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Fabian Wolf

Fabian Wolf appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
physics.atom-phquant-phComputer VisionArtificial Intelligencecond-mat.mes-hallSoftware Engineering

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Published work

10 published item(s)

preprint2026arXiv

Quantifying the human visual exposome with vision language models

The visual environment is a fundamental yet unquantified determinant of mental health. While the concept of the environmental exposome is well established, current methods rely on coarse geospatial proxies or biased self reports, failing to capture the first person visual context of daily life. We addressed this gap by coupling ecological momentary assessment with vision language models (VLMs) to quantify the semantic richness of human visual experience. Across 2674 participant generated photographs, VLM derived estimates of greenness robustly predicted momentary affect and chronic stress, consistent with established benchmarks. We then developed a semi autonomous large language model (LLM) based pipeline that mined over seven million scientific publications to extract nearly 1000 environmental features empirically linked to mental health. When applied to real world imagery, up to 33 percent of VLM extracted context ratings significantly correlated with affect and stress. These findings establish a scalable objective paradigm for visual exposomics, enabling high throughput decoding of how the visible world is associated with mental health.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Recognition-free Question Answering on Handwritten Document Collections

In recent years, considerable progress has been made in the research area of Question Answering (QA) on document images. Current QA approaches from the Document Image Analysis community are mainly focusing on machine-printed documents and perform rather limited on handwriting. This is mainly due to the reduced recognition performance on handwritten documents. To tackle this problem, we propose a recognition-free QA approach, especially designed for handwritten document image collections. We present a robust document retrieval method, as well as two QA models. Our approaches outperform the state-of-the-art recognition-free models on the challenging BenthamQA and HW-SQuAD datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation

Performances of Handwritten Text Recognition (HTR) models are largely determined by the availability of labeled and representative training samples. However, in many application scenarios labeled samples are scarce or costly to obtain. In this work, we propose a self-training approach to train a HTR model solely on synthetic samples and unlabeled data. The proposed training scheme uses an initial model trained on synthetic data to make predictions for the unlabeled target dataset. Starting from this initial model with rather poor performance, we show that a considerable adaptation is possible by training against the predicted pseudo-labels. Moreover, the investigated self-training strategy does not require any manually annotated training samples. We evaluate the proposed method on four widely used benchmark datasets and show its effectiveness on closing the gap to a model trained in a fully-supervised manner.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling

Word spotting is a popular tool for supporting the first exploration of historic, handwritten document collections. Today, the best performing methods rely on machine learning techniques, which require a high amount of annotated training material. As training data is usually not available in the application scenario, annotation-free methods aim at solving the retrieval task without representative training samples. In this work, we present an annotation-free method that still employs machine learning techniques and therefore outperforms other learning-free approaches. The weakly supervised training scheme relies on a lexicon, that does not need to precisely fit the dataset. In combination with a confidence based selection of pseudo-labeled training samples, we achieve state-of-the-art query-by-example performances. Furthermore, our method allows to perform query-by-string, which is usually not the case for other annotation-free methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Initialization of quantum simulators by sympathetic cooling

Simulating computationally intractable many-body problems on a quantum simulator holds great potential to deliver insights into physical, chemical, and biological systems. While the implementation of Hamiltonian dynamics within a quantum simulator has already been demonstrated in many experiments, the problem of initialization of quantum simulators to a suitable quantum state has hitherto remained mostly unsolved. Here, we show that already a single dissipatively driven auxiliary particle can efficiently prepare the quantum simulator in a low-energy state of largely arbitrary Hamiltonians. We demonstrate the scalability of our approach and show that it is robust against unwanted sources of decoherence. While our initialization protocol is largely independent of the physical realization of the simulation device, we provide an implementation example for a trapped ion quantum simulator.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Detection of motional ground state population of a trapped ion using delayed pulses

Efficient preparation and detection of the motional state of trapped ions is important in many experiments ranging from quantum computation to precision spectroscopy. We investigate the stimulated Raman adiabatic passage (STIRAP) technique for the manipulation of motional states in a trapped ion system. The presented technique uses a Raman coupling between two hyperfine ground states in $^{25}$Mg$^+$, implemented with delayed pulses, which removes a single phonon independent of the initial motional state. We show that for a thermal state the STIRAP population transfer is more efficient than a stimulated Raman Rabi pulse on a motional sideband. In contrast to previous implementations, a large detuning of more than 200 times the natural linewidth of the transition is used. This approach renders STIRAP suitable for atoms in which resonant laser fields would populate fluorescing excited states and thus impede the STIRAP process. We use the technique to measure the wavefunction overlap of excited motional states with the motional ground state. This is an important application for photon recoil spectroscopy and other force sensing applications that utilize the high sensitivity of the motional state of trapped ions to external fields. Furthermore, a determination of the ground state population enables a simple measurement of the ion's temperature.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Efficient sympathetic motional ground-state cooling of a molecular ion

