Search

Discrete Choice Modelling serves as a robust framework for modelling human choice behaviour across various disciplines. Building a choice model is a semi structured research process that involves a combination of a priori assumptions, behavioural theories, and statistical methods. This complex set of decisions, coupled with diverse workflows, can lead to substantial variability in model outcomes. To better understand these dynamics, we developed the Serious Choice Modelling Game, which simulates the real world modelling process and tracks modellers' decisions in real time using a stated preference dataset. Participants were asked to develop choice models to estimate Willingness to Pay values to inform policymakers about strategies for reducing noise pollution. The game recorded actions across multiple phases, including descriptive analysis, model specification, and outcome interpretation, allowing us to analyse both individual decisions and differences in modelling approaches. While our findings reveal a strong preference for using data visualisation tools in descriptive analysis, it also identifies gaps in missing values handling before model specification. We also found significant variation in the modelling approach, even when modellers were working with the same choice dataset. Despite the availability of more complex models, simpler models such as Multinomial Logit were often preferred, suggesting that modellers tend to avoid complexity when time and resources are limited. Participants who engaged in more comprehensive data exploration and iterative model comparison tended to achieve better model fit and parsimony, which demonstrate that the methodological choices made throughout the workflow have significant implications, particularly when modelling outcomes are used for policy formulation., Comment: 35 pages, 7 figures

The Zirconium (Z = 40) isotopic chain has attracted interest for more than four decades. The abrupt lowering of the energy of the first $2^+$ state and the increase in the transition strength B(E2; $2_1^\rightarrow 0_1^+$ going from $^{98}$Zr to $^{100}$Zr has been the first example of "quantum phase transition" in nuclear shapes, which has few equivalents in the nuclear chart. Although a multitude of experiments have been performed to measure nuclear properties related to nuclear shapes and collectivity in the region, none of the measured lifetimes were obtained using the Recoil Distance Doppler Shift method in the $\gamma\gamma$-coincidence mode where a gate on the direct feeding transition of the state of interest allows a strict control of systematical errors. This work reports the results of lifetime measurements for the first yrast excited states in $^{98-104}$Zr carried out to extract reduced transition probabilities. The new lifetime values in $\gamma\gamma$-coincidence and $\gamma$-single mode are compared with the results of former experiments. Recent predictions of the Interacting Boson Model with Configuration Mixing, the Symmetry Conserving Configuration Mixing model based on the Hartree-Fock-Bogoliubov approach and the Monte Carlo Shell Model are presented and compared with the experimental data.

Despite the significance of coronal mass ejections (CMEs) in space weather, a comprehensive understanding of their interior morphology remains a scientific challenge, particularly with the advent of many state-of-the-art solar missions such as Parker Solar Probe (Parker) and Solar Orbiter (SO). In this study, we present an analysis of a complex CME as observed by the Wide-Field Imager for Solar PRobe (WISPR) heliospheric imager during Parker's seventh solar encounter. The CME morphology does not fully conform with the general three-part density structure, exhibiting a front and core not significantly bright, with a highly structured overall configuration. In particular, its morphology reveals non-concentric nested rings, which we argue are a signature of the embedded helical magnetic flux rope (MFR) of the CME. For that, we analyze the morphological and kinematical properties of the nested density structures and demonstrate that they outline the projection of the three-dimensional structure of the flux rope as it crosses the lines of sight of the WISPR imager, thereby revealing the magnetic field geometry. Comparison of observations from various viewpoints suggests that the CME substructures can be discerned owing to the ideal viewing perspective, close proximity, and spatial resolution of the observing instrument., Comment: 21 pages, 11 figures

Reliable estimation of treatment effects from observational data is important in many disciplines such as medicine. However, estimation is challenging when unconfoundedness as a standard assumption in the causal inference literature is violated. In this work, we leverage arbitrary (potentially high-dimensional) instruments to estimate bounds on the conditional average treatment effect (CATE). Our contributions are three-fold: (1) We propose a novel approach for partial identification through a mapping of instruments to a discrete representation space so that we yield valid bounds on the CATE. This is crucial for reliable decision-making in real-world applications. (2) We derive a two-step procedure that learns tight bounds using a tailored neural partitioning of the latent instrument space. As a result, we avoid instability issues due to numerical approximations or adversarial training. Furthermore, our procedure aims to reduce the estimation variance in finite-sample settings to yield more reliable estimates. (3) We show theoretically that our procedure obtains valid bounds while reducing estimation variance. We further perform extensive experiments to demonstrate the effectiveness across various settings. Overall, our procedure offers a novel path for practitioners to make use of potentially high-dimensional instruments (e.g., as in Mendelian randomization).

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic., Comment: Accepted version; not Version of Record

Patient trajectories from electronic health records are widely used to predict potential outcomes of treatments over time, which then allows to personalize care. Yet, existing neural methods for this purpose have a key limitation: while some adjust for time-varying confounding, these methods assume that the time series are recorded in discrete time. In other words, they are constrained to settings where measurements and treatments are conducted at fixed time steps, even though this is unrealistic in medical practice. In this work, we aim to predict potential outcomes in continuous time. The latter is of direct practical relevance because it allows for modeling patient trajectories where measurements and treatments take place at arbitrary, irregular timestamps. We thus propose a new method called stabilized continuous time inverse propensity network (SCIP-Net). For this, we further derive stabilized inverse propensity weights for robust prediction of the potential outcomes. To the best of our knowledge, our SCIP-Net is the first neural method that performs proper adjustments for time-varying confounding in continuous time.

Context. Understanding the dwarf galaxy population in low density environments is crucial for testing the LCDM cosmological model. The increase in diversity towards low mass galaxies is seen as an increase in the scatter of scaling relations such as the stellar mass-size and the baryonic Tully-Fisher relation (BTFR), and is also demonstrated by recent in-depth studies of an extreme subclass of dwarf galaxies of low surface brightness, but large physical sizes, called ultra-diffuse galaxies (UDGs). Aims. We select galaxies from the Apertif HI survey, and apply a constraint on their i-band absolute magnitude to exclude high mass systems. The sample consists of 24 galaxies, and span HI mass ranges of 8.6 < log ($M_{HI}/M_{Sun}$) < 9.7 and stellar mass range of 8.0 < log ($M_*/M_{Sun}$) < 9.7 (with only three galaxies having log ($M_*/M_{Sun}$) > 9). Methods. We determine the geometrical parameters of the HI and stellar discs, build kinematic models from the HI data using 3DBarolo, and extract surface brightness profiles in g-, r- and i-band from the Pan-STARRS 1 photometric survey. Results. We find that, at fixed stellar mass, our HI selected dwarfs have larger optical effective radii than isolated, optically-selected dwarfs from the literature. We find misalignments between the optical and HI morphologies for some of our sample. For most of our galaxies, we use the HI morphology to determine their kinematics, and we stress that deep optical observations are needed to trace the underlying stellar discs. Standard dwarfs in our sample follow the same BTFR of high-mass galaxies, whereas UDGs are slightly offset towards lower rotational velocities, in qualitative agreement with results from previous studies. Finally, our sample features a fraction (25%) of dwarf galaxies in pairs that is significantly larger with respect to previous estimates based on optical spectroscopic data., Comment: 22 pages, 11 figures, 5 tables. Accepted for publication in A&A

Biomolecular condensates are complex droplets comprising many different types of molecules that interact using various mechanisms. Condensation is often driven by short-ranged attraction, but net charges can also mediate long-ranged repulsion. Using molecular dynamics simulations and an equilibrium field theory, we show that such opposing interactions can suppress coarsening so that many droplets of equal size coexist at equilibrium. This size control depends strongly on the charge asymmetry between constituents, while the strength of the short-ranged attractions has a weak influence. Our work reveals how electrostatic effects control droplet size, which is relevant for understanding biomolecular condensates and creating synthetic patterns in chemical engineering., Comment: 4 figures and Appendix

Probabilistic regression models trained with maximum likelihood estimation (MLE), can sometimes overestimate variance to an unacceptable degree. This is mostly problematic in the multivariate domain. While univariate models often optimize the popular Continuous Ranked Probability Score (CRPS), in the multivariate domain, no such alternative to MLE has yet been widely accepted. The Energy Score - the most investigated alternative - notoriously lacks closed-form expressions and sensitivity to the correlation between target variables. In this paper, we propose Conditional CRPS: a multivariate strictly proper scoring rule that extends CRPS. We show that closed-form expressions exist for popular distributions and illustrate their sensitivity to correlation. We then show in a variety of experiments on both synthetic and real data, that Conditional CRPS often outperforms MLE, and produces results comparable to state-of-the-art non-parametric models, such as Distributional Random Forest (DRF).

We investigated the electronic properties of the topological insulator Bi$_{2}$Te$_{3}$ by scanning tunneling microscopy and spectroscopy at low temperature. We obtained high-resolution quasiparticle interference data of the topological surface Dirac electrons at different energies. Spin-selective joint density of states calculations were performed for surface and bulk electronic states to interpret the observed quasiparticle interference data. The topological properties of our crystals are demonstrated by the absence of backscattering along with the linear energy dispersion of the dominant scattering vector. In addition, we detect non-dispersive scattering modes which we associate with bulk-surface scattering and, thus, allow an approximate identification of the bulk energy gap range based on our quasiparticle interference data. Measurements of differential conductance maps in magnetic fields up to 15 T have been carried out, but no strong modifications could be observed., Comment: 14 pages, 22 figures

The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a balloon-borne telescope designed to survey star formation over cosmological time scales using intensity mapping in the 420 - 540 GHz frequency range. EXCLAIM uses a fully cryogenic telescope coupled to six on-chip spectrometers featuring kinetic inductance detectors (KIDs) to achieve high sensitivity, allowing for fast integration in dark atmospheric windows. The telescope receiver is cooled to $\approx$ 1.7 K by immersion in a superfluid helium bath and enclosed in a superfluid-tight shell with a meta-material anti-reflection coated silicon window. In addition to the optics and the spectrometer package, the receiver contains the magnetic shielding, the cryogenic segment of the spectrometer readout, and the sub-Kelvin cooling system. A three-stage continuous adiabatic demagnetization refrigerator (CADR) keeps the detectors at 100 mK while a $^4$He sorption cooler provides a 900 mK thermal intercept for mechanical suspensions and coaxial cables. We present the design of the EXCLAIM receiver and report on the flight-like testing of major receiver components, including the superfluid-tight receiver window and the sub-Kelvin coolers., Comment: 13 pages, 7 figures, 1 table

Despite the crucial role early educators play in young children's development, the field has always struggled with poor compensation and inadequate support (McLean et al., 2021). The persistent undervaluation of the ECE sector and the labor provided by the nearly all-female workforce can be traced back to its racist roots, when enslaved Black women were forced to care for White children (Center for the Study of Child Care Employment, 2022a; Lloyd et al., 2021). This report shows how racism continues to affect the ECE workforce. Racial disparities take many forms, from inequities in racial and ethnic representation across provider types and job roles to disparities in compensation. Black and Latina educators, for example, routinely experience lower wages than their peers. Asian and Black educators tend to hold higher levels of educational degrees compared to other groups, but their credentials do not necessarily lead to job advancement or higher pay. Systemic racism is most often understood as a form of racism that is "pervasively and deeply embedded in systems, laws, written or unwritten policies, and entrenched practices and beliefs that produce, condone, and perpetuate widespread unfair treatment and oppression of people of color" (Braveman et al., 2022, p. 171). Yet, systemic racism also fosters an absence of systems or processes that provide recourse for discrimination or protection from inequities. A transformed system could begin to repair the current injustices. (1) For example, if early care and education were treated as a public good, programs would be funded to reflect the true cost of care; (2) A salary scale driven by a combination of years of experience and education would reduce the vast pay gaps, especially between Black educators and their peers of other races and ethnicities with similar education and experience; (3) Professional pathway programs and mentorship initiatives could be designed specifically to expand access to leadership roles for underrepresented communities. Efforts like apprenticeships, degree-completion programs, and fellowships have proven incredibly successful (Copeman Petig et al., 2019; Kipnis et al., 2012; Smith et al., 2023). However, such opportunities are not built into ECE systems, but are often pilots, demonstration projects, or very limited in reach (Malone et al., 2021); and (3) Data to pinpoint disparities could track how or if they are reduced over time. North Carolina, for example, routinely funds robust data collection, but it is unclear if and how identified disparities are acted on (Child Care Services Association, 2020). [Additional funding for this report was provided by the Blue Shield of California Foundation.]

MICADO is a first light instrument for the Extremely Large Telescope (ELT), set to start operating later this decade. It will provide diffraction limited imaging, astrometry, high contrast imaging, and long slit spectroscopy at near-infrared wavelengths. During the initial phase operations, adaptive optics (AO) correction will be provided by its own natural guide star wavefront sensor. In its final configuration, that AO system will be retained and complemented by the laser guide star multi-conjugate adaptive optics module MORFEO (formerly known as MAORY). Among many other things, MICADO will study exoplanets, distant galaxies and stars, and investigate black holes, such as Sagittarius A* at the centre of the Milky Way. After their final design phase, most components of MICADO have moved on to the manufacturing and assembly phase. Here we summarize the final design of the instrument and provide an overview about its current manufacturing status and the timeline. Some lessons learned from the final design review process will be presented in order to help future instrumentation projects to cope with the challenges arising from the substantial differences between projects for 8-10m class telescopes (e.g. ESO-VLT) and the next generation Extremely Large Telescopes (e.g. ESO-ELT). Finally, the expected performance will be discussed in the context of the current landscape of astronomical observatories and instruments. For instance, MICADO will have similar sensitivity as the James Webb Space Telescope (JWST), but with six times the spatial resolution., Comment: Proceedings of the SPIE, Volume 13096, id. 1309611 11 pp. (2024)

Plasmonic near-field transducers (NFTs) play a key role in administering nanoscale heating for a number of applications ranging from medical devices to next generation data processing technology. We present a novel multi-scale approach, combining quantum many-body perturbation theory with finite-element modelling, to predict the electric and thermal material parameters of various Au-based, noble metal (M) alloys. Specifically, we focus on modelling their performance within an NFT designed to focus high-intensity, sub-diffracted light for technologies such as nanoscale etching, manipulation, sensing, and heat-assisted magnetic recording (HAMR). Elemental Au is the long-standing general-purpose NFT medium due its excellent plasmonic performance at relevant wavelengths. However, elemental Au is a soft, ductile material that tends to extrude and deform in response to extreme temperature gradients. Therefore, alloying Au with other noble metals such as Ag, Cu, Pd or Pt, has attracted considerable interest for improved mechanical and thermal robustness while reaching threshold plasmonic generation at standard optoelectronics operating wavelengths (e.g., $\approx 830$~nm) and approximate high-power NFT temperatures ($\approx 400$~K). We predict that certain Au-Ag alloys may offer improved thermal stability as whole-NFT media compared to elemental Au, alongside plasmonic figures of merit comparable to that of Au. Simulations of certain solid solution Au-Pd/Pt alloys enable us to predict significantly enhanced thermal conductivity. We predict that alloying with Pd at low concentrations $\sim 10\%$ may preserve the NFT performance of Au, while offering the benefits of improved thermal and mechanical stability., Comment: 8 pages, 4 figures, 1 table. Includes Supplemental Material with 10 tables

We present a recently established setup for laser spectroscopy at CRYRING@ESR at the GSI Helmholtz Centre for Heavy Ion Research. Here, laser spectroscopy can be performed on stored and cooled ion bunches and coasting beams. First spectra of $^{24,25}$Mg$^+$ ions are presented that were recorded by classical Doppler-limited fluorescence spectroscopy as well as $\Lambda$-spectroscopy using counter- and copropagating laser beams that are Doppler-shifted by several nm., Comment: 10 pages, 8 figures, submitted to conference proceedings of "1st Workshop on Polarized Beam and Target-Physics and Applications"

We identify the physics responsible for the critical reduction of current losses in magnetically insulated transmission lines (MITLs) before magnetic (self-) insulation has been established. Focusing on time-dependent physics, a drive current with a prototypical sine squared temporal profile introduces sufficiently strong time dependence that steady state results alone become insufficient for the complete understanding of current losses. We find that the effects of time dependence are most pronounced in long MITLs, i.e., MITLs in which an electromagnetic wave traverses its length in time comparable to the current pulse length. The time-dependent physics can be described as non-local, diamagnetic electromagnetic response of space charge limited currents. As the length of the MITL is reduced (in the above sense), current losses converge to those based on the well known Child-Langmuir law in static external fields. We present a simple one-dimensional (1D) model that encapsulates the essence of this physics. We find excellent agreement with 2D particle-in-cell (PIC) simulations for two MITL geometries, Cartesian parallel plate and azimuthally symmetric straight coaxial. Based on the 1D model, we explore various scaling dependencies of current losses with relevant parameters, e.g., peak current, peak pulse time, geometrical dimensions, etc. We also propose an improved physics model of magnetic insulation, which could help improve predictions of current losses by common circuit element codes, such as BERTHA. Lastly, we describe how to calculate temperature rise due to electron impact, as a diagnostic within the 1D model. Keywords: magnetically insulated transmission line; MITL; parallel plate; coaxial; magnetic insulation; non-local; diamagnetic; particle-in-cell; PIC; kinetic; space charge limited; SCL; electron emission; BERTHA; circuit element model; Z machine.

The growing privacy concerns surrounding face image data demand new techniques that can guarantee user privacy. One such face recognition technique that claims to achieve better user privacy is Federated Face Recognition (FRR), a subfield of Federated Learning (FL). However, FFR faces challenges due to the heterogeneity of the data, given the large number of classes that need to be handled. To overcome this problem, solutions are sought in the field of personalized FL. This work introduces three new data partitions based on the CelebA dataset, each with a different form of data heterogeneity. It also proposes Hessian-Free Model Agnostic Meta-Learning (HF-MAML) in an FFR setting. We show that HF-MAML scores higher in verification tests than current FFR models on three different CelebA data partitions. In particular, the verification scores improve the most in heterogeneous data partitions. To balance personalization with the development of an effective global model, an embedding regularization term is introduced for the loss function. This term can be combined with HF-MAML and is shown to increase global model verification performance. Lastly, this work performs a fairness analysis, showing that HF-MAML and its embedding regularization extension can improve fairness by reducing the standard deviation over the client evaluation scores., Comment: To appear in the IEEE FLTA 2024 proceedings

Reconstruction of 3D scenes from 2D images is a technical challenge that impacts domains from Earth and planetary sciences and space exploration to augmented and virtual reality. Typically, reconstruction algorithms first identify common features across images and then minimize reconstruction errors after estimating the shape of the terrain. This bundle adjustment (BA) step optimizes around a single, simplifying scalar value that obfuscates many possible causes of reconstruction errors (e.g., initial estimate of the position and orientation of the camera, lighting conditions, ease of feature detection in the terrain). Reconstruction errors can lead to inaccurate scientific inferences or endanger a spacecraft exploring a remote environment. To address this challenge, we present VECTOR, a visual analysis tool that improves error inspection for stereo reconstruction BA. VECTOR provides analysts with previously unavailable visibility into feature locations, camera pose, and computed 3D points. VECTOR was developed in partnership with the Perseverance Mars Rover and Ingenuity Mars Helicopter terrain reconstruction team at the NASA Jet Propulsion Laboratory. We report on how this tool was used to debug and improve terrain reconstruction for the Mars 2020 mission.

The Tarantula Nebula in the Large Magellanic Cloud is known for its high star formation activity. At its center lies the young massive star cluster R136, providing a significant amount of the energy that makes the nebula shine so brightly at many wavelengths. Recently, young massive star clusters have been suggested to also efficiently produce high-energy cosmic rays, potentially beyond PeV energies. Here, we report the detection of very-high-energy $\gamma$-ray emission from the direction of R136 with the High Energy Stereoscopic System, achieved through a multicomponent, likelihood-based modeling of the data. This supports the hypothesis that R136 is indeed a very powerful cosmic-ray accelerator. Moreover, from the same analysis, we provide an updated measurement of the $\gamma$-ray emission from 30 Dor C, the only superbubble detected at TeV energies presently. The $\gamma$-ray luminosity above $0.5\,\mathrm{TeV}$ of both sources is $(2-3)\times 10^{35}\,\mathrm{erg}\,\mathrm{s}^{-1}$. This exceeds by more than a factor of 2 the luminosity of HESS J1646$-$458, which is associated with the most massive young star cluster in the Milky Way, Westerlund 1. Furthermore, the $\gamma$-ray emission from each source is extended with a significance of $>3\sigma$ and a Gaussian width of about $30\,\mathrm{pc}$. For 30 Dor C, a connection between the $\gamma$-ray emission and the nonthermal X-ray emission appears likely. Different interpretations of the $\gamma$-ray signal from R136 are discussed., Comment: 10+11 pages, 4+6 figures. Corresponding authors: L. Mohrmann, N. Komin

Materials combining topologically non-trivial behavior and superconductivity offer a potential route for quantum computation. However, the set of available materials intrinsically realizing these properties are scarce. Recently, surface superconductivity has been reported in PtBi$_2$ in its trigonal phase and an inherent Weyl semimetal phase has been predicted. Here, based on scanning tunneling microscopy experiments, we reveal the signature of topological Fermi arcs in the normal state patterns of the quasiparticle interference. We show that the scattering between Fermi arcs dominates the interference spectra, providing conclusive evidence for the relevance of Weyl fermiology for the surface electronic properties of trigonal PtBi$_2$.

The probabilities for gamma-ray and particle emission as a function of the excitation energy of a decaying nucleus are valuable observables for constraining the ingredients of the models that describe the de-excitation of nuclei near the particle emission threshold. These models are essential in nuclear astrophysics and applications. In this work, we have for the first time simultaneously measured the gamma-ray and neutron emission probabilities of 208Pb. The measurement was performed in inverse kinematics at the Experimental Storage Ring (ESR) of the GSI/FAIR facility, where a 208Pb beam interacted through the 208Pb(p,p') reaction with a hydrogen gas jet target. Instead of detecting the gamma-rays and neutrons emitted by 208Pb, we detected the heavy beam-like residues produced after gamma and neutron emission. These heavy residues were fully separated by a dipole magnet of the ESR and were detected with outstanding efficiencies. The comparison of the measured probabilities with model calculations has allowed us to test and select different descriptions of the gamma-ray strength function and the nuclear level density available in the literature., Comment: 23 pages, 14 figures

Estimating heterogeneous treatment effects (HTEs) over time is crucial in many disciplines such as personalized medicine. For example, electronic health records are commonly collected over several time periods and then used to personalize treatment decisions. Existing works for this task have mostly focused on model-based learners (i.e., learners that adapt specific machine-learning models). In contrast, model-agnostic learners -- so-called meta-learners -- are largely unexplored. In our paper, we propose several meta-learners that are model-agnostic and thus can be used in combination with arbitrary machine learning models (e.g., transformers) to estimate HTEs over time. Here, our focus is on learners that can be obtained via weighted pseudo-outcome regressions, which allows for efficient estimation by targeting the treatment effect directly. We then provide a comprehensive theoretical analysis that characterizes the different learners and that allows us to offer insights into when specific learners are preferable. Finally, we confirm our theoretical insights through numerical experiments. In sum, while meta-learners are already state-of-the-art for the static setting, we are the first to propose a comprehensive set of meta-learners for estimating HTEs in the time-varying setting.

Near-field coupling between nanolasers enables collective high-power lasing but leads to complex spectral reshaping and multimode operation, limiting the emission brightness, spatial coherence and temporal stability. Many lasing architectures have been proposed to circumvent this limitation, based on symmetries, topology, or interference. We show that a much simpler and robust method exploiting phase-delayed coupling, where light exchanged by the lasers carries a phase, can enable stable single-mode operation. Phase-delayed coupling changes the modal amplification: for pump powers close to the anyonic parity-time (PT) symmetric exceptional point, a high phase delay completely separates the mode thresholds, leading to single mode operation. This is shown by stability analysis with nonlinear coupled mode theory and stochastic differential equations for two coupled nanolasers and confirmed by realistic semi-analytical treatment of a dimer of lasing nanospheres. Finally, we extend the mode control to large arrays of nanolasers, featuring lowered thresholds and higher power. Our work promises a novel solution to engineer bright and stable single-mode lasing from nanolaser arrays with important applications in photonic chips for communication and lidars.

Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effects are limited to binary/discrete treatments and make highly restrictive assumptions such as known propensity scores. In this work, we provide a novel conformal prediction method for potential outcomes of continuous treatments. We account for the additional uncertainty introduced through propensity estimation so that our conformal prediction intervals are valid even if the propensity score is unknown. Our contributions are three-fold: (1) We derive finite-sample prediction intervals for potential outcomes of continuous treatments. (2) We provide an algorithm for calculating the derived intervals. (3) We demonstrate the effectiveness of the conformal prediction intervals in experiments on synthetic and real-world datasets. To the best of our knowledge, we are the first to propose conformal prediction for continuous treatments when the propensity score is unknown and must be estimated from data.

Linearizability is a well-known correctness property for concurrent and distributed systems. In the past, it was also used to prove the design and implementation of replicated state-machines correct. State-machine replication (SMR) is a concept to achieve fault-tolerant services by replicating the application code and maintaining its deterministic execution in all correct replicas. Correctness of SMR needs to address both, the execution of the application code -- the state machine -- and the replication framework that takes care of communication, checkpointing and recovery. We show that linearizability is overly restrictive for SMR as it excludes many applications from being replicated. It cannot deal with conditional waits and bidirectional data flows between concurrent executions. Further, linearizability does not allow for vertical composition, e.g., nested invocations. In this work we argue that interval linearizability, a recently defined relaxation of linearizability, is the appropriate correctness criterion for SMR systems. This no longer puts any constraints on the application code. Instead the focus for correctness proofs of an SMR system is on the deterministic execution of the application within correct replicas under the assumptions of the chosen failure model. Thus, this work not only clears some myths about linearizable SMR but also relieves correctness proofs from linearizability proofs.

We present a study of a general spin Hamiltonian with terms up to fourth order. With the coherent states the semiclassical potential is obtained and with catastrophe theory its parameter space is constructed. When the fourth order parameters are large enough the parameter space has regions where the semiclassical potential has three wells. By applying an oscillating magnetic field a trajectory in parameter space crosses the Maxwell set multiple times resulting in many ground state quantum phase transitions. Using the coherent states we are able to visualize the localization of the ground state on the Bloch sphere as the magnetic field is varied., Comment: 18 pages, 6 figures

PSR B1259-63 is a gamma-ray binary system that hosts a pulsar in an eccentric orbit, with a 3.4 year period, around an O9.5Ve star. At orbital phases close to periastron passages, the system radiates bright and variable non-thermal emission. We report on an extensive VHE observation campaign conducted with the High Energy Stereoscopic System, comprised of ~100 hours of data taken from $t_p-24$ days to $t_p+127$ days around the system's 2021 periastron passage. We also present the timing and spectral analyses of the source. The VHE light curve in 2021 is consistent with the stacked light curve of all previous observations. Within the light curve, we report a VHE maximum at times coincident with the third X-ray peak first detected in the 2021 X-ray light curve. In the light curve -- although sparsely sampled in this time period -- we see no VHE enhancement during the second disc crossing. In addition, we see no correspondence to the 2021 GeV flare in the VHE light curve. The VHE spectrum obtained from the analysis of the 2021 dataset is best described by a power law of spectral index $\Gamma = 2.65 \pm 0.04_{\text{stat}}$ $\pm 0.04_{\text{sys}}$, a value consistent with the previous H.E.S.S. observations of the source. We report spectral variability with a difference of $\Delta \Gamma = 0.56 ~\pm~ 0.18_{\text{stat}}$ $~\pm~0.10_{\text{sys}}$ at 95% c.l., between sub-periods of the 2021 dataset. We also find a linear correlation between contemporaneous flux values of X-ray and TeV datasets, detected mainly after $t_p+25$ days, suggesting a change in the available energy for non-thermal radiation processes. We detect no significant correlation between GeV and TeV flux points, within the uncertainties of the measurements, from $\sim t_p-23$ days to $\sim t_p+126$ days. This suggests that the GeV and TeV emission originate from different electron populations., Comment: accepted to A&A

In recent years, quantum nanophotonics has forged a rich nexus of nanotechnology with photonic quantum information processing, offering remarkable prospects for advancing quantum technologies beyond their current technical limits in terms of physical compactness, energy efficiency, operation speed, temperature robustness and scalability. In this perspective, we highlight a number of recent studies that reveal the especially compelling potential of nanoplasmonic cavity quantum electrodynamics for driving quantum technologies down to nanoscale spatial and ultrafast temporal regimes, whilst elevating them to ambient temperatures. Our perspective encompasses innovative proposals for quantum plasmonic biosensing, driving ultrafast single-photon emission and achieving near-field multipartite entanglement in the strong coupling regime, with a notable emphasis on the use of industry-grade devices. We conclude with an outlook emphasizing how the bespoke characteristics and functionalities of plasmonic devices are shaping contemporary research directives in ultrafast and room-temperature quantum nanotechnologies., Comment: 9 pages, 2 figures

Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce errors and increase the resolution of ESMs. However, existing methods, such as quantile mapping, cannot effectively improve spatial patterns or temporal dynamics. We address this problem with a purely generative machine learning approach, combining unpaired domain translation with a super-resolution foundation model. Our results show realistic spatial patterns and temporal dynamics as well as reduced distributional biases in the processed ESM simulation.

The phenomenology of hadronic states at high energy is well described in the framework of Quantum Chromodynamics. The theory, well established by now, cannot be applied to the description of quark-antiquark states at low energy unless their degrees of freedom are treated as effective ones, a condition resulting from confinement. Own to the similarities with other quantum many-body systems we have adopted a non-perturbative scheme where quarks are treated as quasiparticles, which further interact between them, allowing meson-like states to be described as collective superposition of quasiparticle-pairs. The results of the calculations, performed in the context of the random phase approximation method, show that this scheme is a suitable one to describe meson states up to energies of the order of couple of GeV.

Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, are not suitable. With further development, SMFCs show great promise for use in robust and affordable outdoor sensor networks, particularly for farmers. One of the greatest challenges in the development of this technology is understanding and predicting the fluctuations of SMFC energy generation, as the electro-generative process is not yet fully understood. Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 minutes to 1 hour, with results ranging from 2.33% to 5.71% MAPE for median voltage prediction. For each timescale, we use quantile regression to obtain point estimates and to establish bounds on the uncertainty of these estimates. When comparing the median predicted vs. actual values for the total energy generated during the testing period, the magnitude of prediction errors ranged from 2.29% to 16.05%. To demonstrate the real-world utility of this research, we also simulate how the models could be used in an automated environment where SMFC-powered devices shut down and activate intermittently to preserve charge, with promising initial results. Our deep learning-based prediction and simulation framework would allow a fully automated SMFC-powered device to achieve a median 100+% increase in successful operations, compared to a naive model that schedules operations based on the average voltage generated in the past.

The steel industry is a major contributor to CO2 emissions, accounting for 7% of global emissions. The European steel industry is seeking to reduce its emissions by increasing the use of electric arc furnaces (EAFs), which can produce steel from scrap, marking a major shift towards a circular steel economy. Here, we show by combining trade with business intelligence data that this shift requires a deep restructuring of the global and European scrap trade, as well as a substantial scaling of the underlying business ecosystem. We find that the scrap imports of European countries with major EAF installations have steadily decreased since 2007 while globally scrap trade started to increase recently. Our statistical modelling shows that every 1,000 tonnes of EAF capacity installed is associated with an increase in annual imports of 550 tonnes and a decrease in annual exports of 1,000 tonnes of scrap, suggesting increased competition for scrap metal as countries ramp up their EAF capacity. Furthermore, each scrap company enables an increase of around 79,000 tonnes of EAF-based steel production per year in the EU. Taking these relations as causal and extrapolating to the currently planned EAF capacity, we find that an additional 730 (SD 140) companies might be required, employing about 35,000 people (IQR 29,000-50,000) and generating an additional estimated turnover of USD 35 billion (IQR 27-48). Our results thus suggest that scrap metal is likely to become a strategic resource. They highlight the need for a massive restructuring of the industry's supply networks and identify the resulting growth opportunities for companies.

In dynamical systems reconstruction (DSR) we seek to infer from time series measurements a generative model of the underlying dynamical process. This is a prime objective in any scientific discipline, where we are particularly interested in parsimonious models with a low parameter load. A common strategy here is parameter pruning, removing all parameters with small weights. However, here we find this strategy does not work for DSR, where even low magnitude parameters can contribute considerably to the system dynamics. On the other hand, it is well known that many natural systems which generate complex dynamics, like the brain or ecological networks, have a sparse topology with comparatively few links. Inspired by this, we show that geometric pruning, where in contrast to magnitude-based pruning weights with a low contribution to an attractor's geometrical structure are removed, indeed manages to reduce parameter load substantially without significantly hampering DSR quality. We further find that the networks resulting from geometric pruning have a specific type of topology, and that this topology, and not the magnitude of weights, is what is most crucial to performance. We provide an algorithm that automatically generates such topologies which can be used as priors for generative modeling of dynamical systems by RNNs, and compare it to other well studied topologies like small-world or scale-free networks.

Understanding the origin of fast radio bursts (FRBs) has become the main science driver of recent dedicated FRB surveys. Between July 2019 and February 2022, we carried out ALERT, an FRB survey at 1370 MHz using the Apertif instrument installed at the Westerbork Synthesis Radio Telescope (WSRT). Here we report the detection of 18 new FRBs, and we study the properties of the entire 24 burst sample detected during the survey. For five bursts, we identify host galaxy candidates with >50% probability association. We observe an average linear polarisation fraction of $\sim$43% and an average circular polarisation fraction consistent with 0%. A third of the FRBs display multiple components. The sample next reveals a population of highly scattered bursts, which is most likely to have been produced in the immediate circumburst environment. Furthermore, two FRBs show evidence for high rotation measures, reaching |RM|>$10^3$ rad m$^{-2}$ in the source reference frames. Together, the scattering and rotation measures ALERT finds prove that a large fraction of FRBs are embedded in complex media such as star forming regions or supernova remnants. Through the discovery of the third most dispersed FRB so far, we show that one-off FRBs can emit at frequencies in excess of 6 GHz. Finally, we determine an FRB all-sky rate of $459^{+208}_{-155}$ sky$^{-1}$ day$^{-1}$ above a fluence limit of 4.1 Jy ms, and a fluence cumulative distribution with a power law index $\gamma=-1.23\pm0.06\pm0.2$, which is roughly consistent with the Euclidean Universe predictions. Through the high resolution in time, frequency, polarisation and localisation that ALERT featured, we were able to determine the morphological complexity, polarisation, local scattering and magnetic environment, and high-frequency luminosity of FRBs. We find all these strongly resemble those seen in young, energetic, highly magnetised neutron stars., Comment: 40 pages (including 11 of appendix), 37 figures, 5 tables. Accepted for publication in A&A

Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. Yet, existing neural methods for this task either (1) do not perform proper adjustments for time-varying confounders, or (2) suffer from large estimation variance. In order to address both limitations, we introduce the G-transformer (GT). Our GT is a novel, neural end-to-end model which adjusts for time-varying confounders, and provides low-variance estimation of conditional average potential outcomes (CAPOs) over time. Specifically, our GT is the first neural model to perform regression-based iterative G-computation for CAPOs in the time-varying setting. We evaluate the effectiveness of our GT across various experiments. In sum, this work represents a significant step towards personalized decision-making from electronic health records.

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care., Comment: 10 pages, 4 figures, 1 table

The excited states of unstable $^{20}$O were investigated via $\gamma$-ray spectroscopy following the $^{19}$O$(d,p)^{20}$O reaction at 8 $A$MeV. By exploiting the Doppler Shift Attenuation Method, the lifetime of the 2$^+_2$ and 3$^+_1$ states were firmly established. From the $\gamma$-ray branching and E2/M1 mixing ratios for transitions deexciting the 2$^+_2$ and 3$^+_1$ states, the B(E2) and B(M1) were determined. Various chiral effective field theory Hamiltonians, describing the nuclear properties beyond ground states, along with a standard USDB interaction, were compared with the experimentally obtained data. Such a comparison for a large set of $\gamma$-ray transition probabilities with the valence space in medium similarity renormalization group ab-initio calculations was performed for the first time in a nucleus far from stability. It was shown that the ab-initio approaches using chiral EFT forces are challenged by detailed high-precision spectroscopic properties of nuclei. The reduced transition probabilities were found to be a very constraining test of the performance of the ab-initio models., Comment: Supplemental Material available

As both Parker Solar Probe (PSP) and Solar Orbiter (SolO) reach heliocentric distances closer to the Sun, they present an exciting opportunity to study the structure of CMEs in the inner heliosphere. We present an analysis of the global flux rope structure of the 2022 September 5 CME event that impacted PSP at a heliocentric distance of only 0.07 au and SolO at 0.69 au. We compare in situ measurements at PSP and SolO to determine global and local expansion measures, finding a good agreement between magnetic field relationships with heliocentric distance, but significant differences with respect to flux rope size. We use PSP/WISPR images as input to the ELEvoHI model, providing a direct link between remote and in situ observations; we find a large discrepancy between the resulting modeled arrival times, suggesting that the underlying model assumptions may not be suitable when using data obtained close to the Sun, where the drag regime is markedly different in comparison to larger heliocentric distances. Finally, we fit the SolO/MAG and PSP/FIELDS data independently with the 3DCORE model and find that many parameters are consistent between spacecraft, however, challenges are apparent when reconstructing a global 3D structure that aligns with arrival times at PSP and Solar Orbiter, likely due to the large radial and longitudinal separations between spacecraft. From our model results, it is clear the solar wind background speed and drag regime strongly affect the modeled expansion and propagation of CMEs and need to be taken into consideration.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps., Comment: 16 pages, 11 tables, 10 figures, MICCAI

A brief review of the pseudo complex General Relativity (pcGR) will be presented, with its consequences, as the accumulation of a dark energy around a mass and a generalized Machs principle. The main objective in this contribution is to determine the Hawking temperature and the Entropy for various limits: i) The pc-Schwarzschild case with no minimal length present, ii) the pc-Kerr metric without a minimal length and iii) the general case, the pc-Kerr metric with a minimal length present. The physical consequences of a minimal length will be discussed, a possible interpretation of a gravitational Schwinger effect and the appearance of negative temperature. For large masses a minimal length does not show any sensible effect, but only for very small masses, several orders of the Planck mass, where non-trivial effects emerge, important for the production of mini-black holes in the early universe. Our results are more general than being restricted to pcGR. Any other model which assumes a distribution of dark energy around a stellar body produces the same effects. In contrast to these models, pcGR demands the presence of a dark energy term., Comment: 21 pages, 11 figures

Deep neural networks (DNNs) frequently present behaviorally irregular patterns, significantly limiting their practical potentials and theoretical validity in travel behavior modeling. This study proposes strong and weak behavioral regularities as novel metrics to evaluate the monotonicity of individual demand functions (known as the "law of demand"), and further designs a constrained optimization framework with six gradient regularizers to enhance DNNs' behavioral regularity. The proposed framework is applied to travel survey data from Chicago and London to examine the trade-off between predictive power and behavioral regularity for large vs. small sample scenarios and in-domain vs. out-of-domain generalizations. The results demonstrate that, unlike models with strong behavioral foundations such as the multinomial logit, the benchmark DNNs cannot guarantee behavioral regularity. However, gradient regularization (GR) increases DNNs' behavioral regularity by around 6 percentage points (pp) while retaining their relatively high predictive power. In the small sample scenario, GR is more effective than in the large sample scenario, simultaneously improving behavioral regularity by about 20 pp and log-likelihood by around 1.7%. Comparing with the in-domain generalization of DNNs, GR works more effectively in out-of-domain generalization: it drastically improves the behavioral regularity of poorly performing benchmark DNNs by around 65 pp, indicating the criticality of behavioral regularization for enhancing model transferability and application in forecasting. Moreover, the proposed framework is applicable to other NN-based choice models such as TasteNets. Future studies could use behavioral regularity as a metric along with log-likelihood in evaluating travel demand models, and investigate other methods to further enhance behavioral regularity when adopting complex machine learning models.

Climate change exacerbates extreme weather events like heavy rainfall and flooding. As these events cause severe losses of property and lives, accurate high-resolution simulation of precipitation is imperative. However, existing Earth System Models (ESMs) struggle with resolving small-scale dynamics and suffer from biases, especially for extreme events. Traditional statistical bias correction and downscaling methods fall short in improving spatial structure, while recent deep learning methods lack controllability over the output and suffer from unstable training. Here, we propose a novel machine learning framework for simultaneous bias correction and downscaling. We train a generative diffusion model in a supervised way purely on observational data. We map observational and ESM data to a shared embedding space, where both are unbiased towards each other and train a conditional diffusion model to reverse the mapping. Our method can be used to correct any ESM field, as the training is independent of the ESM. Our approach ensures statistical fidelity, preserves large-scale spatial patterns and outperforms existing methods especially regarding extreme events and small-scale spatial features that are crucial for impact assessments.

This paper explores the connection between two recently identified phenomena in deep learning: plasticity loss and neural collapse. We analyze their correlation in different scenarios, revealing a significant association during the initial training phase on the first task. Additionally, we introduce a regularization approach to mitigate neural collapse, demonstrating its effectiveness in alleviating plasticity loss in this specific setting.

The groundbreaking advancements around generative AI have recently caused a wave of concern culminating in a row of lawsuits, including high-profile actions against Stability AI and OpenAI. This situation of legal uncertainty has sparked a broad discussion on the rights of content creators and publishers to protect their intellectual property on the web. European as well as US law already provides rough guidelines, setting a direction for technical solutions to regulate web data use. In this course, researchers and practitioners have worked on numerous web standards and opt-out formats that empower publishers to keep their data out of the development of generative AI models. The emerging AI/ML opt-out protocols are valuable in regards to data sovereignty, but again, it creates an adverse situation for a site owners who are overwhelmed by the multitude of recent ad hoc standards to consider. In our work, we want to survey the different proposals, ideas and initiatives, and provide a comprehensive legal and technical background in the context of the current discussion on web publishers control.