Optical phonons in new ordered perovskite Sr2Cu(Re0.69Ca0.31) Oy system observed by infrared reflectance spectroscopy

We report infrared reflectivity spectra for a new correlated cupric oxide system Sr2Cu(Re0.69Ca0.31)Oy with y ~ 0.6 at several temperatures ranging between 8 and 380 K. The reflectivity spectrum at 300 K comprises of several optical phonons. A couple of residual bands located around 315 and 653 cm-1 exhibit exceptionally large intensity as compared to the other ones. The overall reflectivity spectrum lifts up slightly with increasing temperature. The energy and damping factor of transverse-optical phonons are determined by fitting the imaginary dielectric constant by Lorentz oscillator model and discussed as a function of temperature in terms of lattice anharmonicity.Comment: 9 pages, 3 figures, presented at ISS2005, to appear in Physica

Analyst Forecasts in New Zealand

This study explores analyst annual earnings forecasts in New Zealand. The results show that forecasts of New Zealand firms do not suffer from the pessimistic biases found in studies of forecasts for United States firms. Similar to United States studies, however, loss firm forecasts are significantly less accurate and more optimistic. These results suggest that New Zealand firms do not tend to manage earnings to beat expectations, but poorly performing firms might attempt to deceive investors by decreasing the quality of their information environment. Furthermore, optimism does appear to be impounded in stock prices, as firms with optimistic forecasts underperform firms with pessimistic forecasts by about 30%

Active Sensing as Bayes-Optimal Sequential Decision Making

Sensory inference under conditions of uncertainty is a major problem in both machine learning and computational neuroscience. An important but poorly understood aspect of sensory processing is the role of active sensing. Here, we present a Bayes-optimal inference and control framework for active sensing, C-DAC (Context-Dependent Active Controller). Unlike previously proposed algorithms that optimize abstract statistical objectives such as information maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy [Najemnik & Geisler, 2005], our active sensing model directly minimizes a combination of behavioral costs, such as temporal delay, response error, and effort. We simulate these algorithms on a simple visual search task to illustrate scenarios in which context-sensitivity is particularly beneficial and optimization with respect to generic statistical objectives particularly inadequate. Motivated by the geometric properties of the C-DAC policy, we present both parametric and non-parametric approximations, which retain context-sensitivity while significantly reducing computational complexity. These approximations enable us to investigate the more complex problem involving peripheral vision, and we notice that the difference between C-DAC and statistical policies becomes even more evident in this scenario.Comment: Scheduled to appear in UAI 201

Geological and hydrogeological investigation in West Malaysia

The author has identified the following significant results. The broad synoptic view of the images allowed easy identification of circular features and major fault traces in low lying areas. Sedimentary units were delineated in accordance with the prevailing rock types and where applicable the folding characteristics. Igneous units could easily be differentiated by tone, degree of fracturing, texture, and drainage pattern. The larger fold structures, anticlinoriums and synclinoriums, of the younger sediments on the eastern edge of the central belt could also be easily delineated

A Month-by-Month Examination of Long-Term Stock Returns

This study provides a month-by-month examination of stock returns. The results reconfirm the January Effect as well as indicate a powerful anomaly in September. Investing in the CRSP equal-weighted index in only January turns $1 in 1926 to$87.40 by 2006. The second closest month is July, during which $1 grows to$3.11. September is a poor month to invest. The $1 invested in only September decreases to a mere$0.49. The Halloween Effect vanishes once the monthly anomalies are controlled for. The September Effect is also established in four out of the five international markets tested