Hierarchical active inference for cognitive architectures

This thesis explores the methods based on the free energy principle and active inference for modelling cognition. Active inference is an emerging framework for designing intelligent agents where psychological processes are cast in terms of Bayesian inference. Here, I appeal to it to test the design of a set of cognitive architectures, via simulation. These architectures are defined in terms of generative models where an agent executes a task under the assumption that all cognitive processes aspire to the same objective: the minimization of variational free energy. Chapter 1 introduces the free energy principle and its assumptions about self-organizing systems. Chapter 2 describes how from the mechanics of self-organization can emerge a minimal form of cognition able to achieve autopoiesis. In chapter 3 I present the method of how I formalize generative models for action and perception. The architectures proposed allow providing a more biologically plausible account of more complex cognitive processing that entails deep temporal features. I then present three simulation studies that aim to show different aspects of cognition, their associated behavior and the underlying neural dynamics. In chapter 4, the first study proposes an architecture that represents the visuomotor system for the encoding of actions during action observation, understanding and imitation. In chapter 5, the generative model is extended and is lesioned to simulate brain damage and neuropsychological patterns observed in apraxic patients. In chapter 6, the third study proposes an architecture for cognitive control and the modulation of attention for action selection. At last, I argue how active inference can provide a formal account of information processing in the brain and how the adaptive capabilities of the simulated agents are a mere consequence of the architecture of the generative models. Cognitive processing, then, becomes an emergent property of the minimization of variational free energy.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Networks in Argentine agriculture: a multiple-case study approach

Argentina is among the four largest producers of soybeans, sunflower, corn, and wheat, among other agricultural products. Institutional and policy changes during the 1990s fostered the development of Argentine agriculture and the introduction of innovative process and product technologies (no-till, agrochemicals, GMO, GPS) and new investments in modern, large-scale sunflower and soybean processing plants. In addition to technological changes, a "quiet revolution" occurred in the way agricultural production was carried out and organized: from self-production or ownership agriculture to a contract-based agriculture. The objective of this paper is to explore and describe the emergence of networks in the Argentine crop production sector. The paper presents and describes four cases that currently represent about 50% of total grain and oilseed production in Argentina: "informal hybrid form", "agricultural trust fund", "investor-oriented corporate structure", and "network of networks". In all cases, hybrid forms involve a group of actors linked by common objectives, mainly to gain scale, share resources, and improve the profitability of the business. Informal contracts seem to be the most common way of organizing the agriculture process, but using short-term contracts and sequential interfirm collaboration. Networks of networks involve long-term relationships and social development, and reciprocal interfirm collaboration. Agricultural trust fund and investor-oriented corporate structures have combined interfirm collaboration and medium-term relationships. These organizational forms are highly flexible and show a great capacity to adapt to challenges; they are competitive because they enjoy aligned incentives, flexibility, and adaptability.

Cultural self-identification(s) and personal social networks among first and second-generation immigrants

L’estudi examina les relacions entre (1) les xarxes socials personals de la població immigrant resident a Barcelona i (2) les seves identitats culturals múltiples. L’objectiu principal de l’estudi és entendre com el contingut i l’estructura de les relacions socials dels immigrants facilita o dificulta (1) tenir un sentiment de pertinença a les noves cultures d’acollida, la catalana i la espanyola, i (2) la integració d’aquestes noves identitats socioculturals amb la seva identitat d’origen en una nova identitat bicultural cohesiva. El nostre plantejament inicial era que els immigrants amb xarxes socials més diverses des del punt de vista de la seva composició cultural tindrien més recursos socials i experiències cognitives més diverses , factors que afavoreixen les identificacions múltiples i la participació cívica. Els resultats de l’estudi mostren que el grau d’identificació dels participants amb la seva cultura ètnica o d’origen és força alt i, en certa mesura, més alt en comparació amb les cultures d’acollida ( catalana, cívica i espanyola). Tanmateix, el vincle dels participants amb les cultures d’acollida (p. ex., la cultura catalana) és prou rellevant per a indicar una orientació bicultural (catalana i ètnica). Les anàlisis de correlacions revelen que sentir-se català no impedeix sentir-se part de la comunitat etnocultural d’origen. A més, existeix una interrelació entre l'orientació cultural catalana i la identificació amb les comunitats cíviques locals. De la mateixa manera, tenir competències en llengua catalana no va en detriment de les competències en llengua castellana. Les anàlisis també mostren que factors com l’orientació cultural catalana, l’ús del català i la identificació amb la cultura catalana tenen una correlació positiva amb el grau de chohesio de la indentitat bicultural, afavoreixen el benestar psicològic i disminueixen l’estrès aculturatiu. L’anàlisi de les xarxes socials mostra que la identificació amb la cultura catalana, l’orientació cultural catalana i la integració de la identitat són factors clau per tenir xarxes socials més diverses des del punt de vista ètnic i lingüístic, amb menys membres del col•lectiu d’origen, i amb subgrups o “cliques” culturalment més heterogenis. La identificació espanyola també prediu, en mesura més reduïda, la diversitat de les xarxes. Els nostres resultats contribueixen a la recerca actual i les teories sobre interculturalitat i identitat cultural.

Self-similarity of complex networks and hidden metric spaces

We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.

New copper(I) cyanide networks: interpenetration, self-penetration and polymorphism

The structures Of four alkali-metal copper (I) cyanides, KCu2(CN)(3)(H2O)-H-.-II (I), K2Cu3(CN)(5) (II), CsCu3(CN)(4) (III) and KCu3(CN)(4) (IV) are described. Three of these, ((II)-(IV)), with previously unknown ACN:CuCN ratios have new copper-cyanide frameworks, whilst (1) is a new polymorph of KCu2(CN)(3)(H2O)-H-.. These structures are discussed in terms of assembly from the simple building units Cu(CN)(2/2), Cu(CN)(3/2), Cu(CN)(2/2)(CN)(1/1) and Cu(CN)(4/2). Compounds (I), (II) and (III) are layered materials based on (6,3) nets containing (CuCN)(6) rings (I) and (CuCN)(8) rings (II) and (III). In compound (IV), (4,4) nets containing (CuCN)(12) rings link to generate a three-dimensional network. Both (III) and (IV) are examples of interpenetrating solids in which two and four identical networks interweave, respectively. These materials illustrate the structural versatility of copper (I) in cyanide frameworks. (c) 2006 Elsevier SAS. All rights reserved.

Supramolecular materials for inkjet printing: self-assembling polymer networks

Electronically complementary, low molecular weight polymers that self-assemble through tuneable π-π stacking interactions to form extended supramolecular polymer networks have been developed for inkjet printing applications and successfully deposited using three different printing techniques. Sequential overprinting of the complementary components results in supramolecular network formation through complexation of π-electron rich pyrenyl or perylenyl chain-ends in one component with π-electron deficient naphthalene diimide residues in a chain-folding polyimide. The complementary π-π stacked polymer blends generate strongly coloured materials as a result of charge-transfer absorptions in the visible spectrum, potentially negating the need for pigments or dyes in the ink formulation. Indeed, the final colour of the deposited material can be tailored by changing varying the end-groups of the π electron rich polymer component. Piezoelectric printing techniques were employed in a proof of concept study to allow characterisation of the materials deposited, and a thermal inkjet printer adapted with imaging software enabled a detailed analysis of the ink-drops as they formed, and of their physical properties. Finally, continuous inkjet printing allowed greater volumes of material to be deposited, on a variety of different substrate surfaces, and demonstrated the utility and versatility of this novel type of ink for industrial applications.

Self-optimization of dense wireless sensor networks based on simulated annealing

Wireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.

A transmission power self-optimization technique for wireless sensor networks

Wireless sensor networks (WSNs) are generally used to monitor hazardous events in inaccessible areas. Thus, on one hand, it is preferable to assure the adoption of the minimum transmission power in order to extend as much as possible the WSNs lifetime. On the other hand, it is crucial to guarantee that the transmitted data is correctly received by the other nodes. Thus, trading off power optimization and reliability insurance has become one of the most important concerns when dealing with modern systems based on WSN. In this context, we present a transmission power self-optimization (TPSO) technique for WSNs. The TPSO technique consists of an algorithm able to guarantee the connectivity as well as an equally high quality of service (QoS), concentrating on the WSNs efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind the proposed approach is to trade off WSNs Ef against energy consumption in an environment with inherent noise. Experimental results with different types of noise and electromagnetic interference (EMI) have been explored in order to demonstrate the effectiveness of the TPSO technique.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Bluetooth Enabled Ad-hoc Networks: Performance Evaluation of a Self-healing Scatternet Formation Protocol

Bluetooth wireless technology is a robust short-range communications system designed for low power (10 meter range) and low cost. It operates in the 2.4 GHz Industrial Scientific Medical (ISM) band and it employs two techniques for minimizing interference: a frequency hopping scheme which nominally splits the 2.400 - 2.485 GHz band in 79 frequency channels and a time division duplex (TDD) scheme which is used to switch to a new frequency channel on 625 μs boundaries. During normal operation a Bluetooth device will be active on a different frequency channel every 625 μs, thus minimizing the chances of continuous interference impacting the performance of the system. The smallest unit of a Bluetooth network is called a piconet, and can have a maximum of eight nodes. Bluetooth devices must assume one of two roles within a piconet, master or slave, where the master governs quality of service and the frequency hopping schedule within the piconet and the slave follows the master’s schedule. A piconet must have a single master and up to 7 active slaves. By allowing devices to have roles in multiple piconets through time multiplexing, i.e. slave/slave or master/slave, the Bluetooth technology allows for interconnecting multiple piconets into larger networks called scatternets. The Bluetooth technology is explored in the context of enabling ad-hoc networks. The Bluetooth specification provides flexibility in the scatternet formation protocol, outlining only the mechanisms necessary for future protocol implementations. A new protocol for scatternet formation and maintenance - mscat - is presented and its performance is evaluated using a Bluetooth simulator. The free variables manipulated in this study include device activity and the probabilities of devices performing discovery procedures. The relationship between the role a device has in the scatternet and it’s probability of performing discovery was examined and related to the scatternet topology formed. The results show that mscat creates dense network topologies for networks of 30, 50 and 70 nodes. The mscat protocol results in approximately a 33% increase in slaves/piconet and a reduction of approximately 12.5% of average roles/node. For 50 node scenarios the set of parameters which creates the best determined outcome is unconnected node inquiry probability (UP) = 10%, master node inquiry probability (MP) = 80% and slave inquiry probability (SP) = 40%. The mscat protocol extends the Bluetooth specification for formation and maintenance of scatternets in an ad-hoc network.

"It won't happen to me!": self-disclosure in online social networks

Despite the considerable amount of self-disclosure in Online Social Networks (OSN), the motivation behind this phenomenon is still little understood. Building on the Privacy Calculus theory, this study fills this gap by taking a closer look at the factors behind individual self-disclosure decisions. In a Structural Equation Model with 237 subjects we find Perceived Enjoyment and Privacy Concerns to be significant determinants of information revelation. We confirm that the privacy concerns of OSN users are primarily determined by the perceived likelihood of a privacy violation and much less by the expected damage. These insights provide a solid basis for OSN providers and policy-makers in their effort to ensure healthy disclosure levels that are based on objective rationale rather than subjective misconceptions.

Perceived organizational support and intention to stay in host countries among self-initiated expatriates: the role of career satisfaction and networks

Previous literature in SIE (self-initiated expatriation) has been mostly focused on an individual perspective. Studies on SIEs in organizational context are scarce. The current paper sought to examine the effect of perceived organizational support (POS) on SIE employees’ intention to stay in the host country, mediated by career satisfaction. Furthermore, we examined the moderating roles of career-related social networks with host and home country nationals on the effectiveness of POS. Data from 112 SIE employees in Germany were collected and analyzed. Empirical results partially supported our proposed model: there were significant negative indirect effect between POS and intention to stay, when career network size with home country nationals was high. The direct effect between POS and intention to stay was positive. For HR practice, our paper gave insight to understand SIE employees’ needs for support and mobility preferences, which can help organizations to develop more targeted HR development measures and assignment strategies for them.

Developmental self-construction and -configuration of functional neocortical neuronal networks.

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.