Fast ML estimation for the mixture of factor analyzers via an ECM algorithm

In this brief, we propose a fast expectation conditional maximization (ECM) algorithm for maximum-likelihood (ML) estimation of mixtures of factor analyzers (MFA). Unlike the existing expectation-maximization (EM) algorithms such as the EM in Ghahramani and Hinton, 1996, and the alternating ECM (AECM) in McLachlan and Peel, 2003, where the missing data contains component-indicator vectors as well as latent factors, the missing data in our ECM consists of component-indicator vectors only. The novelty of our algorithm is that closed-form expressions in all conditional maximization (CM) steps are obtained explicitly, instead of resorting to numerical optimization methods. As revealed by experiments, the convergence of our ECM is substantially faster than EM and AECM regardless of whether assessed by central processing unit (CPU) time or number of iterations. © 2008 IEEE.published_or_final_versio

Parental influence on children's home computer use and digital divide in education

Session C6: ICCE Conference on Technology, Pedagogy and Education - Full Papers: C6_F9_284FThis case study compares the data from two secondary schools and attempts to contribute to a better understanding of the construct of parental influence on children's information and communication technology use at home. It identifies five components of parental influence: parents' information and communication technology (ICT) skills, parental monitoring, parental control, parental guidance and parental worries. The relationships among these components were often complex with intriguing similarities and differences among the participants. The findings suggest the existence of certain inequalities in education or, as the authors prefer to call it, the digital divide in education.postprintThe 19th International Conference on Computers in Education (ICCE 2011), Chiang Mai, Thailand, 28 November-2 December 2011. In Proceedings of the 19th ICCE, 2011, p. 595-60

Universal Relations for a Fermi Gas Close to a p-Wave Interaction Resonance

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PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models.

We propose a ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian mixtures models (GMM), assuming that GMMs lie on or near to a manifold of probability distributions that is generated from a low-dimensional hierarchical latent space through parametric mappings. Inspired by principal component analysis (PCA), the generative processes for priors, means and covariance matrices are modeled by their respective latent space and parametric mapping. Then, the dependencies between latent spaces are captured by a hierarchical latent space by a linear or kernelized mapping. The function parameters and hierarchical latent space are learned by minimizing the reconstruction error between ground-truth GMMs and manifold-generated GMMs, measured by Kullback-Leibler Divergence (KLD). Variational approximation is employed to handle the intractable KLD between GMMs and a variational EM algorithm is derived to optimize the objective function. Experiments on synthetic data, flow cytometry analysis, eye-fixation analysis and topic models show that PRIMAL learns a continuous and interpretable manifold of GMM distributions and achieves a minimum reconstruction error

Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM.

The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters ( K ) and the number of hidden states ( S ) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers K and S are automatically determined in two ways. First, we place a prior on the pair (K,S) and approximate their posterior probabilities, from which the values with the maximum posterior are selected. Second, some clusters and states are pruned out implicitly when no data samples are assigned to them, thereby leading to automatic selection of the model complexity. Experiments on synthetic and real data demonstrate that our algorithm performs better than using model selection techniques with maximum likelihood estimation

Quantitative Chevalley-Weil theorem for curves

The classical Chevalley-Weil theorem asserts that for an \'etale covering of projective varieties over a number field K, the discriminant of the field of definition of the fiber over a K-rational point is uniformly bounded. We obtain a fully explicit version of this theorem in dimension 1.Comment: version 4: minor inaccuracies in Lemma 3.4 and Proposition 5.2 correcte

Nanofibers Fabricated Using Triaxial Electrospinning as Zero Order Drug Delivery Systems

A new strategy for creating functional trilayer nanofibers through triaxial electrospinning is demonstrated. Ethyl cellulose (EC) was used as the filament-forming matrix in the outer, middle, and inner working solutions and was combined with varied contents of the model active ingredient ketoprofen (KET) in the three fluids. Triaxial electrospinning was successfully carried out to generate medicated nanofibers. The resultant nanofibers had diameters of 0.74 ± 0.06 μm, linear morphologies, smooth surfaces, and clear trilayer nanostructures. The KET concentration in each layer gradually increased from the outer to the inner layer. In vitro dissolution tests demonstrated that the nanofibers could provide linear release of KET over 20 h. The protocol reported in this study thus provides a facile approach to creating functional nanofibers with sophisticated structural features

Links of cytoskeletal integrity with disease and aging

Aging is a complex feature and involves loss of multiple functions and nonreversible phenotypes. However, several studies suggest it is possible to protect against aging and promote rejuvenation. Aging is associated with many factors, such as telomere shortening, DNA damage, mitochondrial dysfunction, and loss of homeostasis. The integrity of the cytoskeleton is associated with several cellular functions, such as migration, proliferation, degeneration, and mitochondrial bioenergy production, and chronic disorders, including neuronal degeneration and premature aging. Cytoskeletal integrity is closely related with several functional activities of cells, such as aging, proliferation, degeneration, and mitochondrial bioenergy production. Therefore, regulation of cytoskeletal integrity may be useful to elicit antiaging effects and to treat degenerative diseases, such as dementia. The actin cytoskeleton is dynamic because its assembly and disassembly change depending on the cellular status. Aged cells exhibit loss of cytoskeletal stability and decline in functional activities linked to longevity. Several studies reported that improvement of cytoskeletal stability can recover functional activities. In particular, microtubule stabilizers can be used to treat dementia. Furthermore, studies of the quality of aged oocytes and embryos revealed a relationship between cytoskeletal integrity and mitochondrial activity. This review summarizes the links of cytoskeletal properties with aging and degenerative diseases and how cytoskeletal integrity can be modulated to elicit antiaging and therapeutic effects

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

PURPOSE: Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. METHODS: The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. RESULTS: Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. CONCLUSION: ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments