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DarkSide-20k is a novel liquid argon dark matter detector currently under construction at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) that will push the sensitivity for Weakly Interacting Massive Particle (WIMP) detection into the neutrino fog. The core of the apparatus is a dual-phase Time Projection Chamber (TPC), filled with \SI{50} {tonnes} of low radioactivity underground argon (UAr) acting as the WIMP target. NUV-HD-Cryo Silicon Photomultipliers (SiPM)s designed by Fondazione Bruno Kessler (FBK) (Povo, Trento, Italy) were selected as the photon sensors covering two $10.5~\text{m}^2$ Optical Planes, one at each end of the TPC, and a total of $5~\text{m}^2$ photosensitive surface for the liquid argon veto detectors. This paper describes the Quality Assurance and Quality Control (QA/QC) plan and procedures accompanying the production of FBK~NUV-HD-Cryo SiPM wafers manufactured by LFoundry s.r.l. (Avezzano, AQ, Italy). SiPM characteristics are measured at 77~K at the wafer level with a custom-designed probe station. As of May~2024, 603 of the 1400 production wafers (43\% of the total) for DarkSide-20k were tested, including wafers from all 57 production Lots. The wafer yield is $93.6\pm2.5$\%, which exceeds the 80\% specification defined in the original DarkSide-20k production plan.

In this paper we address the challenges of documenting early digital artifacts in collections built to offer historical context for future generations. Through insights from active community members (N=20), we examine current archival needs and obstacles. We assess the potential of the CIDOC Conceptual Reference Model (CRM) for categorizing fragmented digital data. Despite its complexity, CIDOC-CRM proves logical, human-readable, and adaptable, enabling archivists to select minimal yet effective building blocks set to empower community-led heritage projects., Comment: 8 pages, 1 table, 2 figures

This paper presents theoretical and experimental studies on the superconductivity of Pb${_{0.64}}$Bi$_{0.36}$ alloy, which is a prototype of strongly coupled superconductors and exhibits one of the strongest coupling under ambient pressure among the materials studied so far. The critical temperature, the specific heat in the superconducting state, and the magnetic critical fields are experimentally determined. Deviations from the single-gap s-wave BCS-like behavior are observed. The electronic structure, phonons and electron-phonon interactions are analyzed in relation to the metallic Pb, explaining why the Pb-Bi alloy exhibits such a large value of the electron-phonon coupling parameter $\lambda \simeq 2$. Superconductivity is studied using the isotropic Eliashberg formalism as well as the anisotropic density functional theory for superconductors. We find that while Pb is a two-gap superconductor with well-defined separate superconducting gaps, in the Pb-Bi alloy an overlapped three-gap-like structure is formed with a strong anisotropy. Furthermore, the chemical disorder, inherent to this alloy, leads to strong electron scattering, which is found to reduce the critical temperature., Comment: 24 pages, 22 figures + supplemental material. Accepted for publication in Physical Review B

The progress in the fabrication of nanoscale systems based on the two-dimensional electron gas at the interface between LaAlO$_3$ and SrTiO$_3$ (LAO/STO) has created an increased demand for simulations of these nanostructures, which typically range in size from tens to hundreds of nanometers. Due to the low lattice constant of LAO/STO, approximately 0.394 nm, these calculations become extremely time-consuming. Here, we present a scaled tight-binding approximation defined on a mesh with size that can be several times larger than in the ordinary approach. The scaled model is analyzed within the context of quantum transport simulations and electronic structure calculations. Our findings demonstrate that the scaled model closely aligns with the ordinary one up to a scaling factor of 8. These results pave the way for more efficient simulations of LAO/STO nanostructures with realistic sizes relevant to experimental applications., Comment: 11 pages, 13 figures

Methods for analyzing representations in neural systems have become a popular tool in both neuroscience and mechanistic interpretability. Having measures to compare how similar activations of neurons are across conditions, architectures, and species, gives us a scalable way of learning how information is transformed within different neural networks. In contrast to this trend, recent investigations have revealed how some metrics can respond to spurious signals and hence give misleading results. To identify the most reliable metric and understand how measures could be improved, it is going to be important to identify specific test cases which can serve as benchmarks. Here we propose that the phenomena of compositional learning in recurrent neural networks (RNNs) allows us to build a test case for dynamical representation alignment metrics. By implementing this case, we show it enables us to test whether metrics can identify representations which gradually develop throughout learning and probe whether representations identified by metrics are relevant to computations executed by networks. By building both an attractor- and RNN-based test case, we show that the new Dynamical Similarity Analysis (DSA) is more noise robust and identifies behaviorally relevant representations more reliably than prior metrics (Procrustes, CKA). We also show how test cases can be used beyond evaluating metrics to study new architectures. Specifically, results from applying DSA to modern (Mamba) state space models, suggest that, in contrast to RNNs, these models may not exhibit changes to their recurrent dynamics due to their expressiveness. Overall, by developing test cases, we show DSA's exceptional ability to detect compositional dynamical motifs, thereby enhancing our understanding of how computations unfold in RNNs., Comment: 19 pages, 11 figures

This paper presents the multiplicity of neutral and charged hadrons produced in 90 GeV$/c$ proton-carbon interactions from a dataset taken by the NA61/SHINE experiment in 2017. Particle identification via dE/dx was performed for the charged hadrons $\pi^\pm$, $K^\pm$, and $p / \bar{p}$; the neutral hadrons $K^0_S$, $\Lambda$, and $\bar{\Lambda}$ were identified via an invariant mass analysis of their decays to charged hadrons. Double-differential multiplicity results as a function of laboratory momentum and polar angle are presented for each particle species; these results provide vital constraints on the predicted neutrino beam flux for current and future long-baseline neutrino oscillation experiments.

This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were recruited from 149 candidates to form three groups representing experts in the domain of mycology (DE), students with a data science and visualization background (IT) and students from social sciences and humanities (SSH). The main part of the dataset contains 39 transcripts of interviews during which participants were asked to complete a series of tasks and questions related to the interpretation of explanations of decisions of a machine learning model trained to distinguish between edible and inedible mushrooms. The transcripts were complemented with additional data that includes visualizations of explanations presented to the user, results from thematic analysis, recommendations of improvements of explanations provided by the participants, and the initial survey results that allow to determine the domain knowledge of the participant and data analysis literacy. The transcripts were manually tagged to allow for automatic matching between the text and other data related to particular fragments. In the advent of the area of rapid development of XAI techniques, the need for a multidisciplinary qualitative evaluation of explainability is one of the emerging topics in the community. Our dataset allows not only to reproduce the study we conducted, but also to open a wide range of possibilities for the analysis of the material we gathered.

This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intelligence (XAI) algorithms, specifically investigating how humans understand and interact with the explanations provided by these algorithms. To achieve this, we employed a multi-disciplinary approach that included state-of-the-art research methods from social sciences to measure the comprehensibility of explanations generated by a state-of-the-art lachine learning model, specifically the Gradient Boosting Classifier (XGBClassifier). We conducted an extensive empirical user study involving interviews with 39 participants from three different groups, each with varying expertise in data science, data visualization, and domain-specific knowledge related to the dataset used for training the machine learning model. Participants were asked a series of questions to assess their understanding of the model's explanations. To ensure replicability, we built the model using a publicly available dataset from the UC Irvine Machine Learning Repository, focusing on edible and non-edible mushrooms. Our findings reveal limitations in existing XAI methods and confirm the need for new design principles and evaluation techniques that address the specific information needs and user perspectives of different classes of AI stakeholders. We believe that the results of our research and the cross-disciplinary methodology we developed can be successfully adapted to various data types and user profiles, thus promoting dialogue and address opportunities in HCAI research. To support this, we are making the data resulting from our study publicly available.

We theoretically study topological superconductivity in elongated planar Josephson junctions. Josephson junctions host a topological phase accompanied by the presence of zero-energy Majorana-bound states in a certain region of the superconducting phase difference with a span determined by the strength of an external magnetic field. We demonstrate that the topological superconducting phase range can be greatly increased by elongation of the junction, which causes an amplification of the Zeeman-induced phase shift of Andreev bound states. We show that the appearance of trivial in-gap states that occurs in elongated junctions can prohibit the creation of Majorana modes, but it can be mitigated by further proximitization of the junction with additional superconducting contacts. The topological transition in this system can be probed by measurements of the critical current and we show that the elongation of the junction leads to a linear decrease of the transition critical magnetic field beneficial for experimental studies.

DarkSide-20k (DS-20k) is a dark matter detection experiment under construction at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It utilises ~100 t of low radioactivity argon from an underground source (UAr) in its inner detector, with half serving as target in a dual-phase time projection chamber (TPC). The UAr cryogenics system must maintain stable thermodynamic conditions throughout the experiment's lifetime of >10 years. Continuous removal of impurities and radon from the UAr is essential for maximising signal yield and mitigating background. We are developing an efficient and powerful cryogenics system with a gas purification loop with a target circulation rate of 1000 slpm. Central to its design is a condenser operated with liquid nitrogen which is paired with a gas heat exchanger cascade, delivering a combined cooling power of >8 kW. Here we present the design choices in view of the DS-20k requirements, in particular the condenser's working principle and the cooling control, and we show test results obtained with a dedicated benchmarking platform at CERN and LNGS. We find that the thermal efficiency of the recirculation loop, defined in terms of nitrogen consumption per argon flow rate, is 95 % and the pressure in the test cryostat can be maintained within $\pm$(0.1-0.2) mbar. We further detail a 5-day cool-down procedure of the test cryostat, maintaining a cooling rate typically within -2 K/h, as required for the DS-20k inner detector. Additionally, we assess the circuit's flow resistance, and the heat transfer capabilities of two heat exchanger geometries for argon phase change, used to provide gas for recirculation. We conclude by discussing how our findings influence the finalisation of the system design, including necessary modifications to meet requirements and ongoing testing activities., Comment: 45 pages, 24 figures

The dual-phase liquid argon time projection chamber is presently one of the leading technologies to search for dark matter particles with masses below 10 GeV/c$^2$. This was demonstrated by the DarkSide-50 experiment with approximately 50 kg of low-radioactivity liquid argon as target material. The next generation experiment DarkSide-20k, currently under construction, will use 1,000 times more argon and is expected to start operation in 2027. Based on the DarkSide-50 experience, here we assess the DarkSide-20k sensitivity to models predicting light dark matter particles, including Weakly Interacting Massive Particles (WIMPs) and sub-GeV/c$^2$ particles interacting with electrons in argon atoms. With one year of data, a sensitivity improvement to dark matter interaction cross-sections by at least one order of magnitude with respect to DarkSide-50 is expected for all these models. A sensitivity to WIMP--nucleon interaction cross-sections below $1\times10^{-42}$ cm$^2$ is achievable for WIMP masses above 800 MeV/c$^2$. With 10 years exposure, the neutrino fog can be reached for WIMP masses around 5 GeV/c$^2$., Comment: 13 pages, 4 figures, supplementary material (4 figures)

Experiments aimed at direct searches for WIMP dark matter require highly effective reduction of backgrounds and control of any residual radioactive contamination. In particular, neutrons interacting with atomic nuclei represent an important class of backgrounds due to the expected similarity of a WIMP-nucleon interaction, so that such experiments often feature a dedicated neutron detector surrounding the active target volume. In the context of the development of DarkSide-20k detector at INFN Gran Sasso National Laboratory (LNGS), several R&D projects were conceived and developed for the creation of a new hybrid material rich in both hydrogen and gadolinium nuclei to be employed as an essential element of the neutron detector. Thanks to its very high cross-section for neutron capture, gadolinium is one of the most widely used elements in neutron detectors, while the hydrogen-rich material is instrumental in efficiently moderating the neutrons. In this paper results from one of the R&Ds are presented. In this effort the new hybrid material was obtained as a poly(methyl methacrylate) (PMMA) matrix, loaded with gadolinium oxide in the form of nanoparticles. We describe its realization, including all phases of design, purification, construction, characterization, and determination of mechanical properties of the new material.

Nanowire quantum dots, i.e., heterostructures consisting of an axial insertion of low bandgap semiconductor within large band gap semiconductor nanowire, attract interest due to their emerging applications in the field of quantum communication technology. Here, we report on the fabrication of ZnTe/(Zn,Mg)Te nanowire quantum dots by molecular beam epitaxy and on a detailed investigation of the optical emission from individual structures by means of a combined study involving cathodoluminescence and micro-photoluminescence. A distinct dependence of the biexciton binding energy, defined as the spectral distance between the exciton and biexciton emission lines on the length of ZnTe axial insertions, is observed. With increasing dot length, not only does the biexciton binding energy value decrease distinctly, but also its character changes from binding to antibinding. The explanation of this effect relies on the appearance of a piezoelectric field along the nanowire axis, leading to a pronounced separation of electrons and holes. The change from a bound to an unbound character of biexciton energy can be well reproduced by theoretical calculations, which indicate an important contribution of excited hole states to this effect in the case of relatively large quantum dots.

Cross-Modal Retrieval (CMR), which retrieves relevant items from one modality (e.g., audio) given a query in another modality (e.g., visual), has undergone significant advancements in recent years. This capability is crucial for robots to integrate and interpret information across diverse sensory inputs. However, the retrieval space in existing robotic CMR approaches often consists of only one modality, which limits the robot's performance. In this paper, we propose a novel CMR model that incorporates three different modalities, i.e., visual, audio and tactile, for enhanced multi-modal object retrieval, named as VAT-CMR. In this model, multi-modal representations are first fused to provide a holistic view of object features. To mitigate the semantic gaps between representations of different modalities, a dominant modality is then selected during the classification training phase to improve the distinctiveness of the representations, so as to improve the retrieval performance. To evaluate our proposed approach, we conducted a case study and the results demonstrate that our VAT-CMR model surpasses competing approaches. Further, our proposed dominant modality selection significantly enhances cross-retrieval accuracy., Comment: 7 pages, 6 figures, accepted to the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

Most of the intrusion detection methods in computer networks are based on traffic flow characteristics. However, this approach may not fully exploit the potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. We propose a novel approach where packets are stacked into windows and separately recognised, with a 2D image representation suitable for processing with computer vision models. Our investigation utilizes the CIC IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research., Comment: Submitted to Computer Science Journal

Early exits are an important efficiency mechanism integrated into deep neural networks that allows for the termination of the network's forward pass before processing through all its layers. By allowing early halting of the inference process for less complex inputs that reached high confidence, early exits significantly reduce the amount of computation required. Early exit methods add trainable internal classifiers which leads to more intricacy in the training process. However, there is no consistent verification of the approaches of training of early exit methods, and no unified scheme of training such models. Most early exit methods employ a training strategy that either simultaneously trains the backbone network and the exit heads or trains the exit heads separately. We propose a training approach where the backbone is initially trained on its own, followed by a phase where both the backbone and the exit heads are trained together. Thus, we advocate for organizing early-exit training strategies into three distinct categories, and then validate them for their performance and efficiency. In this benchmark, we perform both theoretical and empirical analysis of early-exit training regimes. We study the methods in terms of information flow, loss landscape and numerical rank of activations and gauge the suitability of regimes for various architectures and datasets.

We present a methodology for performing scans of BSM parameter spaces with reinforcement learning (RL). We identify a novel procedure using graph neural networks that is capable of exploring spaces of models without the user specifying a fixed particle content, allowing broad classes of BSM models to be explored. In theory, the technique is applicable to nearly any model space with a pre-specified gauge group. We provide a generic procedure by which a suitable graph grammar can be developed for any BSM model which features user-specified symmetry groups and a finite number of different possible particle species. As a proof of concept, we construct the graph grammar for theories with vector-like leptons that may or may not be charged under a dark U(1) group, inspired by portal matter extensions of the sub-GeV vector portal/kinetic mixing simplified dark matter models. We then use this graph grammar to create a RL environment tasked with creating models with these vector-like leptons that are consistent with a list of a variety of precision observables. The RL agent succeeds in developing models that can address the observed muon anomalous magnetic moment discrepancy while remaining consistent with flavor violation and electroweak precision observables, including both constructions that have previously been studied as well as new models which have not, to our knowledge, previously been identified. By inspecting the resulting ensembles of models that the agent produces and experimenting with different configurations for our RL environment and graph grammar, we also infer various lessons about the development of these environments that can be transferable to RL scans of more complicated model spaces, and comment on future directions for the development of this technique into a more mature tool., Comment: 57 pages, 14 figures, 15 tables

We provide a framework for exploring physics beyond the Standard Model with reinforcement learning using graph representations of new physics theories. The graph structure allows for model-building without a priori specifying definite numbers of new particles. As a case study, we apply our method to a simple class of theories involving vectorlike leptons and a dark U(1) inspired by the portal matter paradigm. Using modern policy gradient methods, the agent successfully explores a model space consisting of both continuous and discrete parameters and identifies consistent theories. The minimal models found include both known and previously unstudied examples that can accommodate the muon anomalous magnetic moment and satisfy precision electroweak and flavor constraints. The method represents a step forward in enabling an automated model-building process for physics beyond the Standard Model., Comment: 10 pages, 2 figures, 2 tables

