On Security and Sparsity of Linear Classifiers for Adversarial Settings

Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to evasion attacks. This can be considered a relevant problem, as linear classifiers have been increasingly used in embedded systems and mobile devices for their low processing time and memory requirements. We exploit recent findings in robust optimization to investigate the link between regularization and security of linear classifiers, depending on the type of attack. We also analyze the relationship between the sparsity of feature weights, which is desirable for reducing processing cost, and the security of linear classifiers. We further propose a novel octagonal regularizer that allows us to achieve a proper trade-off between them. Finally, we empirically show how this regularizer can improve classifier security and sparsity in real-world application examples including spam and malware detection

Elemental Abundance Ratios in Stars of the Outer Galactic Disk. IV. A New Sample of Open Clusters

We present radial velocities and chemical abundances for nine stars in the old, distant open clusters Be 18, Be 21, Be 22, Be 32, and PWM 4. For Be 18 and PWM 4, these are the first chemical abundance measurements. Combining our data with literature results produces a compilation of some 68 chemical abundance measurements in 49 unique clusters. For this combined sample, we study the chemical abundances of open clusters as a function of distance, age, and metallicity. We confirm that the metallicity gradient in the outer disk is flatter than the gradient in the vicinity of the solar neighborhood. We also confirm that the open clusters in the outer disk are metal-poor with enhancements in the ratios [alpha/Fe] and perhaps [Eu/Fe]. All elements show negligible or small trends between [X/Fe] and distance (< 0.02 dex/kpc), but for some elements, there is a hint that the local (RGC < 13 kpc) and distant (RGC > 13 kpc) samples may have different trends with distance. There is no evidence for significant abundance trends versus age (< 0.04 dex/Gyr). We measure the linear relation between [X/Fe] and metallicity, [Fe/H], and find that the scatter about the mean trend is comparable to the measurement uncertainties. Comparison with solar neighborhood field giants shows that the open clusters share similar abundance ratios [X/Fe] at a given metallicity. While the flattening of the metallicity gradient and enhanced [alpha/Fe] ratios in the outer disk suggest a different chemical enrichment history to the solar neighborhood, we echo the sentiments expressed by Friel et al. that definitive conclusions await homogeneous analyses of larger samples of stars in larger numbers of clusters. Arguably, our understanding of the evolution of the outer disk from open clusters is currently limited by systematic abundance differences between various studies.Comment: Accepted for publication in A

Students\u27 Perceptions of STEM Learning After Participating in a Summer Informal Learning Experience

Background: Informal learning environments increase students’ interest in STEM (e.g., Mohr‐Schroeder et al. School Sci Math 114: 291–301, 2014) and increase the chances a student will pursue a STEM career (Kitchen et al. Sci Educ 102: 529–547, 2018). The purpose of this study was to examine the impact of an informal STEM summer learning experience on student participants, to gain in-depth perspectives about how they felt this experience prepared them for their in-school mathematics and science classes as well as how it influenced their perception of STEM learning. Students’ attitudes and perceptions toward STEM are affected by their motivation, experience, and self-efficacy (Brown et al. J STEM Educ Innov Res 17: 27, 2016). The academic and social experiences students’ have are also important. Traditionally, formal learning is taught in a solitary form (Martin Science Education 88: S71–S82, 2004), while, informal learning is brimming with chances to connect and intermingle with peers (Denson et al. J STEM Educ: Innovations and Research 16: 11, 2015). Results: Informal learning environments increase students’ interest in STEM (e.g., Mohr‐Schroeder et al. School Sci Math 114: 291–301, 2014) and increase the chances a student will pursue a STEM career (Kitchen et al. Sci Educ 102: 529–547, 2018). The purpose of this study was to examine the impact of an informal STEM summer learning experience on student participants, to gain in-depth perspectives about how they felt this experience prepared them for their in-school mathematics and science classes as well as how it influenced their perception of STEM learning. Students’ attitudes and perceptions toward STEM are affected by their motivation, experience, and self-efficacy (Brown et al. J STEM Educ Innov Res 17: 27, 2016). The academic and social experiences students’ have are also important. Traditionally, formal learning is taught in a solitary form (Martin Science Education 88: S71–S82, 2004), while, informal learning is brimming with chances to connect and intermingle with peers (Denson et al. J STEM Educ: Innovations and Research 16: 11, 2015). Conclusions: By using authentic STEM workplaces, the STEM summer learning experience fostered a learning environment that extended and deepened STEM content learning while providing opportunity and access to content, settings, and materials that most middle level students otherwise would not have access to. Students also acknowledged the access they received to hands-on activities in authentic STEM settings and the opportunities they received to interact with STEM professionals were important components of the summer informal learning experience

The Gaia-ESO Survey: Chromospheric Emission, Accretion Properties, and Rotation in γ\gamma Velorum and Chamaeleon I

We use the fundamental parameters delivered by the GES consortium in the first internal data release to select the members of $\gamma$ Vel and Cha I among the UVES and GIRAFFE spectroscopic observations. A total of 140 $\gamma$ Vel members and 74 Cha I members were studied. We calculated stellar luminosities through spectral energy distributions, while stellar masses were derived by comparison with evolutionary tracks. The spectral subtraction of low-activity and slowly rotating templates, which are rotationally broadened to match the $v\sin i$ of the targets, enabled us to measure the equivalent widths (EWs) and the fluxes in the H$\alpha$ and H$\beta$ lines. The H$\alpha$ line was also used for identifying accreting objects and for evaluating the mass accretion rate ($\dot M_{\rm acc}$). The distribution of $v\sin i$ for the members of $\gamma$ Vel displays a peak at about 10 km s$^{-1}$ with a tail toward faster rotators. There is also some indication of a different $v\sin i$ distribution for the members of its two kinematical populations. Only a handful of stars in $\gamma$ Vel display signatures of accretion, while many more accretors were detected in the younger Cha~I. Accreting and active stars occupy two different regions in a $T_{\rm eff}$-flux diagram and we propose a criterion for distinguishing them. We derive $\dot M_{\rm acc}$ in the ranges $10^{-11}$-$10^{-9} M_\odot$yr$^{-1}$ and $10^{-10}$-$10^{-7} M_\odot$yr$^{-1}$ for $\gamma$ Vel and Cha I accretors, respectively. We find less scatter in the $\dot M_{\rm acc}-M_\star$ relation derived through the H$\alpha$ EWs, when compared to the H$\alpha$ $10\%W$ diagnostics, in agreement with other authors

The Gaia-ESO Survey: the most metal-poor stars in the Galactic bulge

We present the first results of the EMBLA survey (Extremely Metal-poor BuLge stars with AAOmega), aimed at finding metal-poor stars in the Milky Way bulge, where the oldest stars should now preferentially reside. EMBLA utilises SkyMapper photometry to pre-select metal-poor candidates, which are subsequently confirmed using AAOmega spectroscopy. We describe the discovery and analysis of four bulge giants with -2.72<=[Fe/H]<=-2.48, the lowest metallicity bulge stars studied with high-resolution spectroscopy to date. Using FLAMES/UVES spectra through the Gaia-ESO Survey we have derived abundances of twelve elements. Given the uncertainties, we find a chemical similarity between these bulge stars and halo stars of the same metallicity, although the abundance scatter may be larger, with some of the stars showing unusual [{\alpha}/Fe] ratios.Comment: 7 pages, 5 figures. Accepted for publication by MNRA

The Gaia-ESO Survey: the chemical structure of the Galactic discs from the first internal data release

Most high-resolution spectroscopic studies of the Galactic discs were mostly confined to objects in the solar vicinity. Here we aim at enlarging the volume in which individual chemical abundances are used to characterise both discs, using the first internal data release of the Gaia-ESO survey. We derive and discuss the abundances of eight elements (Mg, Al, Si, Ca, Ti, Fe, Cr, Ni, and Y). The trends of these elemental abundances with iron are very similar to those in the solar neighbourhood. We find a natural division between alpha-rich and alpha-poor stars, best seen in the bimodality of the [Mg/M] distributions in bins of metallicity, which we attribute to thick- and thin-disc sequences, respectively. With the possible exception of Al, the observed dispersion around the trends is well described by the expected errors, leaving little room for astrophysical dispersion. Using previously derived distances from Recio-Blanco et al. (2014b), we further find that the thick-disc is more extended vertically and is more centrally concentrated towards the inner Galaxy than the thin-disc, which indicates a shorter scale-length. We derive the radial and vertical gradients in metallicity, iron, four alpha-element abundances, and Al for the two populations, taking into account the identified correlation between R_GC and |Z|. Radial metallicity gradient is found in the thin disc. The positive radial individual [alpha/M] gradients found are at variance from the gradients observed in the RAVE survey. The thin disc also hosts a negative vertical metallicity gradient, accompanied by positive individual [alpha/M] and [Al/M] gradients. The thick-disc, presents no radial metallicity gradient, a shallower vertical metallicity gradient than the thin-disc, an alpha-elements-to-iron radial gradient in the opposite sense than that of the thin disc, and positive vertical individual [alpha/M] and [Al/M] gradients.Comment: 24 pages, 10 figure

Open clusters towards the Galactic center: chemistry and dynamics. A VLT spectroscopic study of NGC6192, NGC6404, NGC6583

