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We propose a novel scheme for controlling the injection of a high-quality electron bunch into a channel-guided laser plasma accelerator. This all-optical technique, constricted waveguide injection, creates a highly tunable controlled injection structure natively within a plasma waveguide, a key requirement for efficient acceleration of high-quality multi-GeV electron beams. We describe a simple optical setup to tailor the plasma and present start-to-end simulations showing the injection structure formation and the generation of a 1.1 GeV electron beam with 10 pC of charge and 0.35 % energy spread using 1 J of drive laser energy. Highly tunable tailored plasma sources, like those proposed here, enable fine control over the injection and acceleration processes and thus will be crucial for the development of application-focused laser plasma accelerators., Comment: 6 pages, 3 figures

The problem of measuring conditional dependence between two random phenomena arises when a third one (a confounder) has a potential influence on the amount of information shared by the original pair. A typical issue in this challenging problem is the inversion of ill-conditioned autocorrelation matrices. This paper presents a novel measure of conditional dependence based on the use of incomplete unbiased statistics of degree two, which allows to re-interpret independence as uncorrelatedness on a finite-dimensional feature space. This formulation enables to prune data according to the observations of the confounder itself, thus avoiding matrix inversions altogether. Moreover, the proposed approach is articulated as an extension of the Hilbert-Schmidt independence criterion, which becomes expressible through kernels that operate on 4-tuples of data., Comment: Submitted to the IEEE for possible publication. 5 pages, 2 figures

Pedestrian GPS data are key to a better understanding of micro-mobility and micro-behaviour within a neighbourhood. These data can bring new insights into walkability and livability in the context of urban sustainability. However, pedestrian open data are scarce and often lack a context for their transformation into actionable knowledge in a neighbourhood. Citizen science and public involvement practices are powerful instruments for obtaining these data and take a community-centred placemaking approach. The study shares some 3000 GPS recordings corresponding to 19 unique trajectories made and recorded by groups of participants from three distinct communities in a relatively small neighbourhood. The groups explored the neighbourhood through a number of tasks and chose different places to stop and perform various social and festive activities. The study shares not only raw data but also processed records with specific filtering and processing to facilitate and accelerate data usage. Citizen science practices and the data-collection protocols involved are reported in order to offer a complete perspective of the research undertaken jointly with an assessment of how community-centred placemaking and operative mapping are incorporated into local urban transformation actions.

The interaction between surface acoustic waves and magnetization offers an efficient route for electrically controlling magnetic states. Here, we demonstrate the excitation of magnetoacoustic waves in galfenol, a highly magnetostrictive alloy made of iron (72%) and gallium (28%). We quantify the amplitude of the induced magnetization oscillations using magnetic imaging in an X-ray photoelectron microscope and estimate the dynamic magnetoelastic constants through micromagnetic simulations. Our findings demonstrate the potential of galfenol for magnonic applications and reveal that, despite strong magnetoelastic coupling, magnetic interactions and spin-wave dispersion relations significantly influence the overall amplitude of magnetoacoustic waves., Comment: 6 pages, 4 figures

Sheaf Neural Networks (SNNs) naturally extend Graph Neural Networks (GNNs) by endowing a cellular sheaf over the graph, equipping nodes and edges with vector spaces and defining linear mappings between them. While the attached geometric structure has proven to be useful in analyzing heterophily and oversmoothing, so far the methods by which the sheaf is computed do not always guarantee a good performance in such settings. In this work, drawing inspiration from opinion dynamics concepts, we propose two novel sheaf learning approaches that (i) provide a more intuitive understanding of the involved structure maps, (ii) introduce a useful inductive bias for heterophily and oversmoothing, and (iii) infer the sheaf in a way that does not scale with the number of features, thus using fewer learnable parameters than existing methods. In our evaluation, we show the limitations of the real-world benchmarks used so far on SNNs, and design a new synthetic task -- leveraging the symmetries of n-dimensional ellipsoids -- that enables us to better assess the strengths and weaknesses of sheaf-based models. Our extensive experimentation on these novel datasets reveals valuable insights into the scenarios and contexts where SNNs in general -- and our proposed approaches in particular -- can be beneficial.

Interactive Virtual Reality (VR) streaming over Wi-Fi networks encounters significant challenges due to bandwidth fluctuations caused by channel contention and user mobility. Adaptive BitRate (ABR) algorithms dynamically adjust the video encoding bitrate based on the available network capacity, aiming to maximize image quality while mitigating congestion and preserving the user's Quality of Experience (QoE). In this paper, we experiment with ABR algorithms for VR streaming using Air Light VR (ALVR), an open-source VR streaming solution. We extend ALVR with a comprehensive set of metrics that provide a robust characterization of the network's state, enabling more informed bitrate adjustments. To demonstrate the utility of these performance indicators, we develop and test the Network-aware Step-wise ABR algorithm for VR streaming (NeSt-VR). Results validate the accuracy of the newly implemented network performance metrics and demonstrate NeSt-VR's video bitrate adaptation capabilities.

We simulate a molecular Bose-Einstein condensate in the strongly dipolar regime, observing the existence of self-bound droplets, as well as their splitting into multiple droplets by confinement-induced frustration. Our quantum Monte Carlo approach goes beyond the limits of the established effective mean-field theories for dipolar quantum gases, revealing small droplets produced by strong dipolar interactions outside known stable regimes. The simulations include realistic molecular interactions and therefore have direct relevance for current and future experiments., Comment: 6 + 1 pages text + Supplementary Material (appendix). 3 figures + 1 table

For every prime number p and integer $n>1$, a simple, involutive, non-degenerate set-theoretic solution $(X,r$) of the Yang-Baxter equation of cardinality $|X| = p^n$ is constructed. Furthermore, for every non-(square-free) positive integer m which is not the square of a prime number, a non-simple, indecomposable, irretractable, involutive, non-degenerate set-theoretic solution $(X,r)$ of the Yang-Baxter equation of cardinality $|X| = m$ is constructed. A recent question of Castelli on the existence of singular solutions of certain type is also answered affirmatively., Comment: arXiv admin note: text overlap with arXiv:2401.12904, arXiv:2212.06753, arXiv:2112.07271, arXiv:2012.08400

We propose a modular quantum computation architecture based on utilizing multipartite entanglement. Each module consists of a small-scale quantum computer comprising data, memory and interaction qubits. Interaction qubits are used to selectively couple different modules by enhancing interaction strengths via properly adjusting their internal quantum state, where some non-controllable, distance-dependent coupling is used. In this way, different multipartite entangled states with specific entanglement structures shared between modules are generated, and stored in memory qubits. These states are utilized to deterministically perform certain classes of gates or circuits between modules on demand, including parallel controlled-Z gates with arbitrary interaction patterns, multi-qubit gates or whole Clifford circuits, depending on their entanglement structure. The usage of different kinds of multipartite entanglement rather than Bell pairs allows for more efficient and flexible coupling between modules, leading to a scalable quantum computation architecture., Comment: (6 + 6) pages, 4 figures

Active energy compression scheme is presently being investigated for future laser-plasma accelerators. This method enables generating laser-plasma accelerator electron beams with a small, $\sim 10^{-5}$, relative slice energy spread. When modulated by a laser pulse, such beams can produce coherent radiation at very high, $\sim 100$-th harmonics of the modulation laser wavelength, which are hard to access by conventional techniques. The scheme has a potential of providing additional capabilities for future plasma-based facilities by generating stable, tunable, narrow-band radiation.

Protein Language Models (PLMs) have emerged as performant and scalable tools for predicting the functional impact and clinical significance of protein-coding variants, but they still lag experimental accuracy. Here, we present a novel fine-tuning approach to improve the performance of PLMs with experimental maps of variant effects from Deep Mutational Scanning (DMS) assays using a Normalised Log-odds Ratio (NLR) head. We find consistent improvements in a held-out protein test set, and on independent DMS and clinical variant annotation benchmarks from ProteinGym and ClinVar. These findings demonstrate that DMS is a promising source of sequence diversity and supervised training data for improving the performance of PLMs for variant effect prediction., Comment: Machine Learning for Genomics Explorations workshop at ICLR 2024

Alzheimer's disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of Mild Cognitive Impairment (MCI) and early-stage AD. This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals acquired through polysomnography (PSG) for the early detection of AD. Our primary focus is on exploring semi-supervised Deep Learning techniques for the classification of EEG signals due to the clinical scenario characterized by the limited data availability. The methodology entails testing and comparing the performance of semi-supervised SMATE and TapNet models, benchmarked against the supervised XCM model, and unsupervised Hidden Markov Models (HMMs). The study highlights the significance of spatial and temporal analysis capabilities, conducting independent analyses of each sleep stage. Results demonstrate the effectiveness of SMATE in leveraging limited labeled data, achieving stable metrics across all sleep stages, and reaching 90% accuracy in its supervised form. Comparative analyses reveal SMATE's superior performance over TapNet and HMM, while XCM excels in supervised scenarios with an accuracy range of 92 - 94%. These findings underscore the potential of semi-supervised models in early AD detection, particularly in overcoming the challenges associated with the scarcity of labeled data. Ablation tests affirm the critical role of spatio-temporal feature extraction in semi-supervised predictive performance, and t-SNE visualizations validate the model's proficiency in distinguishing AD patterns. Overall, this research contributes to the advancement of AD detection through innovative Deep Learning approaches, highlighting the crucial role of semi-supervised learning in addressing data limitations., Comment: 12 pages, 14 figures. Submitted to IEEE Biomedical and Health Informatics for publication

