No description available.

Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process.

The NASA K2 mission uses photometry to find planets transiting stars of various types. M dwarfs are of high interest since they host more short-period planets than any other type of main-sequence star and transiting planets around M dwarfs have deeper transits compared to other main-sequence stars. In this paper, we present stellar parameters from K and M dwarfs hosting transiting planet candidates discovered by our team. Using the SOFI spectrograph on the European Southern Observatory's New Technology Telescope, we obtained R approximate to 1000 J-, H-, and K-band (0.95-2.52 mu m) spectra of 34 late-type K2 planet and candidate planet host systems and 12 bright K4-M5 dwarfs with interferometrically measured radii and effective temperatures. Out of our 34 late-type K2 targets, we identify 27 of these stars as M dwarfs. We measure equivalent widths of spectral features, derive calibration relations using stars with interferometric measurements, and estimate stellar radii, effective temperatures, masses, and luminosities for the K2 planet hosts. Our calibrations provide radii and temperatures with median uncertainties of 0.059 R-circle dot (16.09%) and 160 K (4.33%), respectively. We then reassess the radii and equilibrium temperatures of known and candidate planets based on our spectroscopically derived stellar parameters. Since a planet's radius and equilibrium temperature depend on the parameters of its host star, our study provides more precise planetary parameters for planets and candidates orbiting late-type stars observed with K2. We find a median planet radius and an equilibrium temperature of approximately 3 R-circle plus and 500 K, respectively, with several systems (K2-18b and K2-72e) receiving near-Earth-like levels of incident irradiation.

Authors: Mikaela Andersson & Louise Petersson Suporvisor: Dan Halvarsson Examiner: Åsa Devine Title: Lost in translation - A qualitative study regarding the translation process of social media activities Keywords: Social media, Translation process, KPIs, Qualitative metrics, Quantitative metrics, Data collection, Data analysis Background: Social media as marketing tools have created many opportunities for marketers and companies that want to increase the social media use. However, the vast amount of data has generated many question marks, so there is a great need to understand how the translation process of social media activities is proceeding to have the ability to understand the effectiveness of the activities. Purpose: The purpose is to describe the translation process regarding the evaluation of social media activities. Research Question: How is the translation process described concerning social media activities? Methodology: The research is founded upon a qualitative approach with a descriptive purpose. It includes multiple-case studies and the data was collected through six semi-structured interviews. The result was derived by descriptions, categorizations and combination of the data. Conclusion: Despite having a fairly stable process including KPIs, metrics, data collection and analysis, there is no general way of translating social media indicators. Every translation process is dependent upon whom the client is, and what the objectives are, so it is difficult to describe the process in detail. However, the above-mentioned process can be viewed as an overall approach.

