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Objectives: To identify a head acceleration event (HAE) severity measure associated with HIA1 removals in elite level rugby union. Methods: HAEs were recorded from 215 men and 325 women with 30 and 28 HIA1 removals from men and women, respectively. Logistical regression were calculated to identify if peak power, maximum principal strain (MPS) and or Head Acceleration Response Metric (HARM) were associated with HIA1 events compared to non-cases. Optimal threshold values were determined using the Youden Index. Area under the curve (AUC) were compared using a paired sample approach. Significant differences were set at p<0.05. Results: All three severity measures were associated with HIA1 removals in both the mens and womens game. Power performed greatest for HIA1 removals in both the mens and womens games, based on overall AUC, sensitivity, and specificity values. HARM and MPS were found to perform lower than PLA in the womens game based on AUC comparisons (p=0.006 and 0.001, respectively), with MPS performing lower than PAA (p=0.001). Conclusion: The findings progress our understanding of HAE measures associated with HIA1 removals. Peak power, a measure based on fundamental mechanics and commonly used in sports performance, may be a suitable HAE severity measure., Comment: 4 Tables, 2 Figures

Periodic Leaky-Wave Antennas (P-LWA) offer highly directive and space-scanning radiation. Unfortunately, they have been plagued by the ``broadside issue'', characterized by a degradation in gain when the antenna's main beam is steered across broadside. While this issue has been addressed by circuit and network approaches, a related fundamental and general electromagnetic theory has been lacking. This paper fills this gap. We first show that a P-LWA is a $\mathcal{PT}$-symmetric system, whose even- and odd-mode coupling in the complex space of temporal frequencies leads to the characteristic pair of two-sheet Riemann surfaces. We observe that the branch cuts of these surfaces, which form the well-known $\mathcal{PT}$-symmetric double pitchfork spectrum, correspond to the ``balanced frequency'' condition, while the branch point at the junction of the pitchforks, is an exceptional point that corresponds to the ``$Q$-balanced'' condition, two conditions that where previously shown to be the conditions for eliminating the broadside issue. In order to acquire an independent and rigorous interpretation of this spectrum, we further transform the coupled complex temporal eigenfrequencies into complex spatial eigenfrequencies. We identify the resulting spatial frequencies as coupled forward-backward modes, with frequency-independent imaginary parts (leakage factors), a condition for P-LWA equalization across broadside. Finally, we derive the scattering parameters of the P-LWA and show that matching is achieved only at one of the two ends of the P-LWA structure. This work both provides a solid foundation to the theory of P-LWAs and represents an original contribution to the field of $\mathcal{PT}$-symmetry.

Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous concepts due to storage/privacy concerns. When faced with this continual learning (CL) setup, most personalization methods fail to find a balance between acquiring new concepts and retaining previous ones -- a challenge that continual personalization (CP) aims to solve. Inspired by the successful CL methods that rely on class-specific information for regularization, we resort to the inherent class-conditioned density estimates, also known as diffusion classifier (DC) scores, for continual personalization of text-to-image diffusion models. Namely, we propose using DC scores for regularizing the parameter-space and function-space of text-to-image diffusion models, to achieve continual personalization. Using several diverse evaluation setups, datasets, and metrics, we show that our proposed regularization-based CP methods outperform the state-of-the-art C-LoRA, and other baselines. Finally, by operating in the replay-free CL setup and on low-rank adapters, our method incurs zero storage and parameter overhead, respectively, over the state-of-the-art., Comment: Work under review, 26 pages of manuscript

Segre-Veronese manifolds are smooth manifolds consisting of partially symmetric rank-$1$ tensors. They are naturally viewed as submanifolds of a Euclidean space of tensors. However, they can also be equipped with other metrics than their induced metrics, such as warped product metrics. We investigate a one-parameter family of warped geometries, which includes the standard Euclidean geometry, and whose parameter controls by how much spherical tangent directions are weighted relative to radial tangent directions. We compute the exponential and logarithmic maps of these warped Segre-Veronese manifolds, including the corresponding starting and connecting geodesics. A closed formula for the intrinsic distance between points on the warped Segre-Veronese manifold is presented. We determine for which warping parameters Segre-Veronese manifolds are geodesically connected and show that they are not geodesically connected in the standard Euclidean geometry. The benefits of connecting geodesics may outweigh using the Euclidean geometry in certain applications. One such application is presented: numerically computing the Riemannian center of mass for averaging rank-$1$ tensors., Comment: 22 pages, 2 figures

Fast radio bursts (FRBs) are millisecond-duration radio waves from the Universe. Even though more than 50 physical models have been proposed, the origin and physical mechanism of FRB emissions are still unknown. The classification of FRBs is one of the primary approaches to understanding their mechanisms, but previous studies classified conventionally using only a few observational parameters, such as fluence and duration, which might be incomplete. To overcome this problem, we use an unsupervised machine-learning model, the Uniform Manifold Approximation and Projection (UMAP) to handle seven parameters simultaneously, including amplitude, linear temporal drift, time duration, central frequency, bandwidth, scaled energy, and fluence. We test the method for homogeneous 977 sub-bursts of FRB 20121102A detected by the Arecibo telescope. Our machine-learning analysis identified five distinct clusters, suggesting the possible existence of multiple different physical mechanisms responsible for the observed FRBs from the FRB 20121102A source. The geometry of the emission region and the propagation effect of FRB signals could also make such distinct clusters. This research will be a benchmark for future FRB classifications when dedicated radio telescopes such as the Square Kilometer Array (SKA) or Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) discover more FRBs than before., Comment: Accepted for publication for PASA

Axionlike particles (ALPs) can be produced in meson decays via tree-level quark-flavor-violating couplings, and decay to a pair of sterile neutrinos if also coupled to them. Such light sterile neutrinos are necessarily long-lived and may give rise to striking signatures of displaced vertices (DVs) at terrestrial experiments. In this work, we study the prospect of the ongoing Belle II experiment for detecting the sterile neutrinos from the ALP, leveraging the $\mathcal{O}(10^{10})$ $B$-mesons produced at the experiment. We propose search strategies for one or two DVs, and perform Monte-Carlo simulations in order to estimate the sensitivity reach of Belle II to active-sterile-neutrino mixing $|V_{eN}|^2$ as functions of the sterile-neutrino mass, under the legitimate assumption of zero background. Our results show that the proposed search strategies can probe values of $|V_{eN}|^2$ orders of magnitude beyond the existing bounds. Compared to the one-DV search, the two-DV one, despite its weaker sensitivities as a result of double exponential suppression of the sterile-neutrino decay positions, possesses the advantage of possible full reconstruction of the signal event allowing for pinning down the masses of the sterile neutrino and the ALP, if a discovery is made., Comment: 8 pages+refs, 4 figs

Quantum Entanglement is a vital phenomenon required for realizing secure quantum networks, so much that distributed entanglement can be re-imagined as a commodity which can be traded to enable and maintain these networks. We explore the idea of commercializing entanglement-based cryptography and future applications where advanced quantum memory systems support less advanced users. We design a sneakernet-based quantum communication network with a central party connecting the users through delayed-choice quantum entanglement swapping, using quantum Low-Density-Parity-Check (qLDPC) encoded qubits on neutral atoms. Our analysis compares this approach with traditional surface codes, demonstrating that qLDPC codes offer superior scaling in terms of resource efficiency and logical qubit count. We show that with near-term attainable patch sizes, one can attain medium-to-high fidelity correlations, paving the way towards large-scale commercial quantum networks.

The optimization of expensive black-box simulators arises in a myriad of modern scientific and engineering applications. Bayesian optimization provides an appealing solution, by leveraging a fitted surrogate model to guide the selection of subsequent simulator evaluations. In practice, however, the objective is often not to obtain a single good solution, but rather a ''basket'' of good solutions from which users can choose for downstream decision-making. This need arises in our motivating application for real-time control of internal combustion engines for flight propulsion, where a diverse set of control strategies is essential for stable flight control. There has been little work on this front for Bayesian optimization. We thus propose a new Diverse Expected Improvement (DEI) method that searches for diverse ''$\epsilon$-optimal'' solutions: locally-optimal solutions within a tolerance level $\epsilon > 0$ from a global optimum. We show that DEI yields a closed-form acquisition function under a Gaussian process surrogate model, which facilitates efficient sequential queries via automatic differentiation. This closed form further reveals a novel exploration-exploitation-diversity trade-off, which incorporates the desired diversity property within the well-known exploration-exploitation trade-off. We demonstrate the improvement of DEI over existing methods in a suite of numerical experiments, then explore the DEI in two applications on rover trajectory optimization and engine control for flight propulsion.

In this paper, we present a simple but compelling argument, focusing on galaxy science, for preserving the main imagers and operational modes of the Hubble Space Telescope (HST) for as long as is technically feasible. While star-formation started at redshifts z$\gtrsim$10$-$13, when the universe was less than 300$-$500 Myr old, the CSFH did not peak until z$\simeq$1.9, and has steadily declined since that time. Hence, at least half of all stars in the universe formed in the era where HST provides its unique rest-frame UV view of unobscured young, massive stars tracing cosmic star-formation. By rendering a subset of the 556.3 hours of available HST images in 12 filters of the Hubble Ultra Deep Field (HUDF) in an appropriate mix of colors, we illustrate the unique capabilities of HST for galaxy science emphasizing that rest-frame UV$-$optical wavelength range. We then contrast this with the 52.7 publicly available hours of JWST/NIRCam images in 8 filters of the same HUDF area from the JADES project, rendering these at the redder near-IR wavelengths to illustrate the unique capabilities of JWST to detect older stellar populations at higher redshifts, as well as very dusty stellar populations and Active Galactic Nuclei (AGN). HST uniquely probes (unobscured) young, hot, massive stars in galaxies, while JWST reveals more advanced stages of older stellar populations, as well as relatively short-lived phases where galaxies produce and shed a lot of dust from intense star-formation, and the very high redshift universe (z$\gtrsim$10$-$11) not accessible by HST. We conclude that HST and JWST are highly complementary facilities that took decades to build to ensure decades of operation. To maximize return on investment on both HST and JWST, ways will need to be found to operate HST imaging instruments in all relevant modes for as long as possible into the JWST mission., Comment: White paper submitted to NASA/STScI for the HST Senior Review of 2024. The HST+JWST PDF images are too big for astro-ph. Please view their full resolution PDF files on: http://www.asu.edu/clas/hst/www/jwst2024/windhorst_HST_SeniorReview2024_v4.pdf

The effective adoption of responsible AI practices in journalism requires a concerted effort to bridge different perspectives, including technological, editorial, journalistic, and managerial. Among the many challenges that could impact information sharing around responsible AI inside news organizations are knowledge silos, where information is isolated within one part of the organization and not easily shared with others. This study aims to explore if, and if so, how, knowledge silos affect the adoption of responsible AI practices in journalism through a cross-case study of four major Dutch media outlets. We examine the individual and organizational barriers to AI knowledge sharing and the extent to which knowledge silos could impede the operationalization of responsible AI initiatives inside newsrooms. To address this question, we conducted 14 semi-structured interviews with editors, managers, and journalists at de Telegraaf, de Volkskrant, the Nederlandse Omroep Stichting (NOS), and RTL Nederland. The interviews aimed to uncover insights into the existence of knowledge silos, their effects on responsible AI practice adoption, and the organizational practices influencing these dynamics. Our results emphasize the importance of creating better structures for sharing information on AI across all layers of news organizations.

As small radiation fields play an ever-increasing role in radiation therapy, accurate dosimetry of these fields becomes critical to ensure high quality dose calculation and treatment optimization. The PRB-0002, a new plastic scintillation detector (PSD) part of the Hyperscint RP-200 dosimetric platform from Medscint, can potentially facilitate small field measurements. In this work our objective is twofold: first we perform a detailed characterization of the field output correction factors of the PRB-0002 over a wide range of field sizes; second we demonstrate how this PSD can be used to determine the field output correction factors for other small field detectors. Uncertainties were carefully considered and a detailed uncertainty budget is provided. EGSnrc Monte Carlo simulations were conducted to determine the impact of detector composition, surrounding materials, dose averaging within the sensitive volume as well as ionization quenching. From these simulations, the field output correction factors of this PSD were determined. Then, by experimental comparisons, field output correction factors for 2 solid state detectors and 3 small volume ion chambers were. With PRB-0002, the material composition was found to be balanced with the ionization quenching making the field output correction factor near unity. For fields between 0.6 $\times$ 0.6 and 30 $\times$ 30~cm$^2$, the field output correction factors of the PRB-0002 were between 1.002 and 0.999 with a total uncertainty of 0.5 %. Analysis of the uncertainty budget showed that an overall uncertainty of 0.59% can be achieved for a 1 $\times$ 1~cm$^2$ using PRB-0002. We conclude that PRB-0002 is a near-ideal detector for small field dosimetry. Furthermore, it can be used to experimentally determine the field output correction factors of other dosimeters with great accuracy.

