Dimension dependent hypercontractivity for Gaussian kernels

We derive sharp, local and dimension dependent hypercontractive bounds on the Markov kernel of a large class of diffusion semigroups. Unlike the dimension free ones, they capture refined properties of Markov kernels, such as trace estimates. They imply classical bounds on the Ornstein-Uhlenbeck semigroup and a dimensional and refined (transportation) Talagrand inequality when applied to the Hamilton-Jacobi equation. Hypercontractive bounds on the Ornstein-Uhlenbeck semigroup driven by a non-diffusive L\'evy semigroup are also investigated. Curvature-dimension criteria are the main tool in the analysis.Comment: 24 page

On a Three-Dimensional Gravity Model with Higher Derivatives

The purpose of this work is to present a model for 3D massive gravity with topological and higher-derivative terms. Causality and unitarity are discussed at tree-level. Power-counting renormalizability is also contemplated.Comment: 9 pages, Latex, no figures; to be published in Gen. Rel. Gra

Comparison of upper body strength gains between men and women after 10 weeks of resistance training

Resistance training (RT) offers benefits to both men and women. However, the studies about the differences between men and women in response to an RT program are not conclusive and few data are available about upper body strength response. The aim of this study was to compare elbow flexor strength gains in men and women after 10 weeks of RT. Forty-four college-aged men (22.63 +/- 2.34 years) and forty-seven college-aged women (21.62 +/- 2.96 years) participated in the study. The RT program was performed two days a week for 10 weeks. Before and after the training period, peak torque (PT) of the elbow flexors was measured with an isokinetic dynamometer. PT values were higher in men in comparison to women in pre-and post-tests (p 0.05). Effect sizes were 0.57 and 0.56 for men and women, respectively. In conclusion, the present study suggests that men and women have a similar upper body strength response to RT

Two-stage carcinogenesis with rat embryo cells in tissue culture.

Transformation of rat embryo fibroblasts in vitro has been investigated using initiation with either benzo(a)pyrene (BaP), 7,12-dimethylbena(a)anthracent (DMBA) or benzo(e)pyrene (BeP) and promotion with either phorbol ester (TPA) or croton oil (Cr.Oil). The criteria used to assess in vitro transformation were (a) the efficiency of cloning in liquid medium, (b) abnormal cellular morphology and (c) the development of malignant tumours following s.c. inoculation of newborn rats. The results show that the cloning efficiency, which remained low in the control cells, was increased to a variable extent in the treated groups. Transformation occurred in all groups, but occurred earliest in cells that were initiated and promoted. Initiation with DMBA or BaP and promotion with TPA or Cr.Oil led to the earliest acquisition of malignancy. Correlations were found between the transformation of cells in vitro and the acquisition of malignant potential, and between the carcinogenic action of the compounds in vitro and their action in vivo, but cloning efficiency was not a reliable indicator of in vitro transformation or of malignancy. In most cases in vitro transformation appeared to precede the acquisition of malignancy, but in two cases it occurred later. The studies also show that BeP, which is a tumour initiator in vivo, also acts in this way in vitro. The conclusion drawn from a discussion of these results and of two-stage carcinogenesis in vivo is that two-stage carcinogenesis can be reproduced in tissue culture; this model may be useful in studies of those mechanisms of chemical carcinogenesis that involve the processes of initiation and promotion

First study on infestation of Excorallana berbicensis (Isopoda: Corallanidae) on six fishes in a reservoir in Brazilian Amazon during dry and rainy seasons.

We analyzed the infestation levels of Excorallana berbicensis on Acestrorhynchus falcirostris, Ageneiosus ucayalensis, Geophagus proximus, Hemiodus unimaculatus, Psectrogaster falcata and Serrasalmus gibbus in a reservoir in the Araguari River basin, northern Brazil, during the dry and rainy seasons. For P. falcata, the infestation levels due to E. berbicensis were greater during the rainy season. For all the species studied, the peak parasite prevalence was in the month of highest rainfall levels and there were two peaks of parasite abundance: one in the month with highest rainfall level and the other in the month of transition from the rainy season to the dry season. In these hosts, around 70% of the E. berbicensis specimens were collected during the rainy season. The body conditions of the hosts also did not suffer any seasonal influence. Despite the differences in seasonal rainfall levels, there was no fluctuation in transparency, turbidity, pH, electric conductivity, temperature and dissolved oxygen levels in the water, due to the stability of these parameters during the seasonal cycle investigated in this artificial Amazon ecosystem. This was the first report on the seasonality of infestation by E. berbicensis associated with fish

Connecting the dots for real-time LiDAR-based object detection with YOLO

© 2018 Australasian Robotics and Automation Association. All rights reserved. In this paper we introduce a generic method for people and vehicle detection using LiDAR data only, leveraging a pre-trained Convolutional Neural Network (CNN) from the RGB domain. Typically with machine learning algorithms, there is an inherent trade-off between the amount of training data available and the need for engineered features. The current state-of-the-art object detection and classification heavily rely on deep CNNs trained on enormous RGB image datasets. To take advantage of this inbuilt knowledge, we propose to fine-tune You only look once (YOLO) network transferring its understanding about object shapes to upsampled LiDAR images. Our method creates a dense depth/intensity map, which highlights object contours, from the 3D-point cloud of a LiDAR scan. The proposed method is hardware agnostic, hence can be used with any LiDAR data, independently on the number of channels or beams. Overall, the proposed pipeline exploits the notable similarity between upsampled LiDAR images and RGB images preventing the need to train a deep CNN from scratch. This transfer learning makes our method data efficient while avoiding the creation of heavily engineered features. Evaluation results show that our proposed LiDAR-only detection model has equivalent performance to its RGB-only counterpart

3D Lidar-IMU Calibration Based on Upsampled Preintegrated Measurements for Motion Distortion Correction

© 2018 IEEE. In this paper, we present a probabilistic framework to recover the extrinsic calibration parameters of a lidar-IMU sensing system. Unlike global-shutter cameras, lidars do not take single snapshots of the environment. Instead, lidars collect a succession of 3D-points generally grouped in scans. If these points are assumed to be expressed in a common frame, this becomes an issue when the sensor moves rapidly in the environment causing motion distortion. The fundamental idea of our proposed framework is to use preintegration over interpolated inertial measurements to characterise the motion distortion in each lidar scan. Moreover, by using a set of planes as a calibration target, the proposed method makes use of lidar point-to-plane distances to jointly calibrate and localise the system using on-manifold optimisation. The calibration does not rely on a predefined target as arbitrary planes are detected and modelled in the first lidar scan. Simulated and real data are used to show the effectiveness of the proposed method