Antipodal random sequences with prescribed second-order statistics: application to Compressive Sensing and UWB system based on DS-CDMA

It is usual to hear a strange short sentence: «Random is better than...». Why is randomness a good solution to a certain engineering problem? There are many possible answers, and all of them are related to the considered topic. In this thesis I will discuss about two crucial topics that take advantage by randomizing some waveforms involved in signals manipulations. In particular, advantages are guaranteed by shaping the second order statistic of antipodal sequences involved in an intermediate signal processing stages. The first topic is in the area of analog-to-digital conversion, and it is named Compressive Sensing (CS). CS is a novel paradigm in signal processing that tries to merge signal acquisition and compression at the same time. Consequently it allows to direct acquire a signal in a compressed form. In this thesis, after an ample description of the CS methodology and its related architectures, I will present a new approach that tries to achieve high compression by design the second order statistics of a set of additional waveforms involved in the signal acquisition/compression stage. The second topic addressed in this thesis is in the area of communication system, in particular I focused the attention on ultra-wideband (UWB) systems. An option to produce and decode UWB signals is direct-sequence spreading with multiple access based on code division (DS-CDMA). Focusing on this methodology, I will address the coexistence of a DS-CDMA system with a narrowband interferer. To do so, I minimize the joint effect of both multiple access (MAI) and narrowband (NBI) interference on a simple matched filter receiver. I will show that, when spreading sequence statistical properties are suitably designed, performance improvements are possible with respect to a system exploiting chaos-based sequences minimizing MAI only.

Permutation-based stochastic ordering using pairwise comparisons

The topic of this work concerns nonparametric permutation-based methods aiming to find a ranking (stochastic ordering) of a given set of groups (populations), gathering together information from multiple variables under more than one experimental designs. The problem of ranking populations arises in several fields of science from the need of comparing G>2 given groups or treatments when the main goal is to find an order while taking into account several aspects. As it can be imagined, this problem is not only of theoretical interest but it also has a recognised relevance in several fields, such as industrial experiments or behavioural sciences, and this is reflected by the vast literature on the topic, although sometimes the problem is associated with different keywords such as: "stochastic ordering", "ranking", "construction of composite indices" etc., or even "ranking probabilities" outside of the strictly-speaking statistical literature. The properties of the proposed method are empirically evaluated by means of an extensive simulation study, where several aspects of interest are let to vary within a reasonable practical range. These aspects comprise: sample size, number of variables, number of groups, and distribution of noise/error. The flexibility of the approach lies mainly in the several available choices for the test-statistic and in the different types of experimental design that can be analysed. This render the method able to be tailored to the specific problem and the to nature of the data at hand. To perform the analyses an R package called SOUP (Stochastic Ordering Using Permutations) has been written and it is available on CRAN.

Modeling multiple state electrostatically formed nanowire transistors

The present thesis work proposes a new physical equivalent circuit model for a recently proposed semiconductor transistor, a 2-drain MSET (Multiple State Electrostatically Formed Nanowire Transistor). It presents a new software-based experimental setup that has been developed for carrying out numerical simulations on the device and on equivalent circuits. As of 2015, we have already approached the scaling limits of the ubiquitous CMOS technology that has been in the forefront of mainstream technological advancement, so many researchers are exploring different ideas in the realm of electrical devices for logical applications, among them MSET transistors. The idea that underlies MSETs is that a single multiple-terminal device could replace many traditional transistors. In particular a 2-drain MSET is akin to a silicon multiplexer, consisting in a Junction FET with independent gates, but with a split drain, so that a voltage-controlled conductive path can connect either of the drains to the source. The first chapter of this work presents the theory of classical JFETs and its common equivalent circuit models. The physical model and its derivation are presented, the current state of equivalent circuits for the JFET is discussed. A physical model of a JFET with two independent gates has been developed, deriving it from previous results, and is presented at the end of the chapter. A review of the characteristics of MSET device is shown in chapter 2. In this chapter, the proposed physical model and its formulation are presented. A listing for the SPICE model was attached as an appendix at the end of this document. Chapter 3 concerns the results of the numerical simulations on the device. At first the research for a suitable geometry is discussed and then comparisons between results from finite-elements simulations and equivalent circuit runs are made. Where points of challenging divergence were found between the two numerical results, the relevant physical processes are discussed. In the fourth chapter the experimental setup is discussed. The GUI-based environments that allow to explore the four-dimensional solution space and to analyze the physical variables inside the device are described. It is shown how this software project has been structured to overcome technical challenges in structuring multiple simulations in sequence, and to provide for a flexible platform for future research in the field.

