Measurement and Modeling of Wireless Off-Body Propagation Characteristics under Hospital Environment at 6-8.5 GHz

© 2013 IEEE. A measurement-based novel statistical path-loss model with a height-dependent factor and a body obstruction (BO) attenuation factor for off-body channel under a hospital environment at 6-8.5 GHz is proposed. The height-dependent factor is introduced to emulate different access point (AP) arrangement scenarios, and the BO factor is employed to describe the effect caused by different body-worn positions. The height-dependent path-loss exponent is validated to fluctuate from 2 to 4 with AP height increasing by employing both computer simulation and classical two-ray model theory. As further validated, the proposed model can provide more flexibility and higher accuracy compared with its existing counterparts. The presented channel model is expected to provide wireless link budget estimation and to further develop the physical layer algorithms for body-centric communication systems under hospital environments

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

Childhood-onset Leber hereditary optic neuropathy

BACKGROUND: The onset of Leber hereditary optic neuropathy (LHON) is relatively rare in childhood. This study describes the clinical and molecular genetic features observed in this specific LHON subgroup. METHODS: Our retrospective study consisted of a UK paediatric LHON cohort of 27 patients and 69 additional cases identified from a systematic review of the literature. Patients were included if visual loss occurred at the age of 12 years or younger with a confirmed pathogenic mitochondrial DNA mutation: m.3460G>A, m.11778G>A or m.14484T>C. RESULTS: In the UK paediatric LHON cohort, three patterns of visual loss and progression were observed: (1) classical acute (17/27, 63%); (2) slowly progressive (4/27, 15%); and (3) insidious or subclinical (6/27, 22%). Diagnostic delays of 3-15 years occurred in children with an insidious mode of onset. Spontaneous visual recovery was more common in patients carrying the m.3460G>A and m.14484T>C mutations compared with the m.11778G>A mutation. Based a meta-analysis of 67 patients with available visual acuity data, 26 (39%) patients achieved a final best-corrected visual acuity (BCVA) ≥0.5 Snellen decimal in at least one eye, whereas 13 (19%) patients had a final BCVA <0.05 in their better seeing eye. CONCLUSIONS: Although childhood-onset LHON carries a relatively better visual prognosis, approximately 1 in 5 patients will remain within the visual acuity criteria for legal blindness in the UK. The clinical presentation can be insidious and LHON should be considered in the differential diagnosis when faced with a child with unexplained subnormal vision and optic disc pallor

Stars made in outflows may populate the stellar halo of the Milky Way

We study stellar-halo formation using six Milky-Way-mass galaxies in FIRE-2 cosmological zoom simulations. We find that 5-40 per cent of the outer (50-300 kpc) stellar halo in each system consists of in-situ stars that were born in outflows from the main galaxy. Outflow stars originate from gas accelerated by superbubble winds, which can be compressed, cool, and form co-moving stars. The majority of these stars remain bound to the halo and fall back with orbital properties similar to the rest of the stellar halo at z = 0. In the outer halo, outflow stars are more spatially homogeneous, metal-rich, and alpha-element-enhanced than the accreted stellar halo. At the solar location, up to ∼10 per cent of our kinematically identified halo stars were born in outflows; the fraction rises to as high as ∼40 per cent for the most metal-rich local halo stars ([Fe/H] &gt;-0.5). Such stars can be retrograde and create features similar to the recently discoveredMilkyWay 'Splash' in phase space.We conclude that theMilkyWay stellar halo could contain local counterparts to stars that are observed to form in molecular outflows in distant galaxies. Searches for such a population may provide a new, near-field approach to constraining feedback and outflow physics. A stellar halo contribution from outflows is a phase-reversal of the classic halo formation scenario of Eggen, Lynden-Bell &amp; Sandange, who suggested that halo stars formed in rapidly infalling gas clouds. Stellar outflows may be observable in direct imaging of external galaxies and could provide a source for metal-rich, extreme-velocity stars in the Milky Way

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients.

Cochlear implants restore hearing in patients with severe to profound deafness by delivering electrical stimuli inside the cochlea. Understanding stimulus current spread, and how it correlates to patient-dependent factors, is hampered by the poor accessibility of the inner ear and by the lack of clinically-relevant in vitro, in vivo or in silico models. Here, we present 3D printing-neural network co-modelling for interpreting electric field imaging profiles of cochlear implant patients. With tuneable electro-anatomy, the 3D printed cochleae can replicate clinical scenarios of electric field imaging profiles at the off-stimuli positions. The co-modelling framework demonstrated autonomous and robust predictions of patient profiles or cochlear geometry, unfolded the electro-anatomical factors causing current spread, assisted on-demand printing for implant testing, and inferred patients' in vivo cochlear tissue resistivity (estimated mean = 6.6 kΩcm). We anticipate our framework will facilitate physical modelling and digital twin innovations for neuromodulation implants

Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

Supermassive black holes do not correlate with galaxy disks or pseudobulges

The masses of supermassive black holes are known to correlate with the properties of the bulge components of their host galaxies. In contrast, they appear not to correlate with galaxy disks. Disk-grown pseudobulges are intermediate in properties between bulges and disks. It has been unclear whether they do or do not correlate with black holes in the same way that bulges do, because too few pseudobulges were classified to provide a clear result. At stake are conclusions about which parts of galaxies coevolve with black holes, possibly by being regulated by energy feedback from black holes. Here we report pseudobulge classifications for galaxies with dynamically detected black holes and combine them with recent measurements of velocity dispersions in the biggest bulgeless galaxies. These data confirm that black holes do not correlate with disks and show that they correlate little or not at all with pseudobulges. We suggest that there are two different modes of black hole feeding. Black holes in bulges grow rapidly to high masses when mergers drive gas infall that feeds quasar-like events. In contrast, small black holes in bulgeless galaxies and galaxies with pseudobulges grow as low-level Seyferts. Growth of the former is driven by global processes, so the biggest black holes coevolve with bulges, but growth of the latter is driven locally and stochastically, and they do not coevolve with disks and pseudobulges.Comment: 6 pages, 3 Postscript figures, 1 table; to appear in Nature (20 January 2011