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Perturbative approaches are methods to efficiently tackle many-body problems, offering both intuitive insights and analysis of correlation effects. However, their application to systems where light and matter are strongly coupled is non-trivial. Specifically, the definition of suitable orbitals for the zeroth-order Hamiltonian represents a significant theoretical challenge. While reviewing previously investigated orbital choices, this work presents an alternative polaritonic orbital basis suitable for the strong coupling regime. We develop a quantum electrodynamical (QED) M{\o}ller-Plesset perturbation theory using orbitals obtained from the strong coupling QED Hartree-Fock. We assess the strengths and limitations of the different approaches and emphasize the essential role of using a consistent molecular orbital framework to achieve an accurate description of cavity-induced electron-photon correlation effects., Comment: 33 pages and 8 Figures

We study the regularity properties of the minimisers of entropic optimal transport providing a natural analogue of the $\varepsilon$-regularity theory of quadratic optimal transport in the entropic setting. More precisely, we show that if the minimiser of the entropic problem satisfies a gradient BMO-type estimate at some scale, the same estimate holds all the way down to the natural length-scale associated to the entropic regularisation. Our result follows from a more general $\varepsilon$-regularity theory for optimal transport costs which can be viewed as perturbations of quadratic optimal transport. We consider such a perturbed cost and require that, under a certain class of admissible affine rescalings, the minimiser remains a local quasi-minimiser of the quadratic problem (in an appropriate sense) and that the cost of "long trajectories" of minimisers (and their rescalings) is small. Under these assumptions, we show that the minimiser satisfies an appropriate $C^{2,\alpha}$ Morrey$\unicode{x2013}$Campanato-type estimate which is valid up to the scale of quasi-minimality., Comment: 22 pages, no figures

An accelerated deployment of renewable energy sources is crucial for a successful transformation of the current energy system, with wind energy playing a key role in this transition. This study addresses the integrated wind farm layout and cable routing problem, a challenging nonlinear optimization problem. We model this problem as an extended version of the Quota Steiner Tree Problem (QSTP), optimizing turbine placement and network connectivity simultaneously to meet specified expansion targets. Our proposed approach accounts for the wake effect - a region of reduced wind speed induced by each installed turbine - and enforces minimum spacing between turbines. We introduce an exact solution framework in terms of the novel Quota Steiner Tree Problem with interference (QSTPI). By leveraging an interference-based splitting strategy, we develop an advanced solver capable of tackling large-scale problem instances. The presented approach outperforms generic state-of-the-art mixed integer programming solvers on our dataset by up to two orders of magnitude. Moreover, we demonstrate that our integrated method significantly reduces the costs in contrast to a sequential approach. Thus, we provide a planning tool that enhances existing planning methodologies for supporting a faster and cost-efficient expansion of wind energy.

In this paper, we investigate the phenomenology of fermionic systems in uniform gravitational fields within the framework of Very Special Relativity (VSR). We especially focus on the case of gravitational spectroscopy with ultracold neutrons, explored in experiments like \emph{q}\textsc{Bounce}. Calculating the leading ($c^0$) and next-to-leading ($c^{-1}$) order corrections to the non-relativistic Hamiltonian in an accelerated frame, we derive the fermionic perturbed energy spectrum. At leading order, we do not find new non-trivial modifications, apart from a mass shift, confirming both the equivalence between inertial and gravitational mass and particle-antiparticle sectors. The next-to-leading order, instead, introduces time-dependent anisotropic contributions depending on the preferred spatial direction from VSR, which can then be used to probe novel Lorentz-violating signatures. Using \emph{q}\textsc{Bounce} sensitivity as a benchmark, we suggest a first rough constraint for these effects. Finally, we propose alternative spin-flipping setups to better probe VSR signatures and foresee potential future research directions.

We measure the radio continuum fluxes at the locations of all Gaia${-}$confirmed members of Taurus${-}$Auriga using Karl G. Jansky Very Large Array Sky Survey data (VLASS; 2${-}$4 GHz, $\sigma_{\rm{VLASS}}{\sim}110{-}140 \mu$Jy, $2.5''$ resolution) spanning 3 VLASS epochs (2019, 2021, and 2023). We present 35 detections coincident with young Taurus${-}$Auriga stars (29 in individual VLASS images, 6 via stacking). We find a strong dependence on spectral type, wherein the fractional detection rate of radio emission coincident with early-type young stellar objects (YSOs) is systematically higher than late-type YSOs, ranging from 25% $\pm$ 13% for B${-}$F YSOs, 21% $\pm$ 11% for G YSOs, 18.4% $\pm$ 6.3% for K0${-}$K4 YSOs, 15.5% $\pm$ 5.4% for K5${-}$K9 YSOs, 7.0% $\pm$ 2.7% for M0${-}$M2 YSOs, 2.3% $\pm$ 0.9% for M3${-}$M6 YSOs, and 1.9% $\pm$ 1.9% for YSOs with SpTs later than M7. We present cumulative density distributions of radio luminosity densities that demonstrate a significant luminosity enhancement for early- versus late-type YSOs. We find 25% of the detected sources to be significantly variable. We discuss possible interpretations of this dependence, which may reflect stellar magnetic activity, binary interactions, or stellar flaring. We find that mid-infrared YSO class is a strong indicator of radio detectability consistent with higher frequency Taurus-Auriga VLA surveys, with class III stars detected at a rate of 8.8% $\pm$ 1.6%, class IIs at 2.0% $\pm$ 1.2%, and combined class 0s, Is and Fs at 8.0% $\pm$ 5.4%., Comment: Accepted for publication in ApJ. 26 pages, inc. 12 figures, 3 tables

We analyze the impact of non-Markovian classical noise on single-qubit randomized benchmarking experiments, in a manner that explicitly models the realization of each gate via realistic finite-duration pulses. Our new framework exploits the random nature of each gate sequence to derive expressions for the full survival probability decay curve which are non-perturbative in the noise strength. In the presence of non-Markovian noise, our approach shows that the decay curve can exhibit a strong dependence on the implementation method, with regimes of both exponential and power law decays. We discuss how these effects can complicate the interpretation of a randomized-benchmarking experiment, but also how to leverage them to probe non-Markovianty.

