CFD modeling of all gas-liquid and vapor-liquid flow regimes predicted by the Baker chart

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Time Consistent Pareto Solutions in Common Access Resource Games with Asymmetric Players

In the analysis of equilibrium policies in a dierential game, if agents have different time preference rates, the cooperative (Pareto optimum) solution obtained by applying the Pontryagin's Maximum Principle becomes time inconsistent. In this work we derive a set of dynamic programming equations (in discrete and continuous time) whose solutions are time consistent equilibrium rules for N-player cooperative dierential games in which agents dier in their instantaneous utility functions and also in their discount rates of time preference. The results are applied to the study of a cake-eating problem describing the management of a common property exhaustible natural resource. The extension of the results to a simple common property renewable natural resource model in innite horizon is also discussed.cooperative solutions, dierential games, asymmetric players, resource games, time-inconsistency, heterogeneous discount rates

Heterogeneous discounting in consumption-investment problems. Time consistent solutions

In this paper we analyze a stochastic continuous time model in finite horizon in which agents discount the instantaneous utility function and the final function at constant but different instantaneous discount rates of time preference. Within this context we can model problems in which, when the time t approaches to the final time, the valuation of the final function increases compared with previous valuations in a way that cannot be explained by using a unique constant or a variable discount rate. We derive a dynamic programming equation whose solutions are time-consistent Markov equilibria. For this class of time preferences, we study the classical consumption and portfolio rules model (Merton, 1971) for CRRA and CARA utility functions for time- consistent agents, and we compare the different equilibria with the time-inconsistent solutions. The introduction of stochastic terminal time is also discussed.dynamic programming, consumption and portfolio rules, heterogeneous discounting, time consistency

Low-temperature magnetization in geometrically frustrated Tb2Ti2O7

The nature of the low temperature ground state of the pyrochlore compound Tb2Ti2O7 remains a puzzling issue. Dynamic fluctuations and short-range correlations persist down to 50 mK, as evidenced by microscopic probes. In parallel, magnetization measurements show irreversibilities and glassy behavior below 200 mK. We have performed magnetization and AC susceptibility measurements on four single crystals down to 57 mK. We did not observe a clear plateau in the magnetization as a function of field along the [111] direction, as suggested by the quantum spin ice model. In addition to a freezing around 200 mK, slow dynamics are observed in the AC susceptibility up to 4 K. The overall frequency dependence cannot be described by a canonical spin-glass behavior.Comment: 5 pages, 4 figures + Supp. Mat (3 pages, 5 figures

Room temperature GW bar detector with opto-mechanical readout

We present the full implementation of a room-temperature gravitational wave bar detector equipped with an opto-mechanical readout. The mechanical vibrations are read by a Fabry--Perot interferometer whose length changes are compared with a stable reference optical cavity by means of a resonant laser. The detector performance is completely characterized in terms of spectral sensitivity and statistical properties of the fluctuations in the system output signal. The new kind of readout technique allows for wide-band detection sensitivity and we can accurately test the model of the coupled oscillators for thermal noise. Our results are very promising in view of cryogenic operation and represent an important step towards significant improvements in the performance of massive gravitational wave detectors.Comment: 7 figures, submitted to Phys. Rev.

Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm