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Multi-physics simulations play a crucial role in understanding complex systems. However, their computational demands are often prohibitive due to high dimensionality and complex interactions, such that actual calculations often rely on approximations. To address this, we introduce a data-driven approach to approximate interactions among degrees of freedom of no direct interest and thus significantly reduce computational costs. Focusing on a semiconductor laser as a case study, we demonstrate the superiority of this method over traditional analytical approximations in both accuracy and efficiency. Our approach streamlines simulations, offering promise for complex multi-physics systems, especially for scenarios requiring a large number of individual simulations., Comment: The simulation code and the regression code is available on GitHub under MIT license (https://github.com/stmeinecke/derrom)

Efficient simulation of the timing jitter in passively mode-locking lasers is key to their numerical investigation and optimization. We introduce a method based on the pulse-period fluctuation auto-correlation function and compare it against established methods with respect to their estimate error. Potential improvements of the computational cost by about two orders of magnitude are reported. This advantage may facilitate larger parameter studies of passively mode-locked laser on small scale clusters or even desktop computers and thereby guide the target-oriented design of future lasers with ultra low timing jitter., Comment: The simulation code is available under MIT license at https://github.com/stmeinecke/QDMLL

Passively mode-locked semiconductor disk lasers have received tremendous attention from both science and industry. Their relatively inexpensive production combined with excellent pulse performance and great emission wavelength flexibility make them suitable laser candidates for applications ranging from frequency comb tomography to spectroscopy. However, due to the interaction of the active medium dynamics and the device geometry, emission instabilities occur at high pump powers and thereby limit their performance potential. Hence, understanding those instabilities becomes critical for an optimal laser design. Using a delay-differential equation model, we are able to detect, understand, and classify three distinct instabilities that limit the maximum achievable pump power for the fundamental mode-locking state and link them to characteristic positive net-gain windows. We furthermore derive a simple analytic approximation in order to quantitatively describe the stability boundary. Our results enable us to predict the optimal laser cavity configuration with respect to positive net-gain instabilities and are therefore of great relevance for the future development of passively mode-locking semiconductor disk lasers., Comment: The simulation code is available under MIT license at https://github.com/stmeinecke/VShape

We present a data-driven approach to efficiently approximate nonlinear transient dynamics in solid-state systems. Our proposed machine-learning model combines a dimensionality reduction stage with a nonlinear vector autoregression scheme. We report an outstanding time-series forecasting performance combined with an easy to deploy model and an inexpensive training routine. Our results are of great relevance as they have the potential to massively accelerate multi-physics simulation software and thereby guide to future development of solid-state based technologies., Comment: The simulation code and the regression code is available on GitHub under MIT license (https://github.com/stmeinecke/derrom)

Simultaneous two-state lasing is a unique property of semiconductor quantum-dot (QD) lasers. This not only changes steady-state characteristics of the laser device but also its dynamic response to perturbations. In this paper we investigate the dynamic stability of QD lasers in an external optical injection setup. Compared to conventional single-state laser devices, we find a strong suppression of dynamical instabilities in two-state lasers. Furthermore, depending on the frequency and intensity of the injected light, pronounced areas of bistability between both lasing frequencies appear, which can be employed for fast optical switching in all-optical photonic computing applications. These results emphasize the suitability of QD semiconductor lasers in future integrated optoelectronic systems where a high level of stability is required.

Decomposing complex unitary evolution into a series of constituent components is a cornerstone of practical quantum information processing. While the decompostion of an $n\times n$ unitary into a series of $2\times2$ subunitaries is well established (i.e. beamsplitters and phase shifters in linear optics), we show how this decomposition can be generalised into a series of $m\times m$ multimode devices, where $m>2$. If the cost associated with building each $m\times m$ multimode device is less than constructing with $\frac{m(m-1)}{2}$ individual $2\times 2$ devices, we show that the decomposition of large unitaries into $m\times m$ submatrices is is more resource efficient and exhibits a higher tolerance to errors, than its $2\times 2$ counterpart. This allows larger-scale unitaries to be constructed with lower errors, which is necessary for various tasks, not least Boson sampling, the quantum Fourier transform and quantum simulations.

Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework., Comment: The Version of Record of this contribution is published in Deep Learning Theory and Applications 4th International Conference, DeLTA 2023 Proceedings, and is available online at https://doi.org/10.1007/978-3-031-39059-3_16

Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better results than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of $0.93$. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low., Comment: Preprint of the short paper presented at the 12th International Conference on Data Science, Technology and Applications

This paper presents a novel exploration of the interaction between generative AI models, visualization, and narrative generation processes, using OpenAI's GPT as a case study. We look at the question "Where Does Generativeness Comes From", which has a simple answer at the intersection of many domains. Drawing on Umberto Eco's "Six Walks in the Fictional Woods", we engender a speculative, transdisciplinary scientific narrative using ChatGPT in different roles: as an information repository, a ghost writer, a scientific coach, among others. The paper is written as a piling of plateaus where the titling of each (sub-)section, the "teaser" images, the headers, and a biblock of text are strata forming a narrative about narratives. To enrich our exposition, we present a visualization prototype to analyze storyboarded narratives, and extensive conversations with ChatGPT. Each link to a ChatGPT conversation is an experiment on writing where we try to use different plugins and techniques to investigate the topics that, ultimately form the content of this portable document file. Our visualization uses a dataset of stories with scene descriptions, textual descriptions of scenes (both generated by ChatGPT), and images (generated by Stable Diffusion using scene descriptions as prompts). We employ a simple graph-node diagram to try to make a "forest of narratives" visible, an example of a vis4gen application that can be used to analyze the output of Large Languange + Image Models., Comment: this is a submission for IEEE alt.vis 2023

Measurements with randomly chosen settings determine many important properties of quantum states without the need for a shared reference frame or calibration. They naturally emerge in the context of quantum communication and quantum computing when dealing with noisy environments, and allow the estimation of properties of complex quantum systems in an easy and efficient manner. In this review, we present the advancements made in utilising randomised measurements in various scenarios of quantum information science. We describe how to detect and characterise different forms of entanglement, including genuine multipartite entanglement and bound entanglement. Bell inequalities are discussed to be typically violated even with randomised measurements, especially for a growing number of particles and settings. Furthermore, we also present an overview on the estimation of non-linear functions of quantum states and shadow tomography from randomised measurements. Throughout the review, we complement the description of theoretical ideas by explaining key experiments., Comment: review paper, 56 pages, 9 figures, P. Cie\'sli\'nski and S. Imai contributed equally as co-first authors

The operation of open-cast lignite mines is a large intervention in nature, making the areas uninhabitable even after closing the mines without renaturation processes. Renaturation of these large areas requires a regional planning process which is tied to many conditions and restrictions, such as environmental protection laws. The related information is available only as unstructured text in a variety of documents. Associated temporal aspects and the geographical borders to these textual information have to be linked manually so far. This process is highly time-consuming, error-prone, and tedious. Therefore, the knowledge of experts is often used, but this does not necessarily include all the relevant information. In this paper, we present a system to support the experts in decision-making of urban planning, renaturation, and redevelopment projects. The system allows to plan new projects, while considering spatial and temporal restrictions extracted from text documents. With this, our presented system can also be used to verify compliance with certain legal regulations, such as nature conservation laws., Comment: 8 pages

Engaging in interdisciplinary projects on the intersection between visualization and humanities research can be a challenging endeavor. Challenges can be finding valuable outcomes for both domains, or how to apply state-of-the-art visual analytics methods like supervised machine learning algorithms. We discuss these challenges when working with cultural heritage data. Further, there is a gap in applying these methods to intangible heritage. To give a reflection on some interdisciplinary projects, we present three case studies focusing on the labeling of cultural heritage collections, the problems and challenges with the data, the participatory design process, and takeaways for the visualization scholars from these collaborations.

Distant viewing approaches have typically used image datasets close to the contemporary image data used to train machine learning models. To work with images from other historical periods requires expert annotated data, and the quality of labels is crucial for the quality of results. Especially when working with cultural heritage collections that contain myriad uncertainties, annotating data, or re-annotating, legacy data is an arduous task. In this paper, we describe working with two pre-annotated sets of medieval manuscript images that exhibit conflicting and overlapping metadata. Since a manual reconciliation of the two legacy ontologies would be very expensive, we aim (1) to create a more uniform set of descriptive labels to serve as a "bridge" in the combined dataset, and (2) to establish a high quality hierarchical classification that can be used as a valuable input for subsequent supervised machine learning. To achieve these goals, we developed visualization and interaction mechanisms, enabling medievalists to combine, regularize and extend the vocabulary used to describe these, and other cognate, image datasets. The visual interfaces provide experts an overview of relationships in the data going beyond the sum total of the metadata. Word and image embeddings as well as co-occurrences of labels across the datasets, enable batch re-annotation of images, recommendation of label candidates and support composing a hierarchical classification of labels., Comment: Revision after DSH Peer Review. Paper is now accepted at DSH

Detecting entanglement in multipartite quantum states is an inherently probabilistic process, typically with a few measured samples. The level of confidence in entanglement detection quantifies the scheme's validity via the probability that the signal comes from a separable state, offering a meaningful figure of merit for big datasets. Yet, with limited samples, avoiding experimental data misinterpretations requires considering not only the probabilities concerning separable states but also the probability that the signal came from an entangled state, i.e. the detection scheme's efficiency. We demonstrate this explicitly and apply a general method to optimize both the validity and the efficiency in small data sets providing examples using at most 20 state copies. The method is based on an analytical model of finite statistics effects on correlation functions which takes into account both a Frequentist as well as a Bayesian approach and is applicable to arbitrary entanglement witnesses., Comment: 15 pages, 5 figures, Hypothesis testing section and Bayesian loss analysis added, data supporting figures available in source files

The 3-Satisfiability Problem (3-SAT) is a demanding combinatorial problem, of central importance among the non-deterministic polynomial (NP) complete problems, with applications in circuit design, artificial intelligence and logistics. Even with optimized algorithms, the solution space that needs to be explored grows exponentially with increasing size of 3-SAT instances. Thus, large 3-SAT instances require excessive amounts of energy to solve with serial electronic computers. Network-based biocomputation (NBC) is a multidisciplinary parallel computation approach with drastically reduced energy consumption. NBC uses biomolecular motors to propel cytoskeletal filaments through nanofabricated networks that encode the mathematical problems. By stochastically exploring possible paths through the networks, the cytoskeletal filaments find possible solutions to the encoded problem instance. Here we first report a novel algorithm that converts 3-SAT into NBC-compatible network format. We demonstrate that this algorithm works in practice, by experimentally solving four small 3-SAT instances (with up to 3 variables and 5 clauses) using the actin-myosin biomolecular motor system. This is a key step towards the broad general applicability of NBC because polynomial conversions to 3-SAT exist for a wide set of important NP-complete problems.

Nanowire lasers can be monolithically and site-selectively integrated onto silicon photonic circuits. To assess their full potential for ultrafast opto-electronic devices, a detailed understanding of their lasing dynamics is crucial. However, the roles played by their resonator geometry and the microscopic processes that mediate energy exchange between the photonic, electronic, and phononic subsystems are largely unexplored. Here, we study the dynamics of GaAs-AlGaAs core-shell nanowire lasers at cryogenic temperatures using a combined experimental and theoretical approach. Our results indicate that these NW lasers exhibit sustained intensity oscillations with frequencies ranging from 160 GHz to 260 GHz. As the underlying physical mechanism, we identified self-induced electron-hole plasma temperature oscillations resulting from a dynamic competition between photoinduced carrier heating and cooling via phonon scattering. These dynamics are intimately linked to the strong interaction between the lasing mode and the gain material, which arises from the wavelength-scale dimensions of these lasers. We anticipate that our results could lead to new approaches for ultrafast intensity and phase modulation of chip-integrated nanoscale semiconductor lasers., Comment: Revised manuscript

Galaxy clusters are filled with hot, diffuse X-ray emitting plasma, with a stochastically tangled magnetic field whose energy is close to equipartition with the energy of the turbulent motions \cite{zweibel1997, Vacca}. In the cluster cores, the temperatures remain anomalously high compared to what might be expected considering that the radiative cooling time is short relative to the Hubble time \cite{cowie1977,fabian1994}. While feedback from the central active galactic nuclei (AGN) \cite{fabian2012,birzan2012,churazov2000} is believed to provide most of the heating, there has been a long debate as to whether conduction of heat from the bulk to the core can help the core to reach the observed temperatures \cite{narayan2001,ruszkowski2002,kunz2011}, given the presence of tangled magnetic fields. Interestingly, evidence of very sharp temperature gradients in structures like cold fronts implies a high degree of suppression of thermal conduction \cite{markevitch2007}. To address the problem of thermal conduction in a magnetized and turbulent plasma, we have created a replica of such a system in a laser laboratory experiment. Our data show a reduction of local heat transport by two orders of magnitude or more, leading to strong temperature variations on small spatial scales, as is seen in cluster plasmas \cite{markevitch2003}.

Highly automated driving functions currently often rely on a-priori knowledge from maps for planning and prediction in complex scenarios like cities. This makes map-relative localization an essential skill. In this paper, we address the problem of localization with automotive-grade radars, using a real-time graph-based SLAM approach. The system uses landmarks and odometry information as an abstraction layer. This way, besides radars, all kind of different sensor modalities including cameras and lidars can contribute. A single, semantic landmark map is used and maintained for all sensors. We implemented our approach using C++ and thoroughly tested it on data obtained with our test vehicles, comprising cars and trucks. Test scenarios include inner cities and industrial areas like container terminals. The experiments presented in this paper suggest that the approach is able to provide a precise and stable pose in structured environments, using radar data alone. The fusion of additional sensor information from cameras or lidars further boost performance, providing reliable semantic information needed for automated mapping.

Open quantum systems are highly relevant, both for practical applications as well as for fundamental questions about the nature of information and its transfer, encompassing for example decoherence and memory effects. Quantum mechanics introduces additional complexity to the transfer of information, e.g., storage of information in non-classical correlations. Yet, some of these aspects tend to be neglected by the usual framework of open system research. In this work we use photons in a waveguide array to implement a quantum simulation of the coupling of a qubit with a low-dimensional discrete environment. Using the trace distance between quantum states as a measure of information, we analyze different types of information transfer. Extending the usual perspective which is focused on the system alone, we also investigate the presence of information in the environment., Comment: 7 pages, 4 figures

The influence of optical feedback on semiconductor lasers has been a widely studied field of research due to fundamental interests as well as the optimization of optical data transmission and computing. Recent publications have shown that it is possible to induce a periodic pulsed like output in quantum-dot and quantum-well laser diodes utilizing the locking of the external cavity modes and the relaxation oscillation frequency. We present an in-depth analysis of this effect. We choose submonolayer quantum dots as a gain system, as these provide a relatively strong amplitude-phase coupling, which has proven to be very beneficial for these locking effects to occur. Introducing a new theoretical model we can correctly reproduce the essential features of the gain system and validate them by comparison to our experimental results. From this starting point we can further explore how the staircase behavior of the oscillation frequency with increasing pump current can be influenced by changing various laser parameters. The staircase behavior is induced by a reordering of the Hopf bifurcations giving birth to the regular pulsed-like oscillations., Comment: 12 pages, 9 figures

Synthesis of organic bi-layers on silicon was realized by a combination of surface functionalization under ultra-high vacuum (UHV) conditions and solution-based click chemistry. The silicon (001) surface was prepared with a high degree of perfection in UHV and functionalized via chemoselective adsorption of ethinyl cyclopropyl cyclooctyne from the gas phase. A second organic layer was then coupled in acetonitrile via the copper-catalyzed alkyne azide click reaction. The samples were directly transferred from UHV via the vapour phase of the solvent into the solution of reactants and back to UHV without contact to ambient conditions. Each reaction step was monitored by means of X-ray photoelectron spectroscopy in UHV; the N 1s spectra clearly indicated the click reaction of the azide group in the two test molecules employed, i.e., methyl-subsituted benzylazide and azide substituted pyrene. In both cases, up to 50 - 60 % of the ethinyl cyclopropyl cyclooctyne molecules on the surface were reacted.

The additive-free tetrazine/enol ether click reaction was performed in ultra-high vacuum (UHV) with an enol ether group covalently linked to a silicon surface: Dimethyl 1,2,4,5-tetrazine-3,6-dicarboxylate molecules were coupled to the enol ether group of a functionalized cyclooctyne which was adsorbed on the silicon (001) surface via the strained triple bond of cyclooctyne. The reaction was observed at a surface temperature of 380 K by means of X-ray photoelectron spectroscopy (XPS). No indications for tetrazine molecules which bind directly to the Si(001) surface via the nitrogen atoms were detected. A moderate energy barrier was deduced for this click reaction in vacuum by means of density functional theory based calculations, in good agreement with the experimental results. This UHV-compatible click reaction thus opens a new, flexible route for synthesizing covalently bound organic architectures.

Understanding magnetic-field generation and amplification in turbulent plasma is essential to account for observations of magnetic fields in the universe. A theoretical framework attributing the origin and sustainment of these fields to the so-called fluctuation dynamo was recently validated by experiments on laser facilities in low-magnetic-Prandtl-number plasmas ($\mathrm{Pm} < 1$). However, the same framework proposes that the fluctuation dynamo should operate differently when $\mathrm{Pm} \gtrsim 1$, the regime relevant to many astrophysical environments such as the intracluster medium of galaxy clusters. This paper reports a new experiment that creates a laboratory $\mathrm{Pm} \gtrsim 1$ plasma dynamo for the first time. We provide a time-resolved characterization of the plasma's evolution, measuring temperatures, densities, flow velocities and magnetic fields, which allows us to explore various stages of the fluctuation dynamo's operation. The magnetic energy in structures with characteristic scales close to the driving scale of the stochastic motions is found to increase by almost three orders of magnitude from its initial value and saturate dynamically. It is shown that the growth of these fields occurs exponentially at a rate that is much greater than the turnover rate of the driving-scale stochastic motions. Our results point to the possibility that plasma turbulence produced by strong shear can generate fields more efficiently at the driving scale than anticipated by idealized MHD simulations of the nonhelical fluctuation dynamo; this finding could help explain the large-scale fields inferred from observations of astrophysical systems., Comment: 12 pages, 9 figures

Organic light emitting diodes (OLEDs) based on thermally activated delayed fluorescence (TADF) show increased efficiencies due to efficient upconversion of non-emissive triplet states to emissive singlets states via reverse intersystem crossing (RISC). To assess the influence of the characteristic efficiency-enhancing RISC process as well as possible efficiency-limiting effects in operational OLEDs, we performed temperature-dependent measurements of transient electroluminescence (trEL). With kinetic modeling, we quantify and separate the impact of different temperature-dependent depopulation processes and contributions to EL in the established donor:acceptor model system m-MTDATA:3TPYMB. The underlying rate equations adapted for EL measurements on TADF systems include radiative and non-radiative first- and second-order effects. In this way, we are able to evaluate the non-radiative recombination and annihilation processes with respect to their efficiency-limiting effects on these OLEDs. On the one hand, we evaluate the depopulation of intermolecular exciplex triplet states via non-radiative direct triplet decay, RISC and triplet-triplet annihilation (TTA). On the other hand, we determine the contribution to EL from the formation of singlet exciplex states via polarons, RISC and TTA. Our results show that TTA accounts for a significant part to triplet depopulation and contributes to EL while limiting the overall device quantum efficiency., Comment: 21 pages, 12 figures

Publicly available grid datasets with electric steady-state equivalent circuit models are crucial for the development and comparison of a variety of power system simulation tools and algorithms. Such algorithms are essential to analyze and improve the integration of distributed energy resources (DERs) in electrical power systems. Increased penetration of DERs, new technologies, and changing regulatory frameworks require the continuous development of the grid infrastructure. As a result, the number and versatility of grid datasets, which are required in power system research, increases. Furthermore, the used grids are created by different methods and intentions. This paper gives orientation within these developments: First, a concise overview of well-known, publicly available grid datasets is provided. Second, background information on the compilation of the grid datasets, including different methods, intentions and data origins, is reviewed and characterized. Third, common terms to describe electric steady-state distribution grids, such as representative grid or benchmark grid, are assembled and reviewed. Recommendations for the use of these grid terms are made., Comment: Preprint submitted to Energies

We propose an information-theoretic quantifier for the advantage gained from cooperation that captures the degree of dependency between subsystems of a global system. The quantifier is distinct from measures of multipartite correlations despite sharing many properties with them. It is directly computable for classical as well as quantum systems and reduces to comparing the respective conditional mutual information between any two subsystems. Exemplarily we show the benefits of using the new quantifier for symmetric quantum secret sharing. We also prove an inequality characterizing the lack of monotonicity of conditional mutual information under local operations and provide intuitive understanding for it. This underlines the distinction between the multipartite dependence measure introduced here and multipartite correlations., Comment: 9 pages, journal version

Quantum entanglement is usually revealed via a well aligned, carefully chosen set of measurements. Yet, under a number of experimental conditions, for example in communication within multiparty quantum networks, noise along the channels or fluctuating orientations of reference frames may ruin the quality of the distributed states. Here we show that even for strong fluctuations one can still gain detailed information about the state and its entanglement using random measurements. Correlations between all or subsets of the measurement outcomes and especially their distributions provide information about the entanglement structure of a state and also enable to witness genuine multipartite entanglement. As long as the rate of entanglement generation is sufficiently high, this method overcomes any type and strength of localized unitary noise., Comment: 8 pages, 4 figures, journal version

One of the characteristic features of quantum mechanics is that every measurement that extracts information about a general quantum system necessarily causes an unavoidable disturbance to the state of this system. A plethora of different approaches has been developed to characterize and optimize this tradeoff. Here, we apply the framework of quantum instruments to investigate the optimal tradeoff and to derive a class of procedures that is optimal with respect to most meaningful measures. We focus our analysis on binary measurements on qubits as commonly used in communication and computation protocols and demonstrate theoretically and in an experiment that the optimal universal asymmetric quantum cloner, albeit ideal for cloning, is not an optimal procedure for measurements and can be outperformed with high significance., Comment: 11 pages, 7 figures. Comments are welcome