A mathematical theory of semantic development in deep neural networks

An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: what are the theoretical principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge by integrating across many individual experiences? We address this question by mathematically analyzing the nonlinear dynamics of learning in deep linear networks. We find exact solutions to this learning dynamics that yield a conceptual explanation for the prevalence of many disparate phenomena in semantic cognition, including the hierarchical differentiation of concepts through rapid developmental transitions, the ubiquity of semantic illusions between such transitions, the emergence of item typicality and category coherence as factors controlling the speed of semantic processing, changing patterns of inductive projection over development, and the conservation of semantic similarity in neural representations across species. Thus, surprisingly, our simple neural model qualitatively recapitulates many diverse regularities underlying semantic development, while providing analytic insight into how the statistical structure of an environment can interact with nonlinear deep learning dynamics to give rise to these regularities

Ribosomal Proteins RPS11 and RPS20, Two Stress-Response Markers of Glioblastoma Stem Cells, Are Novel Predictors of Poor Prognosis in Glioblastoma Patients.

Glioblastoma stem cells (GSC) co-exhibiting a tumor-initiating capacity and a radio-chemoresistant phenotype, are a compelling cell model for explaining tumor recurrence. We have previously characterized patient-derived, treatment-resistant GSC clones (TRGC) that survived radiochemotherapy. Compared to glucose-dependent, treatment-sensitive GSC clones (TSGC), TRGC exhibited reduced glucose dependence that favor the fatty acid oxidation pathway as their energy source. Using comparative genome-wide transcriptome analysis, a series of defense signatures associated with TRGC survival were identified and verified by siRNA-based gene knockdown experiments that led to loss of cell integrity. In this study, we investigate the prognostic value of defense signatures in glioblastoma (GBM) patients using gene expression analysis with Probeset Analyzer (131 GBM) and The Cancer Genome Atlas (TCGA) data, and protein expression with a tissue microarray (50 GBM), yielding the first TRGC-derived prognostic biomarkers for GBM patients. Ribosomal protein S11 (RPS11), RPS20, individually and together, consistently predicted poor survival of newly diagnosed primary GBM tumors when overexpressed at the RNA or protein level [RPS11: Hazard Ratio (HR) = 11.5, p<0.001; RPS20: HR = 4.5, p = 0.03; RPS11+RPS20: HR = 17.99, p = 0.001]. The prognostic significance of RPS11 and RPS20 was further supported by whole tissue section RPS11 immunostaining (27 GBM; HR = 4.05, p = 0.01) and TCGA gene expression data (578 primary GBM; RPS11: HR = 1.19, p = 0.06; RPS20: HR = 1.25, p = 0.02; RPS11+RPS20: HR = 1.43, p = 0.01). Moreover, tumors that exhibited unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) or wild-type isocitrate dehydrogenase 1 (IDH1) were associated with higher RPS11 expression levels [corr (IDH1, RPS11) = 0.64, p = 0.03); [corr (MGMT, RPS11) = 0.52, p = 0.04]. These data indicate that increased expression of RPS11 and RPS20 predicts shorter patient survival. The study also suggests that TRGC are clinically relevant cells that represent resistant tumorigenic clones from patient tumors and that their properties, at least in part, are reflected in poor-prognosis GBM. The screening of TRGC signatures may represent a novel alternative strategy for identifying new prognostic biomarkers

Artificial Neural Networks applied to improve low-cost air quality monitoring precision

It is a fact that air pollution is a major environmental health problem that affects everyone, especially in urban areas. Furthermore, the cost of high-end air pollution monitoring sensors is considerably high, so public administrations are unable to afford to place an elevated number of measuring stations, leading to the loss of information that could be very helpful. Over the last few years, a large number of low-cost sensors have been released, but its use is often problematic, due to their selectivity and precision problems. A calibration process is needed in order to solve an issue with many parameters with no clear relationship among them, which is a field of application of Machine Learning. The objectives of this project are first, integrating three low-cost air quality sensors into a Raspberry Pi and then, training an Artificial Neural Network model that improves precision in the readings made by the sensors.Es un hecho que la contaminación del aire es un gran problema para la salud a nivel mundial, especialmente en zonas urbanas. Además, el coste de los sensores de contaminación de gama alta es considerablemente alto, por lo que los organismos públicos no pueden permitirse emplazar un gran número de estaciones de medida, perdiendo información que podría ser muy útil. A lo largo de los últimos años, han surgido muchos sensores de contaminación de bajo coste, pero su uso suele ser complicado, ya que tienen problemas de selectividad y precisión. Los objetivos de este proyecto son primero integrar tres sensores de contaminación de bajo coste en una Raspberry Pi y sobre todo, entrenar un modelo basado en una red neuronal artificial que mejore la precisión de las lecturas realizadas por los sensores.Està demostrat que la contaminació de l'aire és un gran problema per a la salut a nivell mundial, especialment en zones urbanes. A més, el cost dels sensors de contaminació de gama alta és considerablement alt, motiu pel qual els organismes públics no es poden permetre emplaçar una gran quantitat d'estacions de mesura, perdent informació que podria resultar molt útil. Al llarg dels últims anys, han sorgit molts sensors de contaminació de baix cost, però el seu ús és sovint complicat, ja que tenen problemes de selectivitat i precisió. Els objectius d'aquest projecte són primer de tot integrar tres sensors de contaminació de baix cost en una Raspberry Pi i sobretot, entrenar un model basat en una xarxa neuronal artificial que millori la precisió de les lectures realitzades pels sensors

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis