Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

Total Variation Blind Deconvolution: The Devil Is in the Details

In this paper we study the problem of blind deconvolution. Our analysis is based on the algorithm of Chan and Wong [2] which popularized the use of sparse gradient priors via total variation. We use this algorithm because many methods in the literature are essentially adaptations of this framework. Such algorithm is an iterative alternating energy minimization where at each step either the sharp image or the blur function are reconstructed. Recent work of Levin et al. [14] showed that any algorithm that tries to minimize that same energy would fail, as the desired solution has a higher energy than the no-blur solution, where the sharp image is the blurry input and the blur is a Dirac delta. However, experimentally one can observe that Chan and Wong's algorithm converges to the desired solution even when initialized with the no-blur one. We provide both analysis and experiments to resolve this paradoxical conundrum. We find that both claims are right. The key to understanding how this is possible lies in the details of Chan and Wong's implementation and in how seemingly harmless choices result in dramatic effects. Our analysis reveals that the delayed scaling (normalization) in the iterative step of the blur kernel is fundamental to the convergence of the algorithm. This then results in a procedure that eludes the no-blur solution, despite it being a global minimum of the original energy. We introduce an adaptation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the state of the art.

Optimized aperture shapes for depth estimation

The finite depth of field of a real camera can be used to estimate the depth structure of a scene. The distance of an object from the plane in focus determines the defocus blur size. The shape of the blur depends on the shape of the aperture. The blur shape can be designed by masking the main lens aperture. In fact, aperture shapes different from the standard circular aperture give improved accuracy of depth estimation from defocus blur. We introduce an intuitive criterion to design aperture patterns for depth from defocus. The criterion is independent of a specific depth estimation algorithm. We formulate our design criterion by imposing constraints directly in the data domain and optimize the amount of depth information carried by blurred images. Our criterion is a quadratic function of the aperture transmission values. As such, it can be numerically evaluated to estimate optimized aperture patterns quickly. The proposed mask optimization procedure is applicable to different depth estimation scenarios. We use it for depth estimation from two images with different focus settings, for depth estimation from two images with different aperture shapes as well as for depth estimation from a single coded aperture image. In this work we show masks obtained with this new evaluation criterion and test their depth discrimination capability using a state-of-the-art depth estimation algorithm.

Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Temporally Coherent Disparity Maps Using CRFs with Fast 4D Filtering

State of the art methods for disparity estimation achieve good results for single stereo frames, but temporal coherence in stereo videos is often neglected. In this paper we present a method to compute temporally coherent disparity maps. We define an energy over whole stereo sequences and optimize their Conditional Random Field (CRF) distributions using mean-field approximation. We introduce novel terms for smoothness and consistency between the left and right views, and perform CRF optimization by fast, iterative spatio-temporal filtering with linear complexity in the total number of pixels. Our results rank among the state of the art while having significantly less flickering artifacts in stereo sequences.

Absence of MyD88 results in enhanced TLR3-dependent phosphorylation of IRF3 and increased IFN-(beta) and RANTES production

Toll-like receptors are a group of pattern-recognition receptors that play a crucial role in "danger" recognition and induction of the innate immune response against bacterial and viral infections. TLR3 has emerged as a key sensor of viral dsRNA, resulting in the induction of the anti-viral molecule, IFN- . Thus, a clearer understanding of the biological processes that modulate TLR3 signaling is essential. Previous studies have shown that the TLR adaptor, Mal/TIRAP, an activator of TLR4, inhibits TLR3-mediated IFN- induction through a mechanism involving IRF7. In this study, we sought to investigate whether the TLR adaptor, MyD88, an activator of all TLRs except TLR3, has the ability to modulate TLR3 signaling. Although MyD88 does not significantly affect TLR3 ligand-induced TNF- induction, MyD88 negatively regulates TLR3-, but not TLR4-, mediated IFN- and RANTES production; this process is mechanistically distinct from that employed by Mal/TIRAP. We show that MyD88 inhibits IKK -, but not TBK1-, induced activation of IRF3. In doing so, MyD88 curtails TLR3 ligand-induced IFN- induction. The present study shows that while MyD88 activates all TLRs except TLR3, MyD88 also functions as a negative regulator of TLR3. Thus, MyD88 is essential in restricting TLR3 signaling, thereby protecting the host from unwanted immunopathologies associated with the excessive production of IFN- . Our study offers a new role for MyD88 in restricting TLR3 signaling through a hitherto unknown mechanism whereby MyD88 specifically impairs IKK -mediated induction of IRF3 and concomitant IFN- and RANTES production.

Role of complement and Fc{gamma} receptors in the protective activity of the long pentraxin PTX3 against Aspergillus fumigatus

Pentraxin 3 (PTX3) is a soluble pattern recognition molecule playing a nonredundant role in resistance against Aspergillus fumigatus. The present study was designed to investigate the molecular pathways involved in the opsonic activity of PTX3. The PTX3 N-terminal domain was responsible for conidia recognition, but the full-length molecule was necessary for opsonic activity. The PTX3-dependent pathway of enhanced neutrophil phagocytic activity involved complement activation via the alternative pathway; Fc receptor (Fc R) IIA/CD32 recognition of PTX3-sensitized conidia and complement receptor 3 (CR3) activation; and CR3 and CD32 localization to the phagocytic cup. Gene targeted mice (ptx3, FcR common chain, C3, C1q) validated the in vivo relevance of the pathway. In particular, the protective activity of exogenous PTX3 against A fumigatus was abolished in FcR common chain-deficient mice. Thus, the opsonic and antifungal activity of PTX3 is at the crossroad between complement, complement receptor 3-, and Fc R-mediated recognition. Because short pentraxins (eg, C-reactive protein) interact with complement and Fc R, the present results may have general significance for the mode of action of these components of the humoral arm of innate immunity.

M-ficolin in children with cancer

M-ficolin (ficolin-1) is a complement-activating pattern-recognition molecule structurally related to mannan-binding lectin. It is produced by monocytes and neutrophils, and is found in serum. Its biological role is largely unknown. We assessed M-ficolin concentration in serum from pediatric cancer patients. The aim of this study was to explore association of M-ficolin with clinical and hematological parameters, and to investigate whether the risk of chemotherapy-related infections was related to M-ficolin concentrations in serum.