A causal, time-independent synchronization pattern for collective adaptive systems

Artificial and Natural Computation

Collective behaviour in nature provides a source of inspiration to engineer artificial collective adaptive systems, due to their mechanisms favouring adaptation to environmental changes and enabling complex emergent behaviour to arise from a relatively simple behaviour of individual entities. As part of our ongoing research, we study the social amoeba Dictyostelium discoideum to derive agent-based models and mechanisms that we can then exploit in artificial systems, in particular in swarm robotics. In this paper, we present a selection of agent-based models of the aggregation phase of D. discoideum, their corresponding biological illustrations and how we used them as an inspiration for transposing this behaviour into swarms of Kilobots. We focus on the stream-breaking phenomenon occurring during the aggregation phase of the life cycle of D. discoideum. Results show that the breakup of aggregation streams depends on cell density, motility, motive force and the concentration of cAMP and CF. The breakup also comes with the appearance of late centres. Our computational results show similar behaviour to our biological experiments, using Ax2(ka) strain. For swarm robotics experiments, we focus on signalling and aggregation towards a centre.

Vertebral fractures are the most common osteoporotic fractures in older individuals. Recent studies suggest that the performance of artificial intelligence is equal to humans in detecting osteoporotic fractures, such as fractures of the hip, distal radius, and proximal humerus. However, whether artificial intelligence performs as well in the detection of vertebral fractures on plain lateral spine radiographs has not yet been reported. (1) What is the accuracy, sensitivity, specificity, and interobserver reliability (kappa value) of an artificial intelligence model in detecting vertebral fractures, based on Genant fracture grades, using plain lateral spine radiographs compared with values obtained by human observers? (2) Do patients' clinical data, including the anatomic location of the fracture (thoracic or lumbar spine), T-score on dual-energy x-ray absorptiometry, or fracture grade severity, affect the performance of an artificial intelligence model? (3) How does the artificial intelligence model perform on external validation? Between 2016 and 2018, 1019 patients older than 60 years were treated for vertebral fractures in our institution. Seventy-eight patients were excluded because of missing CT or MRI scans (24% [19]), poor image quality in plain lateral radiographs of spines (54% [42]), multiple myeloma (5% [4]), and prior spine instrumentation (17% [13]). The plain lateral radiographs of 941 patients (one radiograph per person), with a mean age of 76 ± 12 years, and 1101 vertebral fractures between T7 and L5 were retrospectively evaluated for training (n = 565), validating (n = 188), and testing (n = 188) of an artificial intelligence deep-learning model. The gold standard for diagnosis (ground truth) of a vertebral fracture is the interpretation of the CT or MRI reports by a spine surgeon and a radiologist independently. If there were any disagreements between human observers, the corresponding CT or MRI images would be rechecked by them together to reach a consensus. For the Genant classification, the injured vertebral body height was measured in the anterior, middle, and posterior third. Fractures were classified as Grade 1 (< 25%), Grade 2 (26% to 40%), or Grade 3 (> 40%). The framework of the artificial intelligence deep-learning model included object detection, data preprocessing of radiographs, and classification to detect vertebral fractures. Approximately 90 seconds was needed to complete the procedure and obtain the artificial intelligence model results when applied clinically. The accuracy, sensitivity, specificity, interobserver reliability (kappa value), receiver operating characteristic curve, and area under the curve (AUC) were analyzed. The bootstrapping method was applied to our testing dataset and external validation dataset. The accuracy, sensitivity, and specificity were used to investigate whether fracture anatomic location or T-score in dual-energy x-ray absorptiometry report affected the performance of the artificial intelligence model. The receiver operating characteristic curve and AUC were used to investigate the relationship between the performance of the artificial intelligence model and fracture grade. External validation with a similar age population and plain lateral radiographs from another medical institute was also performed to investigate the performance of the artificial intelligence model. The artificial intelligence model with ensemble method demonstrated excellent accuracy (93% [773 of 830] of vertebrae), sensitivity (91% [129 of 141]), and specificity (93% [644 of 689]) for detecting vertebral fractures of the lumbar spine. The interobserver reliability (kappa value) of the artificial intelligence performance and human observers for thoracic and lumbar vertebrae were 0.72 (95% CI 0.65 to 0.80; p < 0.001) and 0.77 (95% CI 0.72 to 0.83; p < 0.001), respectively. The AUCs for Grades 1, 2, and 3 vertebral fractures were 0.919, 0.989, and 0.990, respectively. The artificial intelligence model with ensemble method demonstrated poorer performance for discriminating normal osteoporotic lumbar vertebrae, with a specificity of 91% (260 of 285) compared with nonosteoporotic lumbar vertebrae, with a specificity of 95% (222 of 234). There was a higher sensitivity 97% (60 of 62) for detecting osteoporotic (dual-energy x-ray absorptiometry T-score ≤ -2.5) lumbar vertebral fractures, implying easier detection, than for nonosteoporotic vertebral fractures (83% [39 of 47]). The artificial intelligence model also demonstrated better detection of lumbar vertebral fractures compared with detection of thoracic vertebral fractures based on the external dataset using various radiographic techniques. Based on the dataset for external validation, the overall accuracy, sensitivity, and specificity on bootstrapping method were 89%, 83%, and 95%, respectively. The artificial intelligence model detected vertebral fractures on plain lateral radiographs with high accuracy, sensitivity, and specificity, especially for osteoporotic lumbar vertebral fractures (Genant Grades 2 and 3). The rapid reporting of results using this artificial intelligence model may improve the efficiency of diagnosing vertebral fractures. The testing model is available at http://140.113.114.104/vght_demo/corr/. One or multiple plain lateral radiographs of the spine in the Digital Imaging and Communications in Medicine format can be uploaded to see the performance of the artificial intelligence model. Level II, diagnostic study.

With the development of the modern smart city, sharing-bikes require behaviors prediction for grid-level areas which is essential for intelligent transportation systems. A model which can predict bike sharing demand behaviours accurately can allocate sharing-bikes in advance to satisfy travel demands alongside saving energy, reducing traffic, cutting down waste for those sharing-bikes companies putting excessive sharing-bikes in unsaturated demand areas. In this paper, we abandon the traditional time series prediction method and use a more efficient deep learning method to solve the traffic forecasting problem. Moreover, instead of considering spatial relation and temporal relation relatively, we produced a deep multi-view spatial-temporal network to combine them into one prediction model framework. In the experimental section, we investigate in the experiment on enormous amount of real sharing-bikes application use data in the core region of Beijing to test the performance of the model framework with a 1 km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1 km grid-level scale and compare it with other existing machine learning approaches and prediction models. And the 4G/5G/6G communication technology facilitate the real-time control of the space-time locations of sharing bikes dynamically. Thus, it provides the basis for high-frequency analysis of space-time patterns, especially supported by the 6G large-scale application in the future.

An optimization framework for global optimization of the cable layout topology for offshore wind farm (OWF) is presented. The framework designs and compares closed‐loop and radial layouts for the collection system of OWFs. For the former, a two‐stage stochastic optimization program based on a mixed integer linear programming (MILP) model is developed, while for the latter, a hop‐indexed full binary model is used. The purpose of the framework is to provide a common base for assessing both designs economically, using the same underlying contingency treatment. A discrete Markov model is implemented for calculating the cable failure probability, useful for estimating the time under contingency for multiple power generation scenarios. The objective function supports simultaneous optimization of (i) initial investment (network topology and cable sizing), (ii) total electrical power loss costs and (iii) operation costs due to energy curtailment from cable failures. Constraints are added accounting for common engineering aspects. The applicability of the full method is demonstrated by tackling three differently sized real‐world OWFs. Results show that (i) the profitability of either topology type depends strongly on the project size and wind turbine rating. Closed loop may be a competitive solution for large‐scale projects where large amounts of energy are potentially curtailed. (ii) The stochastic model presents low tractability to tackle large‐scale instances, increasing the required computing time and memory resources. (iii) Strategies must be adopted in order to apply stochastic optimization for modern OWFs, intending analytically or numerically simplification of mathematical models.

This chapter has the objective of describing the structure and placing in a taxonomy the Artificial Adaptive Systems (AAS). These systems form part of the vast world of Artificial Intelligence (AI) nowadays called more properly Artificial Sciences (AS). Artificial Sciences means those sciences for which an understanding of natural and/or cultural processes is achieved by the recreation of those processes through automatic models. In particular, Natural Computation tries to construct automatic models of complex processes, using the local interaction of elementary micro-processes, simulating the original process functioning. Such models organize themselves in space and time and connect in a non-linear way to the global process they are part of, trying to reproduce the complexity through the dynamic creation of specific and independent local rules that transform themselves in relation to the dynamics of the process. Natural Computation constitutes the alternative to Classical Computation (CC). This one, in fact, has great difficulty in facing natural/cultural processes, especially when it tries to impose external rules to understand and reproduce them, trying to formalize these processes in an artificial model. In Natural Computation ambit, Artificial Adaptive Systems are theories which generative algebras are able to create artificial models simulating natural phenomenon. The learning and growing process of the models is isomorphic to the natural process evolution, that is, it’s itself an artificial model comparable with the origin of the natural process. We are dealing with theories adopting the “time of development” of the model as a formal model of “time of process” itself. Artificial Adaptive Systems comprise Evolutive Systems and Learning Systems. Artificial Neural Networks are the more diffused and best-known Learning Systems models in Natural Computation.

Optimization of performance in collective systems often requires altruism. Emergence and stabilization of altruistic behaviors are difficult because the agents incur a cost when behaving altruistically. In this paper we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. Our multi-agent system is made up of autonomous agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so reaching optimal (or near-to-optimal) performances in unknown and changing environments.

Developing environmentally friendly, sustainable, and biocompatible artificial light-harvesting systems has become an essential area of research to understand natural light-harvesting processes involving multistep resonance energy transfer and building efficient energy conversion systems related to energy and optoelectronic applications. In this direction, bio-scaffolded artificial energy transfer systems for panchromatic light collection and sequential energy transfer have fascinated the scientific community. In this review, we have discussed what the dynamic structure and intrinsic physical properties of biomolecules like deoxyribonucleic acid, proteins, and peptides can provide for the development of new optical devices, sustainable and environmentally friendly white emitting materials, and cascaded energy transfer systems for energy harvesting from light. In doing so, we have highlighted some of the recent advances in bio-scaffolds as a platform for the assembly of different types of donor–acceptor chromophores involved in fluorescence energy transfer.

Swarm intelligence, inspired by the collective behavior observed in social organisms, has emerged as a powerful paradigm in both natural and artificial systems. The concept of a swarm refers to a large group of simple agents that interact locally with one another and their environment, giving rise to complex and intelligent behavior at the group level. Swarm intelligence, on the other hand, represents the ability of a swarm to self-organize, adapt, and solve complex problems without central control. In nature, swarms of social insects such as bees, ants, termites, and birds exhibit remarkable abilities in foraging, navigation, resource allocation, and defense. These organisms demonstrate how the interactions of simple individuals can lead to efficient and robust solutions to various challenges faced in their environments. In artificial systems, researchers have successfully translated the principles of swarm intelligence into algorithms and techniques for optimization, decision-making, and problem-solving. Popular swarm intelligence algorithms, such as Ant Colony Optimization, Particle Swarm Optimization, and Artificial Bee Colony, have shown great promise in tackling complex optimization and search tasks. This paper provides an overview of the fundamental concepts of swarm intelligence and explores the similarities and differences between natural and artificial swarms. It delves into the principles of self-organization, decentralized decision-making, and adaptation that underpin swarm intelligence, allowing these systems to cope with dynamic and uncertain environments. Furthermore, the paper examines the application domains of swarm intelligence, ranging from robotics and autonomous systems to data clustering, image processing, and network routing. The potential of swarm robotics in solving real-world challenges, such as environmental monitoring, disaster response, and precision agriculture, is also explored. Swarm intelligence presents a compelling avenue for understanding and harnessing emergent collective behavior in both biological and computational contexts. The interplay of simplicity, local interactions, and adaptation enables swarms to tackle complex problems efficiently, making them a valuable source of inspiration for the design of intelligent systems in various fields. The study of swarm intelligence continues to advance, offering exciting possibilities for creating adaptive, robust, and scalable solutions in the ever-evolving landscape of artificial intelligence and beyond.

Analysis, modelling and simulation of the fragmentation of agglomerates

Labor supply responses to tax changes are a core issue in public economics, reflected by numerous estimates from different methodological approaches. This dissertation contributes to the literature along two main avenues. First, I demonstrate how ex-ante simulations from a static discrete choice model can be validated ex-post by panel data methods estimating the elasticity of taxable earnings (Chapter 2). Second, I analyze the dynamics of labor supply responses to tax changes over time both with regards to panel data approaches (Chapter 3) and incorporated in a structural labor supply framework (Chapter 4). I use Norwegian register and survey data provided by Statistics Norway and focus on labor supply responses in working hours and wage earnings. In Chapter 2, I demonstrate how the standard elasticity of taxable income (ETI) methodology can be used to validate predictions from a structural labor supply model, by analyzing the Norwegian tax reform in 2006. To facilitate comparison, I use the discrete choice model to simulate the effects of the 2006 tax reform on hours of work, and use predicted income levels to obtain a comparable estimate for income elasticity with respect to the net-of-tax rate. In Chapter 3, I analyze the dynamics of earnings responses to tax changes by exploiting substantial exogenous variation in the two-tier surtax schedule for labor income over a period of 14 years (1995-2008). I adopt the dynamic panel data framework by Holmlund and Söderström (2011), and compare with estimates from the conventional static panel approach by Gruber and Saez (2002). I find that the long run responses to tax rate changes in labor supply are about twice as large as the short run responses due to a strong autoregressive effect in earnings. Supplementary conducted simulations suggest that up to 40 percent of the revenue loss due to tax rate cuts in the surtax schedule can be self- financed in the long run by inducing additional generated income. In Chapter 4, I examine the effect of state dependence to policy change responses over time within the framework of an intertemporal structural discrete choice model for women in couples. I allow for state dependence both in preferences and in the set of job opportunities. This causes sluggish labor supply responses to various changes in the wage rate and the tax schedule. Within the first year of a permanent policy change the simulation results suggest that about 50 percent of the full labor supply response can be expected, whereas 90 percent of the response is reached after about 4 years.

