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

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

Printable Metal-Polymer Conductors for Highly Stretchable Bio-Devices

Stretchable, biocompatible devices can bridge electronics and biology. However, most stretchable conductors for such devices are toxic, costly, and regularly break/degrade after several large deformations. Here we show printable, highly stretchable, and biocompatible metal-polymer conductors by casting and peeling off polymers from patterned liquid metal particles, forming surface-embedded metal in polymeric hosts. Our printable conductors present good stretchability (2,316 S/cm at a strain of 500%) and repeatability (ΔR/R <3% after 10,000 cycles), which can satisfy most electrical applications in extreme deformations. This strategy not only overcomes large surface tension of liquid metal but also avoids the undesirable sintering of its particles by stress in deformations, such that stretchable conductors can form on various substrates with high resolution (15 μm), high throughput (∼2,000 samples/hour), and low cost (one-quarter price of silver). We use these conductors for stretchable circuits, motion sensors, wearable glove keyboards, and electroporation of live cells

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

Direct visualization reveals dynamics of a transient intermediate during protein assembly

Interactions between proteins underlie numerous biological functions. Theoretical work suggests that protein interactions initiate with formation of transient intermediates that subsequently relax to specific, stable complexes. However, the nature and roles of these transient intermediates have remained elusive. Here, we characterized the global structure, dynamics, and stability of a transient, on-pathway intermediate during complex assembly between the Signal Recognition Particle (SRP) and its receptor. We show that this intermediate has overlapping but distinct interaction interfaces from that of the final complex, and it is stabilized by long-range electrostatic interactions. A wide distribution of conformations is explored by the intermediate; this distribution becomes more restricted in the final complex and is further regulated by the cargo of SRP. These results suggest a funnel-shaped energy landscape for protein interactions, and they provide a framework for understanding the role of transient intermediates in protein assembly and biological regulation

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

Symbolic Dynamics Analysis of the Lorenz Equations

Recent progress of symbolic dynamics of one- and especially two-dimensional maps has enabled us to construct symbolic dynamics for systems of ordinary differential equations (ODEs). Numerical study under the guidance of symbolic dynamics is capable to yield global results on chaotic and periodic regimes in systems of dissipative ODEs which cannot be obtained neither by purely analytical means nor by numerical work alone. By constructing symbolic dynamics of 1D and 2D maps from the Poincare sections all unstable periodic orbits up to a given length at a fixed parameter set may be located and all stable periodic orbits up to a given length may be found in a wide parameter range. This knowledge, in turn, tells much about the nature of the chaotic limits. Applied to the Lorenz equations, this approach has led to a nomenclature, i.e., absolute periods and symbolic names, of stable and unstable periodic orbits for an autonomous system. Symmetry breakings and restorations as well as coexistence of different regimes are also analyzed by using symbolic dynamics.Comment: 35 pages, LaTeX, 13 Postscript figures, uses psfig.tex. The revision concerns a bug at the end of hlzfig12.ps which prevented the printing of the whole .ps file from page 2

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

Improving automatic source code summarization via deep reinforcement learning

© 2018 Association for Computing Machinery. Code summarization provides a high level natural language description of the function performed by code, as it can benefit the software maintenance, code categorization and retrieval. To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization; b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given. However, it is expected to generate the entire sequence from scratch at test time. This discrepancy can cause an exposure bias issue, making the learnt decoder suboptimal. In this paper, we incorporate an abstract syntax tree structure as well as sequential content of code snippets into a deep reinforcement learning framework (i.e., actor-critic network). The actor network provides the confidence of predicting the next word according to current state. On the other hand, the critic network evaluates the reward value of all possible extensions of the current state and can provide global guidance for explorations. We employ an advantage reward composed of BLEU metric to train both networks. Comprehensive experiments on a real-world dataset show the effectiveness of our proposed model when compared with some state-of-the-art methods