Anomalien havaitseminen GNSS signaaleissa kompleksiarvoisilla LSTM neuroverkoilla

Today, Global Navigation Satellite Systems (GNSS) provide services that many critical systems [1] as well as normal users, need in everyday life. These signals are threatened by unintentional and intentional interference. The received satellite signals are complex-valued by nature, however, state-of-the-art anomaly detection approaches operate in the real domain. Changing the anomaly detection into the complex domain allows for preserving the phase component of the signal data. In this thesis, I developed and tested a fully complex-valued Long Short-Term Memory (LSTM) based autoencoder for anomaly detection. I also developed a method for scaling of complex-numbers that forces both real and imaginary units into the range [-1,1] and does not change the direction of a complex vector. The model is trained and tested both in the time and frequency domains, and the frequency domain is divided into two parts: real and complex domain. The developed model’s training data consists only of clean sample data, and the output of the model is the reconstruction of the model’s input. In testing, it can be determined whether the output is clean or anomalous based on the reconstruction error and the computed threshold value. The results show that the autoencoder model in the real domain outperforms the model trained in the complex domain. This does not indicate that the anomaly detection in the complex domain does not work; rather, the model’s architecture needs improvements, and the amount of training data must be increased to reduce the overfitting of the complex domain and thus improve the anomaly detection capability. It was also investigated that some anomalous sample sequences contain a few large valued spikes while other values in the same data snapshot are smaller. After scaling, the values other than in the spikes get closer to zero. This phenomenon causes small reconstruction errors in the model and yields false predictions in the complex domain

A Latent Variable Approach to Multivariate Quantitative Trait Loci

A novel approach based on latent variable modelling is presented for the analysis of multivariate quantitative and qualitative trait loci. The approach is general in the sense that it enables the joint analysis of many kinds of quantitative and qualitative traits (including count data and censored traits) in a single modelling framework. In the framework, the observations are modelled as functions of latent variables, which are then affected by quantitative trait loci. Separating the analysis in this way means that measurement errors in the phenotypic observations can be included easily in the model, providing robust inferences. The performance of the method is illustrated using two real multivariate datasets, from barley and Scots pine

A novel measure of reliability in Diffusion Tensor Imaging after data rejections due to subject motion

Diffusion Tensor Imaging (DTI) is commonly challenged by subject motion during data acquisition, which often leads to corrupted image data. Currently used procedure in DTI analysis is to correct or completely reject such data before tensor estimations, however assessing the reliability and accuracy of the estimated tensor in such situations has evaded previous studies. This work aims to define the loss of data accuracy with increasing image rejections, and to define a robust method for assessing reliability of the result at voxel level. We carried out simulations of every possible sub-scheme (N=1,073,567,387) of Jones30 gradient scheme, followed by confirming the idea with MRI data from four newborn and three adult subjects. We assessed the relative error of the most commonly used tensor estimates for DTI and tractography studies, fractional anisotropy (FA) and the major orientation vector (V1), respectively. The error was estimated using two measures, the widely used electric potential (EP) criteria as well as the rotationally variant condition number (CN). Our results show that CN and EP are comparable in situations with very few rejections, but CN becomes clearly more sensitive to depicting errors when more gradient vectors and images were rejected. The error in FA and V1 was also found depend on the actual FA level in the given voxel; low actual FA levels were related to high relative errors in the FA and V1 estimates. Finally, the results were confirmed with clinical MRI data. This showed that the errors after rejections are, indeed, inhomogeneous across brain regions. The FA and V1 errors become progressively larger when moving from the thick white matter bundles towards more superficial subcortical structures. Our findings suggest that i) CN is a useful estimator of data reliability at voxel level, and ii) DTI preprocessing with data rejections leads to major challenges when assessing brain tissue with lower FA levels, such as all newborn brain, as well as the adult superficial, subcortical areas commonly traced in precise connectivity analyses between cortical regions.Peer reviewe

Mikä vernakulaari?

Tämän teemanumeron aihe on vernakulaarin käsite. Numeron neljässä artikkelissa vernakulaaria lähestytään käsitehistoriallisesta ja tapaustutkimusten näkökulmasta. Artikkeleissa tarkastellaan ensinnäkin sitä, minkälaisia merkityksiä vernakulaarin käsitteellä on ollut eri aikoina. Toiseksi niissä eritellään, minkälainen asema vernakulaarin käsitteellä sekä sille annetuilla merkityksillä on ollut eri tieteenaloilla ja niiden tutkimuskohteiden määrittelyssä. Kolmanneksi teemanumeron artikkeleissa analysoidaan useita tapaustutkimuksia vernakulaarin käsitteen ja sen tarjoamien näkökulmien kautta samalla käsitettä pohtien. Teemanumeron tavoitteena on tuoda monipuolisesti esiin, miten vernakulaarin käsitettä on tutkimuksessa käytetty, ja mikä sen anti on perinteen- ja kulttuurien tutkimukselle.  Tässä johdannossa esittelemme teemanumeron koostavien artikkeleiden lisäksi vernakulaarin käsitteen taustoja. Käymme läpi myös vernakulaarin käyttöä erityisesti folkloristiikan näkökulmasta, käsitteeseen kytkeytyviä ongelmia ja sen analyyttistä potentiaalia

Lasten ja nuorten terveys ja hyvinvointi – tiedonhankinta ja -tarve kunnallisessa päätöksenteossa

Tutkimuksen tarkoituksena oli kuvata millaisia tiedonhankintakanavia kuntien lautakunnissa käytetään ja millaista tietoa lautakuntien luottamushenkilöt ja viranhaltijat kokevat tarvitsevansa lasten ja nuorten terveyteen ja hyvinvointiin liittyvissä politiikkatoimissa. Tutkimusaineisto (n=13) kerättiin laadullisilla teemahaastatteluilla ja analysoitiin induktiivisella sisällönanalyysilla.Tulosten mukaan pääasiallisina tiedonhankintakanavina käytettiin valtion ja julkisen sektorin sekä kolmannen sektorin organisaatioita, mediaa ja verkostoja. Luottamushenkilöt ja viranhaltijaterosivat toisistaan sekä tiedonhankinnan että tiedon tarpeen suhteen. Luottamushenkilöt luottivat tiedonhankinnassa suuressa määrin viranhaltijoiden valmisteluun ja esittelymateriaaliin,eikä muun omatoimisen tiedonhankinnan katsottu kuuluvan keskeisesti luottamushenkilön tehtäviin. Viranhaltijat puolestaan hankkivat tietoa monista eri lähteistä. Luottamushenkilöidenja viranhaltijoiden työskentelyssä ja päätöksenteossa yhdistyivät virallinen ja epävirallinen tieto. Terveyteen ja hyvinvointiin liittyvän tiedon välittämisessä tulisikin hyödyntää monipuolisiatiedonvälityskanavia ja -menetelmiä, jotta informaatio tavoittaisi poliittiset päättäjät

An outbreak caused by the SARS-CoV-2 Delta variant (B.1.617.2) in a secondary care hospital in Finland, May 2021

An outbreak caused by the SARS-CoV-2 Delta variant (B.1.617.2) spread from one inpatient in a secondary care hospital to three primary care facilities, resulting in 58 infections including 18 deaths in patients and 45 infections in healthcare workers (HCW). Only one of the deceased cases was fully vaccinated. Transmission occurred despite the use of personal protective equipment by the HCW, as advised in national guidelines, and a high two-dose COVID-19 vaccination coverage among permanent staff members in the COVID-19 cohort ward.Peer reviewe

Genomics of clinal local adaptation in Pinus sylvestris under continuous environmental and spatial genetic setting

Understanding the consequences of local adaptation at the genomic diversity is a central goal in evolutionary genetics of natural populations. In species with large continuous geographical distributions the phenotypic signal of local adaptation is frequently clear, but the genetic basis often remains elusive. We examined the patterns of genetic diversity inPinus sylvestris, a keystone species in many Eurasian ecosystems with a huge distribution range and decades of forestry research showing that it is locally adapted to the vast range of environmental conditions. MakingP. sylvestrisan even more attractive subject of local adaptation study, population structure has been shown to be weak previously and in this study. However, little is known about the molecular genetic basis of adaptation, as the massive size of gymnosperm genomes has prevented large scale genomic surveys. We generated a both geographically and genomically extensive dataset using a targeted sequencing approach. By applying divergence-based and landscape genomics methods we identified several loci contributing to local adaptation, but only few with large allele frequency changes across latitude. We also discovered a very large (ca. 300 Mbp) putative inversion potentially under selection, which to our knowledge is the first such discovery in conifers. Our results call for more detailed analysis of structural variation in relation to genomic basis of local adaptation, emphasize the lack of large effect loci contributing to local adaptation in the coding regions and thus point out the need for more attention toward multi-locus analysis of polygenic adaptation