Dyonic Black Holes in String Theory

An exact solution of the low-energy string theory representing static, spherical symmetric dyonic black hole is found. The solution is labeled by their mass, electric charge, magnetic charge and asymptotic value of the scalar dilaton. Some interesting properties of the dyonic black holes are studied. In particular, the Hawking temperature of dyonic black holes depends on both the electric and magnetic charges, and the extremal ones, which have nonzero electric and magnetic charges, have zero temperature but nonzero entropy. These properties are quite different from those of electrically (or magnetically) charged dilaton black holes found by Gibbons {\it et al.} and Garfinkle {\it et al.}, but are the same as those of the dyonic black holes found by Gibbons and Maeda. After this paper was submitted for publication, D. Wiltshire told us that solutions, eqs.(22)-(28), are related to Gibbons-Maeda dyonic black hole solutions by a coordinate transformation and some parameters reparametization \cite{26}. And, we were also informed that many of our results were previously obtained by Kallosh {\it et al.} \cite{27}. The dyonic black hole solutions, eqs.(22)-(28), are also related to those of reference \cite{27} by another coordinateComment: 20 pages, 2 figures not included, LATEX, revised versio

Temperature-dependent permittivity of annealed and unannealed gold films

Due to local field enhancement and subwavelength confinements, nano-plasmonics provide numerous novel applications. Simultaneously, as an efficient nanoscale heat generator from inherent absorption, thermo-plasmonics is emerging as an important branch. However, although significant temperature increase is involved in applications, detailed characterization of metal permittivity at different temperatures and corresponding thermo-derivative are lacking. In this work, we extract the permittivity of gold from 300K to the annealing temperature of 570K. By comparing annealed and unannealed films, more than one-order difference in thermo-derivative of permittivity is revealed, resulting in unexpectedly large variation of plasmonic properties. Our result is valuable not only for characterizing extensively used unannealed nanoparticles, but also for designing future thermo-nano-plasmonic systems.Comment: 6 pages, 4 figures, revised and published on Optics Expres

Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201

Approaching the Intrinsic Bandgap in Suspended High-Mobility Graphene Nanoribbons

We report electrical transport measurements on a suspended ultra-low-disorder graphene nanoribbon(GNR) with nearly atomically smooth edges that reveal a high mobility exceeding 3000 cm2 V-1 s-1 and an intrinsic band gap. The experimentally derived bandgap is in quantitative agreement with the results of our electronic-structure calculations on chiral GNRs with comparable width taking into account the electron-electron interactions, indicating that the origin of the bandgap in non-armchair GNRs is partially due to the magnetic zigzag edges.Comment: 22 pages, 6 figure

Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201

Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin