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Tighter quantum security for fiat-shamir-with-aborts and hash-and-sign-with-retry signatures
Get PDFWe revisit the quantum security (in the QROM) of digital signature schemes that follow the Fiat-Shamir-with-aborts (FSwA) or the probabilistic hash-and-sign with retry/abort (HSwA) design paradigm. Important examples of such signature schemes are Dilithium, SeaSign, Falcon+ and UOV. In particular, we are interested in the UF-CMA-to-UF-NMA reduction for such schemes. We observe that previous such reductions have a reduction loss that is larger than what one would hope for, or require a more stringent notion of zero-knowledge than one would hope for.
We resolve this matter here by means of a novel UF-CMA-to-UF-NMA reduction that applies to FSwA and HSwA signature schemes simultaneously, and that offers an improved reduction loss (without making the zero-knowledge assumption more stringent)
TU_e bouwt mee aan ’s werelds krachtigste digital twin voor elektriciteitsonderzoek - TUE - 9 December 2025
No full textA complete and natural rule set for multi-qutrit Clifford circuits
Get PDFWe present a complete set of rewrite rules for n-qutrit Clifford circuits where n is any non-negative integer. This is the first completeness result for any fragment of quantum circuits in odd prime dimensions. We first generalize Selinger's normal form for n-qubit Clifford circuits to the qutrit setting. Then, we present a rewrite system by which any Clifford circuit can be reduced to this normal form. We then simplify the rewrite rules in this procedure to a small natural set of rules, giving a clean presentation of the group of qutrit Clifford unitaries in terms of generators and relations
Sandwich BUFF: Achieving non-resignability using iterative hash functions
No full textWe revisit the BUFF transform, which was proposed by Cremers et al. (S&P’21) as a means to achieve security properties beyond standard unforgeability for digital signature schemes. One of these properties, non-resignability (NR), has recently drawn some attention due to a strong impossibility result for the original definition of the property. Recent follow-up work then considered a variant (sNR) of the original definition, and showed that it is satisfied by the BUFF transform when the underlying hash function is modeled as a random oracle—while the original impossibility result still applies for the plain model. This raises the natural question of whether the BUFF transform satisfies sNR in a more fine-grained use of the random oracle model, when we consider a real-life iterative-hash-function design (such as Merkle-Damgård or Sponge) and instead idealize the round function. Our discoveries in this direction are two-fold:
First, contrary to what one might expect, we show that there is a simple attack on the non-resignability property sNR of the BUFF-transform when instantiated with an iterative hash function. The attack relies on leaking an intermediate result of the hash computation to the adversary who is challenged to “resign” the message. This negative result once more shows the subtlety in the non-resignability property.
Second, on the positive side, we propose a small modification to the original BUFF transform, which we call Sandwich BUFF (for reasons to become clear), and prove the non-resignability property sNR of Sandwich BUFF both for Merkle-Damgård-based hash functions in the random oracle model, and for Sponge-based hash functions in the random permutation model
Fourier-enhanced reduced-order surrogate modeling for uncertainty quantification in electric machine design
Get PDFThis work proposes a data-driven surrogate modeling framework for cost-effectively inferring the torque of a permanent magnet synchronous machine under geometric design variations. The framework is separated into a reduced-order modeling and an inference part. Given a dataset of torque signals, each corresponding to a different set of design parameters, torque dimension is first reduced by post-processing a discrete Fourier transform and keeping a reduced number of frequency components. This allows to take advantage of torque periodicity and preserve physical information contained in the frequency components. Next, a response surface model is computed by means of machine learning regression, which maps the design parameters to the reduced frequency components. The response surface models of choice are polynomial chaos expansions, feedforward neural networks, and Gaussian processes. Torque inference is performed by evaluating the response surface model for new design parameters and then inverting the dimension reduction. Numerical results show that the resulting surrogate models lead to sufficiently accurate torque predictions for previously unseen design configurations. The framework is found to be significantly advantageous compared to approximating the original (not reduced) torque signal directly, as well as slightly advantageous compared to using principal component analysis for dimension reduction. The combination of discrete Fourier transform-based dimension reduction with Gaussian process-based response surfaces yields the best-in-class surrogate model for this use case. The surrogate models replace the original, high-fidelity model in Monte Carlo-based uncertainty quantification studies, where they provide accurate torque statistics estimates at significantly reduced computational cost
Multi-objective utility actor critic with utility critic for nonlinear utility function
Get PDFIn multi-objective reinforcement learning (MORL), non-linear utility functions pose a significant challenge, as the two optimization criteria—scalarized expected return (SER) and expected scalarized return (ESR)—can diverge substantially. Applying single-objective reinforcement learning methods to solve ESR problems often introduces bias, particularly in the presence of non-linear utilities. Moreover, existing MORL policy-based algorithms, such as EUPG and MOCAC, suffer from numerous hyperparameters, large search spaces, high variance, and low learning efficiency, which frequently result in sub-optimal policies.
