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This work discusses self-improving properties of the Muckenhoupt condition and weighted norm inequalities for the Hardy-Littlewood maximal function on metric measure spaces with a doubling measure. Our main result provides direct proofs of these properties by applying a Whitney covering argument and a technique inspired by the Calder\'on-Zygmund decomposition. In particular, this approach does not rely on reverse H\"older inequalities.

This paper explores the data-aided regularization of the direct-estimate combiner in the uplink of a distributed multiple-input multiple-output system. The network-wide combiner can be computed directly from the pilot signal received at each access point, eliminating the need for explicit channel estimation. However, the sample covariance matrix of the received pilot signal that is used in its computation may significantly deviate from the actual covariance matrix when the number of pilot symbols is limited. To address this, we apply a regularization to the sample covariance matrix using a shrinkage coefficient based on the received data signal. Initially, the shrinkage coefficient is determined by minimizing the difference between the sample covariance matrices obtained from the received pilot and data signals. Given the limitations of this approach in interference-limited scenarios, the shrinkage coefficient is iteratively optimized using the sample mean squared error of the hard-decision symbols, which is more closely related to the actual system's performance, e.g., the symbol error rate (SER). Numerical results demonstrate that the proposed regularization of the direct-estimate combiner significantly enhances the SER, particularly when the number of pilot symbols is limited., Comment: To be presented at IEEE ICASSP 2025

Multi-antenna coded caching (CC) with multicast beamforming typically relies on a complex successive interference cancellation (SIC) structure to decode a superposition of multiple streams received by each user. Signal-level CC schemes require the regeneration and cancellation of interfering signals at the physical layer of each receiver, which complicates practical implementations. To address this, we propose a bit-level multicast scheduling scheme enabling linear, SIC-free decoding of parallel streams by repeatedly transmitting data terms with linearly independent coefficients. Two reference strategies and a novel sparse strategy are considered for constructing the coefficient matrix. The reference cases include the random strategy, which lacks control over matrix construction, and the equal-distant strategy, which balances users' interference and data terms equally. In contrast, the sparse strategy minimizes the number of multicast streams transmitted in parallel during each interval. This approach simplifies both the decoding process and the beamforming design by decoupling the desired data terms for each user and reducing the number of SINR constraints, respectively. To further enhance the symmetric rate, a successive projection algorithm is applied to exploit channel properties and optimize user ordering. With the coefficient matrix and optimized user ordering in place, multicast beamformers are devised to aggregate desired data from relevant multicast streams. Numerical simulations validate the effectiveness of the sparse strategy and user scheduling, demonstrating significant gains in symmetric rate.

Integrating coded caching (CC) into multiple-input multiple-output (MIMO) communications can significantly enhance the achievable degrees of freedom (DoF) in wireless networks. This paper investigates a practical cache-aided asymmetric MIMO configuration with cache ratio $\gamma$, where a server equipped with $L$ transmit antennas communicates with $K$ users, each having $G_k$ receive antennas. We propose three content-aware MIMO-CC strategies: the \emph{min-G} scheme, which treats the system as symmetric by assuming all users have the same number of antennas, equal to the smallest among them; the \emph{Grouping} scheme, which maximizes spatial multiplexing gain separately within each user subset at the cost of some global caching gain; and the \emph{Phantom} scheme, which dynamically redistributes spatial resources using virtual or ``phantom'' antennas at the users, bridging the performance gains of the min-$G$ and Grouping schemes. These strategies jointly optimize the number of users, $\Omega$, and the parallel streams decoded by each user, $\beta_k$, ensuring linear decodability for all target users. Analytical and numerical results confirm that the proposed schemes achieve significant DoF improvements across various system configurations.

