216 research outputs found
Synchronization and Noise: A Mechanism for Regularization in Neural Systems
To learn and reason in the presence of uncertainty, the brain must be capable
of imposing some form of regularization. Here we suggest, through theoretical
and computational arguments, that the combination of noise with synchronization
provides a plausible mechanism for regularization in the nervous system. The
functional role of regularization is considered in a general context in which
coupled computational systems receive inputs corrupted by correlated noise.
Noise on the inputs is shown to impose regularization, and when synchronization
upstream induces time-varying correlations across noise variables, the degree
of regularization can be calibrated over time. The proposed mechanism is
explored first in the context of a simple associative learning problem, and
then in the context of a hierarchical sensory coding task. The resulting
qualitative behavior coincides with experimental data from visual cortex.Comment: 32 pages, 7 figures. under revie
Communications and control for electric power systems: Power system stability applications of artificial neural networks
This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed
Intriguing Properties of Adversarial ML Attacks in the Problem Space
Recent research efforts on adversarial ML have investigated problem-space
attacks, focusing on the generation of real evasive objects in domains where,
unlike images, there is no clear inverse mapping to the feature space (e.g.,
software). However, the design, comparison, and real-world implications of
problem-space attacks remain underexplored. This paper makes two major
contributions. First, we propose a novel formalization for adversarial ML
evasion attacks in the problem-space, which includes the definition of a
comprehensive set of constraints on available transformations, preserved
semantics, robustness to preprocessing, and plausibility. We shed light on the
relationship between feature space and problem space, and we introduce the
concept of side-effect features as the byproduct of the inverse feature-mapping
problem. This enables us to define and prove necessary and sufficient
conditions for the existence of problem-space attacks. We further demonstrate
the expressive power of our formalization by using it to describe several
attacks from related literature across different domains. Second, building on
our formalization, we propose a novel problem-space attack on Android malware
that overcomes past limitations. Experiments on a dataset with 170K Android
apps from 2017 and 2018 show the practical feasibility of evading a
state-of-the-art malware classifier along with its hardened version. Our
results demonstrate that "adversarial-malware as a service" is a realistic
threat, as we automatically generate thousands of realistic and inconspicuous
adversarial applications at scale, where on average it takes only a few minutes
to generate an adversarial app. Our formalization of problem-space attacks
paves the way to more principled research in this domain.Comment: This arXiv version (v2) corresponds to the one published at IEEE
Symposium on Security & Privacy (Oakland), 202
Computer Aided Verification
This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book
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