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

Attention-deficit hyperactivity disorder symptoms and brain morphology:Addressing potential selection bias with inverse probability weighting

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9–11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.</p

Attention-deficit hyperactivity disorder symptoms and brain morphology:Addressing potential selection bias with inverse probability weighting

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9–11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.</p

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

Nitrogen cycle in Great Basin hot springs

Nitrification and denitrification are two important steps in the nitrogen cycle . Nitrification, a two step process, leads to the production of NO3-, (Fig. 1). In the first step, ammonia oxidation, NH3 is oxidized to NO2-, and in the second step, nitrite oxidation, NO2- is oxidized to NO3-. Until recently, very little was know about nitrification in high temperature environments. However, in 2008 a thermophilic archaeon, named “Candidatus Nitrosocaldus yellowstonii”, was shown to mediate ammonia oxidation up to 74°C. More recently , NO2- oxidizing bacteria were discovered that are active in temperatures up to 48°C(4). While NH3 oxidation is generally considered to be the rate limiting step, this may not be the case at high temperatures since accumulation of NO2- has been reported in some hot springs where NH3 is the dominant form of inorganic nitrogen (1)

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

Modeling beam propagation in a moving nonlinear medium

Fully describing light propagation in a rotating, anisotropic medium with thermal nonlinearity requires modeling the interplay between nonlinear refraction, birefringence, and the nonlinear group index. Incorporating these factors into a generalized nonlinear Schr\"odinger equation and fitting them to recent experimental results reveals two key relationships: the photon drag effect can have a nonlinear component that is dependent on the motion of the medium, and the temporal dynamics of the moving birefringent nonlinear medium create distorted figure-eight-like transverse trajectories at the output. The beam trajectory can be accurately modelled with a full understanding of the propagation effects. Efficiently modeling these effects and accurately predicting the beam's output position has implications for optimizing applications in velocimetry and beam-steering. Understanding the roles of competitive nonlinearities gives insight into the creation or suppression of nonlinear phenomena like self-action effects.Comment: 17 pages, 10 figures, 2 table