Fixed versus Dynamic Co-Occurrence Windows in TextRank Term Weights for Information Retrieval

TextRank is a variant of PageRank typically used in graphs that represent documents, and where vertices denote terms and edges denote relations between terms. Quite often the relation between terms is simple term co-occurrence within a fixed window of k terms. The output of TextRank when applied iteratively is a score for each vertex, i.e. a term weight, that can be used for information retrieval (IR) just like conventional term frequency based term weights. So far, when computing TextRank term weights over co- occurrence graphs, the window of term co-occurrence is al- ways ?xed. This work departs from this, and considers dy- namically adjusted windows of term co-occurrence that fol- low the document structure on a sentence- and paragraph- level. The resulting TextRank term weights are used in a ranking function that re-ranks 1000 initially returned search results in order to improve the precision of the ranking. Ex- periments with two IR collections show that adjusting the vicinity of term co-occurrence when computing TextRank term weights can lead to gains in early precision

Large-scale Land Cover Classification in GaoFen-2 Satellite Imagery

Many significant applications need land cover information of remote sensing images that are acquired from different areas and times, such as change detection and disaster monitoring. However, it is difficult to find a generic land cover classification scheme for different remote sensing images due to the spectral shift caused by diverse acquisition condition. In this paper, we develop a novel land cover classification method that can deal with large-scale data captured from widely distributed areas and different times. Additionally, we establish a large-scale land cover classification dataset consisting of 150 Gaofen-2 imageries as data support for model training and performance evaluation. Our experiments achieve outstanding classification accuracy compared with traditional methods.Comment: IGARSS'18 conference pape

GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images

Automatic extraction of buildings in remote sensing images is an important but challenging task and finds many applications in different fields such as urban planning, navigation and so on. This paper addresses the problem of buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS) images, whose spatial resolution is often up to half meters and provides rich information about buildings. Based on the observation that buildings in VHSR-RS images are always more distinguishable in geometry than in texture or spectral domain, this paper proposes a geometric building index (GBI) for accurate building extraction, by computing the geometric saliency from VHSR-RS images. More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images. The resulting GBI is finally measured by integrating the derived geometric saliency of buildings. Experiments on three public and commonly used datasets demonstrate that the proposed GBI achieves the state-of-the-art performance and shows impressive generalization capability. Additionally, GBI preserves both the exact position and accurate shape of single buildings compared to existing methods

Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher

The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reliability of generated results and the credibility of contributing sources, due to the limitations of traditional information retrieval algorithms and the LLM hallucination problem. Aiming to create a "PageRank" for the LLM era, we strive to transform LLM into a relevant, responsible, and trustworthy searcher. We propose a novel generative retrieval framework leveraging the knowledge of LLMs to foster a direct link between queries and online sources. This framework consists of three core modules: Generator, Validator, and Optimizer, each focusing on generating trustworthy online sources, verifying source reliability, and refining unreliable sources, respectively. Extensive experiments and evaluations highlight our method's superior relevance, responsibility, and trustfulness against various SOTA methods.Comment: 14 pages, 4 figures, under peer revie

Feasibility and reliability analysis of LCC DC grids and LCC/VSC hybrid DC grids

Power system interconnections using high-voltage direct-current (HVDC) technologies between different areas can be an effective solution to enhance system efficiency and reliability. Particularly, the multi-terminal DC grids, that could balance and ensure resource adequacy, increase asset utilization and reduce costs. In this paper, the technical feasibility of building DC grids using the line commutated converter based (LCC) and voltage source converter based (VSC) HVDC technologies are discussed. Apart from presenting the technical challenges of building LCC DC grids and LCC/VSC hybrid DC grids, the reliability modeling and analysis of these DC grids are also presented. First, the detailed reliability model of the modular multi-level converters (MMCs) with series connected high-voltage and low-voltage bridges are developed. The active mode redundancy design is considered for the reliability model. To this end, a comprehensive whole system reliability model of the studied systems is developed. The reliability model of each subsystem is modeled in detail. Various reliability indices are calculated using this whole system reliability model. The impacts of the redundancy design of the MMCs on these indices are presented. The studies of this paper provide useful guidance for DC grid design and reliability analysis

Control strategies of full-voltage to half-voltage operation for LCC and hybrid LCC/MMC based UHVDC systems

With the increasing demand of transmitting bulk-power over long-distance, the ultra high-voltage direct-current (UHVDC) transmission systems become an attractive option. Nowadays, not only the line commutated converter (LCC) based systems, but also the modular multilevel converter (MMC) based systems have reached UHVDC levels. The converter stations of UHVDC systems normally utilize two series-connected valve-groups to reduce the difficulties of device manufacturing and transportation. This high-voltage and low-voltage valve-group configuration allows the UHVDC systems to achieve a full-voltage to half-voltage operation which increases the flexibility of the systems. However, the existing research only focuses on the full-voltage to half-voltage control of LCC-UHVDC systems. The control strategies for hybrid LCC/MMC UHVDC systems are underresearched. Moreover, the approaches to reduce the load-shedding caused by the full-voltage to half-voltage control for both LCC and hybrid LCC/MMC based UHVDC systems have not been investigated. In this paper, full-voltage to half-voltage control strategies for both LCC and hybrid LCC/MMC based UHVDC systems have been proposed. Moreover, to avoid load-shedding caused by the half-voltage operation, a power rescheduling method that re-sets the power references of the half-voltage operating and full-voltage operating poles has been proposed. The proposed full-voltage to half-voltage control strategies and power rescheduling method can achieve a stable and fast control process with a minimum power loss. The proposed methods have been verified through the time-domain simulations conducted in PSCAD/EMTDC

Isolation and identification of Tete virus group (Peribunyaviridae: Orthobunyavirus) from Culicoides biting midges collected in Lichuan County, China

In July 2018, a virus (JXLC1806-2) was isolated from Culicoides biting midges collected in Lichuan County, Jiangxi Province, China. The virus isolate showed significant cytopathic effects within 48 hours after inoculation with mammalian cells (BHK-21). JXLC1806-2 virus could form plaques in BHK-21 cells, and the virus titer was 1×105.6 pfu/mL. After inoculation with the virus, suckling mice developed disease and died. The nucleotide and amino sequence analysis showed that the JXLC1806-2 virus genome was composed of S, M and L segments. Phylogenetic analysis showed that the S, M and L genes of JXLC1806-2 virus belonged to the Tete serogroup, Orthobunyavirus, but formed an independent evolutionary branch from the other members of the Tete serogroup. The results showed that the JXLC1806-2 virus, which was named as Lichuan virus, is a new member of Tete serogroup, and this is the first time that a Tete serogroup virus has been isolated in China