Hierarchical self-organization of non-cooperating individuals

Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network) can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score) of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.Comment: Supplementary videos are to be found at http://hal.elte.hu/~nepusz/research/supplementary/hierarchy

LOGIC-BASED FORMULA MANAGEMENT STRATEGIES IN AN ACTUARIAL CONSULTING SYSTEM

In many decision support systems, multiple decision methods and models must be combined for solving a complex problem. Expertise is required for selecting, adapting and coordinating appropriate models. This paper describes the design and implementation of a knowledge-based model management system called the Actuarial Consulting System (ACS). The ACS supports actuaries in making pricing decisions in the domain of life insurance. Actuarial knowledge is organized using a graph formalism called Formula Derivation Network (FDN), represented in Prolog as a hierarchy of predicates. On the user level, a Problem Analyzer converts a problem specification by the user into a search problem on the stored collection of FDNs. Using different search strategies, including human expert rules, the Surface Planner generates an efficient solution strategy (sequence of models). At the lowest level, a Plan Executor retrieves or requests model data and issues appropriate function calls to a subroutine library.Information Systems Working Papers Serie

Intra firm and extra firm networks in the German knowledge economy. Economic development of German agglomerations from a relational perspective

Flows and inter-linkages between and within polycentric metropolitan regions have become a fundamental topic in regional sciences. The knowledge economy as a primary driver of spatial restructuring is forming these relations by generating knowledge within a spatially fine graded division of labor. This process drives companies to cooperate in intra firm and extra firm networks which in turn evoke patterns of interdependent spatial entities. The aim of the paper is twofold. Firstly, we analyze spatial patterns within these firm networks and secondly we combine this network approach with the development of the economic and spatial structure of German agglomerations. Inspired by formal social network analysis and spatial association statistics we apply methods to discover spatial clustering within relational data. We assume that relations between and within polycentric Mega-City Regions in Germany and its neighboring areas constitute a new form of hierarchical urban systems. Network analysis will help to detect locations of high centrality; cluster analyses of location-based data may show specific regional patterns of connectivity. We hypothesize that the position of locations within the functional urban hierarchy depends on the spatial scale of analysis: global, European, national or regional. Furthermore, we combine this relational perspective with an analysis of the economic development within these spatial entities. Here we assume that intensive interaction between functional urban areas has a high influence on their performance over time with regard to output indicators like labor, value-added and gross domestic product. Therefore we apply methods of spatial and network autocorrelation. We hypothesize that relational proximity influences economic development more intensively than effects of agglomeration and geographical proximity do.

Deviation Point Curriculum Learning for Trajectory Outlier Detection in Cooperative Intelligent Transport Systems

Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes' neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased F-measure by 5.06% and 2.4% while compared with generic Node2Vec and Struct2Vec methods.acceptedVersio

Explaining Deep Learning Hidden Neuron Activations using Concept Induction

One of the current key challenges in Explainable AI is in correctly interpreting activations of hidden neurons. It seems evident that accurate interpretations thereof would provide insights into the question what a deep learning system has internally \emph{detected} as relevant on the input, thus lifting some of the black box character of deep learning systems. The state of the art on this front indicates that hidden node activations appear to be interpretable in a way that makes sense to humans, at least in some cases. Yet, systematic automated methods that would be able to first hypothesize an interpretation of hidden neuron activations, and then verify it, are mostly missing. In this paper, we provide such a method and demonstrate that it provides meaningful interpretations. It is based on using large-scale background knowledge -- a class hierarchy of approx. 2 million classes curated from the Wikipedia Concept Hierarchy -- together with a symbolic reasoning approach called \emph{concept induction} based on description logics that was originally developed for applications in the Semantic Web field. Our results show that we can automatically attach meaningful labels from the background knowledge to individual neurons in the dense layer of a Convolutional Neural Network through a hypothesis and verification process.Comment: Submitted to IJCAI-2

Applications of big knowledge summarization

Advanced technologies have resulted in the generation of large amounts of data ( Big Data ). The Big Knowledge derived from Big Data could be beyond humans\u27 ability of comprehension, which will limit the effective and innovative use of Big Knowledge repository. Biomedical ontologies, which play important roles in biomedical information systems, constitute one kind of Big Knowledge repository. Biomedical ontologies typically consist of domain knowledge assertions expressed by the semantic connections between tens of thousands of concepts. Without some high-level visual representation of Big Knowledge in biomedical ontologies, humans cannot grasp the big picture of those ontologies. Such Big Knowledge orientation is required for the proper maintenance of ontologies and their effective use. This dissertation is addressing the Big Knowledge challenge - How to enable humans to use Big Knowledge correctly and effectively (referred to as the Big Knowledge to Use (BK2U) problem) - with a focus on biomedical ontologies. In previous work, Abstraction Networks (AbNs) have been demonstrated successful for the summarization, visualization and quality assurance (QA) of biomedical ontologies. Based on the previous research, this dissertation introduces new AbNs of various granularities for Big Knowledge summarization and extends the applications of AbNs. This dissertation consists of three main parts. The first part introduces two advanced AbNs. One is the weighted aggregate partial-area taxonomy with a parameter to flexibly control the summarization granularity. The second is the Ingredient Abstraction Network (IAbN) for the National Drug File - Reference Terminology (NDF-RT) Chemical Ingredients hierarchy, for which the previously developed AbNs for hierarchies with outgoing relationships, are not applicable. Since NDF-RT\u27s Chemical Ingredients hierarchy has no outgoing relationships. The second part describes applications of the two advanced AbNs. A study utilizing the weighted aggregate partial-area taxonomy for the identification of major topics in SNOMED CT\u27s Specimen hierarchy is reported. A multi-layer interactive visualization system of required granularity for ontology comprehension, based on the weighted aggregate partial-area taxonomy, is demonstrated to comprehend the Neoplasm subhierarchy of National Cancer Institute thesaurus (NCIt). The IAbN is applied for drug-drug interaction (DDI) discovery. The third part reports eight family-based QA studies on NCIt\u27s Neoplasm, Gene, and Biological Process hierarchies, SNOMED CT\u27s Infectious disease hierarchy, the Chemical Entities of Biological Interest ontology, and the Chemical Ingredients hierarchy in NDF-RT. There is no one-size-fits-all QA method and it is impossible to find a QA method for each individual ontology. Hence, family-based QA is an effective way, i.e., one QA technique could be applicable to a whole family of structurally similar ontologies. The results of these studies demonstrate that complex concepts and uncommonly modeled concepts are more likely to have errors. Furthermore, the three studies on overlapping concepts in partial-area taxonomies reported in this dissertation combined with previous three studies prove the success of overlapping concepts as a QA methodology for a whole family of 76 similar ontologies in BioPortal

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.Defense Advanced Research Projects Research Agency (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC subcontract 801881-BS under prime contract HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (SBE-0354378

The Evolution of Knowledge Transfer Boundary Networks in Healthcare

A particular concern within healthcare is the issue of research-informed practice. Failure to translate knowledge efficiently from research into practice potentially has consequences in terms of the quality of care or wasted resources, leading to an inefficient and unproductive health system. Effective techniques and approaches to address this knowledge gap (often called the ‘second translational gap’) are required. Literature suggests there is no ‘magic bullet’ to move healthcare research into improved clinical practice. This difficulty is linked, at least in part, to the organisational complexity of health systems including the National Health Service; there are multiple stakeholders, networks and professional and organisational silos. This study draws on data collection and analysis of a healthcare intervention borne from policy aimed specifically at addressing the second translational gap, i.e. moving research into clinical practice effectively and efficiently. The intervention was entitled the ‘Collaboration and Leadership in Applied Health Research and Care’ (CLAHRC), of which nine examples have been deployed in local health systems. North West London CLAHRC is an appropriate case study as its approach is consciously designed to create collaboration by establishing new networks that span different local health organisations and professions. The study is longitudinal and therefore enables a dynamic perspective that explores the impact of this carefully managed programme of activities on knowledge network evolution within this local context. Using a range of mixed methods, including semi-structured interviews, observation and Social Network Analysis I aimed to uncover how knowledge networks are instigated, how they are successfully developed and also how they are sustained over time, to deliver evidence-based medicine. The findings demonstrate and discuss the process through which a knowledge boundary network evolves and ultimately attains sustainability. It highlights how a mandated, structured inception and continued facilitation leads to increased interaction, a reduction in hierarchy and collaboration across boundaries. The findings are analysed with reference to extant literature and ultimately they contribute to the body of knowledge with regard to boundary network and community development. Finally, this study outlines the implications to future research and in particular the importance of the study to both healthcare practice and policy.Open Acces