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The National Technical Assistance Center on Transition: the Collaborative (NTACT:C) Collaborative Assessment Guide for Transition Planning (CAG) is intended to help students, family members, educators, vocational rehabilitation counselors, human services and health agency staff, and other partners develop a coordinated assessment approach for transition planning and service delivery. The set of resources which make up the CAG includes sections designed for students and families. It also includes a supplement to highlight the assessment requirements of the Individuals with Disabilities Education Act (IDEA, 2004) and the Rehabilitation Act of 1973 (Rehab Act), as amended by title IV of the Workforce Innovation and Opportunity Act (WIOA) as well as a supplement of Definitions referenced throughout. Finally, it includes a supplement of sample assessment tools. The assessment process for identifying post-school goals and relevant transition services for students and youth consists of six separate but interconnected processes: (1) Determining What to Assess; (2) Determining Stakeholders; (3) Selecting Appropriate Assessments; (4) Conducting Assessments; (5) Reviewing Assessment Results; and (6) Using Assessment Data.

Anti-seizure medications (ASMs) are the mainstay of treatment for epilepsy, yet their effect on seizure spread is not fully understood. Higher ASM doses have been associated with shorter and less severe seizures. Our objective was to test if this effect was due to limiting seizure spread through early termination of otherwise unchanged seizures. We retrospectively examined intracranial EEG (iEEG) recordings in 15 subjects that underwent ASM tapering during pre-surgical monitoring. We estimated ASM plasma concentrations based on pharmaco-kinetic modelling. In each subject, we identified seizures that followed the same onset and initial spread patterns, but some seizures terminated early (truncated seizures), and other seizures continued to spread (continuing seizures). We compared ASM concentrations at the times of truncated seizures and continuing seizures. We found no substantial difference between ASM concentrations when truncated vs. continuing seizures occurred (Mean difference = 4%, sd = 29%, p=0.6). Our results indicate that ASM did not appear to halt established seizures in this cohort. Further research is needed to understand how ASM may modulate seizure duration and severity.

The mini proceedings of the "Fourth International Workshop on the Extension Project for the J-PARC Hadron Experimental Facility (HEF-ex 2024) [https://kds.kek.jp/event/46965]" held at J-PARC, February 19-21, 2024, are presented. The workshop was devoted to discussing the physics case that connects both the present and the future Hadron Experimental Facility at J-PARC, covering a wide range of topics in flavor, hadron, and nuclear physics related to both experimental and theoretical activities being conducted at the facility.

We study active steering protocols for weakly measured qubits in the presence of error channels due to amplitude and phase noise. If the error rate is sufficiently small, the protocol approaches and stabilizes a predesignated pure target state with high fidelity and high purity, and thus implements autonomous state stabilization. We present numerical simulation results for one and two qubits, taking Andreev qubit circuits as example. As function of the error rate, a sharp threshold separates an error-correcting weak-damping regime from a strong-damping regime where the target state cannot be reached anymore. At the threshold, the purity gap closes., Comment: 15 pages, 8 figures

Corner trees, introduced in "Even-Zohar and Leng, 2021, Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms", allow for the efficient counting of certain permutation patterns. Here we identify corner trees as a subset of finite (strict) double posets, which we term twin-tree double posets. They are contained in both twin double posets and tree double posets, giving candidate sets for generalizations of corner tree countings. We provide the generalization of an algorithm proposed by Even-Zohar/Leng to a class of tree double posets, thereby enlarging the space of permutations that can be counted in O(n^{5/3}).

Computationally efficient nonlinear model predictive control relies on elaborate discrete-time optimal control problem (OCP) formulations trading off accuracy with respect to the continuous-time problem and associated computational burden. Such formulations, however, are in general not easy to implement within specialized software frameworks tailored to numerical optimal control. This paper introduces a new multi-phase OCP interface for the open-source software acados allowing to conveniently formulate such problems and generate fast solvers that can be used for nonlinear model predictive control (NMPC). While multi-phase OCP (MOCP) formulations occur naturally in many applications, this work focuses on MOCP formulations that can be used to efficiently approximate standard continuous-time OCPs in the context of NMPC. To this end, the paper discusses advanced control parametrizations, such as closed-loop costing and piecewise polynomials with varying degree, as well as partial tightening and formulations that leverage models of different fidelity. An introductory example is presented to showcase the usability of the new interface. Finally, three numerical experiments demonstrate that NMPC controllers based on multi-phase formulations can efficiently trade-off computation time and control performance., Comment: Preprint. Article submitted to journal Optimal Control Applications and Methods on July 12, 2024. 23 pages

Using 1533 type Ia supernovae (SNe Ia) from the five-year sample of the Dark Energy Survey (DES), we investigate the effects of projected galactocentric separation between the SNe and their host galaxies on their light curves and standardization. We show, for the first time, that the difference in SN Ia post-standardization brightnesses between high and low-mass hosts reduces from $0.078\pm0.011$ mag in the full sample to $0.036 \pm 0.018$ mag for SNe Ia located in the outer regions of their host galaxies, while increasing to $0.100 \pm 0.014$ mag for SNe in the inner regions. In these inner regions, the step can be reduced (but not removed) using a model where the $R_V$ of dust along the line-of-sight to the SN changes as a function of galaxy properties. To explain the remaining difference, we use the distributions of the SN Ia stretch parameter to test whether the inferred age of SN progenitors are more varied in the inner regions of galaxies. We find that the proportion of high-stretch SNe Ia in red (older) environments is more prominent in outer regions and that the outer regions stretch distributions are overall more homogeneous compared to inner regions, but conclude that this effect cannot explain the reduction in significance of any Hubble residual step in outer regions. We conclude that the standardized distances of SNe Ia located in the outer regions of galaxies are less affected by their global host galaxy properties than those in the inner regions., Comment: 17 pages, 13 figures

We report the first result from a dark photon dark matter search in the mass range from ${78.62}$ to $83.95~\mathrm{\mu eV}/c^2$ with a dielectric haloscope prototype for MADMAX (Magnetized Disc and Mirror Axion eXperiment). Putative dark photons would convert to observable photons within a stack consisting of three sapphire disks and a mirror. The emitted power of this system is received by an antenna and successively digitized using a low-noise receiver. No dark photon signal has been observed. Assuming unpolarized dark photon dark matter with a local density of $\rho_{\chi}=0.3~\mathrm{GeV/cm^3}$ we exclude a dark photon to photon mixing parameter $\chi > 3.0 \times 10^{-12}$ over the full mass range and $\chi > 1.2 \times 10^{-13}$ at a mass of $80.57~\mathrm{\mu eV}/c^2$ with a 95\% confidence level. This is the first physics result from a MADMAX prototype and exceeds previous constraints on $\chi$ in this mass range by up to almost three orders of magnitude., Comment: v1

The present state of cosmology is facing a crisis where there is a fundamental disagreement in measurements of the Hubble constant ($H_{0}$), with significant tension between the early and late universe methods. Type Ia supernovae (SNe Ia) are important to measuring $H_{0}$ through the astronomical distance ladder. However, there remains potential to better standardise SN Ia light curves by using known dependencies on host galaxy properties after the standard light curve width and colour corrections have been applied to the peak SN Ia luminosities. To explore this, we use the 5-year photometrically identified SNe Ia sample obtained by the Dark Energy Survey, along with host galaxy spectra obtained by the Australian Dark Energy Survey. Using host galaxy spectroscopy, we find a significant trend with the equivalent width (EW) of the [OII] $\lambda\lambda$ 3727, 29 doublet, a proxy for specific star formation rate, and Hubble residuals. We find that the correlation with [OII] EW is a powerful alternative to the commonly used mass step after initial light curve corrections. We applied our [OII] EW correction to a sample of 20 SN Ia hosted by calibrator galaxies observed using WiFeS, and examined the impact on both the SN Ia absolute magnitude and $H_{0}$. We then explored different [OII] EW corrections and found $H_{0}$ values ranging between $72.80$ to $73.28~\mathrm{km} \mathrm{s}^{-1} \mathrm{Mpc}^{-1}$. Notably, even after using an additional [OII] EW correction, the impact of host galaxy properties in standardising SNe Ia appears limited in reducing the current tension ($\sim$5$\sigma$) with the Cosmic Microwave Background result for $H_{0}$., Comment: 16 pages, 13 figures. Submitting to MNRAS

Characterization of the redshift distribution of ensembles of galaxies is pivotal for large scale structure cosmological studies. In this work, we focus on improving the Self-Organizing Map (SOM) methodology for photometric redshift estimation (SOMPZ), specifically in anticipation of the Dark Energy Survey Year 6 (DES Y6) data. This data set, featuring deeper and fainter galaxies than DES Year 3 (DES Y3), demands adapted techniques to ensure accurate recovery of the underlying redshift distribution. We investigate three strategies for enhancing the existing SOM-based approach used in DES Y3: 1) Replacing the Y3 SOM algorithm with one tailored for redshift estimation challenges; 2) Incorporating $\textit{g}$-band flux information to refine redshift estimates (i.e. using $\textit{griz}$ fluxes as opposed to only $\textit{riz}$); 3) Augmenting redshift data for galaxies where available. These methods are applied to DES Y3 data, and results are compared to the Y3 fiducial ones. Our analysis indicates significant improvements with the first two strategies, notably reducing the overlap between redshift bins. By combining strategies 1 and 2, we have successfully managed to reduce redshift bin overlap in DES Y3 by up to 66$\%$. Conversely, the third strategy, involving the addition of redshift data for selected galaxies as an additional feature in the method, yields inferior results and is abandoned. Our findings contribute to the advancement of weak lensing redshift characterization and lay the groundwork for better redshift characterization in DES Year 6 and future stage IV surveys, like the Rubin Observatory.

Self-supervised learning (SSL) methods are popular since they can address situations with limited annotated data by directly utilising the underlying data distribution. However, the adoption of such methods is not explored enough in ultrasound (US) imaging, especially for fetal assessment. We investigate the potential of dual-encoder SSL in utilizing unlabelled US video data to improve the performance of challenging downstream Standard Fetal Cardiac Planes (SFCP) classification using limited labelled 2D US images. We study 7 SSL approaches based on reconstruction, contrastive loss, distillation, and information theory and evaluate them extensively on a large private US dataset. Our observations and findings are consolidated from more than 500 downstream training experiments under different settings. Our primary observation shows that for SSL training, the variance of the dataset is more crucial than its size because it allows the model to learn generalisable representations, which improve the performance of downstream tasks. Overall, the BarlowTwins method shows robust performance, irrespective of the training settings and data variations, when used as an initialisation for downstream tasks. Notably, full fine-tuning with 1% of labelled data outperforms ImageNet initialisation by 12% in F1-score and outperforms other SSL initialisations by at least 4% in F1-score, thus making it a promising candidate for transfer learning from US video to image data., Comment: Simplifying Medical Ultrasound: 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance-redshift relation. While obtaining a host-galaxy spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of millions of SNe. Here we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts (photo-z) inferred directly from the SN light-curve in combination with Gaussian and full p(z) priors from host-galaxy photo-z estimates. Using the DES 5-year photometrically-classified SN sample, we consider several photo-z algorithms as host-galaxy photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional-Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3x2 point analyses. With detailed catalog-level simulations of the DES 5-year sample, we find that the simulated w can be recovered within $\pm$0.02 when using SN+SOMPZ or DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases in w consistent with simulations within ~1$\sigma$ for three of the five photo-z variants. We further evaluate how photo-z systematics interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays the foundation for next-generation fully photometric SNe Ia cosmological analyses., Comment: 19 pages, 9 figures. Submitting to MNRAS, comments welcome

Robot navigation around humans can be a challenging problem since human movements are hard to predict. Stochastic model predictive control (MPC) can account for such uncertainties and approximately bound the probability of a collision to take place. In this paper, to counteract the rapidly growing human motion uncertainty over time, we incorporate state feedback in the stochastic MPC. This allows the robot to more closely track reference trajectories. To this end the feedback policy is left as a degree of freedom in the optimal control problem. The stochastic MPC with feedback is validated in simulation experiments and is compared against nominal MPC and stochastic MPC without feedback. The added computation time can be limited by reducing the number of additional variables for the feedback law with a small compromise in control performance.

We present a study of the weak lensing matter profiles of 698 South Pole Telescope (SPT) thermal Sunyaev-Zel'dovich effect (tSZE) selected galaxy clusters in the redshift range $0.25

MADMAX, a future experiment to search for axion dark matter, is based on a novel detection concept called the dielectric haloscope. It consists of a booster composed of several dielectric disks positioned with $\mu$m precision. A prototype composed of one movable disk was built to demonstrate the mechanical feasibility of such a booster in the challenging environment of the experiment: high magnetic field to convert the axions into photons and cryogenic temperature to reduce the thermal noise. It was tested both inside a strong magnetic field up to 1.6 T and at cryogenic temperatures down to 35K. The measurements of the velocity and positioning accuracy of the disk are shown and are found to match the MADMAX requirements., Comment: 14 pages, 9 figures

We demonstrate the existence of topological phase transitions in interacting, symmetry-protected quantum matter at finite temperatures. Using a combined numerical and analytical approach, we study a one-dimensional Su-Schrieffer-Heeger model with added Hubbard interactions, where no thermodynamic phase transition occurs at finite temperatures. The transition is signalled by a quantized, non-local bulk topological order parameter. It is driven by defects, which are enabled by the combination of interaction and thermal activation, with no counterpart in the non-interacting limit. The defects localize topological zero modes, which, when sufficiently abundant, cause the order parameter to vanish. This phenomenon, interpreted via bulk-boundary correspondence, reflects the loss of a topological edge mode at a well-defined critical temperature in the thermodynamic limit. Unlike zero-temperature topological transitions, these finite-temperature transitions lack thermodynamic signatures but remain observable in controlled quantum systems, such as ultracold fermionic atoms in optical lattices., Comment: 6+4 pages, 7 figures

In the last decade, the concept of path signature has found great success in data science applications, where it provides features describing the path. This is partly explained by the fact that it is possible to compute the signature of a path in linear time, owing to a dynamic programming principle, based on Chen's identity. The path signature can be regarded as a specific example of product or time-/path-ordered integral. In other words, it can be seen as a 1-parameter object build on iterated integrals over a path. Increasing the number of parameters by one, which amounts to considering iterated integrals over surfaces, is more complicated. An observation that is familiar in the context of higher gauge theory where multiparameter iterated integrals play an important role. The 2-parameter case is naturally related to a non-commutative version of Stokes' theorem, which is understood to be fundamentally linked to the concept of crossed modules of groups. Indeed, crossed modules with non-trivial kernel of the feedback map permit to compute features of a surface that go beyond what can be expressed by computing line integrals along the boundary of a surface. A good candidate for the crossed analog of free Lie algebra then seems to be a certain free crossed module over it. Building on work by Kapranov, we study the analog to the classical path signature taking values in such a free crossed module of Lie algebra. In particular, we provide a Magnus-type expression for the logarithm of surface signature as well as a sewing lemma for the crossed module setting.

Measuring Deeply Virtual Compton Scattering on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). Neutron targets play a complementary role to transversely polarized proton targets in the determination of the GPD $E$. This poorly known and poorly constrained GPD is essential to obtain the contribution of the quarks' angular momentum to the spin of the nucleon. DVCS on the neutron was measured for the first time selecting the exclusive final state by detecting the neutron, using the Jefferson Lab longitudinally polarized electron beam, with energies up to 10.6 GeV, and the CLAS12 detector. The extracted beam-spin asymmetries, combined with DVCS observables measured on the proton, allow a clean quark-flavor separation of the imaginary parts of the GPDs $H$ and $E$., Comment: 7 pages, 6 figures

