Search

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of $4.2\pm0.1$ tonne-years from 280 live days of LZ operation, of which $3.3\pm0.1$ tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from $^{214}$Pb $\beta$ decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of $^{124}$Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the data set to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses $\geq$9 GeV/$c^2$. The strongest SI exclusion set is $2.1\times10^{-48}$ cm$^{2}$ at the 90% confidence level at a mass of 36 GeV/$c^2$, and the best SI median sensitivity achieved is $5.0\times10^{-48}$ cm$^{2}$ for a mass of 40 GeV/$c^2$., Comment: 9 pages, 7 figures. See https://www.hepdata.net/record/155182 for a data release related to this paper

Floods are among the most common and devastating natural hazards, imposing immense costs on our society and economy due to their disastrous consequences. Recent progress in weather prediction and spaceborne flood mapping demonstrated the feasibility of anticipating extreme events and reliably detecting their catastrophic effects afterwards. However, these efforts are rarely linked to one another and there is a critical lack of datasets and benchmarks to enable the direct forecasting of flood extent. To resolve this issue, we curate a novel dataset enabling a timely prediction of flood extent. Furthermore, we provide a representative evaluation of state-of-the-art methods, structured into two benchmark tracks for forecasting flood inundation maps i) in general and ii) focused on coastal regions. Altogether, our dataset and benchmark provide a comprehensive platform for evaluating flood forecasts, enabling future solutions for this critical challenge. Data, code & models are shared at https://github.com/Multihuntr/GFF under a CC0 license., Comment: Accepted at NeurIPS 2024 Datasets & Benchmarks

In-orbit automated servicing is a promising path towards lowering the cost of satellite operations and reducing the amount of orbital debris. For this purpose, we present a pipeline for automated satellite docking port detection and state estimation using monocular vision data from standard RGB sensing or an event camera. Rather than taking snapshots of the environment, an event camera has independent pixels that asynchronously respond to light changes, offering advantages such as high dynamic range, low power consumption and latency, etc. This work focuses on satellite-agnostic operations (only a geometric knowledge of the actual port is required) using the recently released Lockheed Martin Mission Augmentation Port (LM-MAP) as the target. By leveraging shallow data-driven techniques to preprocess the incoming data to highlight the LM-MAP's reflective navigational aids and then using basic geometric models for state estimation, we present a lightweight and data-efficient pipeline that can be used independently with either RGB or event cameras. We demonstrate the soundness of the pipeline and perform a quantitative comparison of the two modalities based on data collected with a photometrically accurate test bench that includes a robotic arm to simulate the target satellite's uncontrolled motion., Comment: Submitted to IEEE ICRA 2025

In recent years, automatic speech recognition (ASR) models greatly improved transcription performance both in clean, low noise, acoustic conditions and in reverberant environments. However, all these systems rely on the availability of hundreds of hours of labelled training data in specific acoustic conditions. When such a training dataset is not available, the performance of the system is heavily impacted. For example, this happens when a specific acoustic environment or a particular population of speakers is under-represented in the training dataset. Specifically, in this paper we investigate the effect of accented speech data on an off-the-shelf ASR system. Furthermore, we suggest a strategy based on zero-shot text-to-speech to augment the accented speech corpora. We show that this augmentation method is able to mitigate the loss in performance of the ASR system on accented data up to 5% word error rate reduction (WERR). In conclusion, we demonstrate that by incorporating a modest fraction of real with synthetically generated data, the ASR system exhibits superior performance compared to a model trained exclusively on authentic accented speech with up to 14% WERR., Comment: Accepted to the Asilomar 2023 Conference

The broad physics reach of the LUX-ZEPLIN (LZ) experiment covers rare phenomena beyond the direct detection of dark matter. We report precise measurements of the extremely rare decay of $^{124}$Xe through the process of two-neutrino double electron capture (2$\nu$2EC), utilizing a $1.39\,\mathrm{kg} \times \mathrm{yr}$ isotopic exposure from the first LZ science run. A half-life of $T_{1/2}^{2\nu2\mathrm{EC}} = (1.09 \pm 0.14_{\text{stat}} \pm 0.05_{\text{sys}}) \times 10^{22}\,\mathrm{yr}$ is observed with a statistical significance of $8.3\,\sigma$, in agreement with literature. First empirical measurements of the KK capture fraction relative to other K-shell modes were conducted, and demonstrate consistency with respect to recent signal models at the $1.4\,\sigma$ level., Comment: 15 pages, 3 figures

In this article we assess the comprehension of physics concepts by Physical Science and Engineering students enrolled in their first semester at the University of Johannesburg (UJ), South Africa ($2022$). We employ different graphical measures to explore similarities and differences using the results of both pre- and post-test data from the Force Concept Inventory assessment tool, from which we calculate dominant misconceptions (DMs) and gains. We also use alluvial diagrams to track the choices made by these two groups of students from pre- to post-test stages. In our analysis, we find that DMs results indicate that participating Engineering students outperformed Physical Science students on average. However, the same types of normalised DMs persist at the post-test level. This is very useful when tracking persistent misconceptions, where when using repeated measures and alluvial diagrams with smaller groups of students, we find that Physical Science students tend to make more chaotic choices., Comment: 25 pages, 8 figures, APA7; BibTeX issues resolved

Machine learning models are exceptionally effective in capturing complex non-linear relationships of high-dimensional datasets and making accurate predictions. However, their intrinsic ``black-box'' nature makes it difficult to interpret them or guarantee ``safe behavior'' when deployed in high-risk applications such as feedback control, healthcare and finance. This drawback acts as a significant barrier to their wider application across many scientific and industrial domains where the interpretability of the model predictions is as important as accuracy. Leveraging the latest developments in interpretable machine learning, we develop a method to parameterise ``black-box'' models, effectively transforming them into ``grey-box'' models. We apply this approach to plasma diagnostics by creating a parameterised synthetic Soft X-Ray imaging $-$ Thomson Scattering diagnostic, which predicts high temporal resolution electron temperature and density profiles from the measured soft X-ray emission. The ``grey-box'' model predictions are benchmarked against the trained ``black-box'' models as well as a diverse range of plasma conditions. Our model-agnostic approach can be applied to various machine learning architectures, enabling direct comparisons of model interpretations., Comment: 32 pages, 15 figures

We explore the recently proposed explainable acoustic neural embedding~(XANE) system that models the background acoustics of a speech signal in a non-intrusive manner. The XANE embeddings are used to estimate specific parameters related to the background acoustic properties of the signal which allows the embeddings to be explainable in terms of those parameters. We perform ablation studies on the XANE system and show that estimating all acoustic parameters jointly has an overall positive effect. Furthermore, we illustrate the value of XANE embeddings by performing clustering experiments on unseen test data and show that the proposed embeddings achieve a mean F1 score of 92\% for three different tasks, outperforming significantly the WavLM based signal embeddings and are complimentary to speaker embeddings., Comment: arXiv admin note: substantial text overlap with arXiv:2406.05199

We construct infinitely many pairwise non-diffeomorphic smooth structures on a definite $4$-manifold with non-cyclic fundamental group $\mathbb{Z}/2\times \mathbb{Z}/2$., Comment: 11 pages, comments welcome!

We present a novel method for extracting neural embeddings that model the background acoustics of a speech signal. The extracted embeddings are used to estimate specific parameters related to the background acoustic properties of the signal in a non-intrusive manner, which allows the embeddings to be explainable in terms of those parameters. We illustrate the value of these embeddings by performing clustering experiments on unseen test data and show that the proposed embeddings achieve a mean F1 score of 95.2\% for three different tasks, outperforming significantly the WavLM based signal embeddings. We also show that the proposed method can explain the embeddings by estimating 14 acoustic parameters characterizing the background acoustics, including reverberation and noise levels, overlapped speech detection, CODEC type detection and noise type detection with high accuracy and a real-time factor 17 times lower than an external baseline method.

Accretion discs are fundamental to much of astronomy. They can occur around stars both young and old, around compact objects they provide a window into the extremes of physics, and around supermassive black holes in galaxy centres they generate spectacular luminosities that can outshine the entire galaxy. However, our understanding of the inner workings of accretion discs remains far from complete. Here we revisit a conundrum in the observations of some of the simplest accreting systems; the Cataclysmic Variables (CVs). The high-accretion-rate states of (non-magnetic) CVs can be divided into the short-lived outbursts ($\sim$ a week) typical of dwarf novae (DNe) and the long-lived (and sometimes perpetual) high states of nova-like (NL) CVs. Since both sorts of high-state occur in approximately steady-state accretion discs with similar properties and accretors, we would expect them to display similar spectral energy distributions. However, previous analyses based on UV spectra from the {\it International Ultraviolet Explorer} have shown that their spectral energy distributions are different. We perform a re-analysis of the data using up to date calibrations and distance (and thus dereddening) estimates to test whether this difference persists and whether it is statistically significant over the sample. We find that it does persist and it is statistically significant. We propose routes to investigating this discrepancy further and discuss the implications this has for other accreting systems, such as X-ray binaries, Active Galactic Nuclei and protoplanetary discs., Comment: Accepted for publication in MNRAS. 12 pages, 8 figures

Weakly interacting massive particles (WIMPs) may interact with a virtual pion that is exchanged between nucleons. This interaction channel is important to consider in models where the spin-independent isoscalar channel is suppressed. Using data from the first science run of the LUX-ZEPLIN dark matter experiment, containing 60 live days of data in a 5.5~tonne fiducial mass of liquid xenon, we report the results on a search for WIMP-pion interactions. We observe no significant excess and set an upper limit of $1.5\times10^{-46}$~cm$^2$ at a 90\% confidence level for a WIMP mass of 33~GeV/c$^2$ for this interaction.

The Data Acquisition System (DAQ) for the LUX-ZEPLIN (LZ) dark matter detector is described. The signals from 745 PMTs, distributed across three subsystems, are sampled with 100-MHz 32-channel digitizers (DDC-32s). A basic waveform analysis is carried out on the on-board Field Programmable Gate Arrays (FPGAs) to extract information about the observed scintillation and electroluminescence signals. This information is used to determine if the digitized waveforms should be preserved for offline analysis. The system is designed around the Kintex-7 FPGA. In addition to digitizing the PMT signals and providing basic event selection in real time, the flexibility provided by the use of FPGAs allows us to monitor the performance of the detector and the DAQ in parallel to normal data acquisition. The hardware and software/firmware of this FPGA-based Architecture for Data acquisition and Realtime monitoring (FADR) are discussed and performance measurements are described., Comment: 18 pages, 24 figures

In the last three decades, the Steered Response Power (SRP) method has been widely used for the task of Sound Source Localization (SSL), due to its satisfactory localization performance on moderately reverberant and noisy scenarios. Many works have analyzed and extended the original SRP method to reduce its computational cost, to allow it to locate multiple sources, or to improve its performance in adverse environments. In this work, we review over 200 papers on the SRP method and its variants, with emphasis on the SRP-PHAT method. We also present eXtensible-SRP, or X-SRP, a generalized and modularized version of the SRP algorithm which allows the reviewed extensions to be implemented. We provide a Python implementation of the algorithm which includes selected extensions from the literature.

LUX-ZEPLIN (LZ) is a tonne-scale experiment searching for direct dark matter interactions and other rare events. It is located at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, USA. The core of the LZ detector is a dual-phase xenon time projection chamber (TPC), designed with the primary goal of detecting Weakly Interacting Massive Particles (WIMPs) via their induced low energy nuclear recoils. Surrounding the TPC, two veto detectors immersed in an ultra-pure water tank enable reducing background events to enhance the discovery potential. Intricate calibration systems are purposely designed to precisely understand the responses of these three detector volumes to various types of particle interactions and to demonstrate LZ's ability to discriminate between signals and backgrounds. In this paper, we present a comprehensive discussion of the key features, requirements, and performance of the LZ calibration systems, which play a crucial role in enabling LZ's WIMP-search and its broad science program. The thorough description of these calibration systems, with an emphasis on their novel aspects, is valuable for future calibration efforts in direct dark matter and other rare-event search experiments.

The first science run of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon time project chamber operating in the Sanford Underground Research Facility in South Dakota, USA, has reported leading limits on spin-independent WIMP-nucleon interactions and interactions described from a non-relativistic effective field theory (NREFT). Using the same 5.5~t fiducial mass and 60 live days of exposure we report on the results of a relativistic extension to the NREFT. We present constraints on couplings from covariant interactions arising from the coupling of vector, axial currents, and electric dipole moments of the nucleon to the magnetic and electric dipole moments of the WIMP which cannot be described by recasting previous results described by an NREFT. Using a profile-likelihood ratio analysis, in an energy region between 0~keV$_\text{nr}$ to 270~keV$_\text{nr}$, we report 90% confidence level exclusion limits on the coupling strength of five interactions in both the isoscalar and isovector bases., Comment: 7 pages, 4 figures

Hurricanes and coastal floods are among the most disastrous natural hazards. Both are intimately related to storm surges, as their causes and effects, respectively. However, the short-term forecasting of storm surges has proven challenging, especially when targeting previously unseen locations or sites without tidal gauges. Furthermore, recent work improved short and medium-term weather forecasting but the handling of raw unassimilated data remains non-trivial. In this paper, we tackle both challenges and demonstrate that neural networks can implicitly assimilate sparse in situ tide gauge data with coarse ocean state reanalysis in order to forecast storm surges. We curate a global dataset to learn and validate the dense prediction of storm surges, building on preceding efforts. Other than prior work limited to known gauges, our approach extends to ungauged sites, paving the way for global storm surge forecasting., Comment: Accepted at CVPR EarthVision 2024

Steered Response Power (SRP) is a widely used method for the task of sound source localization using microphone arrays, showing satisfactory localization performance on many practical scenarios. However, its performance is diminished under highly reverberant environments. Although Deep Neural Networks (DNNs) have been previously proposed to overcome this limitation, most are trained for a specific number of microphones with fixed spatial coordinates. This restricts their practical application on scenarios frequently observed in wireless acoustic sensor networks, where each application has an ad-hoc microphone topology. We propose Neural-SRP, a DNN which combines the flexibility of SRP with the performance gains of DNNs. We train our network using simulated data and transfer learning, and evaluate our approach on recorded and simulated data. Results verify that Neural-SRP's localization performance significantly outperforms the baselines., Comment: Presented at Asilomar Conference on Signals, Systems, and Computers

Studies have shown that in noisy acoustic environments, providing binaural signals to the user of an assistive listening device may improve speech intelligibility and spatial awareness. This paper presents a binaural speech enhancement method using a complex convolutional neural network with an encoder-decoder architecture and a complex multi-head attention transformer. The model is trained to estimate individual complex ratio masks in the time-frequency domain for the left and right-ear channels of binaural hearing devices. The model is trained using a novel loss function that incorporates the preservation of spatial information along with speech intelligibility improvement and noise reduction. Simulation results for acoustic scenarios with a single target speaker and isotropic noise of various types show that the proposed method improves the estimated binaural speech intelligibility and preserves the binaural cues better in comparison with several baseline algorithms., Comment: Accepted to ICASSP 2024

Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC., Comment: 7 pages, 4 figures, Proceeding of Connected the Dots Workshop (CTD 2023)

Searches for dark matter with liquid xenon time projection chamber experiments have traditionally focused on the region of the parameter space that is characteristic of weakly interacting massive particles, ranging from a few GeV/$c^2$ to a few TeV/$c^2$. Models of dark matter with a mass much heavier than this are well motivated by early production mechanisms different from the standard thermal freeze-out, but they have generally been less explored experimentally. In this work, we present a re-analysis of the first science run (SR1) of the LZ experiment, with an exposure of $0.9$ tonne$\times$year, to search for ultraheavy particle dark matter. The signal topology consists of multiple energy deposits in the active region of the detector forming a straight line, from which the velocity of the incoming particle can be reconstructed on an event-by-event basis. Zero events with this topology were observed after applying the data selection calibrated on a simulated sample of signal-like events. New experimental constraints are derived, which rule out previously unexplored regions of the dark matter parameter space of spin-independent interactions beyond a mass of 10$^{17}$ GeV/$c^2$., Comment: 9 pages, 7 figures

The effects of confinement on electron transport in GaN have been studied via an ensemble Monte-Carlo code. Excellent agreement is obtained with experimental data from the literature up to moderate fields. In agreement with experimental results, negative-differential-conductivity is not observed in velocity-field curves. The reasons for this are discussed in detail. The dynamics of the non-equilibrium confined electron-LO phonon system is studied via a bulk phonon spectrum. In contrast to the bulk electron case hot or non-equilibrium phonons do not play a significant role in determining the transport properties. This is explained via the energy and momentum conservation rules for the polar optical phonon scattering. In addition, impact ionization is shown to be insignificant for the applied fields considered., Comment: 5 pages, 6 figures

The Planetary Instrument for X-ray Lithochemistry (PIXL) is a rasterable focused-beam X-ray fluorescence (XRF) spectrometer mounted on the arm of National Aeronautics and Space Administration's (NASA) Mars 2020 Perseverance rover. To ensure that PIXL would be capable of performing accurate in-flight compositional analysis of martian targets, in situ, an elemental calibration was performed pre-flight on the PIXL flight instrument in a simulated martian environment. The details of this calibration, and implications for measuring unknown materials on Mars are the subjects of this paper. The major goals of this calibration were both to align the spectrometer to perform accurate elemental analysis and, to derive a matrix of uncertainties that are applied to XRF measurements of all elements in unknown materials. A small set of pure element and pure compound targets and geologically relevant reference materials were measured with the flight hardware in a simulated martian environment. Elemental calibration and quantifications were carried out using PIXL's XRF quantification software (PIQUANT). Uncertainties generated were implemented into the PIQUANT software version employed by the PIXL's data visualization software (PIXLISE). We outline in this work, a list of factors that impact micro-XRF accuracy, the methodology and steps involved in the calibration, details on the fabrication of the uncertainty matrix, instructions on the use and interpretations of the uncertainties applied to unknowns and an assessment on the limitations and areas open to future improvement as part of subsequent calibration efforts.

Recent advances in far-infrared detector technology have led to increases in raw sensitivity of more than an order of magnitude over previous state-of-the-art detectors. With such sensitivity, photon noise becomes the dominant noise component, even when using cryogenically cooled optics, unless a method of restricting the spectral bandpass is employed. The leading instrument concept features reflecting diffraction gratings which post-disperse the light that has been modulated by a polarizing Fourier transform spectrometer (FTS) onto a detector array, thereby reducing the photon noise on each detector. This paper discusses the development of a cryogenic (4 K) diffraction grating spectrometer which operates over the wavelength range from 285 - 500 $\mu$m and was used to post-disperse the output from a room-temperature polarizing FTS. Measurements of the grating spectral response and diffraction efficiency are presented as a function of both wavelength and polarization to characterize the instrumental performance., Comment: 9 pages, 14 figures. The following article has been accepted by Review of Scientific Instruments. It can be found at https://doi.org/10.1063/5.0177603

This paper studies modulation spectrum features ($\Phi$) and mel-frequency cepstral coefficients ($\Psi$) in joint speaker diarization and identification (JSID). JSID is important as speaker diarization on its own to distinguish speakers is insufficient for many applications, it is often necessary to identify speakers as well. Machine learning models are set up using convolutional neural networks (CNNs) on $\Phi$ and recurrent neural networks $\unicode{x2013}$ long short-term memory (LSTMs) on $\Psi$, then concatenating into fully connected layers. Experiment 1 shows models on both $\Phi$ and $\Psi$ have better diarization error rates (DERs) than models on either alone; a CNN on $\Phi$ has DER 29.09\%, compared to 27.78\% for a LSTM on $\Psi$ and 19.44\% for a model on both. Experiment 1 also investigates aleatoric uncertainties and shows the model on both $\Phi$ and $\Psi$ has mean entropy 0.927~bits (out of 4~bits) for correct predictions compared to 1.896~bits for incorrect predictions which, along with entropy histogram shapes, shows the model helpfully indicates where it is uncertain. Experiment 2 investigates epistemic uncertainties as well as aleatoric using Monte Carlo dropout (MCD). It compares models on both $\Phi$ and $\Psi$ with models trained on x-vectors ($X$), before applying Kalman filter smoothing on epistemic uncertainties for resegmentation and model ensembles. While the two models on $X$ (DERs 10.23\% and 9.74\%) outperform those on $\Phi$ and $\Psi$ (DER 17.85\%) after their individual Kalman filter smoothing, combining them using a Kalman filter smoothing method improves the DER to 9.29\%. Aleatoric uncertainties are higher for incorrect predictions. Both Experiments show models on $\Phi$ do not distinguish overlapping speakers as well as anticipated. However, Experiment 2 shows model ensembles do better with overlapping speakers than individual models do., Comment: 12 pages, 7 figures

Following the first science results of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon time projection chamber operating from the Sanford Underground Research Facility in Lead, South Dakota, USA, we report the initial limits on a model-independent non-relativistic effective field theory describing the complete set of possible interactions of a weakly interacting massive particle (WIMP) with a nucleon. These results utilize the same 5.5 t fiducial mass and 60 live days of exposure collected for the LZ spin-independent and spin-dependent analyses while extending the upper limit of the energy region of interest by a factor of 7.5 to 270 keVnr. No significant excess in this high energy region is observed. Using a profile-likelihood ratio analysis, we report 90% confidence level exclusion limits on the coupling of each individual non-relativistic WIMP-nucleon operator for both elastic and inelastic interactions in the isoscalar and isovector bases., Comment: 17 pages 11 figures

This paper reports on the design and outcomes of the ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids. The scenario was a listener attending to a target speaker in a noisy, domestic environment. There were multiple interferers and head rotation by the listener. The challenge extended the second Clarity Enhancement Challenge (CEC2) by fixing the amplification stage of the hearing aid; evaluating with a combined metric for speech intelligibility and quality; and providing two evaluation sets, one based on simulation and the other on real-room measurements. Five teams improved on the baseline system for the simulated evaluation set, but the performance on the measured evaluation set was much poorer. Investigations are on-going to determine the exact cause of the mismatch between the simulated and measured data sets. The presence of transducer noise in the measurements, lower order Ambisonics harming the ability for systems to exploit binaural cues and the differences between real and simulated room impulse responses are suggested causes, Comment: ICASSP 2023

This article discusses a three-year study (2020 - 2022) of dominant misconceptions (DMs) for a large cohort of first-year physics course students at the University of Johannesburg (UJ), South Africa. Our study considered pre-test scores on the Force Concept Inventory using a graphical method, where we found statistical differences between the mean DM scores for the 2020 cohort, as compared to the 2021 and 2022 cohort; possibly due to the onset of COVID lockdowns. We also compared our data from South Africa with cohorts based in Spain and the Kingdom of Saudi Arabia, where the method of DMs was also applied. From this comparison, we found some differences in the preconception knowledge of the cohorts. Furthermore, we included an analysis of DMs through the `gender lens' for the South African cohort, finding no statistically significant difference between the means for DM scores of students who identify as male or female. Finally, given the diverse language backgrounds and levels of matriculation preparation for university level physics courses, we have also shown how quickly responding to student misconceptions can be efficiently addressed using the method of DMs., Comment: 15 pages, 3 figures, accepted for publication in Physics Education

Observations of clusters suffer from issues such as completeness, projection effects, resolving individual stars and extinction. As such, how accurate are measurements and conclusions are likely to be? Here, we take cluster simulations (Westerlund2- and Orion- type), synthetically observe them to obtain luminosities, accounting for extinction and the inherent limits of Gaia, then place them within the real Gaia DR3 catalogue. We then attempt to rediscover the clusters at distances of between 500pc and 4300pc. We show the spatial and kinematic criteria which are best able to pick out the simulated clusters, maximising completeness and minimising contamination. We then compare the properties of the 'observed' clusters with the original simulations. We looked at the degree of clustering, the identification of clusters and subclusters within the datasets, and whether the clusters are expanding or contracting. Even with a high level of incompleteness (e.g. $<2\%$ stellar members identified), similar qualitative conclusions tend to be reached compared to the original dataset, but most quantitative conclusions are likely to be inaccurate. Accurate determination of the number, stellar membership and kinematic properties of subclusters, are the most problematic to correctly determine, particularly at larger distances due to the disappearance of cluster substructure as the data become more incomplete, but also at smaller distances where the misidentification of asterisms as true structure can be problematic. Unsurprisingly, we tend to obtain better quantitative agreement of properties for our more massive Westerlund2-type cluster. We also make optical style images of the clusters over our range of distances., Comment: 19 pages, 10 figures, 6 tables. Accepted for publication in MNRAS

Change detection (CD) methods have been applied to optical data for decades, while the use of hyperspectral data with a fine spectral resolution has been rarely explored. CD is applied in several sectors, such as environmental monitoring and disaster management. Thanks to the PRecursore IperSpettrale della Missione operativA (PRISMA), hyperspectral-from-space CD is now possible. In this work, we apply standard and deep-learning (DL) CD methods to different targets, from natural to urban areas. We propose a pipeline starting from coregistration, followed by CD with a full-spectrum algorithm and by a DL network developed for optical data. We find that changes in vegetation and built environments are well captured. The spectral information is valuable to identify subtle changes and the DL methods are less affected by noise compared to the statistical method, but atmospheric effects and the lack of reliable ground truth represent a major challenge to hyperspectral CD., Comment: Accepted at Big Data from Space 2023 (BiDS); 4 pages, 4 figures

We present the discovery of an unusual set of flares in the nova-like variable V704 And. Using data from AAVSO, ASAS-SN, and ZTF, of the nova-like variable V704 And, we have discovered a trio of brightening events that occurred during the high state. These events elevate the optical brightness of the source from $\sim13.5$ magnitude to $\sim12.5$ magnitude. The events last for roughly a month, and exhibit the unusual shape of a slow rise and faster decay. Just after the third event we obtained data from regular monitoring with Swift, although by this time the flares had ceased and the source returned to its pre-flare level of activity in the high-state. The Swift observations confirm that during the high-state the source is detectable in the X-rays, and provide simultaneous UV and optical fluxes. As the source is already in the high-state prior to the flares, and thus the disc is expected to already be in the high-viscosity state, we conclude that the driver of the variations must be changes in the mass transfer rate from the companion star and we discuss possible mechanisms for such short-timescale mass transfer variations to occur., Comment: 5 pages + appendix. Accepted for publication in A&A Letters

We study the kinematics and dynamics of a highly compliant membrane disk placed head-on in a uniform flow. With increasing flow velocity, the membrane deforms nonlinearly into increasingly parachute-like shapes. These aerodynamically elongated materials exhibit a modified drag law, which is linked to the elastohydrodynamic interactions. We predict the unsteady structural response of the membranes using a nonlinear, aeroelastic model -- in excellent agreement with experimental measurements of deformations and force fluctuations. With simultaneous membrane interface tracking, force measurements and flow tracing, we reveal that a peculiar skewness in the membrane's oscillations triggers turbulence production in the wake, thereby modulating the drag. The present work provides a demonstration of the complex interplay between soft materials and fluid turbulence, leading to new, emergent system properties., Comment: 5 pages, 4 figures, Phys. Rev. Lett. (in press)

In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) algorithms, information from a high dimensional, multichannel audio signals received by many distributed microphones is combined with information describing acoustic properties of the scene, such as the microphones' coordinates in space, to estimate the position of a sound source. We introduce Dual Input Neural Networks (DI-NNs) as a simple and effective way to model these two data types in a neural network. We train and evaluate our proposed DI-NN on scenarios of varying difficulty and realism and compare it against an alternative architecture, a classical Least-Squares (LS) method as well as a classical Convolutional Recurrent Neural Network (CRNN). Our results show that the DI-NN significantly outperforms the baselines, achieving a five times lower localization error than the LS method and two times lower than the CRNN in a test dataset of real recordings.