The ABACOC Algorithm: a Novel Approach for Nonparametric Classification of Data Streams

Stream mining poses unique challenges to machine learning: predictive models are required to be scalable, incrementally trainable, must remain bounded in size (even when the data stream is arbitrarily long), and be nonparametric in order to achieve high accuracy even in complex and dynamic environments. Moreover, the learning system must be parameterless ---traditional tuning methods are problematic in streaming settings--- and avoid requiring prior knowledge of the number of distinct class labels occurring in the stream. In this paper, we introduce a new algorithmic approach for nonparametric learning in data streams. Our approach addresses all above mentioned challenges by learning a model that covers the input space using simple local classifiers. The distribution of these classifiers dynamically adapts to the local (unknown) complexity of the classification problem, thus achieving a good balance between model complexity and predictive accuracy. We design four variants of our approach of increasing adaptivity. By means of an extensive empirical evaluation against standard nonparametric baselines, we show state-of-the-art results in terms of accuracy versus model size. For the variant that imposes a strict bound on the model size, we show better performance against all other methods measured at the same model size value. Our empirical analysis is complemented by a theoretical performance guarantee which does not rely on any stochastic assumption on the source generating the stream

Constraints on feedback processes during the formation of early-type galaxies

Galaxies are found to obey scaling relations between a number of observables. These relations follow different trends at the low- and the high-mass ends. The processes driving the curvature of scaling relations remain uncertain. In this letter, we focus on the specific family of early-type galaxies, deriving the star formation histories of a complete sample of visually classified galaxies from SDSS-DR7 over the redshift range 0.01<z<0.025, covering a stellar mass interval from 10^9 to 3 x 10^11 Msun. Our sample features the characteristic "knee" in the surface brightness vs. mass distribution at Mstar~3 x 10^10 Msun. We find a clear difference between the age and metallicity distributions of the stellar populations above and beyond this knee, which suggests a sudden transition from a constant, highly efficient mode of star formation in high-mass galaxies, gradually decreasing towards the low-mass end of the sample. At fixed mass, our early-type sample is more efficient in building up the stellar content at early times in comparison to the general population of galaxies, with half of the stars already in place by redshift z~2 for all masses. The metallicity-age trend in low-mass galaxies is not compatible with infall of metal-poor gas, suggesting instead an outflow-driven relation.Comment: 12 pages,3 figures, accepted for publication in ApJ

Towards weighing the condensation energy to ascertain the Archimedes force of vacuum

The force exerted by the gravitational field on a Casimir cavity in terms of Archimedes force of vacuum is discussed, the force that can be tested against observation is identified, and it is shown that the present technology makes it possible to perform the first experimental tests. The use of suitable high-Tc superconductors as modulators of Archimedes force is motivated. The possibility is analyzed of using gravitational wave interferometers as detectors of the force, transported through an optical spring from the Archimedes vacuum force apparatus to the gravitational interferometer test masses to maintain the two systems well separated. The use of balances to actuate and detect the force is also analyzed, the different solutions are compared, and the most important experimental issues are discussed.Comment: Revtex, 33 pages, 8 figures. In the final version, the title has been changed, and all sections have been improved, while 2 appendices have been adde

High-spectral-purity laser system for the AURIGA detector optical readout

We describe a low-frequency-noise laser system conceived for the readout of small mechanical vibrations. The system consists of a Nd:YAG source stabilized to a high-finesse Fabry–Perot cavity and achieves the best performance in the range 1–10 kHz with a minimum residual noise of 4×10-3 Hz/Hz. We perform an extended characterization of the frequency stability by means of an independent optical cavity and we also measure the residual fluctuations after transmission through an optical fiber. Our apparatus is optimized for use in an optical readout for the gravitational wave detector AURIGA, where a laser system with the characteristics reported here will allow an improvement of one order of magnitude in the detector sensitivity

Improving communication skill training in patient centered medical practice for enhancing rational use of laboratory tests: The core of bioinformation for leveraging stakeholder engagement in regulatory science.

Requests for laboratory tests are among the most relevant additional tools used by physicians as part of patient's health problemsolving. However, the overestimation of complementary investigation may be linked to less reflective medical practice as a consequence of a poor physician-patient communication, and may impair patient-centered care. This scenario is likely to result from reduced consultation time, and a clinical model focused on the disease. We propose a new medical intervention program that specifically targets improving the patient-centered communication of laboratory tests results, the core of bioinformation in health care. Expectations are that medical students training in communication skills significantly improve physicians-patient relationship, reduce inappropriate use of laboratorial tests, and raise stakeholder engagement

A comparison of CMB Angular Power Spectrum Estimators at Large Scales: the TT case

In the context of cosmic microwave background (CMB) data analysis, we compare the efficiency at large scale of two angular power spectrum algorithms, implementing, respectively, the quadratic maximum likelihood (QML) estimator and the pseudo spectrum (pseudo-Cl) estimator. By exploiting 1000 realistic Monte Carlo (MC) simulations, we find that the QML approach is markedly superior in the range l=[2-100]. At the largest angular scales, e.g. l < 10, the variance of the QML is almost 1/3 (1/2) that of the pseudo-Cl, when we consider the WMAP kq85 (kq85 enlarged by 8 degrees) mask, making the pseudo spectrum estimator a very poor option. Even at multipoles l=[20-60], where pseudo-Cl methods are traditionally used to feed the CMB likelihood algorithms, we find an efficiency loss of about 20%, when we considered the WMAP kq85 mask, and of about 15% for the kq85 mask enlarged by 8 degrees. This should be taken into account when claiming accurate results based on pseudo-Cl methods. Some examples concerning typical large scale estimators are provided.Comment: 9 pages, 7 figures. Accepted for publication in MNRA