RadiX-Net: Structured Sparse Matrices for Deep Neural Networks

The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them. Research over the past few decades has explored the prospect of sparsifying DNNs before, during, and after training by pruning edges from the underlying topology. The resulting neural network is known as a sparse neural network. More recent work has demonstrated the remarkable result that certain sparse DNNs can train to the same precision as dense DNNs at lower runtime and storage cost. An intriguing class of these sparse DNNs is the X-Nets, which are initialized and trained upon a sparse topology with neither reference to a parent dense DNN nor subsequent pruning. We present an algorithm that deterministically generates RadiX-Nets: sparse DNN topologies that, as a whole, are much more diverse than X-Net topologies, while preserving X-Nets' desired characteristics. We further present a functional-analytic conjecture based on the longstanding observation that sparse neural network topologies can attain the same expressive power as dense counterpartsComment: 7 pages, 8 figures, accepted at IEEE IPDPS 2019 GrAPL workshop. arXiv admin note: substantial text overlap with arXiv:1809.0524

Integration of Direct-Write (DW) and Ultrasonic Consolidation (UC) Technologies to Create Advanced Structures with Embedded Electrical Circuitry

In many instances conductive traces are needed in small, compact and enclosed areas. However, with traditional manufacturing techniques, embedded electrical traces or antenna arrays have not been a possibility. By integrating Direct Write and Ultrasonic Consolidation technologies, electronic circuitry, antennas and other devices can be manufactured directly into a solid metal structure and subsequently completely enclosed. This can achieve a significant reduction in mass and volume of a complex electronic system without compromising performance.Mechanical Engineerin

Population pressure and global markets drive a decade of forest cover change in Africa\u27s Albertine Rift

Africa\u27s Albertine Rift region faces a juxtaposition of rapid human population growth and protected areas, making it one of the world\u27s most vulnerable biodiversity hotspots. Using satellite-derived estimates of forest cover change, we examined national socioeconomic, demographic, agricultural production, and local demographic and geographic variables, to assess multilevel forces driving local forest cover loss and gain outside protected areas during the first decade of this century. Because the processes that drive forest cover loss and gain are expected to be different, and both are of interest, we constructed models of significant change in each direction. Although rates of forest cover change varied by country, national population change was the strongest driver of forest loss for all countries – with a population doubling predicted to cause 2.06% annual cover loss, while doubling tea production predicted to cause 1.90%. The rate of forest cover gain was associated positively with increased production of the local staple crop cassava, but negatively with local population density and meat production, suggesting production drivers at multiple levels affect reforestation. We found a small but significant decrease in loss rate as distance from protected areas increased, supporting studies suggesting higher rates of landscape change near protected areas. While local population density mitigated the rate of forest cover gain, loss was also correlated with lower local population density, an apparent paradox, but consistent with findings that larger scale forces outweigh local drivers of deforestation. This implicates demographic and market forces at national and international scales as critical drivers of change, calling into question the necessary scales of forest protection policy in this biodiversity hotspot. Using a satellite derived estimate of forest cover change for both loss and gain added a dynamic component to more traditionally static and unidirectional studies, significantly improving our understanding of landscape processes and drivers at work

Test-Retest Reliability of the SWAY Balance Mobile Application

The SWAY Balance Mobile Application is an FDA-cleared balance testing system which uses the built-in tri-axial accelerometers of a mobile electronic device to objectively assess postural movement. The system was designed to provide a means of quantitative balance assessment in clinical and on-field environments. The purpose of this study was to determine the intrasession and intersession reliability, as well as the minimum difference to be considered real, of the SWAY Balance Mobile Application

Olaparib-induced Adaptive Response Is Disrupted by FOXM1 Targeting that Enhances Sensitivity to PARP Inhibition

FOXM1 transcription factor network is activated in over 84% of cases in high-grade serous ovarian cancer (HGSOC), and FOXM1 upregulates the expression of genes involved in the homologous recombination (HR) DNA damage and repair (DDR) pathway. However, the role of FOXM1 in PARP inhibitor response has not yet been studied. This study demonstrates that PARP inhibitor (PARPi), olaparib, induces the expression and nuclear localization of FOXM1. On the basis of ChIP-qPCR, olaparib enhances the binding of FOXM1 to genes involved in HR repair. FOXM1 knockdown by RNAi or inhibition by thiostrepton decreases FOXM1 expression, decreases the expression of HR repair genes, such as BRCA1 and RAD51, and enhances sensitivity to olaparib. Comet and PARP trapping assays revealed increases in DNA damage and PARP trapping in FOXM1-inhibited cells treated with olaparib. Finally, thiostrepton decreases the expression of BRCA1 in rucaparib-resistant cells and enhances sensitivity to rucaparib. Collectively, these results identify that FOXM1 plays an important role in the adaptive response induced by olaparib and FOXM1 inhibition by thiostrepton induces “BRCAness” and enhances sensitivity to PARP inhibitors

Genetic associations with childhood brain growth, defined in two longitudinal cohorts

Genome-wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross-sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome-wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention-deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population-based cohort (Generation R: 257 participants). The growth of the brain's major regions (cerebral cortex, white matter, basal ganglia, and cerebellum) and one region of interest (the right lateral prefrontal cortex) were defined on all individuals from two aMRIs, and a GWAS and a pathway analysis were performed. In addition, association between polygenic risk for ADHD and brain growth was determined for the LONG cohort. For white matter growth, GWAS meta-analysis identified a genome-wide significant intergenic SNP (rs12386571, P = 9.09 × 10-9 ), near AKR1B10. This gene is part of the aldo-keto reductase superfamily and shows neural expression. No enrichment of neural pathways was detected and polygenic risk for ADHD was not associated with the brain growth phenotypes in the LONG cohort that was enriched for the diagnosis of ADHD. The study illustrates the use of a novel brain growth phenotype defined in vivo for further study

Comparison of noncontrast MRI magnetization transfer and T2‐Weighted signal intensity ratios for detection of bowel wall fibrosis in a Crohn's disease animal model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113105/1/jmri24815.pd