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Structure in the Universe is believed to have evolved out of quantum fluctuations seeded by inflation in the early Universe. These fluctuations lead to density perturbations that grow via gravitational instability into large cosmological structures. In the linear regime, the growth of structure is directly coupled to the velocity field since perturbations are amplified by attracting (and accelerating) matter. Surveys of galaxy redshifts and distances allow one to infer the underlying density and velocity fields. Here, assuming the LCDM standard model of cosmology and applying a Hamiltonian Monte-Carlo algorithm to the grouped Cosmicflows-4 (CF4) compilation of 38,000 groups of galaxies, the large scale structure of the Universe is reconstructed out to a redshift corresponding to about 30, 000 km/s. Our method provides a probabilistic assessment of the domains of gravitational potential minima: basins of attraction (BoA). Earlier Cosmicflows catalogs suggested the Milky Way Galaxy was associated with a BoA called Laniakea. Now with the newer CF4 data, there is a slight probabilistic preference for Laniakea to be part of the much larger Shapley BoA. The largest BoA recovered from the CF4 data is associated with the Sloan Great Wall with a volume within the sample of 15.5 10^6(Mpc/h)^3, which is more than twice the size of the second largest Shapley BoA., Comment: 16 pages, 6 Figures, contact the authors for a nature formatted version accessible via SharedIt

While researchers have been trying to understand the stress and fatigue among pilots, especially pilot trainees, and to develop stress/fatigue models to automate the process of detecting stress/fatigue, they often do not consider biases such as sex in those models. However, in a critical profession like aviation, where the demographic distribution is disproportionately skewed to one sex, it is urgent to mitigate biases for fair and safe model predictions. In this work, we investigate the perceived stress/fatigue of 69 college students, including 40 pilot trainees with around 63% male. We construct models with decision trees first without bias mitigation and then with bias mitigation using a threshold optimizer with demographic parity and equalized odds constraints 30 times with random instances. Using bias mitigation, we achieve improvements of 88.31% (demographic parity difference) and 54.26% (equalized odds difference), which are also found to be statistically significant., Comment: Accepted at 2024 IEEE-EMBS International Conference on Body Sensor Networks (IEEE BSN 2024)

In the healthcare industry, researchers have been developing machine learning models to automate diagnosing patients with respiratory illnesses based on their breathing patterns. However, these models do not consider the demographic biases, particularly sex bias, that often occur when models are trained with a skewed patient dataset. Hence, it is essential in such an important industry to reduce this bias so that models can make fair diagnoses. In this work, we examine the bias in models used to detect breathing patterns of two major respiratory diseases, i.e., chronic obstructive pulmonary disease (COPD) and COVID-19. Using decision tree models trained with audio recordings of breathing patterns obtained from two open-source datasets consisting of 29 COPD and 680 COVID-19-positive patients, we analyze the effect of sex bias on the models. With a threshold optimizer and two constraints (demographic parity and equalized odds) to mitigate the bias, we witness 81.43% (demographic parity difference) and 71.81% (equalized odds difference) improvements. These findings are statistically significant., Comment: Accepted at 2024 IEEE-EMBS International Conference on Body Sensor Networks (IEEE BSN 2024)

In this paper we introduce the so-called Generalized Naive Bayes structure as an extension of the Naive Bayes structure. We give a new greedy algorithm that finds a good fitting Generalized Naive Bayes (GNB) probability distribution. We prove that this fits the data at least as well as the probability distribution determined by the classical Naive Bayes (NB). Then, under a not very restrictive condition, we give a second algorithm for which we can prove that it finds the optimal GNB probability distribution, i.e. best fitting structure in the sense of KL divergence. Both algorithms are constructed to maximize the information content and aim to minimize redundancy. Based on these algorithms, new methods for feature selection are introduced. We discuss the similarities and differences to other related algorithms in terms of structure, methodology, and complexity. Experimental results show, that the algorithms introduced outperform the related algorithms in many cases., Comment: 44 pages, 19 figures

This work investigates the impact of different Modified Gravity (MG) models on the large-scale structures (LSS) properties in relation to the cosmic web (CW), using N-body simulations of f(R) and nDGP models. We analyse the impact of the MG effect on the density field through density distribution and clustering statistics, and assess its influence on halo properties by examining the halo mass function and spin. We find that the PDF of dark matter density fields shift towards lower densities for stronger variants of f(R) and nDGP. Additionally, when segregated into CW environments, the stronger variants show a higher mean density in knots, and a lower mean density in voids compared to LCDM. For higher-order clustering statistics relative to LCDM, the scale-dependent f(R) variants exhibit a greater non-monotonic deviation as a function of scale when segregated into environments, compared to nDGP. Additionally, the halo mass function separated into CW environments shows a similar behaviour, introducing complex trends as a function of mass for f(R) and nDGP models. We also report up to a 15% enhancement in the angular momentum of halos in f(R) gravity models compared to LCDM, with similar differences when considering environmental segregation. We demonstrate that this difference in the spin arises largely due to different tidal torquing across the various MG models. Therefore, studying higher-order statistics of the cosmological fields and halo properties separated into CW components probes the additional physics contained within the MG models. We conclude that considering the effect of CW in MG studies increases the constraining power of these LSS statistics, and can further aid the distinction between the cosmologies that have an identical expansion history to the standard LCDM but differing underlying physics, such as the MG models presented in this work.

We present a mathematical model of lophotrichous bacteria, motivated by Pseudomonas putida, which swim through fluid by rotating a cluster of multiple flagella extended from near one pole of the cell body. Although the flagella rotate individually, they are typically bundled together, enabling the bacterium to exhibit three primary modes of motility: push, pull, and wrapping. One key determinant of these modes is the coordination between motor torque and rotational direction of motors. The computational variations in this coordination reveal a wide spectrum of dynamical motion regimes, which are modulated by hydrodynamic interactions between flagellar filaments. These dynamic modes can be categorized into two groups based on the collective behavior of flagella, i.e., bundled and unbundled configurations. For some of these configurations, experimental examples from fluorescence microscopy recordings of swimming P. putida cells are also presented. Furthermore, we analyze the characteristics of stable bundles, such as push and pull, and investigate the dependence of swimming behaviors on the elastic properties of the flagella.

By exciting a series of $1\mathrm{s}^{2}\, ^{1}\mathrm{S}_{0} \to 1\mathrm{s}n\mathrm{p}\, ^{1}\mathrm{P}_{1}$ transitions in helium-like nitrogen ions with linearly polarized monochromatic soft X-rays at the Elettra facility, we found a change in the angular distribution of the fluorescence sensitive to the principal quantum number $n$. In particular it is observed that the ratio of emission in directions parallel and perpendicular to the polarization of incident radiation increases with higher $n$. We find this $n$-dependence to be a manifestation of the Hanle effect, which served as a practical tool for lifetime determinations of optical transitions since its discovery in 1924. In contrast to traditional Hanle effect experiments, in which one varies the magnetic field and considers a particular excited state, we demonstrate a 'soft X-ray Hanle effect' which arises in a static magnetic field but for a series of excited states. By comparing experimental data with theoretical predictions, we were able to determine lifetimes ranging from hundreds of femtoseconds to tens of picoseconds of the $1\mathrm{s}n\mathrm{p}\, ^{1}\mathrm{P}_{1}$ levels, which find excellent agreement with atomic-structure calculations. We argue that dedicated soft X-ray measurements could yield lifetime data that is beyond current experimental reach and cannot yet be predicted with sufficient accuracy., Comment: 7 pages, 4 figures

The transition energies of the two $1s$-core-excited soft X-ray lines (dubbed q and r) from $1s^2 2s ^1S_{1/2}$ to the respective upper levels $1s(^{2}S)2s2p(^{3}P) ^{2}P_{3/2}$ and $^{2}P_{1/2}$ of Li-like oxygen, fluorine and neon were measured and calibrated using several nearby transitions of He-like ions. The major remaining source of energy uncertainties in monochromators, the periodic fluctuations produced by imperfect angular encoder calibration, is addressed by a simultaneously running photoelectron spectroscopy measurement. This leads to an improved energy determination of 5 parts per million, showing fair agreement with previous theories as well as with our own, involving a complete treatment of the autoionizing states studied here. Our experimental results translate to an uncertainty of only 1.6\,km/s for the oxygen line qr-blend used to determine the outflow velocities of active galactic nuclei, ten times smaller than previously possible., Comment: 7 pages, 4 figures

Nonlinear wave mixing in the X-ray range can provide valuable insights into the structural and electron dynamics of atomic and molecular systems on ultrafast time scales, with state- and site-selectivity and atomic resolution. This promising experimental toolbox was so far limited by requiring at least one near-visible laser, thus preventing core-shell two-dimensional X-ray spectroscopy. In this work, we demonstrate the generation of background-free all-X-ray four-wave mixing (XFWM) signals from a dilute gaseous sample (Ne). The measured and simulated two-dimensional spectral maps ($\omega_{\text{in}},\omega_{\text{out}}$) show multiple contributions involving the coherent response from core electrons. Notably, two-color resonant XFWM signals, essential for generalized multi-color schemes that allow to locally probe the electronic excitation of matter, are observed in neutral Ne. Moreover, stimulated Ne$^+$ emission in each of the propagating X-ray pulses leads to an increase of the temporal coherence in a narrow-bandwidth, which results in the coherent mixing of three X-ray lasers. Preliminary X-ray excitation experiments making use of multi-color time-delayed X-ray pulses demonstrate temporal resolution capability and show a time dependency consistent with a signal dominated by resonant XFWM processes. This first all-X-ray four-wave-mixing approach represents a major breakthrough towards multidimensional X-ray correlation spectroscopy and the general application of nonlinear all-X-ray wave-mixing.

This study investigates thermal transport across nanocrystalline diamond/AlGaN interfaces, crucial for enhancing thermal management in AlGaN/AlGaN-based devices. Chemical vapor deposition growth of diamond directly on AlGaN resulted in a disordered interface with a high thermal boundary resistance (TBR) of 20.6 m^2-K/GW. We employed sputtered carbide interlayers (e.g., $B_4C$, $SiC$, $B_4C/SiC$) to reduce thermal boundary resistance in diamond/AlGaN interfaces. The carbide interlayers resulted in record-low thermal boundary resistance values of 3.4 and 3.7 m^2-K/GW for Al$_{0.65}$Ga$_{0.35}$N samples with $B_4C$ and $SiC$ interlayers, respectively. STEM imaging of the interface reveals interlayer thicknesses between 1.7-2.5 nm, with an amorphous structure. Additionally, Fast-Fourier Transform (FFT) characterization of sections of the STEM images displayed sharp crystalline fringes in the AlGaN layer, confirming it was properly protected from damage from hydrogen plasma during the diamond growth. In order to accurately measure the thermal boundary resistance we develop a hybrid technique, combining time-domain thermoreflectance and steady-state thermoreflectance fitting, offering superior sensitivity to buried thermal resistances. Our findings underscore the efficacy of interlayer engineering in enhancing thermal transport and demonstrate the importance of innovative measurement techniques in accurately characterizing complex thermal interfaces. This study provides a foundation for future research in improving thermal properties of semiconductor devices through interface engineering and advanced measurement methodologies.

Attosecond X-ray pulses are the key to studying electron dynamics at their natural time scale involving specific electronic states. They are promising to build the conceptual bridge between physical and chemical photo-reaction processes. Free-electron lasers have demonstrated their capability of generating intense attosecond X-ray pulses. However, harnessing them for time-resolving experiments and investigations of nonlinear X-ray absorption mechanisms remains a cutting-edge challenge. We have characterised X-ray pulses with durations of down to 700$\,$attoseconds and peak powers up to 200$\,$GW at $\sim$ 1$\,$keV photon energy via angular streaking at the SQS instrument of the European XFEL. As direct application, we present results of nonlinear X-ray-matter interaction via state-specific spectroscopy on a transient system. Using the derived spectral and temporal information of each pulse, we deliberately steer the probability for formation of double-core vacancies in neon gas atoms through excitation or ionisation of the second inner-shell electron after K-shell ionisation. Our results advance the field of attosecond science with highly intense and fully characterised X-ray pulses to the site-specific investigation of electronic motion in transient media.

While bacterial swimming has been well characterized in uniform liquid environments, only little is known about how bacteria propagate through complex environments, such as gel-like matrices or porous media that are typically encountered in tissue or soil. Here, we study swimming motility of the soil bacterium Pseudomonas putida (P. putida) in polysaccharide matrices formed by different concentrations of agar. P. putida cells display intermittent run-motility in the gel, where run times are exponentially distributed and intermittently occurring dwell times follow a waiting-time distribution with a power-law decay. An analysis of the turn angle distribution suggests that both, flagella mediated turning as well as mechanical trapping in the agar matrix play a role in the overall swimming pattern. Based on the experimentally observed motility pattern and measured waiting-time distributions, we propose a minimal active particle model which correctly describes the observed time dependence of the mean square displacement of the bacterial swimmers., Comment: 6 pages, 4 figures

The nonlinear interaction of x-rays with matter is at the heart of understanding and controlling ultrafast molecular dynamics from an atom-specific viewpoint, providing new scientific and analytical opportunities to explore the structure and dynamics of small quantum systems. At increasingly high x-ray intensity, the sensitivity of ultrashort x-ray pulses to specific electronic states and emerging short-lived transient intermediates is of particular relevance for our understanding of fundamental multi-photon absorption processes. In this work, intense x-ray free-electron laser (XFEL) pulses at the European XFEL (EuXFEL) are combined with a gas cell and grating spectrometer for a high-intensity transmission spectroscopy study of multiphoton-induced ultrafast molecular fragmentation dynamics in CF$_4$. This approach unlocks the direct intra-pulse observation of transient fragments, including neutral atoms, by their characteristic absorption lines in the transmitted broad-band x-ray spectrum. The dynamics with and without initially producing fluorine K-shell holes are studied by tuning the central photon energy. The absorption spectra are measured at different FEL intensities to observe nonlinear effects. Transient isolated fluorine atoms and ions are spectroscopically recorded within the ultrashort pulse duration of few tens of femtoseconds. An isosbestic point that signifies the correlated transition between intact neutral CF$_4$ molecules and charged atomic fragments is observed near the fluorine K-edge. The dissociation dynamics and the multiphoton absorption-induced dynamics encoded in the spectra are theoretically interpreted. Overall, this study demonstrates the potential of high-intensity x-ray transmission spectroscopy to study ultrafast molecular dynamics with sensitivity to specific intermediate species and their electronic structure., Comment: 30 pages, with 7 figures, submitted to Phys. Rev. X

We derive the scalar-tensor modification of the gravitational field of an ultrarelativistic particle beam and its effect on a test particle that is used as sensor. To do so, we solve the linearized scalar-tensor gravity field equations sourced by an energy-momentum tensor of a moving point particle. The geodesic equation and the geodesic deviation equation then predict the acceleration of the test particle as well as the momentum transfer due to a passing source. Comparing the momentum transfer predicted by general relativity and scalar tensor gravity, we find that there exists a relevant parameter regime where this difference increases significantly with the velocity of the source particle. Since ultrarelativistic particles are available at accelerators like the Large Hadron Collider, ultra-precise acceleration sensors in the vicinity of the particle beam could potentially detect deviations from general relativity or constrain modified gravity models.

The X-ray morphologies of clusters of galaxies display significant variations, reflecting their dynamical histories and the nonlinear dependence of X-ray emissivity on the density of the intracluster gas. Qualitative and quantitative assessments of X-ray morphology have long been considered a proxy for determining whether clusters are dynamically active or "relaxed." Conversely, the use of circularly or elliptically symmetric models for cluster emission can be complicated by the variety of complex features realized in nature, spanning scales from Mpc down to the resolution limit of current X-ray observatories. In this work, we use mock X-ray images from simulated clusters from THE THREE HUNDRED project to define a basis set of cluster image features. We take advantage of clusters' approximate self similarity to minimize the differences between images before encoding the remaining diversity through a distribution of high order polynomial coefficients. Principal component analysis then provides an orthogonal basis for this distribution, corresponding to natural perturbations from an average model. This representation allows novel, realistically complex X-ray cluster images to be easily generated, and we provide code to do so. The approach provides a simple way to generate training data for cluster image analysis algorithms, and could be straightforwardly adapted to generate clusters displaying specific types of features, or selected by physical characteristics available in the original simulations., Comment: 12 pages, 9 figures. To appear in ApJ

