Agent Based Modeling and Simulation: An Informatics Perspective

The term computer simulation is related to the usage of a computational model in order to improve the understanding of a system's behavior and/or to evaluate strategies for its operation, in explanatory or predictive schemes. There are cases in which practical or ethical reasons make it impossible to realize direct observations: in these cases, the possibility of realizing 'in-machina' experiments may represent the only way to study, analyze and evaluate models of those realities. Different situations and systems are characterized by the presence of autonomous entities whose local behaviors (actions and interactions) determine the evolution of the overall system; agent-based models are particularly suited to support the definition of models of such systems, but also to support the design and implementation of simulators. Agent-Based models and Multi-Agent Systems (MAS) have been adopted to simulate very different kinds of complex systems, from the simulation of socio-economic systems to the elaboration of scenarios for logistics optimization, from biological systems to urban planning. This paper discusses the specific aspects of this approach to modeling and simulation from the perspective of Informatics, describing the typical elements of an agent-based simulation model and the relevant research.Multi-Agent Systems, Agent-Based Modeling and Simulation

Plasmonic Gold Helices for the visible range fabricated by oxygen plasma purification of electron beam induced deposits

Electron beam induced deposition (EBID) currently provides the only direct writing technique for truly three-dimensional nanostructures with geometrical features below 50 nm. Unfortunately, the depositions from metal-organic precursors suffer from a substantial carbon content. This hinders many applications, especially in plasmonics where the metallic nature of the geometric surfaces is mandatory. To overcome this problem a post-deposition treatment with oxygen plasma at room temperature was investigated for the purification of gold containing EBID structures. Upon plasma treatment, the structures experience a shrinkage in diameter of about 18 nm but entirely keep their initial shape. The proposed purification step results in a core-shell structure with the core consisting of mainly unaffected EBID material and a gold shell of about 20 nm in thickness. These purified structures are plasmonically active in the visible wavelength range as shown by dark field optical microscopy on helical nanostructures. Most notably, electromagnetic modeling of the corresponding scattering spectra verified that the thickness and quality of the resulting gold shell ensures an optical response equal to that of pure gold nanostructures

TOWARD A PLATFORM FOR MULTI-LAYERED MULTI-AGENT SITUATED SYSTEM (MMASS)-BASED SIMULATIONS: FOCUSING ON FIELD DIFFUSION

The paper introduces some issues and related solutions adopted in order to realize the MMASS platform. This is a framework to specify and execute simulation applications based on the multilayered multi-agent situated system model (MMASS). MMASS is a model for multi-agent systems (MAS) situated in an environment whose structure is explicitly defined. The behavior and interaction models of MMASS agents are strongly related to the spatial structure of their environment. The MMASS model is the result of a long-term research that has its roots and motivations on application domains and problems that require spatial features to be considered. Our experiences with these problems have concerned the design of domain models and their implementations, according to the MAS approach for simulation purposes. This activity has revealed that currently available tools do not support the management of spatial features of agent environment and interaction mechanisms defined by the MMASS model and thus they are not suitable for our purposes. The paper focuses on the MMASS platform that aims to support the specification and development of applications (mainly, simulations) based on MMASS. Design issues and related solutions that have been adopted in order to manage those aspects that characterize the MMASS model will be shown. After a description of the conceptual model that underlies the MMASS platform and its general architecture, we will overview how the platform supports the specification of agent structured environment, behavior and interaction, and how it supports the execution of agent actions and interactions. Then we will describe issues and adopted solutions (both algorithmic and implementative ones) to manage at-a-distance interaction among MMASS agents

hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications

With the recent success of artificial intelligence in neuroscience, a number of deep learning (DL) models were proposed for classification, anomaly detection, and pattern recognition tasks in electroencephalography (EEG). EEG is a multi-channel time-series that provides information about the individual brain activity for diagnostics, neuro-rehabilitation, and other applications (including emotions recognition). Two main issues challenge the existing DL-based modeling methods for EEG: the high variability between subjects and the low signal-to-noise ratio making it difficult to ensure a good quality in the EEG data. In this paper, we propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction. We properly designed their architectures using the blocks of the well-known EEGNet as the encoder, and proposed a loss function based on dynamic time warping. We tested the models on the public Dataset 2a - BCI Competition IV, where EEG was collected from 9 subjects and 22 channels. hvEEGNet was found to reconstruct the EEG data with very high-fidelity, outperforming most previous solutions (including our vEEGNet-ver3 ). Furthermore, this was consistent across all subjects. Interestingly, hvEEGNet made it possible to discover that this popular dataset includes a number of corrupted EEG recordings that might have influenced previous literature results. We also investigated the training behaviour of our models and related it with the quality and the size of the input EEG dataset, aiming at opening a new research debate on this relationship. In the future, hvEEGNet could be used as anomaly (e.g., artefact) detector in large EEG datasets to support the domain experts, but also the latent representations it provides could be used in other classification problems and EEG data generation

The influence of population size in geometric semantic GP

In this work, we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming for the task of symbolic regression. A large set of experiments, considering different population size values on different regression problems, has been performed. Results show that, on real-life problems, having small populations results in a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, performance on the test instances varies among the different problems: in datasets with a high number of features, models obtained with large populations present a better performance on unseen data, while in datasets characterized by a relative small number of variables a better generalization ability is achieved by using small population size values. When synthetic problems are taken into account, large population size values represent the best option for achieving good quality solutions on both training and test instances

P2-037: Estrogen and progesterone receptors in women with non-small-cell lung cancer: a potential therapeutic target?

BACKGROUND: Thyroid dysfunction and thyroid autoimmunity are prevalent among women of reproductive age and are associated with adverse pregnancy outcomes. Preconception or early pregnancy screening for thyroid dysfunction has been proposed but is not widely accepted. We conducted a systematic review of the literature on the clinical significance of thyroid dysfunction and thyroid autoimmunity before conception and in early pregnancy. METHODS: Relevant studies were identified by searching Medline, EMBASE and the Cochrane Controlled Trials Register. RESULTS: From a total of 14 208 primary selected titles, 43 articles were included for the systematic review and 38 were appropriate for meta-analyses. No articles about hyperthyroidism were selected. Subclinical hypothyroidism in early pregnancy, compared with normal thyroid function, was associated with the occurrence of pre-eclampsia [odds ratio (OR) 1.7, 95% confidence interval (CI) 1.1-2.6] and an increased risk of perinatal mortality (OR 2.7, 95% CI 1.6-4.7). In the meta-analyses, the presence of thyroid antibodies was associated with an increased risk of unexplained subfertility (OR 1.5, 95% CI 1.1-2.0), miscarriage (OR 3.73, 95% CI 1.8-7.6), recurrent miscarriage (OR 2.3, 95% CI 1.5-3.5), preterm birth (OR 1.9, 95% CI 1.1-3.5) and maternal post-partum thyroiditis (OR 11.5, 95% CI 5.6-24) when compared with the absence of thyroid antibodies. CONCLUSIONS: Pregnant women with subclinical hypothyroidism or thyroid antibodies have an increased risk of complications, especially pre-eclampsia, perinatal mortality and (recurrent) miscarriage. Future research, within the setting of clinical trials, should focus on the potential health gain of identification, and effect of treatment, of thyroid disease on pregnancy outcome

Computational methods for resting-state EEG of patients with disorders of consciousness

Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application

The impact of disease extent and severity detected by quantitative ultrasound analysis in the diagnosis and outcome of giant cell arteritis

© The Author(s) 2019. Published by Oxford University Press on behalf of the British Society for Rheumatology.Objectives: To develop a quantitative score based on colour duplex sonography (CDS) to predict the diagnosis and outcome of GCA. Methods; We selected patients with positive CDS and confirmed diagnosis of GCA recruited into the TA Biopsy (TAB) vs Ultrasound in Diagnosis of GCA (TABUL) study and in a validation, independent cohort. We fitted four CDS models including combinations of the following: number and distribution of halos at the TA branches, average and maximum intima–media thickness of TA and axillary arteries. We fitted four clinical/laboratory models. The combined CDS and clinical models were used to develop a score to predict risk of positive TAB and clinical outcome at 6 months. Results: We included 135 GCA patients from TABUL (female: 68%, age 73 (8) years) and 72 patients from the independent cohort (female: 46%, age 75 (7) years). The best-fitting CDS model for TAB used maximum intima–media thickness size and bilaterality of TA and axillary arteries’ halos. The best-fitting clinical model included raised inflammatory markers, PMR, headache and ischaemic symptoms. By combining CDS and clinical models we derived a score to compute the probability of a positive TAB. Model discrimination was fair (area under the receiver operating characteristic curve 0.77, 95% CI: 0.68, 0.84). No significant association was found for prediction of clinical outcome at 6 months. Conclusion: A quantitative analysis of CDS and clinical characteristics is useful to identify patients with a positive biopsy, supporting the use of CDS as a surrogate tool to replace TAB. No predictive role was found for worse prognosis.info:eu-repo/semantics/publishedVersio