Face-based Selection of Corners in 3D Substructuring

In most recent substructuring methods, a fundamental role is played by the coarse space. For some of these methods (e.g. BDDC and FETI-DP), its definition relies on a 'minimal' set of coarse nodes (sometimes called corners) which assures invertibility of local subdomain problems and also of the global coarse problem. This basic set is typically enhanced by enforcing continuity of functions at some generalized degrees of freedom, such as average values on edges or faces of subdomains. We revisit existing algorithms for selection of corners. The main contribution of this paper consists of proposing a new heuristic algorithm for this purpose. Considering faces as the basic building blocks of the interface, inherent parallelism, and better robustness with respect to disconnected subdomains are among features of the new technique. The advantages of the presented algorithm in comparison to some earlier approaches are demonstrated on three engineering problems of structural analysis solved by the BDDC method.

Light-bias induced instability and persistent photoconductivity in In-Zn-O/Ga-In-Zn-O thin film transistors

Stress/recovery measurements demonstrate that even high-performance passivated In-Zn-O/ Ga-In-Zn-O thin film transistors with excellent in-dark stability suffer from light-bias induced threshold voltage shift (ΔV T) and defect density changes. Visible light stress leads to ionisation of oxygen vacancy sites, causing persistent photoconductivity. This makes the material act as though it was n-doped, always causing a negative threshold voltage shift under strong illumination, regardless of the magnitude and polarity of the gate bias.

P-16: Light-bias induced instability and persistent photoconductivity in in-zn-o/ga-in-zn-o thin film transistors

Stress/recovery measurements demonstrate that even highperformance passivated In-Zn-O/ Ga-In-Zn-O thin film transistors with excellent in-dark stability suffer from light-bias induced threshold voltage shift (ΔV T) and defect density changes. Visible light stress leads to ionisation of oxygen vacancy sites, causing persistent photoconductivity. This makes the material act as though it was n-doped, always causing a negative threshold voltage shift under strong illumination, regardless of the magnitude and polarity of the gate bias. © 2011 SID.

Achieving robust face recognition from video by combining a weak photometric model and a learnt generic face invariant

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. The central contribution is an illumination invariant, which we show to be suitable for recognition from video of loosely constrained head motion. In particular there are three contributions: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation to exploit the proposed invariant and generalize in the presence of extreme illumination changes; (ii) we introduce a video sequence re-illumination algorithm to achieve fine alignment of two video sequences; and (iii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve robustness to unseen head poses. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 323 individuals and 1474 video sequences with extreme illumination, pose and head motion variation. Our system consistently achieved a nearly perfect recognition rate (over 99.7% on all four databases). © 2012 Elsevier Ltd All rights reserved.

Occurrence of particle debris field during focused Ga ion beam milling of glassy carbon

To explore the machining characteristics of glassy carbon by focused ion beam (FIB), particles induced by FIB milling on glassy carbon have been studied in the current work. Nano-sized particles in the range of tens of nanometers up to 400 nm can often be found around the area subject to FIB milling. Two ion beam scanning modes - slow single scan and fast repetitive scan - have been tested. Fewer particles are found in single patterns milled in fast repetitive scan mode. For a group of test patterns milled in a sequence, it was found that a greater number of particles were deposited around sites machined early in the sequence. In situ EDX analysis of the particles showed that they were composed of C and Ga. The formation of particles is related to the debris generated at the surrounding areas, the low melting point of gallium used as FIB ion source and the high contact angle of gallium on glassy carbon induces de-wetting of Ga and the subsequent formation of Ga particles. Ultrasonic cleaning can remove over 98% of visible particles. The surface roughness (Ra) of FIB milled areas after cleaning is less than 2 nm. © 2010.

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the Active Appearance Model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases. © 2009 IEEE.

Behavior learning of a face robot based on the characteristics of human instruction

Computer simulation experiments were performed to examine the effectiveness of OR- and comparative-reinforcement learning algorithms. In the simulation, human rewards were given as +1 and -1. Two models of human instruction that determine which reward is to be given in every step of a human instruction were used. Results show that human instruction may have a possibility of including both model-A and model-B characteristics, and it can be expected that the comparative-reinforcement learning algorithm is more effective for learning by human instructions.