102 research outputs found
When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition
Deep learning, in particular Convolutional Neural Network (CNN), has achieved
promising results in face recognition recently. However, it remains an open
question: why CNNs work well and how to design a 'good' architecture. The
existing works tend to focus on reporting CNN architectures that work well for
face recognition rather than investigate the reason. In this work, we conduct
an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a
common ground to make our work easily reproducible. Specifically, we use public
database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing
CNNs trained on private databases. We propose three CNN architectures which are
the first reported architectures trained using LFW data. This paper
quantitatively compares the architectures of CNNs and evaluate the effect of
different implementation choices. We identify several useful properties of
CNN-FRS. For instance, the dimensionality of the learned features can be
significantly reduced without adverse effect on face recognition accuracy. In
addition, traditional metric learning method exploiting CNN-learned features is
evaluated. Experiments show two crucial factors to good CNN-FRS performance are
the fusion of multiple CNNs and metric learning. To make our work reproducible,
source code and models will be made publicly available.Comment: 7 pages, 4 figures, 7 table
Domestication of rice has reduced the occurrence of transposable elements within gene coding regions
Assessing the Microbial Community and Functional Genes in a Vertical Soil Profile with Long-Term Arsenic Contamination
Conceived and designed the experiments: GW. Performed the experiments: JX GL. Analyzed the data: JX JZ GW. Contributed reagents/materials/analysis tools: ST JZ GW. Wrote the paper: JX ZH JDVN JZ GW.Arsenic (As) contamination in soil and groundwater has become a serious problem to public health. To examine how microbial communities and functional genes respond to long-term arsenic contamination in vertical soil profile, soil samples were collected from the surface to the depth of 4 m (with an interval of 1 m) after 16-year arsenic downward infiltration. Integrating BioLog and functional gene microarray (GeoChip 3.0) technologies, we showed that microbial metabolic potential and diversity substantially decreased, and community structure was markedly distinct along the depth. Variations in microbial community functional genes, including genes responsible for As resistance, carbon and nitrogen cycling, phosphorus utilization and cytochrome c oxidases were detected. In particular, changes in community structures and activities were correlated with the biogeochemical features along the vertical soil profile when using the rbcL and nifH genes as biomarkers, evident for a gradual transition from aerobic to anaerobic lifestyles. The C/N showed marginally significant correlations with arsenic resistance (pβ=β0.069) and carbon cycling genes (pβ=β0.073), and significant correlation with nitrogen fixation genes (pβ=β0.024). The combination of C/N, NO3β and P showed the highest correlation (rβ=β0.779, pβ=β0.062) with the microbial community structure. Contradict to our hypotheses, a long-term arsenic downward infiltration was not the primary factor, while the spatial isolation and nutrient availability were the key forces in shaping the community structure. This study provides new insights about the heterogeneity of microbial community metabolic potential and future biodiversity preservation for arsenic bioremediation management.Yeshttp://www.plosone.org/static/editorial#pee
Generation of line pattern in thin polymer films with embedded gold nanoparticles by irradiation with ultrashort, linearly polarized laser pulses
The laser induced photomodification of metal nanoparticles currently attracts considerable technological interest as an uncomplicated low cost method to control an optimize size, shape and spatial arrangement of metal clusters in nanostructured composite materials. Recently, femtosecond (fs) laser pulse assisted shape modification was demonstrated for metal nanoparticles embedded in silica glass, in this contribution we introduce a new and simple, non-tactile method to generate periodically arranged submicron structures in thin organic films with embedded gold nanoparticles. The periodically arranged line pattern within the film appears when the samples are subjected to irradiation with intense, linearly polarized fs-laser pulses. The nanoparticle-containing films, having a thickness of a bout 100 nm, were deposited by alternating plasma polymerization of hexamethyldisilazane and metal evaporation
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