Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we present the first systematic study on medical ST, to our best knowledge, by releasing MultiMed-ST, a large-scale ST dataset for the medical domain, spanning all translation directions in five languages: Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models. With 290,000 samples, this is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains. Secondly, we present the most comprehensive ST analysis in the field's history, to our best knowledge, including: empirical baselines, bilingual-multilingual comparative study, end-to-end vs. cascaded comparative study, task-specific vs. multi-task sequence-to-sequence comparative study, code-switch analysis, and quantitative-qualitative error analysis. All code, data, and models are available online: this https URL

Layered van der Waals materials offer novel opportunities for on-chip waveguiding and development of integrated photonic circuits. In the strong light-matter coupling regime, their nonlinear response can be significantly enhanced, which is crucial for developing active photonic devices. However, probing the nonlinearity of waveguide modes in subwavelength-thick structures is challenging as they are not directly accessible from far-field. Here we apply a novel nonlinear near-field spectroscopic technique based on a GaP solid immersion lens and femtosecond laser excitation to study nonlinearity of guided modes in monolayer WS$_2$ encapsulated in hBN under the strong light-matter coupling regime. We reveal formation of exciton-polaritons with $\sim 50$ meV Rabi splitting and demonstrate a pump-induced transition from strong to weak coupling. Our results show that exciton resonance saturation and broadening lead to an efficient nonlinear response of guided polaritons, which can be employed for developing compact van der Waals photonic switches and modulators.

A theory of dynamic polarization of the nuclear spin system via optically-oriented charge carriers in lead halide perovskites is developed and compared with the experiments performed on a FA$_{0.9}$Cs$_{0.1}$PbI$_{2.8}$Br$_{0.2}$ crystal. The spin Hamiltonians of the electron and hole hyperfine interaction with the nuclear spins of lead and halogen are derived. The hyperfine interaction of the halogen spins with charge carriers is shown to be anisotropic and depending on the position of the halogen nucleus in the cubic elementary cell. The quadrupole splitting is absent for the lead spins, but plays an important role for the halogen spins and affects their dynamic polarization by charge carriers. The Overhauser fields of the dynamically polarized nuclei are calculated as functions of the tilting angle of an external magnetic field and compared with the experimentally measured angular dependence of the Hanle effect. The comparison of the theoretical model with the experimental data reveals an enhanced spin polarization of the lead nuclei, whose mean spin exceeds several times the mean spins of localized electrons and holes. This unexpectedly strong spin polarization is explained by the interaction of the lead nuclei with excitons having a high degree of spin orientation due to their short lifetime after excitation by circularly-polarized light. The dynamic polarization of the quadrupole-split halogen spins manifests itself via the magnetic field they produce at the lead nuclei. This field maintains the magnetization of the lead nuclei at zero external magnetic field. The dynamics of the nuclear spin polarization is measured under optical pumping and in the dark, yielding a nuclear spin-lattice relaxation time on the order of 10 seconds.

Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly tuned. But in many applications, we are not only interested in optimizing ML pipelines solely for predictive accuracy; additional metrics or constraints must be considered when determining an optimal configuration, resulting in a multi-objective optimization problem. This is often neglected in practice, due to a lack of knowledge and readily available software implementations for multi-objective hyperparameter optimization. In this work, we introduce the reader to the basics of multi-objective hyperparameter optimization and motivate its usefulness in applied ML. Furthermore, we provide an extensive survey of existing optimization strategies, both from the domain of evolutionary algorithms and Bayesian optimization. We illustrate the utility of MOO in several specific ML applications, considering objectives such as operating conditions, prediction time, sparseness, fairness, interpretability and robustness.

Galaxy clusters are expected to be dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster's formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at gamma-ray energies and are predicted to be sources of large-scale gamma-ray emission due to hadronic interactions in the intracluster medium. We estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. We perform a detailed spatial and spectral modelling of the expected signal for the DM and the CRp components. For each, we compute the expected CTA sensitivity. The observing strategy of Perseus is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio within the radius $R_{500}$ down to about $X_{500}<3\times 10^{-3}$, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index $\alpha_{\rm CRp}=2.3$. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure $\alpha_{\rm CRp}$ down to about $\Delta\alpha_{\rm CRp}\simeq 0.1$ and the CRp spatial distribution with 10% precision. Regarding DM, CTA should improve the current ground-based gamma-ray DM limits from clusters observations on the velocity-averaged annihilation cross-section by a factor of up to $\sim 5$, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with \tau_{\chi}&gt;10^{27}s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario.

Explainability is a key component in many applications involving deep neural networks (DNNs). However, current explanation methods for DNNs commonly leave it to the human observer to distinguish relevant explanations from spurious noise. This is not feasible anymore when going from easily human-accessible data such as images to more complex data such as genome sequences. To facilitate the accessibility of DNN outputs from such complex data and to increase explainability, we present a modification of the widely used explanation method layer-wise relevance propagation. Our approach enforces sparsity directly by pruning the relevance propagation for the different layers. Thereby, we achieve sparser relevance attributions for the input features as well as for the intermediate layers. As the relevance propagation is input-specific, we aim to prune the relevance propagation rather than the underlying model architecture. This allows to prune different neurons for different inputs and hence, might be more appropriate to the local nature of explanation methods. To demonstrate the efficacy of our method, we evaluate it on two types of data, images and genomic sequences. We show that our modification indeed leads to noise reduction and concentrates relevance on the most important features compared to the baseline.

Using Fourier analysis, this paper establishes exact security bounds for linear extractors in True Random Number Generators (TRNGs). We provide the first near-optimal total variation security characterization by interpolating between optimal $\ell_{\infty}$ and $\ell_2$ norm results, expressed through code weight enumerators and input bias parameters. Our bounds improve security assessments by an order of magnitude over previous approximations. By scanning ~20,000 codes, we reveal fundamental trade-offs between compression efficiency and cryptographic security. For instance, we show that achieving 80 bits of security can require sacrificing more than 50\% of the code rate when correcting 10\% input bias. Our bounds enhance security evaluation of TRNG post-processing schemes and quantify the inherent cost of randomness extraction in hardware implementations.

The LIGO/Virgo collaboration published the catalogs GWTC-1, GWTC-2.1 and GWTC-3 containing candidate gravitational-wave (GW) events detected during its runs O1, O2 and O3. These GW events can be possible sites of neutrino emission. In this paper, we present a search for neutrino counterparts of 90 GW candidates using IceCube DeepCore, the low-energy infill array of the IceCube Neutrino Observatory. The search is conducted using an unbinned maximum likelihood method, within a time window of 1000 s and uses the spatial and timing information from the GW events. The neutrinos used for the search have energies ranging from a few GeV to several tens of TeV. We do not find any significant emission of neutrinos, and place upper limits on the flux and the isotropic-equivalent energy emitted in low-energy neutrinos. We also conduct a binomial test to search for source populations potentially contributing to neutrino emission. We report a non-detection of a significant neutrino-source population with this test.

The occurrence of extreme events like heavy precipitation or storms at a certain location often shows a clustering behaviour and is thus not described well by a Poisson process. We construct a general model for the inter-exceedance times in between such events which combines different candidate models for such behaviour. This allows us to distinguish data generating mechanisms leading to clusters of dependent events with exponential inter-exceedance times in between clusters from independent events with heavy-tailed inter-exceedance times, and even allows us to combine these two mechanisms for better descriptions of such occurrences. We propose a modification of the Cram\'er-von Mises distance for model fitting. An application to mid-latitude winter cyclones illustrates the usefulness of our work.

Cherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based gamma-ray observatory operating in the energy range from 20 GeV up to 300 TeV, with two sites in La Palma (Spain) and Paranal (Chile). It will consist of telescopes of three sizes, covering different parts of the large energy range. We report on the performance of Large-Sized Telescope prototype (LST-1) in the detection and characterization of extragalactic gamma-ray sources, with a focus on the reconstructed gamma-ray spectra and variability of classical bright BL Lacertae objects, which were observed during the early commissioning phase of the instrument. LST-1 data from known bright gamma-ray blazars - Markarian 421, Markarian 501, 1ES 1959+650, 1ES 0647+250, and PG 1553+113 - were collected between July 10, 2020, and May 23, 2022, covering a zenith angle range of 4 deg to 57 deg. The reconstructed light curves were analyzed using a Bayesian block algorithm to distinguish the different activity phases of each blazar. Simultaneous Fermi-LAT data were utilized to reconstruct the broadband $\gamma$-ray spectra for the sources during each activity phase. High-level reconstructed data in a format compatible with gammapy are provided together with measured light curves and spectral energy distributions (SEDs) for several bright blazars and an interpretation of the observed variability in long and short timescales. Simulations of historical flares are generated to evaluate the sensitivity of LST-1. This work represents the first milestone in monitoring bright BL Lacertae objects with a CTAO telescope.

Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing techniques in order to mitigate computational challenges and enhance generative performance within high energy physics data. The fully quantum diffusion model replaces Gaussian noise with random unitary matrices in the forward process and incorporates a variational quantum circuit within the U-Net in the denoising architecture. We run evaluations on the structurally complex quark and gluon jets dataset from the Large Hadron Collider. The results demonstrate that the fully quantum and hybrid models are competitive with a similar classical model for jet generation, highlighting the potential of using quantum techniques for machine learning problems.

This study applies Bayesian models to predict hotel booking cancellations, a key challenge affecting resource allocation, revenue, and customer satisfaction in the hospitality industry. Using a Kaggle dataset with 36,285 observations and 17 features, Bayesian Logistic Regression and Beta-Binomial models were implemented. The logistic model, applied to 12 features and 5,000 randomly selected observations, outperformed the Beta-Binomial model in predictive accuracy. Key predictors included the number of adults, children, stay duration, lead time, car parking space, room type, and special requests. Model evaluation using Leave-One-Out Cross-Validation (LOO-CV) confirmed strong alignment between observed and predicted outcomes, demonstrating the model's robustness. Special requests and parking availability were found to be the strongest predictors of cancellation. This Bayesian approach provides a valuable tool for improving booking management and operational efficiency in the hotel industry.

We investigate the spin properties of charge carriers in vertically coupled InAs/InAlGaAs quantum dots grown by molecular beam epitaxy, emitting at telecom C-band wavelengths, with a silicon $\delta$-doped layer. Using time-resolved pump-probe Faraday ellipticity measurements, we systematically study single-, two-, and four-layer quantum dot (QD) configurations to quantify how vertical coupling affects key spin-coherence parameters. Our measurements reveal distinct layer-dependent effects: (1) Adding a second QD layer flips the resident charge from electrons to holes, consistent with optically induced electron tunneling into lower-energy dots and resultant hole charging. (2) Starting from the four-layer sample, the pump-probe signal develops an additional non-oscillating, decaying component absent in single- and two-layer samples, attributed to multiple layer growth changing the strain environment, which reduces heavy-hole and light-hole mixing. (3) With four-layers or more, hole spin mode locking (SML) can be observed, enabling quantitative extraction of the hole coherence time $T_2 \approx 13$\,ns from SML amplitude saturation. We also extract longitudinal spin relaxation ($T_1$) and transverse ($T_2^*$) spin dephasing times, g-factors, and inhomogeneous dephasing parameters for both electrons and holes across all layer configurations. The hole spin dephasing times $T_2^*$ remain relatively constant (2.26-2.73\,ns) across layer counts, while longitudinal relaxation times $T_1$ decrease with increasing layers (from 1.03\,$\mu$s for single-layer to 0.31\,$\mu$s for four-layer samples). These findings provide potential design guidelines for engineering spin coherence in telecom-band QDs for quantum information applications.

This introductory text on the basics of quantum mechanics is intended to serve as a kind of travel guide through the quantum world. It starts by asking whether quantum physics is important, or weird, or incomprehensible. It explains why particles sometimes behave like waves, and how uncertainty and randomness enter physics, before explaining a number of historically important experiments. Modern topics, like magnetic resonance imaging (MRI) and quantum computing are also covered. Essential concepts, such as the uncertainty principle, are analyzed in depth, employing a slightly increased dose of mathematics. This is the English version of the first part of a manual intended as a companion to the "Treffpunkt Quantenmechanik" (meeting point quantum mechanics), a laboratory at TU Dortmund University, where high-school students can get acquainted with the wonderful world of quantum physics. The second part of the manual contains detailed instructions for the individual experiments available in the lab and is not available on the Internet.

Exciton-phonon interactions govern the energy level spectrum and thus the optical response in semiconductors. In this respect, lead-halide perovskite nanocrystals represent a unique system, for which the interaction with optical phonons is particularly strong, giving rise to a ladder of multiple exciton states which can be optically excited with femtosecond pulses. We establish a new regime of coherent exciton-polaron dynamics with exceptionally long coherence times (T2 ~300 ps) in an ensemble of CsPbI3 nanocrystals embedded in a glass matrix. Using transient two-pulse photon echo at 2 K temperature, we observe quantum beats between the exciton-polaron states. Within a four-level model, we directly quantify the exciton-phonon coupling strength through the Huang-Rhys factors of 0.05-0.1 and 0.02-0.04 for low-energy optical phonons with energies of 3.2 and 5.1 meV, respectively. The pronounced size dependence of both coupling strengths and phonon lifetimes offers a path to tune the optical transitions between polaron states and to tailor the coherent optical dynamics in perovskite semiconductors for solid-state quantum technologies.