Les sursauts gamma sont des sources astrophysiques parmi les plus brillantes du ciel. Dans le modèle standard actuel, leur émission prompte (X et gamma) est due à des particules chargées accélérées au sein de jets relativistes émis à la formation de trous noirs de masses stellaire. L'émission rémanente observée de la radio aux X serait due à l'interaction de ces jets avec le milieu interstellaire. Le LAT, détecteur à création de paire du télescope spatial Fermi, permet depuis juin 2008 l'étude du ciel gamma de 20 MeV à plus de 300 GeV avec des performances inégalées. Le GBM, détecteur de sources transitoires de Fermi (8 keV à 40 MeV) a observé ~450 sursauts gamma, dont ~18 ont été observés jusqu'au domaine du GeV. Une localisation précise de ces sursauts et la synergie de Fermi avec les autres observatoires permettent l'étude des rémanences associées et une meilleure interprétation des observations. L'étude de sursauts gamma de 8 keV au domaine du GeV est présentée. Les localisations obtenues avec le LAT sont étudiées ainsi que leurs erreurs. Des analyses spectrales des émissions promptes combinant les données du GBM et du LAT sont exposées, ainsi que leur interprétation. Une analyse alternative basée sur une sélection relâchée des données LAT est présentée et caractérisée. L'utilisation des événements d'énergies inférieures à 100 MeV améliore l'analyse temporelle et spectrale des émissions promptes. La recherche d'émission gamma prolongée est présentée, ainsi que l'étude de l'émission rémanente de GRB 090510 observé des UV au GeV par Fermi et Swift. Enfin, un modèle d'émission prompte par les chocs internes, développé à l'IAP, est comparé aux observations de Fermi. / Gamma-Ray Bursts (GRB) are among the brightest gamma-ray sources in the sky. The current standard framework associates their prompt gamma-ray emission to charged particles accelerated in relativistic jets issued by newly-formed stellar-mass black holes. The radio to X-ray afterglow emission is due to the interaction between these jets and the interstellar medium.The LAT, pair-creation instrument onboard Fermi gamma-ray space telescope, performs unprecedented observation of the gamma-ray sky at energies of 20 MeV to over 300 GeV since its launch in june 2008. Fermi's transient sources detector, GBM, observed prompt emissions of ~450 GRB between 8 keV and 40 MeV. ~18 of these GRB were also studied up to GeV energies with the LAT. Accurate GRB localizations and Fermi's synergy with other observatories allows the study of GRB afterglows, and therefore a better interpretation of these observations.The analyses of GRB emissions between 8 keV to GeV energies is presented here. Localizations based on LAT data and their biases are studied. Spectral analyses of combined GBM and LAT data are shown, and their theoretical interpretations explained.An alternative analysis based on a relaxed selection of LAT data is presented and fully characterized. It allows to recover and use low-energy LAT statistics in temporal and spectral analyses of GRB prompt emission.Searches for long-lived high-energy emission from GRB are presented. The analysis of GRB 090510 afterglow emission from eV to GeV energies is described.Finally, Fermi bright GRB prompt emissions are compared to an internal shock model developed at IAP.

We present results from MMT/Hectochelle spectroscopy of 390 red giant candidate stars along the line of sight to the recently discovered Galactic satellite Crater 2. Modeling the joint distribution of stellar positions, velocities, and metallicities as a mixture of Crater 2 and Galactic foreground populations, we identify similar to 62 members of Crater 2, for which we resolve a line-of-sight velocity dispersion of sigma(nu los) = 2.7(-0.3)(+0.3) km s(-1) and a. mean velocity of <nu(los)> = 87.5(-0.4)(+0.4) km s(-1) (solar rest frame). We also resolve a metallicity dispersion of sigma([Fe/H]) = 0.22(-0.03)(+0.04) dex and a mean of <[Fe/H]> = 1.98(-0.1)(+0.1) dex that is 0.28 +/- 0.14 dex poorer than estimated from photometry. Despite Crater 2's relatively large size (projected halflight radius R-h similar to 1 kpc) and intermediate luminosity (M-V similar to -8), its velocity dispersion is the coldest that has been resolved for any dwarf galaxy. These properties make Crater 2 the most extreme low-density outlier in dynamical as well as structural scaling relations among the Milky Way's dwarf spheroidals. Even so, under assumptions of dynamical equilibrium and negligible contamination by unresolved binary stars, the observed velocity distribution implies a gravitationally dominant dark matter halo, with a dynamical mass of. 4.4(-0.9)(+1.2) x 10(6) M-circle dot and a mass-to-light ratio of 53(-11)(+15) M-circle dot/L-V,L-circle dot enclosed within a radius of similar to 1 kpc, where the equivalent circular velocity is 4.3(-0.5)(+0.5) km s(-1).

Aims. This article describes the implementation of a focal plane based wavefront control loop on the high-contrast imaging instrument SCExAO (Subaru Coronagraphic Extreme Adaptive Optics). The sensor relies on the Fourier analysis of conventional focal-plane images acquired after an asymmetric mask is introduced in the pupil of the instrument. Methods. This absolute sensor is used here in a closed-loop to compensate for the non-common path errors that normally affects any imaging system relying on an upstream adaptive optics system. This specific implementation was used to control low-order modes corresponding to eight zernike modes (from focus to spherical). Results. This loop was successfully run on-sky at the Subaru Telescope and is used to offset the SCExAO deformable mirror shape used as a zero-point by the high-order wavefront sensor. The paper details the range of errors this wavefront-sensing approach can operate within and explores the impact of saturation of the data and how it can be bypassed, at a cost in performance. Conclusions. Beyond this application, because of its low hardware impact, the asymmetric pupil Fourier wavefront sensor (APF-WFS) can easily be ported in a wide variety of wavefront sensing contexts, for ground-as well space-borne telescopes, and for telescope pupils that can be continuous, segmented or even sparse. The technique is powerful because it measures the wavefront where it really matters, at the level of the science detector.

