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The upcoming UCNProBe experiment at Los Alamos National Laboratory will measure the $\beta$-decay rate of free neutrons with different systematic uncertainties than previous beam-based neutron lifetime experiments. We have developed a new $^{10}$B-coated YAP:Ce scintillator whose properties are presented. The advantage of the YAP:Ce scintillator is its high Fermi potential, which reduces the probability for upscattering of ultracold neutrons, and its short decay time, which is important at high counting rates. Birks' coefficient of YAP:Ce was measured to be ($5.56^{+0.05}_{-0.30})\times 10^{-4}$ cm/MeV and light losses due to 120 nm of $^{10}$B-coating to be about 60%. The loss of light from YAP:Ce due to transmission through deuterated polystyrene scintillator was about 50%. The efficiency for counting neutrons that are captured on the $^{10}$B coating is (86.82 $\pm$ 2.61)%. Measurement with ultracold neutrons showed that YAP:Ce crystal counted 8% to 28% more UCNs compared to ZnS screen. This may be due to an uneven coating of $^{10}$B on the rough surface.

The security of blockchain systems depends on the distribution of mining power across participants. If sufficient mining power is controlled by one entity, they can force their own version of events. This may allow them to double spend coins, for example. For Proof of Work (PoW) blockchains, however, the distribution of mining power cannot be read directly from the blockchain and must instead be inferred from the number of blocks mined in a specific sample window. We introduce a framework to quantify this statistical uncertainty for the Nakamoto coefficient, which is a commonly-used measure of blockchain decentralization. We show that aggregating blocks over a day can lead to considerable uncertainty, with Bitcoin failing more than half the hypothesis tests ({\alpha} = 0.05) when using a daily granularity. For these reasons, we recommend that blocks are aggregated over a sample window of at least 7 days. Instead of reporting a single value, our approach produces a range of possible Nakamoto coefficient values that have statistical support at a particular significance level {\alpha}.

In this study we present the analysis of six solar flare events that occurred in 2022, using new data from the third-generation Miniature X-Ray Solar Spectrometer (MinXSS), also known as the Dual-zone Aperture X-ray Solar Spectrometer (DAXSS). The primary focus of this study is on the flare's "onset phase", which is characterized by elevated soft X-ray emissions even before the flare's impulsive phase. We analyze the temporal evolution of plasma temperature, emission measure, and elemental abundance factors during the flare onset phase, by fitting the DAXSS spectra with the Astrophysical Plasma Emission Code (APEC) model. The model fitting results indicate that the flaring-plasma is already at a high temperature (10-15 MK) during the onset period. The temperature rises during the onset phase, followed by a decrease and subsequent increase during the impulsive phase. Elemental abundance factors show a trend of falling below pre-flare values during the onset phase, with some recovery before the impulsive phase. During the impulsive phase, the abundance factors decrease from elevated coronal values to about photospheric values. We also analyze images from the 193 Angstrom channel of the Atmospheric Imaging Assembly (AIA), highlighting the formation or brightening of coronal loop structures during the onset phase. Two distinct onset loop configurations are observed which are referred to as 1-loop and 2-loop onsets. Both DAXSS and AIA observations indicate that the flare onset phase exhibits similar hot coronal plasma properties as the impulsive phase, suggesting that the onset phase may act as a preconditioning effect for some flares., Comment: 15 pages, 8 figures, 2 tables

This investigation examines the efficacy of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) over fixed effect models when performing intersectional studies. The research questions are: 1) What are typical strata representation rates and outcomes on physics research-based assessments? 2) To what extent do MAIHDA models create more accurate predicted strata outcomes than fixed effects models? 3) To what extent do MAIHDA models allow the modeling of smaller strata sample sizes? We simulated 3,000 datasets based on real-world data from 5,955 students on the LASSO platform. We found that MAIHDA created more accurate and precise predictions than fixed effect models. We also found that using MAIHDA could allow researchers to disaggregate their data further, creating smaller group sample sizes while maintaining more accurate findings than fixed effect models. We recommend using MAIHDA over fixed effect models for intersectional investigations., Comment: 6 tables, 6 figures

We introduce a new class of computer called a quantum frequential computer. They harness quantum properties in a different way to conventional quantum computers to generate a quadratic computational run time advantage for all algorithms as a function of the power consumed. They come in two variants: type 1 can process classical algorithms only while type 2 can also process quantum ones. In a type-1 quantum frequential computer, only the control is quantum, while in a type 2 the logical space is also quantum. We also prove that a quantum frequential computer only requires a classical data bus to function. This is useful, because it means that only a relatively small part of the overall architecture of the computer needs to be quantum in a type-1 quantum frequential computer in order to achieve a quadratic run time advantage. As with classical and conventional quantum computers, quantum frequential computers also generate heat and require cooling. We also characterise these requirements., Comment: 17 pages plus appendices

Qubits that can be efficiently controlled are essential for the development of scalable quantum hardware. While resonant control is used to execute high-fidelity quantum gates, the scalability is challenged by the integration of high-frequency oscillating signals, qubit crosstalk and heating. Here, we show that by engineering the hopping of spins between quantum dots with site-dependent spin quantization axis, quantum control can be established with discrete signals. We demonstrate hopping-based quantum logic and obtain single-qubit gate fidelities of 99.97\%, coherent shuttling fidelities of 99.992\% per hop, and a two-qubit gate fidelity of 99.3\%, corresponding to error rates that have been predicted to allow for quantum error correction. We also show that hopping spins constitute a tuning method by statistically mapping the coherence of a 10-quantum dot system. Our results show that dense quantum dot arrays with sparse occupation could be developed for efficient and high-connectivity qubit registers., Comment: main text with 18 pages and 3 figures, supplementary materials with 64 pages and 26 figures, in a single file

The Cosmological Advanced Survey Telescope for Optical and UV Research (CASTOR) is a planned flagship space telescope, covering the blue-optical and UV part of the spectrum. Here we introduce the CASTOR image simulator, a Python GalSim package-based script capable of generating mock CASTOR images from an input catalogue. We generate example images from the CASTOR Wide, Deep, and Ultra-Deep surveys using simulated light-cones from the Santa Cruz Semi-Analytic Model. We make predictions for the performance of these surveys by comparing galaxies that are extracted from each image using Source Extractor to the input catalogue. We find that the Wide, Deep, and Ultra-Deep surveys will be 75% complete for point sources down to ~27, 29 and 30 mag, respectively, in the UV, u, and g filters, with the UV-split and u-split filters reaching a shallower depth. With a large area of ~2200 deg$^2$, the Wide survey will detect hundreds of millions of galaxies out to z~4, mostly with $M_\ast \gtrsim 10^9 M_\odot$. The Ultra-Deep survey will probe to z~5, detecting galaxies with $M_\ast \gtrsim 10^7 M_\odot$. These galaxy samples will enable precision measurements of the distribution of star formation in the cosmic web, connecting the growth of stellar mass to the assembly of dark matter halos over two thirds of the history of the Universe, and other core goals of CASTOR's legacy surveys. These image simulations and the tools developed to generate them will be a vital planning tool to estimate CASTOR's performance and iterate the telescope and survey designs prior to launch., Comment: Accepted for publication in MNRAS

Hallucinations and delusions are often grouped together within the positive symptoms of psychosis. However, recent evidence suggests they may be driven by distinct computational and neural mechanisms. Examining the time course of their emergence may provide insights into the relationship between these underlying mechanisms. Participants from the second (N = 719) and third (N = 699) iterations of the North American Prodrome Longitudinal Study (NAPLS 2 and 3) were assessed for timing of CHR-P-level delusion and hallucination onset. Pre-onset symptom patterns in first-episode psychosis patients (FEP) from the Prevention and Early Intervention Program for Psychosis (PEPP-Montreal; N = 694) were also assessed. Symptom onset was determined at baseline assessment and the evolution of symptom patterns examined over 24 months. In all three samples, participants were more likely to report the onset of delusion-spectrum symptoms prior to hallucination-spectrum symptoms (odds ratios (OR): NAPLS 2 = 4.09; NAPLS 3 = 4.14; PEPP, Z = 7.01, P < 0.001) and to present with only delusions compared to only hallucinations (OR: NAPLS 2 = 5.6; NAPLS 3 = 11.11; PEPP = 42.75). Re-emergence of delusions after remission was also more common than re-emergence of hallucinations (Ps < 0.05), and hallucinations more often resolved first (Ps < 0.001). In both CHR-P samples, ratings of delusional ideation fell with the onset of hallucinations (P = 0.007). Delusions tend to emerge before hallucinations and may play a role in their development. Further work should examine the relationship between the mechanisms driving these symptoms and its utility for diagnosis and treatment.

Over the past few years, several microring resonator (MRR)-based analog photonic architectures have been proposed to accelerate general matrix-matrix multiplications (GEMMs), which are found in abundance in deep learning workloads.These architectures have dramatically grown in popularity because they offer exceptional throughput and energy efficiency compared to their electronic counterparts. However, such architectures, due to their traditional realization based on the silicon-on-insulator (SOI) material platform, face two shortcomings. First, the high-index contrast of the SOI platform incurs high scattering losses, which mandates the provisioning of high optical input power.Second, SOI waveguides are susceptible to two-photon absorption, which can incur substantial optical signal losses at moderate-to-high signal fan-in. These shortcomings have severely detrimental effects on the achievable parallelism, throughput, and energy efficiency of SOI MRR-based GEMM accelerators. To address these shortcomings, we present a novel Silicon Nitride (SiN)-Based Photonic GEMM Accelerator called SiNPhAR. SiNPhAR architecture employs SiN-based active and passive devices to implement analog GEMM functions. Since the SiN material exhibits lower index contrast and no TPA, the optical signal losses in our SiNPhAR architecture are very low. This advantage significantly enhances the achievable processing parallelism, throughput, and energy efficiency of SiNPhAR architecture, compared to SOI-based photonic GEMM accelerators from prior work. We quantify and compare these benefits of SiNPhAR architecture via our cross-layer evaluation for a benchmark workload comprising four modern deep neural network models. From the system-level performance analysis, SiNPhAR demonstrates at least 1.7x better throughput FPS while consuming at least 2.8x better energy efficiency (FPS/W) than prior SOI-based GEMM accelerators., Comment: To Appear at ISQED 2024

Single-photon emitters serve as building blocks for many emerging concepts in quantum photonics. The recent identification of bright, tunable, and stable emitters in hexagonal boron nitride (hBN) has opened the door to quantum platforms operating across the infrared to ultraviolet spectrum. While it is widely acknowledged that defects are responsible for single-photon emitters in hBN, crucial details regarding their origin, electronic levels, and orbital involvement remain unknown. Here, we employ a combination of resonant inelastic X-ray scattering and photoluminescence spectroscopy in defective hBN unveiling an elementary excitation at 285 meV that gives rise to a plethora of harmonics correlated with single-photon emitters. We discuss the importance of N $\pi^*$ antibonding orbitals in shaping the electronic states of the emitters. The discovery of the elementary excitations of hBN provides new fundamental insights into quantum emission in low-dimensional materials, paving the way for future investigations in other platforms.

The Cosmological Advanced Survey Telescope for Optical and ultraviolet Research (CASTOR) is a proposed Canadian-led 1m-class space telescope that will carry out ultraviolet and blue-optical wide-field imaging, spectroscopy, and photometry. CASTOR will provide an essential bridge in the post-Hubble era, preventing a protracted UV-optical gap in space astronomy and enabling an enormous range of discovery opportunities from the solar system to the nature of the Cosmos, in conjunction with the other great wide-field observatories of the next decade (e.g., Euclid, Roman, Vera Rubin). FORECASTOR (Finding Optics Requirements and Exposure times for CASTOR) will supply a coordinated suite of mission-planning tools that will serve as the one-stop shop for proposal preparation, data reduction, and analysis for the CASTOR mission. We present the first of these tools: a pixel-based, user-friendly, extensible, multi-mission exposure time calculator (ETC) built in Python, including a modern browser-based graphical user interface that updates in real time. We then provide several illustrative examples of FORECASTOR's use that advance the design of planned legacy surveys for the CASTOR mission: a search for the most massive white dwarfs in the Magellanic Clouds; a study of the frequency of flaring activity in M stars, their distribution and impacts on habitability of exoplanets; mapping the proper motions of faint stars in the Milky Way; wide and deep galaxy surveys; and time-domain studies of active galactic nuclei., Comment: Updated references and acknowledgements to match published version. 24 pages, 16 figures, 3 tables, published in AJ

