Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

Relationships between students' conceptions of constructivist learning and their regulation and processing strategies

The present study investigated relationships between students' conceptions of constructivist learning on the one hand, and their regulation and processing strategies on the other hand. Students in a constructivist, problem-based learning curriculum were questioned about their conceptions of knowledge construction and self-regulated learning, as well as their beliefs regarding their own (in)ability to learn and motivation to learn. Two hypothesized models were tested within 98 psychology students, using a structural equation modelling approach: The first model implemented regulation and processing variables of the Inventory of Learning Styles [ILS, Vermunt (Learning styles and regulation of learning in higher education - towards process-oriented instruction in autonomous thinking, 1992)], the second model of the Motivated Strategies for Learning Questionnaire [MSLQ, Pintrich and de Groot (Journal of Educational Psychology, 82, 33-40, 1990)]. Results showed that structural relations exist between conceptions of constructivist learning and regulation and processing strategies. Furthermore, students who express doubt with regard to their own learning capacities are at risk for adopting an inadequate regulation strategy. A three-tiered structure of conceptual, controlling, and operational level appeared valid for the MSLQ variables, but not entirely for those of the ILS

Sex Differences in Social Attention in Infants at Risk for Autism

We studied visual attention to emotional faces in 10-month-old infant siblings of children with ASD (ASD-sibs; N = 70) and a siblings of typically developing children (N = 29) using static stimuli. Contrary to our predictions, we found no evidence for atypical gaze behavior in ASD-sibs when boys and girls were analyzed together. However, a sex difference was found in ASD-sibs' visual attention to the mouth. Male ASD-sibs looked more at the mouth across emotions compared to male controls and female ASD-sibs. In contrast, female ASD-sibs looked less at the mouth compared to female controls. These findings suggest that some aspects of early emerging atypical social attention in ASD-sibs may be sex specific

Overexpression of transmembrane protein 168 in the mouse nucleus accumbens induces anxiety and sensorimotor gating deficit

Transmembrane protein 168 (TMEM168) comprises 697 amino acid residues, including some putative transmembrane domains. It is reported that TMEM168 controls methamphetamine (METH) dependence in the nucleus accumbens (NAc) of mice. Moreover, a strong link between METH dependence-induced adaptive changes in the brain and mood disorders has been evaluated. In the present study, we investigated the effects of accumbal TMEM168 in a battery of behavioral paradigms. The adeno-associated virus (AAV) Tmem168 vector was injected into the NAc of C57BL/6J mice (NAc-TMEM mice). Subsequently, the accumbal TMEM168 mRNA was increased approximately by seven-fold when compared with the NAc-Mock mice (controls). The NAc-TMEM mice reported no change in the locomotor activity, cognitive ability, social interaction, and depression-like behaviors; however, TMEM168 overexpression enhanced anxiety in the elevated-plus maze and light/dark box test. The increased anxiety was reversed by pretreatment with the antianxiety drug diazepam (0.3 mg/kg i.p.). Moreover, the NAc-TMEM mice exhibited decreased prepulse inhibition (PPI) in the startle response test, and the induced schizophrenia-like behavior was reversed by pretreatment with the antipsychotic drug risperidone (0.01 mg/kg i.p.). Furthermore, accumbal TMEM168 overexpression decreased the basal levels of extracellular GABA in the NAc and the high K+ (100 mM)-stimulated GABA elevation; however, the total contents of GABA in the NAc remained unaffected. These results suggest that the TMEM168-regulated GABAergic neuronal system in the NAc might become a novel target while studying the etiology of anxiety and sensorimotor gating deficits

Clustering gene expression data with a penalized graph-based metric

<p>Abstract</p> <p>Background</p> <p>The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.</p> <p>Results</p> <p>In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We show clustering results on several public gene expression datasets and simulated artificial problems to evaluate the behavior of the new metric.</p> <p>Conclusions</p> <p>In all cases the PKNNG metric shows promising clustering results. The use of the PKNNG metric can improve the performance of commonly used pairwise-distance based clustering methods, to the level of more advanced algorithms. A great advantage of the new procedure is that researchers do not need to learn a new method, they can simply compute distances with the PKNNG metric and then, for example, use hierarchical clustering to produce an accurate and highly interpretable dendrogram of their high-dimensional data.</p

Pacing and Decision Making in Sport and Exercise: The Roles of Perception and Action in the Regulation of Exercise Intensity

In pursuit of optimal performance, athletes and physical exercisers alike have to make decisions about how and when to invest their energy. The process of pacing has been associated with the goal-directed regulation of exercise intensity across an exercise bout. The current review explores divergent views on understanding underlying mechanisms of decision making in pacing. Current pacing literature provides a wide range of aspects that might be involved in the determination of an athlete's pacing strategy, but lacks in explaining how perception and action are coupled in establishing behaviour. In contrast, decision-making literature rooted in the understanding that perception and action are coupled provides refreshing perspectives on explaining the mechanisms that underlie natural interactive behaviour. Contrary to the assumption of behaviour that is managed by a higher-order governor that passively constructs internal representations of the world, an ecological approach is considered. According to this approach, knowledge is rooted in the direct experience of meaningful environmental objects and events in individual environmental processes. To assist a neuropsychological explanation of decision making in exercise regulation, the relevance of the affordance competition hypothesis is explored. By considering pacing as a behavioural expression of continuous decision making, new insights on underlying mechanisms in pacing and optimal performance can be developed. © 2014 Springer International Publishing Switzerland

Significance testing as perverse probabilistic reasoning

Truth claims in the medical literature rely heavily on statistical significance testing. Unfortunately, most physicians misunderstand the underlying probabilistic logic of significance tests and consequently often misinterpret their results. This near-universal misunderstanding is highlighted by means of a simple quiz which we administered to 246 physicians at two major academic hospitals, on which the proportion of incorrect responses exceeded 90%. A solid understanding of the fundamental concepts of probability theory is becoming essential to the rational interpretation of medical information. This essay provides a technically sound review of these concepts that is accessible to a medical audience. We also briefly review the debate in the cognitive sciences regarding physicians' aptitude for probabilistic inference