298 research outputs found
ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network
With the development of next generation sequencing techniques, it is fast and
cheap to determine protein sequences but relatively slow and expensive to
extract useful information from protein sequences because of limitations of
traditional biological experimental techniques. Protein function prediction has
been a long standing challenge to fill the gap between the huge amount of
protein sequences and the known function. In this paper, we propose a novel
method to convert the protein function problem into a language translation
problem by the new proposed protein sequence language "ProLan" to the protein
function language "GOLan", and build a neural machine translation model based
on recurrent neural networks to translate "ProLan" language to "GOLan"
language. We blindly tested our method by attending the latest third Critical
Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the
performance of our methods on selected proteins whose function was released
after CAFA competition. The good performance on the training and testing
datasets demonstrates that our new proposed method is a promising direction for
protein function prediction. In summary, we first time propose a method which
converts the protein function prediction problem to a language translation
problem and applies a neural machine translation model for protein function
prediction.Comment: 13 pages, 5 figure
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach
Partial person re-identification (re-id) is a challenging problem, where only
several partial observations (images) of people are available for matching.
However, few studies have provided flexible solutions to identifying a person
in an image containing arbitrary part of the body. In this paper, we propose a
fast and accurate matching method to address this problem. The proposed method
leverages Fully Convolutional Network (FCN) to generate fix-sized spatial
feature maps such that pixel-level features are consistent. To match a pair of
person images of different sizes, a novel method called Deep Spatial feature
Reconstruction (DSR) is further developed to avoid explicit alignment.
Specifically, DSR exploits the reconstructing error from popular dictionary
learning models to calculate the similarity between different spatial feature
maps. In that way, we expect that the proposed FCN can decrease the similarity
of coupled images from different persons and increase that from the same
person. Experimental results on two partial person datasets demonstrate the
efficiency and effectiveness of the proposed method in comparison with several
state-of-the-art partial person re-id approaches. Additionally, DSR achieves
competitive results on a benchmark person dataset Market1501 with 83.58\%
Rank-1 accuracy.Comment: 8 pages, 11 figures, accepted by CVPR 201
Submillisecond-response polymer network liquid crystal phase modulators at 1.06-mu m wavelength
A fast-response and scattering-free polymer network liquid crystal (PNLC) light modulator is demonstrated at lambda = 1.06 mu m wavelength. A decay time of 117 mu s for 2 pi phase modulation is obtained at 70 degrees C, which is similar to 650 x faster than that of the host nematic LCs. The major tradeoff is the increased operating voltage. Potential applications include spatial light modulators and adaptive optics
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Ultra-Sensitive Piezo-Resistive Sensors Constructed with Reduced Graphene Oxide/Polyolefin Elastomer (RGO/POE) Nanofiber Aerogels.
Flexible wearable pressure sensors have received extensive attention in recent years because of the promising application potentials in health management, humanoid robots, and human machine interfaces. Among the many sensory performances, the high sensitivity is an essential requirement for the practical use of flexible sensors. Therefore, numerous research studies are devoted to improving the sensitivity of the flexible pressure sensors. The fiber assemblies are recognized as an ideal substrate for a highly sensitive piezoresistive sensor because its three-dimensional porous structure can be easily compressed and can provide high interconnection possibilities of the conductive component. Moreover, it is expected to achieve high sensitivity by raising the porosity of the fiber assemblies. In this paper, the three-dimensional reduced graphene oxide/polyolefin elastomer (RGO/POE) nanofiber composite aerogels were prepared by chemical reducing the graphene oxide (GO)/POE nanofiber composite aerogels, which were obtained by freeze drying the mixture of the GO aqueous solution and the POE nanofiber suspension. It was found that the volumetric shrinkage of thermoplastic POE nanofibers during the reduction process enhanced the compression mechanical strength of the composite aerogel, while decreasing its sensitivity. Therefore, the composite aerogels with varying POE nanofiber usage were prepared to balance the sensitivity and working pressure range. The results indicated that the composite aerogel with POE nanofiber/RGO proportion of 3:3 was the optimal sample, which exhibits high sensitivity (ca. 223 kPa-1) and working pressure ranging from 0 to 17.7 kPa. In addition, the composite aerogel showed strong stability when it is either compressed with different frequencies or reversibly compressed and released 5000 times
Exploring the Formation Mechanism of Radical Technological Innovation: An MLP Approach
This paper identifies three stages in the radical technological innovation process, namely formation process in niches, breaking out of niches and entering regimes, and new regime formation. It then adopts Multi-level Perspective (MLP) to explore the formation process, operating mechanism, breakthrough path, and impact factors of radical technological innovation. A three-phase model, which includes formation of radical innovation, breakout of radical innovation, and new regimes construction, is proposed to analyze radical technological innovation. The model is adopted in a case study to analyze the leapfrogging development of technologies in China’s mobile communication industry. This paper enriches technological innovation theory and provides supports for policy making and guidance for industries/enterprises practices regarding technological innovation in emerging economies
Technological Innovation Research: A Structural Equation Modelling Approach
The paper explores the relationship among technological innovation, technological trajectory transition, and firms’ innovation performance. Technological innovation is studied from the perspectives of innovation novelty and innovation openness. Technological trajectory transition is categorized into creative cumulative technological trajectory transition and creative disruptive technological trajectory transition. A structural equation model is developed and tested with data collected by surveying 366 Chinese firms. The results indicate that both innovation novelty and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition both positively affect firms’ innovation performance. However, neither innovation novelty nor creative cumulative technological trajectory transition positively affects firms’ innovation performance. Implications for managers and directions for future studies are discussed
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