Attributes and action recognition based on convolutional neural networks and spatial pyramid VLAD encoding

© Springer International Publishing AG 2017.Determination of human attributes and recognition of actions in still images are two related and challenging tasks in computer vision, which often appear in fine-grained domains where the distinctions between the different categories are very small. Deep Convolutional Neural Network (CNN) models have demonstrated their remarkable representational learning capability through various examples. However, the successes are very limited for attributes and action recognition as the potential of CNNs to acquire both of the global and local information of an image remains largely unexplored. This paper proposes to tackle the problem with an encoding of a spatial pyramid Vector of Locally Aggregated Descriptors (VLAD) on top of CNN features. With region proposals generated by Edgeboxes, a compact and efficient representation of an image is thus produced for subsequent prediction of attributes and classification of actions. The proposed scheme is validated with competitive results on two benchmark datasets: 90.4% mean Average Precision (mAP) on the Berkeley Attributes of People dataset and 88.5% mAP on the Stanford 40 action dataset

Mechanical properties related to the relaxor-ferroelectric phase transition of titanium-doped lead magnesium niobate

2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein

Synthesized grain size distribution in the interstellar medium

We examine a synthetic way of constructing the grain size distribution in the interstellar medium (ISM). First we formulate a synthetic grain size distribution composed of three grain size distributions processed with the following mechanisms that govern the grain size distribution in the Milky Way: (i) grain growth by accretion and coagulation in dense clouds, (ii) supernova shock destruction by sputtering in diffuse ISM, and (iii) shattering driven by turbulence in diffuse ISM. Then, we examine if the observational grain size distribution in the Milky Way (called MRN) is successfully synthesized or not. We find that the three components actually synthesize the MRN grain size distribution in the sense that the deficiency of small grains by (i) and (ii) is compensated by the production of small grains by (iii). The fraction of each {contribution} to the total grain processing of (i), (ii), and (iii) (i.e., the relative importance of the three {contributions} to all grain processing mechanisms) is 30-50%, 20-40%, and 10-40%, respectively. We also show that the Milky Way extinction curve is reproduced with the synthetic grain size distributions.Comment: 10 pages, 6 figures, accepted for publication in Earth, Planets, and Spac

Fracturing ranked surfaces

Discretized landscapes can be mapped onto ranked surfaces, where every element (site or bond) has a unique rank associated with its corresponding relative height. By sequentially allocating these elements according to their ranks and systematically preventing the occupation of bridges, namely elements that, if occupied, would provide global connectivity, we disclose that bridges hide a new tricritical point at an occupation fraction $p=p_{c}$, where $p_{c}$ is the percolation threshold of random percolation. For any value of $p$ in the interval $p_{c}< p \leq 1$, our results show that the set of bridges has a fractal dimension $d_{BB} \approx 1.22$ in two dimensions. In the limit $p \rightarrow 1$, a self-similar fracture is revealed as a singly connected line that divides the system in two domains. We then unveil how several seemingly unrelated physical models tumble into the same universality class and also present results for higher dimensions

Nanostructured 3D Constructs Based on Chitosan and Chondroitin Sulphate Multilayers for Cartilage Tissue Engineering

Nanostructured three-dimensional constructs combining layer-by-layer technology (LbL) and template leaching were processed and evaluated as possible support structures for cartilage tissue engineering. Multilayered constructs were formed by depositing the polyelectrolytes chitosan (CHT) and chondroitin sulphate (CS) on either bidimensional glass surfaces or 3D packet of paraffin spheres. 2D CHT/CS multi-layered constructs proved to support the attachment and proliferation of bovine chondrocytes (BCH). The technology was transposed to 3D level and CHT/CS multi-layered hierarchical scaffolds were retrieved after paraffin leaching. The obtained nanostructured 3D constructs had a high porosity and water uptake capacity of about 300%. Dynamical mechanical analysis (DMA) showed the viscoelastic nature of the scaffolds. Cellular tests were performed with the culture of BCH and multipotent bone marrow derived stromal cells (hMSCs) up to 21 days in chondrogenic differentiation media. Together with scanning electronic microscopy analysis, viability tests and DNA quantification, our results clearly showed that cells attached, proliferated and were metabolically active over the entire scaffold. Cartilaginous extracellular matrix (ECM) formation was further assessed and results showed that GAG secretion occurred indicating the maintenance of the chondrogenic phenotype and the chondrogenic differentiation of hMSCs

Strong signature of natural selection within an FHIT intron implicated in prostate cancer risk

Previously, a candidate gene linkage approach on brother pairs affected with prostate cancer identified a locus of prostate cancer susceptibility at D3S1234 within the fragile histidine triad gene (FHIT), a tumor suppressor that induces apoptosis. Subsequent association tests on 16 SNPs spanning approximately 381 kb surrounding D3S1234 in Americans of European descent revealed significant evidence of association for a single SNP within intron 5 of FHIT. In the current study, resequencing and genotyping within a 28.5 kb region surrounding this SNP further delineated the association with prostate cancer risk to a 15 kb region. Multiple SNPs in sequences under evolutionary constraint within intron 5 of FHIT defined several related haplotypes with an increased risk of prostate cancer in European-Americans. Strong associations were detected for a risk haplotype defined by SNPs 138543, 142413, and 152494 in all cases (Pearson's χ2 = 12.34, df 1, P = 0.00045) and for the homozygous risk haplotype defined by SNPs 144716, 142413, and 148444 in cases that shared 2 alleles identical by descent with their affected brothers (Pearson's χ2 = 11.50, df 1, P = 0.00070). In addition to highly conserved sequences encompassing SNPs 148444 and 152413, population studies revealed strong signatures of natural selection for a 1 kb window covering the SNP 144716 in two human populations, the European American (π = 0.0072, Tajima's D= 3.31, 14 SNPs) and the Japanese (π = 0.0049, Fay & Wu's H = 8.05, 14 SNPs), as well as in chimpanzees (Fay & Wu's H = 8.62, 12 SNPs). These results strongly support the involvement of the FHIT intronic region in an increased risk of prostate cancer. © 2008 Ding et al

Temperature Dependence of Spin-Split Peaks in Transverse Electron Focusing

We present experimental results of transverse electron-focusing measurements performed using n-type GaAs. In the presence of a small transverse magnetic field (B⊥), electrons are focused from the injector to detector leading to focusing peaks periodic in B⊥. We show that the odd-focusing peaks exhibit a split, where each sub-peak represents a population of a particular spin branch emanating from the injector. The temperature dependence reveals that the peak splitting is well defined at low temperature whereas it smears out at high temperature indicating the exchange-driven spin polarisation in the injector is dominant at low temperatures

Electrically Tunable Excitonic Light Emitting Diodes based on Monolayer WSe2 p-n Junctions

Light-emitting diodes are of importance for lighting, displays, optical interconnects, logic and sensors. Hence the development of new systems that allow improvements in their efficiency, spectral properties, compactness and integrability could have significant ramifications. Monolayer transition metal dichalcogenides have recently emerged as interesting candidates for optoelectronic applications due to their unique optical properties. Electroluminescence has already been observed from monolayer MoS2 devices. However, the electroluminescence efficiency was low and the linewidth broad due both to the poor optical quality of MoS2 and to ineffective contacts. Here, we report electroluminescence from lateral p-n junctions in monolayer WSe2 induced electrostatically using a thin boron nitride support as a dielectric layer with multiple metal gates beneath. This structure allows effective injection of electrons and holes, and combined with the high optical quality of WSe2 it yields bright electroluminescence with 1000 times smaller injection current and 10 times smaller linewidth than in MoS2. Furthermore, by increasing the injection bias we can tune the electroluminescence between regimes of impurity-bound, charged, and neutral excitons. This system has the required ingredients for new kinds of optoelectronic devices such as spin- and valley-polarized light-emitting diodes, on-chip lasers, and two-dimensional electro-optic modulators.Comment: 13 pages main text with 4 figures + 4 pages upplemental material