Robust dimensionality reduction for human action recognition

Human action recognition can be approached by combining an action-discriminative feature set with a classifier. However, the dimensionality of typical feature sets joint with that of the time dimension often leads to a curse-of-dimensionality situation. Moreover, the measurement of the feature set is subject to sometime severe errors. This paper presents an approach to human action recognition based on robust dimensionality reduction. The observation probabilities of hidden Markov models (HMM) are modelled by mixtures of probabilistic principal components analyzers and mixtures of t-distribution sub-spaces, and compared with conventional Gaussian mixture models. Experimental results on two datasets show that dimensionality reduction helps improve the classification accuracy and that the heavier-tailed t-distribution can help reduce the impact of outliers generated by segmentation errors. © 2010 Crown Copyright

Face hallucination based on nonparametric Bayesian learning

© 2015 IEEE. In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination

Compressive Sensing of time series for human action recognition

Compressive Sensing (CS) is an emerging signal processing technique where a sparse signal is reconstructed from a small set of random projections. In the recent literature, CS techniques have demonstrated promising results for signal compression and reconstruction [9, 8, 1]. However, their potential as dimensionality reduction techniques for time series has not been significantly explored to date. To this aim, this work investigates the suitability of compressive-sensed time series in an application of human action recognition. In the paper, results from several experiments are presented: (1) in a first set of experiments, the time series are transformed into the CS domain and fed into a hidden Markov model (HMM) for action recognition; (2) in a second set of experiments, the time series are explicitly reconstructed after CS compression and then used for recognition; (3) in the third set of experiments, the time series are compressed by a hybrid CS-Haar basis prior to input into HMM; (4) in the fourth set, the time series are reconstructed from the hybrid CS-Haar basis and used for recognition. We further compare these approaches with alternative techniques such as sub-sampling and filtering. Results from our experiments show unequivocally that the application of CS does not degrade the recognition accuracy; rather, it often increases it. This proves that CS can provide a desirable form of dimensionality reduction in pattern recognition over time series. © 2010 Crown Copyright

An infinite adaptive online learning model for segmentation and classification of streaming data

© 2014 IEEE. In recent years, the desire and need to understand streaming data has been increasing. Along with the constant flow of data, it is critical to classify and segment the observations on-the-fly without being limited to a rigid number of classes. In other words, the system needs to be adaptive to the streaming data and capable of updating its parameters to comply with natural changes. This interesting problem, however, is poorly addressed in the literature, as many of the common studies focus on offline classification over a pre-defined class set. In this paper, we propose a novel adaptive online system based on Markov switching models with hierarchical Dirichlet process priors. This infinite adaptive online approach is capable of segmenting and classifying the streaming data over infinite classes, while meeting the memory and delay constraints of streaming contexts. The model is further enhanced by a 'predictive batching' mechanism, that is able to divide the flowing data into batches of variable size, imitating the ground-truth segments. Experiments on two video datasets show significant performance of the proposed approach in frame-level accuracy, segmentation recall and precision, while determining the accurate number of classes in acceptable computational time

Admissibility of a wide cluster solution in "anisotropic" higher-order traffic flow models

We analytically investigate a wide cluster solution and show that it is not admitted in some of the traffic flow models in the literature. For those traffic flow models that admit the wide cluster solution, the relationship between two important control parameters and the critical densities that divide an equilibrium solution into stable and unstable regions is thoroughly discussed in detail. We find that such wide clusters exist with a free traffic density in an unstable region, and with one or three critical densities. These results are different from the cases in the well-known higher-order traffic flow models of Payne and Whitham [H. J. Payne, "Models of freeway traffic and control," in Mathematical Models of Public Systems, A. G. Bekey, ed., Simulation Council Proc. Ser. 1, La Jolla, CA, 1971, pp. 51-61], [G. B. Whitham, Linear and Nonlinear Waves, John Wiley and Sons, New York, 1974], Kühne [R. D. Kühne, "Macroscopic freeway model for dense traffic-stop-start waves and incident detection," in Proceedings of the 9th International Symposium on Transportation and Traffic Theory, J. Volmuller and R. Hamerslag, eds., VNU Science Press, Utrecht, 1984, pp. 21-42], and Kerner and Konhäuser [B. S. Kerner and P. Konhäuser, Phys. Rev. E (3), 50 (1994), pp. 54-83]. © 2007 Society for Industrial and Applied Mathematics.published_or_final_versio

CDK1-PDK1-PI3K/Akt signaling pathway regulates embryonic and induced pluripotency

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Compensation defects in annealed undoped liquid encapsulated Czochralski InP

As-grown undoped n-type semiconducting and annealed undoped semi-insulating (SI) liquid encapsulated Czochralski (LEC) InP has been studied by temperature dependent Hall measurement, photoluminescence spectroscopy, infrared absorption, and photocurrent spectroscopy. P-type conduction SI InP can frequently be obtained by annealing undoped LEC InP. This is caused by a high concentration of thermally induced native acceptor defects. In some cases, it can be shown that the thermally induced n-type SI property of undoped LEC InP is caused by a midgap donor compensating for the net shallow acceptors. The midgap donor is proposed to be a phosphorus antisite related defect. Traps in annealed SI InP have been detected by photocurrent spectroscopy and have been compared with reported results. The mechanisms of defect formation are discussed. © 1999 American Institute of Physics.published_or_final_versio

Cytosine-to-Uracil Deamination by SssI DNA Methyltransferase

The prokaryotic DNA(cytosine-5)methyltransferase M.SssI shares the specificity of eukaryotic DNA methyltransferases (CG) and is an important model and experimental tool in the study of eukaryotic DNA methylation. Previously, M.SssI was shown to be able to catalyze deamination of the target cytosine to uracil if the methyl donor S-adenosyl-methionine (SAM) was missing from the reaction. To test whether this side-activity of the enzyme can be used to distinguish between unmethylated and C5-methylated cytosines in CG dinucleotides, we re-investigated, using a sensitive genetic reversion assay, the cytosine deaminase activity of M.SssI. Confirming previous results we showed that M.SssI can deaminate cytosine to uracil in a slow reaction in the absence of SAM and that the rate of this reaction can be increased by the SAM analogue 5’-amino-5’-deoxyadenosine. We could not detect M.SssI-catalyzed deamination of C5-methylcytosine (m5C). We found conditions where the rate of M.SssI mediated C-to-U deamination was at least 100-fold higher than the rate of m5C-to-T conversion. Although this difference in reactivities suggests that the enzyme could be used to identify C5-methylated cytosines in the epigenetically important CG dinucleotides, the rate of M.SssI mediated cytosine deamination is too low to become an enzymatic alternative to the bisulfite reaction. Amino acid replacements in the presumed SAM binding pocket of M.SssI (F17S and G19D) resulted in greatly reduced methyltransferase activity. The G19D variant showed cytosine deaminase activity in E. coli, at physiological SAM concentrations. Interestingly, the C-to-U deaminase activity was also detectable in an E. coli ung+ host proficient in uracil excision repair

The polygenic nature of telomere length and the anti-ageing properties of lithium

Telomere length is a promising biomarker for age-related disease and a potential anti-ageing drug target. Here, we study the genetic architecture of telomere length and the repositioning potential of lithium as an anti-ageing medication. LD score regression applied to the largest telomere length genome-wide association study to-date, revealed SNP-chip heritability estimates of 7.29%, with polygenic risk scoring capturing 4.4% of the variance in telomere length in an independent cohort (p = 6.17 × 10-5). Gene-enrichment analysis identified 13 genes associated with telomere length, with the most significant being the leucine rich repeat gene, LRRC34 (p = 3.69 × 10-18). In the context of lithium, we confirm that chronic use in a sample of 384 bipolar disorder patients is associated with longer telomeres (p = 0.03). As complementary evidence, we studied three orthologs of telomere length regulators in a Caenorhabditis elegans model of lithium-induced extended longevity and found all transcripts to be affected post-treatment (p  0.05). Consequently, this suggests that lithium may be catalysing the activity of endogenous mechanisms that promote telomere lengthening, whereby its efficacy eventually becomes limited by each individual's inherent telomere maintenance capabilities. Our work indicates a potential use of polygenic risk scoring for the prediction of adult telomere length and consequently lithium's anti-ageing efficacy

Chinese Americans’ Views and Use of Family Health History: A Qualitative Study

Objective Family health history (FHH) plays a significant role in early disease detection and preven- tion. Although Asian Americans are the fastest growing U.S. immigrant group, no data exists regarding Chinese Americans’ (the largest Asian subgroup) views and use of FHH. This study examines this important issue. Methods Forty-nine adults from southern U.S. Chinese American communities participated in this qualitative, semi-structured, in-depth interview study. Interviews were audio recorded, tran- scribed, and analyzed with a content analysis approach. Results Although the majority of participants perceived the importance of collecting FHH, most lacked FHH knowledge and failed to collect FHH information. Barriers affecting FHH collec- tion and discussion among family members included long-distance separation from family members, self-defined “healthy family,� and Chinese cultural beliefs. Lack of doctors’ inqui- ries, never/rarely visiting physicians, self-defined “healthy family,� perceived insignificance of discussing FHH with doctors, and Chinese cultural beliefs were the obstacles in commu- nicating FHH with physicians. Conclusions Chinese Americans had limited usage of their FHH and faced cultural, distance, knowl- edge-, and healthcare system-related barriers that influenced their FHH use. Developing FHH education programs for Chinese Americans is highly recommended