arXiv subjects

Dandan Li

Publications and source records attributed to Dandan Li.

22 records · Page 2Linked to original sources

arXiv2018

C-DLSI: An Extended LSI Tailored for Federated Text Retrieval

As the web expands in data volume and in geographical distribution, centralized search methods become inefficient, leading to increasing interest in cooperative information retrieval, e.g., federated text retrieval (FTR). Different from existing centralized information retrieval (IR) methods, in which search is done on a logically centralized document collection, FTR is composed of a number of peers, each of which is a complete search engine by itself. To process a query, FTR requires firstly the identification of promising peers that host the relevant documents and secondly the retrieval of the most relevant documents from the selected peers. Most of the existing methods only apply traditional IR techniques that treat each text collection as a single large document and utilize term matching to rank the collections. In this paper, we formalize the problem and identify the properties of FTR, and analyze the feasibility of extending LSI with clustering to adapt to FTR, based on which a novel approach called Cluster-based Distributed Latent Semantic Indexing (C-DLSI) is proposed. C-DLSI distinguishes the topics of a peer with clustering, captures the local LSI spaces within the clusters, and consider the relations among these LSI spaces, thus providing more precise characterization of the peer. Accordingly, novel descriptors of the peers and a compatible local text retrieval are proposed. The experimental results show that C-DLSI outperforms existing methods.

cs.IR↗

arXiv2018

Representing Sets as Summed Semantic Vectors

Representing meaning in the form of high dimensional vectors is a common and powerful tool in biologically inspired architectures. While the meaning of a set of concepts can be summarized by taking a (possibly weighted) sum of their associated vectors, this has generally been treated as a one-way operation. In this paper we show how a technique built to aid sparse vector decomposition allows in many cases the exact recovery of the inputs and weights to such a sum, allowing a single vector to represent an entire set of vectors from a dictionary. We characterize the number of vectors that can be recovered under various conditions, and explore several ways such a tool can be used for vector-based reasoning.

cs.AI↗

arXiv2018

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem. We present a new convolutional neural network (CNN) architecture by introducing a novel hierarchical feature integration mechanism, named Hi-Fi, to address the skeleton detection problem. The proposed CNN-based approach has a powerful multi-scale feature integration ability that intrinsically captures high-level semantics from deeper layers as well as low-level details from shallower layers. % By hierarchically integrating different CNN feature levels with bidirectional guidance, our approach (1) enables mutual refinement across features of different levels, and (2) possesses the strong ability to capture both rich object context and high-resolution details. Experimental results show that our method significantly outperforms the state-of-the-art methods in terms of effectively fusing features from very different scales, as evidenced by a considerable performance improvement on several benchmarks.

cs.CV↗

arXiv2014

Tunable Cobalt Vacancies and Related Properties in LaCoxAs2

The origin of transition metal vacancies and their effects on the properties of ThCr2Si2-type compounds have been less studied and poorly understood. Here we carefully investigate the structure, physical properties, and electronic structure for a series of lanthanum cobalt arsenides with nominal composition of LaCoxAs2 (1.6 < = x < = 2.1). It is revealed that the occupancy of Co can be tuned between 1.98(1) and 1.61(1). The structural analyses based on X-ray and neutron diffractions show the existence of Co vacancies results from charge balance due to the formation of bond between As-As. These Co vacancies affect the magnetic and electrical properties greatly, adjusting the Curie temperature from 205 to 47 K and increasing the resistivity by more than 100%. First principles calculations indicate that the Co vacancies weaken the spin polarization and reduce the density of states at the Fermi level, resulting in decreased Curie temperature and increased resistivity, respectively. Our results address the importance of transition metal vacancies in ThCr2Si2-type materials and offer a reliable route to tune the magnetism of ThCr2Si2-type structure.

cond-mat.str-el↗