On the universality of the scaling of fluctuations in traffic on complex networks

We study the scaling of fluctuations with the mean of traffic in complex networks using a model where the arrival and departure of "packets" follow exponential distributions, and the processing capability of nodes is either unlimited or finite. The model presents a wide variety of exponents between 1/2 and 1 for this scaling, revealing their dependence on the few parameters considered, and questioning the existence of universality classes. We also report the experimental scaling of the fluctuations in the Internet for the Abilene backbone network. We found scaling exponents between 0.71 and 0.86 that do not fit with the exponent 1/2 reported in the literature.Comment: 4 pages, 4 figure

Mutações no gene HFE (C282Y, H63D, S65C) em uma população brasileira

Hereditary hemochromatosis (HH) is the most common genetic disorder occurring in individuals of northern European descent. The clinical characteristic of this disease is the gradual accumulation of iron in internal organs, which ultimately leads to organ failure and death. The defective gene in the majority of cases, HFE, was identified in 1996. Three allelic variants of the HFE gene have been correlated with HH: C282Y is significantly associated with HH; H63D and S65C have unclear relationships. In this report, these mutations were analyzed in 8 patients with HH and in 148 healthy individuals (blood donors). To detect the mutations, exons 2 and 4 of the HFE gene were amplified by PCR followed by restriction endonucleases cleavage. In patients with HH, three individuals were homozygous for the C282Y mutation, one showed compound heterozygous (C282Y/H63D), one was heterozygous for the C282Y and 3 presented with no mutations. In healthy individuals, the allele frequency observed was 0.014 for C282Y, 0.108 for H63D and 0.010 for S65C. The frequency of mutations was significantly higher in Caucasians compared with non-Caucasians. These data are concordant with the previous literature and with the ethnical origin of the population studied.A Hemocromatose hereditária (HH) é a alteração genética mais comumente encontrada em descendentes de Europeus, em especial da região Norte. A alteração clínica característica é a acúmulo gradual do ferro em órgãos internos, que evolui para lesão orgânica e morte. Na maioria dos casos, o gene alterado é o HFE, que foi identificado em 1996. Três variantes alélicas do gene HFE foram correlacionados com a HH: a C282Y, significativamente associada com a HH; e a H63D e a S65C, que apresentam uma relação obscura com esta doença. Neste relato, foi analisada a presença destas mutações em 8 pacientes com HH e em 148 indivíduos saudáveis (doadores de sangue). Para detecção das mutações, foi realizado PCR dos exons 2 e 4 do gene HFE, seguido pela clivagem com endonucleases específicas. No grupo de pacientes com HH, observou-se 3 indivíduos homozigotos para a mutação de C282Y, um heterozigoto composto (C282Y/H63D), um heterozigoto para C282Y e ausência de mutações nos outros 3 pacientes. Nos indivíduos saudáveis, a freqüência observada foi de 0,014 para o alelo C282Y, 0,108 para o H63D e 0,010 para o S65C. A presença de mutações foi significantemente maior nos indivíduos brancos, comparando com os não brancos. Estes dados são concordantes com a literatura prévia e com a origem étnica da população estudada.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade Federal de São Paulo (UNIFESP) EPMUNIFESP, EPMSciEL

