A Neural Networks Committee for the Contextual Bandit Problem

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.Comment: 21st International Conference on Neural Information Processin

Spectrally efficient transmit diversity scheme for differentially modulated multicarrier transmissions

Cyclic delay diversity is a simple, yet effective, transmit diversity scheme for multicarrier based transmissions employing coherent digital linear modulation schemes. It is shown that, for satisfactory operation, the scheme requires additional channel estimation overhead compared to single antenna and traditional space–time coded transmissions owing to the inherent increase in frequency selective fading. The authors analyse the additional channel estimation overhead requirement for a Hiperlan #2 style system with two transmit antennas operating in a NLOS indoor environment. The analysis shows that an additional overhead of 500% is required for the candidate system compared to a single antenna system. It is also shown that by employing differential modulation the channel estimation overhead can be eliminated with significant performance improvement compared to a system employing a practical channel estimation scheme. This novel combination, termed ‘differentially modulated cyclic delay diversity, is shown to yield a highly spectral efficient, yet simple transmit diversity solution for multi-carrier transmissions

Analysis of low-temperature direct-condensing vapor-chamber fin and conducting fin radiators

Analysis of flat, direct-condensing finned-tube space radiator with vapor chamber, and central fin tube geometries for low temperature Rankine space power electric generating syste

Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic

We consider the problem of using a heuristic policy to improve the value approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in non-adversarial settings such as planning with large-state space Markov Decision Processes. Current improvements to UCT focus on either changing the action selection formula at the internal nodes or the rollout policy at the leaf nodes of the search tree. In this work, we propose to add an auxiliary arm to each of the internal nodes, and always use the heuristic policy to roll out simulations at the auxiliary arms. The method aims to get fast convergence to optimal values at states where the heuristic policy is optimal, while retaining similar approximation as the original UCT in other states. We show that bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs better compared to the original UCT algorithm and its variants in two benchmark experiment settings. We also examine conditions under which UCT-Aux works well.Comment: 16 pages, accepted for presentation at ECML'1

Integrating Ontologies and Relational Data

In recent years, an increasing number of scientific and other domains have attempted to standardize their terminology and provide reasoning capabilities through ontologies, in order to facilitate data exchange. This has spurred research into Web-based languages, formalisms, and especially query systems based on ontologies. Yet we argue that DBMS techniques can be extended to provide many of the same capabilities, with benefits in scalability and performance. We present OWLDB, a lightweight and extensible approach for the integration of relational databases and description logic based ontologies. One of the key differences between relational databases and ontologies is the high degree of implicit information contained in ontologies. OWLDB integrates the two schemes by codifying ontologies\u27 implicit information using a set of sound and complete inference rules for SHOIN (the description logic behind OWL ontologies. These inference rules can be translated into queries on a relational DBMS instance, and the query results (representing inferences) can be added back to this database. Subsequently, database applications can make direct use of this inferred, previously implicit knowledge, e.g., in the annotation of biomedical databases. As our experimental comparison to a native description logic reasoner and a triple store shows, OWLDB provides significantly greater scalability and query capabilities, without sacrifcing performance with respect to inference

Fungos endofíticos associados a acículas de Pinus taeda.

O presente trabalho objetivou estudar os fungos endofíticos em acículas de árvores jovens de Pinus taeda L. e avaliar o efeito da posição de coleta na árvore. As amostras foram coletadas em duas alturas (30-50 cm e 100-130 cm acima do solo) e nas quatro posições cardeais (norte, sul, leste e oeste), em plantas com 18 meses de idade, localizadas em Colombo, PR, Brasil. As acículas foram submetidas a assepsia e fragmentos com 10 mm de comprimento foram plaqueados em meio BDA e incubados a 28 °C, sob fotofase de 12 h, por 15 dias. Para a identificação, as estruturas reprodutivas dos fungos foram produzidas pelo método do microcultivo. Foram isolados e identificados dezessete gêneros: Alternaria, Aspergillus, Cladosporium, Colletotrichum, Coniothyrium, Diplodia, Drechslera, Hansfordia, Monocillium, Nodulisporium, Panidio, Papulaspora, Pestalotiopsis, Phialophora, Pithomyces, Rhizoctonia e Xylaria Alguns morfotipos sem identificação foram Mycelia sterilia e fungos demaciáceos. O número de isolados da altura 30-50 cm foi significativamente maior que na outra altura. Não foi observada diferença significativa no número de isolados entre as posições cardeais de uma mesma altura. Diferenças significativas foram observadas entre os gêneros isolados e Xylaria foi o gênero mais frequente

Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation

Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot