Automatic normal orientation in point clouds of building interiors

Orienting surface normals correctly and consistently is a fundamental problem in geometry processing. Applications such as visualization, feature detection, and geometry reconstruction often rely on the availability of correctly oriented normals. Many existing approaches for automatic orientation of normals on meshes or point clouds make severe assumptions on the input data or the topology of the underlying object which are not applicable to real-world measurements of urban scenes. In contrast, our approach is specifically tailored to the challenging case of unstructured indoor point cloud scans of multi-story, multi-room buildings. We evaluate the correctness and speed of our approach on multiple real-world point cloud datasets

Task-Projected Hyperdimensional Computing for Multi-Task Learning

Brain-inspired Hyperdimensional (HD) computing is an emerging technique for cognitive tasks in the field of low-power design. As a fast-learning and energy-efficient computational paradigm, HD computing has shown great success in many real-world applications. However, an HD model incrementally trained on multiple tasks suffers from the negative impacts of catastrophic forgetting. The model forgets the knowledge learned from previous tasks and only focuses on the current one. To the best of our knowledge, no study has been conducted to investigate the feasibility of applying multi-task learning to HD computing. In this paper, we propose Task-Projected Hyperdimensional Computing (TP-HDC) to make the HD model simultaneously support multiple tasks by exploiting the redundant dimensionality in the hyperspace. To mitigate the interferences between different tasks, we project each task into a separate subspace for learning. Compared with the baseline method, our approach efficiently utilizes the unused capacity in the hyperspace and shows a 12.8% improvement in averaged accuracy with negligible memory overhead.Comment: To be published in 16th International Conference on Artificial Intelligence Applications and Innovation

Rainfall analysis using conventional and non-conventional rainfall information on monthly scale

The aim of this paper is to describe the technique used to create the merged analysis of rainfall over the Indian and adjoining region (1.5° to 35.5° N and 63.5° to 97.5° E). The technique is tested for monthly gridded fields of rainfall for a 2–year monsoon period (2001 and 2003) on a 1° x 1° latitude–longitude grid by merging rainfall estimates from different sources, viz satellite based estimates, rain gauge analysis and numerical weather prediction model rainfall. First, in order to reduce the random error involved in the satellite rainfall estimates and the model predictions, satellite and model estimates are combined linearly based on a maximum likelihood estimate method. In this case the weight for each component is inversely proportional to the squares of the individual random errors. The weight is determined by comparing the components with the concurrent gauge analysis. As the combined analysis contains bias from the individual input data sources, the combined analysis is then blended with the analysis based on gauge observations. It is seen that the merged analysis produced here is closer to the observations than the individual sources. It is observed that the magnitude and distribution of the orographic heavy rainfall along the Western Ghats of India is very different and more realistic compared to the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP). When compared with the India Meteorological Department analysis, it is found that the merged analysis shows higher correlation than the satellite and model predicted rainfall. From the results it can be concluded that this study has shown promising results and the analyses can be used as a bench mark for evaluating model simulations which serves as a basis for real–time monitoring. Based on these promising results, long term datasets on high resolution grid for daily and monthly scale over Indian and adjoining region will be generated, which in turn can be used to study spatial and temporal variability of rainfall over Indian and adjoining regio

Virtual outreach: economic evaluation of joint teleconsultations for patients referred by their general practitioner for a specialist opinion

Objectives To test the hypotheses that, compared with conventional outpatient consultations, joint teleconsultation (virtual outreach) would incur no increased costs to the NHS, reduce costs to patients, and reduce absences from work by patients and their carers.Design Cost consequences study alongside randomised controlled trial.Setting Two hospitals in London and Shrewsbury and 29 general practices in inner London and Wales.Participants 3170 patients identified; 2094 eligible for inclusion and willing to participate. 1051 randomised to virtual outreach and 1043 to standard outpatient appointments.Main outcome measures NHS costs, patient costs, health status (SF-12), time spent attending index consultation, patient satisfaction.Results Overall six month costs were greater for the virtual outreach consultations (pound724 per patient) than for conventional outpatient appointments (pound625): difference in means pound99 ($162; is not an element of138) (95% confidence interval pound10 to pound187, P=0.03). if the analysis is restricted to resource items deemed "attributable" to the index consultation, six month costs were still greater for virtual outreach: difference in means pound108 (pound73 to pound142, P < 0.0001). In both analyses the index consultation accounted for the excess cost. Savings to patients in terms of costs and time occurred in both centres: difference in mean total patient cost 8 pound (5 pound to 10 pound, P < 0.0001). Loss of productive time was less in the virtual outreach group: difference in mean cost pound11 (pound10 to pound12, P < 0.0001).Condusion The main hypothesis that virtual outreach would be cost neutral is rejected, but the hypotheses that costs to patients and losses in productivity would be lower are supported

Self-reported sleep duration and napping and incident heart failure: prospective associations in the British Regional Heart Study

Abstract Objectives We have examined the associations of self-reported night-time sleep duration and daytime sleep with incident heart failure (HF) in men with and without pre-existing cardiovascular disease (CVD). Design Population-based prospective study Setting General practices in 24 British towns Participants 3723 men aged 60-79 years without prevalent HF followed up for 9 years. Measurements Incident HF cases were obtained from primary care records. Assessment of sleep was based on self-reported sleep duration at night and daytime napping. Results Self-reported short night-time sleep duration and daytime sleep of > 1 hour were associated with pre-existing CVD, breathlessness, depression, poor health, physical inactivity and manual social class. In all men, self-reported daytime sleep of >1 hour duration was associated with significantly increased risk of HF after adjustment for potential confounders [adjusted HR=1.69 (1.06,2.71] compared to those who reported no daytime napping. Self-reported night-time sleep duration was not associated with HF risk except in men with pre-existing CVD. In these men, compared to night-time sleep of 7 hours the adjusted HRs for HF were 2.91 (1.31,6.45), 1.89 (0.89,4.03), 1.29 (0.61,2.71) and 1.80 (0.71,4.61) for those sleeping 9 h respectively. Snoring was not associated with HF risk. Conclusion Self-reported daytime napping of > 1 hour is associated with increased risk of HF in older men. Self-reported short sleep (<6h) in men with CVD is associated with particularly high risk of developing HF

Evolution of an atmospheric boundary layer at a tropical semi-arid station, Anand during boreal summer month of May - A case study

The evolution of an Atmospheric Boundary Layer (ABL) over a semi-arid land station, Anand, (22°35â²N, 72°55â²E, 45.1 m asl) in India, during the summer month of May, is examined using surface meteorological and radiosonde temperature and humidity data collected during LASPEX-97 for a 5-day period from 13-17 May 1997. These 5 days remained undisturbed, and clear sky weather conditions prevailed. However, the data obtained on these days are helpful in understanding the diurnal variation of the ABL over a land station. There are 5 observations per day at an interval of 3 h beginning with 0530 IST. The 0530 IST ascents are chosen to find out the initial ABL heights which exhibit the nocturnal cooling conditions. It is observed from the analysis of θv, θe, θes, q, and P profiles that the nocturnal boundary layer is stable with an inversion close to the ground. The top of an inversion layer is characterized by a θe minimum and a θes maximum. After dawn, the ABL grows to a height of 827 m at 0830 IST. Aloft, a residual layer up to 3200 m is observed. The daytime strong insolation causes formation of an unstable boundary layer close to the ground at 1130 IST with an elevated stable layer between 550 and 930 m. It is only by 1430 IST that the stable layer gets completely wiped out and a convective mixed layer develops up to a height of 3280 m. Lack of moisture inhibits formation of clouds. Hence the ABL at a semi-arid station like Anand is stable in the morning with residual layer aloft and develops into a dry convective boundary layer in the afternoon and evening. Growth of the convective boundary layer (CBL) is observed to be very rapid as it reaches a height up to 3280 m by the afternoon