Purpose: Chest X-rays are essential for diagnosing pulmonary conditions, but limited access in resource-constrained settings can delay timely diagnosis. Electrocardiograms (ECGs), in contrast, are widely available, non-invasive, and often acquired earlier in clinical workflows. This study aims to assess whether ECG features and patient demographics can predict chest radiograph findings using an interpretable machine learning approach. Methods: Using the MIMIC-IV database, Extreme Gradient Boosting (XGBoost) classifiers were trained to predict diverse chest radiograph findings from ECG-derived features and demographic variables. Recursive feature elimination was performed independently for each target to identify the most predictive features. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) with bootstrapped 95% confidence intervals. Shapley Additive Explanations (SHAP) were applied to interpret feature contributions. Results: Models successfully predicted multiple chest radiograph findings with varying accuracy. Feature selection tailored predictors to each target, and including demographic variables consistently improved performance. SHAP analysis revealed clinically meaningful contributions from ECG features to radiographic predictions. Conclusion: ECG-derived features combined with patient demographics can serve as a proxy for certain chest radiograph findings, enabling early triage or pre-screening in settings where radiographic imaging is limited. Interpretable machine learning demonstrates potential to support radiology workflows and improve patient care.

Estimating the position of a speech source based on time-differences-of-arrival (TDOAs) is often adversely affected by background noise and reverberation. A popular method to estimate the TDOA between a microphone pair involves maximizing a generalized cross-correlation with phase transform (GCC-PHAT) function. Since the TDOAs across different microphone pairs satisfy consistency relations, generally only a small subset of microphone pairs are used for source position estimation. Although the set of microphone pairs is often determined based on a reference microphone, recently a more robust method has been proposed to determine the set of microphone pairs by computing the minimum spanning tree (MST) of a signal graph of GCC-PHAT function reliabilities. To reduce the influence of noise and reverberation on the TDOA estimation accuracy, in this paper we propose to compute the GCC-PHAT functions of the MST based on an average of multiple cross-power spectral densities (CPSDs) using an incremental method. In each step of the method, we increase the number of CPSDs over which we average by considering CPSDs computed indirectly via other microphones from previous steps. Using signals recorded in a noisy and reverberant laboratory with an array of spatially distributed microphones, the performance of the proposed method is evaluated in terms of TDOA estimation error and 2D source position estimation error. Experimental results for different source and microphone configurations and three reverberation conditions show that the proposed method considering multiple CPSDs improves the TDOA estimation and source position estimation accuracy compared to the reference microphone- and MST-based methods that rely on a single CPSD as well as steered-response power-based source position estimation.

Machine learning (ML) methods are becoming increasingly important in the design economic scenario generators for internal models. Validation of data-driven models differs from classical theory-based models. We discuss two novel aspects of such a validation: first, checking dependencies between risk factors and second, detecting unwanted memorization effects. The first task becomes necessary since in ML-based methods dependencies are no longer derived from a financial-mathematical theory but are driven by data. The need for the latter task arises since it cannot be ruled out that ML-based models merely reproduce the empirical data rather than generating new scenarios. To address the first issue, we propose to use an existing test from the literature. For the second issue, we introduce and discuss a novel memorization ratio. Numerical experiments based on real market data are included and an autoencoder-based scenario generator is validated with these two methods.

Energy research software (ERS) is a central cornerstone to facilitate energy research. However, ERS is developed by researchers who, in many cases, lack formal training in software engineering. This reduces the quality of ERS, leading to limited reproducibility and reusability. To address these issues, we developed ten central recommendations for the development of ERS, covering areas such as conceptualization, development, testing, and publication of ERS. The recommendations are based on the outcomes of two workshops with a diverse group of energy researchers and aim to improve the awareness of research software engineering in the energy domain. The recommendations should enhance the quality of ERS and, therefore, the reproducibility of energy research.

We provide a unified framework to obtain numerically certain quantities, such as the distribution function, absolute moments and prices of financial options, from the characteristic function of some (unknown) probability density function using the Fourier-cosine expansion (COS) method. The classical COS method is numerically very efficient in one-dimension, but it cannot deal very well with certain integrands in general dimensions. Therefore, we introduce the damped COS method, which can handle a large class of integrands very efficiently. We prove the convergence of the (damped) COS method and study its order of convergence. The method converges exponentially if the characteristic function decays exponentially. To apply the (damped) COS method, one has to specify two parameters: a truncation range for the multivariate density and the number of terms to approximate the truncated density by a cosine series. We provide an explicit formula for the truncation range and an implicit formula for the number of terms. Numerical experiments up to five dimensions confirm the theoretical results.

We identify and classify topologically protected singularities for the reflection coefficient of transdimensional plasmonic systems. Originating from nonlocal electromagnetic response due to vertical electron confinement in the system, such singularities lead to lateral (angular) Goos-Hänchen shifts on the millimeter (milliradian) scale in the visible range, greatly exceeding those reported previously for artificially designed metasurfaces, offering new opportunities for quantum material development.

Many-body physics aims to understand emergent properties of systems made of many interacting objects. This article reviews recent progress on the topic of radiative heat transfer in many-body systems consisting of thermal emitters interacting in the near-field regime. Near-field radiative heat transfer is a rapidly emerging field of research in which the cooperative behavior of emitters gives rise to peculiar effects which can be exploited to control heat flow at the nanoscale. Using an extension of the standard Polder and van Hove stochastic formalism to deal with thermally generated fields in $N$-body systems, along with their mutual interactions through multiple scattering, a generalized Landauer-like theory is derived to describe heat exchange mediated by thermal photons in arbitrary reciprocal and non-reciprocal multi-terminal systems. In this review, we use this formalism to address both transport and dynamics in these systems from a unified perspective. Our discussion covers: (i) the description of non-additivity of heat flux and its related effects, including fundamental limits as well as the role of nanostructuring and material choice, (ii) the study of equilibrium states and multistable states, (iii) the relaxation dynamics (thermalization) toward local and global equilibria, (iv) the analysis of heat transport regimes in ordered and disordered systems comprised of a large number of objects, density and range of interactions, and (v) the description of thermomagnetic effects in magneto-optical systems and heat transport mechanisms in non-Hermitian many-body systems. We conclude this review by listing outstanding challenges and promising future research directions.

We investigate the dynamics of wave packets in a parabolic optical lattice formed by combining an optical lattice with a global parabolic trap. Our study examines the phase space representation of the system's eigenstates by comparing them to the classical phase space of a pendulum, to which the system effectively maps. The analysis reveals that quantum states can exhibit mixed dynamics by straddling the separatrix. A key finding is that the dynamics around the separatrix enables the controlled creation of highly non-classical states, distinguishing them from the classical oscillatory or rotational dynamics of the pendulum. By considering a finite momentum of the initial wave packet, we demonstrate various dynamical regimes. Furthermore, a slight energy mismatch between nearly-degenerate states localized at opposite turning points of the trap potential results in controlled long-range dynamical tunneling. These results can be interpreted as quantum beating between a clockwise rotating and a counterclockwise rotating pendulum.

Fourier pricing methods such as the Carr-Madan formula or the COS method are classic tools for pricing European options for advanced models such as the Heston model. These methods require tuning parameters such as a damping factor, a truncation range, a number of terms, etc. Estimating these tuning parameters is difficult or computationally expensive. Recently, machine learning techniques have been proposed for fast pricing: they are able to learn the functional relationship between the parameters of the Heston model and the option price. However, machine learning techniques suffer from error control and require retraining for different error tolerances. In this research, we propose to learn the tuning parameters of the Fourier methods (instead of the prices) using machine learning techniques. As a result, we obtain very fast algorithms with full error control: Our approach works with any error tolerance without retraining, as demonstrated in numerical experiments using the Heston model.

We reinvestigate the mechanism of near-field heat transfer rectification between two Weyl semimetal nanoparticles and a planar Weyl semimetal substrate via the coupling to non-reciprocal surface modes. We first show that the previously predicted rectification ratio of 2673 is incorrect and should rather be 1502. Furthermore we show that depending on the distance between the nanoparticles there can be a much more efficient heat flux rectification with ratios of about 6000. Furthermore, we identify a previously overlooked range of forward rectification and a range of strong backward rectification with rectification ratios larger than 8000 for relatively small Weyl node separations. We investigate the mechanism behind this large heat flux rectification and study its sensitivity with respect to certain material parameters and temperature showing that even larger rectification ratios up to 15000 are possible highlighting that certain Weyl semimetals are strong candidates for highly efficient heat flux rectification.

While experimentation with synthetic stimuli in abstracted listening situations has a long standing and successful history in hearing research, an increased interest exists on closing the remaining gap towards real-life listening by replicating situations with high ecological validity in the lab. This is important for understanding the underlying auditory mechanisms and their relevance in real-life situations as well as for developing and evaluating increasingly sophisticated algorithms for hearing assistance. A range of 'classical' stimuli and paradigms have evolved to de-facto standards in psychoacoustics, which are simplistic and can be easily reproduced across laboratories. While they ideally allow for across laboratory comparisons and reproducible research, they, however, lack the acoustic stimulus complexity and the availability of visual information as observed in everyday life communication and listening situations. This contribution aims to provide and establish an extendable set of complex auditory-visual scenes for hearing research that allow for ecologically valid testing in realistic scenes while also supporting reproducibility and comparability of scientific results. Three virtual environments are provided (underground station, pub, living room), consisting of a detailed visual model, an acoustic geometry model with acoustic surface properties as well as a set of acoustic measurements in the respective real-world environments. The current data set enables i) audio-visual research in a reproducible set of environments, ii) comparison of room acoustic simulation methods with "ground truth" acoustic measurements, iii) a condensation point for future extensions and contributions for developments towards standardized test cases for ecologically valid hearing research in complex scenes.

