Should we use tactics or patterns to build secure systems?

Abstrac

Monitoring and thermal performance evaluation of two building envelope solutions in an apartment building

A bio-based multi-layer building envelope assembly has been developed for its integration in newly built and retrofitted buildings. Forest-based materials and biocomposite profiles are used as an alternative to fossil-based insulants and metallic framing, providing a well-insulated and low-thermal-bridge technical solution. The wall assembly has been installed as the external envelope of one apartment of a housing block in Donostia-San Sebastián (Basque Country, Spain). A comparative study has been performed for the bio-based wall and the reference wall of the building. Their in-situ thermal resistance has been obtained by means of three different methods: (1) the steady-state average method, (2) a semi-dynamic method from heat balance at the internal surface, and (3) a dynamic multiple regression method. Reasonably consistent results have been obtained with the three methods: a discussion is provided on the influence of measuring periods and boundary conditions. Outputs from this experimental campaign are valuable as a counterpoint to desktop studies and tests under controlled laboratory conditions. Learnings and outputs from the present study should contribute to a better understanding of the in-situ performance of building envelope assemblies and their assessment methods

Synthesis and antiacetylcholinesterase activity of new D-glyceraldehyde heterocyclic derivatives

We report herein the convenient procedures for the syntheses of different heterocyclic compounds from 2,3-O-isopropylidene-D-glyceraldehyde using intramolecular cyclization, 1,3-dipolar cycloaddition or bimolecular coupling reactions. The products were characterized by ¹H and 13C NMR spectroscopy and elemental analysis. The new heterocycles and their derivatives were evaluated as inhibitors of acetylcholinesterase enzyme

A global analysis of Spitzer and new HARPS data confirms the loneliness and metal-richness of GJ 436 b

Context. GJ 436b is one of the few transiting warm Neptunes for which a detailed characterisation of the atmosphere is possible, whereas its non-negligible orbital eccentricity calls for further investigation. Independent analyses of several individual datasets obtained with Spitzer have led to contradicting results attributed to the different techniques used to treat the instrumental effects. Aims. We aim at investigating these previous controversial results and developing our knowledge of the system based on the full Spitzer photometry dataset combined with new Doppler measurements obtained with the HARPS spectrograph. We also want to search for additional planets. Methods. We optimise aperture photometry techniques and the photometric deconvolution algorithm DECPHOT to improve the data reduction of the Spitzer photometry spanning wavelengths from 3-24 {\mu}m. Adding the high precision HARPS radial velocity data, we undertake a Bayesian global analysis of the system considering both instrumental and stellar effects on the flux variation. Results. We present a refined radius estimate of RP=4.10 +/- 0.16 R_Earth, mass MP=25.4 +/- 2.1 M_Earth and eccentricity e= 0.162 +/- 0.004 for GJ 436b. Our measured transit depths remain constant in time and wavelength, in disagreement with the results of previous studies. In addition, we find that the post-occultation flare-like structure at 3.6 {\mu}m that led to divergent results on the occultation depth measurement is spurious. We obtain occultation depths at 3.6, 5.8, and 8.0 {\mu}m that are shallower than in previous works, in particular at 3.6 {\mu}m. However, these depths still appear consistent with a metal-rich atmosphere depleted in methane and enhanced in CO/CO2, although perhaps less than previously thought. We find no evidence for a potential planetary companion, stellar activity, nor for a stellar spin-orbit misalignment. [ABRIDGED]Comment: 25 pages, 26 figures, 8 tables, accepted for publication in A&

Hot Exoplanet Atmospheres Resolved with Transit Spectroscopy (HEARTS) I. Detection of hot neutral sodium at high altitudes on WASP-49b

