Institution profile

University of Antwerp

Academic institutioneurope · be
Official website
Research library169linked papers
Opportunities0open roles
Selected work

Representative Papers

Opportunities and Challenges for Virtual Reality Streaming over Millimeter-Wave: An Experimental Analysis

Oct 05, 2022International Conference on Network of the Future

This study addresses transmission instability of millimeter-wave (mmWave) VR streaming under mobile and dynamic occlusion conditions. We develop the first experimental 802.11ad testbed supporting controllable motion-induced blockage modeling, empirically revealing critical bottlenecks: severe throughput degradation (>60%) during line-of-sight (LOS) interruptions, non-line-of-sight (NLoS) throughput volatility, and TCP protocol mismatch. To overcome these, we propose a novel TCP parameter self-adaptation framework tailored to mmWave channel characteristics—achieving 35% improvement in streaming stability without modifying the protocol stack. Our work constitutes the first systematic empirical validation of feasibility boundaries and optimization pathways for mmWave-enabled immersive VR wireless delivery. It establishes a reproducible experimental paradigm and practical tuning methodology for low-latency, high-bandwidth extended reality (XR) communications.

8 citationsRead paper

Generating Realistic Synthetic Head Rotation Data for Extended Reality using Deep Learning

Oct 10, 2022IXR@MM

To address the scarcity and high acquisition cost of real-world head-motion time-series data for XR systems, this work pioneers the adaptation of TimeGAN to head-rotation sequence modeling. We propose a conditional multivariate time-series generation framework that jointly models angular velocity and Euler angles, integrating LSTM-based generators and discriminators to ensure both dynamic consistency and statistical fidelity. Evaluated on real head-motion datasets, our synthesized data achieves a 37% reduction in Fréchet Inception Distance (FID), yields a 29% error reduction in downstream head-motion prediction models, and attains 92% perceptual realism as validated by expert blind evaluation. This work overcomes the critical bottleneck of head-motion data scarcity, substantially enhancing the generalization capability of prediction models. It establishes a novel paradigm for generating high-fidelity synthetic head-motion data, enabling improved real-time rendering and interaction in XR applications.

3 citationsRead paper

Least trimmed squares regression with missing values and cellwise outliers

Mar 04, 2026

This study addresses the limitations of traditional regression methods in simultaneously handling case-wise and cell-wise outliers as well as missing data, particularly under skewed distributions where out-of-sample prediction performance often deteriorates. To overcome these challenges, the authors propose a novel robust regression approach built upon the Least Trimmed Squares (LTS) framework. This method is the first to provide a theoretical breakdown point guarantee against cell-wise contamination and incorporates an embedded imputation mechanism tailored for asymmetric data distributions. Empirical evaluations demonstrate that the proposed technique substantially enhances both robustness and predictive accuracy in complex scenarios where outliers and missing values coexist.

1 citationsRead paper

Which Algorithms Can Graph Neural Networks Learn?

Feb 13, 2026

In recent years, there has been growing interest in understanding neural architectures'ability to learn to execute discrete algorithms, a line of work often referred to as neural algorithmic reasoning. The goal is to integrate algorithmic reasoning capabilities into larger neural pipelines. Many such architectures are based on (message-passing) graph neural networks (MPNNs), owing to their permutation equivariance and ability to deal with sparsity and variable-sized inputs. However, existing work is either largely empirical and lacks formal guarantees or it focuses solely on expressivity, leaving open the question of when and how such architectures generalize beyond a finite training set. In this work, we propose a general theoretical framework that characterizes the sufficient conditions under which MPNNs can learn an algorithm from a training set of small instances and provably approximate its behavior on inputs of arbitrary size. Our framework applies to a broad class of algorithms, including single-source shortest paths, minimum spanning trees, and general dynamic programming problems, such as the $0$-$1$ knapsack problem. In addition, we establish impossibility results for a wide range of algorithmic tasks, showing that standard MPNNs cannot learn them, and we derive more expressive MPNN-like architectures that overcome these limitations. Finally, we refine our analysis for the Bellman-Ford algorithm, yielding a substantially smaller required training set and significantly extending the recent work of Nerem et al. [2025] by allowing for a differentiable regularization loss. Empirical results largely support our theoretical findings.

1 citationsRead paper

Implications of Russia’s full-scale invasion of Ukraine for the international mobility of Ukrainian scholars

Nov 01, 2025Scientometrics

This study investigates the impact of Russia’s full-scale invasion of Ukraine on the international mobility and scholarly output of Ukrainian researchers. Drawing on Scopus data from 2020 to 2023, it examines shifts in mobility patterns, destination countries, and research impact among scholars affiliated with Ukrainian universities and the National Academy of Sciences. Employing bibliometric analysis, Field-Weighted Citation Impact (FWCI) metrics, and statistical testing, the research reveals a significant increase in researcher outflows during 2022–2023, with Germany and Poland replacing Russia as primary destinations. Notably, internationally mobile academics from universities exhibited a higher proportion of highly cited publications, and overall mobility did not diminish average research impact, underscoring a structural transformation in scholarly migration under wartime conditions.

1 citationsRead paper
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