Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

Macroscopic phase segregation in superconducting K0.73Fe1.67Se2 as seen by muon spin rotation and infrared spectroscopy

Using muon spin rotation (\muSR) and infrared spectroscopy we investigated the recently discovered superconductor K0.73Fe1.67Se2 with Tc = 32 K. We show that the combined data can be consistently described in terms of a macroscopically phase segregated state with a matrix of ~88% volume fraction that is insulating and strongly magnetic and inclusions with a ~12% volume fraction which are metallic, superconducting and non-magnetic. The electronic properties of the latter, in terms of the normal state plasma frequency and the superconducting condensate density, appear to be similar as in other iron selenide or arsenide superconductors.Comment: 22 pages, 8 figures. (citation list correction.

A comprehensive evaluation of full reference image quality assessment algorithms

2012 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, FL, 30-3 October 2012Recent years have witnessed a growing interest in developing objective image quality assessment (IQA) algorithms that can measure the image quality consistently with subjective evaluations. For the full reference (FR) IQA problem, great progress has been made in the past decade. On the other hand, several new large scale image datasets have been released for evaluating FR IQA methods in recent years. Meanwhile, no work has been reported to evaluate and compare the performance of state-of-the-art and representative FR IQA methods on all the available datasets. In this paper, we aim to fulfill this task by reporting the performance of eleven selected FR IQA algorithms on all the seven public IQA image datasets. Our evaluation results and the associated discussions will be very helpful for relevant researchers to have a clearer understanding about the status of modern FR IQA indices. Evaluation results presented in this paper are also online available at http://sse.tongji.edu.cn/linzhang/IQA/IQA. htm.Department of ComputingRefereed conference pape

Multitemporal Very High Resolution from Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 images and a video captured by the Iris camera on-board the International Space Station. The problems addressed and the techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and mixed ideas and methodologies from the remote sensing, video processing, and computer vision. In particular, the winning team developed a deep learning method to jointly address spatial scene labeling and temporal activity modeling using the available image and video data. The second place team proposed a random field model to simultaneously perform coregistration of multitemporal data, semantic segmentation, and change detection. The methodological key ideas of both these approaches and the main results of the corresponding experimental validation are discussed in this paper

Extraction of Electron Self-Energy and Gap Function in the Superconducting State of Bi_2Sr_2CaCu_2O_8 Superconductor via Laser-Based Angle-Resolved Photoemission

Super-high resolution laser-based angle-resolved photoemission measurements have been performed on a high temperature superconductor Bi_2Sr_2CaCu_2O_8. The band back-bending characteristic of the Bogoliubov-like quasiparticle dispersion is clearly revealed at low temperature in the superconducting state. This makes it possible for the first time to experimentally extract the complex electron self-energy and the complex gap function in the superconducting state. The resultant electron self-energy and gap function exhibit features at ~54 meV and ~40 meV, in addition to the superconducting gap-induced structure at lower binding energy and a broad featureless structure at higher binding energy. These information will provide key insight and constraints on the origin of electron pairing in high temperature superconductors.Comment: 4 pages, 4 figure

Graphene-based modulation-doped superlattice structures

The electronic transport properties of graphene-based superlattice structures are investigated. A graphene-based modulation-doped superlattice structure geometry is proposed and consist of periodically arranged alternate layers: InAs/graphene/GaAs/graphene/GaSb. Undoped graphene/GaAs/graphene structure displays relatively high conductance and enhanced mobilities at elevated temperatures unlike modulation-doped superlattice structure more steady and less sensitive to temperature and robust electrical tunable control on the screening length scale. Thermionic current density exhibits enhanced behaviour due to presence of metallic (graphene) mono-layers in superlattice structure. The proposed superlattice structure might become of great use for new types of wide-band energy gap quantum devices.Comment: 5 figure

Superconductivity at the Border of Electron Localization and Itinerancy

The superconducting state of iron pnictides and chalcogenides exists at the border of antiferromagnetic order. Consequently, these materials could provide clues about the relationship between magnetism and unconventional superconductivity. One explanation, motivated by the so-called bad-metal behaviour of these materials, proposes that magnetism and superconductivity develop out of quasi-localized magnetic moments which are generated by strong electron-electron correlations. Another suggests that these phenomena are the result of weakly interacting electron states that lie on nested Fermi surfaces. Here we address the issue by comparing the newly discovered alkaline iron selenide superconductors, which exhibit no Fermi-surface nesting, to their iron pnictide counterparts. We show that the strong-coupling approach leads to similar pairing amplitudes in these materials, despite their different Fermi surfaces. We also find that the pairing amplitudes are largest at the boundary between electronic localization and itinerancy, suggesting that new superconductors might be found in materials with similar characteristics.Comment: Version of the published manuscript prior to final journal-editting. Main text (23 pages, 4 figures) + Supplementary Information (14 pages, 7 figures, 3 tables). Calculation on the single-layer FeSe is added. Enhancement of the pairing amplitude in the vicinity of the Mott transition is highlighted. Published version is at http://www.nature.com/ncomms/2013/131115/ncomms3783/full/ncomms3783.htm