18,178 research outputs found
Quantum criticality and nodal superconductivity in the FeAs-based superconductor KFe2As2
The in-plane resistivity and thermal conductivity of
FeAs-based superconductor KFeAs single crystal were measured down to 50
mK. We observe non-Fermi-liquid behavior at =
5 T, and the development of a Fermi liquid state with when
further increasing field. This suggests a field-induced quantum critical point,
occurring at the superconducting upper critical field . In zero field
there is a large residual linear term , and the field dependence of
mimics that in d-wave cuprate superconductors. This indicates that
the superconducting gaps in KFeAs 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
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