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Spin-polarized antiferromagnets have recently gained significant interest because they combine the advantages of both ferromagnets (spin polarization) and antiferromagnets (absence of net magnetization) for spintronics applications. In particular, spin-polarized antiferromagnetic metals can be useful as active spintronics materials because of their high electrical and thermal conductivities and their ability to host strong interactions between charge transport and magnetic spin textures. We review spin and charge transport phenomena in spin-polarized antiferromagnetic metals, in which the interplay of metallic conductivity and spin-split bands offers novel practical applications and new fundamental insights into antiferromagnetism. We focus on three types of antiferromagnets: canted antiferromagnets, noncollinear antiferromagnets, and collinear altermagnets. We also discuss how the investigation of spin-polarized antiferromagnetic metals can open doors to future research directions., Comment: 15 pages, 4 figures, to be published in Annual Review of Condensed Matter Physics, Vol.16

We study the impact of forbidding short cycles to the edge density of $k$-planar graphs; a $k$-planar graph is one that can be drawn in the plane with at most $k$ crossings per edge. Specifically, we consider three settings, according to which the forbidden substructures are $3$-cycles, $4$-cycles or both of them (i.e., girth $\ge 5$). For all three settings and all $k\in\{1,2,3\}$, we present lower and upper bounds on the maximum number of edges in any $k$-planar graph on $n$ vertices. Our bounds are of the form $c\,n$, for some explicit constant $c$ that depends on $k$ and on the setting. For general $k \geq 4$ our bounds are of the form $c\sqrt{k}n$, for some explicit constant $c$. These results are obtained by leveraging different techniques, such as the discharging method, the recently introduced density formula for non-planar graphs, and new upper bounds for the crossing number of $2$-- and $3$-planar graphs in combination with corresponding lower bounds based on the Crossing Lemma., Comment: Appears in the Proceedings of the 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)

We show that every planar graph has a monotone topological 2-page book embedding where at most (4n-10)/5 (of potentially 3n-6) edges cross the spine, and every edge crosses the spine at most once; such an edge is called a biarc. We can also guarantee that all edges that cross the spine cross it in the same direction (e.g., from bottom to top). For planar 3-trees we can further improve the bound to (3n-9)/4, and for so-called Kleetopes we obtain a bound of at most (n-8)/3 edges that cross the spine. The bound for Kleetopes is tight, even if the drawing is not required to be monotone. A Kleetope is a plane triangulation that is derived from another plane triangulation T by inserting a new vertex v_f into each face f of T and then connecting v_f to the three vertices of f., Comment: Appears in the Proceedings of the 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)

Entropic Optimal Transport (EOT), also referred to as the Schr\"odinger problem, seeks to find a random processes with prescribed initial/final marginals and with minimal relative entropy with respect to a reference measure. The relative entropy forces the two measures to share the same support and only the drift of the controlled process can be adjusted, the diffusion being imposed by the reference measure. Therefore, at first sight, Semi-Martingale Optimal Transport (SMOT) problems (see [1]) seem out of the scope of applications of Entropic regularization techniques, which are otherwise very attractive from a computational point of view. However, when the process is observed only at discrete times, and become therefore a Markov chain, its relative entropy can remain finite even with variable diffusion coefficients, and discrete semi-martingales can be obtained as solutions of (multi-marginal) EOT problems.Given a (smooth) semi-martingale, the limit of the relative entropy of its time discretizations, scaled by the time step converges to the so-called ``specific relative entropy'', a convex functional of its variance process, similar to those used in SMOT.In this paper we use this observation to build an entropic time discretization of continuous SMOT problems. This allows to compute discrete approximations of solutions to continuous SMOT problems by a multi-marginal Sinkhorn algorithm, without the need of solving the non-linear Hamilton-Jacobi-Bellman pde's associated to the dual problem, as done for example in [1, 2]. We prove a convergence result of the time discrete entropic problem to the continuous time problem, we propose an implementation and provide numerical experiments supporting the theoretical convergence.

New vapor-liquid equilibrium (VLE) data are continuously being measured and new parameter values, e.g., for the nonrandom two-liquid (NRTL) model are estimated and published. The parameter $\alpha$, the nonrandomness parameter of NRTL, is often heuristically fixed to a value in the range of 0.1 to 0.47. This can be seen as a manual application of a (subset selection) regularization method. In this work, the identifiability of the NRTL model for describing the VLE is analyzed. It is shown that fixing $\alpha$ is not always a good decision and sometimes leads to worse prediction properties of the final parameter estimates. Popular regularization techniques are compared and Generalized Orthogonalization is proposed as an alternative to this heuristic. In addition, the sequential Optimal Experimental Design and Parameter Estimation (sOED-PE) method is applied to study the influence of the regularization methods on the performance of the sOED-PE loop., Comment: 51 pages, 6 figures

In perovskite oxide heterostructures, bulk functional properties coexist with emergent physical phenomena at epitaxial interfaces. Notably, charge transfer at the interface between two insulating oxide layers can lead to the formation of a two-dimensional electron gas (2DEG) with possible applications in, e.g., high-electronmobility transistors and ferroelectric field-effect transistors. So far, the realization of oxide 2DEGs is, however, largely limited to the interface between the single-crystal substrate and epitaxial film, preventing their deliberate placement inside a larger device architecture. Additionally, the substrate-limited quality of perovskite oxide interfaces hampers room-temperature 2DEG performance due to notoriously low electron mobility. In this work, we demonstrate the controlled creation of an interfacial 2DEG at the epitaxial interface between perovskite oxides BaSnO$_3$ and LaInO$_3$ with enhanced room-temperature electron mobilities up to 119 cm$^2$/Vs - the highest room-temperature value reported so far for a perovskite oxide 2DEG. Using a combination of state-of-the-art deposition modes during oxide molecular beam epitaxy, our approach opens up another degree of freedom in optimization and $in$-$situ$ control of the interface between two epitaxial oxide layers away from the substrate interface. We thus expect our approach to apply to the general class of perovskite oxide 2DEG systems and to enable their improved compatibility with novel device concepts and integration across materials platforms., Comment: article including supplementary information