This study examines the historical evolution of interdisciplinary research (IDR) over a 40-year period, focusing on its dynamic trends, phases, and key turning points. We apply time series analysis to identify critical years for interdisciplinary citations (CYICs) and categorizes IDR into three distinct phases based on these trends: Period I (1981-2002), marked by sporadic and limited interdisciplinary activity; Period II (2003-2016), characterized by the emergence of large-scale IDR led primarily by Medicine, with significant breakthroughs in cloning and medical technology; and Period III (2017-present), where IDR became a widely adopted research paradigm. Our findings indicate that IDR has been predominantly concentrated within the Natural Sciences, with Medicine consistently at the forefront, and highlights increasing contributions from Engineering and Environmental disciplines as a new trend. These insights enhance the understanding of the evolution of IDR, its driving factors, and the shifts in the focus of interdisciplinary collaborations., Comment: 7 pages, 3 figures

Calving front position variation of marine-terminating glaciers is an indicator of ice mass loss and a crucial parameter in numerical glacier models. Deep Learning (DL) systems can automatically extract this position from Synthetic Aperture Radar (SAR) imagery, enabling continuous, weather- and illumination-independent, large-scale monitoring. This study presents the first comparison of DL systems on a common calving front benchmark dataset. A multi-annotator study with ten annotators is performed to contrast the best-performing DL system against human performance. The best DL model's outputs deviate 221 m on average, while the average deviation of the human annotators is 38 m. This significant difference shows that current DL systems do not yet match human performance and that further research is needed to enable fully automated monitoring of glacier calving fronts. The study of Vision Transformers, foundation models, and the inclusion and processing strategy of more information are identified as avenues for future research.

In this letter, we investigate the terahertz (THz) electro-optic Kerr effect (KE) dynamics in LaAlO3 (LAO), a widely used substrate for thin film preparation. We show that the KE dynamics strongly depend on the material anisotropy due to interference between THz field-induced and strain-induced optical birefringence. Such interference leads to quasi-phase matching conditions of the KE, which becomes strongly frequency dependent. Depending on the THz frequency, the KE exhibits a uni- and bipolar shape of the quadratic response. The demonstrated effects will be present in a wide variety of materials used as substrates in different THz-pump laser-probe experiments and need to be considered in order to disentangle the different contributions to the measured ultrafast dynamic signals., Comment: 4 pages, 4 figures

Machine learning (ML) has become crucial in modern life, with growing interest from researchers and the public. Despite its potential, a significant entry barrier prevents widespread adoption, making it challenging for non-experts to understand and implement ML techniques. The increasing desire to leverage ML is counterbalanced by its technical complexity, creating a gap between potential and practical application. This work introduces asanAI, an offline-first, open-source, no-code machine learning toolkit designed for users of all skill levels. It allows individuals to design, debug, train, and test ML models directly in a web browser, eliminating the need for software installations and coding. The toolkit runs on any device with a modern web browser, including smartphones, and ensures user privacy through local computations while utilizing WebGL for enhanced GPU performance. Users can quickly experiment with neural networks and train custom models using various data sources, supported by intuitive visualizations of network structures and data flows. asanAI simplifies the teaching of ML concepts in educational settings and is released under an open-source MIT license, encouraging modifications. It also supports exporting models in industry-ready formats, empowering a diverse range of users to effectively learn and apply machine learning in their projects. The proposed toolkit is successfully utilized by researchers of ScaDS.AI to swiftly draft and test machine learning ideas, by trainers to effectively educate enthusiasts, and by teachers to introduce contemporary ML topics in classrooms with minimal effort and high clarity., Comment: 7 pages, 8 figures

We created new reddening maps and derived new extinction laws from visual to near-infrared passbands using improved RR~Lyrae period-absolute magnitude-metallicity relations, thus enabling distance estimates for individual bulge RR~Lyrae variables. The extinction law is most uniform in RIK and RJK and the distances to individual RR~Lyrae based on these colors are determined with an accuracy six and four percent, respectively. Using only the near-infrared passbands for distance estimation we inferred the distance to the Galactic center equal to djk = 8.2 +- 0.001(stat) +- 0.53(sys)pc after geometrical correction. We show that variations in the extinction law toward the Galactic bulge can mimic a barred spatial distribution in the bulge RR~Lyrae star population in visual passbands. This arises from a gradient in extinction differences along Galactic longitudes and latitudes, which can create the perception of the Galactic bar, particularly when using visual passband-based distances. A barred angle in the RR~Lyrae spatial distribution disappears when near-infrared passband-based distances are used, as well as when reddening law variations are incorporated in visual passband-based distances. The prominence of the bar, as traced by RR~Lyrae stars, depends on their metallicity, with metal-poor RR~Lyrae stars ([Fe/H]<-1.0dex) showing little to no tilt with respect to the bar. Metal-rich ([Fe/H]>-1.0dex) RR~Lyrae stars do show a barred/bulge signature in spatial properties derived using near-infrared distances, with an angle {\iota} = 18 +- 5deg, consistent with previous bar measurements from the literature. The 5D kinematic analysis, primarily based on transverse velocities, indicates a rotational lag in RR~Lyrae stars compared to red clump giants. Despite variations in the extinction law, our kinematic conclusions are robust across different distance estimation methods., Comment: Accepted for publication in A&A