Materials emitting circularly polarized light (CPL) are highly sought after for applications ranging from efficient displays to quantum information technologies. Established methods for time-resolved CPL characterization have significant limitations, preventing in-depth photophysical insight necessary for materials development. We have designed and built a high-sensitivity (noise level 10^-4), broadband (ca. 400-900 nm), transient (ns resolution, ms range) full-Stokes (CPL and linear polarizations) spectroscopy setup. We demonstrate its broad applicability by measuring compounds with low dissymmetry factors across various timescales, as well as tracking the temporal evolution of linear polarization components alongside associated CPL artefacts. We have written open-source software and used stock optical components to make transient CPL spectroscopy practically accessible to a wide audience, enabling the study of chiral materials in numerous diverse applications.

Given an analytic function $f=u+iv$ in the unit disk $\mathbb{D}$, Zygmund's theorem gives the minimal growth restriction on $u$ which ensures that $v$ is in the Hardy space $h^1$. This need not be true if $f$ is a complex-valued harmonic function. However, we prove that Zygmund's theorem holds if $f$ is a harmonic $K$-quasiregular mapping in $\ID$. Our work makes further progress on the recent Riesz-type theorem of Liu and Zhu (Adv. Math., 2023), and the Kolmogorov-type theorem of Kalaj (J. Math. Anal. Appl., 2025), for harmonic quasiregular mappings. We also obtain a partial converse, thus showing that the proposed growth condition is the best possible. Furthermore, as an application of the classical conjugate function theorems, we establish a harmonic analogue of a well-known result of Hardy and Littlewood., Comment: 12 pages

We present a model for inferring where users look during interaction based on keypress data only. Given a key log, it outputs a scanpath that tells, moment-by-moment, how the user had moved eyes while entering those keys. The model can be used as a proxy for human data in cases where collecting real eye tracking data is expensive or impossible. Our technical insight is three-fold: first, we present an inference architecture that considers the individual characteristics of the user, inferred as a low-dimensional parameter vector; second, we present a novel loss function for synchronizing inferred eye movements with the keypresses; third, we train the model using a hybrid approach with both human data and synthetically generated data. The approach can be applied in interactive systems where predictive models of user behavior are available. We report results from evaluation in the challenging case of touchscreen typing, where the model accurately inferred real eye movements.

Current transport in polysilicon is a complicated process with many factors to consider. The inhomogeneous nature of polysilicon with its differently shaped and sized grains is one such consideration. We have developed a method that enhances existing resistivity models with a two-dimensional extension that incorporates the grain size distribution using a Voronoi-based resistor network. We obtain grain size distributions both from our growth simulations (700 K, 800 K, and 900 K) and experimental analysis. Applying our method, we investigate the effect that variation in grain size produces with cases of different average grain sizes (2 nm to 3 $\mu$m). For example, the resistivity of polysilicon with an average grain size of 175 nm drops from 11 k$\Omega$ $\cdot$ cm to 4.5 k$\Omega$ $\cdot$ cm when compared to conventional one-dimensional modeling. Our study highlights the strong effect of grain size variation on resistivity, revealing that wider distributions result in significant resistivity reductions of up to more than 50%. Due to the larger grains present with a grain size distribution, current transport encounters fewer grain boundaries while the average grain size remains the same resulting in fewer barriers along the current transport path. Incorporating the grain structure into the resistivity modeling facilitates a more detailed and comprehensive characterization of the electrical properties of polysilicon., Comment: 7 pages, 7 figures, 2 tables

We identified the Twitter accounts of 941 climate change policy actors across nine countries, and collected their activities from 2017--2022, totalling 48 million activities from 17,700 accounts at different organizational levels. There is considerable temporal and cross-national variation in how prominent climate-related activities were, but all national policy systems generally responded to climate-related events, such as climate protests, in a similar manner. Examining patterns of interaction within and across countries, we find that these national policy systems rarely directly interact with one another, but are connected through consistently engaging with the same content produced by accounts of international organizations, climate activists, and researchers., Comment: working paper, 16 pages, 6 figures

Cs$_2$AgBiBr$_6$ shows promise for solution-processable optoelectronics, such as photovoltaics, photocatalysis and X-ray detection. However, various spectroscopic studies report rapid charge carrier mobility loss in the first picosecond after photoexcitation, limiting carrier collection efficiencies. The origin of this rapid mobility loss is still unclear. Here, we directly compare hot excitation with excitation over the indirect fundamental bandgap, using transient absorption and THz spectroscopy on the same Cs$_2$AgBiBr$_6$ thin film sample. From transient absorption spectroscopy, we find that hot carriers cool towards the band-edges with a cooling rate of 0.58 ps$^{-1}$, which coincides with the observed mobility loss rate from THz spectroscopy. Hence, our study establishes a direct link between the hot carrier cooling and ultrafast mobility loss on the picosecond timescale.

A collaboration between dataset owners and model owners is needed to facilitate effective machine learning (ML) training. During this collaboration, however, dataset owners and model owners want to protect the confidentiality of their respective assets (i.e., datasets, models and training code), with the dataset owners also caring about the privacy of individual users whose data is in their datasets. Existing solutions either provide limited confidentiality for models and training code, or suffer from privacy issues due to collusion. We present Citadel++, a scalable collaborative ML training system designed to simultaneously protect the confidentiality of datasets, models and training code, as well as the privacy of individual users. Citadel++ enhances differential privacy techniques to safeguard the privacy of individual user data while maintaining model utility. By employing Virtual Machine-level Trusted Execution Environments (TEEs) and improved integrity protection techniques through various OS-level mechanisms, Citadel++ effectively preserves the confidentiality of datasets, models and training code, and enforces our privacy mechanisms even when the models and training code have been maliciously designed. Our experiments show that Citadel++ provides privacy, model utility and performance while adhering to confidentiality and privacy requirements of dataset owners and model owners, outperforming the state-of-the-art privacy-preserving training systems by up to 543x on CPU and 113x on GPU TEEs.

We report an improved device fabrication process employed in the development of an advanced front contact grid design employing external busbars. The advanced fabrication process results in enhanced solar cell performance measured at one-sun illumination. In this grid configuration the busbar area is located outside the active solar cell and the grid fingers travel across the mesa sidewalls. With this design the solar cell size can be scaled down without limitations as the area of the busbar is not restricting the component size. Thus, the demonstrated design is beneficial especially for micro-concentrator solar cells. In general, this approach minimizes the power losses originating from the grid shadowing and dark area related voltage losses. The performance of the proposed design is validated by fabricating GaInP/GaAs/GaInNAsSb triple-junction solar cells employing the grid design with external busbars. Light-biased current-voltage and electroluminescence characteristics of the cells reveal that an additional contact GaAs etching step prior to front contact metal deposition is needed to ensure good photovoltaic performance with a fill factor of 85% at one-sun illumination. The improvement is attributed to removing the plasma-damaged material layer that can extend to a depth beyond 100 nm, leading to resistive losses.

This paper studies reinforcement learning (RL) in infinite-horizon dynamic decision processes with almost-sure safety constraints. Such safety-constrained decision processes are central to applications in autonomous systems, finance, and resource management, where policies must satisfy strict, state-dependent constraints. We consider a doubly-regularized RL framework that combines reward and parameter regularization to address these constraints within continuous state-action spaces. Specifically, we formulate the problem as a convex regularized objective with parametrized policies in the mean-field regime. Our approach leverages recent developments in mean-field theory and Wasserstein gradient flows to model policies as elements of an infinite-dimensional statistical manifold, with policy updates evolving via gradient flows on the space of parameter distributions. Our main contributions include establishing solvability conditions for safety-constrained problems, defining smooth and bounded approximations that facilitate gradient flows, and demonstrating exponential convergence towards global solutions under sufficient regularization. We provide general conditions on regularization functions, encompassing standard entropy regularization as a special case. The results also enable a particle method implementation for practical RL applications. The theoretical insights and convergence guarantees presented here offer a robust framework for safe RL in complex, high-dimensional decision-making problems., Comment: 74 pages