Photometric measurements allow the determination of an asteroid's absolute magnitude, which often represents the sole means to infer its size. Photometric observations can be obtained in a variety of filters that can be unique to a specific observatory. Those observations are then calibrated into specific bands with respect to reference star catalogs. In order to combine all the different measurements for evaluation, photometric observations need to be converted to a common band, typically V-band. Current band-correction schemes in use by IAU's Minor Planet Center, JPL's Center for Near Earth Object Studies and ESA's NEO Coordination Centre use average correction values for the apparent magnitude derived from photometry of asteroids as the corrections are dependent on the typically unknown spectrum of the object to be corrected. By statistically analyzing the photometric residuals of asteroids, we develop a new photometric correction scheme that does not only consider the band, but also accounts for reference catalog and observatory. We describe a new statistical photometry correction scheme for asteroid observations with debiased corrections. Testing this scheme on a reference group of asteroids, we see a 36% reduction in the photometric residuals. Moreover, the new scheme leads to a more accurate and debiased determination of the H-G magnitude system and, in turn, to more reliable inferred sizes. We discuss the significant shift in the corrections with this "DePhOCUS" debiasing system, its limitations, and the impact for photometric and physical properties of all asteroids, especially Near-Earth Objects.

In many automated planning applications, action costs can be hard to specify. An example is the time needed to travel through a certain road segment, which depends on many factors, such as the current weather conditions. A natural way to address this issue is to learn to predict these parameters based on input features (e.g., weather forecasts) and use the predicted action costs in automated planning afterward. Decision-Focused Learning (DFL) has been successful in learning to predict the parameters of combinatorial optimization problems in a way that optimizes solution quality rather than prediction quality. This approach yields better results than treating prediction and optimization as separate tasks. In this paper, we investigate for the first time the challenges of implementing DFL for automated planning in order to learn to predict the action costs. There are two main challenges to overcome: (1) planning systems are called during gradient descent learning, to solve planning problems with negative action costs, which are not supported in planning. We propose novel methods for gradient computation to avoid this issue. (2) DFL requires repeated planner calls during training, which can limit the scalability of the method. We experiment with different methods approximating the optimal plan as well as an easy-to-implement caching mechanism to speed up the learning process. As the first work that addresses DFL for automated planning, we demonstrate that the proposed gradient computation consistently yields significantly better plans than predictions aimed at minimizing prediction error; and that caching can temper the computation requirements.

Fast radio bursts (FRBs) are transient radio signals of extragalactic origins that are subjected to propagation effects such as dispersion and scattering. It follows then that these signals hold information regarding the medium they have traversed and are hence useful as cosmological probes of the Universe. Recently, FRBs were used to make an independent measure of the Hubble Constant $H_0$, promising to resolve the Hubble tension given a sufficient number of detected FRBs. Such cosmological studies are dependent on FRB population statistics, cosmological parameters and detection biases, and thus it is important to accurately characterise each of these. In this work, we empirically characterise the sensitivity of the Fast Real-time Engine for Dedispersing Amplitudes (FREDDA) which is the current detection system for the Australian Square Kilometer Array Pathfinder (ASKAP). We coherently redisperse high-time resolution data of 13 ASKAP-detected FRBs and inject them into FREDDA to determine the recovered signal-to-noise ratios as a function of dispersion measure (DM). We find that for 11 of the 13 FRBs, these results are consistent with injecting idealised pulses. Approximating this sensitivity function with theoretical predictions results in a systematic error of 0.3$\,$km$\,$s$^{-1}\,$Mpc$^{-1}$ on $H_0$ when it is the only free parameter. Allowing additional parameters to vary could increase this systematic by up to $\sim1\,$km$\,$s$^{-1}\,$Mpc$^{-1}$. We estimate that this systematic will not be relevant until $\sim$400 localised FRBs have been detected, but will likely be significant in resolving the Hubble tension., Comment: 8 pages, 6 figures, Published in MNRAS

Driving coherent lattice motion with THz pulses has emerged as a novel pathway for achieving dynamic stabilization of exotic phases that are inaccessible in equilibrium quantum materials. In this work, we present a previously unexplored mechanism for THz excitation of Raman-active phonons in semimetals. We show that intense THz pulses centered at 1 THz can excite the Raman-active $A_{1g}$ phonon mode at 2.9 THz in a bismuth film. We rule out the possibilities of the phonon being excited through conventional anharmonic coupling to other modes or via a THz sum frequency process. Instead, we demonstrate that the THz-driven tunnel ionization provides a plausible means of creating a displacive driving force to initiate the phonon oscillations. Our work highlights a new mechanism for exciting coherent phonons, offering potential for dynamic control over the electronic and structural properties of semimetals and narrow-band semiconductors on ultrafast timescales., Comment: main text + SI, 18 pages, 11 figures

Fast radio burst (FRB) science primarily revolves around two facets: the origin of these bursts and their use in cosmological studies. This work follows from previous redshift-dispersion measure ($z$-DM) analyses in which we model instrumental biases and simultaneously fit population parameters and cosmological parameters to the observed population of FRBs. This sheds light on both the progenitors of FRBs and cosmological questions. Previously, we have completed similar analyses with data from the Australian Square Kilometer Array Pathfinder (ASKAP) and the Murriyang (Parkes) Multibeam system. With this manuscript, we additionally incorporate data from the Deep Synoptic Array (DSA) and the Five-hundred-meter Aperture Spherical Telescope (FAST), invoke a Markov chain Monte Carlo (MCMC) sampler and implement uncertainty in the Galactic DM contributions. The latter leads to larger uncertainties in derived model parameters than previous estimates despite the additional data. We provide refined constraints on FRB population parameters and derive a new constraint on the minimum FRB energy of log$\,E_{\mathrm{min}}$(erg)=39.49$^{+0.39}_{-1.48}$ which is significantly higher than bursts detected from strong repeaters. This result may indicate a low-energy turnover in the luminosity function or may suggest that strong repeaters have a different luminosity function to single bursts. We also predict that FAST will detect 25-41% of their FRBs at $z \gtrsim 2$ and DSA will detect 2-12% of their FRBs at $z \gtrsim 1$., Comment: 17 pages, 7 figures, submitted to PASA

Road unevenness significantly impacts the safety and comfort of various traffic participants, especially vulnerable road users such as cyclists and wheelchair users. This paper introduces StreetSurfaceVis, a novel dataset comprising 9,122 street-level images collected from a crowdsourcing platform and manually annotated by road surface type and quality. The dataset is intended to train models for comprehensive surface assessments of road networks. Existing open datasets are constrained by limited geospatial coverage and camera setups, typically excluding cycleways and footways. By crafting a heterogeneous dataset, we aim to fill this gap and enable robust models that maintain high accuracy across diverse image sources. However, the frequency distribution of road surface types and qualities is highly imbalanced. We address the challenge of ensuring sufficient images per class while reducing manual annotation by proposing a sampling strategy that incorporates various external label prediction resources. More precisely, we estimate the impact of (1) enriching the image data with OpenStreetMap tags, (2) iterative training and application of a custom surface type classification model, (3) amplifying underrepresented classes through prompt-based classification with GPT-4o or similarity search using image embeddings. We show that utilizing a combination of these strategies effectively reduces manual annotation workload while ensuring sufficient class representation., Comment: 11 pages, 2 figures

Applications with irregular data structures, data-dependent control flows and fine-grained data transfers (e.g., real-world graph computations) perform poorly on cache-based systems. We propose the UpDown accelerator that supports fine-grained execution with novel architecture mechanisms - lightweight threading, event-driven scheduling, efficient ultra-short threads, and split-transaction DRAM access with software-controlled synchronization. These hardware primitives support software programmable events, enabling high performance on diverse data structures and algorithms. UpDown also supports scalable performance; hardware replication enables programs to scale up performance. Evaluation results show UpDown's flexibility and scalability enable it to outperform CPUs on graph mining and analytics computations by up to 116-195x geomean speedup and more than 4x speedup over prior accelerators. We show that UpDown generates high memory parallelism (~4.6x over CPU) required for memory intensive graph computations. We present measurements that attribute the performance of UpDown (23x architectural advantage) to its individual architectural mechanisms. Finally, we also analyze the area and power cost of UpDown's mechanisms for software programmability., Comment: 14 pages, 23 figures

Summarizing web graphs is challenging due to the heterogeneity of the modeled information and its changes over time. We investigate the use of neural networks for lifelong graph summarization. Assuming we observe the web graph at a certain time, we train the networks to summarize graph vertices. We apply this trained network to summarize the vertices of the changed graph at the next point in time. Subsequently, we continue training and evaluating the network to perform lifelong graph summarization. We use the GNNs Graph-MLP and GraphSAINT, as well as an MLP baseline, to summarize the temporal graphs. We compare $1$-hop and $2$-hop summaries. We investigate the impact of reusing parameters from a previous snapshot by measuring the backward and forward transfer and the forgetting rate of the neural networks. Our extensive experiments on ten weekly snapshots of a web graph with over $100$M edges, sampled in 2012 and 2022, show that all networks predominantly use $1$-hop information to determine the summary, even when performing $2$-hop summarization. Due to the heterogeneity of web graphs, in some snapshots, the $2$-hop summary produces over ten times more vertex summaries than the $1$-hop summary. When using the network trained on the last snapshot from 2012 and applying it to the first snapshot of 2022, we observe a strong drop in accuracy. We attribute this drop over the ten-year time warp to the strongly increased heterogeneity of the web graph in 2022.

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 Motsch-Tadmor (MT) model is a variant of the Cucker-Smale model with a normalized communication weight function. The normalization poses technical challenges in analyzing the collective behavior due to the absence of conservation of momentum. We study three quantitative estimates for the discrete-time MT model considering the first-order Euler discretization. First, we provide a sufficient framework leading to the asymptotic mono-cluster flocking. The proposed framework is given in terms of coupling strength, communication weight function, and initial data. Second, we show that the continuous transition from the discrete MT model to the continuous MT model can be made uniformly in time using the finite-time convergence result and asymptotic flocking estimate. Third, we present uniform-in-time stability estimates for the discrete MT model. We also provide several numerical examples and compare them with analytical results.

Some prominent discretisation methods such as finite elements provide a way to approximate a function of $d$ variables from $n$ values it takes on the nodes $x_i$ of the corresponding mesh. The accuracy is $n^{-s_a/d}$ in $L^2$-norm, where $s_a$ is the order of the underlying method. When the data are measured or computed with systematical experimental noise, some statistical regularisation might be desirable, with a smoothing method of order $s_r$ (like the number of vanishing moments of a kernel). This idea is behind the use of some regularised discretisation methods, whose approximation properties are the subject of this paper. We decipher the interplay of $s_a$ and $s_r$ for reconstructing a smooth function on regular bounded domains from $n$ measurements with noise of order $\sigma$. We establish that for certain regimes with small noise $\sigma$ depending on $n$, when $s_a > s_r$, statistical smoothing is not necessarily the best option and {\it not regularising} is more beneficial than {\it statistical regularising}. We precisely quantify this phenomenon and show that the gain can achieve a multiplicative order $n^{(s_a-s_r)/(2s_r+d)}$. We illustrate our estimates by numerical experiments conducted in dimension $d=1$ with $\mathbb P_1$ and $\mathbb P_2$ finite elements.

