18,423 research outputs found
Data-driven approximations of dynamical systems operators for control
The Koopman and Perron Frobenius transport operators are fundamentally
changing how we approach dynamical systems, providing linear representations
for even strongly nonlinear dynamics. Although there is tremendous potential
benefit of such a linear representation for estimation and control, transport
operators are infinite-dimensional, making them difficult to work with
numerically. Obtaining low-dimensional matrix approximations of these operators
is paramount for applications, and the dynamic mode decomposition has quickly
become a standard numerical algorithm to approximate the Koopman operator.
Related methods have seen rapid development, due to a combination of an
increasing abundance of data and the extensibility of DMD based on its simple
framing in terms of linear algebra. In this chapter, we review key innovations
in the data-driven characterization of transport operators for control,
providing a high-level and unified perspective. We emphasize important recent
developments around sparsity and control, and discuss emerging methods in big
data and machine learning.Comment: 37 pages, 4 figure
When Machine Learning Meets Big Data: A Wireless Communication Perspective
We have witnessed an exponential growth in commercial data services, which
has lead to the 'big data era'. Machine learning, as one of the most promising
artificial intelligence tools of analyzing the deluge of data, has been invoked
in many research areas both in academia and industry. The aim of this article
is twin-fold. Firstly, we briefly review big data analysis and machine
learning, along with their potential applications in next-generation wireless
networks. The second goal is to invoke big data analysis to predict the
requirements of mobile users and to exploit it for improving the performance of
"social network-aware wireless". More particularly, a unified big data aided
machine learning framework is proposed, which consists of feature extraction,
data modeling and prediction/online refinement. The main benefits of the
proposed framework are that by relying on big data which reflects both the
spectral and other challenging requirements of the users, we can refine the
motivation, problem formulations and methodology of powerful machine learning
algorithms in the context of wireless networks. In order to characterize the
efficiency of the proposed framework, a pair of intelligent practical
applications are provided as case studies: 1) To predict the positioning of
drone-mounted areal base stations (BSs) according to the specific tele-traffic
requirements by gleaning valuable data from social networks. 2) To predict the
content caching requirements of BSs according to the users' preferences by
mining data from social networks. Finally, open research opportunities are
identified for motivating future investigations.Comment: This article has been accepted by IEEE Vehicular Technology Magazin
Institutional Metaphors for Designing Large-Scale Distributed AI versus AI Techniques for Running Institutions
Artificial Intelligence (AI) started out with an ambition to reproduce the
human mind, but, as the sheer scale of that ambition became apparent, quickly
retreated into either studying specialized intelligent behaviours, or proposing
overarching architectural concepts for interfacing specialized intelligent
behaviour components, conceived of as agents in a kind of organization. This
agent-based modeling paradigm, in turn, proves to have interesting applications
in understanding, simulating, and predicting the behaviour of social and legal
structures on an aggregate level. This chapter examines a number of relevant
cross-cutting concerns, conceptualizations, modeling problems and design
challenges in large-scale distributed Artificial Intelligence, as well as in
institutional systems, and identifies potential grounds for novel advances.Comment: invited chapter, before proofrea
Network of Bandits insure Privacy of end-users
In order to distribute the best arm identification task as close as possible
to the user's devices, on the edge of the Radio Access Network, we propose a
new problem setting, where distributed players collaborate to find the best
arm. This architecture guarantees privacy to end-users since no events are
stored. The only thing that can be observed by an adversary through the core
network is aggregated information across users. We provide a first algorithm,
Distributed Median Elimination, which is optimal in term of number of
transmitted bits and near optimal in term of speed-up factor with respect to an
optimal algorithm run independently on each player. In practice, this first
algorithm cannot handle the trade-off between the communication cost and the
speed-up factor, and requires some knowledge about the distribution of players.
Extended Distributed Median Elimination overcomes these limitations, by playing
in parallel different instances of Distributed Median Elimination and selecting
the best one. Experiments illustrate and complete the analysis. According to
the analysis, in comparison to Median Elimination performed on each player, the
proposed algorithm shows significant practical improvements
Inside the brain of an elite athlete: The neural processes that support high achievement in sports
Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance
A Survey on Matrix Completion: Perspective of Signal Processing
Matrix completion (MC) is a promising technique which is able to recover an
intact matrix with low-rank property from sub-sampled/incomplete data. Its
application varies from computer vision, signal processing to wireless network,
and thereby receives much attention in the past several years. There are plenty
of works addressing the behaviors and applications of MC methodologies. This
work provides a comprehensive review for MC approaches from the perspective of
signal processing. In particular, the MC problem is first grouped into six
optimization problems to help readers understand MC algorithms. Next, four
representative types of optimization algorithms solving the MC problem are
reviewed. Ultimately, three different application fields of MC are described
and evaluated.Comment: 12 pages, 9 figure
Software tools for the cognitive development of autonomous robots
Robotic systems are evolving towards higher degrees of autonomy. This paper reviews the cognitive tools available nowadays for the fulfilment of abstract or long-term goals as well as for learning and modifying their behaviour.Peer ReviewedPostprint (author's final draft
Matters of Gravity, the newsletter of the APS Topical Group on Gravitation, Fall 2002
Contents:
* Community news:
Einstein prize update, by Clifford Will
World year of physics, by Richard H. Price
We hear that... by Jorge Pullin
* Research briefs:
R-mode epitaph? by John Friedman and Nils Andersson
Gravitational waves from bumpy neutron stars, by Ben Owen
LIGO science operations begin!, by Gary Sanders
* Conference reports:
Fourth international LISA symposium, by Peter Bender
Initial Data for Binary Systems, by Gregory Cook
Report on Joint LSC/Source Modeling Meeting, by Patrick Brady
Greek Relativity Conference, NEB-X by Kostas Kokkotas and Nick Stergioulas
Gravity, Astrophysics, and Strings @ the Black Sea, by Plamen Fiziev
Quantum field theory at ESI, by Robert Wald
School on quantum gravity in Chile, by Don Marolf
Apples with apples workshop, by Miguel Alcubierre
Radiation reaction focus session and 5th Capra meeting, by Eanna Flanagan
Numerical Relativity Workshop at IMA, by Manuel TiglioComment: 37 pages, LaTeX with html.sty and psfig, 2 figures. Jorge Pullin
(editor). PDF and html versions in http://www.phys.lsu.edu/mo
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Progress in AI Planning Research and Applications
Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning
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