Multi-stage optimization of a deep model: A case study on ground motion modeling.

In this study, a multi-stage optimization procedure is proposed to develop deep neural network models which results in a powerful deep learning pipeline called intelligent deep learning (iDeepLe). The proposed pipeline is then evaluated by a challenging real-world problem, the modeling of the spectral acceleration experienced by a particle during earthquakes. This approach has three main stages to optimize the deep model topology, the hyper-parameters, and its performance, respectively. This pipeline optimizes the deep model via adaptive learning rate optimization algorithms for both accuracy and complexity in multiple stages, while simultaneously solving the unknown parameters of the regression model. Among the seven adaptive learning rate optimization algorithms, Nadam optimization algorithm has shown the best performance results in the current study. The proposed approach is shown to be a suitable tool to generate solid models for this complex real-world system. The results also show that the parallel pipeline of iDeepLe has the capacity to handle big data problems as well

A Graph-Based Semantics Workbench for Concurrent Asynchronous Programs

A number of novel programming languages and libraries have been proposed that offer simpler-to-use models of concurrency than threads. It is challenging, however, to devise execution models that successfully realise their abstractions without forfeiting performance or introducing unintended behaviours. This is exemplified by SCOOP---a concurrent object-oriented message-passing language---which has seen multiple semantics proposed and implemented over its evolution. We propose a "semantics workbench" with fully and semi-automatic tools for SCOOP, that can be used to analyse and compare programs with respect to different execution models. We demonstrate its use in checking the consistency of semantics by applying it to a set of representative programs, and highlighting a deadlock-related discrepancy between the principal execution models of the language. Our workbench is based on a modular and parameterisable graph transformation semantics implemented in the GROOVE tool. We discuss how graph transformations are leveraged to atomically model intricate language abstractions, and how the visual yet algebraic nature of the model can be used to ascertain soundness.Comment: Accepted for publication in the proceedings of FASE 2016 (to appear

The Role of the Subgenual Anterior Cingulate Cortex and Amygdala in Environmental Sensitivity to Infant Crying

This work was supported by the Swiss National Science Foundation (SNF): Grant 51A240-104890 to FHW and ES, and the Swiss National Science Foundation (SNF): Grant PA00P1_145418 to IM and the Freiwillige Akademische Gesellschaft to IM

Do consanguineous parents of a child affected by an autosomal recessive disease have more DNA identical-by-descent than similarly-related parents with healthy offspring? Design of a case-control study

<p>Abstract</p> <p>Background</p> <p>The offspring of consanguineous relations have an increased risk of congenital/genetic disorders and early mortality. Consanguineous couples and their offspring account for approximately 10% of the global population. The increased risk for congenital/genetic disorders is most marked for autosomal recessive disorders and depends on the degree of relatedness of the parents. For children of first cousins the increased risk is 2-4%. For individual couples, however, the extra risk can vary from zero to 25% or higher, with only a minority of these couples having an increased risk of at least 25%. It is currently not possible to differentiate between high-and low-risk couples. The quantity of DNA identical-by-descent between couples with the same degree of relatedness shows a remarkable variation. Here we hypothesize that consanguineous partners with children affected by an autosomal recessive disease have more DNA identical-by-descent than similarly-related partners who have only healthy children. The aim of the study is thus to establish whether the amount of DNA identical-by-descent in consanguineous parents of children with an autosomal recessive disease is indeed different from its proportion in consanguineous parents who have healthy children only.</p> <p>Methods/Design</p> <p>This project is designed as a case-control study. Cases are defined as consanguineous couples with one or more children with an autosomal recessive disorder and controls as consanguineous couples with at least three healthy children and no affected child. We aim to include 100 case couples and 100 control couples. Control couples are matched by restricting the search to the same family, clan or ethnic origin as the case couple. Genome-wide SNP arrays will be used to test our hypothesis.</p> <p>Discussion</p> <p>This study contains a new approach to risk assessment in consanguineous couples. There is no previous study on the amount of DNA identical-by-descent in consanguineous parents of affected children compared to the consanguineous parents of healthy children. If our hypothesis proves to be correct, further studies are needed to obtain different risk figure estimates for the different proportions of DNA identical-by-descent. With more precise information about their risk status, empowerment of couples can be improved when making reproductive decisions.</p