Rarely selected distractors in high stakes medical multiple-choice examinations and their recognition by item authors: A simulation and survey

Background Many medical exams use 5 options for multiple choice questions (MCQs), although the literature suggests that 3 options are optimal. Previous studies on this topic have often been based on non-medical examinations, so we sought to analyse rarely selected, 'non-functional' distractors (NF-D) in high stakes medical examinations, and their detection by item authors as well as psychometric changes resulting from a reduction in the number of options. Methods Based on Swiss Federal MCQ examinations from 2005-2007, the frequency of NF-D (selected by <1% or <5% of the candidates) was calculated. Distractors that were chosen the least or second least were identified and candidates who chose them were allocated to the remaining options using two extreme assumptions about their hypothetical behaviour: In case rarely selected distractors were eliminated, candidates could randomly choose another option - or purposively choose the correct answer, from which they had originally been distracted. In a second step, 37 experts were asked to mark the least plausible options. The consequences of a reduction from 4 to 3 or 2 distractors - based on item statistics or on the experts' ratings - with respect to difficulty, discrimination and reliability were modelled. Results About 70% of the 5-option-items had at least 1 NF-D selected by <1% of the candidates (97% for NF-Ds selected by <5%). Only a reduction to 2 distractors and assuming that candidates would switch to the correct answer in the absence of a 'non-functional' distractor led to relevant differences in reliability and difficulty (and to a lesser degree discrimination). The experts' ratings resulted in slightly greater changes compared to the statistical approach. Conclusions Based on item statistics and/or an expert panel's recommendation, the choice of a varying number of 3-4 (or partly 2) plausible distractors could be performed without marked deteriorations in psychometric characteristics.

A multiple source model for 6 MV photon beam dose calculations using Monte Carlo

A multiple source model (MSM) for the 6 MV beam of a Varian Clinac 2300 C/D was developed by simulating radiation transport through the accelerator head for a set of square fields using the GEANT Monte Carlo (MC) code. The corresponding phase space (PS) data enabled the characterization of 12 sources representing the main components of the beam defining system. By parametrizing the source characteristics and by evaluating the dependence of the parameters on field size, it was possible to extend the validity of the model to arbitrary rectangular fields which include the central 3 x 3 cm2 field without additional precalculated PS data. Finally, a sampling procedure was developed in order to reproduce the PS data. To validate the MSM, the fluence, energy fluence and mean energy distributions determined from the original and the reproduced PS data were compared and showed very good agreement. In addition, the MC calculated primary energy spectrum was verified by an energy spectrum derived from transmission measurements. Comparisons of MC calculated depth dose curves and profiles, using original and PS data reproduced by the MSM, agree within 1% and 1 mm. Deviations from measured dose distributions are within 1.5% and 1 mm. However, the real beam leads to some larger deviations outside the geometrical beam area for large fields. Calculated output factors in 10 cm water depth agree within 1.5% with experimentally determined data. In conclusion, the MSM produces accurate PS data for MC photon dose calculations for the rectangular fields specified.

Development of record-breaking statistics for climatological time-series analysis

The number of record-breaking events expected to occur in a strictly stationary time-series depends only on the number of values in the time-series, regardless of distribution. This holds whether the events are record-breaking highs or lows and whether we count from past to present or present to past. However, these symmetries are broken in distinct ways by trends in the mean and variance. We define indices that capture this information and use them to detect weak trends from multiple time-series. Here, we use these methods to answer the following questions: (1) Is there a variability trend among globally distributed surface temperature time-series? We find a significant decreasing variability over the past century for the Global Historical Climatology Network (GHCN). This corresponds to about a 10% change in the standard deviation of inter-annual monthly mean temperature distributions. (2) How are record-breaking high and low surface temperatures in the United States affected by time period? We investigate the United States Historical Climatology Network (USHCN) and find that the ratio of record-breaking highs to lows in 2006 increases as the time-series extend further into the past. When we consider the ratio as it evolves with respect to a fixed start year, we find it is strongly correlated with the ensemble mean. We also compare the ratios for USHCN and GHCN (minus USHCN stations). We find the ratios grow monotonically in the GHCN data set, but not in the USHCN data set. (3) Do we detect either mean or variance trends in annual precipitation within the United States? We find that the total annual and monthly precipitation in the United States (USHCN) has increased over the past century. Evidence for a trend in variance is inconclusive.

Benchmarking with Spine Tango: potentials and pitfalls

The newly released online statistics function of Spine Tango allows comparison of own data against the aggregated results of the data pool that all other participants generate. This comparison can be considered a very simple way of benchmarking, which means that the quality of what one organization does is compared with other similar organizations. The goal is to make changes towards better practice if benchmarking shows inferior results compared with the pool. There are, however, pitfalls in this simplified way of comparing data that can result in confounding. This means that important influential factors can make results appear better or worse than they are in reality and these factors can only be identified and neutralized in a multiple regression analysis performed by a statistical expert. Comparing input variables, confounding is less of a problem than comparing outcome variables. Therefore, the potentials and limitations of automated online comparisons need to be considered when interpreting the results of the benchmarking procedure.

A comparison of the strength of biodiversity effects across multiple functions

In order to predict which ecosystem functions are most at risk from biodiversity loss, meta-analyses have generalised results from biodiversity experiments over different sites and ecosystem types. In contrast, comparing the strength of biodiversity effects across a large number of ecosystem processes measured in a single experiment permits more direct comparisons. Here, we present an analysis of 418 separate measures of 38 ecosystem processes. Overall, 45 % of processes were significantly affected by plant species richness, suggesting that, while diversity affects a large number of processes not all respond to biodiversity. We therefore compared the strength of plant diversity effects between different categories of ecosystem processes, grouping processes according to the year of measurement, their biogeochemical cycle, trophic level and compartment (above- or belowground) and according to whether they were measures of biodiversity or other ecosystem processes, biotic or abiotic and static or dynamic. Overall, and for several individual processes, we found that biodiversity effects became stronger over time. Measures of the carbon cycle were also affected more strongly by plant species richness than were the measures associated with the nitrogen cycle. Further, we found greater plant species richness effects on measures of biodiversity than on other processes. The differential effects of plant diversity on the various types of ecosystem processes indicate that future research and political effort should shift from a general debate about whether biodiversity loss impairs ecosystem functions to focussing on the specific functions of interest and ways to preserve them individually or in combination.

Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

Randomized comparisons among health informatics students identify hypertutorial features as improving web-based instruction.

Hypertutorials optimize five features - presentation, learner control, practice, feedback, and elaborative learning resources. Previous research showed graduate students significantly and overwhelmingly preferred Web-based hypertutorials to conventional "Book-on-the-Web" statistics or research design lessons. The current report shows that the source of hypertutorials' superiority in student evaluations of instruction lies in their hypertutorial features. Randomized comparisons between the two methodologies were conducted in two successive iterations of a graduate level health informatics research design and evaluation course. The two versions contained the same text and graphics, but differed in the presence or absence of hypertutorial features: Elaborative learning resources, practice, feedback, and amount of learner control. Students gave high evaluations to both Web-based methodologies, but consistently rated the hypertutorial lessons as superior. Significant differences localized in the hypertutorial subscale that measured student responses to hypertutorial features.

Quality control for retinal OCT in multiple sclerosis: validation of the OSCAR-IB criteria.

BACKGROUND Retinal optical coherence tomography (OCT) permits quantification of retinal layer atrophy relevant to assessment of neurodegeneration in multiple sclerosis (MS). Measurement artefacts may limit the use of OCT to MS research. OBJECTIVE An expert task force convened with the aim to provide guidance on the use of validated quality control (QC) criteria for the use of OCT in MS research and clinical trials. METHODS A prospective multi-centre (n = 13) study. Peripapillary ring scan QC rating of an OCT training set (n = 50) was followed by a test set (n = 50). Inter-rater agreement was calculated using kappa statistics. Results were discussed at a round table after the assessment had taken place. RESULTS The inter-rater QC agreement was substantial (kappa = 0.7). Disagreement was found highest for judging signal strength (kappa = 0.40). Future steps to resolve these issues were discussed. CONCLUSION Substantial agreement for QC assessment was achieved with aid of the OSCAR-IB criteria. The task force has developed a website for free online training and QC certification. The criteria may prove useful for future research and trials in MS using OCT as a secondary outcome measure in a multi-centre setting.

Deficit actual tool use in schizophrenia is linked to structural alterations in key regions of planning and executing of tool use and connecting fibers

Introduction: Schizophrenia patients frequently suffer from complex motor abnormalities including fine and gross motor disturbances, abnormal involuntary movements, neurological soft signs and parkinsonism. These symptoms occur early in the course of the disease, continue in chronic patients and may deteriorate with antipsychotic medication. Furthermore gesture performance is impaired in patients, including the pantomime of tool use. Whether schizophrenia patients would show difficulties of actual tool use has not yet been investigated. Human tool use is complex and relies on a network of distinct and distant brain areas. We therefore aim to test if schizophrenia patients had difficulties in tool use and to assess associations with structural brain imaging using voxel based morphometry (VBM) and tract based spatial statistics (TBSS). Methode: In total, 44 patients with schizophrenia (DSM-5 criteria; 59% men, mean age 38) underwent structural MR imaging and performed the Tool-Use test. The test examines the use of a scoop and a hammer in three conditions: pantomime (without the tool), demonstration (with the tool) and actual use (with a recipient object). T1-weighted images were processed using SPM8 and DTI-data using FSL TBSS routines. To assess structural alterations of impaired tool use we first compared gray matter (GM) volume in VBM and white matter (WM) integrity in TBSS data of patients with and without difficulties of actual tool use. Next we explored correlations of Tool use scores and VBM and TBSS data. Group comparisons were family wise error corrected for multiple tests. Correlations were uncorrected (p < 0.001) with a minimum cluster threshold of 17 voxels (equivalent to a map-wise false positive rate of alpha < 0.0001 using a Monte Carlo procedure). Results: Tool use was impaired in schizophrenia (43.2% pantomime, 11.6% demonstration, 11.6% use). Impairment was related to reduced GM volume and WM integrity. Whole brain analyses detected an effect in the SMA in group analysis. Correlations of tool use scores and brain structure revealed alterations in brain areas of the dorso-dorsal pathway (superior occipital gyrus, superior parietal lobule, and dorsal premotor area) and the ventro-dorsal pathways (middle occipital gyrus, inferior parietal lobule) the action network, as well as the insula and the left hippocampus. Furthermore, significant correlations within connecting fiber tracts - particularly alterations within the bilateral corona radiata superior and anterior as well as the corpus callosum -were associated with Tool use performance. Conclusions: Tool use performance was impaired in schizophrenia, which was associated with reduced GM volume in the action network. Our results are in line with reports of impaired tool use in patients with brain lesions particularly of the dorso-dorsal and ventro-dorsal stream of the action network. In addition an effect of tool use on WM integrity was shown within fiber tracts connecting regions important for planning and executing tool use. Furthermore, hippocampus is part of a brain system responsible for spatial memory and navigation.The results suggest that structural brain alterations in the common praxis network contribute to impaired tool use in schizophrenia.

