tools such as atomic force microscopy and fluorescence. The process of A dimerization was evaluated in terms of free energy landscape, side-chain two-dimensional contact probability maps, -sheet registries, potential mean force as a function of inter-chain distances, secondary structure development and radial solvation distributions. We showed that dimerization is a key event in A amyloid formation; it is highly prompted in the order of pH 5.02.98.4 and determines further amyloid growth. The dimerization is governed by a dynamic interplay of hydrophobic, electrostatic and solvation interactions permitting some variability of -sheets at each pH. These results provide atomistic insight into the complex process of molecular recognition detrimental for amyloid growth and pave the way for better understanding of the molecular basis of amyloid diseases