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The structure of amorphous silicon is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we show that atomistic machine-learning methods can reveal the complex structural and energetic landscape of defects in amorphous silicon. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and more than ten thousand defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned local atomic energies to develop a universal classification of the different types of defects in amorphous silicon. The results suggest a revision of the established floating-bond model by showing that fivefold-coordinated atoms in amorphous silicon exhibit a wide range of local environments, and it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for modelling and understanding defects in disordered materials beyond silicon alone.

Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support setting, AI classification models should ideally avoid confident wrong predictions and maximise the confidence of correct predictions. Models that do this are said to be well-calibrated with regard to confidence. However, relatively little attention has been paid to how to improve calibration when training these models, i.e., to make the training strategy uncertainty-aware. In this work we evaluate three novel uncertainty-aware training strategies comparing against two state-of-the-art approaches. We analyse performance on two different clinical applications: cardiac resynchronisation therapy (CRT) response prediction and coronary artery disease (CAD) diagnosis from cardiac magnetic resonance (CMR) images. The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions. The method reduced the ECE by 17% for CRT response prediction and by 22% for CAD diagnosis when compared to a baseline classifier in which no uncertainty-aware strategy was included. In both applications, as well as reducing the ECE there was a slight increase in accuracy from 69% to 70% and 70% to 72% for CRT response prediction and CAD diagnosis respectively. However, our analysis showed a lack of consistency in terms of optimal models when using different calibration measures. This indicates the need for careful consideration of performance metrics when training and selecting models for complex high-risk applications in healthcare.

Adiabatic decompression of para-quadrupolar materials has significant potential as a cryogenic cooling technology. We focus on TmVO$_4$, an archetypal material that undergoes a continuous phase transition to a ferroquadrupole-ordered state at 2.15 K. Above the phase transition, each Tm ion contributes an entropy of $k_B \ln{2}$ due to the degeneracy of the crystal electric field groundstate. Owing to the large magnetoelastic coupling, which is a prerequisite for a material to undergo a phase transition via the cooperative Jahn-Teller effect, this level splitting, and hence the entropy, can be readily tuned by externally-induced strain. Using a dynamic technique in which the strain is rapidly oscillated, we measure the adiabatic elastocaloric coefficient of single-crystal TmVO$_4$, and thus experimentally obtain the entropy landscape as a function of strain and temperature. The measurement confirms the suitability of this class of materials for cryogenic cooling applications, and provides insight to the dynamic quadrupole strain susceptibility.

Hydrogen loss to space is a key control on the evolution of the Martian atmosphere and the desiccation of the red planet. Thermal escape is thought to be the dominant loss process, but both forward modeling studies and remote sensing observations have indicated the presence of a second, higher-temperature "nonthermal" or "hot" hydrogen component, some fraction of which also escapes. Exothermic reactions and charge/momentum exchange processes produce hydrogen atoms with energy above the escape energy, but H loss via many of these mechanisms has never been studied, and the relative importance of thermal and nonthermal escape at Mars remains uncertain. Here we estimate hydrogen escape fluxes via 47 mechanisms, using newly-developed escape probability profiles. We find that HCO$^+$ dissociative recombination is the most important of the mechanisms, accounting for 30-50% of the nonthermal escape. The reaction CO$_2^+$ + H$_2$ is also important, producing roughly as much escaping H as momentum exchange between hot O and H. Total nonthermal escape from the mechanisms considered amounts to 39% (27%) of thermal escape, for low (high) solar activity. Our escape probability profiles are applicable to any thermospheric hot H production mechanism and can be used to explore seasonal and longer-term variations, allowing for a deeper understanding of desiccation drivers over various timescales. We highlight the most important mechanisms and suggest that some may be important at Venus, where nonthermal escape dominates and much of the literature centers on charge exchange reactions, which do not result in significant escape in this study., Comment: 47 pages, 4 figures, 3 tables. Accepted manuscript. An edited version of this paper was published by AGU

