Convolutional neural networks for on-board cloud screening

AcloudscreeningunitonasatelliteplatformforEarthobservationcanplayanimportant role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art

Thick planar domain wall: its thin wall limit and dynamics

We consider a planar gravitating thick domain wall of the $\lambda \phi^4$ theory as a spacetime with finite thickness glued to two vacuum spacetimes on each side of it. Darmois junction conditions written on the boundaries of the thick wall with the embedding spacetimes reproduce the Israel junction condition across the wall in the limit of infinitesimal thickness. The thick planar domain wall located at a fixed position is then transformed to a new coordinate system in which its dynamics can be formulated. It is shown that the wall's core expands as if it were a thin wall. The thickness in the new coordinates is not constant anymore and its time dependence is given.Comment: 11 pages, to appear in IJMP

A Note on the Generalized Friedmann Equations for a Thick Brane

Within our thick brane approach previously used to obtain the cosmological evolution equations on a thick brane embedded in a five-dimensional Schwarzschild Anti-de Sitter spacetime it is explicitly shown that the consistency of these equations with the energy conservation equation requires that, in general, the thickness of the brane evolves in time. This varying brane thickness entails the possibility that both Newton's gravitational constant $G$ and the effective cosmological constant $\Lambda_4$ are time dependent.Comment: 6 pages,To appear in GR

Improving oil and flavonoid contents of milk thistle under water stress by salicylic acid

Adverse environmental conditions such as water deficit can limit production. However, some of these adverse effects may be overcome by application of plant growth regulators including salicylic acid (SA). Thus, a field experiment was conducted in 2015 to evaluate the effects of SA (0 and 1 mM l-1) on yield components, seed yield and oil and flavonoid contents of milk thistle (Silybum marianum L.) under different irrigation treatments (I1, I2, I3 and I4: irrigation after 70, 110, 150 and 190 mm evaporation from class A pan, respectively). The experiment was arranged as split-plot based on randomized complete block (RCB) design in three replicates. Irrigation treatments and SA levels were located in the main and sub plots, respectively. The results indicated that plant biomass, seeds per plant, 1000 seed weight, seed yield per unit area and harvest index of milk thistle decreased as a consequence of water stress. Oil percentage and yield were also reduced, but flavonoid content enhanced with increasing water deficit. All these traits were considerably augmented by foliar application of SA under non-stress and stressful conditions. Therefore, it was conclude that SA can be used to improve field performance of milk thistle under different environmental conditions

A Process Calculus for Dynamic Networks

In this paper we propose a process calculus framework for dynamic networks in which the network topology may change as computation proceeds. The proposed calculus allows one to abstract away from neighborhood-discovery computations and it contains features for broadcasting at multiple transmission ranges and for viewing networks at different levels of abstraction. We develop a theory of confluence for the calculus and we use the machinery developed towards the verification of a leader-election algorithm for mobile ad hoc networks

ATRA mechanically reprograms pancreatic stellate cells to suppress matrix remodelling and inhibit cancer cell invasion

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a dismal survival rate. Persistent activation of pancreatic stellate cells (PSCs) can perturb the biomechanical homoeostasis of the tumour microenvironment to favour cancer cell invasion. Here we report that ATRA, an active metabolite of vitamin A, restores mechanical quiescence in PSCs via a mechanism involving a retinoic acid receptor beta (RAR-β)-dependent downregulation of actomyosin (MLC-2) contractility. We show that ATRA reduces the ability of PSCs to generate high traction forces and adapt to extracellular mechanical cues (mechanosensing), as well as suppresses force-mediated extracellular matrix remodelling to inhibit local cancer cell invasion in 3D organotypic models. Our findings implicate a RAR-β/MLC-2 pathway in peritumoural stromal remodelling and mechanosensory-driven activation of PSCs, and further suggest that mechanical reprogramming of PSCs with retinoic acid derivatives might be a viable alternative to stromal ablation strategies for the treatment of PDAC