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Quantum sieving for code-based cryptanalysis and its limitations for ISD
No full textSieving using near-neighbor search techniques is a well-known method in lattice-based cryptanalysis, yielding the current best runtime for the shortest vector problem in both the classical and quantum setting. Recently, sieving has also become an important tool in code-based cryptanalysis. Specifically, a variant of the information-set decoding (ISD) framework, commonly used for attacking cryptographically relevant instances of the decoding problem, has been introduced that involves a sieving subroutine. The resulting sieving-based ISD framework yields complexities close to the best-performing classical algorithms for the decoding problem. It is therefore natural to ask how well quantum versions perform. In this work, we introduce the first quantum algorithms for code sieving by designing quantum variants of the aforementioned sieving subroutine. In particular, using quantum-walk techniques, we provide a speed-up over classical code sieving and over a variant using Grover’s algorithm. Our quantum-walk algorithm exploits the structure of the underlying search problem by adding a layer of locality sensitive filtering, inspired by a quantum-walk algorithm for lattice sieving. We complement our asymptotic analysis of the quantum algorithms with numerical results, and observe that our quantum speed-ups for code sieving behave similarly as those observed in lattice sieving. In addition, we show that a natural quantum analog of the sieving-based ISD framework does not provide any speed-up over the first quantum ISD algorithm. Our analysis highlights that the framework should be adapted in order to outperform state-of-the-art quantum ISD algorithms
A Step towards Interpretable Multimodal AI Models with MultiFIX
Get PDFReal-world problems are often dependent on multiple data modalities, making multimodal fusion essential for leveraging diverse information sources. In high-stakes domains, such as in healthcare, understanding how each modality contributes to the prediction is critical to ensure trustworthy and interpretable AI models. We present MultiFIX, an interpretability-driven multimodal data fusion pipeline that explicitly engineers distinct features from different modalities and combines them to make the final prediction. Initially, only deep learning components are used to train a model from data. The black-box (deep learning) components are subsequently either explained using post-hoc methods such as Grad-CAM for images or fully replaced by interpretable blocks, namely symbolic expressions for tabular data, resulting in an explainable model. We study the use of MultiFIX using several training strategies for feature extraction and predictive modeling. Besides highlighting strengths and weaknesses of MultiFIX, experiments on a variety of synthetic datasets with varying degrees of interaction between modalities demonstrate that MultiFIX can generate multimodal models that can be used to accurately explain both the extracted features and their integration without compromising predictive performance
Data-driven reduced modeling of streamer discharges in air
Get PDFWe present a computational framework for simulating filamentary electric discharges, in which channels are represented as conducting cylindrical segments. The framework requires a model that predicts the position, radius, and line conductivity of channels at a next time step. Using this information, the electric conductivity on a numerical mesh is updated, and the new electric potential is computed by solving a variable-coefficient Poisson equation. A parallel field solver with support for adaptive mesh refinement is used, and the framework provides a Python interface for easy experimentation. We demonstrate how the framework can be used to simulate positive streamer discharges in air. First, a dataset of 1000 axisymmetric positive streamer simulations is generated, in which the applied voltage and the electrode geometry are varied. Fit expressions for the streamer radius, velocity, and line conductivity are derived from this dataset, taking as input the size of the high-field region ahead of the streamers. We then construct a reduced model for positive streamers in air, which includes a stochastic branching model. The reduced model compares well with the axisymmetric simulations from the dataset, while allowing spatial and temporal step sizes that are several orders of magnitude larger. 3D simulations with the reduced model resemble experimentally observed discharge morphologies. The model runs efficiently, with 3D simulations with 20+ streamers taking 4–8 minutes on a desktop computer
Cool + Cruel = Dual, and new benchmarks for sparse LWE
Get PDFThe sparse secret Learning with Errors (LWE) problem is a widely used assumption in efficient fully homomorphic constructions. In [Wenger et al. IEEE S\&P 2025] two approaches, `Cool and Cruel’ (C+C) and the machine learning based `SALSA', were benchmarked against the well established primal attack on sparse secrets. The authors concluded that C+C outperforms SALSA and both outperform the primal attack.
In this work we show that the apparently novel C+C is an instantiation of a known dual attack [Albrecht, EUROCRYPT 2017]. To argue this we introduce a framework for dimension reduction in the bounded distance decoding problem that may be of independent interest. Furthermore we prove that the C+C 'phenomenon' is an expression of the geometry of the well known Z-shape basis in q-ary lattices, despite claims to the contrary.
We also show that a correctly parametrised primal attack outperforms C+C both in parameter regimes studied by Wenger et al. and in new parameter regimes. To support this claim, we provide an open source implementation of two variants of the primal attack that are relevant for sparse secret LWE: Drop+Solve [May--Silverman, CaLC 2001] and Guess+Verify [Albrecht et al. SAC 2019]
Accelerating the primal hybrid attack against sparse LWE using GPUs
Get PDFAlthough the lattice-estimator predicts that Learning with Errors instances having small and very sparse secrets can be broken by hybrid attacks with modest computational resources, no efficient open-source implementation of these attacks exists. This work implements the so-called Guess + Verify attack (G+V) analysed by Albrecht et al. (SAC'19), containing three improvements: (1) cuBLASter, a GPU-based implementation of the lattice basis reduction software BLASter by Ducas et al. (ASIACRYPT'25); (2) a dimension reduction technique for the BDD instance; and (3) a batched variant of Babai’s Nearest Plane algorithm.
On bases of dimension 512 and above, cuBLASter outperforms BLASter. We also integrate the GPU implementation of the General Sieve Kernel by Ducas et al. (EUROCRYPT'21) into cuBLASter’s BKZ framework.
Running G+V on the benchmark instances by Wenger et al. (IEEE SP'25), we show that G+V achieves significantly higher success rates than the Cool&Cruel attack (C+C) by Nolte et al. (AFRICACRYPT'24) on almost all instances, and G+V's average CPU and GPU utilization is substantially lower than the minimum reported by C+C
RCQoEA-360VR: Real-time continuous QoE scores for HMD-based 360° VR dataset
Get PDFAs immersive 360° video experiences through head-mounted displays (HMDs) gain widespread adoption, the need for real-time, fine-grained assessment of Quality of Experience (QoE) becomes increasingly critical for optimising user engagement and system performance. This paper introduces RCQoEA-360VR, a novel multi-modal dataset designed for continuous QoE evaluation in virtual reality (VR) environments. In a controlled study (N=32), participants watched five selected 360° video sequences across eight different video quality configurations (from the VQEG database) using a Vive Pro Eye while providing continuous QoE annotations via a touchpad-based input method, enhanced by the DotMorph peripheral visualisation technique. The dataset also includes synchronised physiological signals (electrocardiogram and galvanic skin response), behavioural data (eye and head movements) and post-viewing QoE ratings gathered through a within-VR interface. RCQoEA-360VR addresses a critical gap in existing public datasets by providing a fine-grained, synchronised multimodal data for immersive QoE analysis. It offers a unique and valuable resource for the research community, supporting a wide range of research applications, including QoE prediction, behavioural modelling, adaptive streaming, and implicit perceptual analysis
Rethinking the alignment of psychotherapy dialogue generation with motivational interviewing strategies
Get PDFRecent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI). However, the inherent lack of transparency in LLM outputs presents significant challenges given the sensitive nature of psychotherapy. Applying MI strategies, a set of MI skills, to generate more controllable therapeutic-adherent conversations with explainability provides a possible solution. In this work, we explore the alignment of LLMs with MI strategies by first prompting the LLMs to predict the appropriate strategies as reasoning and then utilizing these strategies to guide the subsequent dialogue generation. We seek to investigate whether such alignment leads to more controllable and explainable generations. Multiple experiments including automatic and human evaluations are conducted to validate the effectiveness of MI strategies in aligning psychotherapy dialogue generation. Our findings demonstrate the potential of LLMs in producing strategically aligned dialogues and suggest directions for practical applications in psychotherapeutic settings
Designing the space archivists: A metadata-driven VR game concept for children to engage with cultural heritage
No full textMotivated to create a children’s VR game for the Netherlands Institute for Sound and Vision (NISV), this research asks how might we design an immersive game for children to meaningfully interact with media and metadata in cultural heritage contexts? First, during a 'design salon,' 13 data and heritage experts challenged children’s ability to interact with metadata. In response, we ran workshops with 19 children focused on understanding abstract media and data. We found that while (1) metadata has many challenges, (2) children understand abstract data when it is grounded in concrete experiences, are (3) motivated to interact with archival media through in immersive and collaborative contexts, and (4) are interested in exploring media diversity through categorisation games with high-level narrative goals. These findings inform our game concept and three core insights for designing immersive experiences for cultural heritage: Considering the Contextual Complexity of Data and Audience Needs, Connecting Data Abstractions to Embodied Narratives Through Categorisation Mechanics, and Supporting Abstract Meaning Making Using the Immersive Affordances of VR
The Automated Negotiating Agents Competition (ANAC) 2024 challenges and results
Get PDFThis paper introduces the main research challenges and results of the 15th International Automated Negotiating Agents Competition (ANAC 2024). The main challenges addressed are learning the reservation value in bilateral negotiation and designing a factory agent employing concurrent negotiation in supply chain management. Additionally, it outlines the future directions for the competition
Moment-sos and spectral hierarchies for polynomial optimization on the sphere and quantum de Finetti theorems
Get PDFWe revisit the convergence analysis of two approximation hierarchies for polynomial optimization on the unit sphere. The first one is based on the moment-sos approach and gives semidefinite bounds for which Fang and Fawzi (2021) showed an analysis in for the th level bound, using the polynomial kernel method. The second hierarchy was recently proposed by Lovitz and Johnston (2023) and gives spectral bounds for which they show a convergence rate in , using a quantum de Finetti theorem of Christandl et al. (2007) that applies to complex Hermitian matrices with a “double” symmetry. We investigate links between these approaches, in particular, via duality of moments and sums of squares.
Our main results include showing that the spectral bounds cannot have a convergence rate better than and that they do not enjoy generic finite convergence. In addition, we propose alternative performance analyses that involve explicit constants depending on intrinsic parameters of the optimization problem. For this we develop a novel “banded” real de Finetti theorem that applies to real matrices with “double” symmetry. We also show how to use the polynomial kernel method to obtain a de Finetti type result in for real maximally symmetric matrices, improving an earlier result in of Doherty and Wehner (2012)