We present the discovery of Aquarius III, an ultra-faint Milky Way satellite galaxy identified in the second data release of the DECam Local Volume Exploration (DELVE) survey. Based on deeper follow-up imaging with DECam, we find that Aquarius III is a low-luminosity ($M_V = -2.5^{+0.3}_{-0.5}$; $L_V = 850^{+380}_{-260} \ L_{\odot}$), extended ($r_{1/2} = 41^{+9}_{-8}$ pc) stellar system located in the outer halo ($D_{\odot} = 85 \pm 4$ kpc). From medium-resolution Keck/DEIMOS spectroscopy, we identify 11 member stars and measure a mean heliocentric radial velocity of $v_{\rm sys} = -13.1^{+1.0}_{-0.9} \ \rm km \ s^{-1}$ for the system and place an upper limit of $\sigma_v < 3.5 \rm \ km \ s^{-1}$ ($\sigma_v < 1.6 \rm \ km \ s^{-1}$) on its velocity dispersion at the 95% (68%) credible level. Based on Calcium-Triplet-based metallicities of the six brightest red giant members, we find that Aquarius III is very metal-poor ([Fe/H]$ = -2.61 \pm 0.21$) with a statistically-significant metallicity spread ($\sigma_{\rm [Fe/H]} = 0.46^{+0.26}_{-0.14}$ dex). We interpret this metallicity spread as strong evidence that the system is a dwarf galaxy as opposed to a star cluster. Combining our velocity measurement with $Gaia$ proper motions, we find that Aquarius III is currently situated near its orbital pericenter in the outer halo ($r_{\rm peri} = 78 \pm 7$ kpc) and that it is plausibly on first infall onto the Milky Way. This orbital history likely precludes significant tidal disruption from the Galactic disk, notably unlike other satellites with comparably low velocity dispersion limits in the literature. Thus, if further velocity measurements confirm that its velocity dispersion is truly below $\sigma_v \lesssim 2 \rm \ km \ s^{-1}$, Aquarius III may serve as a useful laboratory for probing galaxy formation physics in low-mass halos., Comment: 25 pages, 7 figures. Submitted to AAS Journals

The band-projected density operators in a Landau level obey the Girvin-MacDonald-Platzman (GMP) algebra, and a large amount of effort in the study of fractional Chern insulators has been directed towards approximating this algebra in a Chern band. In this paper, we prove that the GMP algebra, up to form factors, is the $\textit{only}$ closed algebra that projected density operators can satisfy in two and three dimensions, highlighting the central place it occupies in the study of Chern bands in general. A number of interesting corollaries follow., Comment: 5 pages

Measurements of galaxy clustering are affected by RSD. Peculiar velocities, gravitational lensing, and other light-cone projection effects modify the observed redshifts, fluxes, and sky positions of distant light sources. We determine which of these effects leave a detectable imprint on several 2-point clustering statistics extracted from the EWSS on large scales. We generate 140 mock galaxy catalogues with the survey geometry and selection function of the EWSS and make use of the LIGER method to account for a variable number of relativistic RSD to linear order in the cosmological perturbations. We estimate different 2-point clustering statistics from the mocks and use the likelihood-ratio test to calculate the statistical significance with which the EWSS could reject the null hypothesis that certain relativistic projection effects can be neglected in the theoretical models. We find that the combined effects of lensing magnification and convergence imprint characteristic signatures on several clustering observables. Their S/N ranges between 2.5 and 6 (depending on the adopted summary statistic) for the highest-redshift galaxies in the EWSS. The corresponding feature due to the peculiar velocity of the Sun is measured with a S/N of order one or two. The $P_{\ell}(k)$ from the catalogues that include all relativistic effects reject the null hypothesis that RSD are only generated by the variation of the peculiar velocity along the line of sight with a significance of 2.9 standard deviations. As a byproduct of our study, we demonstrate that the mixing-matrix formalism to model finite-volume effects in the $P_{\ell}(k)$ can be robustly applied to surveys made of several disconnected patches. Our results indicate that relativistic RSD, the contribution from weak gravitational lensing in particular, cannot be disregarded when modelling 2-point clustering statistics extracted from the EWSS., Comment: 23 pages, 14 figures

Although state-of-the-art classifiers for facial expression recognition (FER) can achieve a high level of accuracy, they lack interpretability, an important feature for end-users. Experts typically associate spatial action units (AUs) from a codebook to facial regions for the visual interpretation of expressions. In this paper, the same expert steps are followed. A new learning strategy is proposed to explicitly incorporate AU cues into classifier training, allowing to train deep interpretable models. During training, this AU codebook is used, along with the input image expression label, and facial landmarks, to construct a AU heatmap that indicates the most discriminative image regions of interest w.r.t the facial expression. This valuable spatial cue is leveraged to train a deep interpretable classifier for FER. This is achieved by constraining the spatial layer features of a classifier to be correlated with AU heatmaps. Using a composite loss, the classifier is trained to correctly classify an image while yielding interpretable visual layer-wise attention correlated with AU maps, simulating the expert decision process. Our strategy only relies on image class expression for supervision, without additional manual annotations. Our new strategy is generic, and can be applied to any deep CNN- or transformer-based classifier without requiring any architectural change or significant additional training time. Our extensive evaluation on two public benchmarks RAF-DB, and AffectNet datasets shows that our proposed strategy can improve layer-wise interpretability without degrading classification performance. In addition, we explore a common type of interpretable classifiers that rely on class activation mapping (CAM) methods, and show that our approach can also improve CAM interpretability., Comment: 4 pages, 2 figures, AI and Digital Health Symposium 2024, October 18th 2024, Montr\'eal

Head-on giant impacts (collisions between planet-size bodies) are frequently used to study the planet formation process as they present an extreme configuration where the two colliding bodies are greatly disturbed. With limited computing resources, focusing on these extreme impacts eases the burden of exploring a large parameter space. Results from head-on impacts are often then extended to study oblique impacts with angle corrections or used as initial conditions for other calculations, for example, the evolution of ejected debris. In this study, we conduct a detailed investigation of the thermodynamic and energy budget evolution of high-energy head-on giant impacts, entering the catastrophic impacts regime, for target masses between 0.001 and 12 M$_{\oplus}$. We demonstrate the complex interplay of gravitational forces, shock dynamics, and thermodynamic processing in head-on impacts at high energy. Our study illustrates that frequent interactions of core material with the liquid side of the vapour curve could have cumulative effects on the post-collision remnants, leading to fragmentary disintegration occurring at lower impact energy. This results in the mass of the largest remnant diverging significantly from previously developed scaling laws. These findings suggest two key considerations: 1) head-on planetary collisions for different target masses do not behave similarly, so caution is needed when applying scaling laws across a broad parameter space; 2) an accurate model of the liquid-vapour phase boundary is essential for modeling giant impacts. Our findings highlight the need for careful consideration of impact configurations in planetary formation studies, as head-on impacts involve a complex interplay between thermodynamic processing, shocks, gravitational forces, and other factors., Comment: Published in MNRAS, 25 pages, 21 figures

Many-body interactions in metal-organic frameworks are fundamental for emergent quantum physics. Unlike their solution counterpart, magnetization at surfaces in low-dimensional analogues is strongly influenced by magnetic anisotropy induced by the substrate and still not well understood. Here, we use on-surface coordination chemistry to synthesize on Ag(111) and superconducting Pb(111) an iron-based spin chain by using pyrene-4,5,9,10-tetraone precursors as ligands. Using low-temperature scanning probe microscopy, we compare their structures and low-energy spin excitations of coordinated Fe atoms with high S = 2 spin-state. Although the chain and coordination centers are identical on both substrates, the long-range spin-spin coupling due to a superexchange through the ligand observed on Ag is absent on Pb(111). We ascribe this reduction of spin-spin interactions on Pb to the depletion of electronic states around the Fermi level in the Pb(111) superconductor as compared to silver.

For the interpretation of spectra of magnetic stellar objects such as magnetic white dwarfs (WDs) highly accurate quantum chemical predictions for atoms and molecules in finite magnetic field are required. Especially the accurate description of electronically excited states and their properties requires established methods such as those from coupled-cluster (CC) theory. However, respective calculations are computationally challenging even for medium-sized systems. Cholesky decomposition (CD) techniques may be used to alleviate memory bottlenecks. In finite-field computations, the latter are increased due to the reduction of permutational symmetry within the electron-repulsion-integrals (ERIs) as well as the need for complex-valued data types. CD enables a memory-efficient, approximate description of the ERIs with rigorous error control and thus the treatment of larger systems at the CC level becomes feasible. In order to treat molecules in a finite magnetic field (ff), we present in this work the working equations of the left and right-hand side equations for ff-EOM-CD-CCSD for various EOM flavours as well as for the approximate ff-EOM-CD-CC2 method. The methods are applied to the study of the modification of the spectral lines of a magnesium transition by a helium atmosphere that can be found in magnetic WD stars.

Goal instructions for autonomous AI agents cannot assume that objects have unique names. Instead, objects in goals must be referred to by providing suitable descriptions. However, this raises problems in both classical planning and generalized planning. The standard approach to handling existentially quantified goals in classical planning involves compiling them into a DNF formula that encodes all possible variable bindings and adding dummy actions to map each DNF term into the new, dummy goal. This preprocessing is exponential in the number of variables. In generalized planning, the problem is different: even if general policies can deal with any initial situation and goal, executing a general policy requires the goal to be grounded to define a value for the policy features. The problem of grounding goals, namely finding the objects to bind the goal variables, is subtle: it is a generalization of classical planning, which is a special case when there are no goal variables to bind, and constraint reasoning, which is a special case when there are no actions. In this work, we address the goal grounding problem with a novel supervised learning approach. A GNN architecture, trained to predict the cost of partially quantified goals over small domain instances is tested on larger instances involving more objects and different quantified goals. The proposed architecture is evaluated experimentally over several planning domains where generalization is tested along several dimensions including the number of goal variables and objects that can bind such variables. The scope of the approach is also discussed in light of the known relationship between GNNs and C2 logics., Comment: 11 pages, Accepted at the 21st International Conference on Principles of Knowledge Representation and Reasoning (KR2024) in the Reasoning, Learning, and Decision Making track

Purpose: To non-heuristically identify dedicated variable flip angle (VFA) schemes optimized for the point-spread function (PSF) and signal-to-noise ratio (SNR) of multiple tissues in 3D FSE sequences with very long echo trains at 7T. Methods: The proposed optimization considers predefined SAR constraints and target contrast using an end-to-end learning framework. The cost function integrates components for contrast fidelity (SNR) and a penalty term to minimize image blurring (PSF) for multiple tissues. By adjusting the weights of PSF/SNR cost-function components, PSF- and SNR-optimized VFAs were derived and tested in vivo using both the open-source Pulseq standard on two volunteers as well as vendor protocols on a 7T MRI system with parallel transmit extension on three volunteers. Results: PSF-optimized VFAs resulted in significantly reduced image blurring compared to standard VFAs for T2w while maintaining contrast fidelity. Small white and gray matter structures, as well as blood vessels, are more visible with PSF-optimized VFAs. Quantitative analysis shows that the optimized VFA yields 50% less deviation from a sinc-like reference PSF than the standard VFA. The SNR-optimized VFAs yielded images with significantly improved SNR in a white and gray matter region relative to standard (81.2\pm18.4 vs. 41.2\pm11.5, respectively) as trade-off for elevated image blurring. Conclusion: This study demonstrates the potential of end-to-end learning frameworks to optimize VFA schemes in very long echo trains for 3D FSE acquisition at 7T in terms of PSF and SNR. It paves the way for fast and flexible adjustment of the trade-off between PSF and SNR for 3D FSE., Comment: Submitted to Magnetic Resonance in Medicine for peer-review

Stabilizer states form a ubiquitous family of quantum states that can be graphically represented through the graph state formalism. A fundamental property of graph states is that applying a local complementation - a well-known and extensively studied graph transformation - results in a graph that represents the same entanglement as the original. In other words, the corresponding graph states are LU-equivalent. This property served as the cornerstone for capturing non-trivial quantum properties in a simple graphical manner, in the study of quantum entanglement but also for developing protocols and models based on graph states and stabilizer states, such as measurement-based quantum computing, secret sharing, error correction, entanglement distribution... However, local complementation fails short to fully characterise entanglement: there exist pairs of graph states that are LU-equivalent but cannot be transformed one into the other using local complementations. Only few is known about the equivalence of graph states beyond local complementation. We introduce a generalization of local complementation which graphically characterises the LU-equivalence of graph states. We use this characterisation to show the existence of a strict infinite hierarchy of equivalences of graph states. Our approach is based on minimal local sets, which are subsets of vertices that are known to cover any graph, and to be invariant under local complementation and even LU-equivalence. We use these structures to provide a type to each vertex of a graph, leading to a natural standard form in which the LU-equivalence can be exhibited and captured by means of generalised local complementation.

Subtle field-theoretical effects suggest the presence of additional Higgs contributions in standard model processes. This has been supported by electroweak lattice calculation, e.\ g.\ for vector boson scattering. These effects can be included in perturbation theory by a suitable augmentation. We use such augmented perturbation theory to determine the impact at next-to-leading order at lepton colliders, from LEP to future machines such as FCC, in collisions with fermion-antifermion final states. After providing the formal background, we outline the calculational procedure, showing that in the fully exclusive process $e^-e^+\to f\bar{f}$ deviations only occur in fixed order at electroweak NNLO, but become relevant at the TeV scale already in resummed tree-level calculations. We discuss further processes where deviations are expected already at fixed-order NLO., Comment: 6 pages, 2 figures, submitted to the proceedings of ICHEP24

Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the true entity in a small set of randomly sampled negative entities. Due to the limited size of the set of negatives, a simple rule-based baseline can achieve state-of-the-art results, which simply ranks entities higher based on the validity of their type. As a consequence of these insights, we reevaluate current approaches for inductive link prediction on several benchmarks using the link prediction protocol usually applied to the transductive setting. As some inductive methods suffer from scalability issues when evaluated in this setting, we propose and apply additionally an improved sampling protocol, which does not suffer from the problem mentioned above. The results of our evaluation differ drastically from the results reported in so far., Comment: Published in RuleML+RR 2024

The electronCT technique is an imaging method based on the multiple Coulomb scattering of relativistic electrons and has potential applications in medical and industrial imaging. It utilizes a pencil beam of electrons in the very high energy electron (VHEE, 50-250 MeV) range and a single detection layer for the determination of the beam profile. The technique constitutes a projectional, two-dimensional imaging method and thus also qualifies for the tomographic reconstruction of samples. Given the simplicity of the technical setup and its location behind the sample, the electronCT technique has potential synergies with VHEE radiotherapy, making use of the same electron source for both treatment and diagnostics and thus being a candidate for in-situ imaging and patient localization. At the same time, several technical challenges arise from the measurement technique when applied for the imaging of living beings. Measurements performed at the ARES linear particle accelerator at an electron energy of 155 MeV using a mouse phantom and a Timepix3 silicon pixel detector assembly demonstrate the feasibility of this technique. Both projectional and tomographic reconstructions are presented and the potential and limits of the technology are discussed., Comment: 19 pages, 11 figures

Dense cold molecular cores/filaments are surrounded by an envelope of translucent gas. Some of the low-J emission lines of CO and HCO$^+$ isotopologues are more sensitive to the conditions either in the translucent environment or in the dense cold one. We propose a cloud model composed of three homogeneous slabs of gas along each line of sight (LoS), representing an envelope and a shielded inner layer. IRAM-30m data from the ORION-B large program toward the Horsehead nebula are used to demonstrate the method's capability. We use the non-LTE radiative transfer code RADEX to model the line profiles from the kinetic temperature $T_{kin}$, the volume density $n_{H_2}$, kinematics and chemical properties of the different layers. We then use a maximum likelihood estimator to simultaneously fit the lines of the CO and HCO$^+$ isotopologues. We constrain column density ratios to limit the variance on the estimates. This simple heterogeneous model provides good fits of the fitted lines over a large part of the cloud. The decomposition of the intensity into three layers allows to discuss the distribution of the estimated physical/chemical properties along the LoS. About 80$\%$ the CO integrated intensity comes from the envelope, while $\sim55\%$ of that of the (1-0) and (2-1) lines of C$^{18}$O comes from the inner layer. The $N(^{13}CO)/N(C^{18}O)$ in the envelope increases with decreasing $A_v$, and reaches $25$ in the pillar outskirts. The envelope $T_{kin}$ varies from 25 to 40 K, that of the inner layer drops to $\sim 15$ K in the western dense core. The inner layer $n_{H_2}$ is $\sim 3\times10^4\,\text{cm}^{-3}$ toward the filament and it increases by a factor $10$ toward dense cores. The proposed method correctly retrieves the physical/chemical properties of the Horsehead nebula and offers promising prospects for less supervised model fits of wider-field datasets.

We discuss the single-quantum positron annihilation with a bound electron in the near threshold region. The angular distribution and the total cross section are considered. We obtain a simple analytical expression for the Coulomb potential and for the screened potential. It is worth noting that the obtained results for the screened potential are universal and independent of the explicit form of the atomic potential. It is shown that screening significantly increases the cross section. We also obtain the analogous results for bound-free $e^+e^-$ photoproduction., Comment: 9 pages, 3 figures

Significant work has been done to develop quantum satellites, which generate entangled pairs in space and distribute them to ground stations separated some distance away. The reverse uplink case, where pairs are generated on the ground and swapped on the satellite using an optical Bell-measurement, has not been seriously considered due to a prevailing assumption that it is practically infeasible. In this letter, we illustrate the feasibility of performing Discrete Variable photonic Bell-measurements in space by conducting a detailed numerical analysis to estimate the channel efficiency and attainable pair fidelity for various satellite-station configurations. Our model accounts for a wide range of physical effects such as atmospheric effects, stray photons, and mode mismatch. Our findings show promise toward the feasibility of photonic Bell-measurements in space, which motivates future research towards large-scale Satellite-based uplink entanglement distribution., Comment: 6 pages, 3 figures

In this letter, we demonstrate for the first time the creation of Josephson-like superconducting nanojunctions using a thermal scanning probe to directly inscribe weak links into microstrips of YBa2Cu3O7-x (YBCO). Our method effectively reduces the critical current (Ic) over an order of magnitude. The resulting nanobridges exhibit clear evidence of Josephson effects, of SNS-type junctions, as shown by both the DC and AC Josephson effects. This approach provides a novel and flexible method for scaling up quantum mechanical circuits that operate at liquid nitrogen temperatures. Additionally, it offers a promising pathway for modifying properties of the junctions in-situ and post fabrication., Comment: 14 pages, 4 figures

We develop a framework for describing the effects of systematic state preparation error in quantum-enhanced atom interferometry on sensing performance. We do this in the context of both spin-squeezed and non-Gaussian states for the two-axis-twisting (TAT), one-axis-twisting (OAT), and twist-and-turn (TNT) state preparation schemes, and derive general conditions for robustness and susceptibility of quantum states to state preparation error. In the spin-squeezing regime, we find that OAT is more susceptible to state preparation error than TAT due to its parameter-dependent phase space rotation. In the non-Gaussian regime, we find that OAT is robust to state preparation errors, which can be explained by a small ratio of off-diagonal to diagonal elements in its Fisher-covariance matrix. In contrast, TNT does not exhibit this robustness. We find that the single parameter unbiased estimators that are habitually used in quantum-enhanced atom interferometry are not always optimal, and that there may be occasions where biased estimators, or two-parameter unbiased estimators, lead to lower net error., Comment: 15 pages, 10 figures

Fabrication of semiconductor heterostructures is now so precise that metrology has become a key challenge for progress in science and applications. It is now relatively straightforward to characterize classic III-V and group IV heterostructures consisting of slabs of different semiconductor alloys with thicknesses of $\sim$5 nm and greater using sophisticated tools such as X-ray diffraction, high energy X-ray photoemission spectroscopy, and secondary ion mass spectrometry. However, profiling thin layers with nm or sub-nm thickness, e.g. atomically thin dopant layers ($\delta$-layers), of impurities required for modulation doping and spin-based quantum and classical information technologies is more challenging. Here, we present theory and experiment showing how resonant-contrast X-ray reflectometry meets this challenge. The technique takes advantage of the change in the scattering factor of atoms as their core level resonances are scanned by varying the X-ray energy. We demonstrate the capability of the resulting element-selective, non-destructive profilometry for single arsenic $\delta$-layers within silicon, and show that the sub-nm electronic thickness of the $\delta$-layers corresponds to sub-nm chemical thickness. In combination with X-ray fluorescence imaging, this enables non-destructive three-dimensional characterization of nano-structured quantum devices. Due to the strong resonances at soft X-ray wavelengths, the technique is also ideally suited to characterize layered quantum materials, such as cuprates or the topical infinite-layer nickelates.

Rest-frame far-ultraviolet (FUV) observations from JWST are revolutionizing our understanding of the high-z galaxies that drove reionization and the mechanisms by which they accomplished it. To fully interpret these observations, we must be able to diagnose how properties of the interstellar medium (ISM; e.g., column density, covering fraction, outflow velocity) directly relate to the absorption features produced. Using the high-S/N and high-resolution FUV spectra of 45 nearby star-forming galaxies from CLASSY, we present the largest uniform, simultaneous characterization of neutral and low-ionization state (LIS) interstellar UV absorption lines (OI, SiII, SII, CII, AlII) across a wide range of galaxy properties. We also present 21-cm HI observations for 35 galaxies, multiple of which are gas-poor or non-detected, possibly indicating the onset of a post-starburst phase. We find that our simultaneous 1-component Voigt profile fits are capable of accurately modeling the LIS absorption for ~75% of galaxies, mitigating challenges associated with saturation, infilling, and degeneracies. While the most massive galaxies require additional components, our 1-component fits return average properties of the absorbing gas and follow the scaling relations described by a single gas cloud. We explore connections between LIS absorption and direct tracers of the neutral ISM (OI, Ly-alpha, HI 21-cm), finding that CII most closely traces the neutral gas trends while other ions exhibit weaker correlations. Given the challenges with directly observing HI at higher-z, we demonstrate that LIS absorption can be a powerful means to study the neutral ISM and present empirical relationships for predicting neutral gas properties., Comment: 25 pages with 16 figures and 2 tables. Long appendix with figure sets and tables. Accepted to ApJ

The half-Heusler compound GdBiPt orders antiferromagnetically around $T_\mathrm{N}=8.5$ K which implies breaking the time reversal symmetry as well as the translational symmetry. This combination preserves the global symmetry. Here, we used rotational anisotropy of second harmonic generation (SHG) to study the symmetry changes associated with this phase transition. GdBiPt crystallizes in the space group $F\overline{4}3m$, which does not change through the phase transition. From powder neutron diffraction, the proposed magnetic point group for the magnetic unit cell is $3m$. We carried out a symmetry analysis of the SHG patterns. Above \tn, the SHG data shows a $C_2$ symmetry which excludes the point group $\overline{4}3m$, as well as the $3m$ point group which represents a $[1 1 1]$ facet of the sample. Thus, we considered the point groups $2$ and $m$, but we have to reject the first one as the associated tensor elements poorly represent the angular dependence SHG. Finally, a superposition consisting of the point groups $3m$ and $m$ fit the data correctly. Below \tn, we had to add a third contribution associated with magnetic points group $m'$ in order to properly describe the SHG signal. The SHG intensity is linearly proportional to the antiferromagnetic order parameter. This allows us to determine $T_\mathrm{N}=9.61\pm0.48$ K and a critical exponent $\beta=0.346\pm0.017$, which are in agreement with the values found in the literature., Comment: 7 pages, 3 figures

We present magnetohydrodynamic simulations of star formation in the multiphase interstellar medium to quantify the impact of non-ionising far-ultraviolet (FUV) radiation. This study is carried out within the framework of the \textsc{Silcc Project}. It incorporates the radiative transfer of ionising radiation and self-consistent modelling of variable FUV radiation from star clusters. Near young star clusters, the interstellar radiation field (ISRF) can reach values of $G_0 \approx 10^4$ (in Habing units), far exceeding the canonical solar neighbourhood value of $G_0 = 1.7$. However, our findings suggest that FUV radiation has minimal impact on the integrated star formation rate compared to other feedback mechanisms such as ionising radiation, stellar winds, and supernovae. Only a slight decrease in star formation burstiness, related to increased photoelectric heating efficiency by the variable FUV radiation field, is detectable. Dust near star-forming regions can be heated up to 60 K via the photoelectric (PE) effect, showing a broad temperature distribution. PE heating rates for variable FUV radiation models show higher peak intensities but lower average heating rates than static ISRF models. Simulations of solar neighbourhood conditions without stellar winds or ionising radiation but with self-consistent ISRF and supernovae show high star formation rates $\sim10^{-1}\,\mathrm{M_\odot\,yr^{-1}\,kpc^{-2}}$, contradicting expectations. Our chemical analysis reveals increased cold neutral medium volume-filling factors (VFF) outside the vicinity of stellar clusters with a variable ISRF. Simultaneously, the thermally unstable gas is reduced, and a sharper separation of warm and cold gas phases is observed. The variable FUV field also promotes a diffuse molecular gas phase with VFF of $\sim5-10$~per cent., Comment: submitted to MNRAS

Direct Preference Optimization (DPO) has emerged as a stable, scalable, and efficient solution for language model alignment. Despite its empirical success, the $\textit{optimization}$ properties, particularly the impact of samplers on its convergence rates, remain underexplored. In this paper, we provide a rigorous analysis of DPO's $\textit{convergence rates}$ with different sampling strategies under the exact gradient setting, revealing a surprising separation: uniform sampling achieves $\textit{linear}$ convergence, while our proposed online sampler achieves $\textit{quadratic}$ convergence. We further adapt the sampler to practical settings by incorporating posterior distributions and $\textit{logit mixing}$, demonstrating significant improvements over previous approaches. On Safe-RLHF dataset, our method exhibits a $4.5$% improvement over vanilla DPO and a $3.0$% improvement over on-policy DPO; on Iterative-Prompt, our approach outperforms vanilla DPO, on-policy DPO, and Hybrid GSHF by over $4.2$%. Our results not only offer insights into the theoretical standing of DPO but also pave the way for potential algorithm designs in the future., Comment: Format adjustment

Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate models. Here we introduce sbijax, a Python package that implements a wide variety of state-of-the-art methods in neural simulation-based inference using a user-friendly programming interface. sbijax offers high-level functionality to quickly construct SBI estimators, and compute and visualize posterior distributions with only a few lines of code. In addition, the package provides functionality for conventional approximate Bayesian computation, to compute model diagnostics, and to automatically estimate summary statistics. By virtue of being entirely written in JAX, sbijax is extremely computationally efficient, allowing rapid training of neural networks and executing code automatically in parallel on both CPU and GPU.

