Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon Abundances in Compact Galactic Planetary Nebulae: An Ultraviolet Spectroscopic Study with the Space Telescope Imaging Spectrograph (STIS)

We surveyed a sample of compact Galactic planetary nebulae (PNe) with the Space Telescope Imaging Spectrograph on the Hubble Space Telescope (HST) to determine their gas-phase carbon abundances. Carbon abundances in PNe constrain the nature of their asymptotic giant branch (AGB) progenitors, as well as cosmic recycling. We measured the carbon abundances, or the limits thereof, of 11 compact Galactic PNe, notably increasing the sample of Galactic PNe whose carbon abundance based on HST ultraviolet spectra is available. The dust content of most targets has been studied elsewhere from Spitzer spectroscopy; given the compact nature of the nebulae, both UV and IR spectra can be directly compared to study gas- and dust-phase carbon. We found that carbon-poor (C/O < 1) compact Galactic PNe have an oxygen-rich dust type, while their carbon-enhanced counterparts (C/O > 1) have carbon-rich dust, confirming the correlation between gas- and dust-phase carbon content that was known for Magellanic Cloud PNe. Based on models of expected final yields from AGB evolution, we interpret the majority of the carbon-poor PNe in this study as the progeny of ∼1.1–1.2 M ⊙ stars that experienced some extra mixing on the red giant branch. They went through the AGB but did not go through the carbon star phase. Most PNe in this group have a bipolar morphology, possibly due to the presence of a subsolar companion. The carbon-enhanced PNe in our sample could be the progeny of stars in the ∼1.5–2.5 M ⊙ range, depending on their original metallicity.
A trace inequality of Ando, Hiai, and Okubo and a monotonicity property of the Golden–Thompson inequality

The Golden–Thompson trace inequality, which states that Tr e H+ K ≤ Tr e H e K, has proved to be very useful in quantum statistical mechanics. Golden used it to show that the classical free energy is less than the quantum one. Here, we make this G–T inequality more explicit by proving that for some operators, notably the operators of interest in quantum mechanics, H = Δ or [Formula: see text] and K = potential, Tr e H+(1− u) K e uK is a monotone increasing function of the parameter u for 0 ≤ u ≤ 1. Our proof utilizes an inequality of Ando, Hiai, and Okubo (AHO): Tr X s Y t X1− s Y1− t ≤ Tr XY for positive operators X, Y and for [Formula: see text], and [Formula: see text]. The obvious conjecture that this inequality should hold up to s + t ≤ 1 was proved false by Plevnik [Indian J. Pure Appl. Math. 47, 491–500 (2016)]. We give a different proof of AHO and also give more counterexamples in the [Formula: see text] range. More importantly, we show that the inequality conjectured in AHO does indeed hold in the full range if X, Y have a certain positivity property—one that does hold for quantum mechanical operators, thus enabling us to prove our G–T monotonicity theorem.
Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification

In training machine learning models for land cover semantic segmentation there is a stark contrast between the availability of satellite imagery to be used as inputs and ground truth data to enable supervised learning. While thousands of new satellite images become freely available on a daily basis, getting ground truth data is still very challenging, time consuming and costly. In this paper we present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery to improve performance on downstream dense land cover classification tasks. Performing an extensive experimental evaluation spanning four countries and two continents we use models pre-trained with our proposed method as initialization points for supervised land cover semantic segmentation and observe significant improvements up to 25% absolute mIoU. In every case tested we outperform random initialization, especially so when ground truth data are scarse. Through a series of ablation studies we explore the qualities of the proposed approach and find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme as a replacement to random initialization for Earth observation tasks. Code will be uploaded soon at https://github.com/michaeltrs/DeepSatModels.
Single quantum emitters with spin ground states based on Cl bound excitons in ZnSe

Defects in wide-bandgap semiconductors are promising qubit candidates for quantum communication and computation. Epitaxially grown II-VI semiconductors are particularly promising host materials due to their direct bandgap and potential for isotopic purification to a spin-zero nuclear background. Here, we show a new type of single photon emitter with potential electron spin qubit based on Cl impurities in ZnSe. We utilize a quantum well to increase the binding energies of donor emission and confirm single photon emission with short radiative lifetimes of 192 ps. Furthermore, we verify that the ground state of the Cl donor complex contains a single electron by observing two-electron satellite emission, leaving the electron in higher orbital states. We also characterize the Zeeman splitting of the exciton transition by performing polarization-resolved magnetic spectroscopy on single emitters. Our results suggest single Cl impurities are suitable as single photon source with potential photonic interface.
Incident Velocity-Recoil Angle Distribution and Angular Recoil-Energy Spectrum of 3-Dimensional WIMP-Nucleus Scattering Events

In this paper, as a supplementary of our study on the angular distribution of the recoil flux of WIMP–scattered target nuclei and on that of the WIMP effective scattering velocity distribution, we investigate the scattering probability distribution of the WIMP incident velocity versus the nuclear recoil angle in narrow recoil energy windows for different WIMP masses and target nuclei. Our simulation results show that, not only the velocity distribution of incident halo WIMPs, but also a factor of the recoil angle could affect the scattering probability distribution of the available incident velocity–recoil angle combination in a given recoil energy window. Consequently, the 1-D WIMP “effective” velocity distribution corresponding to the considered narrow energy window would not be consistent with that cut simply from the (generating) velocity distribution of incident halo WIMPs. And its contribution to the differential WIMP–nucleus scattering event rate in the considered energy window could thus not be simply estimated by integrating over the 1-D theoretical velocity distribution (of entire halo WIMPs). ar X iv :2 20 3. 05 80 5v 1 [ he pph ] 1 1 M ar 2 02 2
The loss landscape of deep linear neural networks: a second-order analysis

We study the optimization landscape of deep linear neural networks with the square loss. It is known that, under weak assumptions, there are no spurious local minima and no local maxima. However, the existence and diversity of non-strict saddle points, which can play a role in first-order algorithms’ dynamics, have only been lightly studied. We go a step further with a full analysis of the optimization landscape at order 2. We characterize, among all critical points, which are global minimizers, strict saddle points, and non-strict saddle points. We enumerate all the associated critical values. The characterization is simple, involves conditions on the ranks of partial matrix products, and sheds some light on global convergence or implicit regularization that have been proved or observed when optimizing linear neural networks. In passing, we provide an explicit parameterization of the set of all global minimizers and exhibit large sets of strict and non-strict saddle points. Keywords— Deep learning, landscape analysis, non-convex optimization, second-order geometry, strict saddle points, non-strict saddle points, global minimizers, implicit regularization
Systematics in the Spectral Energy Distribution Fitting Parameter Estimation of Composite Galaxies

Derivation of physical properties of galaxies using spectral energy distribution (SED) fitting is a powerful method, but can suffer from various systematics arising from model assumptions. Previously, such biases were mostly studied in the context of individual galaxies. In this study, we investigate potential biases arising from performing the SED fitting on the combined light of two galaxies, as would be the case in postmerger systems. We use the GALEX-SDSS-WISE Legacy Catalog of z < 0.3 galaxies to identify 9000 galaxy pairs that could eventually merge. For these we investigate if the UV/optical SED fitting accurately determines the stellar mass and (specific) star formation rate (sSFRs) if the pair was unresolved (merged). The sum of the stellar masses (and star formation rates (SFRs)) of individual galaxies in the pair establishes the ground truth for these quantities. For star-forming galaxies no biases (<0.1 dex) are found in the stellar mass, SFR, or sSFRs. Moderate systematics in SFR (∼0.1 dex) are found for systems with an extreme contrast in dust content between the two galaxies. We conclude that biases that would arise in the determination of masses and SFRs of postmerger systems on account of the two original galaxies having potentially very different star formation histories and different dust properties are small and that the approach with simple two-component star formation histories is adequate. The approach presented in this study, using flux compositing with empirically determined ground truth, offers new opportunities for testing the results of SED fitting in general.
Uniformly Bounded State Estimation Over Multiple Access Channels

