23,344 research outputs found
LATTE: Application Oriented Social Network Embedding
In recent years, many research works propose to embed the network structured
data into a low-dimensional feature space, where each node is represented as a
feature vector. However, due to the detachment of embedding process with
external tasks, the learned embedding results by most existing embedding models
can be ineffective for application tasks with specific objectives, e.g.,
community detection or information diffusion. In this paper, we propose study
the application oriented heterogeneous social network embedding problem.
Significantly different from the existing works, besides the network structure
preservation, the problem should also incorporate the objectives of external
applications in the objective function. To resolve the problem, in this paper,
we propose a novel network embedding framework, namely the "appLicAtion
orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network
structure can be applied to compute the node "diffusive proximity" scores,
which capture both local and global network structures. Based on these computed
scores, Latte learns the network representation feature vectors by extending
the autoencoder model model to the heterogeneous network scenario, which can
also effectively unite the objectives of network embedding and external
application tasks. Extensive experiments have been done on real-world
heterogeneous social network datasets, and the experimental results have
demonstrated the outstanding performance of Latte in learning the
representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl
Successive Refinement of Shannon Cipher System Under Maximal Leakage
We study the successive refinement setting of Shannon cipher system (SCS)
under the maximal leakage constraint for discrete memoryless sources under
bounded distortion measures. Specifically, we generalize the threat model for
the point-to-point rate-distortion setting of Issa, Wagner and Kamath (T-IT
2020) to the multiterminal successive refinement setting. Under mild conditions
that correspond to partial secrecy, we characterize the asymptotically optimal
normalized maximal leakage region for both the joint excess-distortion
probability (JEP) and the expected distortion reliability constraints. Under
JEP, in the achievability part, we propose a type-based coding scheme, analyze
the reliability guarantee for JEP and bound the leakage of the information
source through compressed versions. In the converse part, by analyzing a
guessing scheme of the eavesdropper, we prove the optimality of our
achievability result. Under expected distortion, the achievability part is
established similarly to the JEP counterpart. The converse proof proceeds by
generalizing the corresponding results for the rate-distortion setting of SCS
by Schieler and Cuff (T-IT 2014) to the successive refinement setting. Somewhat
surprisingly, the normalized maximal leakage regions under both JEP and
expected distortion constraints are identical under certain conditions,
although JEP appears to be a stronger reliability constraint
The Happer's puzzle degeneracies and Yangian
We find operators distinguishing the degenerate states for the Hamiltonian
at that was given by Happer
et al to interpret the curious degeneracies of the Zeeman effect for
condensed vapor of Rb. The operators obey Yangian commutation relations.
We show that the curious degeneracies seem to verify the Yangian algebraic
structure for quantum tensor space and are consistent with the representation
theory of .Comment: 8 pages, Latex fil
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