Nuclear spin hyperpolarization utilizing parahydrogen (pH2) has broad applications in chemistry, biochemistry, and medicine. This review focuses on examining the less traveled roads associated with parahydrogen-induced polarization (PHIP) leading to MR signal enhancement. We begin with photo-induced PHIP, which allows the investigation of short-lived processes and photo-activated catalysis on timescales as short as microseconds. Next, we review the partially negative line (PNL) effect, in which distinctive lineshape helps to reveal information about rapid exchange with pH2 and the role of short lived catalytic species. The signal enhancement of a single proton in oneH-PHIP is discussed which challenges the underpinning concept of pairwise hydrogenation. Furthermore, we examine metal-free PHIP facilitated by novel molecular tweezers and radicaloids, thereby demonstrating alternative routes to conventional hydrogenation using metal-based catalysts. Although symmetric molecules incorporating pH2 are NMR silent, we showcase methods that reveal hyperpolarized states through post-hydrogenation reactions. Additionally, we discuss chemical exchange processes that mediate polarization transfer between pH2 and a molecular target, expanding the reach of PHIP without synthesizing specialized precursors. We conclude this review by highlighting the role PHIP has played in uncovering the H2 activation mechanisms of hydrogenases. By providing a detailed review of these diverse phenomena, we aim to broaden the understanding and hidden potential of PHIP, which keeps evolving rapidly close to 40 years after its first discovery., Comment: 17 pages, 14 figures

The Milky way Galaxy is brimming with free-floating objects, including stars, planets and planetesimals. For the purpose of this chapter, we define a free-floating object as a solid body that is not orbited by a considerably more massive body. A planet then is considered free floating if it is not orbiting a star but it may be orbiting another planet. A binary planet, or planet-moon pair that is not orbiting a star, is then considered free floating. Most free-floating objects are not born as such because most objects form in some sort of coordinated environmental effort, such as a star forming region or a circum-stellar disk. Free-floating stars then originate from dissolved clusters. Free floating planets are ejected from their parent star in an internal dynamical encounter with another planet or stripped from the star by other means such as a supernova or a nearby passing star. Free floating (interstellar) planetesimals probably form in a similar fashion as free-floating planets. The number of free-floating objects in the Galaxy can be large. With billions of stars and planets, and trillions of interstellar planetesimals. Although free-floating planets appear to be quite common (a few hundred have been observed), only two interstellar planetesimals have been discovered so far. The expectation, however, is that they outnumber the stars in the Galaxy by a considerable margin. We expect them to be found more frequently once large new instruments come online, such as the Vera Cooper Rubin Observatory., Comment: Preprint of a chapter for the 'Encyclopedia of Astrophysics' (Editor-in-Chief Ilya Mandel, Section Editor Dimitri Veras) to be published by Elsevier as a Reference Module

Non-invasive methods for diagnosing mental health conditions, such as speech analysis, offer promising potential in modern medicine. Recent advancements in machine learning, particularly speech foundation models, have shown significant promise in detecting mental health states by capturing diverse features. This study investigates which pretext tasks in these models best transfer to mental health detection and examines how different model layers encode features relevant to mental health conditions. We also probed the optimal length of audio segments and the best pooling strategies to improve detection accuracy. Using the Callyope-GP and Androids datasets, we evaluated the models' effectiveness across different languages and speech tasks, aiming to enhance the generalizability of speech-based mental health diagnostics. Our approach achieved SOTA scores in depression detection on the Androids dataset., Comment: 6 pages, 4 figures

Where and under what conditions the transfer of energy between electromagnetic fields and particles takes place in the solar wind remains an open question. We investigate the conditions that promote the growth of kinetic instabilities predicted by linear theory, to infer how turbulence and temperature-anisotropy-driven instabilities are interrelated. Using a large dataset from Solar Orbiter, we introduce the radial rate of strain, a novel measure computed from single-spacecraft data, that we interpret as a proxy for the double-adiabatic strain rate. The solar wind exhibits high absolute values of the radial rate of strain at locations with large temperature anisotropy. We measure the kurtosis and skewness of the radial rate of strain from the statistical moments to show that it is non-Gaussian for unstable intervals and increasingly intermittent at smaller scales with a power-law scaling. We conclude that the velocity field fluctuations in the solar wind contribute to the presence of temperature anisotropy sufficient to create potentially unstable conditions., Comment: Accepted for publication in the Journal of Plasma Physics

This paper presents a novel approach to evaluating blue-chip art as a viable asset class for portfolio diversification. We present the Arte-Blue Chip Index, an index that tracks 100 top-performing artists based on 81,891 public transactions from 157 artists across 584 auction houses over the period 1990 to 2024. By comparing blue-chip art price trends with stock market fluctuations, our index provides insights into the risk and return profile of blue-chip art investments. Our analysis demonstrates that a 20% allocation of blue-chip art in a diversified portfolio enhances risk-adjusted returns by around 20%, while maintaining volatility levels similar to the S&P 500., Comment: 15 pages, 1798 words, 3 figures, 3 tables

This paper presents a large scale dataset from a meticulous 360-degree LiDAR (Light Detection and Ranging) scan conducted on Kronborg Castle, a renowned Renaissance fortress located in Elsinore (Helsing{\o}r), Denmark, famously associated with Shakespeare's "Hamlet." Utilising a vertical mounted, gimbal stabilised, 16 channel, 360-degree Velodyne VLP-16 LiDAR scanner, paired with an Intel RealSense L515 depth camera. This research offers an unparalleled digital representation of the castle's intricate architectural details and structural nuances, enabling fellow researchers to conduct experiments utilising the data for SLAM (Simultaneous Localisation and Mapping) as well as floorplan generation., Comment: 4 pages, 4 figures, 3 tables

We study computational problems in financial networks of banks connected by debt contracts and credit default swaps (CDSs). A main problem is to determine \emph{clearing} payments, for instance right after some banks have been exposed to a financial shock. Previous works have shown the $\varepsilon$-approximate version of the problem to be $\mathrm{PPAD}$-complete and the exact problem $\mathrm{FIXP}$-complete. We show that $\mathrm{PPAD}$-hardness hold when $\varepsilon \approx 0.101$, improving the previously best bound significantly. Due to the fact that the clearing problem typically does not have a unique solution, or that it may not have a solution at all in the presence of default costs, several natural decision problems are also of great interest. We show two such problems to be $\exists\mathbb{R}$-complete, complementing previous $\mathrm{NP}$-hardness results for the approximate setting.

