An Evening with Dr. Venus Opal Reese, Motivational Speaker, Business Woman, and Author

This Droppin' Knowledge Lecture Series will challenge the audience to "defy the impossible". Dr. Venus Opal Reese will offer insight on how to achieve success on Thursday, September 17, at 6:30pm in Martin Luther King Hall-Thomas Pawley Theature, 812 E. Dunklin Street. Reese, an inspirational speaker, business mentor and marketing strategist, offers training to professionals, particularly entrepreneurs and executives, on how to "defy their impossible" to reach million-dollar success. Reese has consulted for O Magazines, and appeared on ABC and CBS News. For more information on Dr. Venus Opal Reese, please visit http://defyimpossible.com.

Sequence verification of human creatine kinase (43 kDa) isozymes by high-resolution tandem mass spectrometry.

Amino acid sequencing by recombinant DNA technology, although dramatically useful, is subject to base reading errors, is indirect, and is insensitive to posttranslational processing. Mass spectrometry techniques can provide molecular weight data from even relatively large proteins for such cDNA sequences and can serve as a check of an enzyme's purity and sequence integrity. Multiply-charged ions from electrospray ionization can be dissociated to yield structural information by tandem mass spectrometry, providing a second method for gaining additional confidence in primary sequence confirmation. Here, accurate (+/- 1 Da) molecular weight and molecular ion dissociation information for human muscle and brain creatine kinases has been obtained by electrospray ionization coupled with Fourier-transform mass spectrometry to help distinguish which of several published amino acid sequences for both enzymes are correct. The results herein are consistent with one published sequence for each isozyme, and the heterogeneity indicated by isoelectric focusing due to 1-Da deamidation changes. This approach appears generally useful for detailed sequence verification of recombinant proteins.

Commercial applications of speech interface technology: an industry at the threshold.

Speech interface technology, which includes automatic speech recognition, synthetic speech, and natural language processing, is beginning to have a significant impact on business and personal computer use. Today, powerful and inexpensive microprocessors and improved algorithms are driving commercial applications in computer command, consumer, data entry, speech-to-text, telephone, and voice verification. Robust speaker-independent recognition systems for command and navigation in personal computers are now available; telephone-based transaction and database inquiry systems using both speech synthesis and recognition are coming into use. Large-vocabulary speech interface systems for document creation and read-aloud proofing are expanding beyond niche markets. Today's applications represent a small preview of a rich future for speech interface technology that will eventually replace keyboards with microphones and loud-speakers to give easy accessibility to increasingly intelligent machines.

Olfactory transduction is intrinsically noisy.

The sources of noise that limit olfactory signal detection were investigated in dissociated rat olfactory receptor cells. Near-threshold odorant-evoked currents exhibited large random fluctuation. However, similar fluctuations were observed in the absence of applied odorants when currents were induced by elevating the intracellular cyclic AMP concentration. This suggests that the fluctuations reflect noise intrinsic to the transduction mechanism, rather than the quantal nature of an odorant stimulus. For many odorants, this intrinsic noise may preclude the reliable detection of single odorant molecules.

Robust fitting of Zernike polynomials to noisy point clouds defined over connected domains of arbitrary shape

A new method for fitting a series of Zernike polynomials to point clouds defined over connected domains of arbitrary shape defined within the unit circle is presented in this work. The method is based on the application of machine learning fitting techniques by constructing an extended training set in order to ensure the smooth variation of local curvature over the whole domain. Therefore this technique is best suited for fitting points corresponding to ophthalmic lenses surfaces, particularly progressive power ones, in non-regular domains. We have tested our method by fitting numerical and real surfaces reaching an accuracy of 1 micron in elevation and 0.1 D in local curvature in agreement with the customary tolerances in the ophthalmic manufacturing industry.

Removing noisy mentions for distant supervision

Los métodos para Extracción de Información basados en la Supervisión a Distancia se basan en usar tuplas correctas para adquirir menciones de esas tuplas, y así entrenar un sistema tradicional de extracción de información supervisado. En este artículo analizamos las fuentes de ruido en las menciones, y exploramos métodos sencillos para filtrar menciones ruidosas. Los resultados demuestran que combinando el filtrado de tuplas por frecuencia, la información mutua y la eliminación de menciones lejos de los centroides de sus respectivas etiquetas mejora los resultados de dos modelos de extracción de información significativamente.

Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.