Search

Given conflicting probability estimates for a set of events, how can we quantify how much they conflict? How can we find a single probability distribution that best encapsulates the given estimates? One approach is to minimize a loss function such as binary KL-divergence that quantifies the dissimilarity between the given estimates and the candidate probability distribution. Given a set of events, we characterize the facets of the polytope of coherent probability estimates about those events. We explore two applications of these ideas: eliciting the beliefs of large language models, and merging expert forecasts into a single coherent forecast., Comment: 33 pages, 3 figures, and 4 tables

Layered van der Waals tellurides reveal topologically non-trivial properties that give rise to unconventional magneto-transport phenomena. Additionally, their semimetallic character with high mobility makes them promising candidates for large magneto-thermoelectric effects. Remarkable studies on the very large and unconventional Nernst effect in WTe$_2$ have been reported, raising questions about whether this property is shared across the entire family of van der Waals tellurides. In this study, systematic measurements of the Nernst effect in telluride van der Waals Weyl semimetals are presented. Large linear Nernst coefficients in WTe$_2$ and MoTe$_2$ are identified, and moderate Nernst coefficients with non-linear behavior in magnetic fields are observed in W$_{0.65}$Mo$_{0.35}$Te$_2$, TaIrTe$_4$, and TaRhTe$_4$. Within this sample set, a correlation between the dominant linear-in-magnetic-field component of the Nernst coefficient and mobility is established, aligning with the established Nernst scaling framework, though with a different scaling factor compared to existing literature. This enhancement might be caused by the shared favorable electronic band structure of this family of materials. Conversely, the non-linear component of the Nernst effect in a magnetic field could not be correlated with mobility. This non-linear term is almost absent in the binary compounds, suggesting a multiband origin and strong compensation between electron-like and hole-like carriers. This comprehensive study highlights the potential of van der Waals tellurides for thermoelectric conversion.

Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting such testing in a cost-effective and scalable way. Neural rendering methods have gained popularity, as they can build simulation environments from collected logs in a data-driven manner. However, existing neural radiance field (NeRF) methods for sensor-realistic rendering of camera and lidar data suffer from low rendering speeds, limiting their applicability for large-scale testing. While 3D Gaussian Splatting (3DGS) enables real-time rendering, current methods are limited to camera data and are unable to render lidar data essential for autonomous driving. To address these limitations, we propose SplatAD, the first 3DGS-based method for realistic, real-time rendering of dynamic scenes for both camera and lidar data. SplatAD accurately models key sensor-specific phenomena such as rolling shutter effects, lidar intensity, and lidar ray dropouts, using purpose-built algorithms to optimize rendering efficiency. Evaluation across three autonomous driving datasets demonstrates that SplatAD achieves state-of-the-art rendering quality with up to +2 PSNR for NVS and +3 PSNR for reconstruction while increasing rendering speed over NeRF-based methods by an order of magnitude. See https://research.zenseact.com/publications/splatad/ for our project page.

We present a detailed study of the large-scale shock front in Stephan's Quintet, a byproduct of past and ongoing interactions. Using integral-field spectroscopy from the new William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE), recent 144 MHz observations from the LOFAR Two-metre Sky Survey (LoTSS), and archival data from the Very Large Array and James Webb Space Telescope (JWST), we obtain new measurements of key shock properties and determine its impact on the system. Harnessing the WEAVE large integral field unit's (LIFU) field of view (90 $\times$ 78 arcsec$^{2}$), spectral resolution ($R\sim2500$) and continuous wavelength coverage across the optical band, we perform robust emission line modeling and dynamically locate the shock within the multi-phase intergalactic medium (IGM) with higher precision than previously possible. The shocking of the cold gas phase is hypersonic, and comparisons with shock models show that it can readily account for the observed emission line ratios. In contrast, we demonstrate that the shock is relatively weak in the hot plasma visible in X-rays (with Mach number of $\mathcal{M} \sim 2 - 4$), making it inefficient at producing the relativistic particles needed to explain the observed synchrotron emission. Instead, we propose that it has led to an adiabatic compression of the medium, which has increased the radio luminosity ten-fold. Comparison of the Balmer line-derived extinction map with the molecular gas and hot dust observed with JWST suggests that pre-existing dust may have survived the collision, allowing the condensation of H$_{2}$ - a key channel for dissipating the shock energy., Comment: 23 pages, 15 figures, accepted for publication in MNRAS

The Surface Photometry and Accurate Rotation Curves (SPARC) database has provided the community with mass models for 175 nearby galaxies, allowing different research teams to test different dark matter models, galaxy evolution models, and modified gravity theories. Extensive tests, however, are hampered by the somewhat heterogeneous nature of the HI rotation curves and the limited sample size of SPARC. To overcome these limitations, we are working on BIG-SPARC, a new database that consists of about 4000 galaxies with HI datacubes from public telescope archives (APERTIF, ASKAP, ATCA, GMRT, MeerKAT, VLA, and WSRT) and near infrared photometry from WISE. For these galaxies, we will provide homogeneously derived HI rotation curves, surface brightness profiles, and mass models. BIG-SPARC is expected to increase the size of its predecessor by a factor of more than 20. This is a necessary step to prepare for the additional order of magnitude increase in sample size expected from ongoing and future HI surveys with the Square Kilometre Array (SKA) and its pathfinders, Comment: To be published in the proceedings of IAU Symposium 392 "Neutral Hydrogen in and around Galaxies in the SKA Era", 6 pages, 3 figures, 1 table

We propose a novel extension to the recently developed non-commutative Riemannian foliated branch-cut quantum gravity (BCQG). Based on an extended Faddeev-Jackiw symplectic deformation of the conventional Poisson algebra, we investigate non-commutativity effects on a symplectic topological manifold that provides a natural isomorphic setting composed by a triad of canonically conjugate scalar complex fields which comprise quantum complementary dualities. Based on a complementary analytically continued Friedmann-type equation, combined with a quantum approach based on the Ho\v{r}awa-Lifshitz quantum gravity, we describe the dynamic evolution of the universe's wave function, unfolding unprecedented predictions for the cosmic evolution and inflation. The non-commutative foliated quantum gravity approach offers a new perspective on explaining the accelerated cosmic expansion of the universe, strongly suggesting that non-commutative algebra induces the late accelerated growth of both the universe's wave function and the corresponding scale factor, along with their quantum counterparts. In contrast to the conventional inflationary model, where inflation requires a remarkably fine-tuned set of initial conditions in a patch of the universe, non-commutative foliated quantum gravity, analytically continued to the complex plane, captures short and long scales of spacetime, leading to an evolutionary cosmic dynamic through a topological reconfiguration of the primordial cosmic matter and energy content. This result introduces new speculative framework elements regarding the reconfiguration of matter and energy due to an underlying non-commutative spatio-temporal structure as a driver of spacetime cosmic acceleration.

Owing to their rapid cooling rate and hence loss-limited propagation distance, cosmic-ray electrons and positrons (CRe) at very high energies probe local cosmic-ray accelerators and provide constraints on exotic production mechanisms such as annihilation of dark matter particles. We present a high-statistics measurement of the spectrum of CRe candidate events from 0.3 to 40 TeV with the High Energy Stereoscopic System (H.E.S.S.), covering two orders of magnitude in energy and reaching a proton rejection power of better than $10^{4}$. The measured spectrum is well described by a broken power law, with a break around 1 TeV, where the spectral index increases from $\Gamma_1 = 3.25$ $\pm$ 0.02 (stat) $\pm$ 0.2 (sys) to $\Gamma_2 = 4.49$ $\pm$ 0.04 (stat) $\pm$ 0.2 (sys). Apart from the break, the spectrum is featureless. The absence of distinct signatures at multi-TeV energies imposes constraints on the presence of nearby CRe accelerators and the local CRe propagation mechanisms., Comment: main paper: 8 pages, 4 figures, supplemental material: 12 pages, 14 figures, accepted for publication in Physical Review Letters https://journals.aps.org/prl/

In 1888, Hilbert proved that the cone $\mathcal{P}_{n+1,2d}$ of positive semidefinite forms in $n+1$ variables of degree $2d$ coincides with its subcone $\Sigma_{n+1,2d}$ of those forms that are representable as finite sums of squares if and only if $(n+1,2d) = (2,2d)_{d\geq1}$ or $(n+1,2)_{n\geq1}$ or $(3,4)$. These are the Hilbert cases. In [GHK23, GHK24], we applied the Gram matrix method to construct cones between $\Sigma_{n+1,2d}$ and $\mathcal{P}_{n+1,2d}$, defined by projective varieties containing the Veronese variety. In particular, we introduced and examined a specific cone filtration $$\Sigma_{n+1,2d} = C_0 \subseteq \ldots \subseteq C_n \subseteq C_{n+1} \subseteq \ldots \subseteq C_{k(n,d)-n} = \mathcal{P}_{n+1,2d}$$ and determined each strict inclusion in non-Hilbert cases. This gave us a refinement of Hilbert's 1888 theorem. Here, $k(n,d)+1$ is the dimension of the vector space of forms in $n+1$ variables of degree $d$. In this paper, we show that the intermediate cones $C_i$'s are closed and describe their interiors and boundaries. We discuss the membership problem for the $C_i$'s, present open problems concerning their dual cones and generalizations to cones defined by toric varieties., Comment: [GHK23]: arXiv:2303.13178 [GHK24]: arXiv:2401.03813

Biomimetic and compliant robotic hands offer the potential for human-like dexterity, but controlling them is challenging due to high dimensionality, complex contact interactions, and uncertainties in state estimation. Sampling-based model predictive control (MPC), using a physics simulator as the dynamics model, is a promising approach for generating contact-rich behavior. However, sampling-based MPC has yet to be evaluated on physical (non-simulated) robotic hands, particularly on compliant hands with state uncertainties. We present the first successful demonstration of in-hand manipulation on a physical biomimetic tendon-driven robot hand using sampling-based MPC. While sampling-based MPC does not require lengthy training cycles like reinforcement learning approaches, it still necessitates adapting the task-specific objective function to ensure robust behavior execution on physical hardware. To adapt the objective function, we integrate a visual language model (VLM) with a real-time optimizer (MuJoCo MPC). We provide the VLM with a high-level human language description of the task, and a video of the hand's current behavior. The VLM iteratively adapts the objective function, enabling effective behavior generation. In our experiments, the hand achieves an average ball rolling speed of 0.35 rad/s, successful ball flips, and catching with a 67\% success rate. Our results demonstrate that sampling-based MPC is a promising approach for generating dexterous manipulation skills on biomimetic hands without extensive training cycles., Comment: For a video, see https://youtu.be/6ivbd_jijHA

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.