A near infrared spectroscopic study of the interstellar gas in the starburst core of M82

Researchers used the McDonald Observatory Infrared Grating Spectrometer, to complete a program of spatially resolved spectroscopy of M82. The inner 300 pc of the starburst was observed with 4 inch (50 pc) resolution. Complete J, H and K band spectra with resolution 0.0035 micron (lambda/delta lambda=620 at K) were measured at the near-infrared nucleus of the galaxy. Measurements of selected spectral features including lines of FeII, HII and H2 were observed along the starburst ridge-line, so the relative distribution of the diagnostic features could be understood. This information was used to better define the extinction towards the starburst region, the excitation conditions in the gas, and to characterize the stellar populations there

Internet-of-Things Devices, Intellectual Property, Venture Capital, China Manufacturing, and the Art of a Clean Deal: Who Owns What?

Internet-of-Things Devices, Intellectual Property, Venture Capital, China Manufacturing, and the Art of a Clean Deal: Who Owns What

Control of Carolina redroot (Lachnanthes caroliana) in cranberry with preemergence herbicides

Abstract. New Jersey produced 27 million kg of cranberries in 2015 at a farm value of $22 million (USDA 2017). Cranberry beds in New Jersey are concentrated in the Pine Barrens coastal plain where soil conditions (sandy texture, pH 4.0 to 5.0, good drainage) are optimal for cranberry production. The perennial nature of cranberry production predisposes the crop to a diversity of weed species ranging from herbaceous weeds to woody perennial species. Among perennial weed species, Carolina redroot has been an increasing source of concern for New Jersey cranberry growers regarding the lack of sufficient control from their current management strategies. Carolina redroot is a perennial herbaceous monocotyledonous species member of the Haemodoraceae family whose common name is derived from the orange to red coloration of its roots and rhizome. Information regarding herbicidal control of Carolina redroot is extremely limited and mostly restricted to blueberry production (Myers et al. 2013). In order to address the issues of successfully managing Carolina redroot under extremely specific environmental and cropping conditions, a study was initiated in the spring of 2017 to evaluate the efficiency of three herbicides at different rates for preemergence control of Carolina redroot. A complete lack of control in the twelve weeks that followed the application was noted for the plants that were treated with Norflurazon at 560, 1,120, 2,240, and 4,480 g ai ha-1. Control of Carolina redroot with napropamide applied at 6,720 g ai ha-1 was 74% 28 days after treatment (DAT) and increased to 78% at 83 DAT. Greater control was achieved early in the season with dichlobenil applied at 2,240 or 4,480 g ai ha-1 with 90 and 99% control, respectively, at 28 DAT. However, control with dichlobenil declined between 28 and 83 DAT. Carolina redroot density in the nontreated plots reached 430 plants m-2 56 DAT but was reduced to 275 plants m-2 with napropamide, 95 plants m-2 with dichlobenil at 2,240 g ai ha-1, and 70 plants m-2 with dichlobenil at 4,480 g ai ha-1. Significant damages to the cranberry crop were noted with dichlobenil at 4,480 g ai ha-1, mostly in the form of chlorosis early in the season (19% at 40 DAT) and stunting later (15% at 83 DAT)

2008 UMaine News Press Releases

This is a catalog of press releases put out by the University of Maine Division of Marketing and Communications between January 7, 2008 and December 29, 2008

Spatial Analysis of Arabidopsis thaliana Gene Expression in Response to Turnip mosaic virus Infection

Virus-infected leaf tissues comprise a heterogeneous mixture of cells at different stages of infection. The spatial and temporal relationships between sites of virus accumulation and the accompanying host responses, such as altered host gene expression, are not well defined. To address this issue, we utilized Turnip mosaic virus (TuMV) tagged with the green fluorescent protein to guide the dissection of infection foci into four distinct zones. The abundance of Arabidopsis thaliana mRNA transcripts in each of the four zones then was assayed using the Arabidopsis ATH1 GeneChip oligonucleotide microarray (Affymetrix). mRNA transcripts with significantly altered expression profiles were determined across gradients of virus accumulation spanning groups of cells in and around foci at different stages of infection. The extent to which TuMV-responsive genes were up- or downregulated primarily correlated with the amount of virus accumulation regardless of gene function. The spatial analysis also allowed new suites of coordinately regulated genes to be identified that are associated with chloroplast functions (decreased), sulfate assimilation (decreased), cell wall extensibility (decreased), and protein synthesis and turnover (induced). The functions of these downregulated genes are consistent with viral symptoms, such as chlorosis and stunted growth, providing new insight into mechanisms of pathogenesis

Inducing and exploiting activation sparsity for fast neural network inference

Optimizing convolutional neural networks for fast inference has recently become an extremely active area of research. One of the go-to solutions in this context is weight pruning, which aims to reduce computational and memory footprint by removing large subsets of the connections in a neural network. Surprisingly, much less attention has been given to exploiting sparsity in the activation maps, which tend to be naturally sparse in many settings thanks to the structure of rectified linear (ReLU) activation functions. In this paper, we present an in-depth analysis of methods for maximizing the sparsity of the activations in a trained neural network, and show that, when coupled with an efficient sparse-input convolution algorithm, we can leverage this sparsity for significant performance gains. To induce highly sparse activation maps without accuracy loss, we introduce a new regularization technique, coupled with a new threshold-based sparsification method based on a parameterized activation function called Forced-Activation-Threshold Rectified Linear Unit (FATReLU). We examine the impact of our methods on popular image classification models, showing that most architectures can adapt to significantly sparser activation maps without any accuracy loss. Our second contribution is showing that these these compression gains can be translated into inference speedups: we provide a new algorithm to enable fast convolution operations over networks with sparse activations, and show that it can enable significant speedups for end-to-end inference on a range of popular models on the large-scale ImageNet image classification task on modern Intel CPUs, with little or no retraining cost

Cathode & Electromagnet Qualification Status and Power Processing Unit Development Update for the Ascendant Sub-kW Transcelestial Electric Propulsion System

A review of the component-level flight qualification efforts and power processing unit development status of the Ascendant Sub-kW Transcelestial Electric Propulsion System (ASTRAEUS) program is presented. Component-level qualification efforts were undertaken for the system’s ultra-compact heaterless LaB6 hollow cathode and electromagnets, both of which employ designs bespoke to ASTRAEUS, as they represent the highest failure risks for the thruster. Through parallel long-duration wear and ignition tests, the ASTRAEUS cathode demonstrated invariant discharge performance over more than 5000 h of operation at its maximum operating current of 4 A and demonstrated more than 25,000 ignition cycles. The ASTRAEUS electromagnets completed their environmental qualification through a demonstration of more than 1200 deep thermal cycles with no indication of coil degradation (the test articles previously completed qualification-level vibration and shock testing). ASTRAEUS’s prototype power processing unit has demonstrated more than 92% total power conversion efficiency and class-leading power density & specific power density of 4.5 W/cm3 & 1670 W/kg, respectively. The various power converters found in the ASTRAEUS power processing unit are reviewed with a focus on the methods by which such high performance was achieved