Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The world is in the grip of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing the risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increasing concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced the risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D (25(OH)D) concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome; and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce the risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D3 for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40–60 ng/mL (100–150 nmol/L). For treatment of people who become infected with COVID-19, higher vitamin D3 doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.

Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths.

Our Galaxy, the Milky Way, is a benchmark for understanding disk galaxies. It is the only galaxy whose formation history can be studied using the full distribution of stars from faint dwarfs to supergiants. The oldest components provide us with unique insight into how galaxies form and evolve over billions of years. The Galaxy is a luminous (L⋆) barred spiral with a central box/peanut bulge, a dominant disk, and a diffuse stellar halo. Based on global properties, it falls in the sparsely populated “green valley” region of the galaxy color-magnitude diagram. Here we review the key integrated, structural and kinematic parameters of the Galaxy, and point to uncertainties as well as directions for future progress. Galactic studies will continue to play a fundamental role far into the future because there are measurements that can only be made in the near field and much of contemporary astrophysics depends on such observations.

The Galaxy in Context: Structural, Kinematic, and Integrated Properties

Aims. The aim of this paper is to explore and map the age and abundance structure of the stars in the nearby Galactic disk. Methods. We have conducted a high-resolution spectroscopic study of 714 F and G dwarf and subgiant stars in the Solar neighbourhood. The star sample has been kinematically selected to trace the Galactic thin and thick disks to their extremes, the metal-rich stellar halo, sub-structures in velocity space such as the Hercules stream and the Arcturus moving group, as well as stars that cannot (kinematically) be associated with either the thin disk or the thick disk. The determination of stellar parameters and elemental abundances is based on a standard analysis using equivalent widths and one-dimensional, plane-parallel model atmospheres calculated under the assumption of local thermodynamical equilibrium (LTE). The spectra have high resolution (R = 40 000-110 000) and high signal-to-noise)S/V = 150-300) and were obtained with the FEROS spectrograph on the ESO 1.5 in and 2.2 in telescopes, the SOFIN and PIES spectrographs on the Nordic Optical Telescope, the LIVES spectrograph on the E50 Very Large Telescope, the HARPS spectrograph on the ESO 3.6 m telescope, and the MIKE spectrograph on the Magellan Clay telescope. The abundances from individual Fe I lines were were corrected for non-LTE effects in every step of the analysis. Results. We present stellar parameters, stellar ages, kinematical parameters, orbital parameters, and detailed elemental abundances for 0, Na, Mg, Al, Si, Ca, Ti, Cr, Fe, Ni, Zn, Y. and Ba for 714 nearby 12 and G dwarf stars. Our data show that there is an old and a-enhanced disk population, and a younger and less a-enhanced disk population. While they overlap greatly in metallicity between 0.7 < [Fe/HI] less than or similar to +0.1, they show a bimodal distribution in [alpha/Fe]. This bimodality becomes even clearer if stars where stellar parameters and abundances show larger uncertainties (T-eff less than or similar to 5400 K) are discarded, showing that it is important to constrain the data set to a narrow range in the stellar parameters if small differences between stellar populations are to be revealed. In addition, we find that the a-enhanced population has orbital parameters placing the stellar birthplaces in the inner Galactic disk while the loss-alpha stars mainly come from the outer Galactic disk, fully consistent with the recent claims of a short scale-length for the alpha-enhanced Galactic thick disk. We have also investigated the properties of the Hercules stream and the Arcturus moving group and find that neither of them presents chemical or age signatures that could suggest that they are disrupted clusters or extragalactic accretion remnants from ancient merger events. Instead, they are most likely dynamical features originating within the Galaxy. We have also discovered that a standard 1D. LTE analysis, utilising ionisation and excitation balance of Fe I and Fen lines produces a flat lower main sequence. As the exact cause for this effect is unclear we chose to apply an empirical correction. Turn-off stars and more evolved stars appear to be unaffected. (Less)

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Exploring the Milky Way stellar disk - A detailed elemental abundance study of 714 F and G dwarf stars in the solar neighbourhood

This work aims to present our current best physical understanding of common-envelope evolution (CEE). We highlight areas of consensus and disagreement, and stress ideas which should point the way forward for progress in this important but long-standing and largely unconquered problem. Unusually for CEE-related work, we mostly try to avoid relying on results from population synthesis or observations, in order to avoid potentially being misled by previous misunderstandings. As far as possible we debate all the relevant issues starting from physics alone, all the way from the evolution of the binary system immediately before CEE begins to the processes which might occur just after the ejection of the envelope. In particular, we include extensive discussion about the energy sources and sinks operating in CEE, and hence examine the foundations of the standard energy formalism. Special attention is also given to comparing the results of hydrodynamic simulations from different groups and to discussing the potential effect of initial conditions on the differences in the outcomes. We compare current numerical techniques for the problem of CEE and also whether more appropriate tools could and should be produced (including new formulations of computational hydrodynamics, and attempts to include 3D processes within 1D codes). Finally we explore new ways to link CEE with observations. We compare previous simulations of CEE to the recent outburst from V1309 Sco, and discuss to what extent post-common-envelope binaries and nebulae can provide information, e.g. from binary eccentricities, which is not currently being fully exploited.

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Common envelope evolution: where we stand and how we can move forward

We searched for and detected stellar streams or moving groups in the solar neighbourhood, using the data provided by the 1st RAVE public data release. This analysis is based on distances to RAVE stars estimated from a color-magnitude relation that was calibrated on Hipparcos stars. Our final sample consists of 7015 stars selected to be within 500 pc of the Sun and to have distance errors better than 25%. Together with radial velocities from RAVE and proper motions from various data bases, there are estimates for all 6 phase-space coordinates of the stars in the sample. We characterize the orbits of these stars through suitable proxies for their angular momentum and eccentricity, and compare the observed distribution to the expectations from a smooth distribution. On this basis we identify at least four "phase space overdensities" of stars on very similar orbits in the Solar neighbourhood. We estimate the statistical significance of these overdensities by Monte Carlo simulations. Three of them have been identified previously: the Sirius and Hercules moving group and a stream found independently in 2006 by Arifyanto and Fuchs and Helmi et al. In addition, we have found a new stream candidate on a quite radial orbit, suggesting an origin external to the Milky Way's disk. Also, there is evidence for the Arcturus stream and the Hyades-Pleiades moving group in the sample. This analysis, using only a minute fraction of the final RAVE data set, shows the power of this experiment to probe the phase-space substructure of stars around the Sun.

Identifying stellar streams in the 1st RAVE public data release

Context. Deep photometric surveys of substellar objects in young clusters and high-redshift quasars are affected by contaminant sources at different heliocentric distances. If not correctly taken into account, the contamination may have a strong effect on the Initial Mass Function determination and on the identification of quasars. Aims. We calculate in detail the back- and foreground contamination by field dwarfs of very late spectral types (intermediate and late M, L, and T) in deep surveys and provide the data and tools for the computation. Methods. We adopt the latest models and data from the literature, which include the following: (i) a model of the Galactic thin disc by an exponential law; (ii) the length and height scales of late-type dwarfs; and (iii) the local spatial densities, absolute magnitudes, and colours of dwarfs for each spectral type. Results. We derive a simplified expression for the spatial density in the thin disc that depends on the heliocentric distance and the galactic coordinates (l, b) and integrate this into the truncated cone studied in the survey. As a practical application, we compute the numbers of L- and T-type field dwarfs in very deep (I = 21−29 mag) surveys in the direction of the young σ Orionis cluster. The increasing number of contaminants at the faintest magnitudes could inhibit the study of the opacity mass limit at M < 0.003 M� in the cluster.

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Contamination by field late-M, L, and T dwarfs in deep surveys

We study the velocity distribution of Milky Way disk stars in a kiloparsec-sized region around the Sun, based on ~ 2 million M-type stars from DR7 of SDSS, which have newly re-calibrated absolute proper motions from combining SDSS positions with the USNO-B catalogue. We estimate photometric distances to all stars, accurate to ~ 20 %, and combine them with the proper motions to derive tangential velocities for this kinematically unbiased sample of stars. Based on a statistical de-projection method we then derive the vertical profiles (to heights of Z = 800 pc above the disk plane) for the first and second moments of the three dimensional stellar velocity distribution. We find that = -7 +/- 1 km/s and = -9 +/- 1 km/s, independent of height above the mid-plane, reflecting the Sun's motion with respect to the local standard of rest. In contrast, changes distinctly from -20 +/- 2 km/s in the mid-plane to = -32 km/s at Z = 800 pc, reflecting an asymmetric drift of the stellar mean velocity that increases with height. All three components of the M-star velocity dispersion show a strong linear rise away from the mid-plane, most notably \sigma_{ZZ}, which grows from 18 km/s (Z = 0) to 40 km/s (at Z = 800 pc). We determine the orientation of the velocity ellipsoid, and find a significant vertex deviation of 20 to 25 degrees, which decreases only slightly to heights of Z = 800 pc. Away from the mid-plane, our sample exhibits a remarkably large tilt of the velocity ellipsoid towards the Galactic plane, which reaches 20 deg. at Z = 800 pc and which is not easily explained. Finally, we determine the ratio \sigma^2_{\phi\phi}/\sigma^2_{RR} near the mid-plane, which in the epicyclic approximation implies an almost perfectly flat rotation curve at the Solar radius.

The kinematics of late type stars in the solar cylinder studied with SDSS data

As more and more links between cancer and metabolism are discovered, new approaches to treat cancer using these mechanisms are considered. Dietary restriction of either calories or macronutrients has shown great potential in animal studies to both reduce the incidence and growth of cancer, and to act synergistically with other treatment strategies. These studies have also shown that dietary restriction simultaneously targets many of the molecular pathways that are targeted individually by anticancer drugs. The insulin/insulin-like growth factor-1 (IGF-1) system has thereby emerged as a key regulator of cancer growth pathways. Although lowering of insulin levels with diet or drugs such as metformin and diazoxide seems generally beneficial, some practitioners also utilize strategic elevations of insulin levels in combination with chemotherapeutic drugs. This indicates a broad spectrum of possibilities for modulating the insulin/IGF-1 system in cancer treatment. With a specific focus on dietary restriction, insulin administration and the insulin-lowering drug diazoxide, such modifications of the insulin/IGF-1 system are the topic of this review. Although preclinical data are promising, we point out that insulin regulation and the metabolic response to a certain diet often differ between mice and humans. Thus, the need for collecting more human data has to be emphasized.

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Dietary and pharmacological modification of the insulin/IGF-1 system: exploiting the full repertoire against cancer.

We have detected stellar halo streams in the solar neighborhood using data from the 7th public data release of the Sloan Digital Sky Survey (SDSS), which includes the directed stellar program SEGUE: Sloan Extension For Galactic Understanding and Exploration. In order to derive distances to each star, we used the metallicity-dependent photometric parallax relation from Ivezic et al. (2008) for which we examine and quantify the accuracy. Our final sample consists of 22,321 nearby (d < 2 kpc), metal-poor ([Fe/H] < -0.5) main-sequence stars with 6D estimates of position and space velocity. We characterize the orbits of these stars through suitable kinematic proxies for their "effective" integrals of motion, angular momentum, eccentricity, and orbital polar angle and compare the observed distribution to expectations from a smooth distribution in four [Fe/H] bins. On this basis we identify at least five significant "phase-space overdensities" of stars on very similar orbits in the solar neighborhood to which we can assign unambiguously peaked [Fe/H] distributions. Three of them have been identified previously, including the halo stream discovered by Helmi et al. (1999) at a significance level of 12.0. In addition, we find at least two new genuine halo streams, judged by their kinematics and [Fe/H], at significance levels of 2.9 and 4.8, respectively. For one stream the stars even show coherence in configuration space, matching a spatial overdensity of stars found by Juric et al. (2008) at (R,z) \approx (9.5,0.8) kpc. Our results demonstrate the practical power of our search method to detect substructure in the phase-space distribution of nearby stars without making a-priori assumptions about the detailed form of the gravitational potential.

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Rainer J. Klement

Papers

Identifying stellar streams in the 1st RAVE public data release

Contamination by field late-M, L, and T dwarfs in deep surveys

The kinematics of late type stars in the solar cylinder studied with SDSS data

Dietary and pharmacological modification of the insulin/IGF-1 system: exploiting the full repertoire against cancer.

Halo streams in the 7th SDSS data release