How directed is a directed network?

The trophic levels of nodes in directed networks can reveal their functional properties. Moreover, the trophic coherence of a network, defined in terms of trophic levels, is related to properties such as cycle structure, stability and percolation. The standard definition of trophic levels, however, borrowed from ecology, suffers from drawbacks such as requiring basal nodes, which limit its applicability. Here we propose simple improved definitions of trophic levels and coherence that can be computed on any directed network. We demonstrate how the method can identify node function in examples including ecosystems, supply chain networks, gene expression and global language networks. We also explore how trophic levels and coherence relate to other topological properties, such as non-normality and cycle structure, and show that our method reveals the extent to which the edges in a directed network are aligned in a global direction

Structure Extraction in Printed Documents Using Neural Approaches

This paper addresses the problem of layout and logical structure extraction from image documents. Two classes of approaches are first studied and discussed in general terms: data-driven and model-driven. In the latter, some specific approaches like rule-based or formal grammar are usually studied on very stereotyped documents providing honest results, while in the former artificial neural networks are often considered for small patterns with good results. Our understanding of these techniques let us to believe that a hybrid model is a more appropriate solution for structure extraction. Based on this standpoint, we proposed a Perceptive Neural Network based approach using a static topology that possesses the characteristics of a dynamic neural network. Thanks to its transparency, it allows a better representation of the model elements and the relationships between the logical and the physical components. Furthermore, it possesses perceptive cycles providing some capacities in data refinement and correction. Tested on several kinds of documents, the results are better than those of a static Multilayer Perceptron

Application of kernel functions for accurate similarity search in large chemical databases

Background Similaritysearch in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. Results To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep. Conclusions Efficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases

Teaching and Learning of Fluid Mechanics

This book contains research on the pedagogical aspects of fluid mechanics and includes case studies, lesson plans, articles on historical aspects of fluid mechanics, and novel and interesting experiments and theoretical calculations that convey complex ideas in creative ways. The current volume showcases the teaching practices of fluid dynamicists from different disciplines, ranging from mathematics, physics, mechanical engineering, and environmental engineering to chemical engineering. The suitability of these articles ranges from early undergraduate to graduate level courses and can be read by faculty and students alike. We hope this collection will encourage cross-disciplinary pedagogical practices and give students a glimpse of the wide range of applications of fluid dynamics

Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process

Hans Hjelm. Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process. NEALT Monograph Series, Vol. 1 (2009), 159 pages. © 2009 Hans Hjelm. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/10126

Development of an Integrated Methodology to Estimate Groundwater Vulnerability to Pollution in Karst Areas

Groundwater is a very important resource since water volumes stored underground are much larger compared with those located at the surface, such as rivers and lakes. Aquifers supply a high percentage of freshwater for human consumption as well as supplying economic activities like industry, agriculture, and livestock production. Among them, karst aquifers stand out due to their special hydrologic characteristics and behaviour. In karst aquifers, infiltration occurs faster in comparison with unconsolidated aquifers, due to high permeability features at the surface like dolines, karren, epikarst, and swallow holes that allow precipitating water to recharge the aquifer at higher rates. Nevertheless, these characteristics also increase the aquifer’s susceptibility to being affected by pollution generated by anthropogenic practices. With a low natural pollutant degradation capacity, karst systems mostly experience problems related with water quality rather than water quantity. At present, this represents a significant challenge because a high percentage of the world population is settled on karst areas and is solely dependent upon karst aquifers to fulfil their necessary water supply. A good example to represent this case is the Yucatan Peninsula. The Peninsula is a transboundary limestone platform, covering parts of Mexico, Belize, and Guatemala, whose characteristics do not allow surface streams to generate. Therefore, the karstic aquifer provides water for nearly 4.5 million inhabitants within Mexican territory; this estimation excludes water volumes used for economic activities. The anthropogenic impacts over this karst aquifer have generated problems for water intended for human consumption, furthered by weak environmental regulations that allow the disposal of wastewater without adequate treatment. In the Mexican state of Yucatan, roughly 10% of the population has access to public sewer services where wastewater is treated. Additionally, the use of fertilizers and pesticides is not regulated in agricultural areas, while pig farming is an increasing activity, which fails to keep the necessary standards for the proper disposal of pig slurry. Similar situations can be found around the world, thus the development of plans and strategies to preserve karst groundwater quality that aim to find a balance between resource protection and regional development is increasingly necessary. One important tool emerged to support decisions regarding groundwater protection: the groundwater vulnerability concept. However, due to the hydrologic differences among detritus and karst aquifers, the vulnerability concept, which was first promoted for the former aquifer type, necessitated the development of a specialized vulnerability method to consider the natural characteristics of karst landscapes. Nevertheless, due to the high heterogeneity and anisotropy present in karst systems several methodologies to estimate karst groundwater vulnerability have arisen. Current methodologies are theoretical approximations to differentiate areas where an assumed pollutant particle, released at the surface, is more likely to reach the aquifer due to the natural characteristics of the area. These methods have shown themselves to be useful in defining protection areas and in highlighting regions in which further studies can be performed. However, the high subjectivity and exclusion of anthropogenic influences as part of the analysis is a drawback for these methods. In order to estimate karst groundwater vulnerability for current and future scenarios, an integrated approach is highly necessary. Since most of the methods focus solely on the travel time of a theoretical pollutant from the surface towards groundwater or to a spring, inclusion of pollutants residence time and concentration as parameters to estimate vulnerability is of the uttermost importance. To reach this goal, it is necessary to investigate current intrinsic-based methods in terms of their applicability and regional congruence in order to highlight advantages and probable misclassifications among them and to propose improvements. Pollutant residence time and concentration can be estimated from modelling, which can highlight areas where pollution can represent a problem due to anthropogenic practices, such as wastewater disposal and water extraction fields influencing groundwater flow. Other problems to be contemplated are the data availability and the variable processes by which areas are classified as vulnerable or not. Evaluation of multiple criteria to define degrees of vulnerability is complicated, since several factors, such as subjectivity, data quality, scale, and regional conditions, will always be present. This work presents the results from the application of eight karst groundwater vulnerability methods to the Yucatan karst and outcomes from solute transport. Important considerations are explained in order to improve the workflow for intrinsic groundwater vulnerability assessment. Possible parameters, to be included as part of vulnerability analysis, are evaluated by modelling, demonstrating the importance of anthropogenic impacts for current vulnerability scenarios. Results obtained in this research are displayed as the basis for an Integrated Karst Aquifer Vulnerability (IKAV) method proposed as an alternative for vulnerability studies

Data Service Outsourcing and Privacy Protection in Mobile Internet

Mobile Internet data have the characteristics of large scale, variety of patterns, and complex association. On the one hand, it needs efficient data processing model to provide support for data services, and on the other hand, it needs certain computing resources to provide data security services. Due to the limited resources of mobile terminals, it is impossible to complete large-scale data computation and storage. However, outsourcing to third parties may cause some risks in user privacy protection. This monography focuses on key technologies of data service outsourcing and privacy protection, including the existing methods of data analysis and processing, the fine-grained data access control through effective user privacy protection mechanism, and the data sharing in the mobile Internet

Chapitre 10 : entretien avec Eugene Seneta

  Eugene Seneta [[Seneta]] – professeur émérite à l’université de Sydney (School of Mathematics and Statistics) – est réputé pour ses contributions en probabilités et statistiques dont certaines ont débouché sur des applications aux domaines de la finance. Membre de l’Australian Academy of Sciences depuis 1985, il a aussi beaucoup contribué à l’histoire des probabilités et statistiques ; il revient dans cet entretien sur ses collaborations avec François Jongmans ainsi qu’avec Henri Breny, Be..

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

A new hybrid convolutional neural network and eXtreme gradient boosting classifier for recognizing handwritten Ethiopian characters

Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition. Due to its vast practical applications and financial implications, handwritten character recognition is still an important research area. In this research, the Handwritten Ethiopian Character Recognition (HECR) dataset has been prepared to train the model. The images in the HECR dataset were organized with more than one color pen RGB main spaces that have been size normalized to 28 × 28 pixels. The dataset is a combination of scripts (Fidel in Ethiopia), numerical representations, punctuations, tonal symbols, combining symbols, and special characters. These scripts have been used to write ancient histories, science, and arts of Ethiopia and Eritrea. In this study, a hybrid model of two super classifiers: Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost) is proposed for classification. In this integrated model, CNN works as a trainable automatic feature extractor from the raw images and XGBoost takes the extracted features as an input for recognition and classification. The output error rates of the hybrid model and CNN with a fully connected layer are compared. A 0.4630 and 0.1612 error rates are achieved in classifying the handwritten testing dataset images, respectively. Thus XGBoost as a classifier performs a better result than the traditional fully connected layer