This paper explores the potential for spin-triplet superconductivity in molecular beam epitaxy-grown IV-VI semiconductor superlattices. The findings present compelling evidence for spin-triplet pairing in PbTe/SnTe by the method of soft point-contact spectroscopy and spin-polarised point-contact spectroscopy. The experimental data are understood with the Anderson-Brinkman-Morel model of p-wave electron pairing. It is pointed out that emergent superconductivity can have its origin in topological flat bands obtained due to internal stresses of the sample similar to twisted layer graphene., Comment: 15 pages, 15 figures, supplementary included

Boron nitride exhibits diverse crystal structures, predominantly a layered arrangement with strong intraplanar covalent bonds and weak interplanar van der Waals bonds. While commonly referred to as hexagonal BN (hBN), the sp$^2$-bonded BN atomic planes can also arrange in other configurations like Bernal (bBN) or rhombohedral (rBN) stacking orders. Variations in the orientation and translation of successive atomic layers lead to changes in crystal symmetry, potentially resulting in piezoelectric, pyroelectric or ferroelectric effects. However, distinguishing between different polytypes using conventional methods like X-ray diffraction or Raman spectroscopy presents a significant challenge. In this work, we demonstrate that the optical response of the 4.1 eV defect can serve as an indicator of the polytype. To this end, we study BN samples grown by metalorganic vapor phase epitaxy (MOVPE), which contain different polytypes. The identification of the polytypes was achieved by X-ray diffraction and transmission electron microscopy. Photoluminescence and cathodoluminescence measurements with a high spatial resolution allowed for the deconvolution of the signal into two components from which we can extract a zero-phonon line (ZPL) at 4.096 eV (302.6 nm) for hBN and 4.143 eV (299.2 nm) for rBN. We performed calculations that enable us to identify the defect as a carbon dimer CBCN (C2) and show that the ZPL shift reflects differences in the crystal environment for different polytypes. Furthermore, we demonstrate that different polytypic composition ratios of hBN and rBN can be achieved by MOVPE, which could pave the way for future applications in large-area van der Waals heterostructures.

The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Possible support for the therapists can be estimation of the time at which subject goes into deep relaxation. This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads conducted with the use of dense array electroencephalographic amplifier. The research reported herein aimed at verification whether it is possible to detect differences between those two states and to classify them using deep learning methods and recurrent neural networks such as EEGNet, Long Short-Term Memory-based classifier, 1D Convolutional Neural Network and hybrid model of 1D Convolutional Neural Network and Long Short-Term Memory. The data processing pipeline was presented from the data acquisition, through the initial data cleaning, preprocessing and postprocessing. The classification was based on two datasets: one of them using 26 so-called cognitive electrodes and the other one using signal collected from 256 channels. So far there have not been such comparisons in the application being discussed. The classification results are presented by the validation metrics such as: accuracy, recall, precision, F1-score and loss for each case. It turned out that it is not necessary to collect signals from all electrodes as classification of the cognitive ones gives the results similar to those obtained for the full signal and extending input to 256 channels does not add much value. In Disscussion there were proposed an optimal classifier as well as some suggestions concerning the prospective development of the project., Comment: In review

A search for full energy depositions from bosonic keV-scale dark matter candidates of masses between 65 keV and 1021 keV has been performed with data collected during Phase II of the GERmanium Detector Array (GERDA) experiment. Our analysis includes direct dark matter absorption as well as dark Compton scattering. With a total exposure of 105.5 kg yr, no evidence for a signal above the background has been observed. The resulting exclusion limits deduced with either Bayesian or Frequentist statistics are the most stringent direct constraints in the major part of the 140-1021 keV mass range. As an example, at a mass of 150 keV the dimensionless coupling of dark photons and axion-like particles to electrons has been constrained to $\alpha$'/$\alpha$ < 8.7x10$^{-24}$ and g$_{ae}$ < 3.3x10$^{-12}$ at 90% credible interval (CI), respectively. Additionally, a search for peak-like signals from beyond the Standard Model decays of nucleons and electrons is performed. We find for the inclusive decay of a single neutron in $^{76}$Ge a lower lifetime limit of $\tau_n$ > 1.5x10$^{24}$ yr and for a proton $\tau_p$ > 1.3x10$^{24}$ yr at 90% CI. For the electron decay e$^-\rightarrow\nu_e\gamma$ a lower limit of $\tau_e$ > 5.4x10$^{25}$ yr at 90% CI has been determined., Comment: 20 pages, 12 figures, 7 tables

Coded apertures, traditionally employed in x-ray astronomy for imaging celestial objects, are now being adapted for micro-scale applications, particularly in studying microscopic specimens with synchrotron light diffraction. In this paper, we focus on micro-coded aperture imaging and its capacity to accomplish depth-resolved micro-diffraction analysis within crystalline specimens. We study aperture specifications and scanning parameters by assessing characteristics like size, thickness, and patterns. Numerical experiments assist in assessing their impact on reconstruction quality. Empirical data from a Laue diffraction microscope at a synchrotron undulator beamline supports our findings. Overall, our results offer key insights for optimizing aperture design in advancing micro-scale diffraction imaging at synchrotrons. This study contributes insights to this expanding field and suggests significant advancements, especially when coupled with the enhanced flux anticipated from the global upgrades of synchrotron sources., Comment: 23 pages, 8 figures

We introduce a structured illumination technique for dark-field x-ray microscopy optimized for three-dimensional imaging of ordered materials at sub-micrometer length scales. Our method utilizes a coded aperture to spatially modulate the incident x-ray beam on the sample, enabling the reconstruction of the sample's 3D structure from images captured at various aperture positions. Unlike common volumetric imaging techniques such as tomography, our approach casts a scanning x-ray silhouette of a coded aperture for depth resolution along the axis of diffraction, eliminating any need for sample rotation or rastering, leading to a highly stable imaging modality. This modification provides robustness against geometric uncertainties during data acquisition, particularly for achieving sub-micrometer resolutions where geometric uncertainties typically limit resolution. We introduce the image reconstruction model and validate our results with experimental data on an isolated twin domain within a bulk single crystal of an iron pnictide obtained using a dark-field x-ray microscope. This timely advancement aligns with the enhanced brightness upgrade of the world's synchrotron radiation facilities, opening unprecedented opportunities in imaging., Comment: 20 pages, 5 figures

Encompassing regions that were amongst the first inhabited by humans following the out-of-Africa expansion, hosting populations with the highest levels of archaic hominid introgression, and including Pacific islands that are the most isolated inhabited locations on the planet, Oceania has a rich, but understudied, human genomic landscape. Here we describe the first region-wide analysis of genome-wide data from population groups spanning Oceania and its surroundings, from island and peninsular southeast Asia to Papua New Guinea, east across the Pacific through Melanesia, Micronesia, and Polynesia, and west across the Indian Ocean to related island populations in the Andamans and Madagascar. In total we generate and analyze genome-wide data from 981 individuals from 92 different populations, 58 separate islands, and 30 countries, representing the most expansive study of Pacific genetics to date. In each sample we disentangle the Papuan and more recent Austronesian ancestries, which have admixed in various proportions across this region, using ancestry-specific analyses, and characterize the distinct patterns of settlement, migration, and archaic introgression separately in these two ancestries. We also focus on the patterns of clinically relevant genetic variation across Oceania--a landscape rippled with strong founder effects and island-specific genetic drift in allele frequencies--providing an atlas for the development of precision genetic health strategies in this understudied region of the world.

Full-shell hybrid nanowires (NWs), structures comprising a superconductor shell that encapsulates a semiconductor (SM) core, have attracted considerable attention in the search for Majorana zero modes (MZMs). However, the predicted Rashba spin-orbit coupling (SOC) in the SM is too small to achieve substantial topological minigaps. In addition, the SM wavefunction spreads all across the section of the nanowire, leading typically to a finite background of trivial subgap states with which MZMs may coexist. To overcome both problems, we explore the advantages of utilizing core-shell hole-band NWs as the SM part of a full-shell hybrid, with an insulating core and an active SM shell. In particular, we consider InP/GaSb core-shell NWs, which allow to exploit the unique characteristics of the III-V compound SM valence bands. We demonstrate that they exhibit a robust hole SOC that emerges from the combination of the intrinsic spin-orbit interaction of the SM active shell and the confinement effects of the nanostructure, thus depending mainly on SM and geometrical parameters. In other words, the SOC is intrinsic and does not rely on red electric fields, which are non-tunable in a full-shell hybrid geometry. As a result, core-shell SM hole-band NWs are found to be a promising candidate to explore Majorana physics in full-hell hybrid devices, addressing several challenges in the field., Comment: 32 pages, 4 figures; Resubmission to SciPost with minor changes

We study the quasiparticle states and nonlocal correlations of a hybrid structure, comprising two quantum dots interconnected through a short-length topological superconducting nanowire hosting overlaping Majorana modes. We show that the hybridization between different components of this setup gives rise to the emergence of molecular states, which are responsible for nonlocal correlations. We inspect the resulting energy structure, focusing on the inter-dependence between the quasiparticles of individual quantum dots. We predict the existence of nonlocal effects, which could be accessed and probed by crossed Andreev reflection spectroscopy. Our study would be relevant to a recent experimental realization of the minimal Kitaev model [T. Dvir et al., Nature 614, 445 (2023)], by considering its hybrid structure with side-attached quantum dots., Comment: 11 pages, 8 figures

This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using Neural Architecture Search (NAS) methods, specifically Differentiable Architecture Search (DARTS). We present the concept of Implantable Adaptive Cell (IAC), small but powerful modules identified through Partially-Connected DARTS, designed to be injected into the skip connections of an existing and already trained U-shaped model. Our strategy allows for the seamless integration of the IAC into the pre-existing architecture, thereby enhancing its performance without necessitating a complete retraining from scratch. The empirical studies, focusing on medical image segmentation tasks, demonstrate the efficacy of this method. The integration of specialized IAC cells into various configurations of the U-Net model increases segmentation accuracy by almost 2\% points on average for the validation dataset and over 3\% points for the training dataset. The findings of this study not only offer a cost-effective alternative to the complete overhaul of complex models for performance upgrades but also indicate the potential applicability of our method to other architectures and problem domains.

The spin dynamics in two electron double quantum dots embedded in two dimensional electron gas at the interface between SrTiO$_3$ and LaAlO$_3$ is studied by an exact numerical solution of the time-dependent Schr\"odinger equation, in the context of the electric dipole spin resonance experiment. Based on the three band model of $3d$-electrons localized at Ti ions on the square lattice we analyze in details the singlet-triplet transition induced by the AC electric field, in the magnetic field range close to the avoided crossing which appears as a result of the spin-orbit coupling. Our calculations show that for symmetric double quantum dots the single photon spin-flip transitions is prohibited due to the parity symmetry and the transition can occur only by the higher order two-photon processes. For a weakly asymmetric system, when the first order singlet-triplet transitions are released due to the parity symmetry breaking, the spin-flip transition has a character of the Rabi oscillations for a low electric field amplitude. As the amplitude is increased the frequency of the transition is blueshifted (redshifted) for the magnetic field below (above) the single-triplet avoided crossing. Interestingly, for a sufficiently high magnetic field and high AC field amplitude the electric field drives the system across the avoided crossing inducing the spin-flip by the Landau-Zener-Stueckelberg-Majorana transitions with 100\% spin flip probability for a slow sweep. Finally, the optimization of the geometrical parameters of the system with respect to the time of spin-flip of its fidelity is also presented.

Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative diseases. Clinical relevance - Developing a non-invasive biomarker for Parkinson's disease is urgently needed to accurately detect the disease's onset. This would allow for the timely introduction of neuroprotective treatment at the earliest stage and enable the continuous monitoring of intervention outcomes. The ability to detect subtle changes in eye movements allows for early diagnosis, offering a critical window for intervention before more pronounced symptoms emerge. Eye tracking provides objective and quantifiable biomarkers, ensuring reliable assessments of disease progression and cognitive function. The eye gaze analysis using Mixed Reality glasses is wireless, facilitating convenient assessments in both home and hospital settings. The approach offers the advantage of utilizing hardware that requires no additional specialized attachments, enabling examinations through personal eyewear.

We present a speculative exploration of the properties of a proposed new Dark Matter (DM) candidate in a heretofore under-explored region of parameter space. Our proposed ultra-cold candidatae has been a matter of speculation for some time,and has recently been tentatively identified via direct-detection. While unconventional, demonstrated existence of this DM candidate would have wide-ranging implications for a range of fields, from particle cosmology to exobiology and the Search for Extraterrestrial Life (SETI)., Comment: 5 pages, 2 figures. Submitted to Vatican Publishing House

In the present note we focus on conic line arrangements in the plane with quasihomogeneous ordinary singularities from the perspective of weak Ziegler pairs. The foundations of this article come from an active area of research devoted to the freeness and nearly freeness of curve arrangements in the complex projective plane and the socalled Numerical Terao s Conjecture. This conjecture boils down to a very fundamental problem in combinatorial algebraic geometry, namely whether the weak combinatorics of a given arrangement determines the freeness.