arXiv subjects

Daniel Braun

Publications and source records attributed to Daniel Braun.

At least 37 records · Page 2Linked to original sources

arXiv2025

The path towards measuring the gravitational field of proton bunches at accelerators

The Newtonian law describing the gravitational interaction of non-relativistic (slowly moving) gravitating matter, has been tested in many laboratory experiments with very high precision. In contrast, the post Minkowskian predictions for the gravitational field of ultra-relativistic matter, dominated by momentum instead of rest mass, have not been tested directly yet. The intense ultra-relativistic proton beam in the LHC storage ring offers the potential to test general relativity and alternative gravitational theories in this parameter regime for the first time in controlled lab-scale experiments. If successful, this would open the road to a novel use case of the LHC, where non-trivial gravitational physics could be studied likely in a parasitic mode, without the necessity of dedicated filling patterns. While the technical challenges are formidable, they should also lead to the development of ultra-high-sensitive acceleration sensors with abundant applications in other parts of science and technology. The present document summarizes the status of the theoretical studies in this direction, points out the challenges, and possible ways of addressing them. It was submitted as a contribution to the European Strategy for Particle Physics (ESPP) 2026 Update.

hep-ex↗

arXiv2025

Metrology of Gravitational Effects with Mechanical Quantum Systems

Mechanical quantum systems, such as resonators and levitated particles, offer unique opportunities for quantum metrology. Particularly, their significant mass and quantum-level control enable applications in measuring gravitational effects. This article highlights key challenges, alongside potential solutions to advance precision sensing and quantum-gravitational research.

quant-ph↗

arXiv2024

Simulation of the Dissipative Dynamics of Strongly Interacting NV Centers with Tensor Networks

NV centers in diamond are a promising platform for highly sensitive quantum sensors for magnetic fields and other physical quantities. The quest for high sensitivity combined with high spatial resolution leads naturally to dense ensembles of NV centers, and hence to strong, long-range interactions between them. Hence, simulating strongly interacting NVs becomes essential. However, obtaining the exact dynamics for a many-spin system is a challenging task due to the exponential scaling of the Hilbert space dimension, a problem that is exacerbated when the system is modelled as an open quantum system. In this work, we employ the Matrix Product Density Operator (MPDO) method to represent the many-body mixed state and to simulate the dynamics of an ensemble of NVs in the presence of strong long-range couplings due to dipole-dipole forces. We benchmark different time-evolution algorithms in terms of numerical accuracy and stability against time evolution based on exact numerical diagonalization. Subsequently, we simulate the dynamics in the strong interaction regime, and study the impact of decoherence on the accuracy of the MPDO method. Lastly, we investigate the dynamics of quantum Fisher information and discuss under what circumstances a strong interaction can improve sensitivity for magnetic field sensing.

quant-ph↗

arXiv2024

Machine learning of quantum channels on NISQ devices

World-wide efforts aim at the realization of advanced quantum simulators and processors. However, despite the development of intricate hardware and pulse control systems, it may still not be generally known which effective quantum dynamics, or channels, are implemented on these devices. To systematically infer those, we propose a neural-network algorithm approximating generic discrete-time dynamics through the repeated action of an effective quantum channel. We test our approach considering time-periodic Lindblad dynamics as well as non-unitary subsystem dynamics in many-body unitary circuits. Moreover, we exploit it to investigate cross-talk effects on the ibmq_ehningen quantum processor, which showcases our method as a practically applicable tool for inferring quantum channels when the exact nature of the underlying dynamics on the physical device is not known a priori. While the present approach is tailored for learning Markovian dynamics, we discuss how it can be adapted to also capture generic non-Markovian discrete-time evolutions.

quant-ph↗

arXiv2024

Leveraging Annotator Disagreement for Text Classification

It is common practice in text classification to only use one majority label for model training even if a dataset has been annotated by multiple annotators. Doing so can remove valuable nuances and diverse perspectives inherent in the annotators' assessments. This paper proposes and compares three different strategies to leverage annotator disagreement for text classification: a probability-based multi-label method, an ensemble system, and instruction tuning. All three approaches are evaluated on the tasks of hate speech and abusive conversation detection, which inherently entail a high degree of subjectivity. Moreover, to evaluate the effectiveness of embracing annotation disagreements for model training, we conduct an online survey that compares the performance of the multi-label model against a baseline model, which is trained with the majority label. The results show that in hate speech detection, the multi-label method outperforms the other two approaches, while in abusive conversation detection, instruction tuning achieves the best performance. The results of the survey also show that the outputs from the multi-label models are considered a better representation of the texts than the single-label model.

cs.CL↗

arXiv2024

Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots

Visual validation of regression models in scatterplots is a common practice for assessing model quality, yet its efficacy remains unquantified. We conducted two empirical experiments to investigate individuals' ability to visually validate linear regression models (linear trends) and to examine the impact of common visualization designs on validation quality. The first experiment showed that the level of accuracy for visual estimation of slope (i.e., fitting a line to data) is higher than for visual validation of slope (i.e., accepting a shown line). Notably, we found bias toward slopes that are "too steep" in both cases. This lead to novel insights that participants naturally assessed regression with orthogonal distances between the points and the line (i.e., ODR regression) rather than the common vertical distances (OLS regression). In the second experiment, we investigated whether incorporating common designs for regression visualization (error lines, bounding boxes, and confidence intervals) would improve visual validation. Even though error lines reduced validation bias, results failed to show the desired improvements in accuracy for any design. Overall, our findings suggest caution in using visual model validation for linear trends in scatterplots.

cs.CV↗

arXiv2024

AGB-DE: A Corpus for the Automated Legal Assessment of Clauses in German Consumer Contracts

Legal tasks and datasets are often used as benchmarks for the capabilities of language models. However, openly available annotated datasets are rare. In this paper, we introduce AGB-DE, a corpus of 3,764 clauses from German consumer contracts that have been annotated and legally assessed by legal experts. Together with the data, we present a first baseline for the task of detecting potentially void clauses, comparing the performance of an SVM baseline with three fine-tuned open language models and the performance of GPT-3.5. Our results show the challenging nature of the task, with no approach exceeding an F1-score of 0.54. While the fine-tuned models often performed better with regard to precision, GPT-3.5 outperformed the other approaches with regard to recall. An analysis of the errors indicates that one of the main challenges could be the correct interpretation of complex clauses, rather than the decision boundaries of what is permissible and what is not.

cs.CL↗

arXiv2024

Pattern-based quantum functional testing

With the growing number of qubits of quantum information processing devices, the task of fully characterizing these processors becomes increasingly unfeasible. From a practical perspective, one wants to find possible errors in the functioning of the device as quickly as possible, or otherwise establish its correct functioning with high confidence. In response to these challenges, we propose a pattern-based approach inspired by classical memory testing algorithms to evaluate the functionality of a quantum memory, based on plausible failure mechanisms. We demonstrate the method's capability to extract pattern dependencies of important qubit characteristics, such as $T_1$ and $T_2$ times, and to identify and analyze interactions between adjacent qubits. Additionally, our approach enables the detection of different types of crosstalk effects and of signatures indicating non-Markovian dynamics in individual qubits.

quant-ph↗

arXiv2024

Minimal-noise estimation of noncommuting rotations of a spin

We propose an analogue of $\text{SU}(1,1)$ interferometry to measure rotation of a spin by using two-spin squeezed states. Attainability of the Heisenberg limit for the estimation of the rotation angle is demonstrated for maximal squeezing. For a specific direction and strength an advantage in sensitivity for all equatorial rotation axes (and hence non-commuting rotations) over the classical bound is shown in terms of quadratic scaling of the single-parameter quantum Fisher information for the corresponding rotation angles. Our results provide a method for measuring magnetic fields in any direction in the $x$-$y$-plane with the same optimized initial state.

quant-ph↗

arXiv2024

Non-linear media in weakly curved spacetime: optical solitons and probe pulses for gravimetry

That light propagating in a gravitational field gets frequency-shifted is one of the basic consequences of any metric theory of gravity rooted in the equivalence principle. At the same time, also a time dependent material's refractive index can frequency-shift light propagating in it. The mathematical analogy between the two effects is such that the latter has been used to study the optical analogue of a black-hole spacetime. Here, we combine these two effects by showing that light propagation in non-linear media in the presence of a moving refractive index perturbation can lead to a gravity-dependent blueshift. We find that the predicted blueshift surpasses the gravitational redshift even if the medium is considered to be perfectly stiff. In realistic scenarios, by far the strongest frequency shift arises due to the deformation of the dielectric medium and the corresponding photoelastic change of refractive index. This has the potential to facilitate optical sensing of small gravity gradients.

gr-qc↗

arXiv2024

Visual Validation versus Visual Estimation: A Study on the Average Value in Scatterplots

We investigate the ability of individuals to visually validate statistical models in terms of their fit to the data. While visual model estimation has been studied extensively, visual model validation remains under-investigated. It is unknown how well people are able to visually validate models, and how their performance compares to visual and computational estimation. As a starting point, we conducted a study across two populations (crowdsourced and volunteers). Participants had to both visually estimate (i.e, draw) and visually validate (i.e., accept or reject) the frequently studied model of averages. Across both populations, the level of accuracy of the models that were considered valid was lower than the accuracy of the estimated models. We find that participants' validation and estimation were unbiased. Moreover, their natural critical point between accepting and rejecting a given mean value is close to the boundary of its 95% confidence interval, indicating that the visually perceived confidence interval corresponds to a common statistical standard. Our work contributes to the understanding of visual model validation and opens new research opportunities.

cs.CV↗

arXiv2023

Efficient Black-Box Adversarial Attacks on Neural Text Detectors

Neural text detectors are models trained to detect whether a given text was generated by a language model or written by a human. In this paper, we investigate three simple and resource-efficient strategies (parameter tweaking, prompt engineering, and character-level mutations) to alter texts generated by GPT-3.5 that are unsuspicious or unnoticeable for humans but cause misclassification by neural text detectors. The results show that especially parameter tweaking and character-level mutations are effective strategies.

cs.CL↗

arXiv2023

Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v = m * 10e . We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyse error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.

cs.HC↗

arXiv2023

Challenges in Domain-Specific Abstractive Summarization and How to Overcome them

Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as domain-specific abstractive text summarization. This paper identifies three of those limitations as research problems in the context of abstractive text summarization: 1) Quadratic complexity of transformer-based models with respect to the input text length; 2) Model Hallucination, which is a model's ability to generate factually incorrect text; and 3) Domain Shift, which happens when the distribution of the model's training and test corpus is not the same. Along with a discussion of the open research questions, this paper also provides an assessment of existing state-of-the-art techniques relevant to domain-specific text summarization to address the research gaps.

cs.CL↗

arXiv2023

Quantum parameter estimation with many-body fermionic systems and application to the Hall effect

We calculate the quantum Fisher information for a generic many-body fermionic system in a pure state depending on a parameter. We discuss the situations where the parameter is imprinted in the basis states, in the state coefficients, or both. In the case where the parameter dependence of coefficients results from a Hamiltonian evolution, we derive a particularly simple expression for the quantum Fisher information. We apply our findings to the quantum Hall effect, and evaluate the quantum Fisher information associated with the optimal measurement of the magnetic field for a system in the ground state of the effective Hamiltonian. The occupation of electron states with high momentum enforced by the Pauli principle leads to a super-Heisenberg scaling of the sensitivity with a power law that depends on the geometry of the sensor.

quant-ph↗

arXiv2023

Investigating Conversational Search Behavior For Domain Exploration

Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to the complexity of information-seeking behavior, the design of conversational interfaces for retrieving information remains a great challenge. Although prior work has employed user studies to empirically ground the system design, most existing studies are limited to well-defined search tasks or known domains, thus being less exploratory in nature. Therefore, we conducted a laboratory study to investigate open-ended search behavior for navigation through unknown information landscapes. The study comprised of 26 participants who were restricted in their search to a text chat interface. Based on the collected dialogue transcripts, we applied statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains. We not only identify core dialogue acts and their interrelations that enable users to discover domain knowledge, but also derive design suggestions for conversational search systems.

cs.CL↗

arXiv2023

Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches

Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. Although existing studies have already investigated individual approaches to these categories, the experiments in literature do not provide a consistent comparison. This paper addresses this gap by conducting a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes. Different state-of-the-art approaches are benchmarked on four text classification datasets, including a new dataset from the medical domain. Additionally, novel SimCSE and SBERT-based baselines are proposed, as other baselines used in existing work yield weak classification results and are easily outperformed. Finally, the novel similarity-based Lbl2TransformerVec approach is presented, which outperforms previous state-of-the-art approaches in unsupervised text classification. Our experiments show that similarity-based approaches significantly outperform zero-shot approaches in most cases. Additionally, using SimCSE or SBERT embeddings instead of simpler text representations increases similarity-based classification results even further.

cs.CL↗

arXiv2023

Optical solitons in curved spacetime

Light propagation in curved spacetime is at the basis of some of the most stringent tests of Einstein's general relativity. At the same time, light propagation in media is at the basis of several communication systems. Given the ubiquity of the gravitational field, and the exquisite level of sensitivity of optical measurements, the time is ripe for investigations combining these two aspects and studying light propagation in media located in curved spacetime. In this work, we focus on the effect of a weak gravitational field on the propagation of optical solitons in non-linear optical media. We derive a non-linear Schrödinger equation describing the propagation of an optical pulse in an effective, gradient-index medium in flat spacetime, encoding both the material properties and curved spacetime effects. In analyzing the special case of propagation in a 1D optical fiber, we also include the effect of mechanical deformations and show it to be the dominant effect for a fiber oriented in the radial direction in Schwarzschild spacetime.

gr-qc↗