In this paper, we propose a new multi-objective policy search algorithm called Multi-Objective Utility Actor-Critic (MOUAC). For the first time in the field, MOUAC introduces a Utility Critic based on expected state utility to replace Qvalue critic, value function, or distributional critic based on Q-values or value functions. To address the high variance challenges inherent in multi-objective reinforcement learning (MORL), MOUAC also adapts traditional eligibility trace to the multi-objective setting called MnES-return. Empirically, we demonstrate that our algorithm achieves state-of-the-art (SOTA) performance in on-policy multiobjective policy search
The effect of police deployment strategy on emergency response times: An agent-based modelling investigation
No full textObjectives: This study investigates the impact of three police deployment strategies on emergency response times using agent-based modelling (ABM). Specifically, it evaluates the effectiveness of random patrol, stationary deployment (optimal spreading), and last location deployment (idling at last incident location). It further examines how key variables – urbanisation, call volume, and police capacity – moderate these effects. Methods: A detailed ABM was developed using NetLogo, integrating real-world data: historical calls-for-service (CFS), jurisdiction shapefiles, and street network data from the Netherlands. The model simulated police travel and response dynamics across 120 scenarios, varying deployment strategies, urbanisation levels, call volumes, and police capacity levels. Outputs were analysed to assess response times, fast response rates (13 minutes). Methods were pre-registered at https://osf.io/yrwdp/. Results: On average, stationary deployment reduced response times by 35% (SD ±14%), increased fast responses by 74% (SD ±40%), and decreased late responses by 66% (SD ±33%) compared to random patrol. Last location deployment also outperformed random patrol, reducing response times by 13% (SD ±9%), increasing fast responses by 22% (SD ±14%), and reducing late responses by 42% (SD ±36%). The advantages of stationary and last location deployment were most pronounced in rural areas and at lower police capacity levels. Urbanisation reduced the performance gap between strategies, while higher call volumes modestly diminished the relative benefits of stationary deployment. Conclusions: This study highlights the significant impact of police deployment strategies on response times and rapid interventions. These findings underscore the need for further research on rapid response. The modular ABM framework offers a valuable tool for adapting investigations to different policing contexts, enhancing external validity
NORMalize 2025: The Third Workshop on Normative Design and Evaluation of Recommender Systems
Get PDFRecommender systems are one of the most widely used applications of artificial intelligence. Their use can have far-reaching consequences for stakeholders, users, and society at large. In this third edition of the NORMalize workshop, we once again seek to advance the research agenda of normative thinking, considering the norms and values that underpin recommender systems, as well as to introduce the concept to a broader audience. We aim to bring together a growing community of researchers and practitioners across disciplines who want to think about the norms and values that should be considered in the design and evaluation of recommender systems, and to further educate them on how to reflect on, prioritise, and operationalise such norms and values. NORMalize 2025 is a half-day workshop focusing on discussion and interdisciplinary collaboration, building upon its two successful runs at previous RecSys conferences in 2023 and 2024