Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval

In this paper, we propose a simple but effective semantic part-based weighting aggregation (PWA) for image retrieval. The proposed PWA utilizes the discriminative filters of deep convolutional layers as part detectors. Moreover, we propose the effective unsupervised strategy to select some part detectors to generate the "probabilistic proposals", which highlight certain discriminative parts of objects and suppress the noise of background. The final global PWA representation could then be acquired by aggregating the regional representations weighted by the selected "probabilistic proposals" corresponding to various semantic content. We conduct comprehensive experiments on four standard datasets and show that our unsupervised PWA outperforms the state-of-the-art unsupervised and supervised aggregation methods. Code is available at https://github.com/XJhaoren/PWA.Comment: 8 pages, 4 figures. Accepted by AAAI201

Layered SnS2-reduced graphene oxide composite--a high-capacity, high-rate, and long-cycle life sodium-ion battery anode material.

A layered SnS -reduced graphene oxide (SnS -RGO) composite is prepared by a facile hydrothermal route and evaluated as an anode material for sodium-ion batteries (NIBs). The measured electrochemical properties are a high charge specific capacity (630 mAh g at 0.2 A g ) coupled to a good rate performance (544 mAh g at 2 A g ) and long cycle-life (500 mAh g at 1 A g for 400 cycles). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. 2 2 -1 -1 -1 -1 -1 -

Cache Invalidation and Replacement Strategies for Location-Dependent Data in Mobile Environments

Mobile location-dependent information services (LDISs) have become increasingly popular in recent years. However, data caching strategies for LDISs have thus far received little attention. In this paper, we study the issues of cache invalidation and cache replacement for location-dependent data under a geometric location model. We introduce a new performance criterion, called caching efficiency, and propose a generic method for location-dependent cache invalidation strategies. In addition, two cache replacement policies, PA and PAID, are proposed. Unlike the conventional replacement policies, PA and PAID take into consideration the valid scope area of a data value. We conduct a series of simulation experiments to study the performance of the proposed caching schemes. The experimental results show that the proposed location-dependent invalidation scheme is very effective and the PA and PAID policies significantly outperform the conventional replacement policies

Co-learning-aided Multi-modal-deep-learning Framework of Passive DOA Estimators for a Heterogeneous Hybrid Massive MIMO Receiver

Due to its excellent performance in rate and resolution, fully-digital (FD) massive multiple-input multiple-output (MIMO) antenna arrays has been widely applied in data transmission and direction of arrival (DOA) measurements, etc. But it confronts with two main challenges: high computational complexity and circuit cost. The two problems may be addressed well by hybrid analog-digital (HAD) structure. But there exists the problem of phase ambiguity for HAD, which leads to its low-efficiency or high-latency. Does exist there such a MIMO structure of owning low-cost, low-complexity and high time efficiency at the same time. To satisfy the three properties, a novel heterogeneous hybrid MIMO receiver structure of integrating FD and heterogeneous HAD ($\rm{H}^2$AD-FD) is proposed and corresponding multi-modal (MD)-learning framework is developed. The framework includes three major stages: 1) generate the candidate sets via root multiple signal classification (Root-MUSIC) or deep learning (DL); 2) infer the class of true solutions from candidate sets using machine learning (ML) methods; 3) fuse the two-part true solutions to achieve a better DOA estimation. The above process form two methods named MD-Root-MUSIC and MDDL. To improve DOA estimation accuracy and reduce the clustering complexity, a co-learning-aided MD framework is proposed to form two enhanced methods named CoMDDL and CoMD-RootMUSIC. Moreover, the Cramer-Rao lower bound (CRLB) for the proposed $\rm{H}^2$AD-FD structure is also derived. Experimental results demonstrate that our proposed four methods could approach the CRLB for signal-to-noise ratio (SNR) > 0 dB and the proposed CoMDDL and MDDL perform better than CoMD-RootMUSIC and MD-RootMUSIC, particularly in the extremely low SNR region

Information Dissemination via Wireless Broadcast

The advent of sensor, wireless and portable device technologies will soon enable us to embed computing technologies transparently in the environment to provide uninterrupted services for our daily life. With temperature and location sensors and wireless access points embedded in a