Elämä omin kuvin ja sanoin. Omaelämäkerrallisuus, identiteetti ja subjekti Ulli Lustin sarjakuvassa Heute ist der letzte Tag vom Rest deines Lebens

Ulli Lustin omaelämäkerrallinen Heute ist der letzte Tag vom Rest deines Lebens (2009) (suom. Tänään on loppuelämäsi viimeinen päivä, 2013) on 2000-luvun kansainvälisesti menestynein saksankielinen sarjakuva. 1980-luvulle sijoittuva teos kuvaa nuoren punkkari-Ullin vaiheikasta matkaa Wienistä Italiaan ja sen halki. Matkakertomus syvenee kasvukertomukseksi, sillä matkan tapahtumat saavat Ullin tarkastelemaan maailmaa ja itseään uusin silmin. Teos edustaa taidesarjakuvaa viime vuosikymmeninä hallinnutta elämäkerrallisen ja dokumentaarisen sarjakuvan kautta. Fiktion rajojen ylittäminen on nähty pyrkimyksenä laajentaa sarjakuvan ilmaisurepertuaaria ja vahvistaa sarjakuvan arvostusta kulttuurin kentällä. Ei-fiktiivisen sarjakuvan aalto on vaikuttanut myös sarjakuvatutkimukseen, jonka piirissä elämäkerrallisuus ja dokumentaarisuus ovat tämän hetken tutkituimpia ilmiöitä. Tutkielmani asettuu osaksi omaelämäkerrallisen sarjakuvan tutkimusta, mutta pyrin huomioimaan analyysissani myös modernin omaelämäkerran laajemman, aina modernin ajan alkuun ulottuvan kirjoittamisen ja tutkimisen perinteen. Tarkastelen erityisesti sitä, miten teos kertoo omaelämäkerrallisen tarinan ja miten se käsittelee omaelämäkertalajille keskeisiä identiteetin, itseymmärryksen ja autonomisen toimijuuden kysymyksiä. Yhdistän sarjakuvailmaisun analyysin tutkielmassani kulttuurihistorialliseen ja sosiologiseen otteeseen. Ymmärrän sarjakuvan omalakisena ilmaisumuotonaan, mutta hyödynnän analyysissani tarpeen mukaan myös kirjallisuuden ja elokuvan tutkimusta. Osoitan tutkielmassani, että teoksen monimuotoinen kerronta korostaa sekä omaelämäkerran kykyä käsitellä erityisiä ja henkilökohtaisia kokemuksia että sen kykyä kurottaa kohti kulttuurien jaettuja ymmärtämisen ja kokemisen tapoja. Ullin identiteetti kiinnittyy teoksessa 1980-luvun vastakulttuureihin, joita kuvataan dokumentaarisen tarkasti. Subjektiivista kokemusmaailmaa, joka kulkee kerronnassa historiallisen todellisuuden rinnalla, lähestytään puolestaan ekspressiivisin ilmaisukeinon. Teoksen lähtökohtana ovat tekijän henkilökohtaiset kokemukset, mutta kerronta punoo tarinan kulttuurimme yhteisiin kertomusmalleihin ja myytteihin. Omaelämäkerrallinen tarina tarjoaa keinon käsitellä tekijän minuuden lisäksi myös historiallis-sosiaalista todellisuutta ja maailmasta kertomisen tapoja. Tässä piilee omaelämäkerran poliittinen potentiaali, jota myös Lustin teos hyödyntää kommentoidessaan sukupuolten välistä epätasa-arvoa.

Vuokrataloyhtiö muuttuvassa toimintaympäristössä

Suurin osa Suomen vuokrataloyhtiöistä perustettiin 60- ja 70-luvuilla. Tuolloin oli selkeä kuva asuntojen tarpeesta, sijainneista, koosta ja varustetasosta. Asuntotuotantoon kohdistetut rahoitusmuodot koettiin onnistuneiksi ja toimiviksi. Toimintaympäristön muutokset ovat tänään huomattavasti nopeampia kuin tutkimuksen kohteena olevan yhtiön perustamisen aikaan, yli neljäkymmentä vuotta sitten. Vuokrataloyhtiön on pystyttävä varautumaan yllättäviin muutoksiin ja pyrkiä tunnistamaan muuttuvat elementit. Tämä diplomityö pyrkii selvittämään, mitä vaihtoehtoisia tulevaisuudenkuvia on olemassa. Työ perustuu samalla tulevaisuudentutkimuksen kirjallisuusteoriaan ja sen peruslähtökohtiin. Empiirinen osuus perustuu tutkittavan vuokrataloyhtiön historiaan, nykyisyyteen ja tulevaisuuteen tehtyjen ratkaisujen dokumentteihin. Vuokrataloyhtiön ympäristö on täynnä tunnistettavia ominaisuuksia, verkostoja ja rakenteellisia aukkoja. Ympäristöstä tulee osata poimia ne asiat, joiden perusteella eri skenaario- ja ennakointimenetelmät laaditaan. Ennakointi ei ole ennustamista. Skenaario - ja ennakointimenetelmien hyödyntäminen on avoin prosessi, joka sisältää tutkimuskysymyksiä, lähtökohtia, joita tarkennetaan prosessin edetessä. Vuokrataloyhtiön on osattava hyödyntää nämä menetelmät osana strategian suunnitteluaan, jotta se pärjää myös tulevaisuudessa muuttuvassa toimintaympäristössään.

An Efficient Parameterization of Dynamic Neural Networks for Nonlinear System Identification

Dynamic neural networks (DNNs), which are also known as recurrent neural networks, are often used for nonlinear system identification. The main contribution of this letter is the introduction of an efficient parameterization of a class of DNNs. Having to adjust less parameters simplifies the training problem and leads to more parsimonious models. The parameterization is based on approximation theory dealing with the ability of a class of DNNs to approximate finite trajectories of nonautonomous systems. The use of the proposed parameterization is illustrated through a numerical example, using data from a nonlinear model of a magnetic levitation system.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Application of neural network to hybrid systems with binary inputs

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

A forward-constrained regression algorithm for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward-constrained regression (FCR) manner. The proposed algorithm selects significant kernels one at a time, while the leave-one-out (LOO) test score is minimized subject to a simple positivity constraint in each forward stage. The model parameter estimation in each forward stage is simply the solution of jackknife parameter estimator for a single parameter, subject to the same positivity constraint check. For each selected kernels, the associated kernel width is updated via the Gauss-Newton method with the model parameter estimate fixed. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

A-optimality orthogonal forward regression algorithm using branch and bound

In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S. Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

A robust nonlinear identification algorithm using PRESS statistic and forward regression

This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual Sums of Squares (PRESS) statistic and for-ward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

Inference From Aging Information

For many learning tasks the duration of the data collection can be greater than the time scale for changes of the underlying data distribution. The question we ask is how to include the information that data are aging. Ad hoc methods to achieve this include the use of validity windows that prevent the learning machine from making inferences based on old data. This introduces the problem of how to define the size of validity windows. In this brief, a new adaptive Bayesian inspired algorithm is presented for learning drifting concepts. It uses the analogy of validity windows in an adaptive Bayesian way to incorporate changes in the data distribution over time. We apply a theoretical approach based on information geometry to the classification problem and measure its performance in simulations. The uncertainty about the appropriate size of the memory windows is dealt with in a Bayesian manner by integrating over the distribution of the adaptive window size. Thus, the posterior distribution of the weights may develop algebraic tails. The learning algorithm results from tracking the mean and variance of the posterior distribution of the weights. It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps.

A classification of Pepper yellow mosaic virus isolates into pathotypes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)