The enclosed data release consists of a subset of the calibration data from the Majorana Demonstrator experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimination cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release is specifically designed to support the training and testing of Artificial Intelligence (AI) and Machine Learning (ML) algorithms upon our data. This document is structured as follows. Section I provides an overview of the dataset's content and format; Section II outlines the location of this dataset and the method for accessing it; Section III presents the NPML Machine Learning Challenge associated with this dataset; Section IV contains a disclaimer from the Majorana collaboration regarding the use of this dataset; Appendix A contains technical details of this data release. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li)., Comment: DataPlanet Access: https://dataplanet.ucsd.edu/dataset.xhtml?persistentId=perma:83.ucsddata/UQWQAV

Electronic nematicity, a state in which rotational symmetry is spontaneously broken, has become a familiar characteristic of many strongly correlated materials. One widely studied example is the discovered Ising-nematicity and its interplay with superconductivity in tetragonal iron pnictides. Since nematic directors in crystalline solids are restricted by the underlying crystal symmetry, recently identified quantum material systems with three-fold rotational (C$_3$) symmetry offer a new platform to investigate nematic order with three-state Potts character. Here, we report reversible strain control of the three-state Potts nematicity in a zigzag antiferromagnetic insulator, FePSe$_3$. Probing the nematicity via optical linear dichroism, we demonstrate either $2{\pi}/3$ or ${\pi}/2$ rotation of nematic director by uniaxial strain. The nature of the nematic phase transition can also be controlled such that it undergoes a smooth crossover transition, a Potts nematic transition, or a Ising nematic flop transition. Further elastocaloric measurements demonstrate signatures of two coupled phase transitions, indicating that the nematic phase is a vestigial order arose from the antiferromagnetism. The ability to tune the nematic order with in-situ strain further enables the extraction of nematic susceptibility, which exhibits a divergent behavior near the magnetic ordering temperature that is corroborated with both linear dichroism and elastocaloric measurements. Our work points to an active control approach to manipulate and explore nematicity in three-state Potts correlated materials., Comment: 24 pages, 5 figures, 6 additional figures. Added elastocaloric measurements and text describing them along with edits. Resubmission on January 17th

The Robotics Major at the University of Michigan was successfully launched in the 2022-23 academic year as an innovative step forward to better serve students, our communities, and our society. Building on our guiding principle of "Robotics with Respect" and our larger Robotics Pathways model, the Michigan Robotics Major was designed to define robotics as a true academic discipline with both equity and excellence as our highest priorities. Understanding that talent is equally distributed but opportunity is not, the Michigan Robotics Major has embraced an adaptable curriculum that is accessible through a diversity of student pathways and enables successful and sustained career-long participation in robotics, AI, and automation professions. The results after our planning efforts (2019-22) and first academic year (2022-23) have been highly encouraging: more than 100 students declared Robotics as their major, completion of the Robotics major by our first two graduates, soaring enrollments in our Robotics classes, thriving partnerships with Historically Black Colleges and Universities. This document provides our original curricular proposal for the Robotics Undergraduate Program at the University of Michigan, submitted to the Michigan Association of State Universities in April 2022 and approved in June 2022. The dissemination of our program design is in the spirit of continued growth for higher education towards realizing equity and excellence. The most recent version of this document is also available on Google Docs through this link: https://ocj.me/robotics_major, Comment: 49 pages, approximately 25 figures

Elastocaloric measurements of the ferroquadrupolar/nematic rare-earth intermetallic TmAg$_2$ are presented. TmAg$_2$ undergoes a cooperative Jahn-Teller-like ferroquadrupolar phase transition at 5K, in which the Tm$^{3+}$ ion's local $4f$ electronic ground state doublet spontaneously splits and develops an electric quadrupole moment which breaks the rotational symmetry of the tetragonal lattice. The elastocaloric effect, which is the temperature change in the sample induced by adiabatic strains the sample experiences, is sensitive to quadrupolar fluctuations in the paranematic phase which couple to the induced strain. We show that elastocaloric measurements of this material reveal a Curie-Weiss like nematic susceptibility with a Weiss temperature of $\approx 2.7K$, in agreement with previous elastic constant measurements. Furthermore, we establish that a magnetic field along the c-axis acts as an effective transverse field for the quadrupole moments.

Invasive pine trees pose a threat to biodiversity in a variety of Southern Hemisphere countries, but understanding of the dynamics of invasions and the factors that retard or accelerate spread is limited. Here, we consider the past models of wilding pine spread and develop a new model of pine invasion. We show that many prior models feature parameter estimates which are not biologically supported and rely on a conjecture to obtain an asymptotic spread speed of invasive pine populations, the main output of these models. In contrast to prior approaches, we use partial differential equations to model an invasion. We show that invasions are almost static for a significant period of time before rapidly accelerating to spread at a constant rate, matching observed behaviour in at least some field sites. Our work suggests that prior methods for estimating invasion speeds may not accurately predict spread and are sensitive to assumptions about the distribution of parameters. However, we present alternative estimation methods and suggest directions for further research., Comment: 36 pages, 9 figures

In this paper, we prove that two integrals from Gradshteyn and Ryzhik (2014) [1] (namely, Eqs. 3.937 1 and 3.937 2) provide incorrect results in certain conditions. We derive those conditions herein and provide the corrections required for those two formulas. We furthermore derive improved formulas for the solutions to those integrals that are less complicated, avoid the errors of the original formulas, and work under a larger range of parameter values. The improved formulas are used to verify the results of a few other related integrals from [1]; the previous results need correction in some instances, or are correct but can be extended in other instances. Lastly, we also consider the extended case of complex-valued parameters and derive the resulting formulas., Comment: 18 pages, 0 figures. Published in SCIENTIA Series A: Mathematical Sciences. Changes since v2: (1) Two new sub-equations (6.19a) and (6.19b) added. (2) Formatting changed for appearance in SCIENTIA. (3) Version number & date in ref. [4] updated. Changes v1 -> v2: (1) Submission venue changed (Elsevier J. Comput. Appl. Math. -> SCIENTIA). (2) Added MSC codes. (3) Moved footnotes into main body

This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region. Our approach utilizes visual features obtained from a single-step featurization method applied to freely available 10m/px Sentinel-2 surface reflectance satellite imagery. These visual features are combined with ten survey questions in a proxy means test (PMT) to estimate whether a household is below the poverty line. We show that the inclusion of visual features reduces the mean error in poverty rate estimates from 4.09% to 3.88% over a nationally representative out-of-sample test set. In addition to including satellite imagery features in proxy means tests, we propose an approach for selecting a subset of survey questions that are complementary to the visual features extracted from satellite imagery. Specifically, we design a survey variable selection approach guided by the full survey and image features and use the approach to determine the most relevant set of small survey questions to include in a PMT. We validate the choice of small survey questions in a downstream task of predicting the poverty rate using the small set of questions. This approach results in the best performance -- errors in poverty rate decrease from 4.09% to 3.71%. We show that extracted visual features encode geographic and urbanization differences between regions., Comment: In 2023 ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS 23) Short Papers Track

Nitrogen-doping and furnace-baking are advanced high-Q0 recipes developed for 1.3 GHz TESLA-type cavities. These treatments will significantly benefit the high-Q0 linear accelerator community if they can be successfully adapted to different cavity styles and frequencies. Strong frequency- and geometry- dependence of these recipes makes the technology transfer amongst different cavity styles and frequencies far from straightforward, and requires rigorous study. Upcoming high-Q0 continuous-wave linear accelerator projects, such as the proposed Michigan State University Facility for Rare Isotope Beam Energy Upgrade, and the underway Fermilab's Proton Improvement Plan-II, could benefit enormously from adapting these techniques to their beta_opt = 0.6 ~650 MHz 5-cell elliptical superconducting rf cavities, operating at an accelerating gradient of around ~17 MV/m. This is the first investigation of the adaptation of nitrogen doping and medium temperature furnace baking to prototype 644 MHz beta_opt = 0.65 cavities, with the aim of demonstrating the high-Q0 potential of these recipes in these novel cavities for future optimization as part of the FRIB400 project R&D. We find that nitrogen-doping delivers superior Q0, despite the sub-GHz operating frequency of these cavities, but is sensitive to the post-doping electropolishing removal step and experiences elevated residual resistance. Medium temperature furnace baking delivers reasonable performance with decreased residual resistance compared to the nitrogen doped cavity, but may require further recipe refinement. The gradient requirement for the FRIB400 upgrade project is comfortably achieved by both recipes., Comment: 16 pages, 5 figures

The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented impact in the popular media and public consciousness, giving rise to a mixture of excitement and fear as to their capabilities and potential uses, and shining a light on natural language processing research which had not previously received so much attention. The developments offer great promise for education technology, and in this paper we look specifically at the potential for incorporating large language models in AI-driven language teaching and assessment systems. We consider several research areas and also discuss the risks and ethical considerations surrounding generative AI in education technology for language learners. Overall we find that larger language models offer improvements over previous models in text generation, opening up routes toward content generation which had not previously been plausible. For text generation they must be prompted carefully and their outputs may need to be reshaped before they are ready for use. For automated grading and grammatical error correction, tasks whose progress is checked on well-known benchmarks, early investigations indicate that large language models on their own do not improve on state-of-the-art results according to standard evaluation metrics. For grading it appears that linguistic features established in the literature should still be used for best performance, and for error correction it may be that the models can offer alternative feedback styles which are not measured sensitively with existing methods. In all cases, there is work to be done to experiment with the inclusion of large language models in education technology for language learners, in order to properly understand and report on their capacities and limitations, and to ensure that foreseeable risks such as misinformation and harmful bias are mitigated., Comment: Accepted at the AIED2023 workshop: Empowering Education with LLMs - the Next-Gen Interface and Content Generation

This paper presents a first-principles study of the Debye-Waller factor and the Debye temperature for amorphous silicon ($a$-Si) from lattice-dynamical calculations and direct molecular-dynamics simulations using density-functional theory (DFT). The effects of temperature and structural disorder on the intensity of the diffraction maxima and the vibrational mean-square displacement (MSD) of Si atoms are studied in the harmonic approximation, with particular emphasis on the bond-length disorder, the presence of coordination defects, and microvoids in $a$-Si networks. It has been observed that the MSDs associated with tetrahedrally-bonded Si atoms are considerably lower than their dangling-bond counterparts -- originating from isolated and vacancy-induced clustered defects -- and those on the surface of microvoids, leading to an asymmetric non-gaussian tail in the distribution of atomic displacements. An examination of the effect of anharmonicity on the MSD at high temperatures using direct $ab$ $initio$ molecular-dynamics simulations (without the harmonic approximation) suggests that the vibrational motion in $a$-Si is practically unaffected by anharmonic effects at temperatures below 400 K, as far as the present DFT calculations are concerned. The Debye temperature of $a$-Si is found to be in the range of 488--541 K from specific-heat and MSD calculations using first-principles lattice-dynamical calculations in the harmonic approximation, which matches closely with the experimental value of 487--528 K obtained from specific-heat measurements of $a$-Si at low temperatures., Comment: 14 pages, 17 figures

Description of the data release 10.13139/OLCF/1969085 (https://doi.ccs.ornl.gov/ui/doi/426) from the measurements of the CsI[Na] response to low energy nuclear recoils by the COHERENT collaboration. The release corresponds to the results published in "D. Akimov et al 2022 JINST 17 P10034". We share the data in the form of raw ADC waveforms, provide benchmark values, and share plots to enhance the transparency and reproducibility of our results. This document describes the contents of the data release as well as guidance on the use of the data.

