Effect of the Coulomb repulsion on the {\it ac} transport through a quantum dot

We calculate in a linear response the admittance of a quantum dot out of equilibrium. The interaction between two electrons with opposite spins simultaneously residing on the resonant level is modeled by an Anderson Hamiltonian. The electron correlations lead to the appearence of a new feature in the frequency dependence of the conductance. For certain parameter values there are two crossover frequencies between a capacitive and an inductive behavior of the imaginary part of the admittance. The experimental implications of the obtained results are briefly discussed.Comment: 13 pages, REVTEX 3.0, 2 .ps figures from [email protected], NUB-308

Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM

Position Reconstruction in Drift Chambers operated with Xe, CO2 (15%)

We present measurements of position and angular resolution of drift chambers operated with a Xe,CO$_2$(15%) mixture. The results are compared to Monte Carlo simulations and important systematic effects, in particular the dispersive nature of the absorption of transition radiation and non-linearities, are discussed. The measurements were carried out with prototype drift chambers of the ALICE Transition Radiation Detector, but our findings can be generalized to other drift chambers with similar geometry, where the electron drift is perpendicular to the wire planes.Comment: 30 pages, 18 figure

Atenuación y distribución de probabilidad de intensidades sísmicas para Colombia y el Occidente de Venezuela

Este artículo utiliza una base de datos de mapas de isosistas (artículo acompañante) para desarrollar dos aproximaciones. Primero, se generan ecuaciones de atenuación tradicionales de intensidad que relaciona variables continuas y discretas, y segunda se presenta un método alternativo de tratamiento del problema que desarrolla funciones probabilísticas conjuntas mixtas discretas-continuas que permite estimar directamente las probabilidades de ocurrencia o excedencia de diferencia de intensidades dada la distancia a un sitio y la intensidad epicentral. La distribución condicional de la distancia dado un nivel de intensidad o diferencia de intensidad es continua y se representa por una distribución lognormal. La distribución de intensidades es discreta y se representa por una función de Poisson bimodal. La representación bimodal se puede deber a reflexiones de onda en la frontera entre la litósfera y la astenósfera. Ambas aproximaciones se aplican para sismos de subducción y eventos superficiales ocurridos en Colombia y el Occidente de VenezuelaThis paper uses an intensity database (parallel paper) to generate first traditional attenuation equations, and second, a model for the conditional excedeence probability of a level of seismic intensity, given distance to a site and the epicentral intensity. The latter model is comprised of mixed discrete-continuous joint probability distributions. The conditional distribution of distance for a given intensity or intensity difference is continuous and is represented by a lognormal distribution. The distribution of intensities is discrete and is represented by bi-modal Poisson functions. The bi-modal representation may be due to wave reflections at the boundary between the lithosphere and the asthenosphere, in the region. Two models are proposed, one for subduction earthquakes and the other for shallow earthquakes. The models are applied to Colombia and Western Venezuela

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

Background and Objectives: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. Methods: We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. Results: We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Conclusions: Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS

Synthesis and White-Light Emission of ZnO/HfO2: Eu Nanocables

ZnO/HfO2:Eu nanocables were prepared by radio frequency sputtering with electrospun ZnO nanofibers as cores. The well-crystallized ZnO/HfO2:Eu nanocables showed a uniform intact core–shell structure, which consisted of a hexagonal ZnO core and a monoclinic HfO2 shell. The photoluminescence properties of the samples were characterized. A white-light band emission consisted of blue, green, and red emissions was observed in the nanocables. The blue and green emissions can be attributed to the zinc vacancy and oxygen vacancy defects in ZnO/HfO2:Eu nanocables, and the yellow–red emissions are derived from the inner 4f-shell transitions of corresponding Eu3+ ions in HfO2:Eu shells. Enhanced white-light emission was observed in the nanocables. The enhancement of the emission is ascribed to the structural changes after coaxial synthesis