Load-Sharing Policies in Parallel Simulation of Agent-Based Demographic Models

Execution parallelism in agent-Based Simulation (ABS) allows to deal with complex/large-scale models. This raises the need for runtime environments able to fully exploit hardware parallelism, while jointly offering ABS-suited programming abstractions. In this paper, we target last-generation Parallel Discrete Event Simulation (PDES) platforms for multicore systems. We discuss a programming model to support both implicit (in-place access) and explicit (message passing) interactions across concurrent Logical Processes (LPs). We discuss different load-sharing policies combining event rate and implicit/explicit LPs’ interactions. We present a performance study conducted on a synthetic test case, representative of a class of agent-based models

Load-sharing policies in parallel simulation of agent-based demographic models

Execution parallelism in agent-Based Simulation (ABS) allows to deal with complex/large-scale models. This raises the need for runtime environments able to fully exploit hardware parallelism, while jointly offering ABS-suited programming abstractions. In this paper, we target last-generation Parallel Discrete Event Simulation (PDES) platforms for multicore systems. We discuss a programming model to support both implicit (in-place access) and explicit (message passing) interactions across concurrent Logical Processes (LPs). We discuss different load-sharing policies combining event rate and implicit/explicit LPs’ interactions. We present a performance study conducted on a synthetic test case, representative of a class of agent-based models.Peer ReviewedPostprint (author's final draft

On characterization of the core of lane covering games via dual solutions

The lane covering game (LCG) is a cooperative game where players cooperate to reduce the cost of cycles that cover their required lanes on a network. We discuss the possibilities/impossibilities of a complete characterization of the core via dual solutions in LCGs played among a collection of shippers, each with a number of service require-ments along some lanes, and show that such a complete characterization is possible if each shipper has at most one service requirement

A Graph-Based Web Services Discovery Framework for IoT EcoSystem

Nowadays, the Internet of Things (IoT) represents an important topic and research domain with multiple objectives. However, most IoTs communicate poorly across the multitude of network interfaces. It should be preferably used a single universal application layer protocol for the devices and services interconnection, regardless of how they are physically connected. The IoT paradigm boosts the device connectivity and the user accessibility benefits of services introduced within the network of connected objects associated with a context-awareness. Within this frame of reference, Web service is the appropriate technological approach to exhibit a set of related IoT functionalities loosely coupled with other services discovered or composed through the Web. In this work, we consider the heterogeneity of connecting technologies for IoT and the applications and devices integration in a single interoperable framework as a research objective. With this in mind, we introduce a five layers multigraph model for Web Services discovery and recommendation, and we address Web services-based applications for IoT data integration. The launched service discovery process permits the interaction between the user/application and the IoT environment. In this context, the choice of suitable services represents a challenge that covers the functionality and the required quality to combine composite services, namely mashups for IoT data management and interconnection. For proof of concept, we test a RESTful Web Services framework as an experimental platform to animate a graph-based approach for dynamic IoT services discovery. We develop a recommender system that performs graph analytics to produce a set of services according to the user's request. The quality of the recommendation process is evaluated by analyzing the correlation of user satisfaction

Analysis and Control of the Linear Threshold Model of Cascades in Large-Scale Networks. A Local Mean-Field Approach.

The spread of new ideas, behaviors and technology may exhibit cascading effects in social, economic and technological networks. These phenomena generally depend on the topology of the network as well as the nature of the local agents' dynamics. In this thesis we consider the Linear Threshold Model, deployed on random graphs. The model describes a binary activation process in a network of agents. At every iteration, each agent compares the number of active neighbors with a personal activation threshold, which determines the subsequent active or inactive state of the agent. The threshold condition can also be interpreted as a graphical game with coordination structure. In representing processes of technology adoption, it is more suitable a Permanent Activation variant of the Linear Threshold Model where active agents can never deactivate. We proved a sufficient condition under which the two version of the model coincide. We analyzed the linear threshold model on a large random network, specifically the directed configuration model with heterogeneous agents. The tree-like local structure of the random networks allows to approximate the evolution of the expected fractional activation with a recursive equation. This equation, called Local Mean-Field dynamic, describes the evolution of the expected activation on an infinite tree with the same statistical properties of the original network. We proved a concentration theorem: for a generic instance of the network, the probability that the activation process and the Local Mean Field dynamic are close converges to one exponentially fast in the network size. If the activation thresholds are constant, the analysis reduces to the study of the fixed point of a scalar autonomous system and the corresponding trajectories. This analysis gives the asymptotic extension of the activation: we observed that in networks with sufficiently heterogeneous thresholds selective activation may occur. With constant thresholds the approach can be extended to study the Permanent Activation dynamic. Remarkably, the Local Mean Field dynamic equation and the concentration theorem continue to hold when the thresholds are dynamically adjusted, making the approach amendable to the design of control strategies. We formulated an optimal control problem and we considered a simplified version on a regular network. We compared the optimal solution with two sub-optimal strategies, developed with the aim to identify an heuristics for the problem's solution. Several aspects of the research discussed in the this Dissertation can be further investigated and generalized. To mention one, the comparison of the analysis presented here with other network topologies and possibly real network data

Optimising airline maintenance scheduling decisions

Airline maintenance scheduling (AMS) studies how plans or schedules are constructed to ensure that a fleet is efficiently maintained and that airline operational demands are met. Additionally, such schedules must take into consideration the different regulations airlines are subject to, while minimising maintenance costs. In this thesis, we study different formulations, solution methods, and modelling considerations, for the AMS and related problems to propose two main contributions. First, we present a new type of multi-objective mixed integer linear programming formulation which challenges traditional time discretisation. Employing the concept of time intervals, we efficiently model the airline maintenance scheduling problem with tail assignment considerations. With a focus on workshop resource allocation and individual aircraft flight operations, and the use of a custom iterative algorithm, we solve large and long-term real-world instances (16000 flights, 529 aircraft, 8 maintenance workshops) in reasonable computational time. Moreover, we provide evidence to suggest, that our framework provides near-optimal solutions, and that inter-airline cooperation is beneficial for workshops. Second, we propose a new hybrid solution procedure to solve the aircraft recovery problem. Here, we study how to re-schedule flights and re-assign aircraft to these, to resume airline operations after an unforeseen disruption. We do so while taking operational restrictions into account. Specifically, restrictions on aircraft, maintenance, crew duty, and passenger delay are accounted for. The flexibility of the approach allows for further operational restrictions to be easily introduced. The hybrid solution procedure involves the combination of column generation with learning-based hyperheuristics. The latter, adaptively selects exact or metaheuristic algorithms to generate columns. The five different algorithms implemented, two of which we developed, were collected and released as a Python package (Torres Sanchez, 2020). Findings suggest that the framework produces fast and insightful recovery solutions

On the classification and evaluation of prefetching schemes

Abstract available: p. [2

Aeronautical engineering: A continuing bibliography with indexes (supplement 306)

This bibliography lists 181 reports, articles, and other documents recently introduced into the NASA STI Database. Subject coverage includes the following: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning

The paper introduces the application of information geometry to describe the ground states of Ising models by utilizing parity-check matrices of cyclic and quasi-cyclic codes on toric and spherical topologies. The approach establishes a connection between machine learning and error-correcting coding. This proposed approach has implications for the development of new embedding methods based on trapping sets. Statistical physics and number geometry applied for optimize error-correcting codes, leading to these embedding and sparse factorization methods. The paper establishes a direct connection between DNN architecture and error-correcting coding by demonstrating how state-of-the-art architectures (ChordMixer, Mega, Mega-chunk, CDIL, ...) from the long-range arena can be equivalent to of block and convolutional LDPC codes (Cage-graph, Repeat Accumulate). QC codes correspond to certain types of chemical elements, with the carbon element being represented by the mixed automorphism Shu-Lin-Fossorier QC-LDPC code. The connections between Belief Propagation and the Permanent, Bethe-Permanent, Nishimori Temperature, and Bethe-Hessian Matrix are elaborated upon in detail. The Quantum Approximate Optimization Algorithm (QAOA) used in the Sherrington-Kirkpatrick Ising model can be seen as analogous to the back-propagation loss function landscape in training DNNs. This similarity creates a comparable problem with TS pseudo-codeword, resembling the belief propagation method. Additionally, the layer depth in QAOA correlates to the number of decoding belief propagation iterations in the Wiberg decoding tree. Overall, this work has the potential to advance multiple fields, from Information Theory, DNN architecture design (sparse and structured prior graph topology), efficient hardware design for Quantum and Classical DPU/TPU (graph, quantize and shift register architect.) to Materials Science and beyond.Comment: 71 pages, 42 Figures, 1 Table, 1 Appendix. arXiv admin note: text overlap with arXiv:2109.08184 by other author