Sisustaminen on kuin käsialaa : Carin Bryggman ja Lasse Ollinkari sisustusarkkitehdin ammatissa 1940- ja 1950-luvulla

"Interior Design is Like Handwriting." Carin Bryggman and Lasse Ollinkari as Interior Designers in the 1940s and 1950s My dissertation deals with the emergence of the interior designer's profession in Finland with focus on the 1940s and 1950s, the postwar years of reconstruction and modernism, as the historical context. The topic is addressed at both the collective and individual levels. Specific subjects of study are the training of interior designers (also known as interior architects), the association of Finnish interior architects (Sisustusarkkitehdit SIO), the professional field and its public image and two leading designers, Carin Bryggman (1920 1993) and Lasse Ollinkari (1921 1993). Though respected figures within the field, Bryggman and Ollinkari have otherwise remained little known and studied. My study presents a great deal of new empiria. The main materials consist of the documents of related institutions and the archives of Bryggman and Ollinkari, in which drawings and photographs figure prominently. The drawings illustrate in a new way the variety of professional tasks in the field. My results are also based on a large body of interviewed material. The materials are approached from two theoretical perspectives, with gender and margins as core concepts from the perspective of women's studies. The even gender division of Finnish interior designers revealed a difference with regard to neighbouring occupations and other countries. I claim that the division of tasks was not defined by gender. The second theoretical basis is the sociological study of professions. The high professional status achieved by interior designers is shown by the fact that of the many related titles in Finnish and Swedish, such as "furniture draughtsman" or "interior artist", interior architect became the established one, despite opposition from architects. My hypothesis that the professionalization of interior designers took place during the two postwar decades proved to be correct. The profession emerged through specialized education and became established with the founding of its own professional organization. From the outset, the goal was to mark a distinction between professionals of interior and furniture design and other designers and architects. Interior designers became a strong and successful modern professional group, involved in a wide range of projects from objects to interiors. Keywords: interior designers, interior architects, interior art, occupations, gender, professions, interior design, furniture, home, public space, Carin Bryggman, Lasse Ollinkari, the Sisustusarkkitehdit SIO association, 1940s and 1950s, reconstruction, modernism.

A la memoria del abnegado maestro licenciado don Pedro Rubio y Piloña : homenaje de cariño y gratitud en el primer aniversario de su sentida muerte.

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Minimalistinen yhdyssanateoria ja käsitteiden yhdistely

The thesis aims to link the biolinguistic research program and the results of studies in comceptual combination from cognitive psychology. The thesis derives a theory of syntactic structure of noun and adjectival compounds from the Empty Lexicon Hypothesis. Two compound-forming operations are described: root-compounding and word-compounding. The aptness of theory is tested with finnish and greek compounds. From the syntactic theory semantic requirements for conceptual system are derived, especially requirements for handling morphosyntactic features. These requirements are compared to three formidable theories of conceptual combination: relation theory CARIN, Dual-Process theory and C3-theory. The claims of explanatory power of relational distributions of modifier in CARIN-theory ared discarded, as the method for sampling and building relational distributions is not reliable and the algorithmic instantiation of theory does not compute what it claims to compute. From relational theory there still remains results supporting existence of 'easy' relations for certain concepts. Dual-Process theory is found to provide results that cannot in theory be affected by linguistic system, but the basic idea of property compounds is kept. C3-theory is found to be not computationally realistic, but the basic results of diagnosticity and local properties (domains) of conceptual system are solid. The three conceptual combination models are rethought as a problem of finding the shortest route between the two concepts. The new basis for modeling is suggested to be bare conceptual landscape with morphosyntactiic or semantic features working as guidance and structural features of landscape basically unknown, but such as they react to features from linguistic system. Minimalistic principles to conceptual modeling are suggested.

语义Web规则标记语言OWLRule+的设计与实现

语义Web是对未来Web体系结构的一个伟大设想 ,其研究分层次进行 目前足够成熟的最高层是以OWL语言为代表的ontology层 但它的语义仅限于描述逻辑 ,该逻辑主要表示对象和类的层次结构 ,而规则的表达能力弱 因此在其之上需要一种表达力更丰富的逻辑语言 设计了一种新的语义Web规则标记语言OWLRule+:其语法扩展了OWL ;语义基于CARIN ,一种结合描述逻辑和Horn规则的表示语言 ;实现基于Jess规则推理机 实例学习展示了它对目前的Webontology语言在规则表示和推理能力上的扩展

An active learning approach for rapid characterization of endothelial cells in human tumors.

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

Adaptive temporal compressive sensing for video

This paper introduces the concept of adaptive temporal compressive sensing (CS) for video. We propose a CS algorithm to adapt the compression ratio based on the scene's temporal complexity, computed from the compressed data, without compromising the quality of the reconstructed video. The temporal adaptivity is manifested by manipulating the integration time of the camera, opening the possibility to realtime implementation. The proposed algorithm is a generalized temporal CS approach that can be incorporated with a diverse set of existing hardware systems. © 2013 IEEE.

Bayesian Gaussian Copula Factor Models for Mixed Data.

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.

A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2.

There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent.

Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection.

Exposure to influenza viruses is necessary, but not sufficient, for healthy human hosts to develop symptomatic illness. The host response is an important determinant of disease progression. In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection, we inoculated 17 healthy adults with live influenza (H3N2/Wisconsin) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours. Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections. We show that symptomatic hosts invoke, simultaneously, multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress. In contrast, asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses. We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection. Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature, suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Nested dictionary learning for hierarchical organization of imagery and text

A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted from patches or superpixels (using any existing method for image feature extraction). Each image is associated with a path through the tree (from root to a leaf), and each of the multiple patches in a given image is associated with one node in that path. Nodes near the tree root are shared between multiple paths, representing image characteristics that are common among different types of images. Moving toward the leaves, nodes become specialized, representing details in image classes. If available, words (text) are also jointly modeled, with a path-dependent probability over words. The tree structure is inferred via a nested Dirichlet process, and a retrospective stick-breaking sampler is used to infer the tree depth and width.

Coded aperture compressive temporal imaging.

We use mechanical translation of a coded aperture for code division multiple access compression of video. We discuss the compressed video's temporal resolution and present experimental results for reconstructions of > 10 frames of temporal data per coded snapshot.

Task-driven adaptive statistical compressive sensing of gaussian mixture models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.

Lognormal and gamma mixed negative binomial regression

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior information, such as sparsity in the regression coefficients. By placing a gamma distribution prior on the NB dispersion parameter r, and connecting a log-normal distribution prior with the logit of the NB probability parameter p, efficient Gibbs sampling and variational Bayes inference are both developed. The closed-form updates are obtained by exploiting conditional conjugacy via both a compound Poisson representation and a Polya-Gamma distribution based data augmentation approach. The proposed Bayesian inference can be implemented routinely, while being easily generalizable to more complex settings involving multivariate dependence structures. The algorithms are illustrated using real examples. Copyright 2012 by the author(s)/owner(s).