A quantum spin liquid (QSL) is a state of matter characterized by fractionalized quasiparticle excitations, quantum entanglement, and a lack of long-range magnetic order. However, QSLs have evaded definitive experimental observation. Several Yb$^{3+}$-based triangular lattice antiferromagnets with effective $S$ = $\frac{1}{2}$ have been suggested to stabilize the QSL state as the ground state. Here, we build a comprehensive magnetic temperature phase diagram of a high-quality single crystalline KYbSe$_2$ via heat capacity and magnetocaloric effect down to 30 mK with magnetic field applied along the $a$-axis. At zero magnetic field, we observe the magnetic long-range order at $T_N$ = 0.29 K entering 120 degrees ordered state in heat capacity, consistent with neutron scattering studies. Analysis of the low-temperature ($T$) specific heat ($C$) at zero magnetic field indicates linear $T$-dependence of $C/T$ and a broad hump of $C/T$ in the proximate QSL region above $T_N$. By applying magnetic field, we observe the up-up-down phase with 1/3 magnetization plateau and oblique phases, in addition to two new phases. These observations strongly indicate that while KYbSe$_2$ closely exhibits characteristics resembling an ideal triangular lattice, deviations may exist, such as the effect of the next-nearest-neighbor exchange interaction, calling for careful consideration for spin Hamiltonian modeling. Further investigations into tuning parameters, such as chemical pressure, could potentially induce an intriguing QSL phase in the material.

The application of atom probe tomography (APT) to frozen liquids is limited by difficulties in specimen preparation. Here, we report on the use of nanoporous Cu needles as a physical framework to hold water ice for investigation using APT. Nanoporous Cu needles are prepared by the electropolishing and dealloying of Cu-Mn matchstick precursors. Cryogenic scanning electron microscopy and focused-ion beam milling reveal a hierarchical, dendritic, highly-wettable microstructure. The atom probe mass spectrum is dominated by peaks of Cu+ and H(H2O)n+ up to n <= 3, and the reconstructed volume shows the protrusion of a Cu ligament into an ice-filled pore. The continuous Cu ligament network electrically connects the apex to the cryostage, leading to enhanced electric field at the apex and increased cooling, both of which simplify the mass spectrum compared to previous reports.

The chemical enrichment of the early Universe is a crucial element in the formation and evolution of galaxies, and Population III (PopIII) stars must play a vital role in this process. In this study, we examine metal enrichment from massive stars in the early Universe's embryonic galaxies. Using radiation hydrodynamic simulations and stellar evolution modelling, we calculated the expected metal yield from these stars. Specifically, we applied accretion rates from a previous radiation-hydrodynamic simulation to inform our stellar evolution modelling, executed with the Geneva code, across 11 selected datasets, with final stellar masses between 500 and 9000 Msol. Our results demonstrate that the first generation of Pop III stars within a mass range of 2000 to 9000 Msol result in N/O, C/O and O/H ratios compatible with the values observed in very high-z galaxies GN-z11 and CEERS 1019. The ejecta of these Pop III stars are predominantly composed of He, H, and N. Our Pop III chemical enrichment model of the halo can accurately reproduce the observed N/O and C/O ratios, and, by incorporating a hundred times more zero-metallicity interstellar material with the stellar ejecta, it accurately attains the observed O/H ratio. Thus, a sub-population of extremely massive PopIII stars, with masses surpassing approximately 2000 Msol, effectively reproduces the CNO elemental abundances observed in high-z JWST galaxies to date., Comment: Accepted in A&A, 13 pages, 4 figures

