A method to polarise antiprotons in storage rings and create polarised antineutrons

An intense circularely polarised photon beam interacts with a cooled antiproton beam in a storage ring. Due to spin dependent absorption cross sections for the reaction gamma+antiproton > pi- + antineutron a built-up of polarisation of the stored antiprotons takes place. Figures-of-merit around 0.1 can be reached in principle over a wide range of antiproton energies. In this process antineutrons with Polarisation > 70% emerge. The method is presented for the case of 300 MeV/c cooled antiproton beam

Secure Vehicular Communication Systems: Implementation, Performance, and Research Challenges

Vehicular Communication (VC) systems are on the verge of practical deployment. Nonetheless, their security and privacy protection is one of the problems that have been addressed only recently. In order to show the feasibility of secure VC, certain implementations are required. In [1] we discuss the design of a VC security system that has emerged as a result of the European SeVeCom project. In this second paper, we discuss various issues related to the implementation and deployment aspects of secure VC systems. Moreover, we provide an outlook on open security research issues that will arise as VC systems develop from today's simple prototypes to full-fledged systems

The sleeping brain's connectivity and family environment: characterizing sleep EEG coherence in an infant cohort.

Brain connectivity closely reflects brain function and behavior. Sleep EEG coherence, a measure of brain's connectivity during sleep, undergoes pronounced changes across development under the influence of environmental factors. Yet, the determinants of the developing brain's sleep EEG coherence from the child's family environment remain unknown. After characterizing high-density sleep EEG coherence in 31 healthy 6-month-old infants by detecting strongly synchronized clusters through a data-driven approach, we examined the association of sleep EEG coherence from these clusters with factors from the infant's family environment. Clusters with greatest coherence were observed over the frontal lobe. Higher delta coherence over the left frontal cortex was found in infants sleeping in their parents' room, while infants sleeping in a room shared with their sibling(s) showed greater delta coherence over the central parts of the frontal cortex, suggesting a link between local brain connectivity and co-sleeping. Finally, lower occipital delta coherence was associated with maternal anxiety regarding their infant's sleep. These interesting links between sleep EEG coherence and family factors have the potential to serve in early health interventions as a new set of targets from the child's immediate environment

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Accounting Profession in Singapore; Professional Accounting in Foreign Country Series

https://egrove.olemiss.edu/aicpa_guides/1692/thumbnail.jp

The Gerasimov-Drell-Hearn Sum Rule and the Spin Structure of the Nucleon

The Gerasimov-Drell-Hearn sum rule is one of several dispersive sum rules that connect the Compton scattering amplitudes to the inclusive photoproduction cross sections of the target under investigation. Being based on such universal principles as causality, unitarity, and gauge invariance, these sum rules provide a unique testing ground to study the internal degrees of freedom that hold the system together. The present article reviews these sum rules for the spin-dependent cross sections of the nucleon by presenting an overview of recent experiments and theoretical approaches. The generalization from real to virtual photons provides a microscope of variable resolution: At small virtuality of the photon, the data sample information about the long range phenomena, which are described by effective degrees of freedom (Goldstone bosons and collective resonances), whereas the primary degrees of freedom (quarks and gluons) become visible at the larger virtualities. Through a rich body of new data and several theoretical developments, a unified picture of virtual Compton scattering emerges, which ranges from coherent to incoherent processes, and from the generalized spin polarizabilities on the low-energy side to higher twist effects in deep inelastic lepton scattering.Comment: 32 pages, 19 figures, review articl

When ferrocene and diiron organometallics meet: triiron vinyliminium complexes exhibit strong cytotoxicity and cancer cell selectivity

Cationic triiron complexes resulting from the conjugation of the ferrocenyl skeleton (Fc) with a diiron bis-cyclopentadienyl core through a variable vinyliminium linker, [Fe2Cp2(CO)(mu-CO){mu-eta(1):eta(3)-C(Fc)CHCN(R)(R')}]CF3SO3 ([2a-i]CF3SO3, Cp = eta(5)-C5H5, R, R'= alkyl, aryl), were synthesised in 70-94% yield, and the homologous nitrate salt was also prepared in one case ([2h]NO3). The neutral derivatives [Fe2Cp2(CO)(mu-CO){mu-eta(1):eta(3)-C(Fc)CHC(CN)NMe2}], 3, and [FeCp(CO){CN(Me)(Xyl)CHC(Fc)C(=O)}], 4 (Xyl = 2,6-C6H3Me2), were obtained in ca. 70% yield by reactions of the respective precursors [2h]CF3SO3 and [2i]CF3SO3 with NBu4CN and pyrrolidine, respectively. All products were purified by alumina chromatography and fully characterised by analytical and spectroscopic methods, and by single crystal X-ray diffraction in the cases of [2a]CF3SO3 and 3. The cytotoxicity of the complexes was assessed on A2780, A2780cisR and BxPC-3 cancer cell lines, and the nontumoral Balb/3T3 clone A31. Most of the cationic complexes display IC50 values in the low micromolar/nanomolar range concerning the cancer cell lines, and up to 35 times higher values on the nontumoral cells. In order to shed light on the mode of action, selected complexes were further characterised by cyclic voltammetry and spectroelectrochemical experiments, and assessed for their potential to trigger ROS production and to interact with a range of biomolecules, i.e. a synthetic dodecapeptide as a simplified model for thioredoxin reductase (TrxR-pept), some model proteins (cytochrome c, hen egg-white lysozyme, ubiquitin, bovine serum albumin, superoxide dismutase and human carbonic anhydrase) and one single-stranded oligonucleotide (ODN2)

An infant sleep electroencephalographic marker of thalamocortical connectivity predicts behavioral outcome in late infancy

Infancy represents a critical period during which thalamocortical brain connections develop and mature. Deviations in the maturation of thalamocortical connectivity are linked to neurodevelopmental disorders. There is a lack of early biomarkers to detect and localize neuromaturational deviations, which can be overcome with mapping through high-density electroencephalography (hdEEG) assessed in sleep. Specifically, slow waves and spindles in non-rapid eye movement (NREM) sleep are generated by the thalamocortical system, and their characteristics, slow wave slope and spindle density, are closely related to neuroplasticity and learning. Spindles are often subdivided into slow (11.0-13.0 Hz) and fast (13.5-16.0 Hz) frequencies, for which not only different functions have been proposed, but for which also distinctive developmental trajectories have been reported across the first years of life. Recent studies further suggest that information processing during sleep underlying sleep-dependent learning is promoted by the temporal coupling of slow waves and spindles, yet slow wave-spindle coupling remains unexplored in infancy. Thus, we evaluated three potential biomarkers: 1) slow wave slope, 2) spindle density, and 3) the temporal coupling of slow waves with spindles. We use hdEEG to first examine the occurrence and spatial distribution of these three EEG features in healthy infants and second to evaluate a predictive relationship with later behavioral outcomes. We report four key findings: First, infants' EEG features appear locally: slow wave slope is maximal in occipital and frontal areas, whereas slow and fast spindle density is most pronounced frontocentrally. Second, slow waves and spindles are temporally coupled in infancy, with maximal coupling strength in the occipital areas of the brain. Third, slow wave slope, fast spindle density, and slow wave-spindle coupling are not associated with concurrent behavioral status (6 months). Fourth, fast spindle density in central and frontocentral regions at age 6 months predicts overall developmental status at age 12 months, and motor skills at age 12 and 24 months. Neither slow wave slope nor slow wave-spindle coupling predict later behavioral development. We further identified spindle frequency as a determinant of slow and fast spindle density, which accordingly, also predicts motor skills at 24 months. Our results propose fast spindle density, or alternatively spindle frequency, as early EEG biomarker for identifying thalamocortical maturation, which can potentially be used for early diagnosis of neurodevelopmental disorders in infants. These findings are in support of a role of sleep spindles in sensorimotor microcircuitry development. A crucial next step will be to evaluate whether early therapeutic interventions may be effective to reverse deviations in identified individuals at risk