Cytoskeletal motor proteins are essential to the function of a wide range of intra-cellular mechano-systems. The biophysical characterization of the movement of motor proteins along their filamentous tracks is therefore of large importance. Towards this end, in vitro stepping motility assays are commonly used to determine the motor’s velocities and runlengths. However, comparing results from such experiments has proved difficult due to influences from variations in the experimental setups, the experimental conditions and the data analysis methods. This work describes a novel unified method to evaluate traces of fluorescently-labeled, processive dimeric motor proteins and proposes an algorithm to correct the measurements for finite filament length as well as photobleaching. Statistical errors of the proposed evaluation method are estimated by a bootstrap method. Numerical simulation and experimental data from GFP-labeled kinesin-1 motors stepping along immobilized microtubules was used to verify the proposed approach and it was shown (i) that the velocity distribution should be fitted by a t location-scale probability density function rather than a normal distribution, (ii) that the temperature during the experiments should be controlled with a precision well below 1 K, (iii) that the impossibility to measure events shorter than the image acquisition time needs to be accounted for, (iv) that the motor’s runlength can be estimated independent of the filament length distribution, and (v) that the dimeric nature of the motors needs to be considered when correcting for photobleaching. This allows for a better statistical comparison of motor proteins influenced by other external factors e.g. ionic strength, ATP concentration, or post-translational modifications of the filaments. In this context, the described method was then applied to experimental data to investigate the influence of the nucleotide state of the microtubule on the motility behavior of the kinesin-1 motor proteins. Here, a small but significant difference in the velocity measurements was found, but no significant difference in the runlength and interaction time measurements. Consequently, this work provides a framework for the evaluation of a wide range of experiments with single fluorescently-labeled motor proteins.

In many practical scenarios, prediction for high-dimensional observations can be accurately performed using only a fraction of the existing features. However, the set of relevant predictive features, known as the sparsity pattern, varies across data. For instance, features that are informative for a subset of observations might be useless for the rest. In fact, in such cases, the dataset can be seen as an aggregation of samples belonging to several low-dimensional sub-models, potentially due to different generative processes. My thesis introduces several techniques for identifying sparse predictive structures and the areas of the feature space where these structures are effective. This information allows the training of models which perform better than those obtained through traditional feature selection. We formalize Informative Projection Recovery, the problem of extracting a set of low-dimensional projections of data which jointly form an accurate solution to a given learning task. Our solution to this problem is a regression-based algorithm that identifies informative projections by optimizing over a matrix of point-wise loss estimators. It generalizes to a number of machine learning problems, offering solutions to classification, clustering and regression tasks. Experiments show that our method can discover and leverage low-dimensional structure, yielding accurate and compact models. Our method is particularly useful in applications involving multivariate numeric data in which expert assessment of the results is of the essence. Additionally, we developed an active learning framework which works with the obtained compact models in finding unlabeled data deemed to be worth expert evaluation. For this purpose, we enhance standard active selection criteria using the information encapsulated by the trained model. The advantage of our approach is that the labeling effort is expended mainly on samples which benefit models from the hypothesis class we are considering. Additionally, the domain experts benefit from the availability of informative axis aligned projections at the time of labeling. Experiments show that this results in an improved learning rate over standard selection criteria, both for synthetic data and real-world data from the clinical domain, while the comprehensible view of the data supports the labeling process and helps preempt labeling errors.

The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nominal, ordered, numeric, and censored data as well as multivariate data at mixed scales. Based on a rich and exible conceptual framework that embeds different permutation test procedures into a common theory, a computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions. As a consequence, the computational tools in coin inherit the exibility of the underlying theory and conditional inference functions for important special cases can be set up easily. Conditional versions of classical tests|such as tests for location and scale problems in two or more samples, independence in two- or three-way contingency tables, or association problems for censored, ordered categorical or multivariate data|can easily be implemented as special cases using this computational toolbox by choosing appropriate transformations of the observations. The paper gives a detailed exposition of both the internal structure of the package and the provided user interfaces along with examples on how to extend the implemented functionality. (authors' abstract)