Dual detection system for AIMP PFM data processing

Dual detection system for processing AIMP satellite pulse frequency modulated telemetry dat

A quality control monitoring system for satellite telemetry data information systems

Quality control monitoring system for satellite telemetry data information system

Predicted and actual spacecraft radio frequency interference for PFM telemetry

Predicted and actual spacecraft radio frequency interference for pulse frequency modulation telemetr

Satellite data recovery and quality for low elevation tracking angles

Satellite data recovery and quality for low elevation tracking angle

Study of parametric performance of a two-stage repetitively pulsed plasma engine /REPPAC/

Parametric performance of two-stage repetitively pulsed plasma engin

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio among the joined datasets. More generally, no single proposed method can efficiently join two spatial datasets in a robust manner with respect to their data distributions. Some approaches do well for datasets with contrasting densities while others do better with similar densities. None of them does well when the datasets have locally divergent data distributions. In this paper we develop TRANSFORMERS, an efficient and robust spatial join approach that is indifferent to such variations of distribution among the joined data. TRANSFORMERS achieves this feat by departing from the state-of-the-art through adapting the join strategy and data layout to local density variations among the joined data. It employs a join method based on data-oriented partitioning when joining areas of substantially different local densities, whereas it uses big partitions (as in space-oriented partitioning) when the densities are similar, while seamlessly switching among these two strategies at runtime. We experimentally demonstrate that TRANSFORMERS outperforms state-of-the-art approaches by a factor of between 2 and 8

Transcription factor signal transducer and activator of transcription 5 promotes growth of human prostate cancer cells in vivo

Purpose: Stat5a/b is the key mediator of prolactin (Prl) effects in prostate cancer cells via activation of Jak2. Prl is locally produced growth factor in human prostate cancer. Prl protein expression and constitutive activation of Stat5a/b are associated with high histological grade of clinical prostate cancer. Moreover, activation of Stat5a/b in primary prostate cancer predicts early disease recurrence. Here, we inhibited Stat5a/b by several different methodological approaches. Our goal was to establish a proof-of-principle that Stat5a/b is critical for prostate cancer cell viability in vitro and for prostate tumor growth in vivo. Experimental Design: We inhibited Stat5a/b protein expression by antisense oligonucleotides or RNA interference and transcriptional activity of Stat5a/b by adenoviral expression of a dominant-negative mutant of Stat5a/b in prostate cancer cells in culture. Moreover, Stat5a/b activity was suppressed in human prostate cancer xenograft tumors in nude mice. Stat5a/b regulation of BclXL and Cyclin-D1 protein levels was demonstrated by antisense suppression of Stat5a/b protein expression followed by Western blotting. Results and Conclusions: We show here that inhibition of Stat5a/b by antisense oligonuleotides, RNA interference, or adenoviral expression of DNStat5a/b all effectively kill prostate cancer cells. Moreover, we demonstrate that Stat5a/b is critical for human prostate cancer xenograft growth in nude mice. Stat5a/b effects on the viability of on prostate cancer cells involve Stat5a/b-regulation of BclXL and Cyclin-D1 protein levels, but not the expression or activation of Stat3. This work establishes Stat5a/b as a therapeutic target protein for prostate cancer. Pharmacological inhibition of Stat5a/b in prostate cancer can be achieved by small-molecule inhibitors of transactivation, dimerization or DNA-binding of Stat5a/b

Data-driven prediction of tool wear using Bayesian-regularized artificial neural networks

The prediction of tool wear helps minimize costs and enhance product quality in manufacturing. While existing data-driven models using machine learning and deep learning have contributed to the accurate prediction of tool wear, they often lack generality and require substantial training data for high accuracy. In this paper, we propose a new data-driven model that uses Bayesian Regularized Artificial Neural Networks (BRANNs) to precisely predict milling tool wear. BRANNs combine the strengths and leverage the benefits of artificial neural networks (ANNs) and Bayesian regularization, whereby ANNs learn complex patterns and Bayesian regularization handles uncertainty and prevents overfitting, resulting in a more generalized model. We treat both process parameters and monitoring sensor signals as BRANN input parameters. We conducted an extensive experimental study featuring four different experimental data sets, including the NASA Ames milling dataset, the 2010 PHM Data Challenge dataset, the NUAA Ideahouse tool wear dataset, and an in-house performed end-milling of the Ti6Al4V dataset. We inspect the impact of input features, training data size, hidden units, training algorithms, and transfer functions on the performance of the proposed BRANN model and demonstrate that it outperforms existing state-of-the-art models in terms of accuracy and reliability

Sequential Monte Carlo Instant Radiosity

Instant Radiosity and its derivatives are interactive methods for efficiently estimating global (indirect) illumination. They represent the last indirect bounce of illumination before the camera as the composite radiance field emitted by a set of virtual point light sources (VPLs). In complex scenes, current algorithms suffer from a difficult combination of two issues: it remains a challenge to distribute VPLs in a manner that simultaneously gives a high-quality indirect illumination solution for each frame, and does so in a temporally coherent manner. We address both issues by building, and maintaining over time, an adaptive and temporally coherent distribution of VPLs in locations where they bring indirect light to the image. We introduce a novel heuristic sampling method that strives to only move as few of the VPLs between frames as possible. The result is, to the best of our knowledge, the first interactive global illumination algorithm that works in complex, highly-occluded scenes, suffers little from temporal flickering, supports moving cameras and light sources, and is output-sensitive in the sense that it places VPLs in locations that matter most to the final result

Sequential Monte Carlo Instant Radiosity

Instant Radiosity and its derivatives are interactive methods for efficiently estimating global (indirect) illumination. They represent the last indirect bounce of illumination before the camera as the composite radiance field emitted by a set of virtual point light sources (VPLs). In complex scenes, current algorithms suffer from a difficult combination of two issues: it remains a challenge to distribute VPLs in a manner that simultaneously gives a high-quality indirect illumination solution for each frame, and to do so in a temporally coherent manner. We address both issues by building, and maintaining overtime, an adaptive and temporally coherent distribution of VPLs in locations where they bring indirect light to the image. We introduce a novel heuristic sampling method that strives to only move as few of the VPLs between frames as possible. The result is, to the best of our knowledge, the first interactive global illumination algorithm that works in complex, highly-occluded scenes, suffers little from temporal flickering, supports moving cameras and light sources, and is output-sensitive in the sense that it places VPLs in locations that matter most to the final result