A major hurdle for building a large-scale quantum computer is to scale up the number of qubits while maintaining connectivity between them. In trapped-ion devices, this connectivity can be provided by physically moving subregisters consisting of a few ions across the processor. The topology of the connectivity is given by the layout of the ion trap where one-dimensional and two-dimensional arrangements are possible. Here, we focus on an architecture based on a rectangular two-dimensional lattice, where each lattice site contains a subregister with a linear string of ions. We refer to this architecture as the Quantum Spring Array (QSA). Subregisters placed in neighboring lattice sites can be coupled by bringing the respective ion strings close to each other while avoiding merging them into a single trapping potential. Control of the separation of subregisters along one axis of the lattice, known as the axial direction, uses quasi-static voltages, while the second axis, the radial, requires control of radio frequency signals. In this work, we investigate key elements of the 2D lattice quantum computation architecture along both axes: We show that the coupling rate between neighboring lattice sites increases with the number of ions per site and the motion of the coupled system can be resilient to noise. The coherence of the coupling is assessed, and an entangled state of qubits in separate trapping regions along the radial axis is demonstrated. Moreover, we demonstrate control over radio frequency signals to adjust radial separation between strings, and thus tune their coupling rate. We further map the 2D lattice architecture to code primitives for fault-tolerant quantum error correction, providing a step towards a quantum processor architecture that is optimized for large-scale fault-tolerant operation., Comment: 23 pages, 19 figures (15 in main text, 4 in appendices)

Due to their tunable material properties, sorptive materials have a wide range of applications in energy storage, water treatment, carbon capture, analytical chemistry, and more. One crucial factor in determining the effectiveness of such materials is their skeletal density, or "true density" because it is often used to calculate key metrics, such as storage capacities. In this paper, we present skeletal density measurements through helium pycnometry for several types of adsorbent carbon materials derived from either corncob, sawdust, coffee grounds, polyvinylidene chloride (PVDC), graphitic carbon nitride (GCN), or metal organic frameworks (MOFs). The measured skeletal density of sawdust-based activated carbon was 2.02 +/- 0.05 g/$cm^{ 3}$. The measured skeletal density of coffee-based activated carbon was 2.23 +/- 0.06 $cm^{ 3}$. We also expound upon the impact that skeletal density has upon hydrogen excess adsorption measurements and other calculated engineering quantities. If a skeletal density is underestimated by 10%, it can affect the room temperature excess adsorption by at least 5% at 100 bar and by 7% at 200 bar, depending on the material type., Comment: 12 pages, 4 figures, 2 tables

We analyze Planck scale induced modifications of the relativistic time dilation using data from the Muon Storage Ring experiment at CERN. By examining the lifetimes of muons, we establish, for the first time, a constraint on such quantum gravity-inspired deformations using this channel., Comment: 4 pages

We consider imitation learning with access only to expert demonstrations, whose real-world application is often limited by covariate shift due to compounding errors during execution. We investigate the effectiveness of the Continuity-based Corrective Labels for Imitation Learning (CCIL) framework in mitigating this issue for real-world fine manipulation tasks. CCIL generates corrective labels by learning a locally continuous dynamics model from demonstrations to guide the agent back toward expert states. Through extensive experiments on peg insertion and fine grasping, we provide the first empirical validation that CCIL can significantly improve imitation learning performance despite discontinuities present in contact-rich manipulation. We find that: (1) real-world manipulation exhibits sufficient local smoothness to apply CCIL, (2) generated corrective labels are most beneficial in low-data regimes, and (3) label filtering based on estimated dynamics model error enables performance gains. To effectively apply CCIL to robotic domains, we offer a practical instantiation of the framework and insights into design choices and hyperparameter selection. Our work demonstrates CCIL's practicality for alleviating compounding errors in imitation learning on physical robots.