Using realistic laser profiles for simulations of laser-plasma interaction is critical to reproduce experimental measurements, but the interface between experiments and simulations can be challenging. Similarly, start-to-end simulations with different codes may require error-prone manipulations to convert between different representations of a laser pulse. In this work, we propose LASY, an open-source Python library to simplify these workflows. Developed through an international collaboration between experimental, theoretical and computational physicists, LASY can be used to initialize a laser profile from an experimental measurement, from a simulation, or from analytics, manipulate it, and write it into a file in compliance with the openPMD standard. This profile can then be used as an input of a simulation code., Comment: 6 pages, 4 figures, EAAC2023 conference proceedings

Linear colliders rely on high-quality flat beams to achieve the desired event rate, while avoiding potentially deleterious beamstrahlung effects. Here, we show that flat beams in plasma accelerators can be subject to quality degradation due to emittance mixing. This effect occurs when the beam particles' betatron oscillations in a nonlinearly coupled wakefield become resonant in the horizontal and vertical planes. Emittance mixing can lead to a substantial decrease of the luminosity, the main quantity determining the event rate. In some cases, the use of laser drivers or flat particle beam drivers may decrease the fraction of resonant particles and, hence, mitigate emittance deterioration.

This book explores infinite-type translation surfaces and is intended as an introductory text for graduate and PhD students, as well as a reference for more advanced researchers. Chapter 1 introduces the three definitions of translation surfaces and meticulously proves their equivalence. It is enriched with numerous examples that are revisited throughout the book. Chapter 2 provides a detailed examination of the topological classification of infinite-type surfaces, the construction of infinite coverings of finite-type translation surfaces, and the structure of points within the metric completion. Chapter 3 investigates the affine symmetries of infinite-type translation surfaces, with special emphasis on infinite coverings of finite-type surfaces, the Hooper-Thurston-Veech construction, and affine homeomorphisms of finite-area infinite-type translation surfaces. Chapter 4 introduces infinite interval exchange transformations and employs them to demonstrate that the dynamics of translation flows are significantly more complex in the infinite-type context. The two appendices address hyperbolic geometry and the spectra of infinite graphs, respectively., Comment: 264 pages, 69 figures

We show that the unsteadiness of turbulence has a drastic effect on turbulence parameters and in particle cluster formation. To this end we use direct numerical simulations of particle laden flows with a steady forcing that generates an unsteady large-scale flow. Particle clustering correlates with the instantaneous Taylor-based flow Reynolds number, and anti-correlates with its instantaneous turbulent energy dissipation constant. A dimensional argument for these correlations is presented. In natural flows, unsteadiness can result in extreme particle clustering, which is stronger than the clustering expected from averaged inertial turbulence effects.

Background: Proximal Junctional Failure (PJF) is a post-operative complication in adult spine surgery, often requiring reoperation. Osteotomy is often used in revision surgeries, leading to 34.8% complications. Hence, suboptimal decisions might be extending hardware without osteotomy, which yields to severe Global Alignment and Proportion (GAP) scores. High GAPs increase PJF risk, but Hardware Density Reduction (HDR) might limit it. Methods: Two clinical cases were evaluated: 1) Initially operated with hardware extended to T10, GAP 10; 2) PJF at T11 and hardware extended to T3, GAP 11. Two patient-personalized spine FE models were constructed through Statistical Shape Modelling (SSM) and mesh morphing. Intervertebral Disk (IVD) fiber strain, screw pull-out force, and rod stress were evaluated for the cases 1) and 2), also for 91 virtual HDR scenarios with different GAP scores, using Finite Element (FE) simulations. Different rod and bone material properties were also assessed. Results: HDR could decrease IVD fiber strain (-70% at most) and increase screw pull-out forces (+142% at most) for cases with Ti rod and normal bone. Cr-Co rod and osteopenia, and osteoporotic bones had high PJF risk. Trade-off analyses could determine the best configurations avoiding PJF. Virtual cohort study showed that GAP 12 and 13 could not avoid PJF in any HDR scenarios either with Ti or Cr-Co rods. HDR in a UIV T10 virtual patient with GAP 11 could not de-risk in case of Cr-Co rods. UIV T3 with GAP 13 could not benefit any HDR strategy, independently of rod properties. In contrast, Ti rods might allow HDR to de-risk GAP 12 patients with UIV T3. Conclusions: HDR could avoid PJF in the patients with medium high GAP scores, depending on the screw reduction pattern, and bone and rod material properties. Remarkably, HDR technique might avoid excessive spine surgeries and minimize the surgery cost.

A new class of indecomposable, irretractable, involutive, non-degenerate set-theoretic solutions of the Yang--Baxter equation is constructed. This class complements the class of such solutions constructed in \cite{CO22} and together they generalize the class of solutions described in \cite[Theorem 4.7{CO21}. Necessary and sufficient conditions are found in order that these new solutions are simple. For a rich subclass of these solutions the structure of their permutation groups, considered as left braces, is determined. In particular, these results answer a question stated in \cite{CO21}. In the finite case, all these solutions have square cardinality. A new class of finite simple solutions of non-square cardinality such that their permutation groups are simple left braces is also constructed., Comment: arXiv admin note: text overlap with arXiv:2112.07271 typos in the formulation of Theorem 3.1 corrected

Hot Jupiters were many of the first exoplanets discovered in the 1990s, but in the decades since their discovery, the mysteries surrounding their origins remain. Here, we present nine new hot Jupiters (TOI-1855 b, TOI-2107 b, TOI-2368 b, TOI-3321 b, TOI-3894 b, TOI-3919 b, TOI-4153 b, TOI-5232 b, and TOI-5301 b) discovered by NASA's TESS mission and confirmed using ground-based imaging and spectroscopy. These discoveries are the first in a series of papers named the Migration and Evolution of giant ExoPlanets (MEEP) survey and are part of an ongoing effort to build a complete sample of hot Jupiters orbiting FGK stars, with a limiting Gaia $G$-band magnitude of 12.5. This effort aims to use homogeneous detection and analysis techniques to generate a set of precisely measured stellar and planetary properties that is ripe for statistical analysis. The nine planets presented in this work occupy a range of masses (0.55 Jupiter masses (M$_{\rm{J}}$) $<$ M$_{\rm{P}}$ $<$ 3.88 M$_{\rm{J}}$) and sizes (0.967 Jupiter radii (R$_{\rm{J}}$) $<$ R$_{\rm{P}}$ $<$ 1.438 R$_{\rm{J}}$) and orbit stars that range in temperature from 5360 K $<$ Teff $<$ 6860 K with Gaia $G$-band magnitudes ranging from 11.1 to 12.7. Two of the planets in our sample have detectable orbital eccentricity: TOI-3919 b ($e = 0.259^{+0.033}_{-0.036}$) and TOI-5301 b ($e = 0.33^{+0.11}_{-0.10}$). These eccentric planets join a growing sample of eccentric hot Jupiters that are consistent with high-eccentricity tidal migration, one of the three most prominent theories explaining hot Jupiter formation and evolution., Comment: 35 pages, 7 tables, and 14 figures. Submitted to AAS Journals on 2023 Dec 28

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather, to planning renewable energy use. Here, we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, the European Centre for Medium-Range Forecasts (ECMWF)'s ensemble forecast, ENS. Unlike traditional approaches, which are based on numerical weather prediction (NWP), GenCast is a machine learning weather prediction (MLWP) method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-hour steps and 0.25 degree latitude-longitude resolution, for over 80 surface and atmospheric variables, in 8 minutes. It has greater skill than ENS on 97.4% of 1320 targets we evaluated, and better predicts extreme weather, tropical cyclones, and wind power production. This work helps open the next chapter in operational weather forecasting, where critical weather-dependent decisions are made with greater accuracy and efficiency., Comment: Main text 11 pages, Appendices 76 pages

We use surface acoustic waves of 1 and 3 GHz in hybrid piezoelectric-magnetic systems with either cobalt or nickel as a magnetic layer to generate magnetoacoustic waves and directly image them using stroboscopic X-ray magnetic circular dichroism imaging. Our measurements visualize and quantify the amplitudes of both acoustic and magnetic components of the magnetoacoustic waves, which are generated in the ferromagnetic layer and can propagate over millimeter distances. Additionally, we quantifiedy the magnetoelastic strain component for cobalt and nickel through micromagnetic simulations. We obtained a drop in the magnetoacoustic signal at 3 GHz suggesting a speed limit for the efficient magnetoelastic coupling in our hybrid devices., Comment: 9 pages, 5 figures

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.