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In this paper, we study the problem of constructing projective systematic authentication schemes based on binary linear codes. In systematic authentication, a tag for authentication is generated and then appended to the information, also referred to as the source, to be sent from the sender. Existing approaches to leverage projective constructions focus primarily on codes over large alphabets, and the projection is simply into one single symbol of the codeword. In this work, we extend the projective construction and propose a general projection process in which the source, which is mapped to a higher dimensional codeword in a given code, is first projected to a lower dimensional vector. The resulting vector is then masked to generate the tag. To showcase the new method, we focus on leveraging binary linear codes and, in particular, Reed-Muller (RM) codes for the proposed projective construction. More specifically, we propose systematic authentication schemes based on RM codes, referred to as RM-Acodes. We provide analytical results for probabilities of deception, widely considered as the main metrics to evaluate the performance of authentication systems. Through our analysis, we discover and discuss explicit connections between the probabilities of deception and various properties of RM codes.

Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios. To handle domain-generalized unknown attacks, we introduce a new method, DGUA-FAS, which consists of a Transformer-based feature extractor and a synthetic unknown attack sample generator (SUASG). The SUASG network simulates unknown attack samples to assist the training of the feature extractor. Experimental results show that our method achieves superior performance on domain generalization FAS with known or unknown attacks., Comment: IEEE International Conference on Image Processing (ICIP 2023)

We consider a fully-decentralized scenario in which no central trusted entity exists and all clients are honest-but-curious. The state-of-the-art approaches to this problem often rely on cryptographic protocols, such as multiparty computation (MPC), that require mapping real-valued data to a discrete alphabet, specifically a finite field. These approaches, however, can result in substantial accuracy losses due to computation overflows. To address this issue, we propose A-MPC, a private analog MPC protocol that performs all computations in the analog domain. We characterize the privacy of individual datasets in terms of $(\epsilon, \delta)$-local differential privacy, where the privacy of a single record in each client's dataset is guaranteed against other participants. In particular, we characterize the required noise variance in the Gaussian mechanism in terms of the required $(\epsilon,\delta)$-local differential privacy parameters by solving an optimization problem. Furthermore, compared with existing decentralized protocols, A-MPC keeps the privacy of individual datasets against the collusion of all other participants, thereby, in a notably significant improvement, increasing the maximum number of colluding clients tolerated in the protocol by a factor of three compared with the state-of-the-art collaborative learning protocols. Our experiments illustrate that the accuracy of the proposed $(\epsilon,\delta)$-locally differential private logistic regression and linear regression models trained in a fully-decentralized fashion using A-MPC closely follows that of a centralized one performed by a single trusted entity.

Scanning probe microscopes scan and manipulate a sharp tip in the immediate vicinity of a sample surface. The limited bandwidth of the feedback mechanism used for stabilizing the separation between the tip and the sample makes the fragile nanoscopic tip very susceptible to mechanical instabilities. We propose, demonstrate and characterize a new alternative device based on bulging a thin substrate against a second substrate and rolling them with respect each other. We showcase the power of this method by placing gold nanoparticles and semiconductor quantum dots on the two opposite substrates and positioning them with nanometer precision to enhance the fluorescence intensity and emission rate. We exhibit the passive mechanical stability of the system over more than one hour. The design concept presented in this work holds promise in a variety of other contexts, where nanoscopic features have to be positioned and kept near contact with each other.

This paper investigates problems associated with the valuation of callable American volatility put options. Our approach involves modeling volatility dynamics as a mean-reverting 3/2 volatility process. We first propose a pricing formula for the perpetual American knock-out put. Under the given conditions, the value of perpetual callable American volatility put options is discussed.

Multiexcitonic transitions and emission of several photons per excitation comprise a very attractive feature of semiconductor quantum dots for optoelectronics applications. However, these higher-order radiative processes are usually quenched in colloidal quantum dots by Auger and other non-radiative decay channels. To increase the multiexcitonic quantum efficiency, several groups have explored plasmonic enhancement, so far with moderate results. By controlled positioning of individual quantum dots in the near field of gold nanocone antennas, we enhance the radiative decay rates of monoexcitons and biexcitons by 109 and 100 folds at quantum efficiencies of 60% and 70%, respectively, in very good agreement with the outcome of numerical calculations. We discuss the implications of our work for future fundamental and applied research in nano-optics., Comment: 31 pages, 6 figures, 1 table

Aluminum, as a metallic material for plasmonics, is of great interest because it extends the applications of surface plasmon resonance into the ultraviolet (UV) region and excels noble metals in the natural abundance, cost and compatibility with modern semiconductor fabrication process. Here, we present UV to near-infrared (NIR) plasmonic resonance of single-crystalline aluminum nanoslits and nanoholes. The high-definition nanostructures are fabricated with focused ion-beam (FIB) milling into an ultrasmooth single-crystalline aluminum film grown on a semiconducting GaAs substrate with molecular beam epitaxy (MBE) method. The single-crystalline aluminum film shows improved reflectivity and reduced two-photon photoluminescence (TPPL) due to the ultrasmooth surface. Both linear scattering and non-linear TPPL are studied in detail. The nanoslit arrays show clear Fano-like resonance and the nanoholes are found to support both photonic modes and localized surface plasmonic resonance. We also found that TPPL generation is more efficient when the excitation polarization is parallel rather than perpendicular to the edge of the aluminum film. Such counter-intuitive phenomenon is attributed to the high refractive index of the GaAs substrate. We show that the polarization of TPPL from aluminum well preserves the excitation polarization and is independent of the crystal orientation of the film or substrate. Our study gains insight into the optical property of aluminum nanostructures on high-index semiconducting GaAs substrate and illustrates a practical route to implement plasmonic devices onto semiconductors for future hybrid nanodevices., Comment: 25 pages, 5 figures and Supporting Information