Chromatic Signatures in the Microlensing of GRB Afterglows

We calculate the radial surface brightness profile of the image of a Gamma-Ray-Burst (GRB) afterglow. The afterglow spectrum consists of several power-law segments separated by breaks. The image profile changes considerably across each of the spectral breaks. It also depends on the density profile of the ambient medium into which the GRB fireball propagates. Gravitational microlensing by an intervening star can resolve the afterglow image. We calculate the predicted magnification history of GRB afterglows as a function of observed frequency and ambient medium properties. We find that intensive monitoring of a microlensed afterglow lightcurve can be used to reconstruct the parameters of the fireball and its environment, and provide constraints on particle acceleration and magnetic field amplification in relativistic blast waves.Comment: Final version, as published in ApJ Letter

Sustainable Building without Certification: An Exploration of Implications and Trends

Background: Sustainable buildings play a pivotal role in meeting the United Nations Sustainable Development Goals (UN SDGs). However, the criteria and process for certification associated with sustainable building rating systems have been seen by many as either cumbersome or too expensive. As a result, some buildings are constructed following sustainable building guidelines without necessarily pursuing external certification. This paper takes a critical look at sustainable building without certification in the US and addresses 3 questions: (1) What is the rationale behind not pursuing certification? (2) When certification is not part of the objective, how are particular sustainability criteria selected? (3) To what extent do sustainable building projects that undergo certification differ from those that do not, and what are the potential implications for building performance? Methods: The study is based on a survey of thirty-two professionals in the building, engineering, and construction industries, followed by semistructured interviews with nine participants about their experience with sustainable building certification. Results: The main rationale for not pursuing certification was associated with cost. The results also suggest that while buildings that have been formally certified may have higher capital costs, they are perceived more favorably with regard to brand reputation, marketability, credibility, meeting sustainability goals, building performance, and value to occupants than buildings without certification. Conclusions: This study provides insights into the implications of assessment-related decisions in building design and construction as we look to transform our societies into more sustainable, healthier, and livable places, and support global goals for sustainable development

AI Governance for Businesses

Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there is limited work on AI governance targeted to companies and corporations. This work views AI products as systems, where key functionality is delivered by machine learning (ML) models leveraging (training) data. We derive a conceptual framework by synthesizing literature on AI and related fields such as ML. Our framework decomposes AI governance into governance of data, (ML) models and (AI) systems along four dimensions. It relates to existing IT and data governance frameworks and practices. It can be adopted by practitioners and academics alike. For practitioners the synthesis of mainly research papers, but also practitioner publications and publications of regulatory bodies provides a valuable starting point to implement AI governance, while for academics the paper highlights a number of areas of AI governance that deserve more attention

Semaphorin 4D Promotes Skeletal Metastasis in Breast Cancer.

Bone density is controlled by interactions between osteoclasts, which resorb bone, and osteoblasts, which deposit it. The semaphorins and their receptors, the plexins, originally shown to function in the immune system and to provide chemotactic cues for axon guidance, are now known to play a role in this process as well. Emerging data have identified Semaphorin 4D (Sema4D) as a product of osteoclasts acting through its receptor Plexin-B1 on osteoblasts to inhibit their function, tipping the balance of bone homeostasis in favor of resorption. Breast cancers and other epithelial malignancies overexpress Sema4D, so we theorized that tumor cells could be exploiting this pathway to establish lytic skeletal metastases. Here, we use measurements of osteoblast and osteoclast differentiation and function in vitro and a mouse model of skeletal metastasis to demonstrate that both soluble Sema4D and protein produced by the breast cancer cell line MDA-MB-231 inhibits differentiation of MC3T3 cells, an osteoblast cell line, and their ability to form mineralized tissues, while Sema4D-mediated induction of IL-8 and LIX/CXCL5, the murine homologue of IL-8, increases osteoclast numbers and activity. We also observe a decrease in the number of bone metastases in mice injected with MDA-MB-231 cells when Sema4D is silenced by RNA interference. These results are significant because treatments directed at suppression of skeletal metastases in bone-homing malignancies usually work by arresting bone remodeling, potentially leading to skeletal fragility, a significant problem in patient management. Targeting Sema4D in these cancers would not affect bone remodeling and therefore could elicit an improved therapeutic result without the debilitating side effects

Microwave Electronics

Contains reports on three research projects

Probing the Mass Fraction of MACHOs in Extragalactic Halos

Current microlensing searches calibrate the mass fraction of the Milky Way halo which is in the form of Massive Compact Halo Objects (MACHOs). We show that surveys like the Sloan Digital Sky Survey (SDSS) can probe the same quantity in halos of distant galaxies. Microlensing of background quasars by MACHOs in intervening galaxies would distort the equivalent width distribution of the quasar emission lines by an amplitude that depends on the projected quasar-galaxy separation. For a statistical sample of detectable at the >2sigma level out to a quasar-galaxy impact parameter of several tens of kpc, as long as extragalactic halos are made of MACHOs. Detection of this signal would test whether the MACHO fraction inferred for the Milky-Way halo is typical of other galaxies.Comment: 12 pages, 2 figures, submitted to ApJ Letter

Measuring the Size of Quasar Broad-Line Clouds Through Time Delay Light-Curve Anomalies of Gravitational Lenses

Intensive monitoring campaigns have recently attempted to measure the time delays between multiple images of gravitational lenses. Some of the resulting light-curves show puzzling low-level, rapid variability which is unique to individual images, superimposed on top of (and concurrent with) longer time-scale intrinsic quasar variations which repeat in all images. We demonstrate that both the amplitude and variability time-scale of the rapid light-curve anomalies, as well as the correlation observed between intrinsic and microlensed variability, are naturally explained by stellar microlensing of a smooth accretion disk which is occulted by optically-thick broad-line clouds. The rapid time-scale is caused by the high velocities of the clouds (~5x10^3 km/s), and the low amplitude results from the large number of clouds covering the magnified or demagnified parts of the disk. The observed amplitudes of variations in specific lenses implies that the number of broad-line clouds that cover ~10% of the quasar sky is ~10^5 per 4 pi steradian. This is comparable to the expected number of broad line clouds in models where the clouds originate from bloated stars.Comment: 19 pages, 9 figures. Submitted to Ap

SegICP: Integrated Deep Semantic Segmentation and Pose Estimation

Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1cm position error and <5^\circ$ angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.Comment: IROS camera-read