A Deep Learning Approach to Ancient Egyptian Hieroglyphs Classification

Nowadays, advances in Artificial Intelligence (AI), especially in machine and deep learning, present new opportunities to build tools that support the work of specialists in areas apparently far from the information technology field. One example of such areas is that of ancient Egyptian hieroglyphic writing. In this study, we explore the ability of different convolutional neural networks (CNNs) to classify pictures of ancient Egyptian hieroglyphs coming from two different datasets of images. Three well-known CNN architectures (ResNet-50, Inception-v3 and Xception) were taken into consideration and trained on the available images. The paradigm of transfer learning was tested as well. In addition, modifying the architecture of one of the previous networks, we developed a specifically dedicated CNN, named Glyphnet, tailoring its complexity to our classification task. Performance comparison tests were carried out and Glyphnet showed the best performances with respect to the other CNNs. In conclusion, this work shows how the ancient Egyptian hieroglyphs identification task can be supported by the deep learning paradigm, laying the foundation for information tools supporting automatic documents recognition, classification and, most importantly, the language translation task

Applying cumulative effects to strategically advance large-scale ecosystem restoration

International efforts to restore degraded ecosystems will continue to expand over the coming decades, yet the factors contributing to the effectiveness of long-term restoration across large areas remain largely unexplored. At large scales, outcomes are more complex and synergistic than the additive impacts of individual restoration projects. Here, we propose a cumulative-effects conceptual framework to inform restoration design and implementation and to comprehensively measure ecological outcomes. To evaluate and illustrate this approach, we reviewed long-term restoration in several large coastal and riverine areas across the US: the greater Florida Everglades; Gulf of Mexico coast; lower Columbia River and estuary; Puget Sound; San Francisco Bay and Sacramento–San Joaquin Delta; Missouri River; and northeastern coastal states. Evidence supported eight modes of cumulative effects of interacting restoration projects, which improved outcomes for species and ecosystems at landscape and regional scales. We conclude that cumulative effects, usually measured for ecosystem degradation, are also measurable for ecosystem restoration. The consideration of evidence-based cumulative effects will help managers of large-scale restoration capitalize on positive feedback and reduce countervailing effects

RPC-based Muon Identification System for the neutrino detector of the SHiP experiment

The SHiP experiment has been proposed at CERN to shed light on phenomena still unexplained in the framework of the Standard Model, such as the nature of dark matter, the baryonic asymmetry of the Universe and the neutrino oscillations, searching for hints of New Physics. A section of the detector will be dedicated to the study of neutrino physics with special emphasis on tau neutrino properties, still very poorly measured. A system to identify the muons produced in neutrino interactions, based on RPC technology, has been proposed and it is presented in detail in this paper

Patterns of Hybrid Loss of Imprinting Reveal Tissue- and Cluster-Specific Regulation

Background: Crosses between natural populations of two species of deer mice, Peromyscus maniculatus (BW), and P. polionotus (PO), produce parent-of-origin effects on growth and development. BW females mated to PO males (bw6po) produce growth-retarded but otherwise healthy offspring. In contrast, PO females mated to BW males (PO6BW) produce overgrown and severely defective offspring. The hybrid phenotypes are pronounced in the placenta and include PO6BW conceptuses which lack embryonic structures. Evidence to date links variation in control of genomic imprinting with the hybrid defects, particularly in the PO6BW offspring. Establishment of genomic imprinting is typically mediated by gametic DNA methylation at sites known as gDMRs. However, imprinted gene clusters vary in their regulation by gDMR sequences. Methodology/Principal Findings: Here we further assess imprinted gene expression and DNA methylation at different cluster types in order to discern patterns. These data reveal PO6BW misexpression at the Kcnq1ot1 and Peg3 clusters, both of which lose ICR methylation in placental tissues. In contrast, some embryonic transcripts (Peg10, Kcnq1ot1) reactivated the silenced allele with little or no loss of DNA methylation. Hybrid brains also display different patterns of imprinting perturbations. Several cluster pairs thought to use analogous regulatory mechanisms are differentially affected in the hybrids. Conclusions/Significance: These data reinforce the hypothesis that placental and somatic gene regulation differs significantly, as does that between imprinted gene clusters and between species. That such epigenetic regulatory variatio

DTT - Divertor Tokamak Test facility: A testbed for DEMO

The effective treatment of the heat and power exhaust is a critical issue in the road map to the realization of the fusion energy. In order to provide possible, reliable, well assessed and on-time answers to DEMO, the Divertor Tokamak Test facility (DTT) has been conceived and projected to be carried out and operated within the European strategy in fusion technology. This paper, based on the invited plenary talk at the 31st virtual SOFT Conference 2020, provides an overview of the DTT scientific proposal, which is deeply illustrated in the 2019 DTT Interim Design Report

SND@LHC: The Scattering and Neutrino Detector at the LHC

SND@LHC is a compact and stand-alone experiment designed to perform measurements with neutrinos produced at the LHC in the pseudo-rapidity region of ${7.2 < \eta < 8.4}$. The experiment is located 480 m downstream of the ATLAS interaction point, in the TI18 tunnel. The detector is composed of a hybrid system based on an 830 kg target made of tungsten plates, interleaved with emulsion and electronic trackers, also acting as an electromagnetic calorimeter, and followed by a hadronic calorimeter and a muon identification system. The detector is able to distinguish interactions of all three neutrino flavours, which allows probing the physics of heavy flavour production at the LHC in the very forward region. This region is of particular interest for future circular colliders and for very high energy astrophysical neutrino experiments. The detector is also able to search for the scattering of Feebly Interacting Particles. In its first phase, the detector will operate throughout LHC Run 3 and collect a total of 250 $\text{fb}^{-1}$