We present the joint tomographic analysis of galaxy-galaxy lensing and galaxy clustering in harmonic space, using galaxy catalogues from the first three years of observations by the Dark Energy Survey (DES Y3). We utilise the redMaGiC and MagLim catalogues as lens galaxies and the METACALIBRATION catalogue as source galaxies. The measurements of angular power spectra are performed using the pseudo-$C_\ell$ method, and our theoretical modelling follows the fiducial analyses performed by DES Y3 in configuration space, accounting for galaxy bias, intrinsic alignments, magnification bias, shear magnification bias and photometric redshift uncertainties. We explore different approaches for scale cuts based on non-linear galaxy bias and baryonic effects contamination. Our fiducial covariance matrix is computed analytically, accounting for mask geometry in the Gaussian term, and including non-Gaussian contributions and super-sample covariance terms. To validate our harmonic space pipelines and covariance matrix, we used a suite of 1800 log-normal simulations. We also perform a series of stress tests to gauge the robustness of our harmonic space analysis. In the $\Lambda$CDM model, the clustering amplitude $S_8 =\sigma_8(\Omega_m/0.3)^{0.5}$ is constrained to $S_8 = 0.704\pm 0.029$ and $S_8 = 0.753\pm 0.024$ ($68\%$ C.L.) for the redMaGiC and MagLim catalogues, respectively. For the $w$CDM, the dark energy equation of state is constrained to $w = -1.28 \pm 0.29$ and $w = -1.26^{+0.34}_{-0.27}$, for redMaGiC and MagLim catalogues, respectively. These results are compatible with the corresponding DES Y3 results in configuration space and pave the way for harmonic space analyses using the DES Y6 data., Comment: To be submitted to MNRAS

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

At asymptotically late times ultrametricity can emerge from the persistent slow aging dynamics of the glass phase. We show that this suffices to recover the breaking of replica symmetry in mean-field spin glasses from the late time limit of the time evolution using the Keldysh path integral. This provides an alternative approach to replica symmetry breaking by connecting it rigorously to the dynamic formulation. Stationary spin glasses are thereby understood to spontaneously break thermal symmetry, or the Kubo-Martin-Schwinger relation of a state in global thermal equilibrium. We demonstrate our general statements for the spherical quantum $p$-spin model and the quantum Sherrington-Kirkpatrick model in the presence of transverse and longitudinal fields. In doing so, we also derive their dynamical Ginzburg-Landau effective Keldysh actions starting from microscopic quantum models., Comment: 17 pages, 4 figures, submitted version

The residuals of the distance moduli of Type Ia supernovae (SN Ia) relative to a Hubble diagram fit contain information about the inhomogeneity of the universe, due to weak lensing magnification by foreground matter. By correlating the residuals of the Dark Energy Survey Year 5 SN Ia sample (DES-SN5YR) with extra-galactic foregrounds from the DES Y3 Gold catalog, we detect the presence of lensing at $6.0 \sigma$ significance. This is the first detection with a significance level above $5\sigma$. Constraints on the effective mass-to-light ratios and radial profiles of dark-matter haloes surrounding individual galaxies are also obtained. We show that the scatter of SNe Ia around the Hubble diagram is reduced by modifying the standardisation of the distance moduli to include an easily calculable de-lensing (i.e., environmental) term. We use the de-lensed distance moduli to recompute cosmological parameters derived from SN Ia, finding in Flat $w$CDM a difference of $\Delta \Omega_{\rm M} = +0.036$ and $\Delta w = -0.056$ compared to the unmodified distance moduli, a change of $\sim 0.3\sigma$. We argue that our modelling of SN Ia lensing will lower systematics on future surveys with higher statistical power. We use the observed dispersion of lensing in DES-SN5YR to constrain $\sigma_8$, but caution that the fit is sensitive to uncertainties at small scales. Nevertheless, our detection of SN Ia lensing opens a new pathway to study matter inhomogeneity that complements galaxy-galaxy lensing surveys and has unrelated systematics., Comment: Submitted to MNRAS

We present $griz$ photometric light curves for the full 5 years of the Dark Energy Survey Supernova program (DES-SN), obtained with both forced Point Spread Function (PSF) photometry on Difference Images (DIFFIMG) performed during survey operations, and Scene Modelling Photometry (SMP) on search images processed after the survey. This release contains $31,636$ DIFFIMG and $19,706$ high-quality SMP light curves, the latter of which contains $1635$ photometrically-classified supernovae that pass cosmology quality cuts. This sample spans the largest redshift ($z$) range ever covered by a single SN survey ($0.1

We report constraints on a variety of non-standard cosmological models using the full 5-year photometrically-classified type Ia supernova sample from the Dark Energy Survey (DES-SN5YR). Both Akaike Information Criterion (AIC) and Suspiciousness calculations find no strong evidence for or against any of the non-standard models we explore. When combined with external probes, the AIC and Suspiciousness agree that 11 of the 15 models are moderately preferred over Flat-$\Lambda$CDM suggesting additional flexibility in our cosmological models may be required beyond the cosmological constant. We also provide a detailed discussion of all cosmological assumptions that appear in the DES supernova cosmology analyses, evaluate their impact, and provide guidance on using the DES Hubble diagram to test non-standard models. An approximate cosmological model, used to perform bias corrections to the data holds the biggest potential for harbouring cosmological assumptions. We show that even if the approximate cosmological model is constructed with a matter density shifted by $\Delta\Omega_m\sim0.2$ from the true matter density of a simulated data set the bias that arises is sub-dominant to statistical uncertainties. Nevertheless, we present and validate a methodology to reduce this bias.

We present a precise measurement of cosmological time dilation using the light curves of 1504 type Ia supernovae from the Dark Energy Survey spanning a redshift range $0.1\lesssim z\lesssim 1.2$. We find that the width of supernova light curves is proportional to $(1+z)$, as expected for time dilation due to the expansion of the Universe. Assuming type Ia supernovae light curves are emitted with a consistent duration $\Delta t_{\rm em}$, and parameterising the observed duration as $\Delta t_{\rm obs}=\Delta t_{\rm em}(1+z)^b$, we fit for the form of time dilation using two methods. Firstly, we find that a power of $b \approx 1$ minimises the flux scatter in stacked subsamples of light curves across different redshifts. Secondly, we fit each target supernova to a stacked light curve (stacking all supernovae with observed bandpasses matching that of the target light curve) and find $b=1.003\pm0.005$ (stat) $\pm\,0.010$ (sys). Thanks to the large number of supernovae and large redshift-range of the sample, this analysis gives the most precise measurement of cosmological time dilation to date, ruling out any non-time-dilating cosmological models at very high significance., Comment: 14 pages, 13 figures. Updated in response to reviewer feedback. Accepted for publication in the Monthly Notices of the Royal Astronomical Society (MNRAS)

We measure the current expansion rate of the Universe, Hubble's constant $H_0$, by calibrating the absolute magnitudes of supernovae to distances measured by Baryon Acoustic Oscillations. This `inverse distance ladder' technique provides an alternative to calibrating supernovae using nearby absolute distance measurements, replacing the calibration with a high-redshift anchor. We use the recent release of 1829 supernovae from the Dark Energy Survey spanning $0.01\lt z \lt1.13$ anchored to the recent Baryon Acoustic Oscillation measurements from DESI spanning $0.30 \lt z_{\mathrm{eff}} \lt 2.33$. To trace cosmology to $z=0$, we use the third-, fourth- and fifth-order cosmographic models, which, by design, are agnostic about the energy content and expansion history of the universe. With the inclusion of the higher-redshift DESI-BAO data, the third-order model is a poor fit to both data sets, with the fourth-order model being preferred by the Akaike Information Criterion. Using the fourth-order cosmographic model, we find $H_0=67.19^{+0.66}_{-0.64}\mathrm{~km} \mathrm{~s}^{-1} \mathrm{~Mpc}^{-1}$, in agreement with the value found by Planck without the need to assume Flat-$\Lambda$CDM. However the best-fitting expansion history differs from that of Planck, providing continued motivation to investigate these tensions.

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

QCD gives rise to a rich spectrum of excited baryon states. Understanding their internal structure is important for many areas of nuclear physics, such as nuclear forces, dense matter, and neutrino-nucleus interactions. Generalized parton distributions (GPDs) are an established tool for characterizing the QCD structure of the ground-state nucleon. They are used to create 3D tomographic images of the quark/gluon structure and quantify the mechanical properties such as the distribution of mass, angular momentum and forces in the system. Transition GPDs extend these concepts to $N \rightarrow N^\ast$ transitions and can be used to characterize the 3D structure and mechanical properties of baryon resonances. They can be probed in high-momentum-transfer exclusive electroproduction processes with resonance transitions $e + N \rightarrow e' + M + N^\ast$, such as deeply-virtual Compton scattering ($M = \gamma$) or meson production ($M = \pi, K$, $etc.$), and in related photon/hadron-induced processes. This White Paper describes a research program aiming to explore baryon resonance structure with transition GPDs. This includes the properties and interpretation of the transition GPDs, theoretical methods for structures and processes, first experimental results from JLab 12 GeV, future measurements with existing and planned facilities (JLab detector and energy upgrades, COMPASS/AMBER, EIC, EicC, J-PARC, LHC ultraperihperal collisions), and the theoretical and experimental developments needed to realize this program.

Active matter encompasses many-particle systems with self-propelling units, such as flocks of birds or schools of fish. Here, we show how self-propelling domain walls can be realised in a solid-state system when a ferrimagnet is weakly driven out of thermal equilibrium by an oscillating field. This activates the Goldstone mode, inducing a rotation of the antiferromagnetic xy-order in a clockwise or anticlockwise direction, determined by the sign of the ferromagnetic component. Two opposite directions of rotation meet at a ferromagnetic domain wall, resulting in 'dynamical frustration', with three main consequences. (i) Domain walls move actively in a direction chosen by spontaneous symmetry breaking. Their speed is proportional to the square root of the driving power across large parameter regimes. (ii) In one dimension (1D), after a quench into the ferrimagnetic phase, this motion and strong hydrodynamic interactions lead to a linear growth of the magnetic correlation length over time, much faster than in equilibrium. (iii) The dynamical frustration makes the system highly resilient to noise. The correlation length of the weakly driven 1D system can be orders of magnitude larger than in the corresponding equilibrium system with the same noise level.

This study evaluates the capabilities of ChatGPT versions 3.5 and 4 in generating code across a diverse range of programming languages. Our objective is to assess the effectiveness of these AI models for generating scientific programs. To this end, we asked ChatGPT to generate three distinct codes: a simple numerical integration, a conjugate gradient solver, and a parallel 1D stencil-based heat equation solver. The focus of our analysis was on the compilation, runtime performance, and accuracy of the codes. While both versions of ChatGPT successfully created codes that compiled and ran (with some help), some languages were easier for the AI to use than others (possibly because of the size of the training sets used). Parallel codes -- even the simple example we chose to study here -- also difficult for the AI to generate correctly., Comment: 9 pages, 3 figures

