Modeling near-field radiative heat transfer from sharp objects using a general 3d numerical scattering technique

We examine the non-equilibrium radiative heat transfer between a plate and finite cylinders and cones, making the first accurate theoretical predictions for the total heat transfer and the spatial heat flux profile for three-dimensional compact objects including corners or tips. We find qualitatively different scaling laws for conical shapes at small separations, and in contrast to a flat/slightly-curved object, a sharp cone exhibits a local \emph{minimum} in the spatially resolved heat flux directly below the tip. The method we develop, in which a scattering-theory formulation of thermal transfer is combined with a boundary-element method for computing scattering matrices, can be applied to three-dimensional objects of arbitrary shape.Comment: 5 pages, 4 figures. Corrected background information in the introduction, results and discussion unchange

Training a HyperDimensional Computing Classifier using a Threshold on its Confidence

Hyperdimensional computing (HDC) has become popular for light-weight and energy-efficient machine learning, suitable for wearable Internet-of-Things (IoT) devices and near-sensor or on-device processing. HDC is computationally less complex than traditional deep learning algorithms and achieves moderate to good classification performance. This article proposes to extend the training procedure in HDC by taking into account not only wrongly classified samples, but also samples that are correctly classified by the HDC model but with low confidence. As such, a confidence threshold is introduced that can be tuned for each dataset to achieve the best classification accuracy. The proposed training procedure is tested on UCIHAR, CTG, ISOLET and HAND dataset for which the performance consistently improves compared to the baseline across a range of confidence threshold values. The extended training procedure also results in a shift towards higher confidence values of the correctly classified samples making the classifier not only more accurate but also more confident about its predictions

Raven Eye: A Mobile Computing Solution for Site Exploitation

Site exploitation (SE) remains a critical mission for operators on the battlefield.  Since SE is a fairly new operation in the military, soldiers face specific challenges that hinder them from conducting a successful SE operation.  This paper details the design of a system, Raven Eye, which endeavors to improve the efficiency and effectiveness of SE.  Raven Eye is an Android based system that collects, stores, and sends SE data.  Raven Eye allows operators to collect exploited site data by capturing photos, videos, and biometrics.  Operators can annotate and tag recorded items.  Lastly, the operators transform data stored and collected via Raven Eye to a standardized report that accelerates follow-on analysis by intelligence personnel. 

Co-learning synaptic delays, weights and adaptation in spiking neural networks

Spiking neural networks (SNN) distinguish themselves from artificial neural networks (ANN) because of their inherent temporal processing and spike-based computations, enabling a power-efficient implementation in neuromorphic hardware. In this paper, we demonstrate that data processing with spiking neurons can be enhanced by co-learning the connection weights with two other biologically inspired neuronal features: 1) a set of parameters describing neuronal adaptation processes and 2) synaptic propagation delays. The former allows the spiking neuron to learn how to specifically react to incoming spikes based on its past. The trained adaptation parameters result in neuronal heterogeneity, which is found in the brain and also leads to a greater variety in available spike patterns. The latter enables to learn to explicitly correlate patterns that are temporally distanced. Synaptic delays reflect the time an action potential requires to travel from one neuron to another. We show that each of the co-learned features separately leads to an improvement over the baseline SNN and that the combination of both leads to state-of-the-art SNN results on all speech recognition datasets investigated with a simple 2-hidden layer feed-forward network. Our SNN outperforms the ANN on the neuromorpic datasets (Spiking Heidelberg Digits and Spiking Speech Commands), even with fewer trainable parameters. On the 35-class Google Speech Commands dataset, our SNN also outperforms a GRU of similar size. Our work presents brain-inspired improvements to SNN that enable them to excel over an equivalent ANN of similar size on tasks with rich temporal dynamics.Comment: 15 pages, 8 figure

Structural Modeling of a Novel CAPN5 Mutation that Causes Uveitis and Neovascular Retinal Detachment

CAPN5 mutations have been linked to autosomal dominant neovascular inflammatory vitreoretinopathy (ADNIV), a blinding autoimmune eye disease. Here, we link a new CAPN5 mutation to ADNIV and model the three-dimensional structure of the resulting mutant protein. In our study, a kindred with inflammatory vitreoretinopathy was evaluated by clinical eye examinations, DNA sequencing, and protein structural modeling to investigate the disease-causing mutation. Two daughters of an affected mother demonstrated symptoms of stage III ADNIV, with posterior uveitis, cystoid macular edema, intraocular fibrosis, retinal neovascularization, retinal degeneration, and cataract. The women also harbored a novel guanine to thymine (c.750G>T, p.Lys250Asn) missense mutation in exon 6 of CAPN5, a gene that encodes a calcium-activated cysteine protease, calpain-5. Modeling based on the structures of all known calpains revealed the mutation falls within a calcium-sensitive flexible gating loop that controls access to the catalytic groove. Three-dimensional modeling placed the new mutation in a region adjacent to two previously identified disease-causing mutations, all three of which likely disrupt hydrogen bonding within the gating loop, yielding a CAPN5 with altered enzymatic activity. This is the third case of a CAPN5 mutation leading to inherited uveitis and neovascular vitreoretinopathy, suggesting patients with ADNIV features should be tested for CAPN5 mutations. Structural modeling of novel variants can be used to support mechanistic consequences of the disease-causing variants

