Complex scaling behavior in animal foraging patterns

This dissertation attempts to answer questions from two different areas of biology, ecology and neuroscience, using physics-based techniques. In Section 2, suitability of three competing random walk models is tested to describe the emergent movement patterns of two species of primates. The truncated power law (power law with exponential cut off) is the most suitable random walk model that characterizes the emergent movement patterns of these primates. In Section 3, an agent-based model is used to simulate search behavior in different environments (landscapes) to investigate the impact of the resource landscape on the optimal foraging movement patterns of deterministic foragers. It should be noted that this model goes beyond previous work in that it includes parameters such as spatial memory and satiation, which have received little consideration to date in the field of movement ecology. When the food availability is scarce in a tropical forest-like environment with feeding trees distributed in a clumped fashion and the size of those trees are distributed according to a lognormal distribution, the optimal foraging pattern of a generalist who can consume various and abundant food types indeed reaches the Lévy range, and hence, show evidence for Lévy-flight-like (power law distribution with exponent between 1 and 3) behavior. Section 4 of the dissertation presents an investigation of phase transition behavior in a network of locally coupled self-sustained oscillators as the system passes through various bursting states. The results suggest that a phase transition does not occur for this locally coupled neuronal network. The data analysis in the dissertation adopts a model selection approach and relies on methods based on information theory and maximum likelihood

On the role and opportunities in teamwork design for advanced multi-robot search systems

Intelligent robotic systems are becoming ever more present in our lives across a multitude of domains such as industry, transportation, agriculture, security, healthcare and even education. Such systems enable humans to focus on the interesting and sophisticated tasks while robots accomplish tasks that are either too tedious, routine or potentially dangerous for humans to do. Recent advances in perception technologies and accompanying hardware, mainly attributed to rapid advancements in the deep-learning ecosystem, enable the deployment of robotic systems equipped with onboard sensors as well as the computational power to perform autonomous reasoning and decision making online. While there has been significant progress in expanding the capabilities of single and multi-robot systems during the last decades across a multitude of domains and applications, there are still many promising areas for research that can advance the state of cooperative searching systems that employ multiple robots. In this article, several prospective avenues of research in teamwork cooperation with considerable potential for advancement of multi-robot search systems will be visited and discussed. In previous works we have shown that multi-agent search tasks can greatly benefit from intelligent cooperation between team members and can achieve performance close to the theoretical optimum. The techniques applied can be used in a variety of domains including planning against adversarial opponents, control of forest fires and coordinating search-and-rescue missions. The state-of-the-art on methods of multi-robot search across several selected domains of application is explained, highlighting the pros and cons of each method, providing an up-to-date view on the current state of the domains and their future challenges

ComplexWorld Position Paper

The Complex ATM Position Paper is the common research vehicle that defines the high-level, strategic scientific vision for the ComplexWorld Network. The purpose of this document is to provide an orderly and consistent scientific framework for the WP-E complexity theme. The specific objectives of the position paper are to: - analyse the state of the art within the different research areas relevant to the network, identifying the major accomplishments and providing a comprehensive set of references, including the main publications and research projects; - include a complete list of , a list of application topics, and an analysis of which techniques are best suited to each one of those applications; - identify and perform an in-depth analysis of the most promising research avenues and the major research challenges lying at the junction of ATM and complex systems domains, with particular attention to their impact and potential benefits for the ATM community; - identify areas of common interest and synergies with other SESAR activities, with special attention to the research topics covered by other WP-E networks. An additional goal for future versions of this position paper is to develop an indicative roadmap on how these research challenges should be accomplished, providing a guide on how to leverage on different aspects of the complexity research in Air Transport

The Statistical Mechanics of Learning and Co-Adaptation in Heterogeneous Populations

Adaptation is a fundamental mechanism of growth. Scientists have developed statistical models in numerous contexts to characterize growth and its emergent behaviors, such as inequality, competition, and cooperation. However, we still lack a general adaptive mechanism that explains the emergence of growth in uncertain environments, preventing systematic exploration of the origins of agent heterogeneities. In this dissertation, I derive a theory of statistical growth among agents adapting to their environments. I then show several key results. First, that the average growth rate of agents' resources is governed by the information they hold about their environment. It follows that the learning process can attenuate growth rate disparities, reducing the long-term effects of heterogeneity on inequality. Second, I show how groups that optimally combine complementary information about resources maximize their effective growth rate. I show that these advantages are quantified by the information synergy embedded in the conditional probability of environmental states given agents’ signals, such that groups with a greater diversity of signals maximize their collective information. Lastly, using simple, pairwise agent interactions, I show how agent preferences converge when driven by observation of each other's behaviors. These results demonstratew how the formal properties of information underlie the statistical dynamics of many complex processes across biological and social phenomena

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

A machine learning ride in the physics theme park: from quantum to biophysics

Tesi amb menció de Doctorat Internacional(English) The integration of artificial intelligence into research is propelling progress and discoveries across the entire scientific landscape. Artificial intelligence tools boost the development of novel scientific insights and theories by processing extensive data sets, guiding exploration and hypothesis formation, enhancing experimental setups, and even enabling autonomous discovery. In this thesis, we harness the power of machine learning, a sub-field of artificial intelligence, to study non-deterministic systems, which are amongst the hardest to characterize. On one hand, we address problems inherent to the study of quantum systems and the development of quantum technologies. Quantum physics presents formidable challenges due to the associated exponential complexity with the size of the system at hand, as well as its intrinsic stochastic nature and the presence of intricate correlations between its components. We employ reinforcement learning, a machine learning technique that excels at dealing with vast hypothesis spaces, to address some of these challenges. Notably, reinforcement learning has demonstrated super-human performance in multiple complex games like Go, which present similar characteristics to the problems encountered in the study of quantum physics. We use it to systematically simplify complex common problems in condensed matter and quantum information processing tasks, as well as to implement robust calibration schemes for quantum computers. On the other hand, we focus on the characterization of complex stochastic processes, such as diffusion. Understanding diffusion processes is crucial to unravel the complex underlying physical and biological mechanisms governing them. This involves extracting meaningful parameters from the analysis of stochastic trajectories described by tracked particles. However, accurately capturing and analyzing the trajectories presents multiple challenges, stemming from the combination of their random nature, complex dynamics, and experimental drawbacks, such as noise. We develop machine learning algorithms to accurately extract such parameters, even when they vary with time, and demonstrate their applicability in experimental scenarios. Furthermore, we apply similar techniques to study the diffusion of internet users browsing an e-commerce website, predicting their likelihood to make a purchase before closing the session.(Català) La integració de la intel·ligència artificial a la recerca accelera el progrés cap a nous descobriments en tot l’'àmbit científic. Les eines d’'intel·ligència artificial contribueixen al desenvolupament de noves teories i coneixements processant grans quantitats de dades, guiant l’'exploració i la formulació d'’hipòtesis, millorant els experiments i, fins i tot, fent possible descobriments automàtics. En aquesta tesi, aprofitem el poder de l’'aprenentatge automàtic (“"machine learning”"), un camp de la intel·ligència artificial, per estudiar sistemes no-deterministes, que es troben entre els més difícils de caracteritzar. Per una banda, tractem problemes inherents a l’'estudi de sistemes quàntics i del desenvolupament de noves tecnologies quàntiques. La física quàntica planteja reptes formidables derivats de la complexitat exponencial amb la mida del sistema considerat, en combinació amb una naturalesa intrínsicament estocàstica i la presència de correlacions complexes entre elements del sistema. Per tractar alguns d’'aquests reptes, fem servir aprenentatge de reforç (“"reinforcement leraning”"), una tècnica de l'aprenentatge automàtic capaç d’'explorar grans espais d'’hipòtesis. Per exemple, empleant aquestes tècniques, s'’ha aconseguit superar als millors jugadors del món en jocs complexes com el Go, que presenten característiques similars a problemes emergents en l’'estudi de la quàntica. En el nostre cas, desenvolupem mètodes per simplificar problemes complexes comuns en els camps de la matèria condensada i de la informació quàntica de forma sistemàtica, i disenyem protocols robustos de cal·libració d’'ordinadors quàntics. Per l’altra banda, ens dediquem a la caracterització de processos estocàstics complexes, com és la difusió. Entendre els processos de difusió és essencial per descobrir els mecanismes físics i biològics que els governen, el que comporta l’'anàlisi de trajectòries estocàstiques descrites per partícules per tal d’extruere’n paràmetres significatius del sistema. Aquest anàlisi, però, presenta grans reptes des de l’'adquisició de les trajectòries fins al seu estudi posterior que provenen, principalment, de la combinació de la seva naturales aleatòria, amb la presència de dinàmiques complexes i altres inconvenients experimentals, com ara el soroll. Utilitzant tècniques d’aprenentatge automàtic, desenvolupem algoritmes per analitzar aquestes trajectòries i extreure’n els paràmetres d’interès acuradament, fins i tot quan aquests canvien amb el temps. Després, utilitzem aquests algoritmes per estudiar diferents experiments en sistemes biològics i, també, per estudiar les trajectòries descrites per usuaris navegant una pàgina de comerç online. En aquest últim cas, en comptes d'extreure paràmetres físics, inferim si l'usuari farà una compra abans de tancar la sessió.Postprint (published version

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches.Comment: 46 pages, 9 figure

Toward Dynamic Social-Aware Networking Beyond Fifth Generation

The rise of the intelligent information world presents significant challenges for the telecommunication industry in meeting the service-level requirements of future applications and incorporating societal and behavioral awareness into the Internet of Things (IoT) objects. Social Digital Twins (SDTs), or Digital Twins augmented with social capabilities, have the potential to revolutionize digital transformation and meet the connectivity, computing, and storage needs of IoT devices in dynamic Fifth-Generation (5G) and Beyond Fifth-Generation (B5G) networks. This research focuses on enabling dynamic social-aware B5G networking. The main contributions of this work include(i) the design of a reference architecture for the orchestration of SDTs at the network edge to accelerate the service discovery procedure across the Social Internet of Things (SIoT); (ii) a methodology to evaluate the highly dynamic system performance considering jointly communication and computing resources; (iii) a set of practical conclusions and outcomes helpful in designing future digital twin-enabled B5G networks. Specifically, we propose an orchestration for SDTs and an SIoT-Edge framework aligned with the Multi-access Edge Computing (MEC) architecture ratified by the European Telecommunications Standards Institute (ETSI). We formulate the optimal placement of SDTs as a Quadratic Assignment Problem (QAP) and propose a graph-based approximation scheme considering the different types of IoT devices, their social features, mobility patterns, and the limited computing resources of edge servers. We also study the appropriate intervals for re-optimizing the SDT deployment at the network edge. The results demonstrate that accounting for social features in SDT placement offers considerable improvements in the SIoT browsing procedure. Moreover, recent advancements in wireless communications, edge computing, and intelligent device technologies are expected to promote the growth of SIoT with pervasive sensing and computing capabilities, ensuring seamless connections among SIoT objects. We then offer a performance evaluation methodology for eXtended Reality (XR) services in edge-assisted wireless networks and propose fluid approximations to characterize the XR content evolution. The approach captures the time and space dynamics of the content distribution process during its transient phase, including time-varying loads, which are affected by arrival, transition, and departure processes. We examine the effects of XR user mobility on both communication and computing patterns. The results demonstrate that communication and computing planes are the key barriers to meeting the requirement for real-time transmissions. Furthermore, due to the trend toward immersive, interactive, and contextualized experiences, new use cases affect user mobility patterns and, therefore, system performance.Cotutelle -yhteisväitöskirj