The Corralitos Observatory program for the detection of lunar transient phenomena

This is a final report on the establishment, observing procedures, and observational results of a survey program for the detection of lunar transient phenomena (LTP's) by electro-optical image conversion means. For survey, a unique detection system with an image orthicon was used as the primary element in conjunction with a 24-in. f/20 Cassegrainian telescope. Observations in three spectral ranges, with 6,466 man-hours of observing, were actually performed during the period from October 27, 1965, to April 26, 1972. Within this entire period, no color or feature change within the detection capabilities of the instrumentation was observed, either independently or in follow up of amateur LTP reports, with the exception of one general bluing and several localized bluings (probably ascribable to the effects of the terrestrial atmosphere) that were observed solely by the Corralitos system. A table is presented indicating amateur and professional reports of LTP's and the results of efforts to confirm these reports through the Corralitos system

Nonlinear Schroedinger equation with two symmetric point interactions in one dimension

We consider a time-dependent one-dimensional nonlinear Schroedinger equation with a symmetric potential double well represented by two delta interactions. Among our results we give an explicit formula for the integral kernel of the unitary semigroup associated with the linear part of the Hamiltonian. Then we establish the corresponding Strichartz-type estimate and we prove local existence and uniqueness of the solution to the original nonlinear problem

Stem Cells in the Nervous System

Given their capacity to regenerate cells lost through injury or disease, stem cells offer new vistas into possible treatments for degenerative diseases and their underlying causes. As such, stem cell biology is emerging as a driving force behind many studies in regenerative medicine. This review focuses on the current understanding of the applications of stem cells in treating ailments of the human brain, with an emphasis on neurodegenerative diseases. Two types of neural stem cells are discussed: endogenous neural stem cells residing within the adult brain and pluripotent stem cells capable of forming neural cells in culture. Endogenous neural stem cells give rise to neurons throughout life, but they are restricted to specialized regions in the brain. Elucidating the molecular mechanisms regulating these cells is key in determining their therapeutic potential as well as finding mechanisms to activate dormant stem cells outside these specialized microdomains. In parallel, patient-derived stem cells can be used to generate neural cells in culture, providing new tools for disease modeling, drug testing, and cell-based therapies. Turning these technologies into viable treatments will require the integration of basic science with clinical skills in rehabilitation

On the lowest eigenvalue of Laplace operators with mixed boundary conditions

In this paper we consider a Robin-type Laplace operator on bounded domains. We study the dependence of its lowest eigenvalue on the boundary conditions and its asymptotic behavior in shrinking and expanding domains. For convex domains we establish two-sided estimates on the lowest eigenvalues in terms of the inradius and of the boundary conditions

Fast Approximate Spoken Term Detection from Sequence of Phonemes

We investigate the detection of spoken terms in conversational speech using phoneme recognition with the objective of achieving smaller index size as well as faster search speed. Speech is processed and indexed as a sequence of one best phoneme sequence. We propose the use of a probabilistic pronunciation model for the search term to compensate for the errors in the recognition of phonemes. This model is derived using the pronunciation of the word and the phoneme confusion matrix. Experiments are performed on the conversational telephone speech database distributed by NIST for the 2006 spoken term detection. We achieve about 1500 times smaller index size and 14 times faster search speed compared to the state-of-the-art system using phoneme lattice at the cost of relatively lower detection performance

Education as inefficient resource against depressive symptoms in the Czech Republic: Cross-sectional analysis of the HAPIEE study

Background: Increasing educational level of the population could be a strategy to prevent depression. We investigated whether education may offer a greater benefit for mental health to women and to individuals living in socioeconomically disadvantaged areas. Methods: We performed a cross-sectional study using data on 6964 Czech participants of the Health, Alcohol and Psychosocial factors in Eastern Europe study (on average 58 years old; 53% women). Binary logistic regression was used to examine the association of education with depressive symptoms, adjusting for several groups of covariates. Interactions were tested between education and sex as well as between education and socioeconomic advantage of the area of residence. Results: Higher education was strongly associated with lower odds of depressive symptoms, independently of sociodemographic characteristics, health behavior and somatic diseases. This association was attenuated after adjusting for other markers of individual socioeconomic position (work activity, material deprivation and household items). There were no interactions between education and either sex or socioeconomic advantage of the area of residence. Conclusions: We did not find an independent association between education and depressive symptoms after controlling for other socioeconomic markers in a sample with a formative history of communistic ideologies. Women or individuals from socioeconomically disadvantaged areas do not seem to gain a larger mental health benefit from education

Analysis of Confusion Matrix to Combine Evidence for Phoneme Recognition

In this work we analyze and combine evidences from different classifiers for phoneme recognition using information from the confusion matrices. Speech signals are processed to extract the Perceptual Linear Prediction (PLP) and Multi-RASTA (MRASTA) features. Neural network classifiers with different architectures are built using these features. The classifiers are analyzed using their confusion matrices. The motivation behind this analysis is to come up with some objective measures which indicate the complementary nature of information in each of the classifiers. These measures are useful for combining a subset of classifiers. The classifiers can be combined using different combination schemes like product, sum, minimum and maximum rules. The significance of the objective measures is demonstrated in terms the results of combination. Classifiers selected through the proposed objective measures seem to provide the best performance