Julia implementation of a scaled projection neural network for quasi-variational inequalities with state-dependent constraint set S(x) = m(x) + S and fixed symmetric positive-definite matrix M. Integrates the continuous-time dynamics dx/dt = lambda * [P_{S(x),M^{-1}}(x - alpha * M * F(x)) - x] (projection in the M^{-1} metric) by adaptive ODE integration (OrdinaryDiffEq.jl) and by a discrete Krasnosel'skii-Mann iteration; sparse obstacle-type projections are computed by a KKT-verified primal-dual active-set method.
Deep Neural Networks (DNNs) exhibit acute vulnerabilities to intermediate activation layer perturbations engineered through out-of-distribution (OOD) noise injection and feature-steering gradient updates. Conventional defensive paradigms—such as adversarial retraining or external…
Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, tra…
Neural networks are extraordinarily effective and almost entirely opaque: their competence is real but unreadable, and adapting them means retraining usually via a data-center process, not something that happens in the moment, in context. Symbolic systems are the inverse, legible…
Nigeria's oil and gas pipeline network spanning over 5,000 km of trunk lines and more than 3,000 km of flow lines loses an estimated one billion US dollars annually to pipeline failures, environmental incidents, and non-productive time. The dominant monitoring approach in operati…
Current quantum portfolio optimization pipelines rely on Random Matrix Theory (RMT) forcorrelation matrix cleaning, a static linear method that cannot adapt when market regimesshift. Meanwhile, D-Wave's own operational audit shows the quantum processor runs for just0.68% of total…