arXiv subjects

Florian Heinrich

Publications and source records attributed to Florian Heinrich.

5 recordsLinked to original sources

arXiv2026

Emotional Engagement in Narrative Medical Visualization: An Electrodermal Activity and Eye-Tracking Study

Narrative visualization embeds data in visual stories to make medical information more relatable for non-experts. Despite the growing use of character elements in health communication, evidence on whether individual characters support affective responses remains inconclusive. Physiological evidence independent of verbal self-report is especially scarce, although such measures should complement participants' self-reported experiences. We present a mixed-methods study using electrodermal activity (EDA), eye tracking, and questionnaires to compare two medical data stories: an individual, character-based version and a general, population-level version without an individual protagonist. We examine how this framing influences physiological arousal, visual attention, viewing behavior, and self-reported affective response. Story-level EDA comparisons provide only limited support for stronger arousal in the character-based story. Stronger evidence comes from eye-tracking-based peak classification, where character illustrations were robustly associated with EDA peaks, and from questionnaire responses showing more negative empathy-related emotions for the individual story. The general story elicited more awe and joy, suggesting that individual and population-level framings may support different emotional qualities. We also observed a preliminary story-order effect: participants who first saw the individual story showed higher peak-based physiological arousal, although this effect cannot be fully disentangled from fatigue, novelty, or learning effects. By combining physiological, gaze-based, questionnaire, and lightweight qualitative evidence, our work advances time-resolved assessment of narrative medical visualization and highlights the need to interpret arousal, attention, curiosity, and self-reported emotions together.

cs.HC↗

arXiv2026

Robustness and Regularization in Hierarchical Re-Basin

This paper takes a closer look at Git Re-Basin, an interesting new approach to merge trained models. We propose a hierarchical model merging scheme that significantly outperforms the standard MergeMany algorithm. With our new algorithm, we find that Re-Basin induces adversarial and perturbation robustness into the merged models, with the effect becoming stronger the more models participate in the hierarchical merging scheme. However, in our experiments Re-Basin induces a much bigger performance drop than reported by the original authors.

cs.LG↗

arXiv2026

Charge carrier generation in RNDR-DEPFET Detectors

Depleted p-channel field effect transistor detectors with repetitive-non-destructive readout (RNDR-DEPFETs) achieve a deep sub-electron noise by averaging several independent measurements of one single event. During the repetitive readout collected electrons are transferred between two readout nodes within each pixel to enable electron number-resolved measurements. The pixels serve as a unit cell of an active pixel sensor to achieve a high level of parallelization and fast readout. These properties are exploited in the DANAE experiment, which aims for the direct detection of light dark matter based with the event signature of electron recoils. We present the experimental characterization of an $64\times64$ RNDR-DEPFET pixel detector with a focus on the charge carrier generation rate. This technology achieves a high time resolution, which increases its sensitivity on rare events with a signal of two or more electrons due to the Poisson distribution of thermal generated electrons.

physics.ins-det↗

arXiv2025

Path Matters: Industrial Data Meet Quantum Optimization

Real-world optimization problems must undergo a series of transformations before becoming solvable on current quantum hardware. Even for a fixed problem, the number of possible transformation paths -- from industry-relevant formulations through binary constrained linear programs (BILPs), to quadratic unconstrained binary optimization (QUBO), and finally to a hardware-executable representation -- is remarkably large. Each step introduces free parameters, such as Lagrange multipliers, encoding strategies, slack variables, rounding schemes or algorithmic choices -- making brute-force exploration of all paths intractable. In this work, we benchmark a representative subset of these transformation paths using a real-world industrial production planning problem with industry data: the optimization of work allocation in a press shop producing vehicle parts. We focus on QUBO reformulations and algorithmic parameters for both quantum annealing (QA) and the Linear Ramp Quantum Approximate Optimization Algorithm (LR-QAOA). Our goal is to identify a reduced set of effective configurations applicable to similar industrial settings. Our results show that QA on D-Wave hardware consistently produces near-optimal solutions, whereas LR-QAOA on IBM quantum devices struggles to reach comparable performance. Hence, the choice of hardware and solver strategy significantly impacts performance. The problem formulation and especially the penalization strategy determine the solution quality. Most importantly, mathematically-defined penalization strategies are equally successful as hand-picked penalty factors, paving the way for automated QUBO formulation. Moreover, we observe a strong correlation between simulated and quantum annealing performance metrics, offering a scalable proxy for predicting QA behavior on larger problem instances.

quant-ph↗

arXiv2023

Rebricking frames and bases

In 1949, Denis Gabor introduced the ``complex signal'' (nowadays called ``analytic signal'') by combining a real function $f$ with its Hilbert transform $Hf$ to a complex function $f+ iHf$. His aim was to extract phase information, an idea that has inspired techniques as the monogenic signal and the complex dual tree wavelet transform. In this manuscript, we consider two questions: When do two real-valued bases or frames $\{f_{n} : n\in\mathbb{N}\}$ and $\{g_{n} : n\in\mathbb{N}\}$ form a complex basis or frame of the form $\{f_{n} + i g_{n}: n\in\mathbb{N}\}$? And for which bounded linear operators $A$ forms $\{f_{n} + i A f_{n} : n\in\mathbb{N}\}$ a complex-valued orthonormal basis, Riesz basis or frame, when $\{f_{n} : n\in\mathbb{N}\}$ is a real-valued orthonormal basis, Riesz basis or frame? We call this approach \emph{rebricking}. It is well-known that the analytic signals don't span the complex vector space $L^{2}(\mathbb{R}; \mathbb{C})$, hence $H$ is not a rebricking operator. We give a full characterization of rebricking operators for bases, in particular orthonormal and Riesz bases, Parseval frames, and frames in general. We also examine the special case of finite dimensional vector spaces and show that we can use any real, invertible matrix for rebricking if we allow for permutations in the imaginary part.

math.FA↗