Assessing the Potential of Multiple Imputation in Sequence Analysis

Sequence analysis and optimal matching are useful heuristic tools for the descriptive analysis of heterogeneous individual pathways such as educational careers, job sequences or patterns of family formation. However, to date it remains unclear how to handle the inevitable problems caused by missing values with regard to such analysis. Multiple Imputation (MI) offers a possible solution for this problem but it has not been tested in the context of sequence analysis. Against this background, we contribute to the literature by assessing the potential of MI in the context of sequence analyses using an empirical example. Methodologically, we draw upon the work of Brendan Halpin and extend it to additional types of missing value patterns. Our empirical case is a sequence analysis of panel data with substantial attrition that examines the typical patterns and the persistence of sex segregation in school-to-work transitions in Switzerland. The preliminary results indicate that MI is a valuable methodology for handling missing values due to panel mortality in the context of sequence analysis. MI is especially useful in facilitating a sound interpretation of the resulting sequence types.

Statistical Methods for Differential Expressions of Genes Detected in Multiple-Condition Experiment of Microarray

Most studies of differential gene-expressions have been conducted between two given conditions. The two-condition experimental (TCE) approach is simple in that all genes detected display a common differential expression pattern responsive to a common two-condition difference. Therefore, the genes that are differentially expressed under the other conditions other than the given two conditions are undetectable with the TCE approach. In order to address the problem, we propose a new approach called multiple-condition experiment (MCE) without replication and develop corresponding statistical methods including inference of pairs of conditions for genes, new t-statistics, and a generalized multiple-testing method for any multiple-testing procedure via a control parameter C. We applied these statistical methods to analyze our real MCE data from breast cancer cell lines and found that 85 percent of gene-expression variations were caused by genotypic effects and genotype-ANAX1 overexpression interactions, which agrees well with our expected results. We also applied our methods to the adenoma dataset of Notterman et al. and identified 93 differentially expressed genes that could not be found in TCE. The MCE approach is a conceptual breakthrough in many aspects: (a) many conditions of interests can be conducted simultaneously; (b) study of association between differential expressions of genes and conditions becomes easy; (c) it can provide more precise information for molecular classification and diagnosis of tumors; (d) it can save lot of experimental resources and time for investigators.^

Multivariate methods for correlated effects sizes

Current statistical methods for estimation of parametric effect sizes from a series of experiments are generally restricted to univariate comparisons of standardized mean differences between two treatments. Multivariate methods are presented for the case in which effect size is a vector of standardized multivariate mean differences and the number of treatment groups is two or more. The proposed methods employ a vector of independent sample means for each response variable that leads to a covariance structure which depends only on correlations among the $p$ responses on each subject. Using weighted least squares theory and the assumption that the observations are from normally distributed populations, multivariate hypotheses analogous to common hypotheses used for testing effect sizes were formulated and tested for treatment effects which are correlated through a common control group, through multiple response variables observed on each subject, or both conditions.^ The asymptotic multivariate distribution for correlated effect sizes is obtained by extending univariate methods for estimating effect sizes which are correlated through common control groups. The joint distribution of vectors of effect sizes (from $p$ responses on each subject) from one treatment and one control group and from several treatment groups sharing a common control group are derived. Methods are given for estimation of linear combinations of effect sizes when certain homogeneity conditions are met, and for estimation of vectors of effect sizes and confidence intervals from $p$ responses on each subject. Computational illustrations are provided using data from studies of effects of electric field exposure on small laboratory animals. ^