The combination of a powerful and broadly applicable nuclear hyperpolarization technique with emerging (near-)zero-field modalities offer novel opportunities in a broad range of nuclear magnetic resonance spectroscopy and imaging applications, including biomedical diagnostics, monitoring catalytic reactions within metal reactors and many others. These are discussed along with a roadmap for future developments., Comment: 12 pages, 5 figures

This paper describes the design of a robust controller for position control in systems with sandwiched backlash. The backlash, which is nonsmooth and nonlinear, is inevitable in the operation of many systems, but it can have destructive effects on the stability and performance of feedback systems. In this work, a robust controller is designed using a modified linear internal model control framework. Different controller architectures are considered and compared based on an experimental case study. The experimental testbased is composed of a three-platform structure driven by a stepper motor. The backlash is introduced into the system in a non-destructive and controllable manner by closing an internal nonlinear feedback loop around the stepper motor. The robustness of the designed controller to a large amount of backlash is verified experimentally, and while the stability is maintained, some residual vibrations are observed. The effects on the residual vibration levels of including nonlinear elements in the controller and changing the controller's settling time are also examined experimentally. The robustness to changes and mismodelling of the linear system, with and without the backlash, is described, as is the tracking of a smooth sinusoidal command signal with a growing amount of backlash. Based on the case study, it is concluded that combining the linear internal model control design method with a small dead zone results in a highly robust controller both with respect to the backlash and to changes in the linear system, which ensure stability and good performance. The required robustness is achieved by tuning the controller's settling time and the dead zone width parameters., Comment: Preprint

Over recent years, denoising diffusion generative models have come to be considered as state-of-the-art methods for synthetic data generation, especially in the case of generating images. These approaches have also proved successful in other applications such as tabular and graph data generation. However, due to computational complexity, to this date, the application of these techniques to graph data has been restricted to small graphs, such as those used in molecular modeling. In this paper, we propose SaGess, a discrete denoising diffusion approach, which is able to generate large real-world networks by augmenting a diffusion model (DiGress) with a generalized divide-and-conquer framework. The algorithm is capable of generating larger graphs by sampling a covering of subgraphs of the initial graph in order to train DiGress. SaGess then constructs a synthetic graph using the subgraphs that have been generated by DiGress. We evaluate the quality of the synthetic data sets against several competitor methods by comparing graph statistics between the original and synthetic samples, as well as evaluating the utility of the synthetic data set produced by using it to train a task-driven model, namely link prediction. In our experiments, SaGess, outperforms most of the one-shot state-of-the-art graph generating methods by a significant factor, both on the graph metrics and on the link prediction task.

Differential privacy (DP) is the prevailing technique for protecting user data in machine learning models. However, deficits to this framework include a lack of clarity for selecting the privacy budget $\epsilon$ and a lack of quantification for the privacy leakage for a particular data row by a particular trained model. We make progress toward these limitations and a new perspective by which to visualize DP results by studying a privacy metric that quantifies the extent to which a model trained on a dataset using a DP mechanism is ``covered" by each of the distributions resulting from training on neighboring datasets. We connect this coverage metric to what has been established in the literature and use it to rank the privacy of individual samples from the training set in what we call a privacy profile. We additionally show that the privacy profile can be used to probe an observed transition to indistinguishability that takes place in the neighboring distributions as $\epsilon$ decreases, which we suggest is a tool that can enable the selection of $\epsilon$ by the ML practitioner wishing to make use of DP., Comment: Approved for Public Release; Distribution Unlimited. PA #:AFRL-2022-3639

The capability to reach ultracold atomic temperatures in compact instruments has recently been extended into space. Ultracold temperatures amplify quantum effects, while free-fall allows further cooling and longer interactions time with gravity - the final force without a quantum description. On Earth, these devices have produced macroscopic quantum phenomena such as Bose-Einstein condensation (BECs), superfluidity, and strongly interacting quantum gases. Quantum sensors interfering the superposition of two ultracold atomic isotopes have tested the Universality of Free Fall (UFF), a core tenet of Einstein's classical gravitational theory, at the $10^{-12}$ level. In space, cooling the elements needed to explore the rich physics of strong interactions and preparing the multiple species required for quantum tests of the UFF has remained elusive. Here, utilizing upgraded capabilities of the multi-user Cold Atom Lab (CAL) instrument within the International Space Station (ISS), we report the first simultaneous production of a dual species Bose-Einstein condensate in space (formed from $^{87}$Rb and $^{41}$K), observation of interspecies interactions, as well as the production of $^{39}$K ultracold gases. We have further achieved the first space-borne demonstration of simultaneous atom interferometry with two atomic species ($^{87}$Rb and $^{41}$K). These results are an important step towards quantum tests of UFF in space, and will allow scientists to investigate aspects of few-body physics, quantum chemistry, and fundamental physics in novel regimes without the perturbing asymmetry of gravity.

Field-induced superconductivity is a rare phenomenon where an applied magnetic field enhances or induces superconductivity. This fascinating effect arises from a complex interplay between magnetism and superconductivity, and it offers the tantalizing technological possibility of an infinite magnetoresistance superconducting spin valve. Here, we demonstrate field-induced superconductivity at a record-high temperature of T=9K in two samples of the ferromagnetic superconductor Eu(Fe$_{0.88}$Co$_{0.12}$)$_{2}$As$_{2}$. We combine tunable uniaxial stress and applied magnetic field to shift the temperature range of the zero-resistance state between 4K and 10K. We use x-ray diffraction and spectroscopy measurements under stress and field to demonstrate that stress tuning of the nematic order and field tuning of the ferromagnetism act as independent tuning knobs of the superconductivity. Finally, DFT calculations and analysis of the Eu dipole field reveal the electromagnetic mechanism of the field-induced superconductivity., Comment: Main text: 15 pages, 5 figures; Supplement: 15 pages, 10 supplementary figures

Data-driven (DD) interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational materials science and chemistry for performing atomic-scale simulations. Despite this, apart from a few notable exceptions, there is a distinct lack of well-organized, public datasets in common formats available for use with IP development. This deficiency precludes the research community from implementing widespread benchmarking, which is essential for gaining insight into model performance and transferability, and also limits the development of more general, or even universal, IPs. To address this issue, we introduce the ColabFit Exchange, the first database providing open access to a large collection of systematically organized datasets from multiple domains that is especially designed for IP development. The ColabFit Exchange is publicly available at \url{https://colabfit.org/}, providing a web-based interface for exploring, downloading, and contributing datasets. Composed of data collected from the literature or provided by community researchers, the ColabFit Exchange currently (September 2023) consists of 139 datasets spanning nearly 70,000 unique chemistries, and is intended to continuously grow. In addition to outlining the software framework used for constructing and accessing the ColabFit Exchange, we also provide analyses of the data, quantifying the diversity of the database and proposing metrics for assessing the relative diversity of multiple datasets. Finally, we demonstrate an end-to-end IP development pipeline, utilizing datasets from the ColabFit Exchange, fitting tools from the KLIFF software package, and validation tests provided by the OpenKIM framework.

Over the last few years, significant attention has been devoted to studying the kagome materials AV$_3$Sb$_5$ (A = K, Rb, Cs) due to their unconventional superconductivity and charge density wave (CDW) ordering. Recently ScV$_6$Sn$_6$ was found to host a CDW below $\approx$90K, and, like AV$_3$Sb$_5$, it contains a kagome lattice comprised only of V ions. Here we present a comprehensive magnetotransport study on ScV$_6$Sn$_6$. We discovered several anomalous transport phenomena above the CDW ordering temperature, including insulating behavior in interlayer resistivity, a strongly temperature-dependent Hall coefficient, and violation of Kohler's rule. All these anomalies can be consistently explained by a progressive decrease in carrier densities with decreasing temperature, suggesting the formation of a pseudogap. Our findings suggest that high-temperature CDW fluctuations play a significant role in determining the normal state electronic properties of ScV$_6$Sn$_6$.

From correlations in measurement outcomes alone, can two otherwise isolated parties establish whether such correlations are atemporal? That is, can they rule out that they have been given the same system at two different times? Classical statistics says no, yet quantum theory disagrees. Here, we introduce the necessary and sufficient conditions by which such quantum correlations can be identified as atemporal. We demonstrate the asymmetry of atemporality under time reversal, and reveal it to be a measure of spatial quantum correlation distinct from entanglement. Our results indicate that certain quantum correlations possess an intrinsic arrow of time, and enable classification of general quantum correlations across space-time based on their (in)compatibility with various underlying causal structures., Comment: 29 pages

The MAJORANA DEMONSTRATOR was a search for neutrinoless double-beta decay ($0\nu\beta\beta$) in the $^{76}$Ge isotope. It was staged at the 4850-foot level of the Sanford Underground Research Facility (SURF) in Lead, SD. The experiment consisted of 58 germanium detectors housed in a low background shield and was calibrated once per week by deploying a $^{228}$Th line source for 1 to 2 hours. The energy scale calibration determination for the detector array was automated using custom analysis tools. We describe the offline procedure for calibration of the Demonstrator germanium detectors, including the simultaneous fitting of multiple spectral peaks, estimation of energy scale uncertainties, and the automation of the calibration procedure.

The Dark Energy Spectroscopic Instrument (DESI) completed its five-month Survey Validation in May 2021. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra., Comment: 43 pages, 7 figures, 17 tables, accepted for publication in the Astronomical Journal

The Dark Energy Spectroscopic Instrument (DESI) was designed to conduct a survey covering 14,000 deg$^2$ over five years to constrain the cosmic expansion history through precise measurements of Baryon Acoustic Oscillations (BAO). The scientific program for DESI was evaluated during a five month Survey Validation (SV) campaign before beginning full operations. This program produced deep spectra of tens of thousands of objects from each of the stellar (MWS), bright galaxy (BGS), luminous red galaxy (LRG), emission line galaxy (ELG), and quasar target classes. These SV spectra were used to optimize redshift distributions, characterize exposure times, determine calibration procedures, and assess observational overheads for the five-year program. In this paper, we present the final target selection algorithms, redshift distributions, and projected cosmology constraints resulting from those studies. We also present a `One-Percent survey' conducted at the conclusion of Survey Validation covering 140 deg$^2$ using the final target selection algorithms with exposures of a depth typical of the main survey. The Survey Validation indicates that DESI will be able to complete the full 14,000 deg$^2$ program with spectroscopically-confirmed targets from the MWS, BGS, LRG, ELG, and quasar programs with total sample sizes of 7.2, 13.8, 7.46, 15.7, and 2.87 million, respectively. These samples will allow exploration of the Milky Way halo, clustering on all scales, and BAO measurements with a statistical precision of 0.28% over the redshift interval $z<1.1$, 0.39% over the redshift interval $1.1

We describe a first measurement of the radiation from a $^{\bf 178m}$Hf sample to search for dark matter. The $\gamma$ flux from this sample, possessed by Los Alamos National Laboratory nuclear chemistry, was measured with a Ge detector at a distance of 4 ft due to its high activity. We search for $\gamma$s that cannot arise from the radioactive decay of $^{\bf 178m}$Hf, but might arise from the production of a nuclear state due to the inelastic scattering with dark matter. The limits obtained on this $\gamma$ flux are then translated into constraints on the parameter space of inelastic dark matter. Finally, we describe the potential reach of future studies with $^{\bf 178m}$Hf.

Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches demand an accurately-labeled, real-world, and diverse dataset for high accuracy and generalizability. As a starting point to address this need, we introduce `VR.net', a dataset offering approximately 12-hour gameplay videos from ten real-world games in 10 diverse genres. For each video frame, a rich set of motion sickness-related labels, such as camera/object movement, depth field, and motion flow, are accurately assigned. Building such a dataset is challenging since manual labeling would require an infeasible amount of time. Instead, we utilize a tool to automatically and precisely extract ground truth data from 3D engines' rendering pipelines without accessing VR games' source code. We illustrate the utility of VR.net through several applications, such as risk factor detection and sickness level prediction. We continuously expand VR.net and envision its next version offering 10X more data than the current form. We believe that the scale, accuracy, and diversity of VR.net can offer unparalleled opportunities for VR motion sickness research and beyond.