Discovery of Shared Semantic Spaces for Multiscene Video Query and Summarization.

The growing rate of public space CCTV installations has generated a need for automated methods for exploiting video surveillance data including scene understanding, query, behaviour annotation and summarization. For this reason, extensive research has been performed on surveillance scene understanding and analysis. However, most studies have considered single scenes, or groups of adjacent scenes. The semantic similarity between different but related scenes (e.g., many different traffic scenes of similar layout) is not generally exploited to improve any automated surveillance tasks and reduce manual effort. Exploiting commonality, and sharing any supervised annotations, between different scenes is however challenging due to: Some scenes are totally un-related -- and thus any information sharing between them would be detrimental; while others may only share a subset of common activities -- and thus information sharing is only useful if it is selective. Moreover, semantically similar activities which should be modelled together and shared across scenes may have quite different pixel-level appearance in each scene. To address these issues we develop a new framework for distributed multiple-scene global understanding that clusters surveillance scenes by their ability to explain each other's behaviours; and further discovers which subset of activities are shared versus scene-specific within each cluster. We show how to use this structured representation of multiple scenes to improve common surveillance tasks including scene activity understanding, cross-scene query-by-example, behaviour classification with reduced supervised labelling requirements, and video summarization. In each case we demonstrate how our multi-scene model improves on a collection of standard single scene models and a flat model of all scenes.Comment: Multi-Scene Traffic Behaviour Analysis ---- Accepted at IEEE Transactions on Circuits and Systems for Video Technolog

Acclimation of morphology and physiology in turf grass to low light environment: A review

This short review elucidated the significance of the research on acclimation of the morphology and physiology in turf grass to low light environment, the mechanism of physiological response and the photosynthetic regulation and control of turf grass to suit low light environment. We also discussed current research problems and provided insight into future relevant research.Key words: Low light, morphological change, physiological acclimation, regulation mechanism, turf grass

A spectral method for elliptic equations: the Dirichlet problem

An elliptic partial differential equation Lu=f with a zero Dirichlet boundary condition is converted to an equivalent elliptic equation on the unit ball. A spectral Galerkin method is applied to the reformulated problem, using multivariate polynomials as the approximants. For a smooth boundary and smooth problem parameter functions, the method is proven to converge faster than any power of 1/n with n the degree of the approximate Galerkin solution. Examples in two and three variables are given as numerical illustrations. Empirically, the condition number of the associated linear system increases like O(N), with N the order of the linear system.Comment: This is latex with the standard article style, produced using Scientific Workplace in a portable format. The paper is 22 pages in length with 8 figure

Lewis Base Passivation Mediates Charge Transfer at Perovskite Heterojunctions

Understanding interfacial charge transfer processes such as trap-mediated recombination and injection into charge transport layers (CTLs) is crucial for the improvement of perovskite solar cells. Herein, we reveal that the chemical binding of charge transport layers to CH3NH3PbI3 defect sites is an integral part of the interfacial charge injection mechanism in both n-i-p and p-i-n architectures. Specifically, we use a mixture of optical and X-ray photoelectron spectroscopy to show that binding interactions occur via Lewis base interactions between electron-donating moieties on hole transport layers and the CH3NH3PbI3 surface. We then correlate the extent of binding with an improvement in the yield and longer lifetime of injected holes with transient absorption spectroscopy. Our results show that passivation-mediated charge transfer has been occurring undetected in some of the most common perovskite configurations and elucidate a key design rule for the chemical structure of next-generation CTLs

Generalization Error in Deep Learning

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results

Real-Time Imaging System using a 12-MHz Forward-Looking Catheter with Single Chip CMUT-on-CMOS Array

Forward looking (FL) imaging catheters would be an important tool for several intravascular ultrasound (IVUS) and intracardiac echocardiography (ICE) applications. Single chip capacitive micromachined ultrasonic transducer (CMUT) arrays fabricated on front-end CMOS electronics with simplified electrical interconnect have been previously developed for highly flexible and compact catheters. In this study, we present a custom built real time imaging system utilizing catheters with single chip CMUT-on-CMOS arrays and show initial imaging results. The fabricated array has a dual-ring structure with 64 transmit (Tx) and 56 receive (Rx) elements. The CMUT arrays fit on a 2.1 mm diameter circular region with all the required front-end electronics. The device operates at 12 MHz center frequency and has around 20 V collapse voltage. The single-chip system requires 13 external connections including 4 Rx channels and power lines. The electrical connections to micro cables in the catheter are made from the top side of the chip using polyimide flex tapes. The device is placed on a 6-Fr catheter shaft and secured with a medical grade silicon rubber. For real time data acquisition, we developed a custom design FPGA based imaging platform to generate digital control sequences for the chip and collect RF data from Rx outputs. We performed imaging experiments using wire phantoms immersed in water to test the real time imaging system. The system has the potential to generate images at 32 fps rate with the particular catheter. The overall system is fully functional and shows promising image performance

Dendritic Spine Shape Analysis: A Clustering Perspective

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.Comment: Accepted for BioImageComputing workshop at ECCV 201

Composite-pulse magnetometry with a solid-state quantum sensor

The sensitivity of quantum magnetometers is challenged by control errors and, especially in the solid-state, by their short coherence times. Refocusing techniques can overcome these limitations and improve the sensitivity to periodic fields, but they come at the cost of reduced bandwidth and cannot be applied to sense static (DC) or aperiodic fields. Here we experimentally demonstrate that continuous driving of the sensor spin by a composite pulse known as rotary-echo (RE) yields a flexible magnetometry scheme, mitigating both driving power imperfections and decoherence. A suitable choice of RE parameters compensates for different scenarios of noise strength and origin. The method can be applied to nanoscale sensing in variable environments or to realize noise spectroscopy. In a room-temperature implementation based on a single electronic spin in diamond, composite-pulse magnetometry provides a tunable trade-off between sensitivities in the microT/sqrt(Hz) range, comparable to those obtained with Ramsey spectroscopy, and coherence times approaching T1

Age at menarche and prevention of hypertension through lifestyle in young Chinese adult women: result from project ELEFANT.

Early and late age at menarche are associated with risk of hypertension, but little is known whether modifiable lifestyle can reduce this risk. METHODS: Our study leverages 60,135 healthy young Chinese women from the Environmental and LifEstyle FActors iN metabolic health throughout life-course Trajectories (ELEFANT) study. Menarche age and lifestyle factors were assessed by self-reported questionnaires and hypertension was diagnosed by physicians. We estimated the odds ratios (ORs) of hypertension associated with menarche age using multivariable logistic regression. We further investigated whether modifiable lifestyles (body mass index, BMI; psychological stress; passive smoking; and imbalanced diet) increased risk in joint analyses. RESULTS: The association between age at menarche and hypertension was U-shaped, with age ≤ 12 at menarche giving the highest OR (1.46, 95% confidence interval [CI], 1.27-1.69) and ≥ 16 the second highest (OR = 1.36, 95% CI = 1.15-1.62). Simultaneous analysis of lifestyle risk factors and age of menarche showed that having one or more modifiable risk factors increased the menarche age-hypertension association. The risk of hypertension among participants with menarche age ≤ 12 decreased from OR 13.21 (95% CI = 5.17-29.36) with four high-risk lifestyle factors to 12.36 (95% CI = 9.51-16.05) with three high-risk factors, 5.24 (95% CI = 4.11-6.69) with two, and 2.76 (95% CI = 2.09-3.60) with one, in comparison to individuals with no high-risk lifestyle factors and menarche age 14. CONCLUSIONS: Our results suggest that modification of lifestyle, including maintenance of normal weight and a balanced diet, are associated with substantially reduce the risk of hypertension in high-risk individuals. Early and late age at menarche are risk factors for the development of hypertension in Western populations, and there is limited evidence that this is also true of Chinese populations. Targeted prevention of hypertension in vulnerable populations would be highly beneficial in efforts to reduce the incidence of cardiovascular disease, but it is not currently known whether lifestyle intervention could reduce hypertension risk. In this study, we analysed the risk of hypertension by age at menarche and four modifiable lifestyle factors (BMI, diet, psychological stress, and smoking tobacco) in a cohort of 60,135 young adult Chinese women (mean age 29). We identified that early and late age at menarche are associated with increased risk of hypertension in young Chinese women. There was joint effects between age at menarche and lifestyles on hypertension only participants with age at menarche ≤12 and being overweight or obese. Modification of lifestyle, including maintenance of normal weight and a balanced diet, can substantially reduce the risk of hypertension in high-risk individuals. In conclusion, our study has revealed that early and late menarche age are associated with the development of hypertension in young Chinese women, and that this risk is modified by healthy lifestyle traits

Analysis of factors influencing the modelling of occupant window opening behaviour in an office building in Beijing, China.

This paper introduces a longitudinal study monitoring occupants’ window opening behaviour in a mixed-mode office building in Beijing, China, when natural ventilation is specifically used for controlling the building’s indoor thermal environment. Based on the field measured data, the influence of factors, including outdoor air temperature, outdoor PM2.5, indoor air temperature, time of day, occupancy and previous window state, on the observed state of windows is analysed. All of them are influential on occupants’ window opening behaviour in the case study building, and so they can be used to model occupants’ window opening behaviour in buildings in China to achieve a better consideration of occupant behaviour in dynamic building performance simulation