Pertactin-negative Bordetella pertussis strains in Canada: characterization of a dozen isolates based on a survey of 224 samples collected in different parts of the country over the last 20 years

SummaryObjectiveTo detect and characterize pertactin-negative Bordetella pertussis in Canada, especially for isolates collected in recent years.MethodsA total of 224 isolates from the years 1994–2013 were screened by Western immuno-blot for expression of pertactin. Pertactin-negative isolates were characterized by serotyping, pulsed-field gel electrophoresis (PFGE), and genotyping of their pertactin, fimbriae 3, pertussis toxin subunit 1, and pertussis toxin gene promoter region, as well as the complete sequence of the pertactin gene.ResultsTwelve isolates were pertactin-negative, giving an overall prevalence of 5.4%. However, no such isolate was found prior to 2011 and 17.8% of 62 isolates examined in 2012 were pertactin-negative. Ten pertactin-negative isolates contained a significant mutation in their pertactin (prn) genes. IS481 was found in the prn genes of eight isolates, while a single point mutation occurred either in the coding region (resulting in a premature stop codon) or in the promoter region (preventing gene transcription) in two other isolates. PFGE analysis also showed multiple profiles suggesting that several independent genetic events might have led to the emergence of these pertactin-negative strains rather than expansion of a single clone.ConclusionsAs reported elsewhere, pertactin-negative B. pertussis has emerged in Canada in recent years, notably in 2012. This coincided with an increase in pertussis activity in Canada. A further systematic study with a larger geographical representative sample is required to determine how these vaccine-negative strains may contribute to the overall changing epidemiology of pertussis in Canada

TMT telescope structure system: design and development progress report

The Thirty Meter Telescope (TMT) project has revised the reference optical configuration from an Aplanatic Gregorian to a Ritchey-Chrétien design. This paper describes the revised telescope structural design and outlines the design methodology for achieving the dynamic performance requirements derived from the image jitter error budget. The usage of transfer function tools which incorporate the telescope structure system dynamic characteristics and the control system properties is described along with the optimization process for the integrated system. Progress on the structural design for seismic considerations is presented. Moreover, mechanical design progress on the mount control system hardware such as the hydrostatic bearings and drive motors, cable wraps and safety system hardware such as brakes and absorbers are also presented

Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong.

BACKGROUND: Health authorities worldwide, especially in the Asia Pacific region, are seeking effective public-health interventions in the continuing epidemic of severe acute respiratory syndrome (SARS). We assessed the epidemiology of SARS in Hong Kong. METHODS: We included 1425 cases reported up to April 28, 2003. An integrated database was constructed from several sources containing information on epidemiological, demographic, and clinical variables. We estimated the key epidemiological distributions: infection to onset, onset to admission, admission to death, and admission to discharge. We measured associations between the estimated case fatality rate and patients' age and the time from onset to admission. FINDINGS: After the initial phase of exponential growth, the rate of confirmed cases fell to less than 20 per day by April 28. Public-health interventions included encouragement to report to hospital rapidly after the onset of clinical symptoms, contact tracing for confirmed and suspected cases, and quarantining, monitoring, and restricting the travel of contacts. The mean incubation period of the disease is estimated to be 6.4 days (95% CI 5.2-7.7). The mean time from onset of clinical symptoms to admission to hospital varied between 3 and 5 days, with longer times earlier in the epidemic. The estimated case fatality rate was 13.2% (9.8-16.8) for patients younger than 60 years and 43.3% (35.2-52.4) for patients aged 60 years or older assuming a parametric gamma distribution. A non-parametric method yielded estimates of 6.8% (4.0-9.6) and 55.0% (45.3-64.7), respectively. Case clusters have played an important part in the course of the epidemic. INTERPRETATION: Patients' age was strongly associated with outcome. The time between onset of symptoms and admission to hospital did not alter outcome, but shorter intervals will be important to the wider population by restricting the infectious period before patients are placed in quarantine