A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness

The robustness of deep neural networks (DNNs) against adversarial attacks has been studied extensively in hopes of both better understanding how deep learning models converge and in order to ensure the security of these models in safety-critical applications. Adversarial training is one approach to strengthening DNNs against adversarial attacks, and has been shown to offer a means for doing so at the cost of applying computationally expensive training methods to the entire model. To better understand these attacks and facilitate more efficient adversarial training, in this paper we develop a novel theoretical framework that investigates how the adversarial robustness of a subnetwork contributes to the robustness of the entire network. To do so we first introduce the concept of semirobustness, which is a measure of the adversarial robustness of a subnetwork. Building on this concept, we then provide a theoretical analysis to show that if a subnetwork is semirobust and there is a sufficient dependency between it and each subsequent layer in the network, then the remaining layers are also guaranteed to be robust. We validate these findings empirically across multiple DNN architectures, datasets, and adversarial attacks. Experiments show the ability of a robust subnetwork to promote full-network robustness, and investigate the layer-wise dependencies required for this full-network robustness to be achieved.Comment: 3 figures, 3 tables, 17 pages, has appendice

Surface Generated Acoustic Wave Biosensors for the Detection of Pathogens: A Review

This review presents a deep insight into the Surface Generated Acoustic Wave (SGAW) technology for biosensing applications, based on more than 40 years of technological and scientific developments. In the last 20 years, SGAWs have been attracting the attention of the biochemical scientific community, due to the fact that some of these devices - Shear Horizontal Surface Acoustic Wave (SH-SAW), Surface Transverse Wave (STW), Love Wave (LW), Flexural Plate Wave (FPW), Shear Horizontal Acoustic Plate Mode (SH-APM) and Layered Guided Acoustic Plate Mode (LG-APM) - have demonstrated a high sensitivity in the detection of biorelevant molecules in liquid media. In addition, complementary efforts to improve the sensing films have been done during these years. All these developments have been made with the aim of achieving, in a future, a highly sensitive, low cost, small size, multi-channel, portable, reliable and commercially established SGAW biosensor. A setup with these features could significantly contribute to future developments in the health, food and environmental industries. The second purpose of this work is to describe the state-of-the-art of SGAW biosensors for the detection of pathogens, being this topic an issue of extremely importance for the human health. Finally, the review discuses the commercial availability, trends and future challenges of the SGAW biosensors for such applications

Theoretical Understanding of the Information Flow on Continual Learning Performance

Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data sequentially. CL performance evaluates the model's ability to continually learn and solve new problems with incremental available information over time while retaining previous knowledge. Despite the numerous previous solutions to bypass the catastrophic forgetting (CF) of previously seen tasks during the learning process, most of them still suffer significant forgetting, expensive memory cost, or lack of theoretical understanding of neural networks' conduct while learning new tasks. While the issue that CL performance degrades under different training regimes has been extensively studied empirically, insufficient attention has been paid from a theoretical angle. In this paper, we establish a probabilistic framework to analyze information flow through layers in networks for task sequences and its impact on learning performance. Our objective is to optimize the information preservation between layers while learning new tasks to manage task-specific knowledge passing throughout the layers while maintaining model performance on previous tasks. In particular, we study CL performance's relationship with information flow in the network to answer the question "How can knowledge of information flow between layers be used to alleviate CF?". Our analysis provides novel insights of information adaptation within the layers during the incremental task learning process. Through our experiments, we provide empirical evidence and practically highlight the performance improvement across multiple tasks.Comment: 7 figures, 16 page

Probing of strong and weak electrolytes with acoustic wave fields

The probing of strong and weak electrolytes using acoustic wave fields is described. Only a very small volume of the solution (≈ 10–100 μl) is required for the measurement. It is shown that ionic mobilities at infinite dilution can be determined for a given class of ions by accurately measuring the concentration at which the maximum attenuation occurs in the propagating acoustic wave under the probing action. This could serve as a means of identifying ion species of a given class. Experiments are performed using acoustic plate mode fields on ZX-LiNbO3, a relatively strong piezoelectric material, and various solutions of the alkali metal ions. For weak electrolytes, it is shown that the present device gives a linear response for low solution concentrations (up to 1 wt.%). Hence, the conductivity and the dielectric constant of the solution can be effectively determined. At higher concentrations, only a qualitative analysis of the variation of the solution parameters can be extracted

Theoretical and experimental study of mass sensitivity of PSAW-APMs on ZX-LiNbO3

Acoustic plate mode (APM) devices have recently been used as sensing elements, both for the physical measurement of fluid properties and in biosensor applications. One of the primary interaction mechanisms in these devices is mass loading caused by the added mass bound to the layered crystal surface. However, the material properties of these thin composite layers are not well characterized or known as is required in order to accurately predict the sensor response. In the present work, perturbation theory is used to derive expressions for the sensitivity of the APM sensors to mass loading and viscoelastic stiffening. Mass sensitivity experiment was conducted on ZX-LiNbO/sub 3/ in a liquid environment to accurately reflect the sensitivity of an actual biosensor and the results are compared to theory. The measured data show a f/sup 2/ dependence for the mass sensitivity for APMs on ZX-LiNbO/sub 3/ in the measured frequency range, which indicates a SAW-like behavior. This behavior is due to the fact that the acoustic plate modes on ZX-LiNBO/sub 3/ are pseudo-SAW (PSAW) derived, and the acoustic energy is confined to the sensing surface. As a result, the APMs on ZX-LiNbO/sub 3/ are referred to as PSAW-APMs. Discussions are given in terms of the added mass which occurs in typical biosensor applications

Design, Packaging and Characterization of a Langasite Monolithic Crystal Filter Viscometer

A two-port monolithic crystal filter (MCF) viscosity sensor has been developed using high coupling langasite material and advanced packaging techniques [1]. The present work details the evaluation of quartz and langasite MCF elements with varying shapes, contours and electrode designs. The study has resulted in a commercial design on 10.25 mm blanks, housed in a TO-8 header with support circuitry contained in a ½” NPT threaded bolt