The Rise of Apps Culture

Based on surveys, analyzes trends in adults' use of mobile software applications, including popular types of apps, number, frequency of use, source, and prevalence compared with other cell phone uses, by age, gender, race/ethnicity, education, and income

Causal Inference by Independent Component Analysis with Applications to Micro- and Macroeconomic Data

Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this paper, we present a recently developed method for exploiting non-Gaussianity in the data for estimating such models, with the aim of capturing the causal structure underlying the data, and show how the method can be applied to both microeconomic data (processes of firm growth and firm performance) as well as macroeconomic data (effects of monetary policy).Causality, Structural VAR, Independent Components Analysis, Non-Gaussianity, Firm Growth, Monetary Policy

Architecture and economics

Import 05/08/2014Diplomová práce se zabývá vztahem architektury a urbánní struktury města Olomouce optikou ekonomického hodnocení místních, potažmo obecných zákonitostí rozvoje sídel. Kvůli snaze o možnost kvantifikace a definovatelnosti dílčích analyzovaných faktorů je částečně omezeno zahrnutí nekvantifikovatelných hodnot území, na druhou stranu však v podmínkách dnešní doby představuje výhradně ekonomicky pojatý model údernější argumenty pro dosažení kýžených cílů. V případě města Olomouce se jedná zejména o problematiku možného rozšiřování sídla (bez logických odůvodnění) a oslabování kompaktní struktury a významných hodnot jak z urbanistického, tak funkčního i ekonomického pojetí. V návaznosti na provedené ekonomické analýzy je těžiště práce soustředěno na lokalizaci vhodného doplnění a posílení stávající struktury. Pro tyto účely jsou vybráni reprezentanti charakteristických oblastí města, přičemž jeden z nich je předmětem praktické části této práce.This thesis is focused on the relationship of architecture and urban structure of the city of Olomouc optics economic evaluation of local, hence the general laws of development of settlements . Because of the effort on how to quantify and definability of partial factors analyzed is partially limited by the inclusion of non-quantifiable values , on the other hand, in terms of our time is the only economically conceived model resonance that arguments for achieving the desired objectives. In the case of the city of Olomouc is mainly the issue of a possible extension of the seat (without logical justification ) a weakening of the compact structure and significant values from both urban and functional and economic concepts. Following the analysis of the economic focus of the work is concentrated on locating a suitable complement and reinforce existing structures . For these purposes are selected representatives characteristic areas of the city, one of which is the subject of the practical part of this work.Prezenční226 - Katedra architekturyvelmi dobř

Model-independent measurements of the sodium magneto-optical trap's excited-state population

We present model-independent measurements of the excited-state population of atoms in a sodium (Na) magneto-optical trap (MOT) using a hybrid ion-neutral trap composed of a MOT and a linear Paul trap (LPT). We photoionize excited Na atoms trapped in the MOT and use two independent methods to measure the resulting ions: directly by trapping them in our LPT, and indirectly by monitoring changes in MOT fluorescence. By measuring the ionization rate via these two independent methods, we have enough information to directly determine the population of MOT atoms in the excited-state. The resulting measurement reveals that there is a range of trapping-laser intensities where the excited-state population of atoms in our MOT follows the standard two-level model intensity-dependence. However, an experimentally determined effective saturation intensity must be used instead of the theoretically predicted value from the two-level model. We measured the effective saturation intensity to be $I_\mathrm{se}=22.9(3)\:\textrm{mW}/\textrm{cm}^2$ for the type-I Na MOT and $I_\mathrm{se}=48.9(7)\;\textrm{mW}/\textrm{cm}^2$ for the type-II Na MOT, approximately 1.7 and 3.6 times the theoretical estimate, respectively. Lastly, at large trapping-laser intensities, our experiment reveals a clear departure from the two-level model at a critical intensity that we believe is due to a state-mixing effect, whose critical intensity can be determined by a simple power broadening model.Comment: 10 pages, 8 figure

April 2nd - Death Visited Today

I drew this piece on April 2nd 2020, the day my grandfather passed away. After a month of quarantine and seeing no one, I felt I had been visited by something I didn\u27t invite in. The day I drew this I felt that I had a lot inside of me to get out, but it was just raw feelings, and I didn\u27t know what to do with it. I was so used to my feelings being molded and mulled into art and writing, but these circles were the best depiction of the hole I felt I was in and it was good to just feel the pen underneath my fingers scraping into the page over and over. I was just constantly reminding myself that he was gone, and it was all I could think and feel. I needed to get it out, to give it a place to live outside of myself so I could know it was true, and so I could ultimately begin to heal