We present a simulation-based cosmological analysis using a combination of Gaussian and non-Gaussian statistics of the weak lensing mass (convergence) maps from the first three years (Y3) of the Dark Energy Survey (DES). We implement: 1) second and third moments; 2) wavelet phase harmonics; 3) the scattering transform. Our analysis is fully based on simulations, spans a space of seven $\nu w$CDM cosmological parameters, and forward models the most relevant sources of systematics inherent in the data: masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration. We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a simulation-based inference approach. Including and combining different non-Gaussian statistics is a powerful tool that strongly improves constraints over Gaussian statistics (in our case, the second moments); in particular, the Figure of Merit $\textrm{FoM}(S_8, \Omega_{\textrm{m}})$ is improved by 70 percent ($\Lambda$CDM) and 90 percent ($w$CDM). When all the summary statistics are combined, we achieve a 2 percent constraint on the amplitude of fluctuations parameter $S_8 \equiv \sigma_8 (\Omega_{\textrm{m}}/0.3)^{0.5}$, obtaining $S_8 = 0.794 \pm 0.017$ ($\Lambda$CDM) and $S_8 = 0.817 \pm 0.021$ ($w$CDM). The constraints from different statistics are shown to be internally consistent (with a $p$-value>0.1 for all combinations of statistics examined). We compare our results to other weak lensing results from the DES Y3 data, finding good consistency; we also compare with results from external datasets, such as \planck{} constraints from the Cosmic Microwave Background, finding statistical agreement, with discrepancies no greater than $<2.2\sigma$., Comment: 24 pages, 13 figures, to be submitted to PRD. Comments welcome!

In high-energy physics, there is a need to investigate alternative silicon sensor concepts that offer cost-efficient, large-area coverage. Sensors based on CMOS imaging technology present such a silicon sensor concept for tracking detectors. The CMOS Strips project investigates passive CMOS strip sensors fabricated by LFoundry in a 150nm technology. By employing the technique of stitching, two different strip sensor formats have been realised. The sensor performance is characterised based on measurements at the DESY II Test Beam Facility. The sensor response was simulated utilising Monte Carlo methods and electric fields provided by TCAD device simulations. This study shows that employing the stitching technique does not affect the hit detection efficiency. A first look at the electric field within the sensor and its impact on generated charge carriers is being discussed., Comment: 5 pages, 6 figures, submitted to Nuclear Instruments and Methods in Physics Research, Section A as a conference proceeding for the 13th International "Hiroshima" Symposium on the Development and Application of Semiconductor Tracking Detectors

We present a method for the numerical computation of Fourier-Bessel transforms on a finite or infinite interval. The function to be transformed needs to be evaluated on a grid of points that is independent of the argument of the Bessel function. We demonstrate the accuracy of the algorithm for a wide range of functions, including those that appear in the context of transverse-momentum dependent parton distributions in Quantum Chromodynamics., Comment: 58 pages, 16 figures. v2: added clarifications; new section about integrands that grow as z goes to infinity

Mixed-integer quadratic programs (MIQPs) are a versatile way of formulating vehicle decision making and motion planning problems, where the prediction model is a hybrid dynamical system that involves both discrete and continuous decision variables. However, even the most advanced MIQP solvers can hardly account for the challenging requirements of automotive embedded platforms. Thus, we use machine learning to simplify and hence speed up optimization. Our work builds on recent ideas for solving MIQPs in real-time by training a neural network to predict the optimal values of integer variables and solving the remaining problem by online quadratic programming. Specifically, we propose a recurrent permutation equivariant deep set that is particularly suited for imitating MIQPs that involve many obstacles, which is often the major source of computational burden in motion planning problems. Our framework comprises also a feasibility projector that corrects infeasible predictions of integer variables and considerably increases the likelihood of computing a collision-free trajectory. We evaluate the performance, safety and real-time feasibility of decision-making for autonomous driving using the proposed approach on realistic multi-lane traffic scenarios with interactive agents in SUMO simulations.

In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance Computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V CPUs implementing the finalized ratified vector specification are being released. The RISC-V vector specification follows in the tradition of vector processors found in the CDC STAR-100, the Cray-1, the Convex C-Series, and the NEC SX machines and accelerators. The family of vector processors offers support for variable-length array processing as opposed to the fixed-length processing functionality offered by SIMD. Vector processors offer opportunities to perform vector-chaining which allows temporary results to be used without the need to resolve memory references. In this work, we use the Octo-Tiger multi-physics, multi-scale, 3D adaptive mesh refinement astrophysics application to study these early RISC-V chips with vector machine support. We report on our experience in porting this modern C++ code (which is built upon several open-source libraries such as HPX and Kokkos) to RISC-V. In addition, we show the impact of the RISC-V Vector extension on a RISC-V single board computer by implementing the std::experimental:simd interface and integrating it with our code. We also compare the application's performance, scalability, and power consumption on desktop-grade RISC-V computer to an A64FX system.

This work considers the problem of optimal lane changing in a structured multi-agent road environment. A novel motion planning algorithm that can capture long-horizon dependencies as well as short-horizon dynamics is presented. Pivotal to our approach is a geometric approximation of the long-horizon combinatorial transition problem which we formulate in the continuous time-space domain. Moreover, a discrete-time formulation of a short-horizon optimal motion planning problem is formulated and combined with the long-horizon planner. Both individual problems, as well as their combination, are formulated as MIQP and solved in real-time by using state-of-the-art solvers. We show how the presented algorithm outperforms two other state-of-the-art motion planning algorithms in closed-loop performance and computation time in lane changing problems. Evaluations are performed using the traffic simulator SUMO, a custom low-level tracking model predictive controller, and high-fidelity vehicle models and scenarios, provided by the CommonRoad environment.

Anti-seizure medications (ASMs) are the primary treatment for epilepsy, yet medication tapering effects have not been investigated in a dose, region, and time-dependent manner, despite their potential impact on research and clinical practice. We examined over 3000 hours of intracranial EEG recordings in 32 subjects during long-term monitoring, of which 22 underwent concurrent ASM tapering. We estimated ASM plasma levels based on known pharmaco-kinetics of all the major ASM types. We found an overall decrease in the power of delta band activity around the period of maximum medication withdrawal in most (80%) subjects, independent of their epilepsy type or medication combination. The degree of withdrawal correlated positively with the magnitude of delta power decrease. This dose-dependent effect was strongly seen across all recorded cortical regions during daytime; but not in sub-cortical regions, or during night time. We found no evidence of differential effect in seizure onset, spiking, or pathological brain regions. The finding of decreased delta band power during ASM tapering agrees with previous literature. Our observed dose-dependent effect indicates that monitoring ASM levels in cortical regions may be feasible for applications such as medication reminder systems, or closed-loop ASM delivery systems. ASMs are also used in other neurological and psychiatric conditions, making our findings relevant to a general neuroscience and neurology audience.

Background: Understanding healthy human brain function is crucial to identify and map pathological tissue within it. Whilst previous studies have mapped intracranial EEG (icEEG) from non-epileptogenic brain regions, these maps do not consider the effects of age and sex. Further, most existing work on icEEG has often suffered from a small sample size due to this modality's invasive nature. Here, we substantially increase the subject sample size compared to existing literature, to create a multi-centre, normative map of brain activity which additionally considers the effects of age, sex and recording site. Methods: Using interictal icEEG recordings from n = 513 subjects originating from 15 centres, we constructed a normative map of non-pathological brain activity by regressing age and sex on relative band power in five frequency bands, whilst accounting for the site effect. Results: Recording site significantly impacted normative icEEG maps in all frequency bands, and age was a more influential predictor of band power than sex. The age effect varied by frequency band, but no spatial patterns were observed at the region-specific level. Certainty about regression coefficients was also frequency band specific and moderately impacted by sample size. Conclusion: The concept of a normative map is well-established in neuroscience research and particularly relevant to the icEEG modality, which does not allow healthy control baselines. Our key results regarding the site and age effect guide future work utilising normative maps in icEEG.

Local-nonlocal coupling approaches combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process is challenging, requiring expertise to identify the interface between local and nonlocal regions. This study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used in a coupling approach. This identification process uses the loading functions and provides as output the selected model at the grid points. Training is based on datasets of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that differ from one another in terms of the data structure. The first approach, referred to as the full-domain input data approach, inputs the full load vector and outputs a full label vector. In this case, the classification process is carried out globally. The second approach consists of a window-based approach, where loads are preprocessed and partitioned into windows and the problem is formulated as a node-wise classification approach in which the central point of each window is treated individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. In particular, it is shown that the windowing approach provides promising results, achieving an accuracy of 0.96 and an F1-score of 0.97. These results underscore the potential of the approach to automate coupling processes, leading to more accurate and computationally efficient solutions for material science applications., Comment: 23 pages, 14 figures, research paper

In this note, we revisit the problem of the pressure-driven transport of a meniscus through a narrow cylindrical capillary or pore. This generic process finds many applications in science and technology. As it is known that Direct Numerical Simulations of moving contact line problems are highly demanding in terms of computational costs, simplified models in the form of ordinary differential equations offer an interesting alternative to perform a mathematical optimization of the flow. Blake and De Coninck studied the pressure-driven transport of a meniscus and identified two major competing mechanisms. While a hydrophilic surface is favorable to enhance the spontaneous imbibition into the pore, the friction is known to be significantly reduced on a hydrophobic surface. Blake and De Coninck showed that, depending on the applied pressure difference, there exists an optimal wettability that minimizes the time required to move the meniscus over a certain distance. We revisit this problem and derive analytical solutions in the limiting cases of negligible inertia and negligible contact line friction., Comment: 12 pages, 4 figures

For continuous decision spaces, nonlinear programs (NLPs) can be efficiently solved via sequential quadratic programming (SQP) and, more generally, sequential convex programming (SCP). These algorithms linearize only the nonlinear equality constraints and keep the outer convex structure of the problem intact. The aim of the presented sequential mixed-integer quadratic programming (MIQP) algorithm for mixed-integer nonlinear problems (MINLPs) is to extend the SQP/SCP methodology to MINLPs and leverage the availability of efficient MIQP solvers. The algorithm employs a three-step method in each iterate: First, the MINLP is linearized at a given iterate. Second, an MIQP with its feasible set restricted to a specific region around the current linearization point is formulated and solved. Third, the integer variables obtained from the MIQP solution are fixed, and only an NLP in the continuous variables is solved. The outcome of the third step is compared to previous iterates, and the best iterate so far is used as a linearization point in the next iterate. Crucially, the objective values and derivatives from all previous iterates are used to formulate the polyhedral region in the second step. The linear inequalities that define the region build on concepts from generalized Benders' decomposition for MINLPs. Although the presented MINLP algorithm is a heuristic method without any global optimality guarantee, it converges to the exact integer solution when applied to convex MINLP with a linear outer structure. The conducted numerical experiments demonstrate that the proposed algorithm is competitive with other open-source solvers for MINLP. Finally, we solve two mixed-integer optimal control problems (MIOCPs) transcribed into MINLPs via direct methods, showing that the presented algorithm can effectively deal with nonlinear equality constraints, a major hurdle for generic MINLP solvers., Comment: Andrea Ghezzi and Wim Van Roy contributed equally to this work. 34 pages, 11 figures, 3 tables

EEG slowing is reported in various neurological disorders including Alzheimer's, Parkinson's and Epilepsy. Here, we investigate alpha rhythm slowing in individuals with refractory temporal lobe epilepsy (TLE), compared to healthy controls, using scalp electroencephalography (EEG) and magnetoencephalography (MEG). We retrospectively analysed data from 17,(46) healthy controls and 22,(24) individuals with TLE who underwent scalp EEG and (MEG) recordings as part of presurgical evaluation. Resting-state, eyes-closed recordings were source reconstructed using the standardized low-resolution brain electrographic tomography (sLORETA) method. We extracted low (slow) 6-9 Hz and high (fast) 10-11 Hz alpha relative band power and calculated the alpha power ratio by dividing low (slow) alpha by high (fast) alpha. This ratio was computed for all brain regions in all individuals. Alpha oscillations were slower in individuals with TLE than controls (p<0.05). This effect was present in both the ipsilateral and contralateral hemispheres, and across widespread brain regions. Alpha slowing in TLE was found in both EEG and MEG recordings. We interpret greater low (slow)-alpha as greater deviation from health.

We study the properties of double white dwarf (DWD) mergers by performing hydrodynamic simulations using the new and improved adaptive mesh refinement code Octo-Tiger. We follow the orbital evolution of DWD systems of mass ratio q=0.7 for tens of orbits until and after the merger to investigate them as a possible origin for R Coronae Borealis (RCB) type stars. We reproduce previous results, finding that during the merger, the Helium WD donor star is tidally disrupted within 20-80 minutes since the beginning of the simulation onto the accretor Carbon-Oxygen WD, creating a high temperature shell around the accretor. We investigate the possible Helium burning in this shell and the merged object's general structure. Specifically, we are interested in the amount of Oxygen-16 dredged-up to the hot shell and the amount of Oxygen-18 produced. This is critical as the discovery of very low Oxygen-16 to Oxygen-18 ratios in RCB stars pointed out the merger scenario as a favorable explanation for their origin. A small amount of hydrogen in the donor may help keep the Oxygen-16 to Oxygen-18 ratios within observational bounds, even if moderate dredge-up from the accretor occurs. In addition, we perform a resolution study to reconcile the difference found in the amount of Oxygen-16 dredge-up between smoothed-particle hydrodynamics and grid-based simulations., Comment: 27 pages, Submitted to MNRAS. Comments are welcome

Extracting precise cosmology from weak lensing surveys requires modelling the non-linear matter power spectrum, which is suppressed at small scales due to baryonic feedback processes. However, hydrodynamical galaxy formation simulations make widely varying predictions for the amplitude and extent of this effect. We use measurements of Dark Energy Survey Year 3 weak lensing (WL) and Atacama Cosmology Telescope DR5 kinematic Sunyaev-Zel'dovich (kSZ) to jointly constrain cosmological and astrophysical baryonic feedback parameters using a flexible analytical model, `baryonification'. First, using WL only, we compare the $S_8$ constraints using baryonification to a simulation-calibrated halo model, a simulation-based emulator model and the approach of discarding WL measurements on small angular scales. We find that model flexibility can shift the value of $S_8$ and degrade the uncertainty. The kSZ provides additional constraints on the astrophysical parameters and shifts $S_8$ to $S_8=0.823^{+0.019}_{-0.020}$, a higher value than attained using the WL-only analysis. We measure the suppression of the non-linear matter power spectrum using WL + kSZ and constrain a mean feedback scenario that is more extreme than the predictions from most hydrodynamical simulations. We constrain the baryon fractions and the gas mass fractions and find them to be generally lower than inferred from X-ray observations and simulation predictions. We conclude that the WL + kSZ measurements provide a new and complementary benchmark for building a coherent picture of the impact of gas around galaxies across observations.

The Finite Elements with Switch Detection (FESD) method is a highly accurate direct transcription method for optimal control of several classes of nonsmooth dynamical systems. This paper extends the FESD method to Projected Dynamical Systems (PDS) and first-order sweeping processes with time-independent sets. This method discretizes an equivalent dynamic complementarity system and exploits the particular structure of the discontinuities present in these systems. In the FESD method, allowing integration step sizes to be degrees of freedom, and introducing additional complementarity constraints, enables the exact detection of nonsmooth events. In contrast to the standard fixed-step Runge-Kutta methods, this approach allows for the recovery of full-order integration accuracy and the correct computation of numerical sensitivities. Numerical examples illustrate the effectiveness of the proposed method in an optimal control context. This method and the examples are included in the open-source software package nosnoc., Comment: 7 pages, 3 figures, Submitted to 2024 IEEE Conference on Decision and Control (CDC)

This paper introduces a novel optimization-based approach for parametric nonlinear system identification. Building upon the prediction error method framework, traditionally used for linear system identification, we extend its capabilities to nonlinear systems. The predictions are computed using a moving horizon state estimator with a constant arrival cost. Eventually, both the system parameters and the arrival cost are estimated by minimizing the sum of the squared prediction errors. Since the predictions are induced by the state estimator, the method can be viewed as the tuning of a state estimator, based on its predictive capacities. The present extension of the prediction error method not only enhances performance for nonlinear systems but also enables learning from multiple trajectories with unknown initial states, broadening its applicability in practical scenarios. Additionally, the novel formulation leaves room for the design of efficient and parallelizable optimization algorithms, since each output prediction only depends on a fixed window of past actions and measurements. In the special case of linear time-invariant systems, we show an important property of the proposed method which suggests asymptotic consistency under reasonable assumptions. Numerical examples illustrate the effectiveness and practicality of the approach, and one of the examples also highlights the necessity for the arrival cost., Comment: Submitted to IEEE Control and Decision Conference in March 2024. Contains 7 pages including 5 figures