Architektury kognitywne, czyli jak zbudować sztuczny umysł

Architektury kognitywne (AK) są próbą stworzenia modeli komputerowych integrujących wiedzę o działaniu umysłu. Ich zadaniem jest implementacja konkretnych schematów działania funkcji poznawczych umożliwiająca testowanie tych funkcji na szerokiej gamie zagadnień. Wiele architektur kognitywnych opracowano w celu symulacji procesu komunikacji pomiędzy człowiekiem i złożonymi maszynami (HCI, Human-Computer Interfaces), symulowania czasów reakcji oraz różnych psychofizycznych zależności. Można to do pewnego stopnia osiągnąć budując modele układu poznawczego na poziomie symbolicznym, z wiedzą w postaci reguł logicznych. Istnieją też projekty, które próbują powiązać procesy poznawcze z aktywacją modułów reprezentujących konkretne obszary mózgu, zgodnie z obserwacjami w eksperymentach z funkcjonalnym rezonansem magnetycznym (fMRI). Dużą grupę stanowią architektury oparte na podejściu logicznym, które mają na celu symulację wyższych czynności poznawczych, przede wszystkim procesów myślenia i rozumowania. Niektóre z projektów rozwoju architektur poznawczych skupiają większe grupy badawcze działające od wielu dziesięcioleci. Ogólnie architektury kognitywne podzielić można na 3 duże grupy: architektury symboliczne (oparte na funkcjonalnym rozumieniu procesów poznawczych); architektury emergentne, oparte na modelach koneksjonistycznych; oraz architektury hybrydowe, wykorzystujące zarówno modele neuronowe jak i reguły symboliczne. W ostatnich latach znacznie wzrosło zainteresowanie architekturami inspirowanymi przez neurobiologię (BICA, Brain Inspired Cognitive Architectures). Jak sklasyfikować różne architektury, jakie wyzwania należy przed nimi postawić, jak oceniać postępy w ich rozwoju, czego nam brakuje do stworzenia pełnego modelu umysłu? Krytyczny przegląd istniejących architektur kognitywnych, ich ograniczeń i możliwości pozwala na sformułowanie ogólnych wniosków dotyczących kierunków ich rozwoju czego nam brakuje do stworzenia pełnego modelu umysłu? Krytyczny przegląd istniejących architektur kognitywnych, ich ograniczeń i możliwości pozwala na sformułowanie ogólnych wniosków dotyczących kierunków ich rozwoju oraz wysunięcie własnych propozycji budowy nowej architektury

Size reduction of complex networks preserving modularity

The ubiquity of modular structure in real-world complex networks is being the focus of attention in many trials to understand the interplay between network topology and functionality. The best approaches to the identification of modular structure are based on the optimization of a quality function known as modularity. However this optimization is a hard task provided that the computational complexity of the problem is in the NP-hard class. Here we propose an exact method for reducing the size of weighted (directed and undirected) complex networks while maintaining invariant its modularity. This size reduction allows the heuristic algorithms that optimize modularity for a better exploration of the modularity landscape. We compare the modularity obtained in several real complex-networks by using the Extremal Optimization algorithm, before and after the size reduction, showing the improvement obtained. We speculate that the proposed analytical size reduction could be extended to an exact coarse graining of the network in the scope of real-space renormalization.Comment: 14 pages, 2 figure

Measurements of eye lens doses in interventional cardiology using OSL and electronic dosemeters

The purpose of this paper is to test the appropriateness of OSL and electronic dosemeters to estimate eye lens doses at interventional cardiology environment. Using TLD as reference detectors, personal dose equivalent was measured in phantoms and during clinical procedures. For phantom measurements, OSL dose values resulted in an average difference of 215% vs. TLD. Tests carried out with other electronic dosemeters revealed differences up to +/- 20% versus TLD. With dosemeters positioned outside the goggles and when TLD doses were > 20 mu Sv, the average difference OSL vs. TLD was 29%. Eye lens doses of almost 700 mu Sv per procedure were measured in two cases out of a sample of 33 measurements in individual clinical procedures, thus showing the risk of high exposure to the lenses of the eye when protection rules are not followed. The differences found between OSL and TLD are acceptable for the purpose and range of doses measured in the survey.Postprint (published version

Enhance the Efficiency of Heuristic Algorithm for Maximizing Modularity Q

Modularity Q is an important function for identifying community structure in complex networks. In this paper, we prove that the modularity maximization problem is equivalent to a nonconvex quadratic programming problem. This result provide us a simple way to improve the efficiency of heuristic algorithms for maximizing modularity Q. Many numerical results demonstrate that it is very effective.Comment: 9 pages, 3 figure

Esthesioneuroblastoma presenting with epifora in a young child

Esthesioneuroblastoma (ENB) is an uncommon tumor believed to arise from the olfactory epithelium.1 This neoplasm has rarely been reported in children, with only 12 cases reported to date among patients younger than 10 years.2 The usual initial symptom in children, as in older patients, is nasal obstruction or epistaxis3; consequently, the tumor is often first seen by an otorhinolaryngologist. We report a case of ENB in a young child in whom the initial symptom was epiphora; to our knowledge, this initial symptom is previously unreported, and ENB must now be considered in the differential diagnosis of epiphora in childhood

Fundamental limits to learning closed-form mathematical models from data

Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem displays a transition from a low-noise phase in which the true model can be learned, to a phase in which the observation noise is too high for the true model to be learned by any method. Both in the low-noise phase and in the high-noise phase, probabilistic model selection leads to optimal generalization to unseen data. This is in contrast to standard machine learning approaches, including artificial neural networks, which are limited, in the low-noise phase, by their ability to interpolate. In the transition region between the learnable and unlearnable phases, generalization is hard for all approaches including probabilistic model selection