The depiction of Alboran Sea Gyre during Donde Va? using remote sensing and conventional data

Experienced oceanographic investigators have come to realize that remote sensing techniques are most successful when applied as part of programs of integrated measurements aimed at solving specific oceanographic problems. A good example of such integration occurred during the multi-platform international experiment, Donde Va? in the Alboran Sea during the period June through October, 1982. The objective of Donde Va? was to derive the interrelationship of the Atlantic waters entering the Mediterranean Sea and the Alboran Sea Gyre. The experimental plan conceived solely with this objective in mind consisted of a variety of remote sensing and conventional platforms: three ships, three aircraft, five current moorings, two satellites and a specialized beach radar (CODAR). Integrated analyses of these multiple-data sets are still being conducted. However, the initial results show detailed structure of the incoming Atlantic jet and Alboran Sea Gyre that would not have been possible by conventional means

The Pioneer maser signal anomaly: Possible confirmation of spontaneous photon blueshifting

The novel physics methodology of subquantum kinetics predicted in 1980 that photons should blueshift their frequency at a rate that varies directly with negative gravitational potential, the rate of blueshifting for photons traveling between Earth and Jupiter having been estimated to average approximately (1.3 +/- 0.65) X 10^-18 s^-1, or (1.1 +/- 0.6) X 10^-18 s^-1 for signals traveling a roundtrip distance of 65 AU through the outer solar system. A proposal was made in 1980 to test this blueshifting effect by transponding a maser signal over a 10 AU round-trip distance between two spacecraft. This blueshift prediction has more recently been corroborated by observations of maser signals transponded to the Pioneer 10 spacecraft. These measurements indicate a frequency shifting of approximately (2.28 +/- 0.4) X 10^-18 s^-1 which lies within 2 sigma of the subquantum kinetics prediction and which cannot be accounted for in terms of known forces acting on the craft. This blueshifting phenomenon implies the existence of a new source of energy which is able to account for the luminosities of red dwarf and brown dwarf stars and planets, and their observed sharing of a common mass-luminosity relation.Comment: 20 pages, 3 figures, 2 table

Satellite imagery and weather for the BESEX area, 15 February - 10 March 1973

The Bering Sea Experiment (BESEX) was conducted in February and March 1973 to study ice cover, sea state and zones of precipitation by means of airborne microwave radiometers over the Bering Sea. The images were computer processed from satellite data tapes. In processing the tapes, compensation was made for satellite attitude and altitude variations, as well as for image rectification. Visual imagery was taken in the 0.4 to 1.1-u range, and infrared imagery in the 8.0 to 13.0-u range

PAC-Bayes and Domain Adaptation

We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk. While this bound stands in the spirit of common domain adaptation works, we derive a second bound (introduced in Germain et al., 2016) that brings a new perspective on domain adaptation by deriving an upper bound on the target risk where the distributions' divergence-expressed as a ratio-controls the trade-off between a source error measure and the target voters' disagreement. We discuss and compare both results, from which we obtain PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian specialization to linear classifiers, we infer two learning algorithms, and we evaluate them on real data.Comment: Neurocomputing, Elsevier, 2019. arXiv admin note: substantial text overlap with arXiv:1503.0694

A New PAC-Bayesian Perspective on Domain Adaptation

We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a new perspective by deriving an upper-bound on the target risk where the distributions' divergence---expressed as a ratio---controls the trade-off between a source error measure and the target voters' disagreement. Our bound suggests that one has to focus on regions where the source data is informative.From this result, we derive a PAC-Bayesian generalization bound, and specialize it to linear classifiers. Then, we infer a learning algorithmand perform experiments on real data.Comment: Published at ICML 201

An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context

This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germain et al. in two ways. We first give another generalization bound tighter and easier to interpret. Moreover, we provide a new analysis of the constant term appearing in the bound that can be of high interest for developing new algorithmic solutions.Comment: NIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice, Dec 2014, Montr{\'e}al, Canad

Study of solid state photomultiplier

Available solid state photomultiplier (SSPM) detectors were tested under low-background, low temperature conditions to determine the conditions producing optimal sensitivity in a space-based astronomy system such as a liquid cooled helium telescope in orbit. Detector temperatures varied between 6 and 9 K, with background flux ranging from 10 to the 13th power to less than 10 to the 6th power photons/square cm-s. Measured parameters included quantum efficiency, noise, dark current, and spectral response. Experimental data were reduced, analyzed, and combined with existing data to build the SSPM data base included herein. The results were compared to analytical models of SSPM performance where appropriate models existed. Analytical models presented here were developed to be as consistent with the data base as practicable. Significant differences between the theory and data are described. Some models were developed or updated as a result of this study