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Convergence of the number of period sets in strings
Get PDFConsider words of length n. The set of all periods of a word of length n is a subset of {0,1,2,…,n-1}. However, not every subset of {0,1,2,…,n-1} can be a valid set of periods. In a seminal paper in 1981, Guibas and Odlyzko proposed encoding the set of periods of a word into a binary string of length n, called an autocorrelation, where a 1 at position i denotes the period i. They considered the question of recognizing a valid period set, and also studied the number κn of valid period sets for strings of length n. They conjectured that lnκn asymptotically converges to a constant times (lnn)2. Although improved lower bounds for lnκn/(lnn)2 were proved in 2001, the question of a tight upper bound has remained open since Guibas and Odlyzko’s paper. Here, we exhibit an upper bound for this fraction, which implies its convergence and closes this longstanding conjecture. Moreover, we extend our result to find similar bounds for the number of correlations: a generalization of autocorrelations that encodes the overlaps between two strings
The Reversible Simulator ─ a data-driven approach for solving forward and inverse problems
No full textflexi-framework/relexi
No full textRelexi is a Reinforcement Learning (RL) framework developed for the high-order HPC flow solver FLEXI
Reimagining workflows to create XR experiences in the creative and cultural sector
No full textThis session was recorded at UnitedXR Europe 2025 - the world's largest XR event in Europe
How to diversify any personalized recommender?
Get PDFIn this paper, we introduce a novel approach to improve the diversity of Top-N recommendations while maintaining accuracy. Our approach employs a user-centric pre-processing strategy aimed at exposing users to a wide array of content categories and topics. We personalize this strategy by selectively adding and removing a percentage of interactions from user profiles. This personalization ensures we remain closely aligned with user preferences while gradually introducing distribution shifts. Our pre-processing technique offers flexibility and can seamlessly integrate into any recommender architecture. We run extensive experiments on two publicly available data sets for news and book recommendations to evaluate our approach. We test various standard and neural network-based recommender system algorithms. Our results show that our approach generates diverse recommendations, ensuring users are exposed to a wider range of items. Furthermore, using pre-processed data for training leads to recommender systems achieving performance levels comparable to, and in some cases, better than those trained on original, unmodified data. Additionally, our approach promotes provider fairness by facilitating exposure to minority categories. (Our GitHub code is available at: https://github.com/SlokomManel/How-to-Diversify-any-Personalized-Recommender-)
Data and code for the paper "Macroscopic parameterization of positive streamer heads in air"
No full textUnveiling cyber threat actors: A hybrid deep learning approach for behavior-based attribution
Get PDFIn this article, we leverage natural language processing and machine learning algorithms to profile threat actors based on their behavioral signatures to establish identification for soft attribution. Our unique dataset comprises various actors and the commands they have executed, with a significant proportion using the Cobalt Strike framework in August 2020–October 2022. We implemented a hybrid deep learning structure combining transformers and convolutional neural networks to benefit global and local contextual information within the sequence of commands, which provides a detailed view of the behavioral patterns of threat actors. We evaluated our hybrid architecture against pre-trained transformer-based models such as BERT, RoBERTa, SecureBERT, and DarkBERT with our high-count, medium-count, and low-count datasets. Hybrid architecture has achieved F1-score of 95.11% and an accuracy score of 95.13% on the high-count dataset, F1-score of 93.60% and accuracy score of 93.77% on the medium-count dataset, and F1-score of 88.95% and accuracy score of 89.25% on the low-count dataset. Our approach has the potential to substantially reduce the workload of incident response experts who are processing the collected cybersecurity data to identify patterns
UTOPYS TU_e bouwt digitale tweeling energiesystemen - industrievandaagnl - 8 December 2025
No full textA quantum speed-up for approximating the top eigenvectors of a matrix
Get PDFFinding a good approximation of the top eigenvector of a given matrix is a basic and important computational problem, with many applications. We give two different quantum algorithms that, given query access to the entries of a Hermitian matrix and assuming a constant eigenvalue gap, output a classicaldescription of a good approximation of the top eigenvector: one algorithm with time complexity and one with time complexity (the first algorithm has a slightly better dependence on the -error of the approximating vector than the second, and uses different techniques of independent interest). Both of our quantum algorithms provide a polynomial speed-up over the best-possible classical algorithm, which needs queries to entries of , and hence time. We extend this to a quantum algorithm that outputs a classical description of the subspace spanned by the top- eigenvectors in time . We also prove a nearly-optimal lower bound of on the quantum query complexity of approximating the top eigenvector. Our quantum algorithms run a version of the classical power method that is robust to certain benign kinds of errors, where we implement each matrix-vector multiplication with small and well-behaved error on a quantum computer, in different ways for the two algorithms. Our first algorithm estimates the matrix-vector product one entry at a time, using a new "Gaussian phase estimation" procedure. Our second algorithm uses block-encoding techniques to compute the matrix-vector product as a quantum state, from which we obtain a classical description by a new time-efficient unbiased pure-state tomography procedure. This procedure uses an essentially optimal number of "conditional sample states"; if we have a state-preparation unitary available rather than just copies of the state, then this -dependence can be improved further quadratically.Our procedure comes with improved statistical properties and faster runtime compared to earlier pure-state tomography algorithms. We also develop an almost optimal time-efficient process-tomography algorithm for reflections around bounded-rank subspaces, providing the basis for our top-eigensubspace estimation algorithm, and in turn providing a pure-state tomography algorithm that only requires a reflection about the state rather than a state preparation unitary as input
CHI NL at CHI 2025
No full textWe are the Dutch ACM SIGCHI Chapter that connects, supports, and represents the Human-Computer Interaction (HCI) community in the Netherlands. Specifically, as CHI NL, we aim to:
connect professionals in HCI in and across academia and industry, support professionals in HCI in academia and industry, and represent professionals in HCI in relevant circles, such as government and the public perception of HCI