The Jaynes-Cummings and quantum Rabi models are fundamental to cavity and circuit quantum electrodynamics, as they describe the simplest form of light-matter interaction, where a single qubit is coupled to a single bosonic mode. A scenario that is commonly encountered in the experimental practice arises when the bosonic mode interacts with an external dissipative thermal bath, making the qubit-boson system open. In this work, we present new analytical solutions of the Lindblad master equations for the open Jaynes-Cummings and quantum Rabi models in the limit of weak qubit-boson coupling $g$, using the holomorphic formalism in Bargmann space. Specifically, we derive the most general solution of the local Lindblad master equation for the open dispersive Jaynes-Cummings model coupled to a thermal bath, with the only assumptions that the initial state of the qubit-boson system is separable. Additionally, we obtain a perturbative analytical solution for the open quantum Rabi model up to second order in $g$. Notably, our findings include a new formula for the qubit's steady state at zeroth order, showing that the stationary populations depend on both qubit and boson frequencies in the quantum Rabi model, but not in the Jaynes-Cummings model. Our results are of general interest to the study of open quantum systems in the context of light-matter interaction.

The utilization of health data for secondary purposes, such as research, sta-tistics, and development, has become increasingly significant in advancing healthcare systems. To foster the above, Finland has established a framework for the secondary use of health and social data through legislative measures and the creation of specialized institutions, which are the first of their kind in the world. In this paper, we give an overview of our implementation for using secondary health and social data in a centralized fashion. As a technical contribution, we also address key implementation aspects related to implementing the framework., Comment: Submitted to the ICSOB 2024 (Approved)

Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources. Here, we develop a foundation model for prostate cancer digital pathology called HistoEncoder by pre-training on 48 million prostate tissue tile images. We demonstrate that HistoEncoder features extracted from tile images with similar histological patterns map closely together in the feature space. HistoEncoder outperforms models pre-trained with natural images, even without fine-tuning or with 1000 times less training data. We describe two use cases that leverage the capabilities of HistoEncoder by fine-tuning the model with a limited amount of data and computational resources. First, we show how HistoEncoder can be used to automatically annotate large-scale datasets with high accuracy. Second, we combine histomics with commonly used clinical nomograms, significantly improving prostate cancer-specific death survival models. Foundation models such as HistoEncoder can allow organizations with limited resources to build effective clinical software tools without needing extensive datasets or significant amounts of computing.

Circulating currents occurring in windings of electric machines received rising interest recent years. Circulating currents represent unwanted currents flowing between parallel-connected conductors. This phenomenon is due to various reasons such as asymmetries in the winding and differences in electric potential between parallel-connected conductors. This effect occurs both at no-load and on-load conditions, and always lead to uneven distribution of the current between the parallel conductors, therefore leading to higher losses, as proven in the authors' previous work. Circulating currents are occurring mainly due to asymmetries and electric potential difference in the active part, meaning that long end windings are advantageous to mitigate the effect of circulating currents. Losses due to circulating currents decrease at a rate proportional to the inverse square of the end windings length. The aim of this paper is to mathematically prove this property and present a case study application in an electric machine.

This work explores the intersection of continual learning (CL) and differential privacy (DP). Crucially, continual learning models must retain knowledge across tasks, but this conflicts with the differential privacy requirement of restricting individual samples to be memorised in the model. We propose using pre-trained models to address the trade-offs between privacy and performance in a continual learning setting. More specifically, we present necessary assumptions to enable privacy-preservation and propose combining pre-trained models with parameter-free classifiers and parameter-efficient adapters that are learned under differential privacy. Our experiments demonstrate their effectiveness and provide insights into balancing the competing demands of continual learning and privacy., Comment: 15 pages, 3 figures, Accepted at Scalable Continual Learning for Lifelong Foundation Models Workshop at 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a federated setting for a real-life use case: invoice processing. The competition introduced a dataset of real invoice documents, along with associated questions and answers requiring information extraction and reasoning over the document images. Thereby, it brings together researchers and expertise from the document analysis, privacy, and federated learning communities. Participants fine-tuned a pre-trained, state-of-the-art Document Visual Question Answering model provided by the organizers for this new domain, mimicking a typical federated invoice processing setup. The base model is a multi-modal generative language model, and sensitive information could be exposed through either the visual or textual input modality. Participants proposed elegant solutions to reduce communication costs while maintaining a minimum utility threshold in track 1 and to protect all information from each document provider using differential privacy in track 2. The competition served as a new testbed for developing and testing private federated learning methods, simultaneously raising awareness about privacy within the document image analysis and recognition community. Ultimately, the competition analysis provides best practices and recommendations for successfully running privacy-focused federated learning challenges in the future., Comment: 27 pages, 6 figures

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

We demonstrate a cryogenic electro-optic sampling (EOS) setup that allows for the measurement of microwave signals at arbitrary positions on a cryogenic chip-scale device. We use a Josephson Arbitrary Waveform Synthesizer (JAWS) to generate quantum-accurate voltage signals and measure them with the EOS setup, allowing for the calibration of its response, yielding traceability of the microwave measurements to a quantum standard. We use the EOS setup to determine the time-domain response of ultrafast cryogenic photodiodes and the electrical reflection coefficient, i.e., the S11 scattering parameter, in a superconducting transmission line. Finally, we introduce an optical femtosecond pulse source which can be used to study the fidelity of superconducting transmission lines and terminations, as well as reflections from elements like Josephson junction arrays imbedded in them., Comment: 16 pages

We give the first parallel algorithm with optimal $\tilde{O}(m)$ work for the classical problem of computing Single-Source Shortest Paths in general graphs with negative-weight edges. In graphs without negative edges, Dijkstra's algorithm solves the Single-Source Shortest Paths (SSSP) problem with optimal $\tilde O(m)$ work, but is inherently sequential. A recent breakthrough by Bernstein, Nanongkai, Wulff-Nilsen; FOCS '22 achieves the same for general graphs. Parallel shortest path algorithms are more difficult and have been intensely studied for decades. Only very recently, multiple lines of research culminated in parallel algorithms with optimal work $\tilde O(m)$ for various restricted settings, such as approximate or exact algorithms for directed or undirected graphs without negative edges. For general graphs, the best known algorithm by [shvinkumar, Bernstein, Cao, Grunau, Haeupler, Jiang, Nanongkai, Su; ESA '24 still requires $m^{1+o(1)}$ work. This paper presents a randomized parallel algorithm for SSSP in general graphs with near-linear work $\tilde O(m)$ and state-of-the-art span $n^{1/2 + o(1)}$. We follow a novel bottom-up approach leading to a particularly clean and simple algorithm. Our algorithm can be seen as a \emph{near-optimal parallel black-box reduction} from SSSP in general graphs to graphs without negative edges. In contrast to prior works, the reduction in this paper is both parallel and essentially without overhead, only affecting work and span by polylogarithmic factors., Comment: To appear at SOSA 2025

Developing a reinforcement learning (RL) agent often involves identifying values for numerous parameters, covering the policy, reward function, environment, and agent-internal architecture. Since these parameters are interrelated in complex ways, optimizing them is a black-box problem that proves especially challenging for nonexperts. Although existing optimization-as-a-service platforms (e.g., Vizier and Optuna) can handle such problems, they are impractical for RL systems, since the need for manual user mapping of each parameter to distinct components makes the effort cumbersome. It also requires understanding of the optimization process, limiting the systems' application beyond the machine learning field and restricting access in areas such as cognitive science, which models human decision-making. To tackle these challenges, the paper presents AgentForge, a flexible low-code platform to optimize any parameter set across an RL system. Available at https://github.com/feferna/AgentForge, it allows an optimization problem to be defined in a few lines of code and handed to any of the interfaced optimizers. With AgentForge, the user can optimize the parameters either individually or jointly. The paper presents an evaluation of its performance for a challenging vision-based RL problem., Comment: This paper has been accepted at the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025)

We study the fine scaling properties of planar self-affine carpets. For Gatzouras--Lalley carpets, we give a precise formula for maximal Hausdorff dimension of a tangent in terms of the Hausdorff dimension of the projection and the Assouad dimension of the corresponding vertical slice. Using regularity properties for the Assouad dimension of non-autonomous self-similar sets, this implies that the set of points with tangents that are as large as possible has full Hausdorff measure, at the critical exponent. On the other hand, we give an explicit example of a Bara\'nski carpet for which the Hausdorff dimension of the set of points for which there exists a maximal tangent has Hausdorff dimension strictly less than the Hausdorff dimension of the original carpet., Comment: 35 pages, 3 figures. This is approximately the final 1/3 of arXiv:2309.11971v1, which has been removed in arXiv:2309.11971v2

Differential privacy (DP) provides robust privacy guarantees for statistical inference, but this can lead to unreliable results and biases in downstream applications. While several noise-aware approaches have been proposed which integrate DP perturbation into the inference, they are limited to specific types of simple probabilistic models. In this work, we propose a novel method for noise-aware approximate Bayesian inference based on stochastic gradient variational inference which can also be applied to high-dimensional and non-conjugate models. We also propose a more accurate evaluation method for noise-aware posteriors. Empirically, our inference method has similar performance to existing methods in the domain where they are applicable. Outside this domain, we obtain accurate coverages on high-dimensional Bayesian linear regression and well-calibrated predictive probabilities on Bayesian logistic regression with the UCI Adult dataset.

Non-autonomous self-similar sets are a family of compact sets which are, in some sense, highly homogeneous in space but highly inhomogeneous in scale. The main purpose of this note is to clarify various regularity properties and separation conditions relevant for the fine local scaling properties of these sets. A simple application of our results is a precise formula for the Assouad dimension of non-autonomous self-similar sets in $\mathbb{R}^d$ satisfying a certain ``bounded neighbourhood'' condition, which generalizes earlier work of Li--Li--Miao--Xi and Olsen--Robinson--Sharples. We also see that the bounded neighbourhood assumption is, in few different senses, as general as possible., Comment: 27 pages. This paper generalizes the results in v1 of arXiv:2309.11971 concerning non-autonomous self-similar sets, which have now been removed in arXiv:2309.11971v2

We prove that the reconstruction of a certain type of length spaces from their travel time data on a closed subset is Lipschitz stable. The travel time data is the set of distance functions from the entire space, measured on the chosen closed subset. The case of a Riemannian manifold with boundary with the boundary as the measurement set appears is a classical geometric inverse problem arising from Gel'fand's inverse boundary spectral problem. Examples of spaces satisfying our assumptions include some non-simple Riemannian manifolds, Euclidean domains with non-trivial topology, and metric trees., Comment: 24 pages, 2 figures

We study stellar core growth in simulations of merging massive ($M_\star>10^{11}\,\mathrm{M}_\odot$) elliptical galaxies by a supermassive black hole (SMBH) displaced by gravitational wave induced recoil velocity. With controlled, dense sampling of the SMBH recoil velocity, we find the core radius originally formed by SMBH binary scouring can grow by a factor of 2-3 when the recoil velocity exceeds $\sim50$ per cent of the central escape velocity, and the mass deficit grows by up to a factor of $\sim4$. Using Bayesian inference we predict the distribution of stellar core sizes formed through this process to peak at $\sim1\,\mathrm{kpc}$. An orbital decomposition of stellar particles within the core reveals that radial orbits dominate over tube orbits when the recoil velocity exceeds the velocity dispersion of the core, whereas tube orbits dominate for the lowest recoil kicks. A change in orbital structure is reflected in the anisotropy parameter, with a central tangential bias present only for recoil velocities less than the local stellar velocity dispersion. Emulating current integral field unit observations of the stellar line-of-sight velocity distribution, we uncover a distinct signature in the Gauss-Hermite symmetric deviation coefficient $h_4$ that uniquely constrains the core size due to binary scouring. This signature is insensitive to the later evolution of the stellar mass distribution due to SMBH recoil. Our results provide a novel method to estimate the SMBH recoil magnitude from observations of local elliptical galaxies, and implies these galaxies primarily experienced recoil velocities less than the stellar velocity dispersion of the core., Comment: 27 pages, 21 figures

Circulating currents in windings refer to unwanted electrical currents flowing between the parallel conductors of a winding. These currents arise due to several phenomena such as asymmetries, imperfections in the winding layout, and differences in electric potential between the parallel conductors. This effect is visible typically in windings of transformers, motors, or generators. At on-load condition, this is equivalent to having a current unevenly distributed between parallel conductors. Circulating currents have two main drawbacks: increased losses in windings and potential degradation of insulation over time. The former is an intuitive property that is widely acknowledged in the literature. This paper presents a formal proof of this fundamental property, building upon the authors' previous work and embedding it within a rigorous mathematical framework. The mathematical definition of circulating currents is provided, along with a case application in an electric machine.

Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with it's detrimental effects. Recent findings in our ED indicate that occupancy ratios exceeding 90% are associated with increased 10-day mortality. In this paper, we aim to predict these crisis periods using retrospective data from a large Nordic ED with a LightGBM model. We provide predictions for the whole ED and individually for it's different operational sections. We demonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of 0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI 0.75-0.83). Consequently we show that forecasting mortality-associated crowding using anonymous administrative data is feasible.

The spatial Sigma-Delta ($\Sigma\Delta$) architecture can be leveraged to reduce the quantization noise and enhance the effective resolution of few-bit analog-to-digital converters (ADCs) at certain spatial frequencies of interest. Utilizing the variational Bayesian (VB) inference framework, this paper develops novel data detection algorithms tailored for massive multiple-input multiple-output (MIMO) systems with few-bit $\Sigma\Delta$ ADCs and angular channel models, where uplink signals are confined to a specific angular sector. We start by modeling the corresponding Bayesian networks for the $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-order $\Sigma\Delta$ receivers. Next, we propose an iterative algorithm, referred to as Sigma-Delta variational Bayes (SD-VB), for MIMO detection, offering low-complexity updates through closed-form expressions of the variational densities of the latent variables. Simulation results show that the proposed $2^{\mathrm{nd}}$-order SD-VB algorithm delivers the best symbol error rate (SER) performance while maintaining the same computational complexity as in unquantized systems, matched-filtering VB with conventional quantization, and linear minimum mean-squared error (LMMSE) methods. Moreover, the $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-order SD-VB algorithms achieve their lowest SER at an antenna separation of one-fourth wavelength for a fixed number of antenna elements. The effects of the steering angle of the $\Sigma\Delta$ architecture, the number of ADC resolution bits, and the number of antennas and users are also extensively analyzed., Comment: 13 pages, 12 figures, under submission for a journal publication

So far, even the highest resolution galaxy formation simulations with gravitational softening have failed to reproduce realistic life cycles of star clusters. We present the first star-by-star galaxy models of star cluster formation to account for hydrodynamics, star formation, stellar evolution and collisional gravitational interactions between stars and compact remnants using the updated SPHGAL+KETJU code, part of the GRIFFIN-project. Gravitational dynamics in the vicinity of $>3$ M$_\odot$ stars and their remnants are solved with a regularised integrator (KETJU) without gravitational softening. Comparisons of idealised star cluster evolution with SPHGAL+KETJU and direct N-body show broad agreement and the failure of simulations that use gravitational softening. In the hydrodynamical dwarf galaxy simulations run with SPHGAL+KETJU, clusters up to $\sim900$ M$_\odot$ are formed compact (effective radii $0.1-1$ pc) and their sizes increase by up to a factor of ten in agreement with previous N-body simulations and the observed sizes of exposed star clusters. The sizes increase rapidly once the clusters become exposed due to photoionising radiation. On average $63\%$ of the gravitationally bound clusters disrupt during the first $100$ Myr of evolution in the galactic tidal field. The addition of collisional dynamics reduces the fraction of supernovae in bound clusters by a factor of $\sim 2.6$, however the global star formation and outflow histories change by less than $30\%$. We demonstrate that the accurate treatment of gravitational encounters with massive stars enables more realistic star cluster life cycles from the earliest stages of cluster formation until disruption in simulated low-mass galaxies., Comment: 20 pages, 12 figures, submitted to MNRAS, comments welcome

Primordial black holes (PBHs) could compose the dark matter content of the Universe. We present the first simulations of cosmological structure formation with PBH dark matter that consistently include collisional few-body effects, post-Newtonian orbit corrections, orbital decay due to gravitational wave emission, and black-hole mergers. We carefully construct initial conditions by considering the evolution during radiation domination as well as early-forming binary systems. We identify numerous dynamical effects due to the collisional nature of PBH dark matter, including evolution of the internal structures of PBH halos and the formation of a hot component of PBHs. We also study the properties of the emergent population of PBH binary systems, distinguishing those that form at primordial times from those that form during the nonlinear structure formation process. These results will be crucial to sharpen constraints on the PBH scenario derived from observational constraints on the gravitational wave background. Even under conservative assumptions, the gravitational radiation emitted over the course of the simulation appears to exceed current limits from ground-based experiments, but this depends on the evolution of the gravitational wave spectrum and PBH merger rate toward lower redshifts., Comment: 46 pages, 26 figures; matches published version

Large power consumption of silicon CMOS electronics is a challenge in very-large-scale integrated circuits and a major roadblock to fault-tolerant quantum computation. Matching the power dissipation of Si-MOSFETs to the thermal budget at deep cryogenic temperatures, below 1 K, requires switching performance beyond levels facilitated by currently available CMOS technologies. We have manufactured fully depleted silicon-on-insulator MOSFETs tailored for overcoming the power dissipation barrier towards sub-1 K applications. With these cryo-optimized transistors we achieve a major milestone of reaching subthreshold swing of 0.3 mV/dec at 420 mK, thereby enabling very-large-scale integration of cryo-CMOS electronics for ultra-low temperature applications.

We prove the equivalence between the Ring Learning With Errors (RLWE) and the Polynomial Learning With Errors (PLWE) problems for the maximal totally real subfield of the $2^r 3^s$-th cyclotomic field for $r \geq 3$ and $s \geq 1$. Moreover, we describe a fast algorithm for computing the product of two elements in the ring of integers of these subfields. This multiplication algorithm has quasilinear complexity in the dimension of the field, as it makes use of the fast Discrete Cosine Transform (DCT). Our approach assumes that the two input polynomials are given in a basis of Chebyshev-like polynomials, in contrast to the customary power basis. To validate this assumption, we prove that the change of basis from the power basis to the Chebyshev-like basis can be computed with $\mathcal{O}(n \log n)$ arithmetic operations, where $n$ is the problem dimension. Finally, we provide a heuristic and theoretical comparison of the vulnerability to some attacks for the $p$-th cyclotomic field versus the maximal totally real subextension of the $4p$-th cyclotomic field for a reasonable set of parameters of cryptographic size.