The strong coherent coupling of quantum emitters to vacuum fluctuations of the light field offers opportunities for manipulating the optical and transport properties of nanomaterials, with potential applications ranging from ultrasensitive all-optical switching to creating polariton condensates. Often, ubiquitous decoherence processes at ambient conditions limit these couplings to such short time scales that the quantum dynamics of the interacting system remains elusive. Prominent examples are strongly coupled exciton-plasmon systems, which, so far, have mostly been investigated by linear optical spectroscopy. Here, we use ultrafast two-dimensional electronic spectroscopy to probe the quantum dynamics of J-aggregate excitons collectively coupled to the spatially structured plasmonic fields of a gold nanoslit array. We observe rich coherent Rabi oscillation dynamics reflecting a plasmon-driven coherent exciton population transfer over mesoscopic distances at room temperature. This opens up new opportunities to manipulate the coherent transport of matter excitations by coupling to vacuum fields.

Accurately estimating the direction-of-arrival (DOA) of a speech source using a compact microphone array (CMA) is often complicated by background noise and reverberation. A commonly used DOA estimation method is the steered response power with phase transform (SRP-PHAT) function, which has been shown to work reliably in moderate levels of noise and reverberation. Since for closely spaced microphones the spatial coherence of noise and reverberation may be high over an extended frequency range, this may negatively affect the SRP-PHAT spectra, resulting in DOA estimation errors. Assuming the availability of an auxiliary microphone at an unknown position which is spatially separated from the CMA, in this paper we propose to compute the SRP-PHAT spectra between the microphones of the CMA based on the SRP-PHAT spectra between the auxiliary microphone and the microphones of the CMA. For different levels of noise and reverberation, we show how far the auxiliary microphone needs to be spatially separated from the CMA for the auxiliary microphone-based SRP-PHAT spectra to be more reliable than the SRP-PHAT spectra without the auxiliary microphone. These findings are validated based on simulated microphone signals for several auxiliary microphone positions and two different noise and reverberation conditions.

We consider the behavior of an Ising ferromagnet obeying the Glauber dynamics under the influence of a fast switching, random external field. In Part I, we introduced a general formalism for describing such systems and presented the mean field theory. In this article we derive results for the one dimensional case, which can be only partially solved. Monte Carlo simulations performed on a square lattice indicate that the main features of the mean field theory survive the presence of strong fluctuations.

The Greeks Delta and Gamma of plain vanilla options play a fundamental role in finance, e.g., in hedging or risk management. These Greeks are approximated in many models such as the widely used Variance Gamma model by Fourier techniques such as the Carr-Madan formula, the COS method or the Lewis formula. However, for some realistic market parameters, we show empirically that these three Fourier methods completely fail to approximate the Greeks. As an application we show that the Delta-Gamma VaR is severely underestimated in realistic market environments. As a solution, we propose to use finite differences instead to obtain the Greeks.

The profit and loss (p&l) attrition for each business year into different risk or risk factors (e.g., interest rates, credit spreads, foreign exchange rate etc.) is a regulatory requirement, e.g., under Solvency 2. Three different decomposition principles are prevalent: one-at-a-time (OAT), sequential updating (SU) and average sequential updating (ASU) decompositions. In this research, using financial market data from 2003 to 2022, we demonstrate that the OAT decomposition can generate significant unexplained p&l and that the SU decompositions depends significantly on the order or labeling of the risk factors. On the basis of an investment in a foreign stock, we further explain that the SU decomposition is not able to identify all relevant risk factors. This potentially effects the hedging strategy of the portfolio manager. In conclusion, we suggest to use the ASU decomposition in practice.

In this research, we show how to expand existing approaches of using generative adversarial networks (GANs) as economic scenario generators (ESG) to a whole internal market risk model - with enough risk factors to model the full band-width of investments for an insurance company and for a one year time horizon as required in Solvency 2. We demonstrate that the results of a GAN-based internal model are similar to regulatory approved internal models in Europe. Therefore, GAN-based models can be seen as a data-driven alternative way of market risk modeling.

Infinite-range spin-glass models with Levy-distributed interactions show a freezing transition similar to disordered spin systems on finite connectivity random graphs. It is shown that despite diverging moments of the local field distribution this transition can be analyzed within the replica approach by working at imaginary temperature and using a variant of the replica method developed for diluted systems and optimization problems. The replica-symmetric self-consistent equation for the distribution of local fields illustrates how the long tail in the distribution of coupling strengths gives rise to a significant fraction of strong bonds per spin which form a percolating backbone at the transition temperature.

Financial institutions and insurance companies that analyze the evolution and sources of profits and losses often look at risk factors only at discrete reporting dates, ignoring the detailed paths. Continuous-time decompositions avoid this weakness and also make decompositions consistent across different reporting grids. We construct a large class of continuous-time decompositions from a new extended version of Itô's formula and uniquely identify a preferred decomposition from the axioms of exactness, symmetry and normalization. This unique decomposition turns out to be a stochastic limit of recursive Shapley values, but it suffers from a curse of dimensionality as the number of risk factors increases. We develop an approximation that breaks this curse when the risk factors almost surely have no simultaneous jumps.