In May 2023, the Georgia Tech Ethics, Technology, and Human Interaction Center organized the Conference on Ethical and Responsible Design in the National AI Institutes. Representatives from the National AI Research Institutes that had been established as of January 2023 were invited to attend; researchers representing 14 Institutes attended and participated. The conference focused on three questions: What are the main challenges that the National AI Institutes are facing with regard to the responsible design of AI systems? What are promising lines of inquiry to address these challenges? What are possible points of collaboration? Over the course of the conference, a revised version of the first question became a focal point: What are the challenges that the Institutes face in identifying ethical and responsible design practices and in implementing them in the AI development process? This document summarizes the challenges that representatives from the Institutes in attendance highlighted., Comment: 9 pages

This paper uses the gyro-moment (GM) approach as a multi-fidelity tool to explore the effect of triangularity on tokamak edge turbulence. Considering experimental data from an L-mode DIII-D discharge, we conduct gyrokinetic (GK) simulations with realistic plasma edge geometry parameters at $\rho=0.95$. We find that employing ten GMs effectively captures essential features of both trapped electron mode (TEM) and ion temperature gradient (ITG) turbulence. By comparing electromagnetic GK simulations with adiabatic electron GK and reduced fluid simulations, we identify the range of validity of the reduced models. We observe that TEMs drive turbulent heat transport under nominal discharge conditions, hindering accurate transport level estimates by both simplified models. However, when TEMs are absent, and turbulence is ITG-driven, an agreement across the different models is observed. Finally, a parameter scan shows that the positive triangularity scenario destabilizes the TEM, therefore, the adiabatic electron model tends to show agreement with the electromagnetic simulations in zero and negative triangularity scenarios. On the other hand, the reduced fluid simulations exhibit limited sensitivity to triangularity changes, shedding light on the importance of retaining kinetic effects to accurately model the impact of triangularity turbulence in the tokamak edge. In conclusion, our multi-fidelity study suggests that a GM hierarchy with a limited number of moments is an ideal candidate for efficiently exploring triangularity effects on micro-scale turbulence., Comment: 22 pages, 9 figures

We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the underlying generative process that explain the observed patient disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical knowledge. By combining the generative approach with medical definitions of different characteristics of SSc, we facilitate the discovery of new aspects of the disease. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering SSc patient trajectories into novel sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series with uncertainty quantification., Comment: Accepted at Machine Learning for Healthcare 2024. arXiv admin note: substantial text overlap with arXiv:2311.08149

Femtosecond laser ablation is a process that bears both fundamental physics interest and has wide industrial applications. For decades, the lack of probes on the relevant time and length scales has prevented access to the highly nonequilibrium phase decomposition processes triggered by laser excitation. Enabled by the unprecedented intense femtosecond X-ray pulses delivered by an X-ray free electron laser, we report here results of time-resolved small angle scattering measurements on the dynamics of nanoscale phase decomposition in thin gold films upon femtosecond laser-induced ablation. By analyzing the features imprinted onto the small angle diffraction patterns, the transient heterogeneous density distributions within the ablation plume as obtained from molecular dynamics simulations get direct experimental confirmation., Comment: Main manuscript with 32 pages incl. 9 figures + supplementary materials with 16 pages incl. 5 figures

Materials with strong spin-orbit coupling and low crystalline symmetry are promising for generating large unconventional spin-orbit torques (SOTs), such as in-plane field-like (FL) torques and out-of-plane damping-like (DL) torques, which can effectively manipulate and deterministically switch an out-of-plane magnetization without the need for additional external in-plane magnetic fields. Here, we report SOTs generated by magnetron-sputtered 1T' MoTe2/Permalloy (Py; Ni80Fe20)/MgO heterostructures using both spin-torque ferromagnetic resonance (ST-FMR) and second harmonic Hall measurements. We observed unconventional FL and DL torques in our samples due to spins polarized normal to the interface of MoTe2 and Py layers, and studied the influence of crystallographic order and MoTe2 layer thickness on the SOTs. By comparing the Raman spectra of 1T' MoTe2 samples prepared in different ways, we found a tensile strain in sputtered MoTe2 films, which might further enhance the generation of unconventional torques by reducing the symmetry of 1T' MoTe2.

The topological semimetal CrPt$_3$ has potential for generating unconventional spin torques due to its ferrimagnetic ordering, topological band structure, and high anomalous Hall effect. CrPt$_3$ exhibits ferrimagnetic behavior only in its chemically ordered phase and is paramagnetic in its chemically disordered phase. By controlling the growth and annealing temperatures epitaxial films of both chemically ordered and disordered phases of CrPt$_3$ are prepared allowing us to investigate the role of magnetic ordering on unconventional torque generation. We use angle dependent spin-torque ferromagnetic resonance and second harmonic Hall measurements to probe the spin torques generated from epitaxial CrPt$_3$ in CrPt$_3$/Cu/Ni$_{81}$Fe$_{19}$ heterostructures. With current applied along specific directions with respect to the crystal order we reveal unconventional spin torques in both ordered and disordered films. When current flows parallel to the $[1\overline{1}1]$ and $[\overline{1}11]$ directions we observe an unconventional field-like torque that is opposite in sign for the two directions, which lack a mirror plane thus allowing unconventional torques to be generated.

Localized vibrations, arising from nonlinearities or symmetry breaking, pose a challenge in engineering, as the resulting high-amplitude vibrations may result in component failure due to fatigue. During operation, the emergence of localization is difficult to predict, partly because of changing parameters over the life cycle of a system. This work proposes a novel, network-based approach to predicting an imminent localized vibration. Synthetic measurement data is used to generate a functional network, which captures the dynamic interplay of the machine parts, complementary to their geometric coupling. Analysis of these functional networks reveals an impending localized vibration and its location. The method is demonstrated using a model system for a bladed disk, a ring composed of coupled nonlinear Duffing oscillators. Results indicate that the proposed method is robust against small parameter uncertainties, added measurement noise, and the length of the measurement data samples. The source code for this work is available., Comment: 10 pages, 4 figures, submitted to Journal of Sound and Vibration

Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A

Automatic markerless estimation of infant posture and motion from ordinary videos carries great potential for movement studies "in the wild", facilitating understanding of motor development and massively increasing the chances of early diagnosis of disorders. There is rapid development of human pose estimation methods in computer vision thanks to advances in deep learning and machine learning. However, these methods are trained on datasets featuring adults in different contexts. This work tests and compares seven popular methods (AlphaPose, DeepLabCut/DeeperCut, Detectron2, HRNet, MediaPipe/BlazePose, OpenPose, and ViTPose) on videos of infants in supine position. Surprisingly, all methods except DeepLabCut and MediaPipe have competitive performance without additional finetuning, with ViTPose performing best. Next to standard performance metrics (object keypoint similarity, average precision and recall), we introduce errors expressed in the neck-mid-hip ratio and additionally study missed and redundant detections and the reliability of the internal confidence ratings of the different methods, which are relevant for downstream tasks. Among the networks with competitive performance, only AlphaPose could run close to real time (27 fps) on our machine. We provide documented Docker containers or instructions for all the methods we used, our analysis scripts, and processed data at https://hub.docker.com/u/humanoidsctu and https://osf.io/x465b/., Comment: 21 pages, 3 figures, 14 tables

Hypothetical axions provide a compelling explanation for dark matter and could be emitted from the hot solar interior. The CERN Axion Solar Telescope (CAST) has been searching for solar axions via their back conversion to X-ray photons in a 9-T 10-m long magnet directed towards the Sun. We report on an extended run with the IAXO (International Axion Observatory) pathfinder detector, doubling the previous exposure time. The detector was operated with a xenon-based gas mixture for part of the new run, providing technical insights for future detector configurations in IAXO. No counts are detected in the 95\% signal-encircling region during the new run, while one is expected. The new data improve the axion-photon coupling limit to 5.7$\times 10^{-11}\,$GeV$^{-1}$ at 95\% C.L., the most restrictive experimental limit to date., Comment: 8 pages, 4 figures (plus 6 pages, 10 figures of supplemental material) Corresponding authors: C. Margalejo (cmargalejo@unizar.es) and J. Ruz (Jaime.Ruz@cern.ch)

We perform stability analysis of a kinetic bacterial chemotaxis model of bacterial self-organization, assuming that bacteria respond sharply to chemical signals. The resulting discontinuous tumbling kernel represents the key challenge for the stability analysis as it rules out a direct linearization of the nonlinear terms. To address this challenge we fruitfully separate the evolution of the shape of the cellular profile from its global motion. We provide a full nonlinear stability theorem in a perturbative setting when chemical degradation can be neglected. With chemical degradation we prove stability of the linearized operator. In both cases we obtain exponential relaxation to equilibrium with an explicit rate using hypocoercivity techniques. To apply a hypocoercivity approach in this setting, we develop two novel and specific approaches: i) the use of the $H^1$ norm instead of the $L^2$ norm, and ii) the treatment of nonlinear terms. This work represents an important step forward in bacterial chemotaxis modeling from a kinetic perspective as most results are currently only available for the macroscopic descriptions, which are usually parabolic in nature. Significant difficulty arises due to the lack of regularization of the kinetic transport operator as compared to the parabolic operator in the macroscopic scaling limit., Comment: 48 pages

Gaussian process regression is a frequently used statistical method for flexible yet fully probabilistic non-linear regression modeling. A common obstacle is its computational complexity which scales poorly with the number of observations. This is especially an issue when applying Gaussian process models to multiple functions simultaneously in various applications of functional data analysis. We consider a multi-level Gaussian process regression model where a common mean function and individual subject-specific deviations are modeled simultaneously as latent Gaussian processes. We derive exact analytic and computationally efficient expressions for the log-likelihood function and the posterior distributions in the case where the observations are sampled on either a completely or partially regular grid. This enables us to fit the model to large data sets that are currently computationally inaccessible using a standard implementation. We show through a simulation study that our analytic expressions are several orders of magnitude faster compared to a standard implementation, and we provide an implementation in the probabilistic programming language Stan., Comment: 48 pages, 3 figures; Figure 1 corrected

Navigating the ultrasound (US) probe to the standardized imaging plane (SIP) for image acquisition is a critical but operator-dependent task in conventional freehand diagnostic US. Robotic US systems (RUSS) offer the potential to enhance imaging consistency by leveraging real-time US image feedback to optimize the probe pose, thereby reducing reliance on operator expertise. However, determining the proper approach to extracting generalizable features from the US images for probe pose adjustment remain challenging. In this work, we propose a SIP navigation framework for RUSS, exemplified in the context of robotic lung ultrasound (LUS). This framework facilitates automatic probe adjustment when in proximity to the SIP. This is achieved by explicitly extracting multiple anatomical features presented in real-time LUS images and performing non-patient-specific template matching to generate probe motion towards the SIP using image-based visual servoing (IBVS). This framework is further integrated with the active-sensing end-effector (A-SEE), a customized robot end-effector that leverages patient external body geometry to maintain optimal probe alignment with the contact surface, thus preserving US signal quality throughout the navigation. The proposed approach ensures procedural interpretability and inter-patient adaptability. Validation is conducted through anatomy-mimicking phantom and in-vivo evaluations involving five human subjects. The results show the framework's high navigation precision with the probe correctly located at the SIP for all cases, exhibiting positioning error of under 2 mm in translation and under 2 degree in rotation. These results demonstrate the navigation process's capability to accomondate anatomical variations among patients.

We consider the problem of \textit{true} open-world semi-supervised node classification, in which nodes in a graph either belong to known or new classes, with the latter not present during training. Existing methods detect and reject new classes but fail to distinguish between different new classes. We adapt existing methods and show they do not solve the problem sufficiently. We introduce a novel end-to-end approach for classification into known classes and new classes based on class prototypes, which we call Prototypical Open-World Learning for Node Classification (POWN). Our method combines graph semi-supervised learning, self-supervised learning, and pseudo-labeling to learn prototype representations of new classes in a zero-shot way. In contrast to existing solutions from the vision domain, POWN does not require data augmentation techniques for node classification. Experiments on benchmark datasets demonstrate the effectiveness of POWN, where it outperforms baselines by up to $20\%$ accuracy on the small and up to $30\%$ on the large datasets. Source code is available at https://github.com/Bobowner/POWN.

Cosmic shear is a powerful probe of cosmology, but it is affected by the intrinsic alignment (IA) of galaxy shapes with the large-scale structure. Upcoming surveys like Euclid and Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require an accurate understanding of IA, particularly for higher-order cosmic shear statistics that are vital for extracting the most cosmological information. In this paper, we report the first detection of third-order IA correlations using the LOWZ galaxy sample from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS). We compare our measurements with predictions from the MICE cosmological simulation and an analytical NLA-inspired model informed by second-order correlations. We also explore the dependence of the third-order correlation on the galaxies' luminosity. We find that the amplitude $A_\mathrm{IA}$ of the IA signal is non-zero at the $4.7\sigma$ ($7.6\sigma$) level for scales between $6 h^{-1} \mathrm{Mpc}$ ($1 h^{-1} \mathrm{Mpc}$) and $20 h^{-1} \mathrm{Mpc}$. For scales above $6 h^{-1}\mathrm{Mpc}$ the inferred AIA agrees both with the prediction from the simulation and estimates from second-order statistics within $1\sigma$ but deviations arise at smaller scales. Our results demonstrate the feasibility of measuring third-order IA correlations and using them for constraining IA models. The agreement between second- and third-order IA constraints also opens the opportunity for a consistent joint analysis and IA self-calibration, promising tighter parameter constraints for upcoming cosmological surveys., Comment: 9 pages + appendix, 4 figures, submitted to Astronomy & Astrophysics

\Ac{MPC} and \ac{RL} are two powerful control strategies with, arguably, complementary advantages. In this work, we show how actor-critic \ac{RL} techniques can be leveraged to improve the performance of \ac{MPC}. The \ac{RL} critic is used as an approximation of the optimal value function, and an actor roll-out provides an initial guess for primal variables of the \ac{MPC}. A parallel control architecture is proposed where each \ac{MPC} instance is solved twice for different initial guesses. Besides the actor roll-out initialization, a shifted initialization from the previous solution is used. Thereafter, the actor and the critic are again used to approximately evaluate the infinite horizon cost of these trajectories. The control actions from the lowest-cost trajectory are applied to the system at each time step. We establish that the proposed algorithm is guaranteed to outperform the original \ac{RL} policy plus an error term that depends on the accuracy of the critic and decays with the horizon length of the \ac{MPC} formulation. Moreover, we do not require globally optimal solutions for these guarantees to hold. The approach is demonstrated on an illustrative toy example and an \ac{AD} overtaking scenario.

In this paper we present a framework in which the relational description of General Relativity can be used to smoothly continue cosmological dynamical systems through the Big Bang without invoking quantum gravity effects. Cosmological spacetimes contain as a key dynamical variable a notion of scale through the volume factor $\nu$. However no cosmological observer is ever able to separate their measuring apparatus from the system they are measuring, in that sense every measurement is a relative one and measurable dynamical variables are in fact dimensionless ratios. This is manifest in the identification of a scaling symmetry or ``Dynamical Similarity" in the Einstein-Hilbert action associated with the volume factor. By quotienting out this scaling symmetry, we form a relational system defined on a contact manifold whose dynamical variables are decoupled from scale. When the phase space is reduced to shape space, we show that there exist unique solutions to the equations of motion that pass smoothly through the initial cosmological singularity in flat FLRW, Bianchi I and Quiescent Bianchi IX cosmologies., Comment: 52 pages, 38 figures

Advanced experiments using THz pump and X-ray probe pulses at modern free-electron lasers (FELs) like the European X-ray FEL require a frequency-tunable, high-power, narrow-band THz source maintaining the repetition rate and pulse structure of the X-ray pulses. This paper reports the first results from a THz source, that is based on a single-pass high-gain THz FEL operating with a central wavelength of 100 micrometers. The THz FEL prototype is currently in operation at the Photo Injector Test facility at DESY in Zeuthen (PITZ) and uses the same type of electron source as the European XFEL photo injector. A self-amplified spontaneous emission (SASE) FEL was envisioned as the main mechanism for generating the THz pulses. Although the THz FEL at PITZ is supposed to use the same mechanism as at X-ray facilities, it cannot be considered as a simple scaling of the radiation wavelength because there is a large difference in the number of electrons per radiation wavelength, which is five orders of magnitude higher for the THz case. The bunching factor arising from the electron beam current profile contributes strongly to the initial spontaneous emission starting the FEL process. Proof-of-principle experiments were done at PITZ using an LCLS-I undulator to generate the first high-power, high-repetition-rate single-pass THz FEL radiation. Electron bunches with a beam energy of ~17 MeV and a bunch charge of up to several nC are used to generate THz pulses with a pulse energy of several tens of microjoules. For example, for an electron beam with a charge of ~2.4 nC, more than 100 microjoules were generated at a central wavelength of 100 micrometers. The narrowband spectrum was also demonstrated by spectral measurements. These proof-of-principle experiments pave the way for a tunable, high-repetition-rate THz source providing pulses with energies in the millijoule range., Comment: (unpublished) for submission to PRAB

Origin and composition dependence of the anisotropic thermomechanical properties are elucidated for Ba1-xSrxZn2Si2O7 (BZS) solid solutions. The high-temperature phase of BZS shows negative thermal expansion (NTE) along one crystallographic axis and highly anisotropic elastic properties characterized by X-ray diffraction experiments and simulations at the density functional theory level. Ab initio molecular dynamics simulations provide accurate predictions of the anisotropic thermal expansion in excellent agreement with experimental observations. The NTE considerably decreases with increasing Sr content x. This is connected with the composition dependence of the vibrational density of states (VDOS) and the anisotropic Gr\"uneisen parameters. The VDOS shifts to higher frequencies between 0-5 THz due to substitution of Ba with Sr. In the same frequency range, vibrational modes contributing most to the NTE are found. In addition, phonon calculations using the quasi-harmonic approximation revealed that the NTE is mainly connected with deformation of four-membered rings formed by SiO4 and ZnO4 tetrahedra. The thermomechanical and vibrational properties obtained in this work provide the basis for future studies facilitating the targeted design of BZS solid solutions as zero or negative thermal expansion material.

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Continuum robots have emerged as a promising technology in the medical field due to their potential of accessing deep sited locations of the human body with low surgical trauma. When deriving physics-based models for these robots, evaluating the models poses a significant challenge due to the difficulty in accurately measuring their intricate shapes. In this work, we present an optimization based 3D shape registration algorithm for estimation of the backbone shape of slender continuum robots as part of a pho togrammetric measurement. Our approach to estimating the backbones optimally matches a parametric three-dimensional curve to images of the robot. Since we incorporate an iterative closest point algorithm into our method, we do not need prior knowledge of the robots position within the respective images. In our experiments with artificial and real images of a concentric tube continuum robot, we found an average maximum deviation of the reconstruction from simulation data of 0.665 mm and 0.939 mm from manual measurements. These results show that our algorithm is well capable of producing high accuracy positional data from images of continuum robots., Comment: 11 pages, 8 figures, 2 algorithms, journal

We present the Flagship galaxy mock, a simulated catalogue of billions of galaxies designed to support the scientific exploitation of the Euclid mission. Euclid is a medium-class mission of the European Space Agency optimised to determine the properties of dark matter and dark energy on the largest scales of the Universe. It probes structure formation over more than 10 billion years primarily from the combination of weak gravitational lensing and galaxy clustering data. The breath of Euclid's data will also foster a wide variety of scientific analyses. The Flagship simulation was developed to provide a realistic approximation to the galaxies that will be observed by Euclid and used in its scientific analyses. We ran a state-of-the-art N-body simulation with four trillion particles, producing a lightcone on the fly. From the dark matter particles, we produced a catalogue of 16 billion haloes in one octant of the sky in the lightcone up to redshift z=3. We then populated these haloes with mock galaxies using a halo occupation distribution and abundance matching approach, calibrating the free parameters of the galaxy mock against observed correlations and other basic galaxy properties. Modelled galaxy properties include luminosity and flux in several bands, redshifts, positions and velocities, spectral energy distributions, shapes and sizes, stellar masses, star formation rates, metallicities, emission line fluxes, and lensing properties. We selected a final sample of 3.4 billion galaxies with a magnitude cut of H_E<26, where we are complete. We have performed a comprehensive set of validation tests to check the similarity to observational data and theoretical models. In particular, our catalogue is able to closely reproduce the main characteristics of the weak lensing and galaxy clustering samples to be used in the mission's main cosmological analysis. (abridged), Comment: Paper submitted as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

The rise of Generative AI (GenAI) brings about transformative potential across sectors, but its dual-use nature also amplifies risks. Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety. China, the United States (US), and the European Union (EU) are at the forefront with initiatives like the Management of Algorithmic Recommendations, the Executive Order, and the AI Act, respectively. However, the rapid evolution of GenAI capabilities often outpaces the development of comprehensive safety measures, creating a gap between regulatory needs and technical advancements. A workshop co-organized by Google, University of Wisconsin, Madison (UW-Madison), and Stanford University aimed to bridge this gap between GenAI policy and technology. The diverse stakeholders of the GenAI space -- from the public and governments to academia and industry -- make any safety measures under consideration more complex, as both technical feasibility and regulatory guidance must be realized. This paper summarizes the discussions during the workshop which addressed questions, such as: How regulation can be designed without hindering technological progress? How technology can evolve to meet regulatory standards? The interplay between legislation and technology is a very vast topic, and we don't claim that this paper is a comprehensive treatment on this topic. This paper is meant to capture findings based on the workshop, and hopefully, can guide discussion on this topic.

In information technology devices, current-driven state switching is crucial in various disciplines including spintronics, where the contribution of heating to the switching mechanism plays an inevitable role. Recently, current-driven antiferromagnetic order switching has attracted considerable attention due to its implications for next-generation spintronic devices. Although the switching mechanisms can be explained by spin dynamics induced by spin torques, some reports have claimed that demagnetization above the Neel temperature due to Joule heating is critical for switching. Here we present a systematic method and an analytical model to quantify the thermal contribution due to Joule heating in micro-electronic devices, focusing on current-driven octupole switching in the non-collinear antiferromagnet, Mn3Sn. The results consistently show that the critical temperature for switching remains relatively constant above the Neel temperature, while the threshold current density depends on the choice of substrate and the base temperature. In addition, we provide an analytical model to calculate the Joule-heating temperature which quantitatively explains our experimental results. From numerical calculations, we illustrate the reconfiguration of magnetic orders during cooling from a demagnetized state of polycrystalline Mn3Sn. This work not only provides deeper insights into magnetization switching in antiferromagnets, but also a general guideline for evaluating the Joule-heating temperature excursions in micro-electronic devices., Comment: Main: 9 pages, 6 figures, Supplementary Material: 9 pages, 8 figures

In this study, we introduce a robust central Gradient-Based Reconstruction (GBR) scheme for the compressible Navier-Stokes equations. The method leverages transformation to characteristic space, allowing selective treatment of waves from the compressible Euler equations. By averaging left- and right-biased state interpolations, a central scheme is achieved for all but the acoustic waves, which require upwinding for stability. Distinct differences were observed between transformations using either primitive or conservative variables. We evaluated the method's robustness and superiority using benchmark problems, including the two-dimensional shock entropy problem, two-dimensional viscous shock tube, and three-dimensional inviscid Taylor-Green vortex. Subsequently, we assessed the method in the context of Wall Modelled Large Eddy Simulations (WMLES), where coarse grids are used to reduce computational cost but also introduce substantial numerical dissipation. Using WMLES, we simulated oblique shock impingement on a Mach 6 disturbed boundary layer and a Mach 7.7 flow over a $15^{\circ}$ compression ramp. Our findings reveal that: 1) transformation to characteristic space using conservative variables leads to more accurate results; 2) minimizing numerical dissipation through centralized interpolation is crucial. In the compression ramp case, boundary layer separation was shifted slightly upstream, and there was an over-prediction of wall heating, likely attributable to the equilibrium-assuming wall model. Overall, this work showcases the method's potential in accurately capturing complex flow dynamics with reduced numerical dissipation.

Current boson star models are largely restricted to global symmetries and lower spin fields. In this work, we generalize these systems of self-gravitating bosonic fields to allow for arbitrary totally antisymmetric tensor fields and arbitrary internal gauge symmetries. We construct a generalized formalism for Yang-Mills-like theories, which allows for arbitrary k-form fields, instead of just vector fields. The k-form fields have gauge symmetries described by semisimple, compact Lie groups. We further derive equations of motion for the k-form fields and connection coefficients of the Lie group. Extensions and applications are also discussed. We present a novel way to fix the group connection using a spacetime connection. As an example, we derive explicitly the connection coefficients for SU(2) in a spherically symmetric spacetime using rectangular vielbeins. The combination of methods presented leads to a powerful, adaptable and practical framework. As a proof of concept, we derive ordinary differential equations for a 0-form field with a SU(2) symmetry. Our framework can be used to model self-gravitating (multi) particle states with internal symmetries, such as pion condensates or dark matter. It is also suited as a tool to approach open problems in modified gravity and string theory., Comment: 58 pages including appendix, both authors are first authors

It has been previously advocated that the presence of the daily and annual modulations of the axion flux on the Earth's surface may dramatically change the strategy of the axion searches. The arguments were based on the so-called Axion Quark Nugget (AQN) dark matter model which was originally put forward to explain the similarity of the dark and visible cosmological matter densities $\Omega_{\rm dark}\sim \Omega_{\rm visible}$. In this framework, the population of galactic axions with mass $ 10^{-6} {\rm eV}\lesssim m_a\lesssim 10^{-3}{\rm eV}$ and velocity $\langle v_a\rangle\sim 10^{-3} c$ will be accompanied by axions with typical velocities $\langle v_a\rangle\sim 0.6 c$ emitted by AQNs. Furthermore, in this framework, it has also been argued that the AQN-induced axion daily modulation (in contrast with the conventional WIMP paradigm) could be as large as $(10-20)\%$, which represents the main motivation for the present investigation. We argue that the daily modulations along with the broadband detection strategy can be very useful tools for the discovery of such relativistic axions. The data from the CAST-CAPP detector have been used following such arguments. Unfortunately, due to the dependence of the amplifier chain on temperature-dependent gain drifts and other factors, we could not conclusively show the presence or absence of a dark sector-originated daily modulation. However, this proof of principle analysis procedure can serve as a reference for future studies., Comment: 18 pages, 8 figures

Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach for modelling system dynamics, showcasing the ability to capture uncertainty and scalability in high-dimensional control scenarios. However, PE models are susceptible to mode collapse, resulting in non-robust control when faced with environments slightly different from the training set. In this paper, we introduce the $\textbf{u}$ncertainty-$\textbf{d}$riven rob$\textbf{u}$st $\textbf{c}$ontrol (UDUC) loss as an alternative objective for training PE models, drawing inspiration from contrastive learning. We analyze the robustness of UDUC loss through the lens of robust optimization and evaluate its performance on the challenging Real-world Reinforcement Learning (RWRL) benchmark, which involves significant environmental mismatches between the training and testing environments.