The Pseudo-Boolean problem deals with linear or polynomial constraints with integer coefficients over Boolean variables. The objective lies in optimizing a linear objective function, or finding a feasible solution, or finding a solution that satisfies as many constraints as possible. In the 2024 Pseudo-Boolean competition, solvers incorporating the SCIP framework won five out of six categories it was competing in. From a total of 1,207 instances, SCIP successfully solved 759, while its parallel version FiberSCIP solved 776. Based on the results from the competition, we further enhanced SCIP's Pseudo-Boolean capabilities. This article discusses the results and presents the winning algorithmic ideas.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

The evolution of micron-sized droplets in clouds is studied with focus on the 'size-gap' regime of 15-40 $\mu m$ radius, where condensation and differential sedimentation are least effective in promoting growth. This bottleneck leads to inaccurate growth models and turbulence can potentially rectify disagreement with in-situ cloud measurements. The role of turbulent collisions, mixing of droplets, and water vapour fluctuations in crossing the 'size-gap' has been analysed in detail. Collisions driven by the coupled effects of turbulent shear and differential sedimentation are shown to grow drizzle sized droplets. Growth is also promoted by turbulence-induced water vapour fluctuations, which maintain polydispersity during the initial condensation driven growth and facilitate subsequent growth by differential sedimentation driven coalescence. The collision rate of droplets is strongly influenced by non-continuum hydrodynamics and so the size evolution beyond the condensation regime is found to be very sensitive to the mean free path of air. Turbulence-induced inertial clustering leads to a moderate enhancement in the growth rate but the intermittency of the turbulent shear rate does not change the coalescence rate significantly. The coupled influence of all these phenomena is evaluated by evolving a large number of droplets within an adiabatically rising parcel of air using a Monte Carlo scheme that captures turbulent intermittency and mixing.

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

Achieving fast and high-fidelity qubit operations is crucial for unlocking the potential of quantum computers. In particular, reaching low gate errors in two-qubit gates has been a long-standing challenge in the field of superconducting qubits due to their typically long duration relative to coherence times. To realize fast gates, we utilize the hybridization between fixed-frequency superconducting qubits with a strongly interacting coupler mode that is tunable in frequency. To reduce population leakage during required adiabatic passages through avoided level crossings, we employ a sensitivity-adaptive closed-loop optimization method to design complex pulse shapes. We compare the performance of Gaussian-square, Fourier-series, and piecewise-constant-slope (PiCoS) pulse parametrizations and are able to reach 99.9 % controlled-Z gate fidelity using a 64 ns long Fourier-series pulse defined by only seven parameters. These high-fidelity values are achieved by analyzing the optimized pulse shapes to identify and systematically mitigate signal-line distortions in the experiment. To improve the convergence speed of the optimization we implement an adaptive cost function, which continuously maximizes the sensitivity. The demonstrated method can be used for tune-up and recalibration of superconducting quantum processors.

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

This work focuses on the numerical study of a recently published class of Runge-Kutta methods designed for mixed-precision arithmetic. We employ the methods in solving partial differential equations on modern hardware. In particular we investigate what speedups are achievable by the use of mixed precision and the dependence of the methods algorithmic compatibility with the computational hardware. We use state-of-the-art software, utilizing the Ginkgo library, which is designed to incorporate mixed precision arithmetic, and perform numerical tests of 3D problems on both GPU and CPU architectures. We show that significant speedups can be achieved but that performance depends on solver parameters and performance of software kernels.

Let F:K be a Galois extension of number fields and Q a prime ideal of O_F lying over the prime P of O_K. By analyzing the Q-adic closure of O_K in O_F we characterize those rings of integers O_K for which every residue class ring of Int(O_K) modulo a non-zero prime ideal is GE2 (meaning that every unimodular pair can be trasformed to (1,0) by a series of elementary transformations).

This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of 1138 papers from the past six years, sourced from four top FPGA conferences. Such selection underscores the increasing integration of ML and FPGA technologies and their mutual importance in technological advancement. Research clearly emphasises inference acceleration (81\%) compared to training acceleration (13\%). Additionally, the findings reveals that CNN dominates current FPGA acceleration research while emerging models like GNN show obvious growth trends. The categorization of the FPGA research papers reveals a wide range of topics, demonstrating the growing relevance of ML in FPGA research. This comprehensive analysis provides valuable insights into the current trends and future directions of FPGA research in the context of ML applications., Comment: 16 pages, 4 figures (Working paper)

In quantum computing systems the quantum states of qubits can be modified among others by applying light pulses. In order to achieve low computing error rates these pulses have to be precisely shaped in magnitude and phase. In practical applications, both acousto-optic (AOM) and electro-optic modulators (EOM) are used for this purpose. The advantages of EOMs, in particular Mach-Zehnder modulators (MZMs), include e.g. higher bandwidth, compactness, good integrability and better noise performance. On the other hand, EOMs are challenging regarding their control voltage regulation as they experience a bias drift, i.e. voltage shifts in the modulator's operating point. Such a shift significantly impairs the quality of the modulation, which is why EOMs usually require a bias control loop to track the DC operating point. This work addresses the estimation of the instantaneous bias voltage using a small pilot tone, where the optical output power of the EOM is tracked by a photodetector feedback signal.

Neural radiance fields are an emerging 3D scene representation and recently even been extended to learn features for scene understanding by distilling open-vocabulary features from vision-language models. However, current method primarily focus on object-centric representations, supporting object segmentation or detection, while understanding semantic relationships between objects remains largely unexplored. To address this gap, we propose RelationField, the first method to extract inter-object relationships directly from neural radiance fields. RelationField represents relationships between objects as pairs of rays within a neural radiance field, effectively extending its formulation to include implicit relationship queries. To teach RelationField complex, open-vocabulary relationships, relationship knowledge is distilled from multi-modal LLMs. To evaluate RelationField, we solve open-vocabulary 3D scene graph generation tasks and relationship-guided instance segmentation, achieving state-of-the-art performance in both tasks. See the project website at https://relationfield.github.io., Comment: Project page: https://relationfield.github.io

Detection of abrupt spatial changes in physical properties representing unique geometric features such as buried objects, cavities, and fractures is an important problem in geophysics and many engineering disciplines. In this context, simultaneous spatial field and geometry estimation methods that explicitly parameterize the background spatial field and the geometry of the embedded anomalies are of great interest. This paper introduces an advanced inversion procedure for simultaneous estimation using the domain independence property of the Karhunen-Lo\`eve (K-L) expansion. Previous methods pursuing this strategy face significant computational challenges. The associated integral eigenvalue problem (IEVP) needs to be solved repeatedly on evolving domains, and the shape derivatives in gradient-based algorithms require costly computations of the Moore-Penrose inverse. Leveraging the domain independence property of the K-L expansion, the proposed method avoids both of these bottlenecks, and the IEVP is solved only once on a fixed bounding domain. Comparative studies demonstrate that our approach yields two orders of magnitude improvement in K-L expansion gradient computation time. Inversion studies on one-dimensional and two-dimensional seepage flow problems highlight the benefits of incorporating geometry parameters along with spatial field parameters. The proposed method captures abrupt changes in hydraulic conductivity with a lower number of parameters and provides accurate estimates of boundary and spatial-field uncertainties, outperforming spatial-field-only estimation methods.

Superconducting circuits incorporating Josephson elements represent a promising hardware platform for quantum technologies. Potential applications include scalable quantum computing, microwave quantum networks, and quantum-limited amplifiers. However, progress in Josephson junction-based quantum technologies is facing the ongoing challenge of minimizing loss channels. This is also true for parametric superconducting devices based on nonlinear Josephson resonators. In this work, we report on the fabrication and characterization of low-loss Josephson parametric devices operated in the GHz frequency range, showing record internal quality factors. Specifically, we achieve internal quality factors significantly above $10^5$ for both Josephson parametric converters and the Josephson parametric amplifiers at low microwave power ranging in the single-photon regime. These low-loss devices mark a significant step forward in realizing high-performance quantum circuits, enabling further advancements in superconducting quantum technologies.

The chemical abundances of elements such as barium and the lanthanides are essential to understand the nucleosynthesis of heavy elements in the early Universe as well as the contribution of different neutron capture processes (for example slow versus rapid) at different epochs. The Chemical Evolution of R-process Elements in Stars (CERES) project aims to provide a homogeneous analysis of a sample of metal-poor stars ( [Fe/H]\<-1.5) to improve our understanding of the nucleosynthesis of neutron capture elements, in particular the r-process elements, in the early Galaxy. Our data consist of a sample of high resolution and high signal-to-noise ratio UVES spectra. The chemical abundances were derived through spectrum synthesis, using the same model atmospheres and stellar parameters as derived in the first paper of the CERES series. We measured chemical abundances or upper limits of seven heavy neutron capture elements (Ba, La, Ce, Pr, Nd, Sm, and Eu) for a sample of 52 metal-poor giant stars. We estimated through the mean shift clustering algorithm that at Ba/H =-2.4 and Fe/H =-2.4 a variation in the trend of X/Ba with X=La,Nd,Sm,Eu, versus Ba/H occurs. This result suggests that, for Ba/H $\<$$-2.4$, Ba nucleosynthesis in the Milky Way halo is primarily due to the $r$-process, while for Ba/H \<-2.4 the effect of the s-process contribution begins to be visible. In our sample, stars with Ba/Eu compatible with a Solar System pure r-process value (hereafter, r-pure) do not show any particular trend compared to other stars, suggesting r-pure stars may form in similar environments to stars with less pure r-process enrichments. Homogeneous investigations of high resolution and signal-to-noise ratio spectra are crucial for studying the heavy elements formation, as they provide abundances that can be used to test nucleosynthesis models as well as Galactic chemical evolution models., Comment: Astronomy & Astrophysics - A&A, In press

A complete understanding of the initial conditions of high-mass star formation and what processes determine multiplicity require the study of the magnetic field (B-field) in young, massive cores. Using ALMA 250 GHz polarization (0.3" = 1000 au) and ALMA 220 GHz high-angular resolution observations (0.05" = 160 au), we have performed a full energy analysis including the B-field at core scales and have assessed what influences the multiplicity inside a massive core previously believed to be in the prestellar phase. With 31 Msun, the G11.92 MM2 core has a young CS outflow with a dynamical time scale of a few thousand years. At high-resolution, the MM2 core fragments into a binary system with a projected separation of 505 au and a binary mass ratio of 1.14. Using the DCF method with an ADF analysis, we estimate in this core a B-field strength of 6.2 mG and a mass-to-flux ratio of 18. The MM2 core is strongly subvirialized with a virial parameter of 0.064, including the B-field. The high mass-to-flux ratio and low virial parameter indicate that this massive core is very likely undergoing runaway collapse, which is in direct contradiction with the core-accretion model. The MM2 core is embedded in a filament that has a velocity gradient consistent with infall. In line with clump-fed scenarios, the core can grow in mass at a rate of 1.9--5.6 x 10^-4 Msun/yr. In spite of the B-field having only a minor contribution to the total energy budget at core scales, it likely plays a more important role at smaller scales by setting the binary properties. Considering energy ratios and a fragmentation criterion at the core scale, the binary could have been formed by core fragmentation. The binary properties (separation and mass ratio), however, are also consistent with radiation-magnetohydrodynamic simulations with super-Alfvenic, supersonic (or sonic) turbulence that form binaries by disk fragmentation., Comment: Accepted for publications in ApJ (9 pages, 3 figures, Appendix)

