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We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.

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Combinatorial testing is an important testing method. It requires the test cases to cover various combinations of parameters of the system under test. The test generation problem for combinatorial testing can be modeled as constructing a matrix which has certain properties. This paper first discusses two combinatorial testing criteria: covering array and orthogonal array, and then proposes a backtracking search algorithm to construct matrices satisfying them. Several search heuristics and symmetry breaking techniques are used to reduce the search time. This paper also introduces some techniques to generate large covering array instances from smaller ones. All the techniques have been implemented in a tool called EXACT (EXhaustive seArch of Combinatorial Test suites). A new optimal covering array is found by this tool.

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Search for low-spin signature inversion in the pi i(13/2) circle times nu i(13/2) bands in odd-odd Au-182,Au-184,Au-186 has been conducted through the standard in-beam gamma-spectroscopy techniques. The experiments for Au-182 and 186Au have been performed in the Japan Atomic Energy Agency (JAEA) via the Sm-152(Cl-35,5n)Au-182 and Yb-172(F-19,5n)Au-186 reactions, respectively. A study of Au-184 has been made using a multi-detector array GASP in LNL, Italy, via the Tb-159(Si-29,4n)Au-184 reaction. The pi i(13/2) circle times nu i(13/2) bands in these three nuclei have been identified and extended up to high-spin states. In particular, the inter-band connection between the pi i(13/2) nu i(13/2) band and the ground-state band in 184 Au has been established, leading to a firm spin-and-parity assignment for the pi i(13/2) circle times nu i(13/2) band. The low-spin signature inversion is found in the pi i(13/2) circle times nu i(13/2) bands in Au-182,Au-184,Au-186 according to our spin-assignment and the signature crossing observed at high-spin states.

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Search for low-spin signature inversion in the pi i(13/2) circle times nu i(13/2) bands in odd-odd Au-182,Au-184,Au-186 has been conducted through the standard in-beam gamma-spectroscopy techniques via the Sm-152(Cl-35,5n) Au-182, Yb-172(F-19,5n) (186)An, and Tb-159(Si-29,4n) (184)An reactions, respectively. The pi i(13/2) circle times nu i(13/2) bands in these three nuclei have been identified and extended up to high-spin states. In particular, the inter-band connection between the pi i(13/2) circle times nu i(13/2) band and the ground-state band in Au-184 has been established, leading to a firm spin-and-parity assignment for the pi i(13/2) circle times nu i(13/2) band. The low-spin signature inversion is found in the pi i(13/2) circle times nu i(13/2) bands according to our spin-assignment and-the signature crossing observed at high-spin states.

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Radium was radiochemically separated from natural thorium. Thin Ra-228 ->beta Ac-228 sources were prepared and exposed to mica fission track detectors, and measured by an HPGe gamma-ray detector. The beta-delayed fission events of Ac-228 were observed and its beta-delayed fission probability was found to be (5 +/- 2)x10(-12).

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The relationship between structures of complex fluorides and spectral structure of Eu(II) ion in complex fluorides (AB(m)F(n)) is investigated by means of pattern recognition methods, such as KNN, ALKNN, BAYES, LLM, SIMCA and PCA. A learning set consisting of 32 f-f transition emission host compounds and 31 d-f transition emission host compounds and a test set consisting of 27 host compounds were characterized by 12 crystal structural parameters. These parameters, i.e. features, were reduced from 12 to 6 by multiple criteria for the classification of these host compounds as f-f transition emission or d-f transition emission. A recognition rate from 79.4 to 96.8% and prediction capabilities from 85.2 to 92.6% were obtained. According to the above results, the spectral structures of Eu(II) ion in seven unknown host lattices were predicted.