9,323 research outputs found
Learning Automata-Based Object Partitioning with Pre-Specified Cardinalities
Master's thesis in Information- and communication technology (IKT591)The Object Migrating Automata (OMA) has been used as a powerful AI-based tool to resolve real-life partitioning problems. Apart from its original version, variants and enhancements that invoke the pursuit concept of Learning Automata, and the phenomena of transitivity, have more recently been used to improve its power. The single major handicap that it possesses is the fact that the number of the objects in each partition must be equal. This thesis deals with the task of relaxing this constraint. Thus, in this thesis, we will consider the problem of designing OMA-based schemes when the number of the objects can be unequal, but prespecified. By opening ourselves to this less-constrained version, we encounter a few problems that deal with the implementation of the inter-partition migration of the objects. This thesis considers how these problems can be solved, and in essence, presents the design, implementation and testing of two OMA-based methods and all its variants, that include the pursuit and transitivity phenomena
Futures Studies in the Interactive Society
This book consists of papers which were prepared within the framework of the research project (No. T 048539) entitled Futures Studies in the Interactive Society (project leader: Éva Hideg) and funded by the Hungarian Scientific Research Fund (OTKA) between 2005 and 2009. Some discuss the theoretical and methodological questions of futures studies and foresight; others present new approaches to or
procedures of certain questions which are very important and topical from the perspective of forecast and foresight practice. Each study was conducted in pursuit of improvement in futures fields
User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution
Author's accepted manuscriptIn this paper, we present a pioneering solution to the problem of user grouping and power allocation in non-orthogonal multiple access (NOMA) systems. The problem is highly pertinent because NOMA is a well-recognized technique for future mobile radio systems. The salient and difcult issues associated with NOMA systems involve the task of grouping users together into the prespecifed time slots, which are augmented with the question of determining how much power should be allocated to the respective users. This problem is, in and of itself, NP-hard. Our solution is the frst reported reinforcement learning (RL)-based solution, which attempts to resolve parts of this issue. In particular, we invoke the object migration automaton (OMA) and one of its variants to resolve the grouping in NOMA systems. Furthermore, unlike the solutions reported in the literature, we do not assume prior knowledge of the channels’ distributions, nor of their coefcients, to achieve the grouping/partitioning. Thereafter, we use the consequent groupings to heuristically infer the power allocation. The simulation results that we have obtained confrm that our learning scheme can follow the dynamics of the channel coefcients efciently, and that the solution is able to resolve the issue dynamicallyacceptedVersio
Learning-based run-time power and energy management of multi/many-core systems: current and future trends
Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the everincreasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Pathetic Politics: An Analysis Of Emotion And Embodiment In First Lady Rhetoric
This dissertation explores the theoretical and practical relationship between our understandings of emotion’s role in political decision-making. In this pursuit I seek a resurrection for pathos’s legitimacy in persuasive studies through the pursuit of the pathetic political realm. This work has three primary concerns: how may pathetic power be accessed, from where does this power originate, and how might political actors enact this power for their own political goals. I draw primarily from theories related to visual rhetoric and the body in order to provide perspective on how the body is politicized through the pathetic realm. Theoretical perspectives are drawn from scholars like Aristotle, Robert Hariman, and Susan Bordo to extricate the positioning of the body within American politics
Partition strategies for incremental Mini-Bucket
Los modelos en grafo probabilĂsticos, tales como los campos aleatorios de
Markov y las redes bayesianas, ofrecen poderosos marcos de trabajo para la
representaciĂłn de conocimiento y el razonamiento en modelos con gran nĂşmero
de variables. Sin embargo, los problemas de inferencia exacta en modelos de
grafos son NP-hard en general, lo que ha causado que se produzca bastante
interés en métodos de inferencia aproximados.
El mini-bucket incremental es un marco de trabajo para inferencia aproximada
que produce como resultado lĂmites aproximados inferior y superior de la
funciĂłn de particiĂłn exacta, a base de -empezando a partir de un modelo con
todos los constraints relajados, es decir, con las regiones más pequeñas posibleincrementalmente
añadir regiones más grandes a la aproximación. Los métodos
de inferencia aproximada que existen actualmente producen lĂmites superiores
ajustados de la funciĂłn de particiĂłn, pero los lĂmites inferiores suelen ser demasiado
imprecisos o incluso triviales.
El objetivo de este proyecto es investigar estrategias de particiĂłn que mejoren
los lĂmites inferiores obtenidos con el algoritmo de mini-bucket, trabajando dentro
del marco de trabajo de mini-bucket incremental.
Empezamos a partir de la idea de que creemos que deberĂa ser beneficioso
razonar conjuntamente con las variables de un modelo que tienen una alta correlaciĂłn,
y desarrollamos una estrategia para la selecciĂłn de regiones basada en
esa idea. Posteriormente, implementamos nuestra estrategia y exploramos formas
de mejorarla, y finalmente medimos los resultados obtenidos usando nuestra
estrategia y los comparamos con varios métodos de referencia.
Nuestros resultados indican que nuestra estrategia obtiene lĂmites inferiores
más ajustados que nuestros dos métodos de referencia. También consideramos
y descartamos dos posibles hipĂłtesis que podrĂan explicar esta mejora.Els models en graf probabilĂstics, com bĂ© els camps aleatoris de Markov i les
xarxes bayesianes, ofereixen poderosos marcs de treball per la representaciĂł
del coneixement i el raonament en models amb grans quantitats de variables.
Tanmateix, els problemes d’inferència exacta en models de grafs son NP-hard
en general, el qual ha provocat que es produeixi bastant d’interès en mètodes
d’inferència aproximats.
El mini-bucket incremental es un marc de treball per a l’inferència aproximada
que produeix com a resultat lĂmits aproximats inferior i superior de la
funció de partició exacta que funciona començant a partir d’un model al qual
se li han relaxat tots els constraints -és a dir, un model amb les regions més
petites possibles- i anar afegint a l’aproximació regions incrementalment més
grans. Els mètodes d’inferència aproximada que existeixen actualment produeixen
lĂmits superiors ajustats de la funciĂł de particiĂł. Tanmateix, els lĂmits
inferiors acostumen a ser massa imprecisos o fins aviat trivials.
El objectiu d’aquest projecte es recercar estratègies de partició que millorin
els lĂmits inferiors obtinguts amb l’algorisme de mini-bucket, treballant dins del
marc de treball del mini-bucket incremental.
La nostra idea de partida pel projecte es que creiem que hauria de ser beneficiĂłs
per la qualitat de l’aproximació raonar conjuntament amb les variables del
model que tenen una alta correlació entre elles, i desenvolupem una estratègia
per a la selecciĂł de regions basada en aquesta idea. Posteriorment, implementem
la nostra estratègia i explorem formes de millorar-la, i finalment mesurem els
resultats obtinguts amb la nostra estratègia i els comparem a diversos mètodes
de referència.
Els nostres resultats indiquen que la nostra estratègia obtĂ© lĂmits inferiors
més ajustats que els nostres dos mètodes de referència. També considerem i
descartem dues possibles hipòtesis que podrien explicar aquesta millora.Probabilistic graphical models such as Markov random fields and Bayesian networks
provide powerful frameworks for knowledge representation and reasoning
over models with large numbers of variables. Unfortunately, exact inference
problems on graphical models are generally NP-hard, which has led to signifi-
cant interest in approximate inference algorithms.
Incremental mini-bucket is a framework for approximate inference that provides
upper and lower bounds on the exact partition function by, starting from
a model with completely relaxed constraints, i.e. with the smallest possible
regions, incrementally adding larger regions to the approximation. Current
approximate inference algorithms provide tight upper bounds on the exact partition
function but loose or trivial lower bounds.
This project focuses on researching partitioning strategies that improve the
lower bounds obtained with mini-bucket elimination, working within the framework
of incremental mini-bucket.
We start from the idea that variables that are highly correlated should be
reasoned about together, and we develop a strategy for region selection based
on that idea. We implement the strategy and explore ways to improve it, and
finally we measure the results obtained using the strategy and compare them to
several baselines.
We find that our strategy performs better than both of our baselines. We
also rule out several possible explanations for the improvement
HoPP: Robust and Resilient Publish-Subscribe for an Information-Centric Internet of Things
This paper revisits NDN deployment in the IoT with a special focus on the
interaction of sensors and actuators. Such scenarios require high
responsiveness and limited control state at the constrained nodes. We argue
that the NDN request-response pattern which prevents data push is vital for IoT
networks. We contribute HoP-and-Pull (HoPP), a robust publish-subscribe scheme
for typical IoT scenarios that targets IoT networks consisting of hundreds of
resource constrained devices at intermittent connectivity. Our approach limits
the FIB tables to a minimum and naturally supports mobility, temporary network
partitioning, data aggregation and near real-time reactivity. We experimentally
evaluate the protocol in a real-world deployment using the IoT-Lab testbed with
varying numbers of constrained devices, each wirelessly interconnected via IEEE
802.15.4 LowPANs. Implementations are built on CCN-lite with RIOT and support
experiments using various single- and multi-hop scenarios
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