Hydrostatics of a fluid between parallel plates at low bond numbers

Two-dimensional liquid vapor interface behavior between parallel plates under static equilibrium and low gravitational acceleratio

Mortality rates of the Alpine Chamois : the influence of snow-meteorological factors

Especially for animals inhabiting alpine areas, winter environmental conditions can be limiting. Cold temperatures, hampered food availability and natural perils are just three of many potential threats that mountain ungulates face in winter. Understanding their sensitivity to climate variability is essential for game management. Here we focus on analyzing the influence of snow and weather conditions on the mortality pattern of Alpine chamois. Our mortality data are derived from a systematic assessment of 6,500 chamois that died of natural causes over the course of 13 years. We use population- and habitat-specific data on snow, climate and avalanche danger to identify the key environmental factors that essentially determine the spatio-temporal variations in chamois mortality. Initially, we show that most fatalities occurred in winter, with a peak around March, when typically snow depths were highest. Death causes related to poor general conditions were the major component of seasonal variations. As for the interannual variations in mortality, snow depth and avalanche risk best explained the occurrence of winters with increased numbers of fatalities. Finally, analyzing differences in mortality rates between populations, we identified sun-exposed winter habitats with little snow accumulation as favourable for alpine chamois

Reflection and interference of electromagnetic waves in inhomogeneous media

Solutions were obtained of the wave equation for a plane horizontally polarized electro-magnetic wave incident on a semi infinite two dimensional inhomogeneous medium. Two problems were considered: An inhomogeneous half space, and an inhomogeneous layer of arbitrary thickness. Solutions of the wave equation were obtained in terms of Hankel functions with complex arguments. Numerical calculations were made of the reflection coefficient R at the interface of the homogeneous medium. The startling results show that the reflection coefficient for a complex dielectric constant with gradient, can be less than that of the same medium with zero gradient

Flash of photons from the early stage of heavy-ion collisions

The dynamics of partonic cascades may be an important aspect for particle production in relativistic collisions of nuclei at CERN SPS and BNL RHIC energies. Within the Parton-Cascade Model, we estimate the production of single photons from such cascades due to scattering of quarks and gluons q g -> q gamma, quark-antiquark annihilation q qbar -> g gamma, or gamma gamma, and from electromagnetic brems-strahlung of quarks q -> q gamma. We find that the latter QED branching process plays the dominant role for photon production, similarly as the QCD branchings q -> q g and g -> g g play a crucial role for parton multiplication. We conclude therefore that photons accompanying the parton cascade evolution during the early stage of heavy-ion collisions shed light on the formation of a partonic plasma.Comment: 4 pages including 3 postscript figure

Agricultural intensification and farmland birds

Samen met onderzoekers van acht Europese universiteiten, heeft Flavia Geiger de effecten van verschillende landbouwpraktijken op boerenlandvogels onderzocht. In negen Europese studiegebieden waren soortenrijkdom en dichtheid van boerenlandvogels lager op akkerbouwbedrijven met een hogere graanopbrengst. Het gebruik van gewasbeschermingsmiddelen had een negatief effect op boerenlandvogels. Soortenrijkdom en dichtheid van broedvogels verschilde niet tussen biologische en gangbare bedrijven

Out of Equilibrium Non-perturbative Quantum Field Dynamics in Homogeneous External Fields

The quantum dynamics of the symmetry broken lambda (Phi^2)^2 scalar field theory in the presence of an homogeneous external field is investigated in the large N limit. We choose as initial state the ground state for a constant external field J .The sign of the external field is suddenly flipped from J to - J at a given time and the subsequent quantum dynamics calculated. Spinodal instabilities and parametric resonances produce large quantum fluctuations in the field components transverse to the external field. This allows the order parameter to turn around the maximum of the potential for intermediate times. Subsequently, the order parameter starts to oscillate near the global minimum for external field - J, entering a novel quasi-periodic regime.Comment: LaTex, 30 pages, 12 .ps figures, improved version to appear in Phys Rev

Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201

Detecting vapour bubbles in simulations of metastable water

International audienceThe investigation of cavitation in metastable liquids with molecular simulations requires an appropriate definition of the volume of the vapour bubble forming within the metastable liquid phase. Commonly used approaches for bubble detection exhibit two significant flaws: first, when applied to water they often identify the voids within the hydrogen bond network as bubbles thus masking the signature of emerging bubbles and, second, they lack thermodynamic consistency. Here, we present two grid-based methods, the M-method and the V-method, to detect bubbles in metastable water specifically designed to address these shortcomings. The M-method incorporates information about neighbouring grid cells to distinguish between liquid- and vapour-like cells, which allows for a very sensitive detection of small bubbles and high spatial resolution of the detected bubbles. The V-method is calibrated such that its estimates for the bubble volume correspond to the average change in system volume and are thus thermodynamically consistent. Both methods are computationally inexpensive such that they can be used in molecular dynamics and Monte Carlo simulations of cavitation. We illustrate them by computing the free energy barrier and the size of the critical bubble for cavitation in water at negative pressure