Leveraging Contextual Cues for Generating Basketball Highlights

The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in. The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies. Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games.Comment: Proceedings of ACM Multimedia 201

The calibratrion of dopplergrams and magnetograms at BBSO

The calibration procedure for the Big Bear Solar Observatory (BBSO) videomagnetograph in which the radial velocity of the sidereal rotation of the Sun is used as a calibrator is described. One of the key points of the procedure is to eliminate the effects of the Earth's motion relative to the Sun and the temperature instability of the birefringent filter by tuning the bandpass of the birefringent filter. The other is to make the light level of the direct image of the videomagnetograph the same both in Doppler and in Zeeman modes in order to reduce the errors introduced by imperfect linearity of the transfer curve of the camera tube. Some practical problems of calibration are discussed for further improvement

The branching Brownian motion seen from its tip

It has been conjectured since the work of Lalley and Sellke (1987) that the branching Brownian motion seen from its tip (e.g. from its rightmost particle) converges to an invariant point process. Very recently, it emerged that this can be proved in several different ways (see e.g. Brunet and Derrida, 2010, Arguin et al., 2010, 2011). The structure of this extremal point process turns out to be a Poisson point process with exponential intensity in which each atom has been decorated by an independent copy of an auxiliary point process. The main goal of the present work is to give a complete description of the limit object via an explicit construction of this decoration point process. Another proof and description has been obtained independently by Arguin et al. (2011).Comment: 47 pages, 3 figure

PDMS/PVA composite ferroelectret for improved energy harvesting performance

This paper address the PDMS ferroelectret discharge issue for improved long- term energy harvesting performance. The PDMS/PVA ferroelectret is fabricated using a 3D-printed plastic mould technology and a functional PVA composite layer is introduced. The PDMS/PVA composite ferroelectret achieved 80% piezoelectric coefficient d33 remaining, compared with 40% without the proposed layer over 72 hours. Further, the retained percentage of output voltage is about 73% over 72 hours

Modelling thermomechanical behaviour of Cr-Mo-V steel

This paper presents a mechanism-based approach for modelling the thermomechanical behaviour of a Cr-Mo-V steel. A set of unified viscoplastic constitutive equations were employed to model dislocation density, recrystallisation and grain size during deformation. The evolution of dislocation density accounts for the build-up of dislocations due to plastic strain, the static and dynamic recovery and the effect of recrystallisation. Recrystallisation occurs when a critical dislocation density is reached after an incubation time, and grain size becomes smaller after such event. Gleeble compression tests were used to obtain Stress-strain curves and evaluate the microstructural evolution at different temperature and strain rate, and the material constants for the model were determined from the experimental data. Copyright © 2010 MS&T10®

Orthogonal learning particle swarm optimization

Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood’s best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSOL algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness