Adversarial training, a widely used technique for fortifying the robustness of machine learning models, has seen its effectiveness further bolstered by modifying loss functions or incorporating additional terms into the training objective. While thes...
Motivated by the importance of contextuality and a work on the robustness of the entanglement of mixed quantum states, the robustness of contextuality (RoC)
We propose RobustQuote, a novel defense framework designed to enhance the adversarial robustness of vision transformers. The core idea is to leverage trusted reference images drawn from a dynamically changing pool unknown to the attacker as contextua...
Despite their remarkable performance, deep learning models still lack robustness guarantees, particularly in the presence of adversarial examples. This significant vulnerability raises concerns about their trustworthiness and hinders their deployment...
Autonomous driving technology has advanced significantly with deep learning, but noise and attacks threaten its real-world deployment. While research has revealed vulnerabilities in individual intelligent tasks, a comprehensive evaluation of these im...
In the practical application of quantum entanglement, entangled particles usually need to be distributed to many distant parties or stored in different quantum memories. In these processes, entangled particles unavoidably interact with their surround...
The interplay between entropy and robustness of gene network is a core mechanism of systems biology. The entropy is a measure of randomness or disorder of a physical system due to random parameter fluctuation and environmental noises in gene regulato...
The continuous operation of modern society is dominated by interdependent networks, such as energy networks, communication networks, and traffic networks. As a result, the robustness of interdependent networks has become increasingly important in rec...
This paper addresses the robust job-shop scheduling problems (RJSSP) with stochastic deteriorating processing times by considering the resilience of the production schedule. To deal with the disturbances caused by the processing time variations, the...
In the aftermath of the financial crisis, it was realized that the mathematical models used for the valuation of financial instruments and the quantification of risk inherent in portfolios consisting of these financial instruments exhibit a substanti...
Despite its central role in ruminant nutrition, little is known about ruminal microbiota robustness, which is understood as the ability of the microbiota to cope with disturbances. The aim of the present review is to offer a comprehensive description...
Deep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To...
Despite the advance in deep learning technology, assuring the robustness of deep neural networks (DNNs) is challenging and necessary in safety-critical environments, including automobiles, IoT devices in smart factories, and medical devices, to name...
Research on the robustness of networks, and in particular the Internet, has gained critical importance in recent decades because more and more individuals, societies and firms rely on this global network infrastructure for communication, knowledge tr...
Max–min algebra (called also fuzzy algebra) is an extremal algebra with operations maximum and minimum. In this paper, we study the robustness of Monge matrices with inexact data over max–min algebra. A matrix with inexact data (also call...
Stochastic robustness of discrete noises has already been proposed and studied in the previous work. Nevertheless, the significant phenomenon of delays is left in the basket both in the deterministic and the stochastic parts of the considered equatio...
The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept. However, th...
The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world network...
Complementarity plays a central role in the conceptual development of quantum mechanics, and also provides practical applications in quantum information technologies. How to properly quantify it is an important problem in quantum foundations, and the...
Graph robustness or network robustness is the ability that a graph or a network preserves its connectivity or other properties after the loss of vertices and edges, which has been a central problem in the research of complex networks. In this paper,...
Boolean chaotic systems solely composed of logic devices have been successfully applied in fields such as random number generation, reservoir computing, and radar detection because of their simple structure and amenability to integration. However, no...
A novel engineering approach for assessing the robustness of fuzzy logic control (FLC) systems with modified parallel distributed compensation (MPDC) is presented. It addresses the problem of successful implementation and operation in industrial envi...
The trend towards lightweight design, driven by increasingly stringent emission targets, poses challenges to conventional joining processes due to the different mechanical properties of the joining partners used to manufacture multi-material systems....
Very Long Baseline Interferometry (VLBI) plays an indispensable role in the realization of global terrestrial and celestial reference frames and in the determination of the full set of the Earth Orientation Parameters (EOP). The main goal of this res...
As the future of nuclear power is uncertain, only choosing one development objective for the coming decades can be risky; while trying to achieve several possible objectives at the same time may lead to a deadlock due to contradiction among them. In...
Critical infrastructures (CI) are the cornerstone of modern society, and they are connected with each other through material, energy, or information. The robustness of interdependent CI systems under attack has been a hot topic in recent years, but p...
Metabolic networks are probably among the most challenging and important biological networks. Their study provides insight into how biological pathways work and how robust a specific organism is against an environment or therapy. Here, we propose a d...
In some statistical methods, the statistical information is provided in terms of the values used by classical estimators, such as the sample mean and sample variance. These estimations are used in a second stage, usually in a classical manner, to be...
Cascading failures between interdependent multilayer networks are being widely studied, especially the trend of robustness caused by the interlinks between networks. However, few researchers pay attention to the effect of the interlink topology on th...
Deep neural network models are vulnerable to attacks from adversarial methods, such as gradient attacks. Evening small perturbations can cause significant differences in their predictions. Adversarial training (AT) aims to improve the model’s a...
In recent years, AI-generated text (AIGT) detection has attracted increasing attention, and some detectors demonstrate high accuracy in benchmark settings. However, the complexity and diversity of AIGT and counter-detection methods in real-world appl...
Genetic regulatory networks have evolved by complexifying their control systems with numerous effectors (inhibitors and activators). That is, for example, the case for the double inhibition by microRNAs and circular RNAs, which introduce a ubiquitous...
A robustness measure is an effective tool to evaluate the anti-interference capacity of the construction schedule. However, most research focuses on solution robustness or quality robustness, and few consider a composite robustness criterion, neglect...
This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two different classes of priors in the neighborhood of the elicited prior are considered. The first one is the well-known
One of the most intriguing phenomenons related to deep learning is the so-called adversarial examples. These samples are visually equivalent to normal inputs, undetectable for humans, yet they cause the networks to output wrong results. The phenomeno...
Networks are widely used to model the interaction between individual dynamic systems. In many instances, the total number of units and interaction coupling are not fixed in time, and instead constantly evolve. In networks, this means that the number...
Artificial intelligence (AI) powers breakthroughs in language processing, computer vision, and scientific discovery; yet, the increasing complexity of frontier models makes their reasoning opaque. This opacity undermines public trust, complicates dep...
Understanding the process of aging is still an important challenge to enable healthy aging and to prevent age-related diseases. Most studies in age research investigate the decline in organ functionality and gene activity with age. The focus on decli...
In this work, we present a robust examination of the Bayesian estimators utilizing the two-parameter Upper truncated XLindley model, a unique Lindley model variant, and the oscillation of posterior risks. We provide the model in a censored scheme alo...
Over the past decade, much research has focused on the behaviour of structures following the failure of a key structural component. Particular attention has been given to sudden column loss, though questions remain as to whether this event-independen...
Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that c...
Vehicle self-localization is one of the most important capabilities for automated driving. Current localization methods already provide accuracy in the centimeter range, so robustness becomes a key factor, especially in urban environments. There is n...
Analyzing the robustness of networks against random failures or malicious attacks is a critical research issue in network science, as it contributes to enhancing the robustness of beneficial networks and effectively dismantling harmful ones. Most stu...
The increasing global demand for natural substances such as the sesquiterpene α-humulene makes optimizing microbial production essential. A production process using the versatile host Cupriavidus necator has been recently improved by adjusting...
Due to their growing number and increasing autonomy, drones and drone swarms are equipped with sophisticated algorithms that help them achieve mission objectives. Such algorithms vary in their quality such that their comparison requires a metric that...
Convolutional neural networks (CNNs) can efficiently extract image features and perform corresponding classification. Typically, the CNN architecture uses the softmax layer to map the extracted features to classification probabilities, and the cost f...
In the presented work, a methodical approach was developed for determining rational operation modes of tribosystems, taking into account their design. This approach makes it possible in the designing stage, according to the predicted operating modes,...
Public–private partnership (PPP) has been increasingly imported to deliver infrastructure and public services around the world. As an emerging public procurement mode, PPP has drawn considerable attention both from academy and industry. We cons...
An important issue for robust inference is to examine the stability of the asymptotic level and power of the test statistic in the presence of contaminated data. Most existing results are derived in finite-dimensional settings with some particular ch...
The adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-d...
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