Cluster-based algorithm selection deals with selecting recommendation algorithms on clusters of users to obtain performance gains. No studies have been attempted for many combinations of clustering approaches and recommendation algorithms. We want to show that clustering users prior to algorithm selection increases the performance of recommendation algorithms. Our study covers eight datasets, four clustering approaches, and eight recommendation algorithms. We select the best performing recommendation algorithm for each cluster. Our work shows that cluster-based algorithm selection is an effective technique for optimizing recommendation algorithm performance. For five out of eight datasets, we report an increase in nDCG@10 between 19.28% (0.032) and 360.38% (0.191) compared to algorithm selection without prior clustering., Comment: 16 pages, 8 figures, 2 tables

We demonstrate a left-right asymmetry control of the photo\-electron angular distribution in multi\-photon ionization of Li atoms by a bichromatic laser field. By delaying the fundamental (780 nm) and its second harmonic relative to each other in steps of 130 atto\-seconds, we can vary the relative phase between the two laser fields with sub-wavelength accuracy and thereby steer the ejected electrons. Good agreement is found between the measurements and calculations at the appropriate intensities of the two harmonics., Comment: 8 pages, 8 figures

The process of cometary activity continues to pose a challenging question in cometary science. The activity modeling of comet 67P/Churyumov-Gerasimenko, based on data from the Rosetta mission, has significantly enhanced our comprehension of cometary activity. But thermophysical models have difficulties in simultaneously explaining the production rates of various gas species and dust. It has been suggested that different gas species might be responsible for the ejection of refractory material in distinct size ranges. This work focuses on investigating abundance and the ejection mechanisms of large ($\gtrsim$ 1 cm) aggregates from the comet nucleus. We aim to determine their properties and map the distribution of their source regions across the comet surface. This can place constraints on activity models for comets. We examined 189 images acquired at five epochs by the OSIRIS/NAC instrument. Our goal was to identify bright tracks produced by individual aggregates as they traversed the camera field of view. We generated synthetic images based on the output of dynamical simulations involving various types of aggregates. By comparing these synthetic images with the observations, we determine the characteristics of the simulated aggregates that most closely resembled the observations. We identified over 30000 tracks present in the OSIRIS images, derived constraints on the characteristics of the aggregates and mapped their origins on the nucleus surface. The aggregates have an average radius of $\simeq5$ cm, and a bulk density consistent with that of the comet's nucleus. Due to their size, gas drag exerts only a minor influence on their dynamical behavior, so an initial velocity is needed in order to bring them into the camera field of view. The source regions of these aggregates are predominantly located near the boundaries of distinct terrains on the surface., Comment: 19 pages, 15 figures. Accepted for publication in Astronomy & Astrophysics

The surge in AI usage demands innovative power reduction strategies. Novel Compute-in-Memory (CIM) architectures, leveraging advanced memory technologies, hold the potential for significantly lowering energy consumption by integrating storage with parallel Matrix-Vector-Multiplications (MVMs). This study addresses the 1T1R RRAM crossbar, a core component in numerous CIM architectures. We introduce an abstract model and a calibration methodology for estimating operational energy. Our tool condenses circuit-level behaviour into a few parameters, facilitating energy assessments for DNN workloads. Validation against low-level SPICE simulations demonstrates speedups of up to 1000x and energy estimations with errors below 1%., Comment: Pre-print of work presented at AICAS 2024. 5 pages, 6 figures

We study the weak equivalence principle in the context of modified dispersion relations, a prevalent approach to quantum gravity phenomenology. We find that generic modified dispersion relations violate the weak equivalence principle. The acceleration in general depends on the mass of the test body, unless the Hamiltonian is either two-homogeneous in the test particles' 4-momenta or the corresponding Lagrangian differs from the homogeneous case by a total derivative only. The key ingredient of this calculation is a $3+1$ decomposition of the parametrization invariant relativistic test particle action derived from the dispersion relation. Additionally, we apply a perturbative expansion in the test particle's spatial velocity and the inverse speed of light. To quantify our result, we provide a general formula for the E\"otv\'os factor of modified dispersion relations. As a specific example, we study the $\kappa$-Poincar\'e dispersion relation. Comparing the ensuing non-vanishing E\"otv\'os factor to recent data from the MICROSCOPE experiment, we obtain a bound of $\kappa^{-1}\geq10^{15}{\rm GeV}/c^2.$., Comment: 18 pages

Interpretable machine learning has emerged as central in leveraging artificial intelligence within high-stakes domains such as healthcare, where understanding the rationale behind model predictions is as critical as achieving high predictive accuracy. In this context, feature selection assumes a pivotal role in enhancing model interpretability by identifying the most important input features in black-box models. While random forests are frequently used in biomedicine for their remarkable performance on tabular datasets, the accuracy gained from aggregating decision trees comes at the expense of interpretability. Consequently, feature selection for enhancing interpretability in random forests has been extensively explored in supervised settings. However, its investigation in the unsupervised regime remains notably limited. To address this gap, the study introduces novel methods to construct feature graphs from unsupervised random forests and feature selection strategies to derive effective feature combinations from these graphs. Feature graphs are constructed for the entire dataset as well as individual clusters leveraging the parent-child node splits within the trees, such that feature centrality captures their relevance to the clustering task, while edge weights reflect the discriminating power of feature pairs. Graph-based feature selection methods are extensively evaluated on synthetic and benchmark datasets both in terms of their ability to reduce dimensionality while improving clustering performance, as well as to enhance model interpretability. An application on omics data for disease subtyping identifies the top features for each cluster, showcasing the potential of the proposed approach to enhance interpretability in clustering analyses and its utility in a real-world biomedical application.

We used the monochromatic soft-x-ray beamline P04 at the synchrotron-radiation facility PETRA III to resonantly excite the strongest $2p-3d$ transitions in neon-like Ni XIX ions, $[2p^6]_{J=0} \rightarrow [(2p^5)_{1/2}\,3d_{3/2}]_{J=1}$ and $[2p^6]_{J=0} \rightarrow [(2p^5)_{3/2}\,3d_{5/2}]_{J=1}$, respectively dubbed 3C and 3D, achieving a resolving power of 15\,000 and signal-to-background ratio of 30. We obtain their natural linewidths, with an accuracy of better than 10\%, as well as the oscillator-strength ratio $f(3C)/f(3D)$ = 2.51(11) from analysis of the resonant fluorescence spectra. These results agree with those of previous experiments, earlier predictions, and our own advanced calculations., Comment: 10 pages, 3 figures, 3 tables, published

We compute accurate potential energy curves of O$_2$ up to O$_2^{2+}$ for states that are energetically accessible in the photon energy range 20 to 42 eV. We find the dissociation limits of these states and the atomic fragments to which they dissociate. We use the Velocity Verlet algorithm to account for the nuclear dynamics of O$_2$ using these computed potential energy curves. Through the use of Monte Carlo simulations which monitor the nuclear motion and electronic structure of the molecule, we obtain kinetic energy release distributions of the O$^+$ + O$^+$ atomic fragments of O$_2$ exposed to an XUV pulse in the photon energy range. We compare these theoretical kinetic energy release distributions with results obtained from experiment., Comment: 11 pages, 9 Figures

In most analytical studies of light ray propagation in curved spacetimes around a gravitating object surrounded by a medium, it is assumed that the medium is a cold nonmagnetized plasma. The distinctive feature of this environment is that the equations of motion of the rays are independent of the plasma velocity, which, however, is not the case in other media. In this paper, we consider the deflection of light rays propagating near a spherically symmetric gravitating object in a moving dispersive medium given by a general refractive index. The deflection is studied when the motion of the medium is defined either as a radially falling onto a gravitating object (e.g., black hole), or rotating in the equatorial plane. For both cases the deflection angles are obtained. These examples demonstrate that fully analytic expressions can be obtained if the Hamiltonian for the rays takes a rather general form as a polynomial in a given momentum component. The general expressions are further applied to three specific choices of refractive indices and these cases are compared. Furthermore, the light rays propagating around a gravitating object surrounded by a generally moving medium are further studied as a small perturbation of the cold plasma model. The deflection angle formula is hence expressed as a sum of zeroth and first order components, where the zeroth order term corresponds to the known cold plasma case and the first order correction can be interpreted as caused by small difference in the refractive index compared to the cold plasma. The results presented in this paper allow to describe the effects caused by the motion of a medium and thus go beyond cold nonmagnetized plasma model.

The electronic structure of atomic quantum systems and their dynamical interaction with light is reflected in transition dipole matrix elements coupling the system's energy eigenstates. In this work, we measure phase shifts of the time-dependent ultrafast absorption to determine the relative signs of. the transition-dipole matrix elements. The measurement relies on precise absolute calibration of the relative timing between the used light pulses, which is achieved by combining attosecond transient absorption and attosecond streaking spectroscopy to simultaneously measure the resonant photoabsorption spectra of laser-coupled doubly excited states in helium, together with the attosecond streaked photoelectron spectra. The streaking measurement reveals the absolute timing and the full temporal profile of the interacting electric fields which is then used to quantify the state-specific dynamics of the measured photoabsorption spectra. By comparing the 1-fs time-scale modulation across the absorption lines corresponding to the 2s2p (1P) and sp2,3+ (1P) doubly excited states between simulation and measurement, we quantify the signs of the transition dipole matrix elements for the laser-coupled autoionizing states 2s2p-2p2 and 2p2-sp2,3+ to be opposite of each other.

Methods. We algorithmically tracked thousands of individual particles through four OSIRIS/NAC image sequences of 67P's near-nucleus coma. We then traced concentrated particle groups back to the nucleus surface, and estimated their potential source regions, size distributions, and projected dynamical parameters. Finally, we compared the observed activity to dust coma simulations. Results. We traced back 409 decimeter-sized particles to four suspected source regions. The regions strongly overlap and are mostly confined to the Khonsu-Atum-Anubis area. The activity may be linked to rugged terrain, and the erosion of fine dust and the ejection of large boulders may be mutually exclusive. Power-law indices fitted to the particle size--frequency distributions range from $3.4 \pm 0.3$ to $3.8 \pm 0.4$. Gas drag fits to the radial particle accelerations provide an estimate for the local gas production rates ($Q_\text{g} = 3.6 \cdot 10^{-5}$ kg s$^{-1}$ m$^{-2}$), which is several times higher than our model predictions based on purely insolation-driven water ice sublimation. Our observational results and our modeling results both reveal that our particles were likely ejected with substantial nonzero initial velocities of around 0.5$-$0.6 m s$^{-1}$. Conclusions. Our findings strongly suggest that the observed ejection of decimeter-sized particles cannot be explained by water ice sublimation and favorable illumination conditions alone. Instead, the local structures and compositions of the source regions likely play a major role. In line with current ejection models of decimeter-sized particles, we deem an overabundance of CO$_2$ ice and its sublimation to be the most probable driver. In addition, because of the significant initial velocities, we suspect the ejection events to be considerably more energetic than gradual liftoffs., Comment: 31 pages, 29 figures

In this paper we show that Cardanos formula for the solution of cubic equations can be reduced to expressions involving only square roots if the real root is rational., Comment: 3 pages

High-\Qm{} mechanical resonators are crucial for applications where low noise and long coherence time are required, as mirror suspensions, quantum cavity optomechanical devices, or nanomechanical sensors. Tensile strain in the material enables the use of dissipation dilution and strain engineering techniques, which increase the mechanical quality factor. These techniques have been employed for high-\Qm{} mechanical resonators made from amorphous materials and, recently, from crystalline materials such as InGaP, SiC, and Si. A strained crystalline film exhibiting substantial piezoelectricity expands the capability of high-\Qm{} nanomechanical resonators to directly utilize electronic degrees of freedom. In this work we realize nanomechanical resonators with \Qm{} up to $2.9\times 10^{7}$ made from tensile-strained \SI{290}{\nano\meter}-thick AlN, which is an epitaxially-grown crystalline material offering strong piezoelectricity. We demonstrate nanomechanical resonators that exploit dissipation dilution and strain engineering to reach a \Qf-product approaching $10^{13}$\,\SI{}{\hertz} at room temperature. We realize a novel resonator geometry, triangline, whose shape follows the Al-N bonds and offers a central pad that we pattern with a photonic crystal. This allows us to reach an optical reflectivity above 80\% for efficient coupling to out-of-plane light. The presented results pave the way for quantum optoelectromechanical devices at room temperature based on tensile-strained AlN.

In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. This work establishes a powerful framework for advancing precision medicine through unsupervised random-forest-based clustering and federated computing. We introduce a novel multi-omics clustering approach utilizing unsupervised random-forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Moreover, our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark data sets as well as on cancer data from The Cancer Genome Atlas (TCGA). Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing.