Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network

In this paper we present a novel instance segmentation algorithm that extends a fully convolutional network to learn to label objects separately without prediction of regions of interest. We trained the new algorithm on a challenging CCTV recording of beef cattle, as well as benchmark MS COCO and Pascal VOC datasets. Extensive experimentation showed that our approach outperforms the state-of-the-art solutions by up to 8% on our data

Diffusion and Localization of Ultra-Cold Particles on Rough Substrates

Diffusion and localization of ultra-cold particles moving along randomly corrugated substrates is analyzed quasianalytically. The particles are either bound to the substrate or pressed to it by the external holding field. The localization length and diffusion coefficient are expressed explicitly via the correlation radius of surface inhomogeneities. This quantum bouncing hall problem with a random rough wall is solved analytically in three limiting cases of longwave particles, large gaps between bound states, and single-state occupancy. Elsewhere, the diffusion coefficient and localization length are evaluated numerically for Gaussian correlation of inhomogeneities. The results are applied to ultra-cold neutrons in the gravitational trap, electrons on helium and hydrogen surfaces, and hydrogen particles bound to helium surface. Experimental observation of weak 2D localization for neutrons and electrons requires further cooling and substrate preparation

Localization of Ultra-Cold Particles over Rough Substrates

Localization and diffusion parameters are calculated for particles adsorbed over inhomogeneous substrates for discrete and quasicontinuous spectra of the adsorbed states. The results are expressed via the angular harmonics of the correlation function of surface roughness. The problem is solved analytically in the limiting cases of longwave particles and large correlation radii of surface inhomogeneities. Elsewhere, the problem is solved numerically for Gaussian correlation of inhomogeneities. Applications to electrons on helium films, mobile adsorbed hydrogen atoms and molecules, ultra-cold neutrons in gravitational or magnetic field, etc., are discussed

Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis

This paper introduces a new approach to the long-term tracking of an object in a challenging environment. The object is a cow and the environment is an enclosure in a cowshed. Some of the key challenges in this domain are a cluttered background, low contrast and high similarity between moving objects - which greatly reduces the efficiency of most existing approaches, including those based on background subtraction. Our approach is split into object localization, instance segmentation, learning and tracking stages. Our solution is benchmarked against a range of semi-supervised object tracking algorithms and we show that the performance is strong and well suited to subsequent analysis. We present our solution as a first step towards broader tracking and behavior monitoring for cows in precision agriculture with the ultimate objective of early detection of lameness

Adsorption hysteresis and capillary condensation in disordered porous solids: a density functional study

We present a theoretical study of capillary condensation of fluids adsorbed in mesoporous disordered media. Combining mean-field density functional theory with a coarse-grained description in terms of a lattice-gas model allows us to investigate both the out-of-equilibrium (hysteresis) and the equilibrium behavior. We show that the main features of capillary condensation in disordered solids result from the appearance of a complex free-energy landscape with a large number of metastable states. We detail the numerical procedures for finding these states, and the presence or absence of transitions in the thermodynamic limit is determined by careful finite-size studies.Comment: 30 pages, 18 figures. To appear in J. Phys.: Condens. Matte

Third Order Optical Nonlinearity of Colloidal Metal Nanoclusters Formed by MeV Ion Implantation

We report the results of characterization of nonlinear refractive index of the composite material produced by MeV Ag ion implantation of LiNbO(sub 3) crystal (z-cut). The material after implantation exhibited a linear optical absorption spectrum with the surface plasmon peak near 430 nm attributed to the colloidal silver nanoclusters. Heat treatment of the material at 500 deg C caused a shift of the absorption peak to 550 nm. The nonlinear refractive index of the sample after heat treatment was measured in the region of the absorption peak with the Z-scan technique using a tunable picosecond laser source (4.5 ps pulse width).The experimental data were compared against the reference sample made of MeV Cu implanted silica with the absorption peak in the same region. The nonlinear index of the Ag implanted LiNbO(sub 3) sample produced at five times less fluence is on average two times greater than that of the reference

Photonuclear physics - Laser light splits atom

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62874/1/404239a0.pd

The use of big data in interdisciplinary research on example of the Greater Mediterranean macroregion

Big data collection and analysis technologies are being intensively introduced into the social, economic and political spheres in countries and macroregions, including the Mediterranean region. This fact actualises the prospects of interdisciplinary research at the intersection of sociology, political science and regional studies. The article examines the benefits and risks of using these technologies in listed areas, taking Spain and the Middle East as examples. Among the main problems and threats, the authors consider the following: personal data confidentiality breach, and increased data manipulation for political purposes, the emergence of illegitimate forms of citizens self-organisation, the transition of Internet users to anonymous accounts due to the practice of “privatization” of personal data by non-state and terrorist groups in regional spaces, preventing the qualitative collection of data. The need to solve these problems at the international level was noted. The range of possibilities offered by working with big data to provide pragmatic and effective socio-political forecasting and operational regional studies is shown