Optimal classifiers with minimum expected error within a Bayesian framework—Part I: Discrete and Gaussian models

We present a discrete choice modeling framework with heterogeneous decision rules accounting for non-trading behavior. The proposed approach builds upon the state-of-the-art probabilistic finite mixture models and tackles non-trading behavior while accounting for inertia effects and serial correlation in the SP data, and contextual effects on the probability of an individual employing a specific decision rule. The framework involves three subpopulations of decision-makers, referred to respectively as pure utility-maximizers, utility-maximizers with strong preference for one alternative, and non-traders non-utility-maximizers employing a non-trading heuristic. The second subpopulation is expected to exhibit non-trading behavior, despite making trade-offs consistent with utility maximization. Our goal is to disentangle the two types of manifested non-trading behavior. We assume that the manifestation of non-trading behavior – by otherwise utility-maximizing individuals – may be driven by important context variables. In order to accommodate this assumption in the modeling framework, we define and add a relative advantage (RA) component in the class-membership model. Finally, we apply the framework to a Swiss stated preferences (SP) mode choice case study, and demonstrate the impact of accounting for non-trading behavior on the value of time estimates.

Preface. Introduction and overview. Elements of systems engineering of digital control. Sensors and instrumentation. Control elements, actuators, and displays. Computer systems hardware. Computer software. Communications. Control laws without theory. Sources of system models. Continuous-time system representations. Sampled-data system representations. Conversions of continuous time to discrete time models. System performance indicators. BIBO stability and simple tests. Nyquist stability theory. Lyapunov stability testing. Steady state response: error constants and system type. Root locus methods for analysis and design. Desirable pole locations. Bode diagrams for frequency domain analysis and design. A special control law: deadbeat control. Controllability. Controller design by pole placement. Observability. State observers. Optimal control by multiplier-type methods. Other optimal control methods. State estimation in noise. State feedback using state estimates. System identification. Adaptive and self-tuning control. Learning control. Robust control. Structures of multivariable controllers. Appendix A: Z-Transform. Appendix B: Review of matrices. Appendix C: Description of random processes. References.

The dynamics of urban growth are the direct consequence of the actions of individuals, and public and private organisations, which act to change the urban landscape simultaneously over space and time. Since previous urban form has a strong influence on the present, a prime concern of urban planners, spatial scientists and government authorities is to under‐ stand how a city has grown in the past in order to predict the growth of the city in the fu‐ ture. This requires flexible tools that allow planners to examine the impacts and potential consequences of applying different development policies, strategies and future plans [1]. However, traditional linear, static and top-down models are unable to adequately capture the processes underlying urban change. The non-linearity of spatial and temporal relation‐ ships and irregular, uncoordinated and uncontrolled local decision-making gives rise to seemingly coordinated global patterns that define the size and shape of cities in familiar ways [2-7]. Cities are now increasingly recognized as complex systems and display many of the characteristic traits of complexity, i.e. non-linearity, self-organization and emergence. Cellular Automata (CA) offer a modeling framework and a set of techniques for modelling the dynamic processes and outcomes of self-organizing systems [8-13]. Since the late 1980s they have demonstrated significant potential benefits for urban modelling through their simplicity, flexibility and transparency [8, 14-17]. CA are capable of generating complex pat‐ terns in aggregate form by using relatively simple local transition rules, i.e. by recursive de‐ velopment decisions being made at individual cells or sites [2, 15, 18]. However, cities are also influenced by global factors representing government polices (such as broader social, economic and technological factors). This has led to a number of hybrid-type urban growth models, which take into consideration local, regional and global factors [19-22]. When inte‐ grated with other technologies such as GIS and remote sensing, the potential of CA for geo‐