There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

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Kernel Methods for Pattern Analysis

We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.

/pdf/canonical-correlation-analysis-an-overview-with-application-2zvylpnrdn.pdf

Canonical Correlation Analysis: An Overview with Application to Learning Methods

The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

https://www.pnas.org/content/pnas/113/28/7900.full.pdf

Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates

IEEE Transactions on Pattern Analysis and Machine Intelligence

The previously observed supraclavicular depot of brown adipose tissue (BAT) in adult humans was commonly believed to be the equivalent of the interscapular thermogenic organ of small mammals. This view was recently disputed on the basis of the demonstration that this depot consists of beige (also called brite) brown adipocytes, a newly identified type of brown adipocyte that is distinct from the classical brown adipocytes that make up the interscapular thermogenic organs of other mammals. A combination of high-resolution imaging techniques and histological and biochemical analyses showed evidence for an anatomically distinguishable interscapular BAT (iBAT) depot in human infants that consists of classical brown adipocytes, a cell type that has so far not been shown to exist in humans. On the basis of these findings, we conclude that infants, similarly to rodents, have the bona fide iBAT thermogenic organ consisting of classical brown adipocytes that is essential for the survival of small mammals in a cold environment.

/pdf/evidence-for-two-types-of-brown-adipose-tissue-in-humans-3y8ytof0kb.pdf

Evidence for two types of brown adipose tissue in humans

This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative MRI. Earlier published studies of this method are summarized, and a previously unpublished validation study, based on 4753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy X-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRIs show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 and 4.6 per cent for fat (computed from AT) and LT, respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of >20 per cent. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat, in combination with rapid scanning protocols and efficient image analysis tools, makes quantitative MRI a powerful tool for advanced body composition assessment.

https://jim.bmj.com/content/jim/66/5/1.10.full.pdf

Advanced body composition assessment: from body mass index to body composition profiling

A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated. Magn Reson Med 45:323-330, 2001.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/1522-2594%28200102%2945%3A2%3C323%3A%3AAID-MRM1041%3E3.0.CO%3B2-%23

Detection of neural activity in functional MRI using canonical correlation analysis.

Adaptive analysis of fMRI data

This paper presents our general strategy for designing learning machines as well as a number of particular designs. The search for methods allowing a sufficient level of adaptivity are based on two main principles: 1) simple adaptive local models; and 2) adaptive model distribution. Particularly important concepts in our work is mutual information and canonical correlation. Examples are given on learning feature descriptors, modeling disparity, synthesis of a global 3-mode model and a setup for reinforcement learning of online video coder parameter control.

/pdf/learning-multidimensional-signal-processing-34q0fom2z4.pdf

Magnus Borga

Papers

Evidence for two types of brown adipose tissue in humans

Advanced body composition assessment: from body mass index to body composition profiling

Detection of neural activity in functional MRI using canonical correlation analysis.

Adaptive analysis of fMRI data

Learning multidimensional signal processing