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Harmonic analysis has provided powerful tools to accurately determine the tune from turn-by-turn data originating from numerical simulations or beam measurements in circular accelerators and storage rings. Methods that have been developed since the 1990s are suitable for stationary signals, i.e., time series whose properties do not vary with time and are represented by stationary signals. However, it is common experience that accelerator physics is a rich source of time series in which the signal amplitude varies over time. Furthermore, the properties of the amplitude variation of the signal often contain essential information about the phenomena under consideration. In this paper, a novel approach is presented, suitable for determining the tune of a non-stationary signal, which is based on the use of the Hilbert transform. The accuracy of the proposed methods is assessed in detail, and an application to the analysis of beam data collected at the CERN Large Hadron Collider is presented and discussed in detail.

Recent experimental and theoretical results have demonstrated the possibility of accelerating electrons in the MeV range by focusing tightly a few-cycle laser beam in ambient air. Using Particle-In-Cell (PIC) simulations, this configuration is revisited within a more accurate modelling approach to analyze and optimize the mechanism responsible for electron acceleration. In particular, an analytical model for a linearly polarized tightly-focused ultrashort laser field is derived and coupled to a PIC code, allowing us to model the interaction of laser beams reflected by high-numerical aperture mirrors with laser-induced plasmas. A set of 3D PIC simulations is performed where the laser wavelength is varied from 800 nm to 7.0 $\mu$m while the normalized amplitude of the electric field is varied from $a_{0} = 3.6$ to $a_{0} = 7.0$. The preferential forward acceleration of electrons, as well as the analysis of the laser intensity evolution in the plasma and data on electron number density, confirm that the relativistic ponderomotive force is responsible for the acceleration. We also demonstrate that the electron kinetic energy reaches a maximum of $\approx1.6$ MeV when the central wavelength is of 2.5 $\mu$m., Comment: 16 pages, 17 figures

Given a reduced abelian $p$-group, we give an upper bound on the Scott complexity of the group in terms of its Ulm invariants. For limit ordinals, we show that this upper bound is tight. This gives an explicit sequence of such groups with arbitrarily high Scott complexity below $\omega_1$. Along the way, we give a largely algebraic characterization of the back-and-forth relations on reduced abelian $p$-groups, making progress on an open problem from the book by Ash and Knight., Comment: 20 pages

The choice of coordinate system in a bearings-only (BO) tracking problem influences the methods used to observe and predict the state of a moving target. Modified Polar Coordinates (MPC) and Log-Polar Coordinates (LPC) have some advantages over Cartesian coordinates. In this paper, we derive closed-form expressions for the target state prior distribution after ownship manoeuvre: the mean, covariance, and higher-order moments in LPC. We explore the use of these closed-form expressions in simulation by modifying an existing BO tracker that uses the UKF. Rather than propagating sigma points, we directly substitute current values of the mean and covariance into the time update equations at the ownship turn. This modified UKF, the CFE-UKF, performs similarly to the pure UKF, verifying the closed-form expressions. The closed-form third and fourth central moments indicate non-Gaussianity of the target state when the ownship turns. By monitoring these metrics and appropriately initialising relative range error, we can achieve a desired output mean estimated range error (MRE). The availability of these higher-order moments facilitates other extensions of the tracker not possible with a standard UKF., Comment: 11 pages, 6 figures. This work has been submitted to the IEEE for possible publication

