Microscopic and self-consistent description for neutron halo in deformed nuclei

A deformed relativistic Hartree-Bogoliubov theory in continuum has been developed for the study of neutron halos in deformed nuclei and the halo phenomenon in deformed weakly bound nuclei is investigated. Magnesium and neon isotopes are studied and some results are presented for the deformed neutron-rich and weakly bound nuclei 44Mg and 36Ne. The core of the former nucleus is prolate, but the halo has a slightly oblate shape. This indicates a decoupling of the halo orbitals from the deformation of the core. The generic conditions for the existence of halos in deformed nuclei and for the occurrence of this decoupling effect are discussed.Comment: 7 pages, 2 figures; invited talk at the XXXV Brazilian Workshop on Nuclear Physics, Sep 2-6, 2012, Maresias, Brazi

Halos in a deformed Relativistic Hartree-Bogoliubov theory in continuum

In this contribution we present some recent results about neutron halos in deformed nuclei. A deformed relativistic Hartree-Bogoliubov theory in continuum has been developed and the halo phenomenon in deformed weakly bound nuclei is investigated. These weakly bound quantum systems present interesting examples for the study of the interdependence between the deformation of the core and the particles in the halo. Magnesium and neon isotopes are studied and detailed results are presented for the deformed neutron-rich and weakly bound nuclei 42Mg. The core of this nucleus is prolate, but the halo has a slightly oblate shape. This indicates a decoupling of the halo orbitals from the deformation of the core. The generic conditions for the existence of halos in deformed nuclei and for the occurrence of this decoupling effect are discussed.Comment: 6 pages, 2 figures; invited talk at the 2nd Int. Conf. on Nuclear Structure & Dynamics (NSD12), Opatija, Croatia, 9-13 July 201

Time-dependent generator coordinate method study of mass-asymmetric fission of actinides

Low-energy positive and negative parity collective states in the equilibrium minimum, and the dynamics of induced fission of actinide nuclei are investigated in a unified theoretical framework based on the generator coordinate method (GCM) with the Gaussian overlap approximation (GOA). The collective potential and inertia tensor, both at zero and finite temperature, are computed using the self-consistent multidimensionally constrained relativistic mean field (MDC-RMF) model, based on the energy density functional DD-PC1. Pairing correlations are treated in the BCS approximation with a separable pairing force of finite range. A collective quadrupole-octupole Hamiltonian characterized by zero-temperature axially-symmetric deformation energy surface and perturbative cranking inertia tensor, is used to model the low-lying excitation spectrum. The fission fragment charge distributions are obtained by propagating the initial collective states in time with the time-dependent GCM+GOA that uses the same quadrupole-octupole Hamiltonian, but with the collective potential and inertia tensor computed at finite temperature. The illustrative charge yields of $^{228}$Th, $^{234}$U, $^{240}$Pu, $^{244}$Cm, and $^{250}$Cf are in very good agreement with experiment, and the predicted mass asymmetry is consistent with the result of a recent microscopic study that has attributed the distribution (peak) of the heavier-fragment nuclei to shell-stabilized octupole deformations.Comment: 10 pages, 8 figures. arXiv admin note: text overlap with arXiv:1809.0614

Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse

Normalization operations are essential for state-of-the-art neural networks and enable us to train a network from scratch with a large learning rate (LR). We attempt to explain the real effect of Batch Normalization (BN) from the perspective of variance transmission by investigating the relationship between BN and Weights Normalization (WN). In this work, we demonstrate that the problem of the shift of the average gradient will amplify the variance of every convolutional (conv) layer. We propose Parametric Weights Standardization (PWS), a fast and robust to mini-batch size module used for conv filters, to solve the shift of the average gradient. PWS can provide the speed-up of BN. Besides, it has less computation and does not change the output of a conv layer. PWS enables the network to converge fast without normalizing the outputs. This result enhances the persuasiveness of the shift of the average gradient and explains why BN works from the perspective of variance transmission. The code and appendix will be made available on https://github.com/lyxzzz/PWSConv.Comment: This paper has been accepted by AAAI2

Mtmr8 is essential for vasculature development in zebrafish embryos

<p>Abstract</p> <p>Background</p> <p>Embryonic morphogenesis of vascular and muscular systems is tightly coordinated, and a functional cooperation of Mtmr8 with PI3K in actin filament modeling and muscle development has been revealed in zebrafish. Here, we attempt to explore the function of Mtmr8 in vasculature development parallel to its function in muscle development.</p> <p>Results</p> <p>During early stage of somitogenesis, <it>mtmr8 </it>expression was detected in both somitic mesodem and ventral mesoderm. Knockdown of <it>mtmr8 </it>by morpholino impairs arterial endothelial marker expression, and results in endothelial cell reduction and vasculogenesis defects, such as retardation in intersegmental vessel development and interruption of trunk dorsal aorta. Moreover, <it>mtmr8 </it>morphants show loss of arterial endothelial cell identity in dorsal aorta, which is effectively rescued by low concentration of PI3K inhibitor, and by over-expression of <it>dnPKA </it>mRNA or <it>vegf </it>mRNA. Interestingly, <it>mtmr8 </it>expression is up-regulated when zebrafish embryos are treated with specific inhibitor of Hedgehog pathway that abolishes arterial marker expression.</p> <p>Conclusion</p> <p>These data indicate that Mtmr8 is essential for vasculature development in zebrafish embryos, and may play a role in arterial specification through repressing PI3K activity. It is suggested that Mtmr8 should represent a novel element of the Hedgehog/PI3K/VEGF signaling cascade that controls arterial specification.</p

Single-photon-memory measurement-device-independent quantum secure direct communication

Quantum secure direct communication (QSDC) uses the quantum channel to transmit information reliably and securely. In order to eliminate the security loopholes resulting from practical detectors, the measurement-device-independent (MDI) QSDC protocol has been proposed. However, block-based transmission of quantum states is utilized in MDI-QSDC, which requires practical quantum memory that is still unavailable at the time of writing. For circumventing this impediment, we propose a single-photon-memory MDI QSDC protocol (SPMQC) for dispensing with high-performance quantum memory. The performance of the proposed protocol is characterized by simulations considering realistic experimental parameters, and the results show that it is feasible to implement SPMQC by relying on present-day technology.Comment: 10 pages, 6 figure

Single-Photon-Memory Measurement-Device-Independent Quantum Secure Direct Communication -- Part I: Its Fundamentals and Evolution

Quantum secure direct communication (QSDC) has attracted a lot of attention, which exploits deep-rooted quantum physical principles to guarantee unconditional security of communication in the face of eavesdropping. We first briefly review the fundamentals of QSDC, and then present its evolution, including its security proof, its performance improvement techniques, and practical implementation. Finally, we discuss the future directions of QSDC.Comment: IEEE Communications Letters, 202

Dynamic Communities in Stock Market

The stock market has the huge effect and influence on a country or region’s economic and financial activities. But we have found that it is very hard for the prediction and control. This illustrates a critical need for new and fundamental understanding of the structure and dynamics of stock markets. Previous research and analysis on stock markets often focused on some assumptions of the game of competition and cooperation. Under the condition of these assumptions, the conclusions often reflect just part of the problem. The stock price is the core reflections of a stock market. So, in this paper, the authors introduce a methodology for constructing stock networks based on stock prices in a stock market and detecting dynamic communities in it. This strategy will help us from a new macroperspective to explore and mine the characteristics and laws hiding in the big data of stock markets. Through statistical analysis of many characteristics of dynamic communities, some interesting phenomena are found in this paper. These results are new findings in finance data analysis field and will potentially contribute to the analysis and decision-making of a financial market. The method presented in this paper can also be used to analyze other similar financial systems

FlowX: Towards Explainable Graph Neural Networks via Message Flows

We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms. While most current methods focus on explaining graph nodes, edges, or features, we argue that, as the inherent functional mechanism of GNNs, message flows are more natural for performing explainability. To this end, we propose a novel method here, known as FlowX, to explain GNNs by identifying important message flows. To quantify the importance of flows, we propose to follow the philosophy of Shapley values from cooperative game theory. To tackle the complexity of computing all coalitions' marginal contributions, we propose a flow sampling scheme to compute Shapley value approximations as initial assessments of further training. We then propose an information-controlled learning algorithm to train flow scores toward diverse explanation targets: necessary or sufficient explanations. Experimental studies on both synthetic and real-world datasets demonstrate that our proposed FlowX and its variants lead to improved explainability of GNNs. The code is available at https://github.com/divelab/DIG