Determining Principal Component Cardinality through the Principle of Minimum Description Length

PCA (Principal Component Analysis) and its variants areubiquitous techniques for matrix dimension reduction and reduced-dimensionlatent-factor extraction. One significant challenge in using PCA, is thechoice of the number of principal components. The information-theoreticMDL (Minimum Description Length) principle gives objective compression-based criteria for model selection, but it is difficult to analytically applyits modern definition - NML (Normalized Maximum Likelihood) - to theproblem of PCA. This work shows a general reduction of NML prob-lems to lower-dimension problems. Applying this reduction, it boundsthe NML of PCA, by terms of the NML of linear regression, which areknown.Comment: LOD 201

Nonparametric Hierarchical Clustering of Functional Data

In this paper, we deal with the problem of curves clustering. We propose a nonparametric method which partitions the curves into clusters and discretizes the dimensions of the curve points into intervals. The cross-product of these partitions forms a data-grid which is obtained using a Bayesian model selection approach while making no assumptions regarding the curves. Finally, a post-processing technique, aiming at reducing the number of clusters in order to improve the interpretability of the clustering, is proposed. It consists in optimally merging the clusters step by step, which corresponds to an agglomerative hierarchical classification whose dissimilarity measure is the variation of the criterion. Interestingly this measure is none other than the sum of the Kullback-Leibler divergences between clusters distributions before and after the merges. The practical interest of the approach for functional data exploratory analysis is presented and compared with an alternative approach on an artificial and a real world data set

Precision mass measurements of radioactive nuclei at JYFLTRAP

The Penning trap mass spectrometer JYFLTRAP was used to measure the atomic masses of radioactive nuclei with an uncertainty better than 10 keV. The atomic masses of the neutron-deficient nuclei around the N = Z line were measured to improve the understanding of the rp-process path and the SbSnTe cycle. Furthermore, the masses of the neutron-rich gallium (Z = 31) to palladium (Z = 46) nuclei have been measured. The physics impacts on the nuclear structure and the r-process paths are reviewed. A better understanding of the nuclear deformation is presented by studying the pairing energy around A = 100.Comment: 4 pages and 4 figures, RNB7 conf. pro

Q_EC values of the Superallowed beta-Emitters 10-C, 34-Ar, 38-Ca and 46-V

The Q_EC values of the superallowed beta+ emitters 10-C, 34-Ar, 38-Ca and 46-V have been measured with a Penning-trap mass spectrometer to be 3648.12(8), 6061.83(8), 6612.12(7) and 7052.44(10) keV, respectively. All four values are substantially improved in precision over previous results.Comment: 9 pages, 7 figures, 5 table

Isomeric states close to doubly magic 132^{132}Sn studied with JYFLTRAP

The double Penning trap mass spectrometer JYFLTRAP has been employed to measure masses and excitation energies for $11/2^-$ isomers in $^{121}$Cd, $^{123}$Cd, $^{125}$Cd and $^{133}$Te, for $1/2^-$ isomers in $^{129}$In and $^{131}$In, and for $7^-$ isomers in $^{130}$Sn and $^{134}$Sb. These first direct mass measurements of the Cd and In isomers reveal deviations to the excitation energies based on results from beta-decay experiments and yield new information on neutron- and proton-hole states close to $^{132}$Sn. A new excitation energy of 144(4) keV has been determined for $^{123}$Cd$^m$. A good agreement with the precisely known excitation energies of $^{121}$Cd$^m$, $^{130}$Sn$^m$, and $^{134}$Sb$^m$ has been found.Comment: 10 pages, 6 figures, submitted to Phys. Rev.

Textiles as Material Gestalt: Cloth as a Catalyst in the Co-designing Process

Textiles is the common language within Emotional Fit, a collaborative research project investigating a person-centred, sustainable approach to fashion for an ageing female demographic (55+). Through the co-designing of a collection of research tools, textiles have acted as a material gestalt for exploring our research participants' identities by tracing their embodied knowledge of fashionable dress. The methodology merges Interpretative Phenomenological Analysis, co-design and a simultaneous approach to textile and garment design. Based on an enhanced understanding of our participants textile preferences, particular fabric qualities have catalysed silhouettes, through live draping and geometric pattern cutting to accommodate multiple body shapes and customisation. Printedtextiles have also been digitally crafted in response to the contours of the garment and body and personal narratives of wear. Sensorial and tactile interactions have informed the engineering and scaling of patterns within zero-waste volumes. The article considers the functional and aesthetic role of textiles

PAC-Bayesian Bounds for Randomized Empirical Risk Minimizers

The aim of this paper is to generalize the PAC-Bayesian theorems proved by Catoni in the classification setting to more general problems of statistical inference. We show how to control the deviations of the risk of randomized estimators. A particular attention is paid to randomized estimators drawn in a small neighborhood of classical estimators, whose study leads to control the risk of the latter. These results allow to bound the risk of very general estimation procedures, as well as to perform model selection

Handwritten digit recognition by bio-inspired hierarchical networks

The human brain processes information showing learning and prediction abilities but the underlying neuronal mechanisms still remain unknown. Recently, many studies prove that neuronal networks are able of both generalizations and associations of sensory inputs. In this paper, following a set of neurophysiological evidences, we propose a learning framework with a strong biological plausibility that mimics prominent functions of cortical circuitries. We developed the Inductive Conceptual Network (ICN), that is a hierarchical bio-inspired network, able to learn invariant patterns by Variable-order Markov Models implemented in its nodes. The outputs of the top-most node of ICN hierarchy, representing the highest input generalization, allow for automatic classification of inputs. We found that the ICN clusterized MNIST images with an error of 5.73% and USPS images with an error of 12.56%

Challenges of Religious Literacy in Education : Islam and the Governance of Religious Diversity in Multi-faith Schools

This chapter seeks take part in an emerging research where religion is approached as a whole school endeavor. Previous research and policy recommendations typically focused on teaching about religion in school, but the accommodation of religious diversity in the wider school culture merits more attention. Based on observations in our multiple case studies, we discuss the multi-level governance of religious diversity in Finnish multi-faith schools with a particular focus on the challenges of religious literacy for educators. The three examples we present focus on the inclusion of Muslims in Finnish schools and in particular on the challenges for educator (1) in interpreting the distinction between religion and culture, (2) in recognizing and handling intra-religious diversity, and (3) in being aware of Protestant conceptions of religion and culture. A theme cutting across these examples is how they reflect the tendencies either to see different situations merely through the lens of religion (religionisation), or not to recognize the importance of religion at all (religion-blindness). We argue that religious literacy should be recognized and developed as a vital part of the intercultural competencies of educators.Peer reviewe