Characteristic Polynomials of Sample Covariance Matrices: The Non-Square Case

We consider the sample covariance matrices of large data matrices which have i.i.d. complex matrix entries and which are non-square in the sense that the difference between the number of rows and the number of columns tends to infinity. We show that the second-order correlation function of the characteristic polynomial of the sample covariance matrix is asymptotically given by the sine kernel in the bulk of the spectrum and by the Airy kernel at the edge of the spectrum. Similar results are given for real sample covariance matrices

Whole-body vibration training induces hypertrophy of the human patellar tendon

I Brage finner du siste tekst-versjon av artikkelen, og den kan inneholde ubetydelige forskjeller fra forlagets pdf-versjon. Forlagets pdf-versjon finner du på onlinelibrary.wiley.com / In Brage you'll find the final text version of the article, and it may contain insignificant differences from the journal's pdf version. The definitive version is available at onlinelibrary.wiley.comAnimal studies suggest that regular exposure to whole-body vibration (WBV) induces an anabolic response in bone and tendon. However, the effects of this type of intervention on human tendon properties and its influence on the muscle-tendon unit function have never been investigated. The aim of this study was to investigate the effect of WBV training on the patellar tendon mechanical, material and morphological properties, the quadriceps muscle architecture and the knee extension torque–angle relationship. Fifty-five subjects were randomized into either a vibration, an active control, or an inactive control group. The active control subjects performed isometric squats on a vibration platform without vibration. Muscle and tendon properties were measured using ultrasonography and dynamometry. Vibration training induced an increase in proximal (6.3%) and mean (3.8%) tendon cross-sectional area, without any appreciable change in tendon stiffness and modulus or in muscle architectural parameters. Isometric torque at a knee angle of 90° increased in active controls (6.7%) only and the torque–angle relation remained globally unchanged in all groups. The present protocol did not appreciably alter knee extension torque production or the musculo-tendinous parameters underpinning this function. Nonetheless, this study shows for the first time that WBV elicits tendon hypertrophy in humans.Seksjon for fysisk prestasjonsevne / Department of Physical Performanc

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware, at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware.Comment: 7 pages, 4 figures, submitted to APL Machine Learnin

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here, we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two-dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware

Screening for breast cancer : medicalization, visualization and the embodied experience

Women’s perspectives on breast screening (mammography and breast awareness) were explored in interviews with midlife women sampled for diversity of background and health experience. Attending mammography screening was considered a social obligation despite women’s fears and experiences of discomfort. Women gave considerable legitimacy to mammography visualizations of the breast, and the expert interpretation of these. In comparison, women lacked confidence in breast awareness practices, directly comparing their sensory capabilities with those of the mammogram, although mammography screening did not substitute breast awareness in a straightforward way. The authors argue that reliance on visualizing technology may create a fragmented sense of the body, separating the at risk breast from embodied experience