This article addresses the problem of distributed state estimation via <italic>multiple access channels</italic> (MACs). We consider a scenario where two encoders are simultaneously communicating their measurements through a noisy channel. First, the zero-error capacity region of the general <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula>-input, single-output MAC is characterized using tools from nonstochastic information theory. Next, we show that a tight condition to be able to achieve uniformly bounded state estimation errors can be given in terms of the channel zero-error capacity region. This criterion relates the channel properties to the plant dynamics. These results pave the way toward understanding information flows in networked control systems with multiple transmitters.
Duke Energy Photovoltaic Integration Study: Carolinas Service Areas

Solar energy collected using photovoltaic (PV) technology is a clean and renewable energy source that offers multiple benefits to the electric utility industry and its customers, such as cost predictability, reduced emissions, and loss reduction by distributed installations. Renewable energy goals established in North Carolina Senate Bill 3 (SB3), in combination with the state tax credit and decreases in the cost of energy from PV panels, have resulted in rapid solar power penetration within the Carolinas services areas of Duke Energy. Continued decreases in PV prices are expected to lead to greater PV penetration rates than currently required in SB3. Despite the potential benefits, significant penetration of PV energy is of concern to the utility industry because of its impact on operating reliability and integration cost to customers, and equally important, how any additional costs may be allocated to different customer groups. Some of these impacts might become limiting factors for PV energy, especially growing distributed generation installed at customer sites. Recognizing the importance of renewable energy developments for a sustainable energy future and economic growth, Duke Energy has commissioned this study to simulate the effects of high-PV penetration rates and to initiate the process of quantifying the impacts. Themore » objective of the study is to inform resource plans, guide operation improvements, and drive infrastructure investments for a steady and smooth transition to a new energy mix that provides optimal values to customers. The study team consists of experts from Pacific Northwest National Laboratory (PNNL), Power Costs, Inc. (PCI), Clean Power Research (CPR), Alstom Grid, and Duke Energy. PNNL, PCI, and CPR performed the study on generation impacts; Duke Energy modeled the transmission cases; and distribution simulations were conducted by Alstom Grid. PNNL analyzed the results from each work stream and produced the report. « less
Response to COVID-19 booster vaccinations in seronegative people with MS.

Importance: Uncertainties remain about the benefit of a 3rd COVID-19 vaccine for people with attenuated response to earlier vaccines. This is of particular relevance for people with multiple sclerosis (pwMS) treated with anti-CD20 therapies and fingolimod, who have substantially reduced antibody responses to initial vaccine course. Objective: To report humoral and T-cell responses following COVID-19 vaccine 3 in pwMS who were seronegative after COVID-19 vaccines 1&2. Design, setting and participants: PwMS taking part in a seroprevalence study without a detectable IgG response following COVID-19 vaccines 1&2 were invited to participate. Participants provided a dried blood spot +/- venous blood sample 2-12 weeks following COVID-19 vaccine 3. Data on demographics, MS treatment, and COVID-19 infection/vaccine dates were derived from the medical notes. Methods: Humoral and T cell responses to SARS-CoV-2 spike protein and nucleocapsid antigen were measured. The relationship between evidence of prior COVID-19 infection and immune response to COVID-19 vaccine 3 was evaluated using Fishers exact test. Results: Of 81 participants, 79 provided a dried blood spot sample, of whom 38 also provided a whole blood sample; 2 provided only whole blood. Anti-SARS-CoV-2-spike IgG seroconversion post-COVID-19 vaccine 3 occurred in 26/79 (33%) participants; 26/40 (65%) had positive T-cell responses. Overall, 31/40 (78%) demonstrated either humoral or cellular immune response post-COVID-19 vaccine 3. There no association between laboratory evidence of prior COVID-19 infection and anti-spike seroconversion following COVID-19 vaccine 3. Conclusions: Approximately one third of pwMS who were seronegative after initial COVID-19 vaccination seroconverted after booster (third) vaccination, supporting the use of boosters in this group. Almost 8 out of 10 had a measurable immune response following 3rd COVID-19 vaccine.
Incidence and Prevalence of Coronavirus Disease 2019 Within a Healthcare Worker Cohort During the First Year of the Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic

Abstract Background Preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2_ infections in healthcare workers (HCWs) is critical for healthcare delivery. We aimed to estimate and characterize the prevalence and incidence of coronavirus disease 2019 (COVID-19) in a US HCW cohort and to identify risk factors associated with infection. Methods We conducted a longitudinal cohort study of HCWs at 3 Bay Area medical centers using serial surveys and SARS-CoV-2 viral and orthogonal serological testing, including measurement of neutralizing antibodies. We estimated baseline prevalence and cumulative incidence of COVID-19. We performed multivariable Cox proportional hazards models to estimate associations of baseline factors with incident infections and evaluated the impact of time-varying exposures on time to COVID-19 using marginal structural models. Results A total of 2435 HCWs contributed 768 person-years of follow-up time. We identified 21 of 2435 individuals with prevalent infection, resulting in a baseline prevalence of 0.86% (95% confidence interval [CI], .53%–1.32%). We identified 70 of 2414 incident infections (2.9%), yielding a cumulative incidence rate of 9.11 cases per 100 person-years (95% CI, 7.11–11.52). Community contact with a known COVID-19 case was most strongly correlated with increased hazard for infection (hazard ratio, 8.1 [95% CI, 3.8–17.5]). High-risk work-related exposures (ie, breach in protective measures) drove an association between work exposure and infection (hazard ratio, 2.5 [95% CI, 1.3–4.8). More cases were identified in HCWs when community case rates were high. Conclusions We observed modest COVID-19 incidence despite consistent exposure at work. Community contact was strongly associated with infections, but contact at work was not unless accompanied by high-risk exposure.
A Metagenomic Approach for Characterizing Antibiotic Resistance Genes in Specific Bacterial Populations: Demonstration with Escherichia coli in Cattle Manure

Bacterial antibiotic resistance genes in the environment are ubiquitous and can pose different levels of human health risks due to their bacterial host association and subsequent mobility. This study introduced a population metagenomic approach to study ARGs and their mobility in specific bacterial populations through a combination of selective cultivation followed by next generation sequencing and bioinformatic analysis of the combined metagenome of isolates. ABSTRACT The high diversity of bacterial antibiotic resistance genes (ARGs) and the different health risks due to their association with different bacterial hosts require environmental ARG risk assessment to have capabilities of both high throughput and host differentiation. Current whole genome sequencing of cultivated isolates is low in throughput, while direct metagenomic next generation sequencing (mNGS) of environmental samples is nonselective with respect to bacterial hosts. This study introduced a population metagenomic approach that combines isolate library construction and mNGS of the population metagenomic DNA, which enables studying ARGs and their association with mobile genetic elements (MGEs) in a specific bacterial population. The population metagenomic approach was demonstrated with the E. coli population in cattle manure, which detected the co-location of multiple ARGs on the same MGEs and their correspondence to the prevalence of resistance phenotypes of the E. coli isolates. When compared with direct mNGS of the cattle manure samples, the E. coli population metagenomes exhibited a significantly different resistome and an overall higher relative abundance of ARGs and horizontal gene transfer risks. IMPORTANCE Bacterial antibiotic resistance genes in the environment are ubiquitous and can pose different levels of human health risks due to their bacterial host association and subsequent mobility. This study introduced a population metagenomic approach to study ARGs and their mobility in specific bacterial populations through a combination of selective cultivation followed by next generation sequencing and bioinformatic analysis of the combined metagenome of isolates. The utility of this approach was demonstrated with the E. coli population in cattle manure samples, which showed that ARGs detected in the E. coli population corresponded to the observed resistance phenotypes, co-location of multiple ARGs on the same mobile genetic elements.
Diagnostic accuracy of complete blood cell count and neutrophil-to-lymphocyte, lymphocyte-to-monocyte, and platelet-to-lymphocyte ratios for neonatal infection

Abstract Background Complete blood cell (CBC) counts and neutrophil-to-lymphocyte (NLR), lymphocyte-to-monocyte (LMR), and platelet-to-lymphocyte ratios (PLR) are simple measurements that are conducted as part of routine diagnostic procedures. Objective To determine the diagnostic importance, specificity, and sensitivity of these measurements for the diagnosis of neonatal infections and in discriminating between neonatal sepsis and various other infections. Methods We conducted a retrospective study of data from a consecutive series of 232 neonatal patients admitted to Yildirim Beyazit University Yenimahalle Training and Research Hospital in Ankara for 2 years from 2016 to 2018. We included patients with a diagnosis of or clinically suspected infection, and healthy neonates were included as controls. Data included CBC counts, and bacterial culture results, considered the criterion standard for the diagnosis of neonatal sepsis. NLR, LMR, and PLR were calculated. We compared data using independent Student t and Mann–Whitney U tests and determined the sensitivity, specificity, and likelihood ratio (LHOR) of the characteristics for neonatal sepsis using receiver operating characteristic curve analyses. Results We included data from 155 neonatal patients with a diagnosis or suspicion of infection and 77 healthy neonates. NLR was significantly higher in neonates with sepsis or fever due to dehydration (P < 0.001) than in neonates with other infections or healthy neonates. LMR was significantly higher in neonates with sepsis or viral infection than in those with other infections or healthy controls (P = 0.003). In neonates with early-onset sepsis (EOS), we found cut-off values of ≥4.79 [area under curve (AUC) 0.845, 95% confidence interval (CI) 0.76–0.93, LHOR 11.6, specificity 98.7%, sensitivity 15%] for NLR, ≥1.24 (AUC 0.295; CI 0.18–0.41, LHOR 1.02, specificity 2.6%, sensitivity 100%) for LMR, and ≥37.72 (AUC 0.268; CI 0.15–0.39, LHOR 0.86, specificity 7.8%, sensitivity 80%) for PLR. We found cut-off values of ≥4.94 (AUC 0.667; CI 0.56–0.77, LHOR 4.16, specificity 98.7%, sensitivity 5.4%) for NLR and ≥10.92 (AUC 0.384; CI 0.26–0.51, LHOR 6.24, specificity 98.7%, sensitivity 8.1%) for LMR in those with late-onset sepsis (LOS). Conclusions CBCs, NLR, LMR, and PLR may be useful for the differential diagnosis of EOS and LOS, and neonates with sepsis from those with other infection. NLR may be a useful diagnostic test to identify neonatal patients with septicemia more quickly than other commonly used diagnostic tests such as blood cultures. NLR has high specificity and LHOR, but low sensitivity.
Public AI imaginaries: How the debate on artificial intelligence was covered in Danish newspapers and magazines 1956–2021

Abstract This article investigates the media's construction of public perceptions of future human–machine relationships related to artificial intelligence (AI) development and reflects on how such perceptions play a role in shaping strategies for the use of AI in Denmark. Through a critical discourse analysis of 253 newspaper and magazine articles published from 1956 to 2021, it shows how conflicting discursive positions are constructed, representing what I refer to as public AI imaginaries. The analysis shows that newspapers and magazines tend not to distinguish between futuristic descriptions of the human–machine relationship of AI and the human-centred principles of intelligence amplification (IA). Furthermore, it demonstrates how principles of IA are reflected in the Danish strategies for AI in practice. While the discursive ambiguity has fuelled public debate, it leaves the term AI relatively vague, thereby creating uncertainty rather than possibilities for a form of human-centered AI in empirical reality.