Multi-view Metric Learning in Vector-valued Kernel Spaces

We consider the problem of metric learning for multi-view data and present a novel method for learning within-view as well as between-view metrics in vector-valued kernel spaces, as a way to capture multi-modal structure of the data. We formulate two convex optimization problems to jointly learn the metric and the classifier or regressor in kernel feature spaces. An iterative three-step multi-view metric learning algorithm is derived from the optimization problems. In order to scale the computation to large training sets, a block-wise Nystr{\"o}m approximation of the multi-view kernel matrix is introduced. We justify our approach theoretically and experimentally, and show its performance on real-world datasets against relevant state-of-the-art methods

Entangled Kernels-Beyond Separability

Publisher Copyright: © 2021 Microtome Publishing. All rights reserved.We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels. Borrowing tools and concepts from the field of quantum computing, such as partial trace and entanglement, we propose a new view on operator-valued kernels and define a general family of kernels that encompasses previously known operator-valued kernels, including separable and transformable kernels. Within this framework, we introduce another novel class of operator-valued kernels called entangled kernels that are not separable. We propose an efficient two-step algorithm for this framework, where the entangled kernel is learned based on a novel extension of kernel alignment to operator-valued kernels. We illustrate our algorithm with an application to supervised dimensionality reduction, and demonstrate its effectiveness with both artificial and real data for multi-output regression.Peer reviewe

Wideband Self-Adaptive RF Cancellation Circuit for Full-Duplex Radio: Operating Principle and Measurements

This paper presents a novel RF circuit architecture for self-interference cancellation in inband full-duplex radio transceivers. The developed canceller is able to provide wideband cancellation with waveform bandwidths in the order of 100 MHz or beyond and contains also self-adaptive or self-healing features enabling automatic tracking of time-varying self-interference channel characteristics. In addition to architecture and operating principle descriptions, we also provide actual RF measurements at 2.4 GHz ISM band demonstrating the achievable cancellation levels with different bandwidths and when operating in different antenna configurations and under low-cost highly nonlinear power amplifier. In a very challenging example with a 100 MHz waveform bandwidth, around 41 dB total cancellation is obtained while the corresponding cancellation figure is close to 60 dB with the more conventional 20 MHz carrier bandwidth. Also, efficient tracking in time-varying reflection scenarios is demonstrated.Comment: 7 pages, to be presented in 2015 IEEE 81st Vehicular Technology Conferenc

DC-DC Converters in Distributed Photovoltaic Electricity System - Analysis, Control and Design

This thesis presents a comprehensive review on switched-mode converters in terms of dynamic behavior and practical limitations that arise from the fundamental properties of the electrical sources and loads, control engineering principles and topological properties of the converters. The main focus is on analyzing the behavior of a single converter used to interface a photovoltaic generator into a high-voltage dc link. The main objective is to introduce interfacing principles with numerous examples and a thorough discussion. The interfacing of photovoltaic generators by means of switched-mode converters has proven to be problematic according to numerous scientific publications indicating operational disadvantages and anomalies. The output characteristics of the photovoltaic generator, which are bound to varying environmental conditions, introduce design challenges. It has been recognized recently that the photovoltaic generator does not contain similar electrical behavior as conventional electrical sources, most notably due to its limited-power characteristics, yielding two distinctive operating regions. Yet, the constraints arising from the properties of the source have not been completely recognized, although the effect of these constraints can be seen from the published research results. When switched-mode converters are used to adapt individual photovoltaic modules into larger system by connecting converters in series or in parallel, severe operational limitations are observed. On the other hand, if the photovoltaic generator is substituted with a source that does not contain similar characteristics, observations may lead to misconclusions as the effect of the photovoltaic generator is not properly modeled. Therefore, claims that are not valid for actual applications with photovoltaic generators may be presented and widely accepted. This thesis presents methods to perform proper analysis of switched-mode converters implemented in distributed photovoltaic applications, by continuing previous work around the subject (Leppäaho, 2011). The dynamic models for series-connected and parallelconnected systems of interfacing converters are given, explaining the observed operational anomalies. Additionally, it is shown by a thorough review that the parallel configuration does not contain the claimed disadvantageous properties and actually provides better performance. A patented converter topology designed for the parallel configuration is presented with comprehensive analysis and practical validation. Finally, the problematics of photovoltaic interfacing is summarized under the interfacing constraints, which give guidelines for design and analysis of interfacing converters

