Learning Structured Inference Neural Networks with Label Relations

Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with fine-grained labels that describe major components, coarse-grained labels that depict high level abstraction or a set of labels that reveal attributes. Such categorization at different concept layers can be modeled with label graphs encoding label information. In this paper, we exploit this rich information with a state-of-art deep learning framework, and propose a generic structured model that leverages diverse label relations to improve image classification performance. Our approach employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. We evaluate our method on benchmark image datasets, and empirical results illustrate the efficacy of our model.Comment: Conference on Computer Vision and Pattern Recognition(CVPR) 201

-Means Based Fingerprint Segmentation with Sensor Interoperability

A critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected by different sensors. This work studies the sensor interoperability of fingerprint segmentation algorithms, which refers to the algorithm's ability to adapt to the raw fingerprints obtained from different sensors. We empirically analyze the sensor interoperability problem, and effectively address the issue by proposing a k-means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on the k-means algorithm, where a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (abbreviated as CMV). SKI also employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The interoperability and robustness of our method are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors

An Optical Design Theory for Focused Fragmentation Warhead

Focusing the dispersing direction of fragments with high-accuracy is one key issue in the design of advancedfocused fragmentation warhead for the application of aerial defence and antimissile. The similarity of mechanismbetween the focusing of geometrical optics and focusing of fragments is analysed in details, and it is proved thatthe driving direction of warhead fragment can be controlled according to the theory of geometrical optics. A newdesign model and method for Focused fragmentation warhead is obtained. The design is preliminary proved to beefficient by some tests, and it is found that 85 per cent of total fragment of the warhead can be focused within thedesigned focusing zone, which is an improvement to the 70 per cent obtained by other methods.Defence Science Journal, 2012, 62(4), pp.205-211, DOI:http://dx.doi.org/10.14429/dsj.62.106

Adaptive evolution and functional constraint at TLR4 during the secondary aquatic adaptation and diversification of cetaceans

<p>Abstract</p> <p>Background</p> <p>Cetaceans (whales, dolphins and porpoises) are a group of adapted marine mammals with an enigmatic history of transition from terrestrial to full aquatic habitat and rapid radiation in waters around the world. Throughout this evolution, the pathogen stress-response proteins must have faced challenges from the dramatic change of environmental pathogens in the completely different ecological niches cetaceans occupied. For this reason, cetaceans could be one of the most ideal candidate taxa for studying evolutionary process and associated driving mechanism of vertebrate innate immune systems such as Toll-like receptors (TLRs), which are located at the direct interface between the host and the microbial environment, act at the first line in recognizing specific conserved components of microorganisms, and translate them rapidly into a defense reaction.</p> <p>Results</p> <p>We used TLR4 as an example to test whether this traditionally regarded pattern recognition receptor molecule was driven by positive selection across cetacean evolutionary history. Overall, the lineage-specific selection test showed that the <it>dN/dS </it>(ω) values along most (30 out of 33) examined cetartiodactylan lineages were less than 1, suggesting a common effect of functional constraint. However, some specific codons made radical changes, fell adjacent to the residues interacting with lipopolysaccharides (LPS), and showed parallel evolution between independent lineages, suggesting that TLR4 was under positive selection. Especially, strong signatures of adaptive evolution on TLR4 were identified in two periods, one corresponding to the early evolutionary transition of the terrestrial ancestors of cetaceans from land to semi-aquatic (represented by the branch leading to whale + hippo) and from semi-aquatic to full aquatic (represented by the ancestral branch leading to cetaceans) habitat, and the other to the rapid diversification and radiation of oceanic dolphins.</p> <p>Conclusions</p> <p>This is the first study thus far to characterize the TLR gene in cetaceans. Our data present evidences that cetacean TLR4 has undergone adaptive evolution against the background of purifying selection in response to the secondary aquatic adaptation and rapid diversification in the sea. It is suggested that microbial pathogens in different environments are important factors that promote adaptive changes at cetacean TLR4 and new functions of some amino acid sites specialized for recognizing pathogens in dramatically contrasted environments to enhance the fitness for the adaptation and survival of cetaceans.</p

Supplementary Material: Discovering Video Clusters from Visual Features and Noisy Tags

The Flip MMC framework proposed in this paper jointly optimizes the model parameters that describe each cluster, finds the best assignment of videos to clusters, and refines the tag labeling to reduce the noise in tag annotation. Similar to MMC, the Flip MMC optimization is a challenging non-convex optimization problem due to the discrete optimization that assigns videos to clusters and refines tag labels. Here this non-convex optimization problem is rewritten in unconstrained format as: min where R w is the the risk function defined in the form of an assignment problem as: where R w (y n ) computes the &quot;mis-clustering&quot; cost of assigning the n-th video to the cluster y n using: In Eq. 3 annotated tags change to t n such that the error of assigning the video x n to y n is minimal while number of changes are being penalized by ∆ t n ,tn . In order to address the unconstrained optimization problem in Eq. 1, we develop a coordinate descent-style approach shown in Algorithm 1. This algorithm alternates between finding the parameters of each cluster (w) and finding an assignment of videos to clusters. The algorithm mainly consists of three steps performed iteratively. First, &quot;mis-clustering&quot; cost is computed in Eq. 3, and then it is used for computing risk function by solving the assignment problem in Eq. 2. Finally, the model parameters are updated given the risk values. The following explains these steps in detail

Low-Loss Polymer-Based Ring Resonator for Resonant Integrated Optical Gyroscopes

Waveguide ring resonator is the sensing element of resonant integrated optical gyroscope (RIOG). This paper reports a polymer-based ring resonator with a low propagation loss of about 0.476 dB/cm for RIOG. The geometrical parameters of the waveguide and the coupler of the resonator were optimally designed. We also discussed the optical properties and gyroscope performance of the polymer resonator which shows a high quality factor of about 105. The polymer-based RIOG exhibits a limited sensitivity of less than 20 deg/h for the low and medium resolution navigation systems