RF-ICE is a signal processing platform for the readout of large arrays of superconducting resonators. Designed for flexibility, the system's low digital latency and ability to independently and dynamically set the frequency and amplitude of probe tones in real time has enabled previously-inaccessible views of resonator behaviour, and opened the door to novel resonator control schemes. We introduce a multi-frequency imaging technique, developed with RF-ICE, which allows simultaneous observation of the entire resonance bandwidth. We demonstrate the use of this technique in the examination of the response of superconducting resonators to variations in applied readout current and thermal loading. We observe that, used in conjunction with a conventional frequency sweep at sufficiently large amplitude to induce resonance bifurcation, the multi-frequency imaging technique reveals a resonator response which is not captured by the frequency sweep measurement alone. We demonstrate that equivalent resonant frequency shifts can be achieved using either thermal, optical, or readout loading, and use this equivalence to counteract a change in thermal loading by digitally modulating the readout current through a resonator. We develop and implement a proof-of-concept closed-loop negative electro-quasiparticle feedback algorithm which first sets and then maintains the resonant frequency of a lumped element kinetic inductance detector while the loading on it is varied. Although this simple implementation is not yet suitable to deploy at scale, it demonstrates the utility of this feedback technique to improve linearity while addressing amplifier distortion, resonator response non-uniformity, and crosstalk. It can be applied to kinetic inductors in non-bolometric operation, and sets the stage for future developments., Comment: 14 pages, 7 figures, appearing in the proceedings of SPIE Astronomical Telescopes and Instrumentation 2024

The t0.technology Control and Readout System (CRS) is a modular microwave control and readout system for mm-wave and radio astronomy, THz imaging, noise radar, and superconducting qubit control. The configuration discussed in this work implements firmware for readout of microwave Kinetic Inductance Detector (KID) arrays. The CRS can operate 4,096 KIDs over 2.5 GHz of complex bandwidth between 0-10 GHz, typically allocated across four independent RF chains at 1,024x multiplexing and 625 MHz of complex bandwidth each. Every CRS can operate as a standalone unit or collectively within one or more backplane-enabled subracks that distribute power, clocking, and synchronization, scaling to an arbitrary number of channels. Each fully populated subrack supports arrays of more than 65,000 KIDs. The signal processing and control software supports recent innovations in multi-probe measurements and dynamic feedback modes, which are described in (Rouble et al. 2024). The CRS has recently been selected as the new baseline readout system for the proposed South Pole Telescope instrument, SPT-3G+. We present the hardware design, firmware capabilities, open-source control and data acquisition software, and the first laboratory characterization measurements., Comment: 18 pages, 9 figures, submitted as a conference proceeding to SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

Increasing concerns about privacy leakage issues in academia and industry arise when employing NLP models from third-party providers to process sensitive texts. To protect privacy before sending sensitive data to those models, we suggest sanitizing sensitive text using two common strategies used by humans: i) deleting sensitive expressions, and ii) obscuring sensitive details by abstracting them. To explore the issues and develop a tool for text rewriting, we curate the first corpus, coined NAP^2, through both crowdsourcing and the use of large language models (LLMs). Compared to the prior works based on differential privacy, which lead to a sharp drop in information utility and unnatural texts, the human-inspired approaches result in more natural rewrites and offer an improved balance between privacy protection and data utility, as demonstrated by our extensive experiments.

A teenager's experience of chronic pain reverberates through multiple interacting aspects of their lives. To self-manage their symptoms, they need to understand how factors such as their sleep, social interactions, emotions and pain intersect; supporting this capability must underlie an effective personalized healthcare solution. While adult use of personal informatics for self-management of various health factors has been studied, solutions intended for adults are rarely workable for teens, who face this complex and confusing situation with unique perspectives, skills and contexts. In this design study, we explore a means of facilitating self-reflection by youth living with chronic pain, through visualization of their personal health data. In collaboration with pediatric chronic pain clinicians and a health-tech industry partner, we designed and deployed MyWeekInSight, a visualization-based self-reflection tool for youth with chronic pain. We discuss our staged design approach with this intersectionally vulnerable population, in which we balanced reliance on proxy users and data with feedback from youth viewing their own data. We report on extensive formative and in-situ evaluation, including a three-week clinical deployment, and present a framework of challenges and barriers faced in clinical deployment with mitigations that can aid fellow researchers. Our reflections on the design process yield principles, surprises, and open questions.

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and well-defined by heterogeneous molecular and micro-environmental histopathologic profiles, which pose a major obstacle in treatment. Correctly diagnosing these tumors and assessing their heterogeneity is crucial for choosing the precise treatment and potentially enhancing patient survival rates. In the gold-standard histopathology-based approach to tumor diagnosis, detecting various morpho-pathological features of distinct histology throughout digitized tissue sections is crucial. Such "features" include the presence of cellular tumor, geographic necrosis, pseudopalisading necrosis, areas abundant in microvascular proliferation, infiltration into the cortex, wide extension in subcortical white matter, leptomeningeal infiltration, regions dense with macrophages, and the presence of perivascular or scattered lymphocytes. With these features in mind and building upon the main aim of the BraTS Cluster of Challenges https://www.synapse.org/brats2024, the goal of the BraTS-Path challenge is to provide a systematically prepared comprehensive dataset and a benchmarking environment to develop and fairly compare deep-learning models capable of identifying tumor sub-regions of distinct histologic profile. These models aim to further our understanding of the disease and assist in the diagnosis and grading of conditions in a consistent manner.

The Lyapunov exponent is well-known in deterministic dynamical systems as a measure for quantifying chaos and detecting coherent regions in physically evolving systems. In this Letter, we show how the Lyapunov exponent can be unified with stochastic sensitivity (which quantifies the uncertainty of an evolving uncertain system whose initial condition is certain) within a finite time uncertainty quantification framework in which both the dynamics and the initial condition of a continuously evolving $ n $-dimensional state variable are uncertain.

We generate a tabletop pulsed relativistic electron beam at 100 Hz repetition rate from vacuum laser acceleration (VLA) by tightly focusing a radially polarized beam into a low-density gas. We demonstrate that strong longitudinal electric fields at the focus can accelerate electrons up to 1.43 MeV by using only 98 GW of peak laser power. The electron energy is measured as a function of laser intensity and gas species, revealing a strong dependence on the atomic ionization dynamics. These experimental results are supported by numerical simulations of particle dynamics in a tightly focused configuration that take ionization into consideration. For the range of intensities considered, it is demonstrated that atoms with higher atomic numbers like krypton can optimally inject electrons at the peak of the laser field, resulting in higher energies and an efficient acceleration mechanism that reaches a significant fraction of the theoretical energy gain limit., Comment: 7 pages, 4 figures, to be submitted for review, supplementary material not included

3D depth sensors using single-photon avalanche diodes (SPADs) are becoming increasingly common in applications such as autonomous navigation and object detection. Recent designs implement on-chip histogramming time-to-digital converters (TDCs) to compress the photon timestamps and reduce the bottleneck in the read-out and processing of large volumes of photon data. However, the use of full histogramming with large SPAD arrays poses significant challenges due to the associated demands in silicon area and power consumption. We propose a TDC-less dToF sensor which uses Spiking Neural Networks (SNN) to process the SPAD events directly. The proposed SNN is trained and tested on synthetic SPAD events, and while it offers five times lower precision in depth prediction than a classic centre-of-mass (CoM) algorithm (applied to histograms of the events), it achieves similar Mean Absolute Error (MAE) with faster processing speeds and significantly lower power consumption is anticipated., Comment: 7 Pages, 9 Figures

Detecting anomalous behaviour of satellites is an important goal within the broader task of space situational awareness. The Two Line Element (TLE) data published by NORAD is the only widely-available, comprehensive source of data for satellite orbits. We present here a filtering approach for detecting anomalies in satellite orbits from TLE data. Optimal proposal particle filters are deployed to track the state of the satellites' orbits. New TLEs that are unlikely given our belief of the current orbital state are designated as anomalies. The change in the orbits over time is modelled using the SGP4 model with some adaptations. A model uncertainty is derived to handle the errors in SGP4 around singularities in the orbital elements. The proposed techniques are evaluated on a set of 15 satellites for which ground truth is available and the particle filters are shown to be superior at detecting the subtle in-track and cross-track manoeuvres in the simulated dataset, as well as providing a measure of uncertainty of detections.

Protein-protein interactions drive many biological processes, including the detection of phytopathogens by plants' R-Proteins and cell surface receptors. Many machine learning studies have attempted to predict protein-protein interactions but performance is highly dependent on training data; models have been shown to accurately predict interactions when the proteins involved are included in the training data, but achieve consistently poorer results when applied to previously unseen proteins. In addition, models that are trained using proteins that take part in multiple interactions can suffer from representation bias, where predictions are driven not by learned biological features but by learning of the structure of the interaction dataset. We present a method for extracting unique pairs from an interaction dataset, generating non-redundant paired data for unbiased machine learning. After applying the method to datasets containing _Arabidopsis thaliana_ and pathogen effector interations, we developed a convolutional neural network model capable of learning and predicting interactions from Chaos Game Representations of proteins' coding genes.

Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data, is challenging due to intractability of the likelihood. To work at all, current simulation based inference methods require the generation of realisations of the model conditional on the data, which can be both tricky to implement and computationally expensive. In this paper we instead construct a neural likelihood approximation that can be trained using unconditional simulation of the underlying model, which is much simpler. We demonstrate our method by performing inference on a number of ecological and epidemiological models, showing that we can accurately approximate the true posterior while achieving significant computational speed ups compared to current best methods.

The scattered field formalism is combined to the particle-in-cell method to model relativistic laser-plasma dynamics in complex field configurations. Despite the strong nonlinearity of the interactions, we demonstrate the validity of this technique theoretically, by using the semi-linear property of Maxwell-Vlasov equations, and empirically, by performing a systematic convergence analysis with simple 1D and 2D examples. Then, we apply this method to the description of electron acceleration in undercritical plasmas from tightly focused linear and radially polarized beams. For this purpose, an analytical solution for a linearly-polarized tightly focused beam is derived. According to our simulations and their analysis, this approach looks very promising for the optimization of laser-induced relativistic electron beams at ultrahigh intensities because it allows for tightly-focused geometries in reduced calculation volumes., Comment: 28 pages, 16 figures

We proposed a Smartphone-based Optical Sectioning (SOS) microscope based on the HiLo technique, with a single smartphone replacing a high-cost illumination source and a camera sensor.We built our SOS with off-the-shelf optical mechanical cage systems with 3D-printed adapters to integrate the smartphone with the SOS main body seamlessly.The liquid light guide can be integrated with the adapter, guiding the smartphone LED light to the digital mirror device with neglectable loss.We used an electrically tunable lens (ETL) instead of a mechanical translation stage to realize low-cost axial scanning. The ETL was conjugated to the objective lens back pupil plane (BPP) to construct a telecentric design by a 4f configuration. This can exempt images of different layers from the variation in magnification. SOS has a 571.5 {\mu}m telecentric scanning range and an 11.7 {\mu}m axial resolution. The broadband smartphone LED torch can effectively excite fluorescent polystyrene (PS) beads. We successfully used SOS for high contrast fluorescent PS beads imaging with different wavelengths and optical sectioning imaging of accumulated fluorescent PS beads. To our knowledge, the proposed SOS is the first smartphone-based HiLo optical sectioning microscopy. It is a powerful, low-cost tool for biomedical research in resource-limited areas., Comment: 19 pages, 10 figures

Prediction via deterministic continuous-time models will always be subject to model error, for example due to unexplainable phenomena, uncertainties in any data driving the model, or discretisation/resolution issues. In this paper, we build upon previous small-noise studies to provide an explicit bound for the error between a general class of stochastic differential equations and corresponding computable linearisations written in terms of a deterministic system. Our framework accounts for non-autonomous coefficients, multiplicative noise, and uncertain initial conditions. We demonstrate the predictive power of our bound on diverse numerical case studies. We confirm that our bound is sharp, in that it accurately predicts the error scaling in the moments of the linearised approximation as both the uncertainty in the initial condition and the magnitude of the noise in the differential equation are altered. This paper also provides an extension of stochastic sensitivity, a recently introduced tool for quantifying uncertainty in dynamical systems, to arbitrary dimensions and establishes the link to our characterisation of stochastic differential equation linearisations., Comment: 31 pages, 7 figures