Multivariate Techniques for Identifying Diffractive Interactions at the LHC

31 pages, 14 figures, 11 tablesClose to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.Peer reviewe

Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating

Genealogical networks, also known as family trees or population pedigrees, are commonly studied by genealogists wanting to know about their ancestry, but they also provide a valuable resource for disciplines such as digital demography, genetics, and computational social science. These networks are typically constructed by hand through a very time-consuming process, which requires comparing large numbers of historical records manually. We develop computational methods for automatically inferring large-scale genealogical networks. A comparison with human-constructed networks attests to the accuracy of the proposed methods. To demonstrate the applicability of the inferred large-scale genealogical networks, we present a longitudinal analysis on the mating patterns observed in a network. This analysis shows a consistent tendency of people choosing a spouse with a similar socioeconomic status, a phenomenon known as assortative mating. Interestingly, we do not observe this tendency to consistently decrease (nor increase) over our study period of 150 years.Comment: This is a pre-print of an article appearing in the proceedings of the Web Conference (WWW 2018

Probabilistinen siirto-oppimismenetelmä ihmisten liikkumisen ennustamiseksi

Human mobility exhibits various daily and weekly routines, such as a home-work-lunch-work pattern which many working individuals follow regularly. In this thesis, a probabilistic method for predicting these mobility patterns is developed. Mobility prediction has a wide range of applications from context-aware smartphone applications to the modeling of epidemic disease spreading. We use two sources of location data: the Mobile Data Challenge (MDC) data set which contains visit sequences recorded automatically through GPS and Wi-Fi sensors and the Foursquare (4sq) data set which consists of manual check-ins people have made in places such as train stations and restaurants. Our first goal is to study how the difference in the nature of the two location disclosure systems is reflected in mobility patterns. Differences related to, e.g., the number of check-ins are identified but the time distributions of the visits/check-ins turn out to be similar, suggesting that the two data sets can be used to complement each other. The second goal is to develop a probabilistic next place prediction method. A model combining the strengths of the current state-of-the-art methods is derived and we show that it outperforms the current methods. Furthermore, the developed method is compatible with 4sq data allowing transfer learning. The final goal is to introduce a transfer learning method in order to use 4sq data to complement the MDC data set. The method we propose is based on mixtures of multinomials and we show that it improves next place prediction accuracy during the first month of the data collection. Thus the proposed transfer learning method helps to tackle the cold start problem that many applications requiring the estimation of probability distributions face.Ihmisten liikkumisessa on havaittavissa useita päivä- ja viikkorytmejä kuten koti-työ-lounas-työ-rytmi, joka on tyypillinen monille työssäkäyville henkilöille. Tässä työssä kehitetään probabilistinen menetelmä ihmisten liikkumisen ennustamiseksi. Liikkumisen ennustamisella on useita sovelluksia alkaen kontekstitietoisista matkapuhelinsovelluksista aina epidemioiden leviämisen mallintamiseen. Työssä käytetään kahta paikkatietoaineistoa: Mobile Data Challenge (MDC) -aineistoa, joka sisältää GPS- ja Wi-Fi-sensoreiden avulla automaattisesti kerättyjä vierailusekvenssejä, sekä Foursquare (4sq) -aineistoa, joka koostuu manuaalisesti kirjatuista vierailuista eri paikkoihin kuten juna-asemiin ja ravintoloihin. Työn ensimmäisenä tavoitteena on tarkastella, miten näiden kahden paikkatiedonkeruumenetelmän erilaisuus näkyy aineistoista löytyvissä liikkumisrytmeissä. Osoittautuu, että eroavaisuuksia löytyy muun muassa tallennettujen vierailujen lukumäärissä, mutta toisaalta vierailujen aikajakaumat ovat samankaltaisia. Tämän perusteella voidaan päätellä, että aineistoja voidaan käyttää täydentämään toisiaan. Työn toisena tavoitteena on kehittää probabilistinen menetelmä henkilön seuraavan sijainnin ennustamiseen. Johdettu menetelmä perustuu tämän hetken parhaisiin menetelmiin, ja työssä osoitetaan, että menetelmä suoriutuu paremmin kuin nykyiset menetelmät. Lisäksi menetelmä on yhteensopiva 4sq-aineiston kanssa, mikä mahdollistaa siirto-oppimisen. Työn kolmantena tavoitteena on kehittää siirto-oppimismenetelmä, joka käyttää 4sq-aineistoa täydentämään MDC-aineistoa. Työssä osoitetaan multinomiaalimikstuurimalleihin perustuvan menetelmän parantavan seuraavan sijainnin ennustustarkkuutta, kun aineistoa on kerättynä alle kuukauden ajalta. Näin ollen menetelmä auttaa ongelmassa, joka kohdataan lukuisissa sovelluksissa, joissa vaaditaan todennäköisyysjakaumien estimointia, mutta joissa aineistoa ei ole aluksi riittävästi