Tootfinder

Decision algorithms for reversibility of one-dimensional non-linear cellular automata under null boundary conditions
Ma Junchi, Chen Weilin, Wang Chen, Lin Defu, Wang Chao
https://arxiv.org/abs/2405.03609

Decision algorithms for reversibility of one-dimensional non-linear cellular automata under null boundary conditions
The property of reversibility is quite meaningful for the classic theoretical computer science model, cellular automata. For the reversibility problem for a CA under null boundary conditions, while linear rules have been studied a lot, the non-linear rules remain unexplored at present. The paper investigates the reversibility problem of general one-dimensional CA on a finite field $\mathbb{Z}_p$, and proposes an approach to optimize the Amoroso's infinite CA surjectivity detection algorithm. Th…

Model X-ray:Detect Backdoored Models via Decision Boundary
Yanghao Su, Jie Zhang, Ting Xu, Tianwei Zhang, Weiming Zhang, Nenghai Yu
https://arxiv.org/abs/2402.17465

Model X-ray:Detect Backdoored Models via Decision Boundary
Deep neural networks (DNNs) have revolutionized various industries, leading to the rise of Machine Learning as a Service (MLaaS). In this paradigm, well-trained models are typically deployed through APIs. However, DNNs are susceptible to backdoor attacks, which pose significant risks to their applications. This vulnerability necessitates a method for users to ascertain whether an API is compromised before usage. Although many backdoor detection methods have been developed, they often operate un…

Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics
Wenwei Zhao, Xiaowen Li, Shangqing Zhao, Jie Xu, Yao Liu, Zhuo Lu
https://arxiv.org/abs/2402.08986

Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics
Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing values to deceive the fusion center, called adversarial spectrum attacks. In this paper, we propose an efficient framework for detecting adversarial spectrum attacks. Our design leverages the concept of the distance to the decision boundary (DDB) observed at the fusion center and c…

This https://arxiv.org/abs/2011.10166 has been replaced.
link: https://scholar.google.com/scholar?q=a

Retirement decision with addictive habit persistence in a jump diffusion market
This paper investigates the optimal retirement decision, investment, and consumption strategies in a market with jump diffusion, taking into account habit persistence and stock-wage correlation. Our analysis considers multiple stocks and a finite time framework, intending to determine the retirement boundary of the ``wealth-habit-wage" triplet $(x, h, w)$. To achieve this, we use the habit reduction method and a duality approach to obtain the retirement boundary of the primal variables and feed…

A mean curvature flow arising in adversarial training
Leon Bungert, Tim Laux, Kerrek Stinson
https://arxiv.org/abs/2404.14402 https://

A mean curvature flow arising in adversarial training
We connect adversarial training for binary classification to a geometric evolution equation for the decision boundary. Relying on a perspective that recasts adversarial training as a regularization problem, we introduce a modified training scheme that constitutes a minimizing movements scheme for a nonlocal perimeter functional. We prove that the scheme is monotone and consistent as the adversarial budget vanishes and the perimeter localizes, and as a consequence we rigorously show that the sch…