Itsehäiriön analoginen RF-kumoaminen

In this thesis a novel RF-canceler for full-duplex operation is presented, designed and implemented. The canceler is built for the full-duplex research group which is currently collaborating with Intel labs. The original design is from Intel. Two revision of cancelers were built and measured. The obtained results prove the canceler design to be effective and it is self-adaptive. The obtained cancellation in self-adaptive mode is order of 30 dB when using 20 MHz wide signal. This design fares well against other designs presented in academia

Automatic identification of land cover types from satellite data with machine learning techniques

This study is part of the TEKES funded Electric Brain -project of VTT and University of Helsinki where the goal is to develop novel techniques for automatic big data analysis. In this study we focus on studying potential methods for automated land cover type classification from time series satellite data. Developing techniques to identify different environments would be beneficial in monitoring the effects of natural phenomena, forest fires, development of urbanization or climate change. We tackle the arising classification problem with two approaches; with supervised and unsupervised machine learning methods. From the former category we use a technique called support vector machine (SVM), while from the latter we consider Gaussian mixture model clustering technique and its simpler variant, k-means. We introduce the techniques used in the study in chapter 1 as well as give motivation for the work. The detailed discussion of the data available for this study and the methods used for analysis is presented in chapter 2. In that chapter we also present the simulated data that is created to be a proof of concept for the methods. The obtained results for both the simulated data and the satellite data are presented in chapter 3 and discussed in chapter 4, along with the considerations for possible future works. The obtained results suggest that the support vector machines could be suitable for the task of automated land cover type identification. While clustering methods were not as successful, we were able to obtain as high as 93 % accuracy with the data available for this study with the supervised implementation.Tutkielma on osa TEKES-rahoitteista VTT:n ja Helsingin yliopiston Electric Brain -projektia, jonka tarkoituksena on kehittää tekniikoita automaattiseen suurien datamäärien käsittelyyn. Tämä työ keskittyy tutkimaan potentiaalisia menetelmiä automaattiseen maanpeittotyyppien tunnistukseen aikasarjaluonteisesta sateliittidatasta. Tällaiset automaattiset seurantamentelmät olisivat hyödyllisiä erilaisten luonnon- ja muiden ilmiöiden tarkkailuun; mahdollisia seurantakohteita ovat esimerkiksi metsäpalot, urbaanien alueiden kehittyminen ja ilmastonmuutoksen aiheuttamien muutosten tarkkailu. Lähestymme luokitteluongelmaa kahdesta lähtökohdasta: ohjatun ja ohjaamattoman koneoppimisen menetelmillä. Ensimmäisestä kategoriasta käytämme tekniikkaa nimeltä tukivektorikone, kun taas jälkimmäisessä keskitymme klusterointiin Gaussisilla sekoitemalleilla ja niiden yksinkertaisemmalla versiolla, k-means -menetelmällä. Esittelemme työssä käytettävät tekniikat ja motivaatiota työlle kappaleessa yksi. Tarkemmin nämä tekniikat käsitellään kappaleessa kaksi, jossa myös esitellään työss\ä käytettävä data, sekä simuloitu data joka on luotu tekniikoiden toimivuuden testaamiseksi. Tulokset sekä simuloidulla että oikealla datalla esitellään kappaleessa kolme. Keskustelemme tuloksista ja mahdollisista laajennoksista työlle kappaleessa neljä. Saadut tulokset viittaavat siihen, että tukivektorikone voisi olla soveltuva menetelmä tämäntyyppiseen sateliittidatan analysointiin. Korkein saavutettu tarkkuus tukivektorikoneilla maanpeittotyyppejä luokitellessa oli 93 %, joka oli huomattavasti parempi kuin klusterointimenetelmillä saavutetut tulokset

Cross-view kernel transfer

We consider the kernel completion problem with the presence of multiple views in the data. In this context the data samples can be fully missing in some views, creating missing columns and rows to the kernel matrices that are calculated individually for each view. We propose to solve the problem of completing the kernel matrices with Cross-View Kernel Transfer (CVKT) procedure, in which the features of the other views are transformed to represent the view under consideration. The transformations are learned with kernel alignment to the known part of the kernel matrix, allowing for finding generalizable structures in the kernel matrix under completion. Its missing values can then be predicted with the data available in other views. We illustrate the benefits of our approach with simulated data, multivariate digits dataset and multi-view dataset on gesture classification, as well as with real biological datasets from studies of pattern formation in early \textit{Drosophila melanogaster} embryogenesis

Caste-specific expression of chemosensory genes in Formica-ants

Sosiaaliset hyönteiset, joihin muurahaiset kuuluvat elävät yhteiskunnissa, joissa esiintyy selvä työnjako lisääntyvien yksilöiden ja työläiskastin välillä. Yhteiskunta voi koostua suurimmillaan jopa miljoonista yksilöistä ja kuningatarten määrä voi vaihdella voimakkaasti. Populaatioita, joissa kussakin pesässä on vain yksi tai muutamia kuningattaria voidaan kutsua sukulaisrakenteisiksi, sillä niissä yksilöiden välinen sukulaisuus yhteiskunnan sisällä on korkeaa. Populaatioita, joissa on runsaasti kuningattaria, ja yhteiskunta muodostuu useista toisiinsa liitoksissa olevista pesistä, voidaan kutsua superkolonioiksi. Tällaisissa yhteiskunnissa yksilöiden välinen sukulaisuus on matalaa ja työläiset edustavat useita geneettisiä linjoja. Lajista ja elinympäristöstä riippuen yhteiskunnat käyttäytyvät tyypillisesti vieraiden pesien yksilöitä kohtaan aggressiivisesti suojellen omaa pesäänsä. Jotta yhteiskunnan puolustus olisi mahdollista, on muurahaisten kyettävä tunnistamaan oman yhteiskuntansa jäsenet tunkeutujista. Pesätovereiden tunnistus on tärkeä tekijä yhteiskuntien ja lajien välisessä vuorovaikutuksessa ja sen ansiosta työläiset voivat suosia omia pesätovereitaan hoivan, puolustuksen tai ravinnonhankinnan kautta ja näin lisätä myös omaa kokonaiskelpoisuuttaan. Pesätovereiden tunnistuskyvyn kannalta kemiallisen informaation aistiminen on merkityksellisessä roolissa. Tunnistukseen liittyviä kemiallisia aineita muurahaiset aistivat pääasiassa tuntosarvissaan ilmentyvien proteiinien avulla. Tässä työssä tutkin seitsemän suomumuurahaislajilla oletetusti kemosensoriseen aistimukseen liittyvien geenien ilmentymistä RT-qPCR -menetelmän avulla. Tutkimuslajini olivat sukulaisrakenteiset karvaloviniska, niittymuurahainen, ja mustamuurahainen sekä superkoloniaaliset kantomuurahainen, kaljuloviniska, samettimuurahainen ja tupsukekomuurahainen. Tarkastelen tuoksuja sitovaa proteiinia (OBP), kemosensoriproteiinia (CSP) ja makuaistinreseptoria (GRT) tuottavien geenien ilmentymistä ja haluan selvittää, eroaako näiden geenien ilmentyminen lajien ja kastien välillä. Työssäni vertaan myös sukulaisrakenteisten ja superkoloniaalisten lajien kemosensoristen geenien kastispesifiä ilmentymistä. Koska työläisillä on muurahaisyhteiskunnassa paljon tehtäviä, joiden hoitaminen vaatii tehokasta aistijärjestelmää, hypoteesini on, että tutkimusgeenini ilmentyvät työläisillä voimakkaammin kuin kuningattarilla. Lisäksi uskon geenien ilmentyvän työläisissä voimakkaammin sukulaisrakenteisilla lajeilla, sillä niiden työläiset ovat superkoloniaalisia lajeja aggressiivisempia tunkeutujia kohtaan ja kohtaavat pesän ulkopuolisia yksilöitä useammin, kuin valtavissa superkolonioissa elävät yksilöt. Superkolonioissa tyypillisten matalasta sukulaisuudesta johtuvien konfliktien vuoksi uskon superkoloniaalisten kuningatarten kemosensorigeenien ilmentymisen olevan sukulaisrakenteisten yhteiskuntien kuningatarten geenin ilmentymistä voimakkaampaa. Tutkimusgeeneistä voimakkaimmin ilmentyi OBP ja vähäisintä oli GRT-geenin ilmentyminen. CSP:n ilmentyminen oli näiden geenien ilmentymisen väliltä. Hypoteesini mukaisesti geenien ilmentyminen oli työläisillä kuningattaria voimakkaampaa OBP:lla ja CSP:lla kaikilla lajeilla ja GRT:lla kuudella seitsemästä lajista. Sukulaisrakenteisten lajien kemosensoristen geenien kastispesifi ilmentyminen ei ollut superkoloniaalisia lajeja voimakkaampaa, vaan lajien välillä havaittiin vaihtelua, joka ei näyttänyt riippuvan populaatioiden polygynia-asteesta. Tutkimuksessani selvisi myös OBP- ja CSP-geenien ilmentymisen olevan korreloitunutta. Tulokseni olivat kastien osalta hypoteesini mukaisia ja voivat kertoa työläisten paremmasta haju- ja makuaistista sekä pesätoveruuden tunnistuskyvystä. Tutkimukseni tarjoaa arvokasta tietoa vielä melko vähän tutkitusta tunnistusjärjestelmään liittyvien kemosensoristen geenien ilmentymisestä ja herättää useita uusia mielenkiintoisia tutkimuskysymyksiä. Kemiallisen aistinjärjestelmän tutkimusta on tehty laajalla skaalalla, mutta geenien ilmentymisen tasolla työsarkaa vielä riittää.Social insects such as ants live in societies and have a strict division of labor between reproductive and worker castes. A colony can consist of even millions of individuals and the number of queens can vary a lot. Populations where each colony comprises just one or few queens are often called kin structured because the relatedness between nestmates is high. Colonies that have lots of queens and the society lives in many connected nests (polydomy) in are referred to as supercolonies. In these colonies relatedness between individuals is low and the workers represent many genetic lineages. Depending on species and the environment where the colony lives societies can behave aggressively towards individuals from other nests to protect their own nest. Ants must be able to recognize members of their own colony from the intruders to be able to protect the nest. Nestmate recognition is a key element in the interaction between nests and species and makes it possible for the workers in the colony to favour their own nestmates in form of care, defence or food acquisition to gain inclusive fitness benefits. To recognise nestmates ants must be able to sense chemical cues. Ants detect these chemical signals through the proteins expressed mainly in their antennas. In this thesis I studied gene expression of genes related to chemosensation in seven Formica species using the RT qPCR method. My study species were kin structured Formica exsecta, F. pratensis and F. fusca and supercolonial F. truncorum, F. pressilabris, F. cinerea and F. aquilonia. My study genes belong to gene families that code for odorant binding proteins (OBP), chemosensory proteins (CSP) and gustatory reseptors (GRT). I want to find out whether the expression of these genes differs between castes, and whether the caste difference varies between kin structured and supercolonial species. Workers have many tasks in the ant colony and to take care of them, they need to have a sophisticated sensory system. For that reason, I expect to find out that the study genes are expressed more in the worker than the queen caste. In addition, I expect the caste difference in gene expression to be higher in the kin structured species than in the supercolonial species. That is because kin structured species behave more aggressively towards intruders and possibly confront intruders more often than the individuals living in supercolonies. Furthermore, in the supercolonies low relatedness between individuals sometimes lead to conflicts inside the nest. For that reason, I suppose queens of the supercolonies express chemosensory genes more than the queens from the kin structured colonies. Overall expression level was the highest for the OBP and the lowest for GRT. The expression level of CSP was in between these extremes. In accordance with my hypothesis gene expression of OBP and CSP was higher in workers in all the study species. GRT expression was worker biased in six of the seven species. Caste difference in expression of chemosensory genes was similar in kin structured and supercolonial species. The expression level varied between species but did not show a pattern depending on the degree of the polygyny. The study revealed that the expression of OBP and CSP is correlated. My results revealed expected worker biased pattern in the expression. The result might be a consequence of better olfactory or taste abilities in the worker caste compared to queens or it may even be consequence of more sophisticated nestmate recognition skills of the workers. This study reveals valuable information about the gene expression of chemosensory genes related to the recognition system in the ants and awakes many new study questions. Chemical sensory system has been studied a lot in the ants, but in the field of expression studies there is still lot to reveal

Partial Trace Regression and Low-Rank Kraus Decomposition

The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs. We here introduce an even more general model, namely the partial-trace regression model, a family of linear mappings from matrix-valued inputs to matrix-valued outputs; this model subsumes the trace regression model and thus the linear regression model. Borrowing tools from quantum information theory, where partial trace operators have been extensively studied, we propose a framework for learning partial trace regression models from data by taking advantage of the so-called low-rank Kraus representation of completely positive maps. We show the relevance of our framework with synthetic and real-world experiments conducted for both i) matrix-to-matrix regression and ii) positive semidefinite matrix completion, two tasks which can be formulated as partial trace regression problems