On Spectral Graph Embedding: A Non-Backtracking Perspective and Graph Approximation

Graph embedding has been proven to be efficient and effective in facilitating graph analysis. In this paper, we present a novel spectral framework called NOn-Backtracking Embedding (NOBE), which offers a new perspective that organizes graph data at a deep level by tracking the flow traversing on the edges with backtracking prohibited. Further, by analyzing the non-backtracking process, a technique called graph approximation is devised, which provides a channel to transform the spectral decomposition on an edge-to-edge matrix to that on a node-to-node matrix. Theoretical guarantees are provided by bounding the difference between the corresponding eigenvalues of the original graph and its graph approximation. Extensive experiments conducted on various real-world networks demonstrate the efficacy of our methods on both macroscopic and microscopic levels, including clustering and structural hole spanner detection.Comment: SDM 2018 (Full version including all proofs

Private Model Compression via Knowledge Distillation

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful $(9.83,10^{-6})$-differential privacy is guaranteed, the compact model trained by RONA can obtain 20$\times$ compression ratio and 19$\times$ speed-up with merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1

Thermal nature of de Sitter spacetime and spontaneous excitation of atoms

We consider, in de Sitter spacetime, both freely falling and static two-level atoms in interaction with a conformally coupled massless scalar field in the de Sitter-invariant vacuum, and separately calculate the contributions of vacuum fluctuations and radiation reaction to the atom's spontaneous excitation rate. We find that spontaneous excitations occur even for the freely falling atom as if there is a thermal bath of radiation at the Gibbons-Hawking temperature and we thus recover, in a different physical context, the results of Gibbons and Hawking that reveals the thermal nature of de Sitter spacetime. Similarly, for the case of the static atom, our results show that the atom also perceives a thermal bath which now arises as a result of the intrinsic thermal nature of de Sitter spacetime and the Unruh effect associated with the inherent acceleration of the atom.Comment: 11 page

Transport through the intertube link between two parallel carbon nanotubes

Quantum transport through the junction between two metallic carbon nanotubes connected by intertube links has been studied within the TB method and Landauer formula. It is found that the conductance oscillates with both of the coupling strength and length. The corresponding local density of states (LDOS) is clearly shown and can be used to explain the reason why there are such kinds of oscillations of the conductances, which should be noted in the design of nanotube-based devices.Comment: 6 pages, 4 figure

Direct tunneling through high-κ\kappa amorphous HfO2_2: effects of chemical modification

We report first principles modeling of quantum tunneling through amorphous HfO$_2$ dielectric layer of metal-oxide-semiconductor (MOS) nanostructures in the form of n-Si/HfO$_2$/Al. In particular we predict that chemically modifying the amorphous HfO$_2$ barrier by doping N and Al atoms in the middle region - far from the two interfaces of the MOS structure, can reduce the gate-to-channel tunnel leakage by more than one order of magnitude. Several other types of modification are found to enhance tunneling or induce substantial band bending in the Si, both are not desired from leakage point of view. By analyzing transmission coefficients and projected density of states, the microscopic physics of electron traversing the tunnel barrier with or without impurity atoms in the high-$\kappa$ dielectric is revealed.Comment: 5 pages, 5 figure

Perturbation theory of von Neumann Entropy

In quantum information theory, von Neumann entropy plays an important role. The entropies can be obtained analytically only for a few states. In continuous variable system, even evaluating entropy numerically is not an easy task since the dimension is infinite. We develop the perturbation theory systematically for calculating von Neumann entropy of non-degenerate systems as well as degenerate systems. The result turns out to be a practical way of the expansion calculation of von Neumann entropy.Comment: 7 page