Monolayers of molybdenum disulfide (MoS2) are the most studied two-dimensional (2D) transition-metal dichalcogenides (TMDs), due to its exceptional optical, electronic, and opto-electronic properties. Recent studies have shown the possibility of incorporating a small amount of magnetic transition metals (e.g., Fe, Co, Mn, V) into MoS2 to form a 2D dilute magnetic semiconductor (2D-DMS). However, the origin of the observed ferromagnetism has remained elusive, due to the presence of randomly generated sulfur vacancies during synthesis that can pair with magnetic dopants to form complex dopant-vacancy configurations altering the magnetic order induced by the dopants. By combining high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) imaging with first-principles density functional theory (DFT) calculations and magnetometry data, we demonstrate the critical effects of sulfur vacancies and their pairings with vanadium atoms on the magnetic ordering in V-doped MoS2 (V-MoS2) monolayers. Additionally, we fabricated a series of field effect transistors on these V-MoS2 monolayers and observed the emergence of p-type behavior as the vanadium concentration increased. Our study sheds light on the origin of ferromagnetism in V-MoS2 monolayers and provides a foundation for future research on defect engineering to tune the electronic and magnetic properties of atomically thin TMD-based DMSs.

Optimization under uncertainty is important in many applications, particularly to inform policy and decision making in areas such as public health. A key source of uncertainty arises from the incorporation of environmental variables as inputs into computational models or simulators. Such variables represent uncontrollable features of the optimization problem and reliable decision making must account for the uncertainty they propagate to the simulator outputs. Often, multiple, competing objectives are defined from these outputs such that the final optimal decision is a compromise between different goals. Here, we present emulation-based optimization methodology for such problems that extends expected quantile improvement (EQI) to address multi-objective optimization. Focusing on the practically important case of two objectives, we use a sequential design strategy to identify the Pareto front of optimal solutions. Uncertainty from the environmental variables is integrated out using Monte Carlo samples from the simulator. Interrogation of the expected output from the simulator is facilitated by use of (Gaussian process) emulators. The methodology is demonstrated on an optimization problem from public health involving the dispersion of anthrax spores across a spatial terrain. Environmental variables include meteorological features that impact the dispersion, and the methodology identifies the Pareto front even when there is considerable input uncertainty.

We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels, to uncover a set of semi-sparse, spatially-discriminative regions in each image. The classification uncertainty of each region is then evaluated. Those areas with the lowest uncertainties are then chosen to be weakly labeled segmentation seeds, at the pixel level, for the second part of the framework. Each of the seed extents are progressively resized according to an unsupervised, information-theoretic loss with structured-prediction regularizers. This reshaping process uses multi-scale, adaptively-weighted features to delineate class-specific transitions in local image content. Content-addressable memories are inserted at various parts of our framework so that it can leverage features from previously seen images to improve segmentation performance for related images. We evaluate our weakly-supervised framework using real-world CSAS imagery that contains over ten seafloor classes and ten target classes. We show that our framework performs comparably to nine fully-supervised deep networks. Our framework also outperforms eleven of the best weakly-supervised deep networks. We achieve state-of-the-art performance when pre-training on natural imagery. The average absolute performance gap to the next-best weakly-supervised network is well over ten percent for both natural imagery and sonar imagery. This gap is found to be statistically significant., Comment: Submitted to the IEEE Journal of Oceanic Engineering

The ever-growing sample of observed supernovae enhances our capacity for comprehensive supernova population studies, providing a richer dataset for understanding the diverse characteristics of Type Ia supernovae and possibly that of their progenitors. Here, we present a data-driven analysis of observed Type Ia supernova photometric light curves collected in the Open Supernova Catalog. Where available, we add the environmental information from the host galaxy. We focus on identifying sub-classes of Type Ia supernovae without imposing the pre-defined sub-classes found in the literature to date. To do so, we employ an implicit-rank minimizing autoencoder neural network for developing low-dimensional data representations, providing a compact representation of the supernova light curve diversity. When we analyze light curves alone, we find that one of our resulting latent variables is strongly correlated with redshift, allowing us to approximately ``de-redshift'' the other latent variables describing each event. After doing so, we find that three of our latent variables account for $\sim$95\% of the variance in our sample, and provide a natural separation between 91T and 91bg thermonuclear supernovae. Of note, the 02cx subclass is not unambiguously delineated from the 91bg sample in our results, nor do either the over-luminous 91T or the under-luminous 91bg/02cx samples form a clearly distinct population from the broader sample of ``other'' SN Ia events. We identify the physical characteristics of supernova light curves which best distinguish SNe 91T from SNe 91bg \& 02cx, and discuss prospects for future refinements and applications to other classes of supernovae as well as other transients., Comment: Astrophysical Journal, accepetd