Statistical Mechanics of Linear and Nonlinear Time-Domain Ensemble Learning

Conventional ensemble learning combines students in the space domain. In this paper, however, we combine students in the time domain and call it time-domain ensemble learning. We analyze, compare, and discuss the generalization performances regarding time-domain ensemble learning of both a linear model and a nonlinear model. Analyzing in the framework of online learning using a statistical mechanical method, we show the qualitatively different behaviors between the two models. In a linear model, the dynamical behaviors of the generalization error are monotonic. We analytically show that time-domain ensemble learning is twice as effective as conventional ensemble learning. Furthermore, the generalization error of a nonlinear model features nonmonotonic dynamical behaviors when the learning rate is small. We numerically show that the generalization performance can be improved remarkably by using this phenomenon and the divergence of students in the time domain.Comment: 11 pages, 7 figure

Clathrate type 2 hydrate formation in vacuo under astrophysical conditions

The properties of clathrate hydrates were used to explain the complex and poorly understood physical processes taking place within cometary nuclei and other icy solar system bodies. Most of all the experiments previously conducted used starting compositions which would yield clathrate types I hydrates. The main criterion for type I vs. type II clathrate hydrate formation is the size of the guest molecule. The stoichiometry of the two structure types is also quite different. In addition, the larger molecules which would form type II clathrate hydrates typically have lower vapor pressures. The result of these considerations is that at temperatures where we identified clathrate formation (120-130 K), it is more likely that type II clathrate hydrates will form. We also formed clathrate II hydrates of methanol by direct vapor deposition in the temperature range 125-135 K

Low-pressure clathrate-hydrate formation in amorphous astrophysical ice analogs

In modeling cometary ice, the properties of clathrate hydrates were used to explain anomalous gas release at large radial distances from the Sun, and the retention of particular gas inventories at elevated temperatures. Clathrates may also have been important early in solar system history. However, there has never been a reasonable mechanism proposed for clathrate formation under the low pressures typical of these environments. For the first time, it was shown that clathrate hydrates can be formed by warming and annealing amorphous mixed molecular ices at low pressures. The complex microstructures which occur as a result of clathrate formation from the solid state may provide an explanation for a variety of unexplained phenomena. The vacuum and imaging systems of an Hitachi H-500H Analytical Electron Microscope was modified to study mixed molecular ices at temperatures between 12 and 373 K. The resulting ices are characterized by low-electron dose Transmission Electron Microscopy (TEM) and Selected Area Electron Diffraction (SAED). The implications of these results for the mechanical and gas release properties of comets are discussed. Laboratory IR data from similar ices are presented which suggest the possibility of remotely observing and identifying clathrates in astrophysical objects

Are There Oscillations in the Baryon/Meson Ratio?

All available data indicate a surplus of baryon states over meson states for energies greater than about 1.5 GeV. Since hadron-scale string theory suggests that their numbers should become equal with increasing energy, it has recently been proposed that there must exist exotic mesons with masses just above 1.7 GeV in order to fill the deficit. We demonstrate that a string-like picture is actually consistent with the present numbers of baryon and meson states, and in fact predicts regular oscillations in their ratio. This suggests a different role for new hadronic states.Comment: 14 pages (RevTeX), McGill/92-0

Learning preferences for large scale multi-label problems

Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable. The main contribution of this work is the proposal of a novel general online preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task

The Effect of High-Intensity Intermittent Exercise on Body Composition of Overweight Young Males

To determine the effect of a 12-week high intensity intermittent exercise (HIIE) intervention on total body, abdominal, trunk, visceral fat mass, and fat free mass of young overweight males. Participants were randomly assigned to either exercise or control group. The intervention group received HIIE three times per week, 20 min per session, for 12 weeks. Aerobic power improved significantly (P < 0.001) by 15% for the exercising group. Exercisers compared to controls experienced significant weight loss of 1.5 kg (P < 0.005) and a significant reduction in total fat mass of 2 kg (P < 0.001). Abdominal and trunk adiposity was also significantly reduced in the exercising group by 0.1 kg (P < 0.05) and 1.5 kg (P < 0.001). Also the exercise group had a significant (P < 0.01) 17% reduction in visceral fat after 12 weeks of HIIE, whereas waist circumference was significantly decreased by week six (P < 0.001). Fat free mass was significantly increased (P < 0.05) in the exercising group by 0.4 kg for the leg and 0.7 kg for the trunk. No significant change (P > 0.05) occurred in levels of insulin, HOMA-IR, and blood lipids. Twelve weeks of HIIE resulted in significant reductions in total, abdominal, trunk, and visceral fat and significant increases in fat free mass and aerobic power

Elucidating Surface Structure with Action Spectroscopy

Surface Action Spectroscopy, a vibrational spectroscopy method developed in recent years at the Fritz Haber Institute is employed for structure determination of clean and H2O-dosed (111) magnetite surfaces. Surface structural information is revealed by using the microscopic surface vibrations as a fingerprint of the surface structure. Such vibrations involve just the topmost atomic layers, and therefore the structural information is truly surface related. Our results strongly support the view that regular Fe3O4(111)/Pt(111) is terminated by the so-called Fetet1 termination, that the biphase termination of Fe3O4(111)/Pt(111) consists of FeO and Fe3O4(111) terminated areas, and we show that the method can differentiate between different water structures in H2O-derived adsorbate layers on Fe3O4(111)/Pt(111). With this, we conclude that the method is a capable new member in the set of techniques providing crucial information to elucidate surface structures. The method does not rely on translational symmetry and can therefore also be applied to systems which are not well ordered. Even an application to rough surfaces is possible

Water and Carbon Dioxide Adsorption on CaO(001) Studied via Single Crystal Adsorption Calorimetry

A new method to analyze microcalorimetry data was employed to study the adsorption energies and sticking probabilities of D2O and CO2 on CaO(001) at several temperatures. This method deconvolutes the line shapes of the heat detector response into an instrument response function and exponential decay functions, which correspond to the desorption of distinct surface species. This allows for a thorough analysis of the adsorption, dissociation, and desorption processes that occur during our microcalorimetry experiments. Our microcalorimetry results, show that D2O adsorbs initially with an adsorption energy of 85–90 kJ/mol at temperatures ranging from 120 to 300 K, consistent with prior spectroscopic studies that indicate dissociation. This adsorption energy decreases with increasing coverage until either D2O multilayers are formed at low temperatures (120 K) or the surface is saturated (150 K). Artificially producing defects on the surface by sputtering prior to dosing D2O sharply increases this adsorption energy, but these defects may be healed after annealing the surface to 1300 K. CO2 adsorbs on CaO(001) with an initial adsorption energy of ~ 125 kJ/mol, and decreases until the saturation coverage is reached, which is a function of surface temperature. The results showed that pre-adsorbed water blocks adsorption sites, lowers the saturation coverage, and lowers the measured adsorption energy of CO2. The calorimetry data further adds to our understanding of D2O and CO2 adsorption on oxide surfaces