A method for validating Rent's rule for technological and biological networks

Rent’s rule is empirical power law introduced in an effort to describe and optimize the wiring complexity of computer logic graphs. It is known that brain and neuronal networks also obey Rent’s rule, which is consistent with the idea that wiring costs play a fundamental role in brain evolution and development. Here we propose a method to validate this power law for a certain range of network partitions. This method is based on the bifurcation phenomenon that appears when the network is subjected to random alterations preserving its degree distribution. It has been tested on a set of VLSI circuits and real networks, including biological and technological ones. We also analyzed the effect of different types of random alterations on the Rentian scaling in order to test the influence of the degree distribution. There are network architectures quite sensitive to these randomization procedures with significant increases in the values of the Rent exponents

An independently validated nomogram for isocitrate dehydrogenase-wild-type glioblastoma patient survival.

BackgroundIn 2016, the World Health Organization reclassified the definition of glioblastoma (GBM), dividing these tumors into isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant GBM, where the vast majority of GBMs are IDH-wild-type. Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a nomogram for IDH-wild-type patients with newly diagnosed GBM.MethodsData were obtained from newly diagnosed GBM patients from the Ohio Brain Tumor Study (OBTS) and the University of California San Francisco (UCSF) for diagnosis years 2007-2017 with the following variables: age at diagnosis, sex, extent of resection, concurrent radiation/temozolomide (TMZ) status, Karnofsky Performance Status (KPS), O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and IDH mutation status. Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the OBTS data and independently validated using the UCSF data. Models were internally validated using 10-fold cross-validation and externally validated by plotting calibration curves.ResultsA final nomogram was validated for IDH-wild-type newly diagnosed GBM. Factors that increased the probability of survival included younger age at diagnosis, female sex, having gross total resection, having concurrent radiation/TMZ, having a high KPS, and having MGMT methylation.ConclusionsA nomogram that calculates individualized survival probabilities for IDH-wild-type patients with newly diagnosed GBM could be useful to physicians for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: https://gcioffi.shinyapps.io/Nomogram_For_IDH_Wildtype_GBM_H_Gittleman/

Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations

open18siThe eddy covariance is a powerful technique to estimate the surface-Atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO2, water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in terms of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in e.g. ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO2 and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-The-Art processing methodsopenSabbatini, Simone; Mammarella, Ivan; Arriga, Nicola; Fratini, Gerardo; Graf, Alexander; Hörtnagl, Lukas; Ibrom, Andreas; Longdoz, Bernard; Mauder, Matthias; Merbold, Lutz; Metzger, Stefan; Montagnani, Leonardo; Pitacco, Andrea; Rebmann, Corinna; Sedlák, Pavel; Šigut, Ladislav; Vitale, Domenico; Papale, DarioSabbatini, Simone; Mammarella, Ivan; Arriga, Nicola; Fratini, Gerardo; Graf, Alexander; Hörtnagl, Lukas; Ibrom, Andreas; Longdoz, Bernard; Mauder, Matthias; Merbold, Lutz; Metzger, Stefan; Montagnani, Leonardo; Pitacco, Andrea; Rebmann, Corinna; Sedlák, Pavel; Šigut, Ladislav; Vitale, Domenico; Papale, Dari

Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set

Learning systems have been focused on creating models capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in the interpretation and explanation of the results. The need for interpretation is greater when these models are used to support decision making. In some areas, this becomes an indispensable requirement, such as in medicine. The goal of this study was to define a simple process to construct a system that could be easily interpreted based on two principles: (1) reduction of attributes without degrading the performance of the prediction systems and (2) selecting a technique to interpret the final prediction system. To describe this process, we selected a problem, predicting cardiovascular disease, by analyzing the well-known Statlog (Heart) data set from the University of California’s Automated Learning Repository. We analyzed the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. We performed an analysis on a large set of classification techniques and performance metrics, demonstrating that it is possible to construct explainable and reliable models that provide high quality predictive performance

Complexity adaptation in video encoders for power limited platforms

With the emergence of video services on power limited platforms, it is necessary to consider both performance-centric and constraint-centric signal processing techniques. Traditionally, video applications have a bandwidth or computational resources constraint or both. The recent H.264/AVC video compression standard offers significantly improved efficiency and flexibility compared to previous standards, which leads to less emphasis on bandwidth. However, its high computational complexity is a problem for codecs running on power limited plat- forms. Therefore, a technique that integrates both complexity and bandwidth issues in a single framework should be considered. In this thesis we investigate complexity adaptation of a video coder which focuses on managing computational complexity and provides significant complexity savings when applied to recent standards. It consists of three sub functions specially designed for reducing complexity and a framework for using these sub functions; Variable Block Size (VBS) partitioning, fast motion estimation, skip macroblock detection, and complexity adaptation framework. Firstly, the VBS partitioning algorithm based on the Walsh Hadamard Transform (WHT) is presented. The key idea is to segment regions of an image as edges or flat regions based on the fact that prediction errors are mainly affected by edges. Secondly, a fast motion estimation algorithm called Fast Walsh Boundary Search (FWBS) is presented on the VBS partitioned images. Its results outperform other commonly used fast algorithms. Thirdly, a skip macroblock detection algorithm is proposed for use prior to motion estimation by estimating the Discrete Cosine Transform (DCT) coefficients after quantisation. A new orthogonal transform called the S-transform is presented for predicting Integer DCT coefficients from Walsh Hadamard Transform coefficients. Complexity saving is achieved by deciding which macroblocks need to be processed and which can be skipped without processing. Simulation results show that the proposed algorithm achieves significant complexity savings with a negligible loss in rate-distortion performance. Finally, a complexity adaptation framework which combines all three techniques mentioned above is proposed for maximizing the perceptual quality of coded video on a complexity constrained platform

Engineering Aggregation Operators for Relational In-Memory Database Systems

In this thesis we study the design and implementation of Aggregation operators in the context of relational in-memory database systems. In particular, we identify and address the following challenges: cache-efficiency, CPU-friendliness, parallelism within and across processors, robust handling of skewed data, adaptive processing, processing with constrained memory, and integration with modern database architectures. Our resulting algorithm outperforms the state-of-the-art by up to 3.7x

On the detection of latent structures in categorical data

With the growing availability of huge amounts of data it is increasingly important to uncover the underlying data generating structures. The present work focusses on the detection of latent structures for categorical data, which have been treated less intensely in the literature. In regression models categorical variables are either the responses or part of the covariates. Alternative strategies have to be used to detect the underlying structures. The first part of this thesis is dedicated to regression models with an excessive number of parameters. More concrete, we consider models with various categorical covariates and a potentially large number of categories. In addition, it is investigated how fixed effects models can be used to model the heterogeneity in longitudinal and cross-sectional data. One interesting aspect is to identify the categories or units that have to be distinguished with respect to their effect on the response. The objective is to detect ``latent groups'' that share the same effects on the response variable. A novel approach to the clustering of categorical predictors or fixed effects is introduced, which is based on recursive partitioning techniques. In contrast to competing methods that use specific penalties the proposed algorithm also works in high-dimensional settings. The second part of this thesis deals with item response models, which can be considered as regression models that aim at measuring ``latent abilities'' of persons. In item response theory one uses indicators such as the answers of persons to a collection of items to infer on the underlying abilities. When developing psychometric tests one has to be aware of the phenomenon of Differential Item Functioning (DIF). An item response model is affected by DIF if the difficulty of an item among equally able persons depends on characteristics of the persons, such as the membership to a racial or ethnic subgroup. A general tree-based method is proposed that simultaneously detects the items and subgroups of persons that carry DIF including a set of variables on different scales. Compared to classical approaches a main advantage is that the proposed method automatically identifies regions of the covariate space that are responsible for DIF and do not have to be prespecified. In addition, extensions to the detection of non-uniform DIF are developed. The last part of the thesis addresses regression models for rating scale data that are frequently used in behavioural research. Heterogeneity among respondents caused by ``latent response styles'' can lead to biased estimates and can affect the conclusion drawn from the observed ratings. The focus is on symmetric response categories and a specific form of response style, namely the tendency to the middle or extreme categories. In ordinal regression models a stronger or weaker concentration in the middle can also be interpreted as varying dispersion. The strength of the proposed models is that they can be embedded into the framework of generalized linear models and therefore inference techniques and asymptotic results for this class of models are available. In addition, a visualization tool is developed that makes the interpretation of effects easy accessible