28,402 research outputs found
On the inward drift of runaway electrons during the plateau phase of runaway current
The well observed inward drift of current carrying runaway electrons during
runaway plateau regime after disruption is studied by considering the phase
space dynamic of runaways in a large aspect ratio toroidal system. We consider
the case where the toroidal field is unperturbed and the toroidal symmetry of
the system is preserved. The balance between the change in canonical angular
momentum and the input of mechanical angular momentum in such system requires
runaways to drift horizontally in configuration space for any given change in
momentum space. The dynamic of this drift can be obtained by taking the
variation of canonical angular momentum. It is then found that runaway
electrons will always drift inward as long as they are decelerating. This drift
motion is essentially non-linear, since the current is carried by runaways
themselves, and any runaway drift relative to the magnetic axis will cause
further displacement of the axis itself. A simplified analytical model is
constructed to describe such inward drift both in ideal wall case and no wall
case, and the runaway current center displacement as a function of parallel
momentum variation is obtained. The time scale of such displacement is
estimated by considering effective radiation drag, which shows reasonable
agreement with observed displacement time scale. This indicates that the phase
space dynamic studied here plays a major role in the horizontal displacement of
runaway electrons during plateau regime.Comment: 25 pages, 9 figures, submitted to Physics of Plasma
Unevenness of Loop Location in Complex Networks
The loop structure plays an important role in many aspects of complex
networks and attracts much attention. Among the previous works, Bianconi et al
find that real networks often have fewer short loops as compared to random
models. In this paper, we focus on the uneven location of loops which makes
some parts of the network rich while some other parts sparse in loops. We
propose a node removing process to analyze the unevenness and find rich loop
cores can exist in many real networks such as neural networks and food web
networks. Finally, an index is presented to quantify the unevenness of loop
location in complex networks.Comment: 7 pages, 6 figure
Deep Binary Reconstruction for Cross-modal Hashing
With the increasing demand of massive multimodal data storage and
organization, cross-modal retrieval based on hashing technique has drawn much
attention nowadays. It takes the binary codes of one modality as the query to
retrieve the relevant hashing codes of another modality. However, the existing
binary constraint makes it difficult to find the optimal cross-modal hashing
function. Most approaches choose to relax the constraint and perform
thresholding strategy on the real-value representation instead of directly
solving the original objective. In this paper, we first provide a concrete
analysis about the effectiveness of multimodal networks in preserving the
inter- and intra-modal consistency. Based on the analysis, we provide a
so-called Deep Binary Reconstruction (DBRC) network that can directly learn the
binary hashing codes in an unsupervised fashion. The superiority comes from a
proposed simple but efficient activation function, named as Adaptive Tanh
(ATanh). The ATanh function can adaptively learn the binary codes and be
trained via back-propagation. Extensive experiments on three benchmark datasets
demonstrate that DBRC outperforms several state-of-the-art methods in both
image2text and text2image retrieval task.Comment: 8 pages, 5 figures, accepted by ACM Multimedia 201
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