Is weight gain after smoking cessation inevitable?

Weight gain after smoking cessation was studied in a naturalistic setting where (1) all smokers quit and (2) risk factors for postcessation weight gain were modified. Participants were 332 military recruits (227 men, 105 women), 86 of whom were smokers who quit during 6 weeks of basic training. Results showed no significant weight changes for smokers who quit. Pretest smoking rates and feat of weight gain were unrelated to changes in weight. Results suggest that an intensive program that limits access to alcohol and foods that are high in fat and that increases physical activity can attenuate weight gain after smoking cessation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Evaluation of Localized Semantics: Data, Methodology, and Experiments

We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark database (Martin et al., in International conference on computer vision, vol. II, pp. 416–421, 2001). The database is extended by manually labeling each segment with its most specific semantic concept in WordNet (Miller et al., in Int. J. Lexicogr. 3(4):235–244, 1990). The evaluation methodology establishes protocols for mapping algorithm specific localization (e.g., segmentations) to our data, handling synonyms, scoring matches at different levels of specificity, dealing with vocabularies with sense ambiguity (the usual case), and handling ground truth regions with multiple labels. Given these protocols, we develop two evaluation approaches. The first measures the range of semantics that an algorithm can recognize, and the second measures the frequency that an algorithm recognizes semantics correctly. The data, the image labeling tool, and programs implementing our evaluation strategy are all available on-line (kobus.ca//research/data/IJCV_2007). We apply this infrastructure to evaluate four algorithms which learn to label image regions from weakly labeled data. The algorithms tested include two variants of multiple instance learning (MIL), and two generative multi-modal mixture models. These experiments are on a significantly larger scale than previously reported, especially in the case of MIL methods. More specifically, we used training data sets up to 37,000 images and training vocabularies of up to 650 words. We found that one of the mixture models performed best on image annotation and the frequency correct measure, and that variants of MIL gave the best semantic range performance. We were able to substantively improve the performance of MIL methods on the other tasks (image annotation and frequency correct region labeling) by providing an appropriate prior.     

A search space “cartography” for guiding graph coloring heuristics

We present a search space analysis and its application in improving local search algorithms for the graph coloring problem. Using a classical distance measure between colorings, we introduce the following clustering hypothesis: the high quality solutions are not randomly scattered in the search space, but rather grouped in clusters within spheres of specific diameter. We first provide intuitive evidence for this hypothesis by presenting a projection of a large set of local minima in the 3D space. An experimental confirmation is also presented: we introduce two algorithms that exploit the hypothesis by guiding an underlying Tabu Search (TS) process. The first algorithm (TS-Div) uses a learning process to guide the basic TS process toward as-yet-unvisited spheres. The second algorithm (TS-Int) makes deep investigations within a bounded region by organizing it as a tree-like structure of connected spheres. We experimentally demonstrate that if such a region contains a global optimum, TS-Int does not fail in eventually finding it. This pair of algorithms significantly outperforms the underlying basic TS algorithm; it can even improve some of the best-known solutions ever reported in the literature (e.g. for dsjc1000.9).     

Infrared balloon experiment: improved instrumental configuration and assessment of instrument performance

During the 2002 environmental satellite Envisat mid-latitude validation campaign, a new upgraded configuration of the Infrared Balloon Experiment (IBEX) Fourier transform spectrometer, which had its first flight in 1978, performed a stratospheric balloon flight across the Mediterranean Sea. Among the substantial upgrades made to the instrument, the use of photon-noise-limited detectors permitted us to reach the theoretical limits in terms of signal-to-noise ratio. Also, important modifications were made to the interferometric system and electronics, such as the installation of a solid-state laser reference source and an onboard data recording system. During the flight, measurement of volume-mixing-ratio vertical profiles of O3, HNO3, N2O, and ClO from an altitude of approximately 38 km were performed with a vertical resolution of approximately 1.5 km.     

Determining the Characteristics of Cosmic-Radiation Nuclei in the Sileye Experiment on Board the Mir Orbital Station

An algorithm for reconstructing the characteristics (charge, mass, and energy) of cosmic-radiation nuclei with 20- to 200-MeV/nucleon energies is described. The detector is a telescope of three two-coordinate planes with two 1-mm-thick iron filters inserted between them. Each plane is composed of two strip silicon detectors with 3.6-mm-wide orthogonally oriented strips, an effective area of 6 × 6 cm², and a thickness of 380 m. The algorithm for reconstructing the nuclei characteristics is based on the analysis of how the specific ionization losses change as the nuclei pass through the filter material. The results of the Monte Carlo simulation are presented for the energy dependence of the telescope acceptance and the energy deposited in the detectors by different nuclei in view of the detector calibration on the nuclear beams of the accelerator. The mass resolution of the telescope is 30, 12, and 5% for He, N, and Al nuclei, respectively. The energy resolution, which is 20%, is much the same for all nuclei.