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Predicting module-lattice reduction
Get PDFIs module-lattice reduction better than unstructured lattice reduction? This question was highlighted as ‘Q8’ in the Kyber NIST standardization submission (Avanzi et al., 2021), as potentially affecting the concrete security of Kyber and other module-lattice-based schemes. Foundational works on module-lattice reduction (Lee, Pellet-Mary, Stehlé, and Wallet, ASIACRYPT 2019; Mukherjee and Stephens-Davidowitz, CRYPTO 2020) confirmed the existence of such module variants of LLL and block-reduction algorithms, but focus only on provable worst-case asymptotic behavior. In this work, we present a concrete average-case analysis of modulelattice reduction. Specifically, we address the question of the expected slope after running module-BKZ, and pinpoint the discriminant of the number field at hand as the main quantity driving this slope. We convert this back into a gain or loss on the blocksize : module-BKZ in a number field of degree requires an SVP oracle of dimension to reach the same slope as unstructured BKZ with blocksize . This asymptotic summary hides further terms that we predict concretely using experimentally verified heuristics. Incidentally, we provide the first open-source implementation of module BKZ for some cyclotomic fields. For power-of-two cyclotomic fields, we have , and conclude that module-BKZ requires a blocksize larger than its unstructured counterpart. On the contrary, for all other cyclotomic fields, , so module-BKZ provides a sublinear gain on the required blocksize, yielding a subexponential speedup of
A framework for simulating subjective experiments: Testing subject screening
Get PDFThe ITU-T recommendations BT.500 and P.910 outline multiple subject screening methodologies for subjective multimedia quality experiments. Yet, their real-world effectiveness remains difficult to verify due to the lack of known ground truth. This paper introduces a comprehensive simulation framework designed to objectively assess subject screening methods by generating synthetic subjective scores with known parameters. Two primary experimental scenarios — typical and super-precise subject models — were evaluated using simulated data. Results indicate that correlation-based screening methods (P.910) outperform kurtosis-based methods (BT.500) in detecting irrelevant subjects, thereby improving the precision of subjective experiment outcomes. Additional contributions include the development of a novel score generation model and the definition of robust evaluation metrics. We hope this paper will serve as the basis for future analysis based on simulations of subjective experiments
Introduction to Quantum Computing for Business
Get PDFHow will businesses use quantum technology in the future? What problems will a quantum computer solve? How long will it take before these devices become commercially relevant? With the first generation of quantum computers on the horizon, understanding their impact is more relevant than ever. Luckily, you don't need a physics degree to understand the functionality of these computers, just like you don't need to know how a transistor works to excel in conventional IT. This book is the perfect introduction to the opportunities and threats of quantum technologies. It equips you with the necessary knowledge to join cutting-edge discussions and make strategic decisions
A biologically inspired filter significance assessment method for model explanation
Get PDFThe interpretability of deep learning models remains a significant challenge, particularly in convolutional neural networks (CNNs) where understanding the contributions of individual filters is crucial for
explainability. In this work, we propose a biologically inspired filter significance assessment method based on Steady-State Visually Evoked Potentials (SSVEPs), a well-established neuroscience principle. Our approach leverages frequency tagging techniques to quantify the importance of convolutional filters by analyzing their frequency-locked responses to periodic contrast modulations in input images. By blending SSVEP-based filter selection into Class Activation Mapping (CAM) frameworks such as Grad-CAM, Grad-CAM++, EigenCAM, and LayerCAM, we enhance model interpretability while reducing attribution noise. Experimental evaluations on ImageNet using VGG-16, ResNet-50, and ResNeXt-50 demonstrate that SSVEP-enhanced CAM methods improve spatial focus in visual explanations, yielding higher energy concentration while maintaining competitive localization accuracy. These findings suggest that our biologically inspired approach offers a robust mechanism for identifying key filters in CNNs, paving the way for more interpretable and transparent deep learning models
Multidimensional quantum walks, recursion, and quantum divide & conquer
Get PDFWe introduce an object called a subspace graph that formalizes the technique of multidimensional quantum walks. Composing subspace graphs allows one to seamlessly combine quantum and classical reasoning, keeping a classical structure in mind, while abstracting quantum parts into subgraphs with simple boundaries as needed. As an example, we show how to combine a switching network with arbitrary quantum subroutines, to compute a composed function. As another application, we give a time-efficient implementation of quantum Divide & Conquer when the sub-problems are combined via a Boolean formula. We use this to quadratically speed up Savitch’s algorithm for directed st-connectivity
Learn to create neighborhoods in real-world vehicle routing problem
No full textWe apply Reinforcement Learning to the destroy routine of a Large Neighborhood Search (LNS) Algorithm for a real-world last mile Pickup and Delivery Problem with Time Windows (PDPTW) with extra compatibility constraints. LNS iteratively improves a solution by destroying and repairing a part of the solution. Since the repair routine of LNS usually is expensive, it is crucial to destroy smartly. Our Reinforcement Learning (RL) model, which is an adaptation of the graph attention encoder-decoder model by Kool et al. [10], aims to create neighborhoods to destroy which yield a high improvement, ultimately speeding up the optimization algorithm. We implement our Smart Neighborhood Creation into two applications: a state-of-the-art application that is used to solve many daily logistic problems and an implementation of LNS for the classical Vehicle Routing Problem with Time Windows (VRPTW). We define three test phases, each with increasing difficulty to learn how to create good neighborhoods. In the first two phases of the first application, our RL model finds higher improvements (3–4 times) and more frequent improvements (8–25% more) than the state-of-the-art optimizer. In the last test phase, which represents the real-world optimization problem, our model needs around 9% fewer iterations to reach the same objective than the algorithm that uses a random neighborhood creation. These results are verified on the VRPTW instances
Procedurally generating natural-looking villages in Minecraft with ant colony optimization algorithms
No full textProcedural content generation has been widely deployed to automatically generate digital content with limited or indirect user input. This paper shows how ant colony optimization algorithms, a multi-agent system usually applied to shortest-path optimization problems, can be adapted to generate natural-looking villages in the video game Minecraft. To achieve this, houses are stochastically placed in a specified region of a Minecraft world, favoring flat and central areas. Next, villagers inhabiting these houses are represented by multiple ant agents each and simulate life in the village by wandering between houses. The shorter and flatter a found path between two houses is, the more likely the path will be followed by future ant agents. After some iterations, a natural path network connects all houses. This process of placing houses and connecting them with paths can be repeated, allowing the village to naturally grow. Survey results show that the generated villages are perceived as more natural than the default ones, both regarding house placements and path trajectories
Dynamic X-ray CT of Spaghetti Noodles
No full textThe dataset consists of a scan of a pasta bundle (Albert Heijn – Spelt), placed inside a bottle. The pasta was suspended at the bottleneck using a sponge to hold it in position. Scanning was focused on the bottleneck region to minimize interference from surrounding water. This scan was performed under dynamic thermal conditions: the bottle was filled with a 50/50 mix of boiling and cold water, leading to a gradual change in temperature during the scan.
This setup tries to mimic cooking conditions, allowing observation of structural changes in the pasta as it is exposed to varying thermal environments. Exact temperature values were not recorded
Reliable determination of sub-nanometer gaps in plasmonic gold dimers for correlation to their optical properties
Get PDFAccurately characterizing sub-nanometer gaps in plasmonic nanoparticle dimers is essential for understanding their optical properties, particularly in the transition from classical to quantum plasmonic behavior. While two-dimensional (scanning) transmission electron microscopy imaging provides high spatial resolution, it lacks the three-dimensional (3D) morphological information needed to reliably extract gap sizes. In this work, we combine electron tomography with a robust data analysis workflow to quantify interparticle gaps in gold nanosphere dimers with sub-nanometer precision. We show that gap size estimates are highly sensitive to reconstruction algorithms, segmentation thresholds, and meshing parameters. To overcome this, we introduce a model-fitting approach based on convolving a step function with a Gaussian, enabling consistent and accurate gap measurements even in the absence of a known ground truth. Validation on simulated datasets confirms pixel-level accuracy, and application to experimental data demonstrates the robustness and general applicability of the method. The resulting 3D reconstructions are directly integrated into electromagnetic simulations, allowing reliable interpretation of the optical response of the dimer. This workflow offers a broadly applicable strategy for correlating morphology and optical function in plasmonic systems and provides a crucial step toward resolving quantum effects in nanoscale light-matter interactions