Deep learning surrogate models for spatial and visual connectivity

Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses. This article investigates the possibility of considerably speeding up the outcomes of such computationally intensive simulations by using machine learning to create models capable of identifying the spatial and visual connectivity potential of a space. To that end, we present the entire process of investigating different machine learning models and a pipeline for training them on such task, from the incorporation of a bespoke spatial and visual connectivity analysis engine through a distributed computation pipeline, to the process of synthesizing training data and evaluating the performance of different neural networks

Spatial Solutions and Solution Spaces: The use of Virtual and Augmented Reality in Design Exploration

The recent wave of Virtual and Augmented Reality (VAR) technologies has coincided with new technologies for processing, analyzing and evaluating large amounts of data. In general, the purpose of Data Visualization is to enable the user to discover and understand patterns in data. Good visualizations present large amounts of data in a way that is easily understood, and good interactive visualizations promote intuitive means of exploring relationships. Over the past few years many researchers have looked into the development of immersive Virtual Environment platforms for Big Data visualization, such as, iViz (Donalek et al, 2014) and the work carried out by Masters of Pie and Lumacode for the Big Data VR Challenge in 2016 (Lumapie, 2016). Filtering, combination and scaling have all been identified elsewhere as important interactive techniques used in contemporary data visualization (Olshannikova et al, 2015). Of these, scaling may be the most familiar to architects: for centuries, designers have attempted to experience architectural space in different scales simultaneously, by using models at different scales (Yaneva, 2005), and by employing various drawing techniques to achieve an embodied perception of the designed space. With the use of VAR technologies this becomes easier than ever. At the same time, designers increasingly must understand not just the experience of a design proposal but also the data associated with it

Design of thermally deformable laminates using machine learning

Recent advances in material science and manufacturing have enabled designers to create objects which respond to changing environmental conditions by controlled deformation, without external mechanical or electrical actuation. The design of such smart materials has mostly been done through trial and error due to their complex nonlinear behavior. This paper will present how this problem is addressed by introducing a novel inverse design workflow. In this, a desired structural deformation is used as an input to a machine learned model which subsequently outputs the required geometric and material properties that will produce said deformation when exposed to an external stimulus. This workflow uses a Generative Adversarial Neural Network (GANN) trained on pairs of input cut-out patterns of laminate layers and their nonlinear Finite Element Analysis (FEA) simulation results. The method offers a significant performance speed-up while maintaining acceptable levels of accuracy, especially at the early design stage. This methodology could be further extended to the design of any nonlinear mechanical deformation

Fabrication of large-area CCD detectors on high-purity, float-zone silicon

In this report on the fabrication of a 1024 x 1024 charge coupled device (CCD) imager to be used as a soft x-ray sensor onboard the Advanced X-ray Astronomical Facility (AXAF), the following conclusions were found: the dislocations that limited the performance of the high resistivity imager were characterized; the sources of stress were identified and the dislocations found were eliminated; and a charge transfer inefficiency (CTI) of 10(exp -6) and read noise as low as 1.3/e was demonstrated. This sensor must have low noise and a low CTI and must be radiation hardened to withstand any radiation damage from a space environment

Are white storks addicted to junk food? Impacts of landfill use on the movement and behaviour of resident white storks (Ciconia ciconia) from a partially migratory population

Background: The migratory patterns of animals are changing in response to global environmental change with many species forming resident populations in areas where they were once migratory. The white stork (Ciconia ciconia) was wholly migratory in Europe but recently guaranteed, year-round food from landfill sites has facilitated the establishment of resident populations in Iberia. In this study 17 resident white storks were fitted with GPS/GSM data loggers (including accelerometer) and tracked for 9.1 ± 3.7 months to quantify the extent and consistency of landfill attendance by individuals during the non-breeding and breeding seasons and to assess the influence of landfill use on daily distances travelled, percentage of GPS fixes spent foraging and non-landfill foraging ranges. Results: Resident white storks used landfill more during non-breeding (20.1 % ± 2.3 of foraging GPS fixes) than during breeding (14.9 % ± 2.2). Landfill attendance declined with increasing distance between nest and landfill in both seasons. During non-breeding a large percentage of GPS fixes occurred on the nest throughout the day (27 % ± 3.0 of fixes) in the majority of tagged storks. This study provides first confirmation of year-round nest use by resident white storks. The percentage of GPS fixes on the nest was not influenced by the distance between nest and the landfill site. Storks travelled up to 48.2 km to visit landfills during non-breeding and a maximum of 28.1 km during breeding, notably further than previous estimates. Storks nesting close to landfill sites used landfill more and had smaller foraging ranges in non-landfill habitat indicating higher reliance on landfill. The majority of non-landfill foraging occurred around the nest and long distance trips were made specifically to visit landfill. Conclusions: The continuous availability of food resources on landfill has facilitated year-round nest use in white storks and is influencing their home ranges and movement behaviour. White storks rely on landfill sites for foraging especially during the non-breeding season when other food resources are scarcer and this artificial food supplementation probably facilitated the establishment of resident populations. The closure of landfills, as required by EU Landfill Directives, will likely cause dramatic impacts on white stork populations

Laser Cooling of Optically Trapped Molecules

Calcium monofluoride (CaF) molecules are loaded into an optical dipole trap (ODT) and subsequently laser cooled within the trap. Starting with magneto-optical trapping, we sub-Doppler cool CaF and then load $150(30)$ CaF molecules into an ODT. Enhanced loading by a factor of five is obtained when sub-Doppler cooling light and trapping light are on simultaneously. For trapped molecules, we directly observe efficient sub-Doppler cooling to a temperature of $60(5)$ $\mu\text{K}$. The trapped molecular density of $8(2)\times10^7$ cm$^{-3}$ is an order of magnitude greater than in the initial sub-Doppler cooled sample. The trap lifetime of 750(40) ms is dominated by background gas collisions.Comment: 5 pages, 5 figure

ERCC1-deficient cells and mice are hypersensitive to lipid peroxidation

Lipid peroxidation (LPO) products are relatively stable and abundant metabolites, which accumulate in tissues of mammals with aging, being able to modify all cellular nucleophiles, creating protein and DNA adducts including crosslinks. Here, we used cells and mice deficient in the ERCC1-XPF endonuclease required for nucleotide excision repair and the repair of DNA interstrand crosslinks to ask if specifically LPO-induced DNA damage contributes to loss of cell and tissue homeostasis. Ercc1-/- mouse embryonic fibroblasts were more sensitive than wild-type (WT) cells to the LPO products: 4-hydroxy-2-nonenal (HNE), crotonaldehyde and malondialdehyde. ERCC1-XPF hypomorphic mice were hypersensitive to CCl4 and a diet rich in polyunsaturated fatty acids, two potent inducers of endogenous LPO. To gain insight into the mechanism of how LPO influences DNA repair-deficient cells, we measured the impact of the major endogenous LPO product, HNE, on WT and Ercc1-/- cells. HNE inhibited proliferation, stimulated ROS and LPO formation, induced DNA base damage, strand breaks, error-prone translesion DNA synthesis and cellular senescence much more potently in Ercc1-/- cells than in DNA repair-competent control cells. HNE also deregulated base excision repair and energy production pathways. Our observations that ERCC1-deficient cells and mice are hypersensitive to LPO implicates LPO-induced DNA damage in contributing to cellular demise and tissue degeneration, notably even when the source of LPO is dietary polyunsaturated fats

A Comparison of Embedded and Nonembedded Print Coverage of the U.S. Invasion and Occupation of Iraq

This study examines the impact of embedded versus nonembedded (unilateral) news coverage during the U.S. invasion and occupation of Iraq. A content analysis was conduycted of the Washington Post, New York Times, Los Angeles Times, and Chicago Tribune news coverage of the invasion and occupation examining whether embedded and nonembedded new reports were different and, if so, how. News reports were examined for differences in tone toward the military, trust in the military, framing, and authoritativeness. The results of the study revealed significant differences in overall tone toward the military, trust in military personnel, framing, and authoritativeness between embedded and nonembedded articles.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline