Cloudlet-based just-in-time indexing of IoT video

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The THUMOS Challenge on Action Recognition for Videos "in the Wild"

Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task. In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos. These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions. The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world. In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures. We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection. We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches. Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos. We conclude by proposing several directions and improvements for future THUMOS challenges.Comment: Preprint submitted to Computer Vision and Image Understandin

Micro-CT study of male genitalia and reproductive system of the Asian citrus psyllid, Diaphorina citri Kuwayama, 1908 (Insecta: Hemiptera, Liviidae)

The Asian citrus psyllid (ACP), Diaphorina citri, is a major vector of the bacteria Candidatus Liberibacter asiaticus and C.L. americanus, which cause Huanglongbing disease (HLB) (aka Citrus greening disease), considered the most serious bacterial disease of citrus trees. As part of a multidisciplinary project on psyllid biology (www.citrusgreening.org), the results presented here concern a detailed anatomical study of the male reproductive system (testes, seminal vesicles, accessory glands, sperm pump, connecting ducts, and aedeagus) using micro-computed tomography (micro-CT). The study summarizes current knowledge on psyllids male reproductive system and represents significant advances in the knowledge of ACP anatomy.This work was supported by USDA-NIFA Award 2014-70016-23028 ªDeveloping an Infrastructure and Product Test Pipeline to Deliver Novel Therapies for Citrus Greening Diseaseº, 2015-2020

Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children

Background: The Paediatric Quality of Life Inventory (PedsQL™) questionnaire is a widely used, generic instrument designed for measuring health-related quality of life (HRQoL); however, it is not preference-based and therefore not suitable for cost–utility analysis. The Child Health Utility Index–9 Dimension (CHU-9D), however, is a preference-based instrument that has been primarily developed to support cost–utility analysis. Objective: This paper presents a method for estimating CHU-9D index scores from responses to the PedsQL™ using data from a randomised controlled trial of prednisolone therapy for treatment of childhood corticosteroid-sensitive nephrotic syndrome. Methods: HRQoL data were collected from children at randomisation, week 16, and months 12, 18, 24, 36 and 48. Observations on children aged 5 years and older were pooled across all data collection timepoints and were then randomised into an estimation (n = 279) and validation (n = 284) sample. A number of models were developed using the estimation data before internal validation. The best model was chosen using multi-stage selection criteria. Results: Most of the models developed accurately predicted the CHU-9D mean index score. The best performing model was a generalised linear model (mean absolute error = 0.0408; mean square error = 0.0035). The proportion of index scores deviating from the observed scores by 13 years) or patient groups with particularly poor quality of life. ISRCTN Registry No: 1664524

Semi-supervised clustering via learnt codeword distances

10.5244/C.22.90BMVC 2008 - Proceedings of the British Machine Vision Conference 200