Quantum criticality and nodal superconductivity in the FeAs-based superconductor KFe2As2

The in-plane resistivity $\rho$ and thermal conductivity $\kappa$ of FeAs-based superconductor KFe$_2$As$_2$ single crystal were measured down to 50 mK. We observe non-Fermi-liquid behavior $\rho(T) \sim T^{1.5}$ at $H_{c_2}$ = 5 T, and the development of a Fermi liquid state with $\rho(T) \sim T^2$ when further increasing field. This suggests a field-induced quantum critical point, occurring at the superconducting upper critical field $H_{c_2}$. In zero field there is a large residual linear term $\kappa_0/T$, and the field dependence of $\kappa_0/T$ mimics that in d-wave cuprate superconductors. This indicates that the superconducting gaps in KFe$_2$As$_2$ have nodes, likely d-wave symmetry. Such a nodal superconductivity is attributed to the antiferromagnetic spin fluctuations near the quantum critical point.Comment: 4 pages, 4 figures - replaces arXiv:0909.485

Flare magnetic reconnection and relativistic particles in the 2003 October 28 event

An X17.2 solar flare occurred on 2003 October 28, accompanied by multi-wavelength emissions and a high flux of relativistic particles observed at 1AU. We present the analytic results of the TRACE, SOHO, RHESSI, ACE, GOES, hard X-ray (INTEGRAL satellite), radio (Onderejov radio telescope), and neutron monitor data. It is found that the inferred magnetic reconnection electric field correlates well with the hard X-ray, gamma-ray, and neutron emission at the Sun. Thus the flare's magnetic reconnection probably makes a crucial contribution to the prompt relativistic particles, which could be detected at 1 AU. Since the neutrons were emitted a few minutes before the injection of protons and electrons, we propose a magnetic-field evolution configuration to explain this delay. We do not exclude the effect of CME-driven shock, which probably plays an important role in the delayed gradual phase of solar energetic particles.Comment: 5 pages, 7 figures, accepted by A&

Learning Edge Representations via Low-Rank Asymmetric Projections

We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph is an important first step for using network information (from social networks, user-item graphs, knowledge bases, etc.) in many machine learning tasks. Unlike previous work, we (1) explicitly model an edge as a function of node embeddings, and we (2) propose a novel objective, the "graph likelihood", which contrasts information from sampled random walks with non-existent edges. Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings. When combined, our contributions enable us to significantly improve the state-of-the-art by learning more concise representations that better preserve the graph structure. We evaluate our method on a variety of link-prediction task including social networks, collaboration networks, and protein interactions, showing that our proposed method learn representations with error reductions of up to 76% and 55%, on directed and undirected graphs. In addition, we show that the representations learned by our method are quite space efficient, producing embeddings which have higher structure-preserving accuracy but are 10 times smaller