Methods of Nonparametric Multivariate Ranking and Selection

In a Ranking and Selection problem, a collection of k populations is given which follow some (partially) unknown probability distributions. The problem is to select the best of the k populations where best is well defined in terms of some unknown population parameter. In many univariate parametric and nonparamentric settings, solutions to these ranking and selection problems exist. In the multivariate case, only parametric solutions have been developed. We have developed several methods for solving nonparametric multivariate ranking and selection problems. The problems considered allow an experimenter to select the best populations based on nonparametric notions of dispersion, location, and distribution. For the first two problems, we use Tukey\u27s Halfspace Depth to define these notions. In the last problem, we make use of a multivariate version of the Kolmogorov-Smirnov Statistic for making selections

ALART: A novel lidar system for vegetation height retrieval from space

We propose a multi-kHz Single-Photon Counting (SPC) space LIDAR, exploiting low energy pulses with high repetition frequency (PRF). The high PRF allows one to overcome the low signal limitations, as many return shots can be collected from nearly the same scattering area. The ALART space instrument exhibits a multi-beam design, providing height retrieval over a wide area and terrain slope measurements. This novel technique, working with low SNRs, allows multiple beam generation with a single laser, limiting mass and power consumption. As the receiver has a certain probability to detect multiple photons from different levels of canopy, a histogram is constructed and used to retrieve the properties of the target tree, by means of a modal decomposition of the reconstructed waveform. A field demonstrator of the ALART space instrument is currently being developed by a European consortium led by cosine | measurement systems and funded by ESA under the TRP program. The demonstrator requirements have been derived to be representative of the target instrument and it will be tested in an equipped tower in woodland areas in the Netherlands. The employed detectors are state-of-the-art CMOS Single-Photon Avalanche Diode (SPAD) matrices with 1024 pixels. Each pixel is independently equipped with an integrated Time-to-Digital Converter (TDC), achieving a timing accuracy that is much lower than the SPAD dead time, resulting in a distance resolution in the centimeter range. The instrument emits nanosecond laser pulses with energy on the order of several J, at a PRF of ~ 10 kHz, and projects on ground a three-beams pattern. An extensive field measurement campaign will validate the employed technologies and algorithms for vegetation height retrieval

Kärntner Slowenen und Sloweninnen - unbekannte / ungeliebte Minderheit im Süden Österreichs

Dieser Beitrag gibt einen historischen Hintergrund für die Ursachen, die bei Kärntner SlowenInnen zu Traumatisierungen führen konnten. Dabei zeigt sich, dass zwar die Jahre zwischen 1938 und 1945 einen gewissen Höhepunkt darstellen, dass aber Ausgrenzung und Diskriminierung aber auch Verfolgung Konstanten im 20. Jahrhundert darstellten.This contribution gives a historical background to the causes which could lead to traumatisation. It appears that for Carinthian Slovenes the years between 1938 and 1945 show a certain climax, but exclusion and discrimination, however, also pursuit are constants in the 10th century

Causal Structure Learning and Effect Identification in Linear Non-Gaussian Models and Beyond