High-resolution optical spectroscopy during the transit of HD 189733b, a prototypical hot Jupiter, allowed the resolution of the Na I D sodium lines in the planet, giving access to the extreme conditions of the planet upper atmosphere. We have undertaken HEARTS, a spectroscopic survey of exoplanet upper atmospheres, to perform a comparative study of hot gas giants and determine how stellar irradiation affect them. Here, we report on the first HEARTS observations of the hot Saturn-mass planet WASP-49b. We observed the planet with the HARPS high-resolution spectrograph at ESO 3.6m telescope. We collected 126 spectra of WASP-49, covering three transits of WASP-49b. We analyzed and modeled the planet transit spectrum, while paying particular attention to the treatment of potentially spurious signals of stellar origin. We spectrally resolve the Na I D lines in the planet atmosphere and show that these signatures are unlikely to arise from stellar contamination. The large contrasts of $2.0\pm0.5\%$ (D$_2$) and $1.8\pm0.7\%$ (D$_1$) require the presence of hot neutral sodium ($2,950^{+400}_{-500}$ K) at high altitudes ($\sim$1.5 planet radius or $\sim$45,000 km). From estimating the cloudiness index of WASP-49b, we determine its atmosphere to be cloud free at the altitudes probed by the sodium lines. WASP-49b is close to the border of the evaporation desert and exhibits an enhanced thermospheric signature with respect to a farther-away planet such as HD 189733b.Comment: Accepted for publication in A&A. 14 page

On merging the fields of neural networks and adaptive data structures to yield new pattern recognition methodologies

The aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. These are the well-established fields of Neural Networks (NNs) and Adaptive Data Structures (ADS) respectively. The field of NNs deals with the training and learning capabilities of a large number of neurons, each possessing minimal computational properties. On the other hand, the field of ADS concerns designing, implementing and analyzing data structures which adaptively change with time so as to optimize some access criteria. In this talk, we shall demonstrate how these fields can be merged, so that the neural elements are themselves linked together using a data structure. This structure can be a singly-linked or doubly-linked list, or even a Binary Search Tree (BST). While the results themselves are quite generic, in particular, we shall, as a prima facie case, present the results in which a Self-Organizing Map (SOM) with an underlying BST structure can be adaptively re-structured using conditional rotations. These rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution. Besides, the neighborhood properties of the neurons suit the best BST that represents the data

Characterization of the K2-18 multi-planetary system with HARPS: A habitable zone super-Earth and discovery of a second, warm super-Earth on a non-coplanar orbit

The bright M dwarf K2-18 at 34 pc is known to host a transiting super-Earth-sized planet orbiting within the star's habitable zone; K2-18b. Given the superlative nature of this system for studying an exoplanetary atmosphere receiving similar levels of insolation as the Earth, we aim to characterize the planet's mass which is required to interpret atmospheric properties and infer the planet's bulk composition. We obtain precision radial velocity measurements with the HARPS spectrograph and couple those measurements with the K2 photometry to jointly model the observed radial velocity variation with planetary signals and a radial velocity jitter model based on Gaussian process regression. We measure the mass of K2-18b to be $8.0 \pm 1.9$ M$_{\oplus}$ with a bulk density of $3.7 \pm 0.9$ g/cm$^3$ which may correspond to a predominantly rocky planet with a significant gaseous envelope or an ocean planet with a water mass fraction $\gtrsim 50$%. We also find strong evidence for a second, warm super-Earth K2-18c at $\sim 9$ days with a semi-major axis 2.4 times smaller than the transiting K2-18b. After re-analyzing the available light curves of K2-18 we conclude that K2-18c is not detected in transit and therefore likely has an orbit that is non-coplanar with K2-18b. A suite of dynamical integrations with varying simulated orbital eccentricities of the two planets are used to further constrain each planet's eccentricity posterior from which we measure $e_b < 0.43$ and $e_c < 0.47$ at 99% confidence. The discovery of the inner planet K2-18c further emphasizes the prevalence of multi-planet systems around M dwarfs. The characterization of the density of K2-18b reveals that the planet likely has a thick gaseous envelope which along with its proximity to the Solar system makes the K2-18 planetary system an interesting target for the atmospheric study of an exoplanet receiving Earth-like insolation.Comment: 13 pages, 8 figures including 4 interactive figures best viewed in Adobe Acrobat. Submitted to Astronomy & Astrophysics. Comments welcom

Semi-supervised classification using tree-based self-organizing maps

This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabeled and labeled instances. First, we learn the structure of the data distribution in an unsupervised manner. After convergence, and once labeled data become available, our strategy tags each of the clusters according to the evidence provided by the instances. Unlike other neighborhood-based schemes, our classifier uses only a small set of representatives whose cardinality can be much smaller than that of the input set. Our experiments show that, on average, the accuracy of such classifier is reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes