Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.

Method for computing state transformations of time-delay systems

This study provides a systematic method for deriving state transformations of a class of time-delay systems with multiple output. The significance of this study is that such state transformations can be used to transform timedelay systems into new coordinates where all the time-delay terms in the system description are associated with the output and input only. Therefore, in the new coordinate system, a Luenberger-type state observer can be readily designed. Subsequently, of the three possible versions of the original state vector, namely, instantaneous, delayed, and a mixed of instantaneous and delayed, a state observer which estimates one of these versions can be obtained. This new finding allows the authors to design state observers for a wider class of time-delay systems. Conditions for the existence of such coordinate changes and an effective algorithm for computing them are provided in this study. A numerical example and simulation results are given to illustrate the simplicity and effectiveness of the proposed method.

Partial state estimation of LTI systems with multiple constant time-delays

Functional observer design for Multi-Input Multi-Output (MIMO) Linear Time-Invariant (LTI) systems with multiple mixed time delays in the states of the system is addressed. Two structures for the design of a minimum-order observer are considered: 1 - delay-dependent, and 2 - internal-delay independent. The parameters of the delay-dependent observer are designed using the Lyapunov Krasovskii approach. The delay-dependent exponential stability of the observer for a specified convergence rate and delay values is guaranteed upon the feasibility of a set of Linear Matrix Inequalities (LMIs) together with a rank condition. Using the descriptor transformation, a modified Jensen's inequality, and improved Park's inequality, the results can be less conservative than the available functional observer design methods that address LTI systems with single state delay. Furthermore, the necessary and sufficient conditions of the asymptotic stability of the internal-delay independent observer are obtained, which are shown to be independent of delay. Two illustrative numerical examples and simulation studies confirm the validity and highlight the performance of the proposed theoretical achievements.

A framework for mixed-type multi-outcome prediction with applications in healthcare

Health analysis often involves prediction of multiple outcomes of mixed-type. Existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multi-outcome prediction. Our proposed framework proposes a cumulative loss function composed of a specific loss function for each outcome type - as an example, least square (continuous outcome), hinge (binary outcome), poisson (count outcome) and exponential (non-negative outcome). Tomodel these outcomes jointly, we impose a commonality across the prediction parameters through a common matrix-Normal prior. The framework is formulated as iterative optimization problems and solved using an efficient Block coordinate descent method (BCD). We empirically demonstrate both scalability and convergence. We apply the proposed model to a synthetic dataset and then on two real-world cohorts: a Cancer cohort and an Acute Myocardial Infarction cohort collected over a two year period. We predict multiple emergency related outcomes - as example, future emergency presentations (binary), emergency admissions (count), emergency length-of-stay-days (non-negative) and emergency time-to-next-admission-day (non-negative). Weshow that the predictive performance of the proposed model is better than several state-of-the-art baselines.

New perspectives on the annihilation electrogenerated chemiluminescence of mixed metal complexes in solution

Preliminary explorations of the annihilation electrogenerated chemiluminescence (ECL) of mixed metal complexes have revealed opportunities to enhance emission intensities and control the relative intensities from multiple luminophores through the applied potentials. However, the mechanisms of these systems are only poorly understood. Herein, we present a comprehensive characterisation of the annihilation ECL of mixtures of tris(2,2′-bipyridine)ruthenium(ii) hexafluorophosphate ([Ru(bpy)3](PF6)2) and fac-tris(2-phenylpyridine)iridium(iii) ([Ir(ppy)3]). This includes a detailed investigation of the change in emission intensity from each luminophore as a function of both the applied electrochemical potentials and the relative concentrations of the two complexes, and a direct comparison with two mixed (Ru/Ir) ECL systems for which emission from only the ruthenium-complex was previously reported. Concomitant emission from both luminophores was observed in all three systems, but only when: (1) the applied potentials were sufficient to generate the intermediates required to form the electronically excited state of both complexes; and (2) the concentration of the iridium complex (relative to the ruthenium complex) was sufficient to overcome quenching processes. Both enhancement and quenching of the ECL of the ruthenium complex was observed, depending on the experimental conditions. The observations were rationalised through several complementary mechanisms, including resonance energy transfer and various energetically favourable electron-transfer pathways.

The experience of anxiety in colonoscopy outpatients: a mixed-method study.

Colonoscopy is commonly used to investigate gastrointestinal symptoms such as pain or changes in bowel habits and may either induce patient anxiety or assist in patient reassurance. Currently, 2 studies investigating negative colonoscopy, reassurance, and anxiety came to conflicting conclusions on this issue. Furthermore, it is possible that differences in coping styles may influence patient anxiety. A mixed-methods study was conducted with 26 precolonoscopy and 24 postcolonoscopy patients to address the conflicting, limited literature regarding colonoscopy, coping, and anxiety. Participants completed postal surveys and interviews were conducted with 16 participants. There was no significant difference between pre- and postcolonoscopy groups on any anxiety measures; however, this was possibly because of individual differences. Significant positive correlations were found between maladaptive coping and state anxiety indicating that healthcare professionals should consider screening for maladaptive coping in patients needing invasive procedures. Neither problem- nor emotion-focused coping showed any significant relationship with state anxiety. Interviews revealed that clinicians and endoscopy nurses should be aware that some patients are not absorbing correct information about colonoscopy, specifically that they may be conscious or experience pain during the procedure. Because of this, clinicians should ensure that patients understand standard practice at their hospital. In addition, interview data suggested that more attention should be given to pain management as it currently may not be adequate during conscious sedation.