We determine the optimal angle of release in shot put. The simplest model - mostly used in textbooks - gives a value of $45^\circ$, while measurements of top athletes cluster around $37 - 38^\circ$. Including simply the height of the athlete the theory prediction goes down to about $42^\circ$ for typical parameters of top athletes. Taking further the correlations of the initial velocity of the shot, the angle of release and the height of release into account we predict values around $37 - 38^\circ$, which coincide perfectly with the measurements.

In this note we discuss the mathematical tools to define trend indicators which are used to describe market trends. We explain the relation between averages and moving averages on the one hand and the so called exponential moving average (EMA) on the other hand. We present a lot of examples and give the definition of the most frequently used trend indicator, the MACD, and discuss its properties.

Determining which rocky exoplanets have atmospheres, and why, is a key goal for the James Webb Space Telescope. So far, emission observations of individual rocky exoplanets orbiting M stars (M-Earths) have not provided definitive evidence for atmospheres. Here, we synthesize emission data for M-Earths and find a trend in measured brightness temperatures (ratioed to its theoretical maximum value) as a function of instellation. However, the statistical evidence of this trend is dependent on the choice of stellar model, and we consider its identification tentative. We show that this trend can be explained by either the onset of thin/tenuous (<1 bar) CO2-rich atmospheres on colder worlds, or a population of bare rocks with stronger space weathering and/or coarser regolith on closer-in worlds. Such grain coarsening may be caused by sintering near the melting point of rock or frequent volcanic resurfacing. Furthermore, we highlight considerations when testing rocky planet hypotheses at the population level, including the choice of instrument, stellar modeling, and how brightness temperatures are derived. We also find that fresh (unweathered) fine-grained surfaces can serve as a false positive to the detection of moderate atmospheric heat redistribution through eclipse observations. However, we argue that such surfaces are unlikely given the ubiquity of space weathering in the Solar System, the low albedo of Solar System airless bodies, and the high stellar wind environments of M-Earths. Emission data from a larger sample of M-Earths will be able to confirm or reject this tentative trend and diagnose its cause through spectral characterization.

Aqueous alkylamine mixtures are studied by computer simulations in order to understand the microscopic origin of the water rich side prominent x-ray scattering pre-peaks reported in a recent study. These pre-peaks are puzzling in view of the apparently contradicting facts that neat amines show pre-peaks much weaker than neat alkanols, while water-rich aqueous alcohols do not. These observations can be intuitively rationalized by noting that the amine head group have two hydrogen atoms when the hydroxyl group have only one, but they oppose the following two facts: i) computer simulations show micro-heterogeneity for both systems; ii) amines mix with water better than alcohols, both over larger concentrations and alkyl tails lengths. The study of the atom-atom pair correlation functions and related structure factors allows to understand the microscopic molecular details. The most interesting observation is that the amine head groups accumulate preferentially at the surface of the water domains, and increasingly better with longer alkyl tail, thus allowing to stabilize both the water and alkylamine domains, hence avoiding macroscopic demixing, except at high water concentrations when amines are scarce to achieve efficient surface saturation. The amine domains appear as disordered bilayers. Hence, aqueous amines are analogous to an inverse micelle melt and as precursor micro-emulsion. This stable micro-segregation produces large domain oscillations in the long range part of the correlation functions, translating into positive pre-peaks and negative anti-peaks in the related structure factors, the latter which contribute destructively to produce the prominent scattering pre-peak observed in the x-ray experiments. The model dependence is shown to be quite important, both for water and solute models. The CHARMM-AA model associated with the SPC/E model seems to be a good compromise.

Synchronization resulting in unified collective behavior of the individual elements of a system that are weakly coupled to each other has long fascinated scientists. Examples range from the periodic oscillation of coupled pendulum clocks to the rhythmic behavior in biological systems. Here we demonstrate this effect in a solid-state platform: spatially remote, auto-oscillating electron-nuclear spin systems in a semiconductor. When two such oscillators separated by up to 40 ${\mu}$m are optically pumped, their individually different frequencies lock to a common value, revealing long-range coherent coupling. For larger separations, the synchronization breaks. The interaction distance matches the electron spin diffusion length, identifying spin transport as the coupling-mediating mechanism and establishing phase coherence over mesoscopic distances. As a consequence, a wide-area optical pump drives all oscillators within the illuminated spot into a single synchronized state, despite their inhomogeneity. This synchronization accounts for the exceptional stability of the resulting auto-oscillations, enabling collective motion in distributed spin systems and paving the way toward coherent spin networks in spintronics.