In the framework of the study of the Galactic metallicity gradient and its time evolution, we present new high-resolution spectroscopic observations obtained with FLAMES and the fiber link to UVES at VLT of three open clusters (OCs) located within $\sim$7~kpc from the Galactic Center (GC): NGC~6192, NGC~6404, NGC~6583. We also present new orbit determination for all OCs with Galactocentric distances (R$_{\rm{GC}}) \leq$8~kpc and metallicity from high-resolution spectroscopy. We aim to investigate the slope of the inner disk metallicity gradient as traced by OCs and at discussing its implication on the chemical evolution of our Galaxy. We have derived memberships of a group of evolved stars for each clusters, obtaining a sample of 4, 4, and 2 member stars in NGC~6192, NGC~6404, and NGC~6583, respectively. Using standard LTE analysis we derived stellar parameters and abundance ratios for the iron-peak elements Fe, Ni, Cr, and for the $\alpha$-elements Al, Mg, Si, Ti, Ca. We calculated the orbits of the OCs currently located within 8~kpc from the GC, and discuss their implication on the present-time radial location. {The average metallicities of the three clusters are all oversolar: [Fe/H]= $+0.12\pm0.04$ (NGC~6192), $+0.11\pm0.04$ (NGC 6404), $+0.37\pm0.03$ (NGC 6583). They are in qualitative agreement with their Galactocentric distances, being all internal OCs, and thus expected to be metal richer than the solar neighborhood. The abundance ratios of the other elements over iron [X/Fe] are consistent with solar values. The clusters we have analysed, together with other OC and Cepheid data, confirm a steep gradient in the inner disk, a signature of an evolutionary rate different than in the outer disk.Comment: 17 pages, 13 figures, A&A accepted for publicatio

Notulae to the Italian flora of algae, bryophytes, fungi and lichens: 12

In this contribution, new data concerning bryophytes, fungi and lichens of the Italian flora are presented. It includes new records, confirmations or exclusions for the bryophyte genera Acaulon, Campylopus, En-tosthodon, Homomallium, Pseudohygrohypnum, and Thuidium, the fungal genera Entoloma, Cortinarius, Mycenella, Oxyporus, and Psathyrella and the lichen genera Anaptychia, Athallia, Baeomyces, Bagliettoa, Calicium, Nephroma, Pectenia, Phaeophyscia, Polyblastia, Protoparmeliopsis, Pyrenula, Ramalina, and San-guineodiscus

The Gaia-ESO Survey : The analysis of high-resolution UVES spectra of FGK-type stars

Date of Acceptance: 01/09/2014Context. The ongoing Gaia-ESO Public Spectroscopic Survey is using FLAMES at the VLT to obtain high-quality medium-resolution Giraffe spectra for about 105 stars and high-resolution UVES spectra for about 5000 stars. With UVES, the Survey has already observed 1447 FGK-type stars. Aims. These UVES spectra are analyzed in parallel by several state-of-the-art methodologies. Our aim is to present how these analyses were implemented, to discuss their results, and to describe how a final recommended parameter scale is defined. We also discuss the precision (method-to-method dispersion) and accuracy (biases with respect to the reference values) of the final parameters. These results are part of the Gaia-ESO second internal release and will be part of its first public release of advanced data products. Methods. The final parameter scale is tied to the scale defined by the Gaia benchmark stars, a set of stars with fundamental atmospheric parameters. In addition, a set of open and globular clusters is used to evaluate the physical soundness of the results. Each of the implemented methodologies is judged against the benchmark stars to define weights in three different regions of the parameter space. The final recommended results are the weighted medians of those from the individual methods. Results. The recommended results successfully reproduce the atmospheric parameters of the benchmark stars and the expected Teff-log g relation of the calibrating clusters. Atmospheric parameters and abundances have been determined for 1301 FGK-type stars observed with UVES. The median of the method-to-method dispersion of the atmospheric parameters is 55 K for Teff, 0.13 dex for log g and 0.07 dex for [Fe/H]. Systematic biases are estimated to be between 50-100 K for Teff, 0.10-0.25 dex for log g and 0.05-0.10 dex for [Fe/H]. Abundances for 24 elements were derived: C, N, O, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Y, Zr, Mo, Ba, Nd, and Eu. The typical method-to-method dispersion of the abundances varies between 0.10 and 0.20 dex. Conclusions. The Gaia-ESO sample of high-resolution spectra of FGK-type stars will be among the largest of its kind analyzed in a homogeneous way. The extensive list of elemental abundances derived in these stars will enable significant advances in the areas of stellar evolution and Milky Way formation and evolution.Peer reviewe

Picking on the family: disrupting android malware triage by forcing misclassification

Machine learning classification algorithms are widely applied to different malware analysis problems because of their proven abilities to learn from examples and perform relatively well with little human input. Use cases include the labelling of malicious samples according to families during triage of suspected malware. However, automated algorithms are vulnerable to attacks. An attacker could carefully manipulate the sample to force the algorithm to produce a particular output. In this paper we discuss one such attack on Android malware classifiers. We design and implement a prototype tool, called IagoDroid, that takes as input a malware sample and a target family, and modifies the sample to cause it to be classified as belonging to this family while preserving its original semantics. Our technique relies on a search process that generates variants of the original sample without modifying their semantics. We tested IagoDroid against RevealDroid, a recent, open source, Android malware classifier based on a variety of static features. IagoDroid successfully forces misclassification for 28 of the 29 representative malware families present in the DREBIN dataset. Remarkably, it does so by modifying just a single feature of the original malware. On average, it finds the first evasive sample in the first search iteration, and converges to a 100% evasive population within 4 iterations. Finally, we introduce RevealDroid*, a more robust classifier that implements several techniques proposed in other adversarial learning domains. Our experiments suggest that RevealDroid* can correctly detect up to 99% of the variants generated by IagoDroid