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Invited Talk: "Is less better in AI disclosures? -- How level of detail affects trust in news readers"
No full textContextual value iteration and deep approximation for Bayesian contextual bandits
Full text linkWe present a Bayesian value-iteration framework for contextual multi-armed bandit problems that treats the agents posterior distribution for the pay-off as the state of the Markov Decision Process. We apply finite-dimensional priors on the unknown reward parameters, and the exogenous context transition kernel. Value iteration on the belief-MDP yields an optimal policy. We illustrate the approach in an airline seat-pricing simulation. To address the curse of dimensionality, we approximate the value function with a dual-stream deep learning network and benchmark our deep value iteration algorithm on a standard contextual bandit instance
awgansekoele/relative-phase-equivariant-deep-neural-systems
No full textGithub repository to reproduce the results of the paper "Relative phase equivariant deep neural systems for physical layer communications"
WELD-E MENTOR INTERVIEW
No full textIn this interview, Moonisa Ahsan talks about mentoring WELD-E, a VOXReality OC project that uses AI and XR to improve remote welding. The system integrates speech recognition, multilingual translation, and mixed reality for safer, more accessible, and efficient manufacturing processes
A note on the genus of the HAWK lattice
Full text linkThe cryptographic scheme and NIST candidate HAWK [DPPvW22; ABC+24] makes use of a
particular module lattice and relies for its security on the assumption that finding module lattice
isomorphisms (module LIP) is hard. To support this assumption, we compute the mass of the
HAWK lattice, which gives a lower bound on the number of isometry classes of module lattices
which cannot be distinguished from the HAWK lattice by an easily computed invariant called
the genus. This number turns out to be so large that an attack based on the genus alone seems
infeasible
On the independence assumption in quasi-cyclic code-based cryptography
Full text linkCryptography based on the presumed hardness of decoding codes
– i.e., code-based cryptography – has recently seen increased interest
due to its plausible security against quantum attackers. Notably, of
the four proposals for the NIST post-quantum standardization process
that were advanced to their fourth round for further review, two were
code-based. The most efficient proposals – including HQC and BIKE,
the NIST submissions alluded to above – in fact rely on the presumed
hardness of decoding structured codes. Of particular relevance to our
work, HQC is based on quasi-cyclic codes, which are codes generated
by matrices consisting of two cyclic blocks.
In particular, the security analysis of HQC requires a precise un-
derstanding of the Decryption Failure Rate (DFR), whose analysis
relies on the following heuristic: given random “sparse” vectors e1, e2
(say, each coordinate is i.i.d. Bernoulli) multiplied by fixed “sparse”
quasi-cyclic matrices A1, A2, the weight of resulting vector e1A1 + e2A2
is very concentrated around its expectation. In the documentation, the
authors model the distribution of e1A1 + e2A2 as a vector with inde-
pendent coordinates (and correct marginal distribution). However, we
uncover cases where this modeling fails. While this does not invalidate
the (empirically verified) heuristic that the weight of e1A1 + e2A2 is
concentrated, it does suggest that the behavior of the noise is a bit more
subtle than previously predicted. Lastly, we also discuss implications
of our result for potential worst-case to average-case reductions for
quasi-cyclic codes
A comprehensive academic and industrial survey of blockchain technology for the energy sector using fuzzy Einstein decision-making
Full text linkThe global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in energy, (ii) an in-depth evaluation of the evolution and viability of blockchain initiatives in energy with the help of expert surveys, and (iii) a novel decision-making model using a q-rung orthopair fuzzy Multi-Attributive Border Approximation (q-ROF-MABAC) method under the Einstein operator. The results were compared with existing decision models to validate consistency and robustness. Nine key blockchain use case categories were identified and ranked based on technical, economic, and governance dimensions. The results demonstrated that integrating expert insights into a fuzzy logic framework helps filter out overhyped claims in the literature and prioritize realistic and high-impact applications such as green certificates, grid services, and peer-to-peer energy trading. The model's rankings remained stable across varying weight configurations, confirming the robustness of the methodology. This study provides an evidence-based decision-support tool for researchers, industry stakeholders, and policymakers to better understand, evaluate, and adopt blockchain technologies in the energy sector
GALÆXI: Solving complex compressible flows with high-order discontinuous Galerkin methods on accelerator-based systems
Full text linkThis work presents GALÆXI as a novel, energy-efficient flow solver for the simulation of compressible flows on unstructured hexahedral meshes leveraging the parallel computing power of modern Graphics Processing Units (GPUs). GALÆXI implements the high-order Discontinuous Galerkin Spectral Element Method (DGSEM) using shock capturing with a finite-volume subcell approach to ensure the stability of the high-order scheme near shocks. This work provides details on the general code design, the parallelization strategy, and the implementation approach for the compute kernels with a focus on the element local mappings between volume and surface data due to the unstructured mesh. The scheme is implemented using a pure distributed memory parallelization based on a domain decomposition, where each GPU handles a distinct region of the computational domain. On each GPU, the computations are assigned to different compute streams which allows to antedate the computation of quantities required for communication while performing local computations from other streams to hide the communication latency. This parallelization strategy allows for maximizing the use of available computational resources. This results in excellent strong scaling properties of GALÆXI up to 1024 GPUs if each GPU is assigned a minimum of one million degrees of freedom. To verify its implementation, a convergence study is performed that recovers the theoretical order of convergence of the implemented numerical schemes. Moreover, the solver is validated using both the incompressible and compressible formulation of the Taylor–Green-Vortex at a Mach number of 0.1 and 1.25, respectively. A mesh convergence study shows that the results converge to the high-fidelity reference solution and that the results match the original CPU implementation. Finally, GALÆXI is applied to a large-scale wall-resolved large eddy simulation of a linear cascade of the NASA Rotor 37. Here, the supersonic region and shocks at the leading edge are captured accurately and robustly by the implemented shock-capturing approach. It is demonstrated that GALÆXI requires less than half of the energy to carry out this simulation in comparison to the reference CPU implementation. This renders GALÆXI as a potent tool for accurate and efficient simulations of compressible flows in the realm of exascale computing and the associated new HPC architectures