Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application

Using Mixture Covariance Matrices to Improve Face and Facial Expression Recognitions

Abstract. In several pattern recognition problems, particularly in image recognition ones, there are often a large number of features available, but the number of training examples for each pattern is significantly less than the dimension of the feature space. This statement implies that the sample group covariance matrices often used in the Gaussian maximum probability classifier are singular. A common solution to this problem is to assume that all groups have equal covariance matrices and to use as their estimates the pooled covariance matrix calculated from the whole training set. This paper uses an alternative estimate for the sample group covariance matrices, here called the mixture covariance, given by an appropriate linear combination of the sample group and pooled covariance matrices. Experiments were carried out to evaluate the performance associated with this estimate in two biometric applications: face and facial expression. The average recognition rates obtained by using the mixture covariance matrices were higher than the usual estimates

Borrelia carolinensis sp. nov., a novel species of the Borrelia burgdorferi sensu lato complex isolated from rodents and a tick from the south-eastern USA

A group of 16 isolates with genotypic characteristics different from those of known species of the Borrelia burgdorferi sensu lato complex were cultured from ear biopsies of the rodents Peromyscus gossypinus and Neotoma floridana trapped at five localities in South Carolina, USA, and from the tick Ixodes minor feeding on N. floridana. Multilocus sequence analysis of members of the novel species, involving the 16S rRNA gene, the 5S–23S (rrf–rrl) intergenic spacer region and the flagellin, ospA and p66 genes, was conducted and published previously and was used to clarify the taxonomic status of the novel group of B. burgdorferi sensu lato isolates. Phylogenetic analysis based on concatenated sequences of the five analysed genomic loci showed that the 16 isolates clustered together but separately from other species in the B. burgdorferi sensu lato complex. The analysed group therefore represents a novel species, formally described here as Borrelia carolinensis sp. nov., with the type strain SCW-22T (=ATCC BAA-1773T =DSM 22119T)

Remote dynamic partial reconfiguration: A threat to Internet-of-Things and embedded security applications

The advent of the Internet of Things has motivated the use of Field Programmable Gate Array (FPGA) devices with Dynamic Partial Reconfiguration (DPR) capabilities for dynamic non-invasive modifications to circuits implemented on the FPGA. In particular, the ability to perform DPR over the network is essential in the context of a growing number of Internet of Things (IoT)-based and embedded security applications. However, the use of remote DPR brings with it a number of security threats that could lead to potentially catastrophic consequences in practical scenarios. In this paper, we demonstrate four examples where the remote DPR capability of the FPGA may be exploited by an adversary to launch Hardware Trojan Horse (HTH) attacks on commonly used security applications. We substantiate the threat by demonstrating remotely-launched attacks on Xilinx FPGA-based hardware implementations of a cryptographic algorithm, a true random number generator, and two processor-based security applications - namely, a software implementation of a cryptographic algorithm and a cash dispensing scheme. The attacks are launched by on-the-fly transfer of malicious FPGA configuration bitstreams over an Ethernet connection to perform DPR and leak sensitive information. Finally, we comment on plausible countermeasures to prevent such attack

Modeling Connectivity in Terms of Network Activity

A new complex network model is proposed which is founded on growth with new connections being established proportionally to the current dynamical activity of each node, which can be understood as a generalization of the Barabasi-Albert static model. By using several topological measurements, as well as optimal multivariate methods (canonical analysis and maximum likelihood decision), we show that this new model provides, among several other theoretical types of networks including Watts-Strogatz small-world networks, the greatest compatibility with three real-world cortical networks.Comment: A working manuscript, 5 pages, 3 figures, 1 tabl

PCA Tomography: how to extract information from datacubes

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies it is possible to obtain datacubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of datacube (data from single field observations, containing two spatial and one spectral dimension) that uses PCA (Principal Component Analysis) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data onto these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal and this information is fundamental for their handling and interpretation. When the datacube shows objects that present uncorrelated physical phenomena, the eigenvector's orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the LINER galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore we show that it is displaced from the centre of its stellar bulge.Comment: 13 pages, 16 figures, accepted for publication on MNRA

Allergen Delivery Inhibitors: A Rationale for Targeting Sentinel Innate Immune Signaling of Group 1 House Dust Mite Allergens through Structure-Based Protease Inhibitor Design

Diverse evidence from epidemiologic surveys and investigations into the molecular basis of allergenicity have revealed that a small cadre of “initiator” allergens promote the development of allergic diseases, such as asthma, allergic rhinitis, and atopic dermatitis. Pre-eminent among these initiators are the group 1 allergens from house dust mites (HDM). In mites, group 1 allergens function as cysteine peptidase digestive enzymes to which humans are exposed by inhalation of HDM fecal pellets. Their protease nature confers the ability to activate high gain signaling mechanisms which promote innate immune responses, leading to the persistence of allergic sensitization. An important feature of this process is that the initiator drives responses both to itself and to unrelated allergens lacking these properties through a process of collateral priming. The clinical significance of group 1 HDM allergens in disease, their serodominance as allergens, and their IgE-independent bioactivities in innate immunity make these allergens interesting therapeutic targets in the design of new small-molecule interventions in allergic disease. The attraction of this new approach is that it offers a powerful, root-cause-level intervention from which beneficial effects can be anticipated by interference in a wide range of effector pathways associated with these complex diseases. This review addresses the general background to HDM allergens and the validation of group 1 as putative targets. We then discuss structure-based drug design of the first-in-class representatives of allergen delivery inhibitors aimed at neutralizing the proteolytic effects of HDM group 1 allergens, which are essential to the development and maintenance of allergic diseases

Relevance of Bcl-x expression in different types of endometrial tissues

<p>Abstract</p> <p>Objectives</p> <p>To explore the roles of Bcl-xl and Bcl-xs in the development and progression of endometrial carcinoma, and to analyze the correlation between Bcl-xl and Bcl-xs.</p> <p>Methods</p> <p>RT-PCR and Western-blot assay were applied to detect the expressions of Bcl-xl and Bcl-xs in endometrial tissues of various histomorphologic types.</p> <p>Results</p> <p>The Bcl-xl expression levels of simple and atypical hyperplasia endometrial tissues were not significantly different from that of normal endometrial tissue (both <it>P </it>> 0.05). On contrary, Bcl-xl expression in endometrial carcinoma tissue was significantly higher than the normal endometrial tissue (<it>P </it>= 0.00), which was correlated with the pathological grading of endometrial carcinoma (F = 5.33, <it>P </it>= 0.02). In addition, Bcl-xs mRNA level in simple hyperplasia endometrial tissue had no significant difference compared to that in normal endometrial tissue (<it>P </it>= 0.12), while the levels of atypical hyperplasia and endometrial carcinoma endometrial tissues were significantly different from the normal endometrial tissue (both <it>P </it>= 0.00). Furthermore, level of Bcl-xs mRNA was correlated with the clinical staging and lymph node metastasis of the endometrial carcinoma (<it>P </it>< 0.05). The expressions of Bcl-xl and Bcl-xs were negatively correlated with each other (<it>r </it>= -0.76).</p> <p>Conclusion</p> <p>The abnormal expressions of Bcl-xs and Bcl-xl were one of the molecular mechanisms for the pathogenesis of endometrial carcinoma, and altered ratio between these two might involve in the onset of endometrial carcinoma.</p

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author