In many fields of science, researchers are keen to learn causal connections among quantities of interest. For instance, in medical studies doctors want to infer the effect of a new drug on the recovery from a particular disease, or economists may be interested in the effect of education on income. The preferred approach to causal inference is to carry out controlled experiments. However, such experiments are not always possible due to ethical, financial or technical restrictions. An important problem is thus the development of methods to infer cause-effect relationships from passive observational data. While this is a rather old problem, in the late 1980s research on this issue gained significant momentum, and much attention has been devoted to this problem ever since. One rather recently introduced framework for causal discovery is given by linear non-Gaussian acyclic models (LiNGAM). In this thesis, we apply and extend this model in several directions, also considering extensions to non-parametric acyclic models. We address the problem of causal structure learning from time series data, and apply a recently developed method using the LiNGAM approach to two economic time series data sets. As an extension of this algorithm, in order to allow for non-linear relationships and latent variables in time series models, we adapt the well-known Fast Causal Inference (FCI) algorithm to such models. We are also concerned with non-temporal data, generalizing the LiNGAM model in several ways: We introduce an algorithm to learn the causal structure among multidimensional variables, and provide a method to find pairwise causal relationships in LiNGAM models with latent variables. Finally, we address the problem of inferring the causal effect of one given variable on another in the presence of latent variables. We first suggest an algorithm in the setting of LiNGAM models, and then introduce a procedure for models without parametric restrictions. Overall, this work provides practitioners with a set of new tools for discovering causal information from passive observational data in a variety of settings.Monilla tieteenaloilla tutkijat etsivät syy-seuraussuhteita kiinnostavina pitämiensä muuttujien välillä. Suorimman lähestymistavan tähän tarjoavat satunnaistetut kontrolloidut kokeet: esimerkiksi kliinisissä kokeissa uuden lääkkeen vaikutusta johonkin sairauteen arvioidaan jakamalla potilaat satunnaisesti kahteen ryhmään, joista toiselle annetaan oikeaa lääkkeettä ja toiselle ainoastaan lumelääkkettä. Lääkkeen todellinen vaikutus selviää ryhmien tuloksia vertailemalla. Monissa tapauksissa tällaiset kokeet eivät kuitenkaan ole mahdollisia. Esimerkiksi taloustieteilijöiden tutkiessa koulutuksen vaikutusta tuloihin, kokeeseen osallistuvien henkilöiden koulutustason suora määrääminen olisi sekä epäeettistä että käytännössä mahdotonta. Näin ollen tutkijat joutuvat usein turvautumaan passiivisesti kerättyyn (ei-kokeelliseen) havaintoaineistoon. Tällainen aineisto ei kuitenkaan välttämättä kerro suoraan kausaalisuhteista, sillä havaitsemattomat muuttujat saattavat oleellisesti vaikeuttaa aineiston hyödyntämistä. Esimerkiksi yksilön kyvyt ja lahjakkuus voivat vaikuttaa suoraan sekä hankittuun koulutukseen että saavutettuun tulotasoon. Tällaisten mittaamattomien ominaisuuksien sivuuttaminen voi aiheuttaa merkittävää harhaa aineiston analyysissä. Tärkeäksi tutkimusaiheeksi on siten muodostunut kausaalisuhteiden oppimisen passiivisesta havaintoaineistosta mahdollistavien menetelmien kehittäminen. Vaikka tutkimusongelma on vanha, aihealueen tutkimus sai 1980-luvun loppupuolella uutta vauhtia ja on ollut hyvin aktiivista siitä lähtien. Tässä väitöskirjassa kehitetään ja sovelletaan menetelmiä syy-seuraussuhteiden oppimiseen ja piilomuuttujien vaikutusten havaitsemiseen passiivisesti kerätyssä havaintoaineistossa. Väitöskirja keskittyy suurelta osin hiljattain kehitettyyn viitekehykseen, joka perustuu lineaarisiin epägaussisiin asyklisiin malleihin (LiNGAM-mallit). Väitöskirjan alkuosa käsittelee aikasarjoja: ensin LiNGAM-pohjaista menetelmää sovelletaan kahteen taloustieteelliseen havaintoaineistoon, jonka jälkeen aikaisemmin tunnettu algoritmi (joka huomioi sekä piilomuuttujat että epälineaariset vaikutussuhteet) laajennetaan käsittelemään aikasarja-aineistoa. Toisaalta väitöskirja tarjoaa myös uusia työkaluja ei-temporaalisen aineiston analyysiin: LiNGAM-mallia laajennetaan kuvaamaan kausaalirakennetta moniulotteisten muuttujien välillä, ja esitetään menetelmä, jolla yksittäisiä syy-seuraussuhteita voidaan löytää piilomuuttujia sallivissa LiNGAM-malleissa. Lopuksi väitöskirja käsittelee kausaalisuhteen voimakkuuden arviointia havaitsemattomien muuttujien vaikeuttaessa aineiston analyysiä, sekä LiNGAM-malleissa että malleissa ilman parametrisia rajoitteita. Kaiken kaikkiaan tämä väitöskirja tarjoaa soveltajille useita uusia työkaluja kausaalitiedon löytämiseen passiivisesta havaintoaineistosta