A compact high-flux source of cold sodium atoms

We present a compact source of cold sodium atoms suitable for the production of quantum degenerate gases and versatile for a multi-species experiment. The magnetic field produced by permanent magnets allows to simultaneously realize a Zeeman slower and a two-dimensional MOT within an order of magnitude smaller length than standard sodium sources. We achieve an atomic flux exceeding 4x10^9 atoms/s loaded in a MOT, with a most probable longitudinal velocity of 20 m/s, and a brightness larger than 2.5x10^(12) atoms/s/sr. This atomic source allowed us to produce a pure BEC with more than 10^7 atoms and a background pressure limited lifetime of 5 minutes.Comment: 8 pages, 6 figures, submitted to Phys. Rev.

Decoherence of number states in phase-sensitive reservoirs

The non-unitary evolution of initial number states in general Gaussian environments is solved analytically. Decoherence in the channels is quantified by determining explicitly the purity of the state at any time. The influence of the squeezing of the bath on decoherence is discussed. The behavior of coherent superpositions of number states is addressed as well.Comment: 5 pages, 2 figures, minor changes, references adde

The squashed entanglement of the noiseless quantum Gaussian attenuator and amplifier

We determine the maximum squashed entanglement achievable between sender and receiver of the noiseless quantum Gaussian attenuators and amplifiers and we prove that it is achieved sending half of an infinitely squeezed two-mode vacuum state. The key ingredient of the proof is a lower bound to the squashed entanglement of the quantum Gaussian states obtained applying a two-mode squeezing operation to a quantum thermal Gaussian state tensored with the vacuum state. This is the first lower bound to the squashed entanglement of a quantum Gaussian state and opens the way to determine the squashed entanglement of all quantum Gaussian channels. Moreover, we determine the classical squashed entanglement of the quantum Gaussian states above and show that it is strictly larger than their squashed entanglement. This is the first time that the classical squashed entanglement of a mixed quantum Gaussian state is determined

Designing All Graphdiyne Materials as Graphene Derivatives: Topologically Driven Modulation of Electronic Properties

Designing new 2D systems with tunable properties is an important subject for science and technology. Starting from graphene, we developed an algorithm to systematically generate 2D carbon crystals belonging to the family of graphdiynes (GDYs) and having different structures and sp/sp(2) carbon ratios. We analyze how structural and topological effects can tune the relative stability and the electronic behavior, to propose a rationale for the development of new systems with tailored properties. A total of 26 structures have been generated, including the already known polymorphs such as alpha-, beta-, and gamma-GDY. Periodic density functional theory calculations have been employed to optimize the 2D crystal structures and to compute the total energy, the band structure, and the density of states. Relative energies with respect to graphene have been found to increase when the values of the carbon sp/sp(2) ratio increase, following however different trends based on the peculiar topologies present in the crystals. These topologies also influence the band structure, giving rise to semiconductors with a finite band gap, zero-gap semiconductors displaying Dirac cones, or metallic systems. The different trends allow identifying some topological effects as possible guidelines in the design of new 2D carbon materials beyond graphene

Sex differences in steroid levels and steroidogenesis in the nervous system : Physiopathological role

The nervous system, in addition to be a target for steroid hormones, is the source of a variety of neuroactive steroids, which are synthesized and metabolized by neurons and glial cells. Recent evidence indicates that the expression of neurosteroidogenic proteins and enzymes and the levels of neuroactive steroids are different in the nervous system of males and females. We here summarized the state of the art of neuroactive steroids, particularly taking in consideration sex differences occurring in the synthesis and levels of these molecules. In addition, we discuss the consequences of sex differences in neurosteroidogenesis for the function of the nervous system under healthy and pathological conditions and the implications of neuroactive steroids and neurosteroidogenesis for the development of sex-specific therapeutic interventions

Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning

Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over popular media channels. However, concerns about interpretability and accountability of AI have been raised by influential thinkers. In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems. Neural-symbolic computing aims at integrating, as foreseen by Valiant, two most fundamental cognitive abilities: the ability to learn from the environment, and the ability to reason from what has been learned. Neural-symbolic computing has been an active topic of research for many years, reconciling the advantages of robust learning in neural networks and reasoning and interpretability of symbolic representation. In this paper, we survey recent accomplishments of neural-symbolic computing as a principled methodology for integrated machine learning and reasoning. We illustrate the effectiveness of the approach by outlining the main characteristics of the methodology: principled integration of neural learning with symbolic knowledge representation and reasoning allowing for the construction of explainable AI systems. The insights provided by neural-symbolic computing shed new light on the increasingly prominent need for interpretable and accountable AI systems