Painlev\'e III′' and the Hankel Determinant Generated by a Singularly Perturbed Gaussian Weight

In this paper, we study the Hankel determinant generated by a singularly perturbed Gaussian weight $$w(x,t)=\mathrm{e}^{-x^{2}-\frac{t}{x^{2}}},\;\;x\in(-\infty, \infty),\;\;t>0.$$ By using the ladder operator approach associated with the orthogonal polynomials, we show that the logarithmic derivative of the Hankel determinant satisfies both a non-linear second order difference equation and a non-linear second order differential equation. The Hankel determinant also admits an integral representation involving a Painlev\'e III$'$. Furthermore, we consider the asymptotics of the Hankel determinant under a double scaling, i.e. $n\rightarrow\infty$ and $t\rightarrow 0$ such that $s=(2n+1)t$ is fixed. The asymptotic expansions of the scaled Hankel determinant for large $s$ and small $s$ are established, from which Dyson's constant appears.Comment: 22 page

Efficiency of the spectral element method with very high polynomial degree to solve the elastic wave equation

International audienc

BITS-Net: Blind Image Transparency Separation Network

This research presents a new approach for blind single-image transparency separation, a significant challenge in image processing. The proposed framework divides the task into two parallel processes: feature separation and image reconstruction. The feature separation task leverages two deep image prior (DIP) networks to recover two distinct layers. An exclusion loss and deep feature separation loss are used to decompose features. For the image reconstruction task, we minimize the difference between the mixed image and the re-mixed image while also incorporating a regularizer to impose natural priors on each layer. Our results indicate that our method performs comparably or outperforms state-of-the-art approaches when tested on various image datasets

Attention-Enhancing Backdoor Attacks Against BERT-based Models

Recent studies have revealed that \textit{Backdoor Attacks} can threaten the safety of natural language processing (NLP) models. Investigating the strategies of backdoor attacks will help to understand the model's vulnerability. Most existing textual backdoor attacks focus on generating stealthy triggers or modifying model weights. In this paper, we directly target the interior structure of neural networks and the backdoor mechanism. We propose a novel Trojan Attention Loss (TAL), which enhances the Trojan behavior by directly manipulating the attention patterns. Our loss can be applied to different attacking methods to boost their attack efficacy in terms of attack successful rates and poisoning rates. It applies to not only traditional dirty-label attacks, but also the more challenging clean-label attacks. We validate our method on different backbone models (BERT, RoBERTa, and DistilBERT) and various tasks (Sentiment Analysis, Toxic Detection, and Topic Classification).Comment: Findings of EMNLP 202

Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.Comment: 32 Pages, 12 figures, accepted to IEEE Transactions on Wireless Communication

A Fast and Scalable Authentication Scheme in IoT for Smart Living

Numerous resource-limited smart objects (SOs) such as sensors and actuators have been widely deployed in smart environments, opening new attack surfaces to intruders. The severe security flaw discourages the adoption of the Internet of things in smart living. In this paper, we leverage fog computing and microservice to push certificate authority (CA) functions to the proximity of data sources. Through which, we can minimize attack surfaces and authentication latency, and result in a fast and scalable scheme in authenticating a large volume of resource-limited devices. Then, we design lightweight protocols to implement the scheme, where both a high level of security and low computation workloads on SO (no bilinear pairing requirement on the client-side) is accomplished. Evaluations demonstrate the efficiency and effectiveness of our scheme in handling authentication and registration for a large number of nodes, meanwhile protecting them against various threats to smart living. Finally, we showcase the success of computing intelligence movement towards data sources in handling complicated services.Comment: 15 pages, 7 figures, 3 tables, to appear in FGC