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Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensional topic space, by utilizing the global word collocation patterns in the same document. These two types of patterns are complementary. In(More)
A vertex cover of an n-vertex graph with perfect matching contains at least n/2 vertices. In this paper, we study the parameterized complexity of the problem vc-pm* that decides if a given graph with perfect matching has a vertex cover of size bounded by n/2 +k. We first present an algorithm of running time O*(4 k ) for a variation of the vertex cover(More)
Indoor localization is the fundamental capability for indoor service robots and indoor applications on mobile devices. To realize that, the cost of sensors is of great concern. In order to decode the signal carried out by the LED beacons, we propose two reliable solutions using common sensors available on consumer electronic devices. Firstly, we introduce a(More)
With the popularity of smart phones and mobile devices, the number of mobile applications (a.k.a. "apps") has been growing rapidly. Detecting semantically similar apps from a large pool of apps is a basic and important problem, as it is beneficial for various applications, such as app recommendation, app search, etc. However, there is no systematic and(More)
when the number of distinct paths in the channel is large. In this paper, we propose a multi-stage beamforming (MSB) scheme for MIMO-OFDM systems employing both the principles of subcarrier level beamforming and symbol level beamform-ing to effectively tradeoff system performance and complexity. Using the proposed MSB scheme and with a little complexity(More)
Obstacle avoidance is the core problem for mobile robots. Its objective is to allow mobile robots to explore an unknown environment without colliding into other objects. It is the basis for various tasks, e.g. surveillance and rescue, etc. Previous approaches mainly focused on geometric models (such as constructing local cost-maps) which could be regarded(More)
Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)-based methods. However some models are opaque to probabilistic interpretation , and MF-based methods, typically solved using Singular Value Decomposition (SVD), may incur loss of corpus information. In addition, it is desirable to incorporate(More)
Author name ambiguity has been a long-standing problem which impairs the accuracy of publication retrieval and bibliometric methods. Most of the existing disambiguation methods are built on similarity measures, e.g., " Jaccard Coefficient " , between two sets of papers to be disambiguated, each set represented by a set of categorical features, e.g.,(More)