Learning Deep Belief Networks from Non-Stationary Streams

Deep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by exploiting the generative properties of the model to incrementally re-train the Deep Belief Network whenever new data are collected. This approach eliminates the need to store past observations and, therefore, requires only constant memory consumption. Hence, our approach can be valuable for life-long learning from non-stationary data streams. © 2012 Springer-Verlag

An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion

© 2014 IEEE.The design of gaits and corresponding control policies for bipedal walkers is a key challenge in robot locomotion. Even when a viable controller parametrization already exists, finding near-optimal parameters can be daunting. The use of automatic gait optimization methods greatly reduces the need for human expertise and time-consuming design processes. Many different approaches to automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this paper, we present some common methods for automatic gait optimization in bipedal locomotion, and analyze their strengths and weaknesses. We experimentally evaluated these gait optimization methods on a bipedal robot, in more than 1800 experimental evaluations. In particular, we analyzed Bayesian optimization in different configurations, including various acquisition functions

Dynamical properties of a strongly correlated model for quarter-filled layered organic molecular crystals

The dynamical properties of an extended Hubbard model, which is relevant to quarter-filled layered organic molecular crystals, are analyzed. We have computed the dynamical charge correlation function, spectral density, and optical conductivity using Lanczos diagonalization and large-N techniques. As the ratio of the nearest-neighbour Coulomb repulsion, V, to the hopping integral, t, increases there is a transition from a metallic phase to a charge ordered phase. Dynamical properties close to the ordering transition are found to differ from the ones expected in a conventional metal. Large-N calculations display an enhancement of spectral weight at low frequencies as the system is driven closer to the charge ordering transition in agreement with Lanczos calculations. As V is increased the charge correlation function displays a plasmon-like mode which, for wavevectors close to (pi,pi), increases in amplitude and softens as the charge ordering transition is approached. We propose that inelastic X-ray scattering be used to detect this mode. Large-N calculations predict superconductivity with dxy symmetry close to the ordering transition. We find that this is consistent with Lanczos diagonalization calculations, on lattices of 20 sites, which find that the binding energy of two holes becomes negative close to the charge ordering transition.Comment: 22 pages, 16 eps figures; caption of Fig. 5 correcte

Bayesian Gait Optimization for Bipedal Locomotion

One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. We propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, we use Bayesian optimization to efficiently find gait parameters that optimize the desired performance metric. As a proof of concept we demonstrate that Bayesian optimization is near-optimal in a classical stochastic optimal control framework. Moreover, we validate our approach to Bayesian gait optimization on a low-cost and fragile real bipedal walker and show that good walking gaits can be efficiently found by Bayesian optimization. © 2014 Springer International Publishing

Effect of dimensionality on the charge-density-wave in few-layers 2H-NbSe2_2

We investigate the charge density wave (CDW) instability in single and double layers, as well as in the bulk 2H-NbSe$_{2}$. We demonstrate that the density functional theory correctly describes the metallic CDW state in the bulk 2H-NbSe$_{2}$. We predict that both mono- and bilayer NbSe$_{2}$ undergo a CDW instability. However, while in the bulk the instability occurs at a momentum $\mathbf{q}_{CDW}\approx{2/3}\mathbf{\Gamma M}$, in free-standing layers it occurs at $\mathbf{q}_{CDW}\approx{1/2}\mathbf{\Gamma M}$. Furthermore, while in the bulk the CDW leads to a metallic state, in a monolayer the ground state becomes semimetallic, in agreement with recent experimental data. We elucidate the key role that an enhancement of the electron-phonon matrix element at $\mathbf{q}\approx\mathbf{q}_{CDW}$ plays in forming the CDW ground state.Comment: 4 pages 5 figure

Bayesian Optimization for Learning Gaits under Uncertainty

© 2015, Springer International Publishing Switzerland.Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parametrization, finding near-optimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and time-consuming design processes. Many different approaches for automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this article, we thoroughly discuss multiple automatic optimization methods in the context of gait optimization. We extensively evaluate Bayesian optimization, a model-based approach to black-box optimization under uncertainty, on both simulated problems and real robots. This evaluation demonstrates that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments

Thermodynamic stabilities of ternary metal borides: An ab initio guide for synthesizing layered superconductors

Density functional theory calculations have been used to identify stable layered Li-$M$-B crystal structure phases derived from a recently proposed binary metal-sandwich (MS) lithium monoboride superconductor. We show that the MS lithium monoboride gains in stability when alloyed with electron-rich metal diborides; the resulting ordered Li$_{2(1-x)}M_x$B$_2$ ternary phases may form under normal synthesis conditions in a wide concentration range of $x$ for a number of group-III-V metals $M$. In an effort to pre-select compounds with the strongest electron-phonon coupling we examine the softening of the in-plane boron phonon mode at $\Gamma$ in a large class of metal borides. Our results reveal interesting general trends for the frequency of the in-plane boron phonon modes as a function of the boron-boron bond length and the valence of the metal. One of the candidates with a promise to be an MgB$_2$-type superconductor, Li$_2$AlB$_4$, has been examined in more detail: according to our {\it ab initio} calculations of the phonon dispersion and the electron-phonon coupling $\lambda$, the compound should have a critical temperature of $\sim4$ K.Comment: 10 pages, 9 figures, submitted to PR

Hyperprolactinemia induced by hCG leads to metabolic disturbances in female mice

The metabolic syndrome is a growing epidemic; it increases the risk for diabetes, cardiovascular disease, fatty liver, and several cancers. Several reports have indicated a link between hormonal imbalances and insulin resistance or obesity. Transgenic (TG) female mice overexpressing the human chorionic gonadotropin β-subunit (hCGβ+ mice) exhibit constitutively elevated levels of hCG, increased production of testosterone, progesterone and prolactin, and obesity. The objective of this study was to investigate the influence of hCG hypersecretion on possible alterations in the glucose and lipid metabolism of adult TG females. We evaluated fasting serum insulin, glucose, and triglyceride levels in adult hCGβ+ females and conducted intraperitoneal glucose and insulin tolerance tests at different ages. TG female mice showed hyperinsulinemia, hypertriglyceridemia, and dyslipidemia, as well as glucose intolerance and insulin resistance at 6 months of age. A 1-week treatment with the dopamine agonist cabergoline applied on 5-week-old hCGβ+ mice, which corrected hyperprolactinemia, hyperandrogenism, and hyperprogesteronemia, effectively prevented the metabolic alterations. These data indicate a key role of the hyperprolactinemia-induced gonadal dysfunction in the metabolic disturbances of hCGβ+ female mice. The findings prompt further studies on the involvement of gonadotropins and prolactin on metabolic disorders and might pave the way for the development of new therapeutic strategies.Fil: Ratner, Laura Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Stevens, Guillermina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Bonaventura, Maria Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Lux, Victoria Adela R.. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Poutanen, Matti. University of Turku; FinlandiaFil: Calandra, Ricardo Saul. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Huhtaniemi, Ilpo T.. University of Turku; FinlandiaFil: Rulli, Susana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentin

Chiral spin texture in the charge-density-wave phase of the correlated metallic Pb/Si(111) monolayer

We investigate the 1/3 monolayer $\alpha$-Pb/Si(111) surface by scanning tunneling spectroscopy (STS) and fully relativistic first-principles calculations. We study both the high-temperature $\sqrt{3}\times\sqrt{3}$ and low-temperature $3\times 3$ reconstructions and show that, in both phases, the spin-orbit interaction leads to an energy splitting as large as $25\%$ of the valence-band bandwidth. Relativistic effects, electronic correlations and Pb-substrate interaction cooperate to stabilize a correlated low-temperature paramagnetic phase with well-developed lower and upper Hubbard bands coexisting with $3\times3$ periodicity. By comparing the Fourier transform of STS conductance maps at the Fermi level with calculated quasiparticle interference from non-magnetic impurities, we demonstrate the occurrence of two large hexagonal Fermi sheets with in-plane spin polarizations and opposite helicities.Comment: 5 pages, 3 figure