2,975 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence
We discuss the emerging new opportunity for building feedback-rich
computational models of social systems using generative artificial
intelligence. Referred to as Generative Agent-Based Models (GABMs), such
individual-level models utilize large language models such as ChatGPT to
represent human decision-making in social settings. We provide a GABM case in
which human behavior can be incorporated in simulation models by coupling a
mechanistic model of human interactions with a pre-trained large language
model. This is achieved by introducing a simple GABM of social norm diffusion
in an organization. For educational purposes, the model is intentionally kept
simple. We examine a wide range of scenarios and the sensitivity of the results
to several changes in the prompt. We hope the article and the model serve as a
guide for building useful diffusion models that include realistic human
reasoning and decision-making
Refounding of Activity Concept ? Towards a Federative Paradigm for Modeling and Simulation
Journal : Simulation, Transactions of the Society for Modeling and Simulation InternationalInternational audienceCurrently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accord-ingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epis-temology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the new simulation activity definition is first applied critically. Then, activity is discussed generally. In epistemology, activity is discussed, in a prospective way, as a possible framework in models of human beliefs and knowledge
Collective predator evasion: Putting the criticality hypothesis to the test
According to the criticality hypothesis, collective biological systems should
operate in a special parameter region, close to so-called critical points,
where the collective behavior undergoes a qualitative change between different
dynamical regimes. Critical systems exhibit unique properties, which may
benefit collective information processing such as maximal responsiveness to
external stimuli. Besides neuronal and gene-regulatory networks, recent
empirical data suggests that also animal collectives may be examples of
self-organized critical systems. However, open questions about
self-organization mechanisms in animal groups remain: Evolutionary adaptation
towards a group-level optimum (group-level selection), implicitly assumed in
the "criticality hypothesis", appears in general not reasonable for
fission-fusion groups composed of non-related individuals. Furthermore,
previous theoretical work relies on non-spatial models, which ignore
potentially important self-organization and spatial sorting effects. Using a
generic, spatially-explicit model of schooling prey being attacked by a
predator, we show first that schools operating at criticality perform best.
However, this is not due to optimal response of the prey to the predator, as
suggested by the "criticality hypothesis", but rather due to the spatial
structure of the prey school at criticality. Secondly, by investigating
individual-level evolution, we show that strong spatial self-sorting effects at
the critical point lead to strong selection gradients, and make it an
evolutionary unstable state. Our results demonstrate the decisive role of
spatio-temporal phenomena in collective behavior, and that individual-level
selection is in general not a viable mechanism for self-tuning of unrelated
animal groups towards criticality
Shear-promoted drug encapsulation into red blood cells: a CFD model and ÎĽ-PIV analysis
The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (ÎĽ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Vocal Interactivity in-and-between Humans, Animals, and Robots
Almost all animals exploit vocal signals for a range of ecologically motivated purposes: detecting predators/prey and marking territory, expressing emotions, establishing social relations, and sharing information. Whether it is a bird raising an alarm, a whale calling to potential partners, a dog responding to human commands, a parent reading a story with a child, or a business-person accessing stock prices using Siri, vocalization provides a valuable communication channel through which behavior may be coordinated and controlled, and information may be distributed and acquired. Indeed, the ubiquity of vocal interaction has led to research across an extremely diverse array of fields, from assessing animal welfare, to understanding the precursors of human language, to developing voice-based human–machine interaction. Opportunities for cross-fertilization between these fields abound; for example, using artificial cognitive agents to investigate contemporary theories of language grounding, using machine learning to analyze different habitats or adding vocal expressivity to the next generation of language-enabled autonomous social agents. However, much of the research is conducted within well-defined disciplinary boundaries, and many fundamental issues remain. This paper attempts to redress the balance by presenting a comparative review of vocal interaction within-and-between humans, animals, and artificial agents (such as robots), and it identifies a rich set of open research questions that may benefit from an interdisciplinary analysis
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