Learning Dictionaries with Bounded Self-Coherence

Sparse coding in learned dictionaries has been established as a successful approach for signal denoising, source separation and solving inverse problems in general. A dictionary learning method adapts an initial dictionary to a particular signal class by iteratively computing an approximate factorization of a training data matrix into a dictionary and a sparse coding matrix. The learned dictionary is characterized by two properties: the coherence of the dictionary to observations of the signal class, and the self-coherence of the dictionary atoms. A high coherence to the signal class enables the sparse coding of signal observations with a small approximation error, while a low self-coherence of the atoms guarantees atom recovery and a more rapid residual error decay rate for the sparse coding algorithm. The two goals of high signal coherence and low self-coherence are typically in conflict, therefore one seeks a trade-off between them, depending on the application. We present a dictionary learning method with an effective control over the self-coherence of the trained dictionary, enabling a trade-off between maximizing the sparsity of codings and approximating an equiangular tight frame.Comment: 4 pages, 2 figures; IEEE Signal Processing Letters, vol. 19, no. 12, 201

Human Capital Investments in Children: A Comparative Analysis of the Role of Parent-Child Shared Time in Selected Countries

Parents invest in their children's human capital in several ways. We investigate the extent to which the levels and composition of parent-child time varies across countries with different welfare regimes: Finland, Germany and the United States. We test the hypothesis of parent-child time as a form of human capital investment in children using a propensity score treatment effects approach that accounts for the possible endogenous nature of time use and human capital investment. Result: There is considerable evidence of welfare regime effects on parent-child shared time. Our results provide mixed support for the hypothesis that non-care related parent-child time is human capital enriching. The strongest support is found in the case of leisure time and eating time.parent-child time, comparative research, welfare regimes, Finland, Germany, USA, treatment effects, propensity score matching

Cu nuclear magnetic resonance study of charge and spin stripe order in La1.875_{1.875}Ba0.125_{0.125}CuO4_4

We present a Cu nuclear magnetic/quadrupole resonance study of the charge stripe ordered phase of LBCO, with detection of previously unobserved ('wiped-out') signal. We show that spin-spin and spin-lattice relaxation rates are strongly enhanced in the charge ordered phase, explaining the apparent signal decrease in earlier investigations. The enhancement is caused by magnetic, rather than charge fluctuations, conclusively confirming the long-suspected assumption that spin fluctuations are responsible for the wipeout effect. Observation of the full Cu signal enables insight into the spin and charge dynamics of the stripe-ordered phase, and measurements in external magnetic fields provide information on the nature and suppression of spin fluctuations associated with charge order. We find glassy spin dynamics, in agreement with previous work, and incommensurate static charge order with charge modulation amplitude similar to other cuprate compounds, suggesting that the amplitude of charge stripes is universal in the cuprates.Comment: 7 pages, 5 figure

Molecular mechanisms of autoimmunity triggered by microbial infection

Autoimmunity can be triggered by microbial infection. In this context, the discovery of Toll-like receptors (TLRs) provides new insights and research perspectives. TLRs induce innate and adaptive antimicrobial immune responses upon exposure to common pathogen-associated molecules, including lipopeptides, lipopolysaccharides, and nucleic acids. They also have the potential, however, to trigger autoimmune disease, as has been revealed by an increasing number of experimental reports. This review summarizes important facts about TLR biology, available data on their role in autoimmunity, and potential consequences for the management of patients with autoimmune disease

Fusing Continuous-valued Medical Labels using a Bayesian Model

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78$\pm$0.63ms, significantly outperforming the best Challenge entry (15.37$\pm$2.13ms) as well as the EM, mean, and median voting strategies (14.76$\pm$0.52ms, 17.61$\pm$0.55ms, and 14.43$\pm$0.57ms respectively with $p<0.0001$)