On the combination of omics data for prediction of binary outcomes

Enrichment of predictive models with new biomolecular markers is an important task in high-dimensional omic applications. Increasingly, clinical studies include several sets of such omics markers available for each patient, measuring different levels of biological variation. As a result, one of the main challenges in predictive research is the integration of different sources of omic biomarkers for the prediction of health traits. We review several approaches for the combination of omic markers in the context of binary outcome prediction, all based on double cross-validation and regularized regression models. We evaluate their performance in terms of calibration and discrimination and we compare their performance with respect to single-omic source predictions. We illustrate the methods through the analysis of two real datasets. On the one hand, we consider the combination of two fractions of proteomic mass spectrometry for the calibration of a diagnostic rule for the detection of early-stage breast cancer. On the other hand, we consider transcriptomics and metabolomics as predictors of obesity using data from the Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) study, a population-based cohort, from Finland

A System for Unsteady Pressure Measurements Revisited

An overview is presented of some recent developments in the field of the design of effective sound absorbers. The first part deals with the application of socalled coupled tubes. For this purpose use is made of a system originally applied for unsteady pressure measurements on oscillating wind tunnel models. The second part deals with an extension of the theory of tubing systems to thin air layers, trapped between flexible walls

Adaptive Quantization for Deep Neural Network

In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNNs generally comes with high computational costs and large memory consumption, which may not be affordable for mobile platforms. Deep model quantization can be used for reducing the computation and memory costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we propose an optimization framework for deep model quantization. First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy. Then, we propose an optimization process based on this measurement for finding optimal quantization bit-width for each layer. This is the first work that theoretically analyse the relationship between parameter quantization errors of individual layers and model accuracy. Our new quantization algorithm outperforms previous quantization optimization methods, and achieves 20-40% higher compression rate compared to equal bit-width quantization at the same model prediction accuracy.Comment: 9 pages main paper + 5 pages supplementary, 8 figures, conferenc

Mediating between AI and highly specialized users

We report part of the design experience gained in X-Media, a system for knowledge management and sharing. Consolidated techniques of interaction design (scenario-based design) had to be revisited to capture the richness and complexity of intelligent interactive systems. We show that the design of intelligent systems requires methodologies (faceted scenarios) that support the investigation of intelligent features and usability factors simultaneously. Interaction designers become mediators between intelligent technology and users, and have to facilitate reciprocal understanding

Reweighted nuclear norm regularization: A SPARSEVA approach

The aim of this paper is to develop a method to estimate high order FIR and ARX models using least squares with re-weighted nuclear norm regularization. Typically, the choice of the tuning parameter in the reweighting scheme is computationally expensive, hence we propose the use of the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) framework to overcome this problem. Furthermore, we suggest the use of the prediction error criterion (PEC) to select the tuning parameter in the SPARSEVA algorithm. Numerical examples demonstrate the veracity of this method which has close ties with the traditional technique of cross validation, but using much less computations.Comment: This paper is accepted and will be published in The Proceedings of the 17th IFAC Symposium on System Identification (SYSID 2015), Beijing, China, 201

Pedestrian behaviour in urban area

The pedestrian behavior is influenced by several factors, including: characteristics of the user, numerousness of group, road infrastructures and environmental factors. These factors were studied by means the collection of data carried out in the city of Oristano (Sardinia-Italy) on eleven sidewalks and five crosswalks. The objective was to study the pedestrians behavior, researching the link between independent variables and the dependent variables that, for sidewalks was only the pedestrian speed while for crosswalks were the speed of crossing, the crossing time, the waiting time and the total time. The regression models were constructed by using ten sidewalks and four crosswalks so ignoring one for each. In the construction, were considered more variables that gradually were excluded on the basis of the p-value. The models thus detected were deemed significant according to their coefficient of determination and were validated with data from the sidewalk or crosswalk excluded from the construction of the same. Both for sidewalks that crosswalks were found some reliable models. The models construction is useful to improve the understanding of the pedestrians behavior and then obtain useful indications to design pedestrian infrastructures with characteristics closer to the real pedestrians behavior. The present study aims to give greater importance to pedestrians, analyzing how they relate with the urban context in which they live and how it conditions their behavior, so as to design infrastructure in which they feel an integral part and main actors of the urban scene, giving them the respect they deserve and a new sense of belonging to the city in which they live

A statistical method (cross-validation) for bone loss region detection after spaceflight.

Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes