713 research outputs found
Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks
Detecting spreading outbreaks in social networks with sensors is of great
significance in applications. Inspired by the formation mechanism of human's
physical sensations to external stimuli, we propose a new method to detect the
influence of spreading by constructing excitable sensor networks. Exploiting
the amplifying effect of excitable sensor networks, our method can better
detect small-scale spreading processes. At the same time, it can also
distinguish large-scale diffusion instances due to the self-inhibition effect
of excitable elements. Through simulations of diverse spreading dynamics on
typical real-world social networks (facebook, coauthor and email social
networks), we find that the excitable senor networks are capable of detecting
and ranking spreading processes in a much wider range of influence than other
commonly used sensor placement methods, such as random, targeted, acquaintance
and distance strategies. In addition, we validate the efficacy of our method
with diffusion data from a real-world online social system, Twitter. We find
that our method can detect more spreading topics in practice. Our approach
provides a new direction in spreading detection and should be useful for
designing effective detection methods
Long-Range triplet Josephson Current Modulated by the Interface Magnetization Texture
We have investigated the Josephson coupling between two s-wave
superconductors separated by the ferromagnetic trilayers with noncollinear
magnetization. We find that the long-range triplet critical current will
oscillate with the strength of the exchange field and the thickness of the
interface layer, when the interface magnetizations are orthogonal to the
central magnetization. This feature is induced by the spatial oscillations of
the spin-triplet state |\uparrow\downarrow>+|\downarrow\uparrow> in the
interface layer. Moreover, the critical current can exhibit a characteristic
nonmonotonic behavior, when the misalignment angle between interface
magnetization and central ferromagnet increases from 0 to \pi/2. This peculiar
behavior will take place under the condition that the original state of the
junction with the parallel magnetizations is the \pi state
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
This paper considers the domain adaptive person re-identification (re-ID)
problem: learning a re-ID model from a labeled source domain and an unlabeled
target domain. Conventional methods are mainly to reduce feature distribution
gap between the source and target domains. However, these studies largely
neglect the intra-domain variations in the target domain, which contain
critical factors influencing the testing performance on the target domain. In
this work, we comprehensively investigate into the intra-domain variations of
the target domain and propose to generalize the re-ID model w.r.t three types
of the underlying invariance, i.e., exemplar-invariance, camera-invariance and
neighborhood-invariance. To achieve this goal, an exemplar memory is introduced
to store features of the target domain and accommodate the three invariance
properties. The memory allows us to enforce the invariance constraints over
global training batch without significantly increasing computation cost.
Experiment demonstrates that the three invariance properties and the proposed
memory are indispensable towards an effective domain adaptation system. Results
on three re-ID domains show that our domain adaptation accuracy outperforms the
state of the art by a large margin. Code is available at:
https://github.com/zhunzhong07/ECNComment: To appear in CVPR 201
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