We construct infinitely many new $\frac{1}{16}$-BPS cohomologies of the 4d maximal super-Yang-Mills theory and interpret them as a black hole wrapped by dual giant graviton hairs. Since the black hole inside a dual giant feels the RR 5-form flux reduced by one unit, its microstate should essentially be an $SU(N-1)$ cohomology. However, due to the fortuitous nature of the black hole microstates, promoting an $SU(N-1)$ black hole state to $SU(N)$ generally fails to yield a cohomology. We show at $N=3$ that suitable fusion products with the dual giants yield cohomologies. The core black hole size is probed by the minimal size of the dual giant which can wrap it. We also discuss two types of large black hole hairs: large conformal descendants of gravitons and large dual giants. We prove that any $SU(N)$ black hole cohomology admits infinitely many hairs of the first type., Comment: 42 pages

In practice, non-specialized interior point algorithms often cannot utilize the massively parallel compute resources offered by modern many- and multi-core compute platforms. However, efficient distributed solution techniques are required, especially for large-scale linear programs. This article describes a new decomposition technique for systems of linear equations implemented in the parallel interior-point solver PIPS-IPM++. The algorithm exploits a matrix structure commonly found in optimization problems: a doubly-bordered block-diagonal or arrowhead structure. This structure is preserved in the linear KKT systems solved during each iteration of the interior-point method. We present a hierarchical Schur complement decomposition that distributes and solves the linear optimization problem; it is designed for high-performance architectures and scales well with the availability of additional computing resources. The decomposition approach uses the border constraints' locality to decouple the factorization process. Our approach is motivated by large-scale unit commitment problems. We demonstrate the performance of our method on a set of mid-to large-scale instances, some of which have more than 10^9 nonzeros in their constraint matrix.

Intermediate mass black holes (10^2 < M_BH< 10^5 Msun) are an open question in our understanding of black hole evolution and growth. They have long been linked to dense star cluster environments thanks to cluster dynamics, but there are a limited number of secure detections. We leverage existing X-ray observations from Chandra X-ray Observatory and optical catalogs from Hubble Space Telescope with new radio observations from the Karl G. Jansky Very Large Array to search for any evidence of accreting black holes in young massive clusters in the nearby galaxy M51. We find that of 43 bright ($L_X > 10^{38}$ erg/s) X-ray point sources in M51, 24 had probable matches to objects including possible associated star clusters in the HST Legacy Extragalactic UV Survey catalog, seven of which were classified as contaminants (background galaxies or foreground stars). We explore the optical properties of the remaining 17 sources, including cluster age and mass estimates, and search for radio counterparts in the 8-12 GHz band. The lack of radio counterparts to X-ray sources we know to be associated with young massive clusters in M51 suggests that we do not significantly detect hard-state IMBHs ~ 10^4 Msun or above. However, more sensitive radio facilities like the Square Kilometre Array and next generation Very Large Array may be able to provide evidence for IMBHs with masses down to ~ 10^3 Msun., Comment: 18 pages, 7 figures, accepted to ApJ

Superconducting microwave resonators are critical to quantum computing and sensing technologies. Additionally, they are common proxies for superconducting qubits when determining the effects of performance-limiting loss mechanisms such as from two-level systems (TLS). The extraction of these loss mechanisms is often performed by measuring the internal quality factor $Q_i$ as a function of power or temperature. In this work, we investigate large temporal fluctuations of $Q_i$ at low powers over periods of 12 to 16 hours (relative standard deviation $\sigma_{Qi}/Q_i = 13\%$). These fluctuations are ubiquitous across multiple resonators, chips and cooldowns. We are able to attribute these fluctuations to variations in the TLS loss tangent due to two main indicators. First, measured fluctuations decrease as power and temperature increase. Second, for interleaved measurements, we observe correlations between low- and medium-power $Q_i$ fluctuations and an absence of correlations with high-power fluctuations. Agreement with the TLS loss tangent mean is obtained by performing measurements over a time span of a few hours. We hypothesize that, in addition to decoherence due to coupling to individual near-resonant TLS, superconducting qubits are affected by these observed TLS loss tangent fluctuations.

Encoding classical data in a quantum state is a key prerequisite of many quantum algorithms. Recently matrix product state (MPS) methods emerged as the most promising approach for constructing shallow quantum circuits approximating input functions, including probability distributions, with only linear number of gates. We derive rigorous asymptotic expansions for the decay of entanglement across bonds in the MPS representation depending on the smoothness of the input function, real or complex. We also consider the dependence of the entanglement on localization properties and function support. Based on these analytical results we construct an improved MPS-based algorithm yielding shallow and accurate encoding quantum circuits. By using Tensor Cross Interpolation we are able to construct utility-scale quantum circuits in a compute- and memory-efficient way. We validate our methods on heavy-tailed distributions important in finance, including on L\'evy distributions. We test the performance of the resulting quantum circuits by executing and sampling from them on IBM quantum devices, for up to 64 qubits.

