Pseudoconvex proximal splitting for L∞problems in multiview geometry

In this paper we study optimization methods for minimizing large-scale pseudoconvex L∞problems in multiview geometry. We present a novel algorithm for solving this class of problem based on proximal splitting methods. We provide a brief derivation of the proposed method along with a general convergence analysis. The resulting meta-algorithm requires very little effort in terms of implementation and instead makes use of existing advanced solvers for non-linear optimization. Preliminary experiments on a number of real image datasets indicate that the proposed method experimentally matches or outperforms current state-of-the-art solvers for this class of problems.

Paradox lost: variable colour-pattern geometry is associated with differences in movement in aposematic frogs

Aposematic signal variation is a paradox: predators are better at learning and retaining the association between conspicuousness and unprofitability when signal variation is low. Movement patterns and variable colour patterns are linked in non-aposematic species: striped patterns generate illusions of altered speed and direction when moving linearly, affecting predators' tracking ability; blotched patterns benefit instead from unpredictable pauses and random movement. We tested whether the extensive colour-pattern variation in an aposematic frog is linked to movement, and found that individuals moving directionally and faster have more elongated patterns than individuals moving randomly and slowly. This may help explain the paradox of polymorphic aposematism: variable warning signals may reduce protection, but predator defence might still be effective if specific behaviours are tuned to specific signals. The interacting effects of behavioural and morphological traits may be a key to the evolution of warning signals. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Geometry and geography

This is an extract from an ongoing project: a ficitionalised memoir in poetic fragments connecting personal trauma with the fall-out from the scientific research in the Cold War. It is influenced by Michel Blanchot's The Writing of the Disaster and an ongoing meditation on the poetics of Marguerite Duras and the poetics of awkwardness.

Greater association of peak neuromuscular performance with cortical bone geometry, bone mass and bone strength than bone density : a study in 417 older women

BACKGROUND: We evaluated which aspects of neuromuscular performance are associated with bone mass, density, strength and geometry. METHODS: 417 women aged 60-94years were examined. Countermovement jump, sit-to-stand test, grip strength, forearm and calf muscle cross-sectional area, areal bone mineral content and density (aBMC and aBMD) at the hip and lumbar spine via dual X-ray absorptiometry, and measures of volumetric vBMC and vBMD, bone geometry and section modulus at 4% and 66% of radius length and 4%, 38% and 66% of tibia length via peripheral quantitative computed tomography were performed. The first principal component of the neuromuscular variables was calculated to generate a summary neuromuscular variable. Percentage of total variance in bone parameters explained by the neuromuscular parameters was calculated. Step-wise regression was also performed. RESULTS: At all pQCT bone sites (radius, ulna, tibia, fibula), a greater percentage of total variance in measures of bone mass, cortical geometry and/or bone strength was explained by peak neuromuscular performance than for vBMD. Sit-to-stand performance did not relate strongly to bone parameters. No obvious differential in the explanatory power of neuromuscular performance was seen for DXA aBMC versus aBMD. In step-wise regression, bone mass, cortical morphology, and/or strength remained significant in relation to the first principal component of the neuromuscular variables. In no case was vBMD positively related to neuromuscular performance in the final step-wise regression models. CONCLUSION: Peak neuromuscular performance has a stronger relationship with leg and forearm bone mass and cortical geometry as well as proximal forearm section modulus than with vBMD.

A convex geometry-based blind source separation method for separating nonnegative sources

This paper presents a convex geometry (CG)-based method for blind separation of nonnegative sources. First, the unaccessible source matrix is normalized to be column-sum-to-one by mapping the available observation matrix. Then, its zero-samples are found by searching the facets of the convex hull spanned by the mapped observations. Considering these zero-samples, a quadratic cost function with respect to each row of the unmixing matrix, together with a linear constraint in relation to the involved variables, is proposed. Upon which, an algorithm is presented to estimate the unmixing matrix by solving a classical convex optimization problem. Unlike the traditional blind source separation (BSS) methods, the CG-based method does not require the independence assumption, nor the uncorrelation assumption. Compared with the BSS methods that are specifically designed to distinguish between nonnegative sources, the proposed method requires a weaker sparsity condition. Provided simulation results illustrate the performance of our method.