Distinguishing humans from computers in the game of go: a complex network approach

We compare complex networks built from the game of go and obtained from databases of human-played games with those obtained from computer-played games. Our investigations show that statistical features of the human-based networks and the computer-based networks differ, and that these differences can be statistically significant on a relatively small number of games using specific estimators. We show that the deterministic or stochastic nature of the computer algorithm playing the game can also be distinguished from these quantities. This can be seen as tool to implement a Turing-like test for go simulators.Comment: 7 pages, 6 figure

Évolution d'une zone d'habitat rural du Ier s. av. J.-C. au IIIe s. ap. J.-C. : Chassieu-Genas L'Épine (Rhône)

International audienc

Novel Version of PageRank, CheiRank and 2DRank for Wikipedia in Multilingual Network Using Social Impact

International audienceNowadays, information describing navigation behaviour of internet users are used in several fields, e-commerce, economy, sociology and data science. Such information can be extracted from different knowledge bases, including business-oriented ones. In this paper, we propose a new model for the PageRank, CheiRank and 2DRank algorithm based on the use of clickstream and pageviews data in the google matrix construction. We used data from Wikipedia and analysed links between over 20 million articles from 11 language editions. We extracted over 1.4 billion source-destination pairs of articles from SQL dumps and more than 700 million pairs from XML dumps. Additionally, we unified the pairs based on the analysis of redirect pages and removed all duplicates. Moreover, we also created a bigger network of Wikipedia articles based on all considered language versions and obtained multilingual measures. Based on real data, we discussed the difference between standard PageRank, Cheirank, 2DRank and measures obtained based on our approach in separate languages and multilingual network of Wikipedia

Introduction

Le voyageur pressé qui, du nord, descend vers Lyon et le sud-est de la France par l’autoroute A6 (fig. coul. 1) fait parfois une halte sur l’aire de Patural à Saint-Georges-de-Reneins (Rhône). Il est à 420 km au sud-est de Paris, à 30 km au sud de Mâcon, et à une quarantaine de kilomètres au nord des faubourgs de Lyon ; il a aperçu la Saône sur sa gauche à quelques reprises, et ne cherche pas à apercevoir, à un kilomètre sur sa droite, la butte sableuse où le bourg de Ludna s’élevait au Ier s..

Изучение спектрально-люминесцентных свойств гамма-пирилоцианинов и их гетероаналогов

Изучены спектрально-люминесцентные свойства гамма-пирилоцианинов и их гетероаналогов с модифицированными комплексными анионами. Показано, что происходит уменьшение квантовых выходов флуоресценции в ряду пирило-, тиапирило-, селенпирило- для моно- и триметинцианинов и при замене в молекуле красителя аниона ClO[4] - на анион TlCl[4] - в результате увеличения вероятности синглет-триплетных переходов под влиянием тяжелых атомов

Artificial neural network for high-throughput spectral data processing in LIBS imaging: application to archaeological mortar

International audienceWith the development of micro-LIBS imaging, the ever-increasing size of datasets (sometimes >1 million spectra) makes the processing of spectral data difficult and time consuming. Advanced statistical methods have become necessary to process these data, but most of them still require strong expertise and are not adapted to fast data treatment or a high throughput analysis. To address these issues, we evaluate, in the present work, the use of an artificial neural network (ANN) for LIBS imaging spectral data processing for the identification of different mineral phases in archaeological lime mortar. Common in ancient architecture, this building material is a complex mixture of lime with one or more aggregates, some components of which are of the same chemical nature (e.g. calcium carbonates). In this study, we trained an artificial neural network (ANN) for automatic detection of different phases in these complex samples. The training of such a predictive model was made possible by building a LIBS dataset of more than 1300 reference spectra, obtained from various selected materials that may be present in mortars. The ANN parameters (pre-treatment of data, number of neurons and of iterations) were optimized to ensure the best recognition of mortar components, while avoiding overtraining. The results demonstrate a fast and accurate identification of each component. The use of an ANN appears to be a strong means to provide an efficient, fast and automated LIBS characterization of archaeological mortar, a concept that could later be generalized to other samples and other scientific fields and methods