411 research outputs found

    A machine learning approach to detect violent behaviour from video

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    The automatic classification of violent actions performed by two or more persons is an important task for both societal and scientific purposes. In this paper, we propose a machine learning approach, based a Support Vector Machine (SVM), to detect if a human action, captured on a video, is or not violent. Using a pose estimation algorithm, we focus mostly on feature engineering, to generate the SVM inputs. In particular, we hand-engineered a set of input features based on keypoints (angles, velocity and contact detection) and used them, under distinct combinations, to study their effect on violent behavior recognition from video. Overall, an excellent classification was achieved by the best performing SVM model, which used keypoints, angles and contact features computed over a 60 frame image input range.Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013

    Tubal lymphangioma associated with ectopic pregnancy: a rare case report

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    Cavernous lymphangioma is a rare benign lymphatic neoplasm which occurs in occur in oral cavity, limbs, and abdomen. Presentation of cavernous lymphangioma of the fallopian tube is extremely rare. Cavernous lymphangioma presenting in a case of ectopic pregnancy is discussed below

    NA+ K+ ATPase post exposure recovery from Lead intoxicated freshwater fish Anabas testudineus.

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    NA+ K+ ATPase are important amongst the several molecules available in the cells, Carbohydrates play an important role in the cellular process  Under extreme stress conditions, carbohydrate membrane bound enzyme such as NA+ K+ ATPase have been known to act as the energy supplier in metabolic pathways and biochemical reactions. In the present investigation fish  treated with an equitoxic dose of 10 ppm of lead nitrate and lead acetate intoxicated fish After a period of 15 days of exposure a batch from lead nitrate exposed fish and a batch from lead acetate exposed fish were transferred to lead-free water. Fishes were scarified on 1, 4, 8, 12 and 15 days for the analysis of of recovery pattern in tissues viz. liver, muscle, kidney, gill and brain .It is found that lead toxicated fishes were recovered after 15 days depends upon physical condition of the fish

    Food Limitation as a Potentially Emerging Contributor to the Asian Vulture Crisis

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    It was believed that the reason for decline in Asian vulture population is the drug, Diclofenac sodium (DFS), used in livestock. Even after declaring the DFS use banned by the government, apparent decrease in the population of vultures was reported. Alternate hypothesis was suggested that food limitation may be a cause of Asian vulture crisis in Pakistan. Very recent shifts in livestock utilization observed in Pakistan may present a significant barrier to vulture recovery. Increased livestock utilization is translated to fewer carcasses. Since 2005, no livestock carcasses were found in 1650 km transect in the habitat of vultures. Carcasses recorded 13 in 1999 gradually declined to almost zero in 2005 and onwards, which suggests DFS may not be the only cause of Asian Vulture Crisi

    Probability densities for the sums of iterates of the sine-circle map in the vicinity of the quasi-periodic edge of chaos

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    We investigate the probability density of rescaled sum of iterates of sine-circle map within quasi-periodic route to chaos. When the dynamical system is strongly mixing (i.e., ergodic), standard Central Limit Theorem (CLT) is expected to be valid, but at the edge of chaos where iterates have strong correlations, the standard CLT is not necessarily to be valid anymore. We discuss here the main characteristics of the central limit behavior of deterministic dynamical systems which exhibit quasi-periodic route to chaos. At the golden-mean onset of chaos for the sine-circle map, we numerically verify that the probability density appears to converge to a q-Gaussian with q<1 as the golden mean value is approached.Comment: 7 pages, 7 figures, 1 tabl

    Modeling supersonic heated jet noise at fixed jet Mach number using an asymptotic approach for the acoustic analogy Green’s function and an optimized turbulence model

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    In this study we show how accurate jet noise predictions can be achieved within Goldstein’s generalized acoustic analogy formulation for heated and un-heated supersonic jets using a previously developed asymptotic theory for the adjoint vector Green’s function and a turbulence model whose independent parameters are determined using an optimization algorithm . In this approach, mean flow non-parallelism enters the lowest order dominant balance producing enhanced amplification at low frequencies, which we believe corresponds to the peak sound at small polar observation angles. The novel aspect of this paper is that we exploit both mean flow and turbulence structure from existent Large Eddy Simulations database of two axi-symmetric round jets at fixed jet Mach number and different nozzle temperature ratios to show (broadly speaking) the efficacy of the asymptotic approach. The empirical parameters that enter via local turbulence length scales within the algebraic-exponential turbulence model are determined by optimizing against near field turbulence data post-processed from the LES calculation. Our results indicate that accurate jet noise predictions are obtained with this approach up to a Strouhal number of 0.5 for both jets without introducing significant empiricism

    Breakdown of Simple Scaling in Abelian Sandpile Models in One Dimension

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    We study the abelian sandpile model on decorated one dimensional chains. We determine the structure and the asymptotic form of distribution of avalanche-sizes in these models, and show that these differ qualitatively from the behavior on a simple linear chain. We find that the probability distribution of the total number of topplings ss on a finite system of size LL is not described by a simple finite size scaling form, but by a linear combination of two simple scaling forms ProbL(s)=1/Lf1(s/L)+1/L2f2(s/L2)Prob_L(s) = 1/L f_1(s/L) + 1/L^2 f_2(s/L^2), for large LL, where f1f_1 and f2f_2 are some scaling functions of one argument.Comment: 10 pages, revtex, figures include
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