12,312 research outputs found
Competing electronic orders on Kagome lattices at van Hove filling
The electronic orders in Hubbard models on a Kagome lattice at van Hove
filling are of intense current interest and debate. We study this issue using
the singular-mode functional renormalization group theory. We discover a rich
variety of electronic instabilities under short range interactions. With
increasing on-site repulsion , the system develops successively
ferromagnetism, intra unit-cell antiferromagnetism, and charge bond order. With
nearest-neighbor Coulomb interaction alone (U=0), the system develops
intra-unit-cell charge density wave order for small , s-wave
superconductivity for moderate , and the charge density wave order appears
again for even larger . With both and , we also find spin bond order
and chiral superconductivity in some particular
regimes of the phase diagram. We find that the s-wave superconductivity is a
result of charge density wave fluctuations and the squared logarithmic
divergence in the pairing susceptibility. On the other hand, the d-wave
superconductivity follows from bond order fluctuations that avoid the matrix
element effect. The phase diagram is vastly different from that in honeycomb
lattices because of the geometrical frustration in the Kagome lattice.Comment: 8 pages with 9 color figure
Interchange reconnection associated with a confined filament eruption: Implications for the source of transient cold-dense plasma in solar winds
The cold-dense plasma is occasionally detected in the solar wind with in situ
data, but the source of the cold-dense plasma remains illusive. Interchange
reconnections (IRs) between closed fields and nearby open fields are well known
to contribute to the formation of solar winds. We present a confined filament
eruption associated with a puff-like coronal mass ejection (CME) on 2014
December 24. The filament underwent successive activations and finally erupted,
due to continuous magnetic flux cancellations and emergences. The confined
erupting filament showed a clear untwist motion, and most of the filament
material fell back. During the eruption, some tiny blobs escaped from the
confined filament body, along newly-formed open field lines rooted around the
south end of the filament, and some bright plasma flowed from the north end of
the filament to remote sites at nearby open fields. The newly-formed open field
lines shifted southward with multiple branches. The puff-like CME also showed
multiple bright fronts and a clear southward shift. All the results indicate an
intermittent IR existed between closed fields of the confined erupting filament
and nearby open fields, which released a portion of filament material (blobs)
to form the puff-like CME. We suggest that the IR provides a possible source of
cold-dense plasma in the solar wind
PABO: Mitigating Congestion via Packet Bounce in Data Center Networks
In today's data center, a diverse mix of throughput-sensitive long flows and
delay-sensitive short flows are commonly presented in shallow-buffered
switches. Long flows could potentially block the transmission of
delay-sensitive short flows, leading to degraded performance. Congestion can
also be caused by the synchronization of multiple TCP connections for short
flows, as typically seen in the partition/aggregate traffic pattern. While
multiple end-to-end transport-layer solutions have been proposed, none of them
have tackled the real challenge: reliable transmission in the network. In this
paper, we fill this gap by presenting PABO -- a novel link-layer design that
can mitigate congestion by temporarily bouncing packets to upstream switches.
PABO's design fulfills the following goals: i) providing per-flow based flow
control on the link layer, ii) handling transient congestion without the
intervention of end devices, and iii) gradually back propagating the congestion
signal to the source when the network is not capable to handle the
congestion.Experiment results show that PABO can provide prominent advantage of
mitigating transient congestions and can achieve significant gain on end-to-end
delay
CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control
Intrinsic motivation is a promising exploration technique for solving
reinforcement learning tasks with sparse or absent extrinsic rewards. There
exist two technical challenges in implementing intrinsic motivation: 1) how to
design a proper intrinsic objective to facilitate efficient exploration; and 2)
how to combine the intrinsic objective with the extrinsic objective to help
find better solutions. In the current literature, the intrinsic objectives are
all designed in a task-agnostic manner and combined with the extrinsic
objective via simple addition (or used by itself for reward-free pre-training).
In this work, we show that these designs would fail in typical sparse-reward
continuous control tasks. To address the problem, we propose Constrained
Intrinsic Motivation (CIM) to leverage readily attainable task priors to
construct a constrained intrinsic objective, and at the same time, exploit the
Lagrangian method to adaptively balance the intrinsic and extrinsic objectives
via a simultaneous-maximization framework. We empirically show, on multiple
sparse-reward continuous control tasks, that our CIM approach achieves greatly
improved performance and sample efficiency over state-of-the-art methods.
Moreover, the key techniques of our CIM can also be plugged into existing
methods to boost their performances
Helton-Howe Trace, the Connes-Chern character and Quantization
We study the Helton-Howe trace and the Connes-Chern character for Toeplitz
operators on weighted Bergman spaces via the idea of quantization. We prove a
local formula for the large -limit of the Connes-Chern character as the
weight goes to infinity. And we show that the Helton-Howe trace of Toeplitz
operators is independent of the weight and obtain a local formula for the
Helton-Howe trace for all weighted Bergman spaces using harmonic analysis and
quantization.Comment: 92 page
Bilinear Graph Neural Network with Neighbor Interactions
Graph Neural Network (GNN) is a powerful model to learn representations and
make predictions on graph data. Existing efforts on GNN have largely defined
the graph convolution as a weighted sum of the features of the connected nodes
to form the representation of the target node. Nevertheless, the operation of
weighted sum assumes the neighbor nodes are independent of each other, and
ignores the possible interactions between them. When such interactions exist,
such as the co-occurrence of two neighbor nodes is a strong signal of the
target node's characteristics, existing GNN models may fail to capture the
signal. In this work, we argue the importance of modeling the interactions
between neighbor nodes in GNN. We propose a new graph convolution operator,
which augments the weighted sum with pairwise interactions of the
representations of neighbor nodes. We term this framework as Bilinear Graph
Neural Network (BGNN), which improves GNN representation ability with bilinear
interactions between neighbor nodes. In particular, we specify two BGNN models
named BGCN and BGAT, based on the well-known GCN and GAT, respectively.
Empirical results on three public benchmarks of semi-supervised node
classification verify the effectiveness of BGNN -- BGCN (BGAT) outperforms GCN
(GAT) by 1.6% (1.5%) in classification accuracy.Codes are available at:
https://github.com/zhuhm1996/bgnn.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International
Joint Conferences on Artificial Intelligence), all rights reserve
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