The ITk is a new silicon tracker for the ATLAS experiment designed to increase detector resolution, readout capacity, and radiation hardness, in preparation for the larger number of simultaneous proton-proton interactions at the High Luminosity LHC. This paper presents the first direct measurement of the material budget of an ATLAS ITk pixel module, performed at a testbeam at the CERN Proton Synchrotron via the multiple scattering of low energy positrons within the module volume. Using a four plane telescope of thin monolithic pixel detectors from the MALTA collaboration, scattering datasets were recorded at a beam energy of $1.2\,\text{GeV}$. Kink angle distributions were extracted from tracks derived with and without information from the ITk pixel module, and were fit to extract the RMS scattering angle, which was converted to a fractional radiation length $x/X_0$. The average $x/X_0$ across the module was measured as $[0.89 \pm 0.01 \text{ (resolution)} \pm 0.01 \text{ (subtraction)} \pm 0.08 \text{ (beam momentum band)}]\%$, which agrees within uncertainties with an estimate of $0.88\%$ derived from material component expectations., Comment: 12 pages, 12 figures

Developments in machine learning and computing power suggest that artificial general intelligence is within reach. This raises the question of artificial consciousness: if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do when we are conscious? Answering this question in a principled manner can only be done on the basis of a theory of consciousness that is grounded in phenomenology and that states the necessary and sufficient conditions for any system, evolved or engineered, to support subjective experience. Here we employ Integrated Information Theory (IIT), which provides principled tools to determine whether a system is conscious, to what degree, and the content of its experience. We consider pairs of systems constituted of simple Boolean units, one of which -- a basic stored-program computer -- simulates the other with full functional equivalence. By applying the principles of IIT, we demonstrate that (i) two systems can be functionally equivalent without being phenomenally equivalent, and (ii) that this conclusion is not dependent on the simulated system's function. We further demonstrate that, according to IIT, it is possible for a digital computer to simulate our behavior, possibly even by simulating the neurons in our brain, without replicating our experience. This contrasts sharply with computational functionalism, the thesis that performing computations of the right kind is necessary and sufficient for consciousness.

Autonomous driving (AD) operates in open-world scenarios, where encountering unknown objects is inevitable. However, standard object detectors trained on a limited number of base classes tend to ignore any unknown objects, posing potential risks on the road. To address this, it is important to learn a generic rather than a class specific objectness from objects seen during training. We therefore introduce an occupancy prediction together with bounding box regression. It learns to score the objectness by calculating the ratio of the predicted area occupied by actual objects. To enhance its generalizability, we increase the object diversity by exploiting data from other domains via Mosaic and Mixup augmentation. The objects outside the AD training classes are classified as a newly added out-of-distribution (OOD) class. Our solution UNCOVER, for UNknown Class Object detection for autonomous VEhicles in Real-time, excels at achieving both real-time detection and high recall of unknown objects on challenging AD benchmarks. To further attain very low false positive rates, particularly for close objects, we introduce a post-hoc filtering step that utilizes geometric cues extracted from the depth map, typically available within the AD system.

The CO-to-H$_2$ conversion factor ($\alpha_\mathrm{CO}$) is expected to vary with dust abundance and grain size distribution through the efficiency of shielding gas from CO-dissociation radiation. We present a comprehensive analysis of $\alpha_\mathrm{CO}$ and grain size distribution for nearby galaxies, using the PAH fraction ($q_\mathrm{PAH}$) as an observable proxy of grain size distribution. We adopt the resolved observations at 2-kpc resolution in 42 nearby galaxies, where $\alpha_\mathrm{CO}$ is derived from measured metallicity and surface densities of dust and HI assuming a fixed dust-to-metals ratio. We use an analytical model for the evolution of H$_2$ and CO, in which the evolution of grain size distribution is controlled by the dense gas fraction ($\eta$). We find that the observed level of $q_\mathrm{PAH}$ is consistent with the diffuse-gas-dominated model ($\eta=0.2$) where dust shattering is more efficient. Meanwhile, the slight decreasing trend of observed $q_\mathrm{PAH}$ with metallicity is more consistent with high-$\eta$ predictions, likely due to the more efficient loss of PAHs by coagulation. We discuss how grain size distribution (indicated by $q_\mathrm{PAH}$) and metallicity impact $\alpha_\mathrm{CO}$; we however did not obtain conclusive evidence that the grain size distribution affects $\alpha_\mathrm{CO}$. Observations and model predictions show similar anti-correlation between $\alpha_\mathrm{CO}$ and 12+log(O/H). Meanwhile, there is a considerable difference in how resolved $\alpha_\mathrm{CO}$ behaves with $q_\mathrm{PAH}$. The observed $\alpha_\mathrm{CO}$ has a positive correlation with $q_\mathrm{PAH}$, while the model-predicted $\alpha_\mathrm{CO}$ does not have a definite correlation with $q_\mathrm{PAH}$. This difference is likely due to the limitation of one-zone treatment in the model., Comment: 12 pages, 7 figures, accepted for publication in MNRAS

We investigate the nonlinear dynamics of vertically emitting Kerr microcavities under detuned optical injection, considering the impact of slow thermal effects. Our model integrates thermal detuning caused by refractive index shifts due to heating. Through numerical and analytical approaches, we uncover a rich spectrum of dynamical behaviors, including excitable thermo-optical pulses, mixed-mode oscillations, and chaotic spiking, governed by a higher-dimensional canard scenario. Introducing a long external feedback loop with time delays comparable to the microcavity photon lifetime but shorter than thermal relaxation timescales, reveals how delay affects excitability and stabilizes temporal localized states. Our findings extend the understanding of excitable systems, demonstrating how thermal and feedback mechanisms interplay to shape nonlinear optical dynamics. Further, our approach paves the way for the study of cavity stabilization and cavity cooling using an additional control beam.

While photodissociation of molecular systems has been extensively studied, the photoinduced formation of chemical bonds remains largely unexplored. Especially for larger aggregates, the electronic and nuclear dynamics involved in the cluster formation process remain elusive. This limitation is rooted in difficulties to prepare reactants at well-defined initial conditions. Here, we overcome this hurdle by exploiting the exceptional solvation properties of helium nanodroplets. We load the droplets with Mg atoms and investigate the dynamical response of the formed Mg$_n$ aggregates to photoexcitation with time-resolved photoelectron spectroscopy. Beside the response expected for conventional Mg$_n$ clusters, consisting of a prompt signal rise and a decay characteristic for van der Waals bonds, the transient spectra also show a delayed photoelectron band peaking at 1.2 ps. This delayed signal rise is characteristic for nuclear dynamics and represents the transition of Mg$_n$ aggregates from a metastable, foam-like configuration, where Mg atoms are stabilized with a previously predicted interatomic spacing of 9.5 A, to a compact cluster. With global fitting analysis and ion-electron coincidence detection, the concerted electronic and nuclear dynamics can be tracked on a fs timescale. We find that cluster formation, proceeding with a ($450\pm180$) fs time constant, is accompanied by the population of highly-excited atomic states. We propose an energy pooling reaction in collisions of two or more excited Mg atoms during cluster formation as the mechanism leading to population of these high-lying Mg states. Additionally, conversion to kinetic energy through electronic relaxation leads to fragmentation and ejection of ionic cluster fragments from the He droplet. These results underline the potential of He droplets for time-resolved studies of bond formation and to uncover involved processes.

Electron microscopy techniques such as electron energy-loss spectroscopy (EELS) facilitate the spatio-spectral characterization of plasmonic nanostructures. In this work, a time-dependent perspective is presented, which significantly enhances the utility of EELS. Specifically, silver nanowires offer the material and geometric features for various high-quality plasmonic excitations. This provides an ideal illustrative system for combined experimental-theoretical analyses of the different plasmonic excitations and their real-time dynamics. It is demonstrated how the plasmonic excitations propagating inside the wire repeatedly interact with the swift electrons in an EELS configuration. In addition, the role of azimuthal modes, often overlooked for very thin wires, is observed and analyzed in both the energy-loss spectrum and the dynamical perspective. Such a complete understanding of the interaction of electrons and plasmonic excitation is key for the design of efficient plasmonic sensors, the study of hot electron dynamics in metals, and applications in the context of electron quantum optics, where full control of the spatial and temporal characteristics of the fields at the nanometer and femtosecond scales is highly desirable.

Interior compositions are key for our understanding of Earth-like exoplanets. The composition of the core can influence the presence of a magnetic dynamo and the strength of gravity on the planetary surface, both of which heavily impact thermal and possible biological processes and thus the habitability for life and its evolution on the planet. However, detailed measurements of the planetary interiors are extremely challenging for small exoplanets, and existing data suggest a wide diversity in planet compositions. Hitherto, only certain photospheric chemical abundances of the host stars have been considered as tracers to explain the diversity of exoplanet compositions. Here we present a homogeneous analysis of stars hosting rocky exoplanets, with ages between 2 and 14 Gyr, revealing a correlation between rocky exoplanet compositions and the ages of the planetary systems. Denser rocky planets are found around younger stars. This suggests that the compositional diversity of rocky exoplanets can be linked to the ages of their host stars. We interpret this to be a result of chemical evolution of stars in the Milky Way, which modifies the material out of which stars and planets form. The results imply that rocky planets which form today, at similar galactocentric radii, may have different formation conditions, and thus different properties than planets which formed several billion years ago, such as the Earth.

Qubits that are intrinsically insensitive to depolarization and dephasing errors promise to significantly reduce the overhead of fault-tolerant quantum computing. At their optimal operating points, the logical states of these qubits exhibit both exponentially suppressed matrix elements and sweet spots in energy dispersion, rendering the qubits immune to depolarization and dephasing, respectively. We introduce a multimode qubit, the protomon, encoded in a fluxonium molecule circuit. Compared to the closely related $0$-$\pi$ qubit, the protomon offers several advantages in theory: resilience to circuit parameter disorder, minimal dephasing from intrinsic harmonic modes, and no dependence on static offset charge. As a proof of concept, we realize four protomon qubits. By tuning the qubits to various operating points identified with calibrated two-tone spectroscopy, we measure depolarization times ranging from 64 to 73 $\mu$s and dephasing times between 0.2 to 0.5 $\mu$s for one selected qubit. The discrepancy between the relatively short measured coherence times and theoretical predictions is not fully understood. This calls for future studies investigating the limiting noise factors, informing the direction for improving coherence times of the protomon qubit., Comment: 13 pages, 6 figures

Recently, a class of long-period radio transients (LPTs) has been discovered, exhibiting emission on timescales thousands of times longer than radio pulsars. Several models had been proposed implicating either a strong magnetic field neutron star, isolated white dwarf pulsar, or a white dwarf binary system with a low-mass companion. While several models for LPTs also predict X-ray emission, no LPTs have been detected in X-rays despite extensive searches. Here we report the discovery of an extremely bright LPT (10-20 Jy in radio), ASKAP J1832-0911, which has coincident radio and X-ray emission, both with a 44.2-minute period. The X-ray and radio luminosities are correlated and vary by several orders of magnitude. These properties are unique amongst known Galactic objects and require a new explanation. We consider a $\gtrsim0.5$ Myr old magnetar with a $\gtrsim 10^{13}$ G crustal field, or an extremely magnetised white dwarf in a binary system with a dwarf companion, to be plausible explanations for ASKAP J1832-0911, although both explanations pose significant challenges to formation and emission theories. The X-ray detection also establishes a new class of hour-scale periodic X-ray transients of luminosity $\sim10^{33}$ erg/s associated with exceptionally bright coherent radio emission., Comment: 52 pages, 17 figures, 3 tables