Aharonov-Bohm interferences from local deformations in graphene

One of the most interesting aspects of graphene is the tied relation between structural and electronic properties. The observation of ripples in the graphene samples both free standing and on a substrate has given rise to a very active investigation around the membrane-like properties of graphene and the origin of the ripples remains as one of the most interesting open problems in the system. The interplay of structural and electronic properties is successfully described by the modelling of curvature and elastic deformations by fictitious gauge fields that have become an ex- perimental reality after the suggestion that Landau levels can form associated to strain in graphene and the subsequent experimental confirmation. Here we propose a device to detect microstresses in graphene based on a scanning-tunneling-microscopy setup able to measure Aharonov-Bohm inter- ferences at the nanometer scale. The interferences to be observed in the local density of states are created by the fictitious magnetic field associated to elastic deformations of the sample.Comment: Some bugs fixe

STM Spectroscopy of ultra-flat graphene on hexagonal boron nitride

Graphene has demonstrated great promise for future electronics technology as well as fundamental physics applications because of its linear energy-momentum dispersion relations which cross at the Dirac point. However, accessing the physics of the low density region at the Dirac point has been difficult because of the presence of disorder which leaves the graphene with local microscopic electron and hole puddles, resulting in a finite density of carriers even at the charge neutrality point. Efforts have been made to reduce the disorder by suspending graphene, leading to fabrication challenges and delicate devices which make local spectroscopic measurements difficult. Recently, it has been shown that placing graphene on hexagonal boron nitride (hBN) yields improved device performance. In this letter, we use scanning tunneling microscopy to show that graphene conforms to hBN, as evidenced by the presence of Moire patterns in the topographic images. However, contrary to recent predictions, this conformation does not lead to a sizable band gap due to the misalignment of the lattices. Moreover, local spectroscopy measurements demonstrate that the electron-hole charge fluctuations are reduced by two orders of magnitude as compared to those on silicon oxide. This leads to charge fluctuations which are as small as in suspended graphene, opening up Dirac point physics to more diverse experiments than are possible on freestanding devices.Comment: Nature Materials advance online publication 13/02/201

Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future

Delayed union of femoral fractures in older rats:decreased gene expression

BACKGROUND: Fracture healing slows with age. While 6-week-old rats regain normal bone biomechanics at 4 weeks after fracture, one-year-old rats require more than 26 weeks. The possible role of altered mRNA gene expression in this delayed union was studied. Closed mid-shaft femoral fractures were induced followed by euthanasia at 0 time (unfractured) or at 1, 2, 4 or 6 weeks after fracture in 6-week-old and 12-15-month-old Sprague-Dawley female rats. mRNA levels were measured for osteocalcin, type I collagen α1, type II collagen, bone morphogenetic protein (BMP)-2, BMP-4 and the type IA BMP receptor. RESULTS: For all of the genes studied, the mRNA levels increased in both age groups to a peak at one to two weeks after fracture. All gene expression levels decreased to very low or undetectable levels at four and six weeks after fracture for both age groups. At four weeks after fracture, the younger rats were healed radiographically, but not the older rats. CONCLUSIONS: (1) All genes studied were up-regulated by fracture in both age groups. Thus, the failure of the older rats to heal promptly was not due to the lack of expression of any of the studied genes. (2) The return of the mRNA gene expression to baseline values in the older rats prior to healing may contribute to their delayed union. (3) No genes were overly up-regulated in the older rats. The slower healing response of the older rats did not stimulate a negative-feedback increase in the mRNA expression of stimulatory cytokines