Cold molecular ions are promising candidates in various fields ranging from precision spectroscopy and test of fundamental physics to ultra-cold chemistry. Control of internal and external degrees of freedom is a prerequisite for many of these applications. Motional ground state cooling represents the starting point for quantum logic-assisted internal state preparation, detection, and spectroscopy protocols. Robust and fast cooling is crucial to maximize the fraction of time available for the actual experiment. We optimize the cooling rate of ground state cooling schemes for single $^{25}\mathrm{Mg}^{+}$ ions and sympathetic ground state cooling of $^{24}\mathrm{MgH}^{+}$. In particular, we show that robust cooling is achieved by combining pulsed Raman sideband cooling with continuous quench cooling. Furthermore, we experimentally demonstrate an efficient strategy for ground state cooling outside the Lamb-Dicke regime.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Non-destructive state detection for quantum logic spectroscopy of molecular ions

Laser spectroscopy of cold and trapped molecular ions is a powerful tool for fundamental physics, including the determination of fundamental constants, the laboratory test for their possible variation, and the search for a possible electric dipole moment of the electron. Optical clocks based on molecular ions sensitive to some of these effects are expected to achieve uncertainties approaching the $10^{-18}$ level. While the complexity of molecular structure facilitates these applications, the absence of cycling transitions poses a challenge for direct laser cooling, quantum state control, and detection. Previously employed state detection techniques based on photo-dissociation or chemical reactions are destructive and therefore inefficient. Here we experimentally demonstrate non-destructive state detection of a single trapped molecular ion through its strong Coulomb coupling to a well-controlled co-trapped atomic ion. An algorithm based on a state-dependent optical dipole force(ODF) changes the internal state of the atom conditioned on the internal state of the molecule. We show that individual states in the molecule can be distinguished by their coupling strength to the ODF and observe black-body radiation-induced quantum jumps between rotational states. Using the detuning dependence of the state detection signal, we implement a variant of quantum logic spectroscopy and improve upon a previous measurement of the $\mathrm{X}^1Σ^+(J=1)\rightarrow\mathrm{A}^1Σ^+(J=0)$ transition in MgH, finding a frequency of 1067.74752(53)THz. We estimate that non-destructive state detection with near 100% efficiency could take less than 10 ms. The technique we demonstrate is applicable to a wide range of molecular ions, enabling further applications in state-controlled quantum chemistry and spectroscopic investigations of molecules serving as probes for interstellar clouds.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Precision isotope shift measurements in Ca$^+$ using highly sensitive detection schemes

We demonstrate an efficient high-precision optical spectroscopy technique for single trapped ions with non-closed transitions. In a double-shelving technique, the absorption of a single photon is first amplified to several phonons of a normal motional mode shared with a co-trapped cooling ion of a different species, before being further amplified to thousands of fluorescence photons emitted by the cooling ion using the standard electron shelving technique. We employ this extension of the photon recoil spectroscopy technique to perform the first high precision absolute frequency measurement of the $^{2}$D$_{3/2}$ $\rightarrow$ $^{2}$P$_{1/2}$ transition in $^{40}$Ca$^{+}$, resulting in a transition frequency of $f=346\, 000\, 234\, 867(96)$ kHz. Furthermore, we determine the isotope shift of this transition and the $^{2}$S$_{1/2}$ $\rightarrow$ $^{2}$P$_{1/2}$ transition for $^{42}$Ca$^{+}$, $^{44}$Ca$^{+}$ and $^{48}$Ca$^{+}$ ions relative to $^{40}$Ca$^{+}$ with an accuracy below 100 kHz. Improved field and mass shift constants of these transitions as well as changes in mean square nuclear charge radii are extracted from this high resolution data.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Product Line Metrics for Legacy Software in Practice

Nowadays, customer products like vehicles do not only contain mechanical parts but also a highly complex software and their manufacturers have to offer many variants of technically very similar systems with sometimes only small differences in their behavior. The proper reuse of software artifacts which realize this behavior using a software product line is discussed in recent literature and appropriate methods and techniques for their management are proposed. However, establishing a software product line for integrating already existing legacy software to reuse valuable resources for future similar products is very company-specific. In this paper, a method is outlined for evaluating objectively a legacy software's potential to create a software product line. This method is applied to several development projects at Volkswagen AG Business Unit Braunschweig to evaluate the software product line potential for steering systems.

0 citations0 reviewsNo code linkedOpen access