Formation of InAs Self-Assembled Quantum Rings on InP

Shape transformations of partially capped self-assembled InAs quantum dots grown on InP are studied. Atomic force microscopy images show large anisotropic redistribution of the island material after coverage by a 1 nm thick InP layer. The anisotropic material redistribution occurs within a few minutes and leads to a change from lens-like to elongated ring-like islands. The shape transformation is not accompanied by dot material compositional change. The formation of InAs/InP quantum rings disagrees with a previous model of InAs/GaAs ring formation that assumes that the driving force for the dot to ring transformation is the difference in surface diffusion velocity of indium and gallium atoms.Comment: 13 pages, including 2 figures and 1 table. Submitted to Appl. Phys. Let

Optimal timing of project control points

Cataloged from PDF version of article.The project control cycle consists of measuring the status of the project, comparing to the plan, analysis of the deviations, and implementing any appropriate corrective actions. We present an analytical framework for determining the optimal timing of project control points throughout the life cycle of the project. Our approach is based on maximizing the amount of information generated by the control points, which depends on the intensity of the activities carried out since the last control point and on the time elapsed since their execution. The optimization problem is solved with a dynamic programming approach. We report the results of numerical experimentation with the model involving di erent types of activity intensity pro®les and several levels of information loss. For each combination, we compared the optimal amount of information to the amount of information obtained with two simpler policies: control at equal time intervals, and control at equal activity contents intervals. We also investigated the e ect of adding more control points on the amount of information generated. Ó 2000 Elsevier Science B.V. All rights reserved

The Psychology of Neurofeedback: Clinical Intervention Even if Applied Placebo

Advocates of neurofeedback make bold claims concerning brain regulation, treatment of disorders, and mental health. Decades of research and thousands of peer-reviewed publications support neurofeedback using electroencephalography (EEG-nf); yet, few experiments isolate the act of receiving feedback from a specific brain signal as a necessary precursor to obtain the purported benefits. Moreover, while psychosocial parameters including participant motivation and expectation, rather than neurobiological substrates, seem to fuel clinical improvement across a wide range of disorders, for-profit clinics continue to sprout across North America and Europe. Here, we highlight the tenuous evidence supporting EEG-nf and sketch out the weaknesses of this approach. We challenge classic arguments often articulated by proponents of EEG-nf and underscore how psychologists and mental health professionals stand to benefit from studying the ubiquitous placebo influences that likely drive these treatment outcomes

Neurofeedback or Neuroplacebo?

This scientific commentary refers to ‘Better than sham? A double-blind placebo-controlled neurofeedback study in primary insomnia’, by Schabus et al.. (doi:10.1093/brain/awx011)

The Climate of Neurofeedback: Scientific Rigour and the Perils of Ideology

Over the last six decades, an in-group with ideological and financial stakes has been conducting sub-par research to develop an ostensibly effective clinical intervention: EEG-neurofeedback. More recently, however, a string of independent studies featuring increased scientific rigour and tighter experimental controls has challenged the foundation on which EEG-neurofeedback stands. Earlier this year, Brain published one of the most robust EEG-neurofeedback experiments to date (Schabus et al., 2017), which sparked a flurry of correspondence concerning the therapeutic value of neurofeedback (Fovet et al., 2017; Schabus, 2017); notably, a parallel discussion continues in Lancet Psychiatry (Micoulaud-Franchi and Fovet, 2016; Thibault and Raz, 2016a; Schönenberg et al., 2017). However, to effectively interpret the pro and con viewpoints, one must appreciate the peculiar culture surrounding the field of EEG-neurofeedback. The present breezy piece provides little-discussed yet highly relevant contextual information often absent from formal papers and technical reports

Dysfunctional transcripts are formed by alternative polyadenylation in OPMD

Molecular Technology and Informatics for Personalised Medicine and HealthFunctional Genomics of Muscle, Nerve and Brain Disorder

New Approximability Results for the Robust k-Median Problem

We consider a robust variant of the classical $k$-median problem, introduced by Anthony et al. \cite{AnthonyGGN10}. In the \emph{Robust $k$-Median problem}, we are given an $n$-vertex metric space $(V,d)$ and $m$ client sets $\set{S_i \subseteq V}_{i=1}^m$. The objective is to open a set $F \subseteq V$ of $k$ facilities such that the worst case connection cost over all client sets is minimized; in other words, minimize $\max_{i} \sum_{v \in S_i} d(F,v)$. Anthony et al.\ showed an $O(\log m)$ approximation algorithm for any metric and APX-hardness even in the case of uniform metric. In this paper, we show that their algorithm is nearly tight by providing $\Omega(\log m/ \log \log m)$ approximation hardness, unless ${\sf NP} \subseteq \bigcap_{\delta >0} {\sf DTIME}(2^{n^{\delta}})$. This hardness result holds even for uniform and line metrics. To our knowledge, this is one of the rare cases in which a problem on a line metric is hard to approximate to within logarithmic factor. We complement the hardness result by an experimental evaluation of different heuristics that shows that very simple heuristics achieve good approximations for realistic classes of instances.Comment: 19 page