17,414 research outputs found
q-Differential equations for q-classical polynomials and q-Jacobi-Stirling numbers
No full textWe introduce, characterise and provide a combinatorial interpretation for the so-called q-Jacobi–Stirling numbers.
This study is motivated by their key role in the (reciprocal) expansion of any power of a second order
q-differential operator having the q-classical polynomials as eigenfunctions in terms of other even order operators,
which we explicitly construct in this work. The results here obtained can be viewed as the q-version of
those given by Everitt et al. and by the first author, whilst the combinatorics of this new set of numbers is a
q-version of the Jacobi–Stirling numbers given by Gelineau and the second author
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Network Q
No full textA press release from Network Q announcing that they will begin featuring Brian McNaught, a gay columnist and author, for a monthly segment
Network Q
No full textA press release from Network Q announcing that they will begin featuring Brian McNaught, a gay columnist and author, for a monthly segment
Tobin's Q and Financial Policy
No full textRecent research in macroeconomics has emphasized the importance of linking the financial and real sectors and the need for working with optimizing models. Tobin’s Q model of investment would appear to provide a framework that can satisfy these two criteria. In contrast to the original presentation of the Q model, the formal development has not recognized that the firm actively participates in a number of financial markets; in this broader context, we show that Q is likely to be an uninformative and possibly misleading signal for investment expenditures . We then endeavor to turn this negative theoretical result to positive advantage in resolving a number of empirical problems with Q models, but the modifications dictated by the theory receive little support from the data.
A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning
No full textArtificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Discrete Mathematics and Optimizatio
Event-Based Communication in Distributed Q-Learning
No full textWe present an approach to reduce the communication of information needed on a Distributed Q-Learning system inspired by Event Triggered Control (ETC) techniques. We consider a baseline scenario of a Distributed Q-Learning problem on a Markov Decision Process (MDP). Following an event-based approach, N agents sharing a value function explore the MDP and compute a trajectory-dependent triggering signal which they use distributedly to decide when to communicate information to a central learner in charge of computing updates on the action-value function. These decision functions form an Event Based distributed Q learning system (EBd-Q), and we derive convergence guarantees resulting from the reduction of communication. We then apply the proposed algorithm to a cooperative path planning problem, and show how the agents are able to learn optimal trajectories communicating a fraction of the information. Additionally, we discuss what effects (desired and undesired) these event-based approaches have on the learning processes studied, and how they can be applied to more complex multi-agent systems.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Manuel Mazo J
Academic Writing Q&A Series 4
No full textThe presentation describes the processes that typically follow article submission. This presentation serves as the basis for a video from the Academic Writing Q&A series that was created as a supplement to the My First Scientific Article webinar. This presentation highlights the critical issues the author needs to take into account during the writing or peer review process that are essential to publishing. The content is useful for Early Career Researchers and for everyone who is interested in scholarly communication and publishing
Undecidability of Q(2)
No full textIt is shown that the compositum Q(2) of all degree 2 extensions of Q has undecidable theory.articl
Exploring a Mixed Method Approach: Simulation Games and Q Methodology
No full textIn this paper we explore the possibilities to combine two research methods we regard as being very useful when interacting with stakeholders in complex systems. We discuss a mixed research methods approach, based on the Q methodology and a simulation game. In a game design process, translating the real or reference system into the game design is an intricate process and rather challenging due to the complexity of today’s societal systems. As shown by various studies, different data techniques are proposed in order to translate reality aspects. One of the proposed data gathering techniques in combination with simulation games is Q methodology. Q methodology is a suitable method to retrieve social perspectives of stakeholders on a particular topic. Yet it is still elusive how the results of a Q methodology can be used in a game design process. In this paper, we explore the possibilities how to combine the two methods and how to translate the results of the Q analysis into a game design concept. In the context of a case within the domain of transport and logistics, we discuss how such mixed research methods approach could look like. We conclude with a future outlook on our research.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Policy Analysi
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