Distributed Asynchronous Object Store (DAOS) is a novel software-defined object store leveraging Non-Volatile Memory (NVM) devices, designed for high performance. It provides a number of interfaces for applications to undertake I/O, ranging from a native object storage API to a DAOS FUSE module for seamless compatibility with existing applications using POSIX file system APIs. In this paper we discuss these interfaces and the options they provide, exercise DAOS through them with various I/O benchmarks, and analyse the observed performance. We also briefly compare the performance with a distributed file system and another object storage system deployed on the same hardware, and showcase DAOS' potential and increased flexibility to support high-performance I/O., Comment: 9 pages, 38 figures, PDSW'24

We measure human capital using the self-reported skill sets of 8.75 million U.S. college graduates from professional profiles on the online platform LinkedIn. We establish that these skills are systematically related to human capital investments such as different types of schooling and work experience. The average profile of the number of reported skills by age looks remarkably similar to the well-established concave age-earnings profiles. More experienced workers and those with higher educational degrees have larger shares of occupation-specific skills, consistent with their acquisition through professional-degree programs and on-the-job experience. Workers who report more, and particularly more specific and managerial, skills are more likely to hold highly paid jobs. Skill differences across workers can account for more earnings variation than detailed vectors of education and experience. We also document a substantial gender gap in reported skills, which starts to manifest when young women reach typical ages of first motherhood. Gender differences in skill profiles can rationalize a substantial proportion of the gender gap in the propensity to work in highly paid jobs. Overall, the results are consistent with an important role of multidimensional skills in accounting for several well-known basic labor-market patterns.

A $k$-rainbow dominating function ($k$RDF) of $G$ is a function that assigns subsets of $ \{1,2,...,k\}$ to the vertices of $G$ such that for vertices $v$ with $f(v)=\emptyset $ we have $\bigcup\nolimits_{u\in N(v)}f(u)=\{1,2,...,k\}$. The weight $w(f)$ of a $k$RDF $f$ is defined as $w(f)=\sum_{v\in V(G)}\left\vert f(v)\right\vert $. The minimum weight of a $k$RDF of $G$ is called the $k$-rainbow domination number of $G$, which is denoted by $\gamma_{rk}(G)$. In this paper, we study the 2-rainbow domination number of the Cartesian product of two cycles. Exact values are given for a number of infinite families and we prove lower and upper bounds for all other cases.

In hidden-sector models, axion-like particles (ALPs) can couple to heavy neutral leptons (HNLs), leading to rich phenomenologies. We study ALPs produced from $D$- and $B$-meson decays via quark-flavor-violating couplings, and decaying exclusively into a pair of HNLs which mix with active neutrinos. The ALP can be either short- or long-lived, depending on the masses of the ALP and the HNL, as well as the corresponding coupling strength. Such GeV-scale HNLs are necessarily long-lived given the current bounds on their mixing parameters. We assess the sensitivities of the LHC far detectors and SHiP, to the long-lived HNLs in such theoretical scenarios. We find that for currently allowed values of the ALP couplings, most of these experiments can probe the active-sterile-neutrino mixing parameters multiple orders of magnitude beyond the present bounds, covering large parameter region targeted with the type-I seesaw mechanism. In addition, our results show that compared to the case of a promptly decaying ALP, considering an ALP of longer lifetimes weakens the sensitivities of the considered experiments to the long-lived HNLs., Comment: 16 pages+refs, 6 figures, 1 table

Experiments in the field of quantum optics often require very low concentrations of dust particles in the laboratory, but the complexity of working routines precludes operation within a proper clean room. Research teams have established a multitude of different approaches, precaution measures, and habits to keep the delicate optics setups free of contamination. Here, we systematically quantify dust particle concentration during day-to-day operation of a quantum optics lab, assess the effectiveness of various measures, and give practical recommendations., Comment: 4 pages, 4 figures, comments welcome!

State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during deployment, where it might be advantageous to call out to a human overseer for help. Efforts to gauge the criticality of different points in time have been developed, but their accuracy is not well established due to a lack of ground truth, and they are not designed to be easily interpretable by end users. Therefore, we seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users. We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions. We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality. Safety margins make these interpretable, when defined as the number of random actions for which performance loss will not exceed some tolerance with high confidence. We demonstrate this approach in several environment-agent combinations; for an A3C agent in an Atari Beamrider environment, the lowest 5% of safety margins contain 47% of agent losses; i.e., supervising only 5% of decisions could potentially prevent roughly half of an agent's errors. This criticality framework measures the potential impacts of bad decisions, even before those decisions are made, allowing for more effective debugging and oversight of autonomous agents., Comment: 17 pages, 10 figures. This work has been submitted to the IEEE for possible publication

Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an extension to the N-BEATS model for univariate time series point forecasting was proposed to include forecast stability as an additional optimization objective, next to accuracy. It was shown that more stable forecasts can be obtained without harming accuracy by minimizing a composite loss function that contains both a forecast error and a forecast instability component, with a static hyperparameter to control the impact of stability. In this paper, we empirically investigate whether further improvements in stability can be obtained without compromising accuracy by applying dynamic loss weighting algorithms, which change the loss weights during training. We show that some existing dynamic loss weighting methods achieve this objective. However, our proposed extension to the Random Weighting approach -- Task-Aware Random Weighting -- shows the best performance.

Nonlinear metasurfaces offer a new paradigm to realize optical nonlinear devices with new and unparalleled behavior compared to nonlinear crystals, due to the interplay between photonic resonances and materials properties. The complicated interdependency between efficiency and emission directionality of the nonlinear optical signal on the existence, localization, and lifetimes of photonic resonances, as well as on the nonlinear susceptibility, makes it extremely difficult to design optimal metasurfaces using conventional materials and geometries. Inverse design using topology optimization is a powerful design tool for photonic structures, but traditional approaches developed for linear photonics are not suitable for such high dimensional nonlinear problems. Here, we use a topology optimization approach to inverse-design a fabrication-robust nonlinear metasurface that includes quantum-engineered resonant nonlinearities in semiconductor heterostructures for efficient and directional second harmonic generation. Furthermore, we also demonstrate that under practical constraints, among all the parameters, the nonlinear modal overlap emerges as the dominant parameter that enhances conversion efficiency, a finding that contrasts with intuition-driven studies that often emphasize Purcell enhancement. Our results open new opportunities for optimizing nonlinear processes in nanophotonic structures for novel light sources, quantum information applications, and communication.