We demonstrate sub-Doppler cooling of calcium atoms using a two-photon transition from the ${^1}S_0$ ground state to the upper $4s5s~{^1}S_0$ state via the ${^1}P_1$ intermediate state. We achieve temperatures as low as $260~\mu\text{K}$ in a magneto-optical trap (MOT), well below the Doppler limit ($T_{\text{D}} = 0.8~\text{mK}$) of the ${^1}P_1$ state. We characterize temperature, lifetime and confinement of the MOT over a range of experimental parameters, observing no reduction in lifetime due to coupling to the higher state. We perform theoretical simulations of the cooling scheme and observe good agreement with the experimental results. The two-photon cooling scheme presented in this work provides an alternative to the standard Doppler cooling applied to alkaline-earth atoms, based on a sequence of two magneto-optical traps. The advantages of our scheme are the possibility of varying the effective linewidth of the ${^1}P_1$ state, a higher transfer efficiency (close to 100$\%$), and a more straightforward experimental implementation., Comment: 8 pages 4+1 figures

The surprising inversion of the orbital- and magnetic-order transition temperatures in the RVO3 series with increasing the rare-earth radius makes the series unique among orbitally-ordered materials. Here, augmenting dynamical mean-field theory with a decomposition of the order parameter into irreducible tensors, we show that this anomalous behavior emerges from an unusual hierarchy of interactions. First, increasing the rare-earth radius, orbital physics comes to be controlled by xz-xz quadrupolar super-exchange rather than by lattice distortion. Next, for antiferromagnetic spin order, orbital super-exchange terms with different spin rank compete, so that the dipolar spin-spin interaction dominates. Eventually, G-type magnetic order (anti-ferro in all directions) can appear already above the orbital ordering transition, and C-type order (anti-ferro in the ab plane) right around it. The strict constraints we found explain why the inversion is rare, giving at the same time criteria to look for similar behavior in other materials., Comment: 5 pages, 4 figures; submitted on 25 Oct 2024

Histopathology, the microscopic study of diseased tissue, is increasingly digitized, enabling improved visualization and streamlined workflows. An important task in histopathology is the segmentation of cells and glands, essential for determining shape and frequencies that can serve as indicators of disease. Deep learning tools are widely used in histopathology. However, variability in tissue appearance and cell morphology presents challenges for achieving reliable segmentation, often requiring manual correction to improve accuracy. This work introduces CellPilot, a framework that bridges the gap between automatic and interactive segmentation by providing initial automatic segmentation as well as guided interactive refinement. Our model was trained on over 675,000 masks of nine diverse cell and gland segmentation datasets, spanning 16 organs. CellPilot demonstrates superior performance compared to other interactive tools on three held-out histopathological datasets while enabling automatic segmentation. We make the model and a graphical user interface designed to assist practitioners in creating large-scale annotated datasets available as open-source, fostering the development of more robust and generalized diagnostic models.

Hourly maxima of 3-second wind gust speeds are prominent indicators of the severity of wind storms, and accurately forecasting them is thus essential for populations, civil authorities and insurance companies. Space-time max-stable models appear as natural candidates for this, but those explored so far are not suited for forecasting and, more generally, the forecasting literature for max-stable fields is limited. To fill this gap, we consider a specific space-time max-stable model, more precisely a max-autoregressive model with advection, that is well-adapted to model and forecast atmospheric variables. We apply it, as well as our related forecasting strategy, to reanalysis 3-second wind gust data for France in 1999, and show good performance compared to a competitor model. On top of demonstrating the practical relevance of our model, we meticulously study its theoretical properties and show the consistency and asymptotic normality of the space-time pairwise likelihood estimator which is used to calibrate the model., Comment: 35 pages + 25 pages of supplementary material, 12 figures, 1 table

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data., Comment: 21 pages, 11 figures, 2 tables

The digitization of complex technical systems, such as Piping and Instrumentation Diagrams (P&IDs), is crucial for efficient maintenance and operation of complex systems in hydraulic and process engineering. Previous approaches often rely on separate modules that analyze diagram elements individually, neglecting the diagram's overall structure. We address this limitation by proposing a novel approach that utilizes the Relationformer, a state-of-the-art deep learning architecture, to extract graphs from P&IDs. Our method leverages the ability of the Relationformer to simultaneously detect objects and their relationships in images, making it suitable for the task of graph extraction from engineering diagrams. We apply our proposed approach to both real-world and synthetically created P&ID datasets, and evaluate its effectiveness by comparing it with a modular digitization approach based on recent literature. We present PID2Graph, the first publicly accessible P&ID dataset featuring comprehensive labels for the graph structure, including symbols, nodes and their connections that is used for evaluation. To understand the effect of patching and stitching of both of the approaches, we compare values before and after merging the patches. For the real-world data, the Relationformer achieves convincing results, outperforming the modular digitization approach for edge detection by more than 25%. Our work provides a comprehensive framework for assessing the performance of P&ID digitization methods and opens up new avenues for research in this area using transformer architectures. The P&ID dataset used for evaluation will be published and publicly available upon acceptance of the paper.