Impact Investing 2.0: The Way Forward

"Impact Investing 2.0: The Way Forward – Insight from 12 Outstanding Funds", created in partnership with the Center for the Advancement of Social Entrepreneurship (CASE) at Duke University and Impact Assets, identifies twelve high-performing funds that have seen both financial and social returns on their investments. This report is designed to be a resource for the broad community interested in the future of impact investing, but especially for impact investing practitioners – those fund managers, investors, entrepreneurs, policymakers and advisors creating and managing new and existing funds and working hard to achieve successful social and financial performance

From Ideas to Practice, Pilots to Strategy: Practical Solutions and Actionable Insights on How to Do Impact Investing

This report is the second publication in the World Economic Forum's Mainstreaming Impact Investing Initiative. The report takes a deeper look at why and how asset owners began to include impact investing in their portfolios and continue to do so today, and how they overcame operational and cultural constraints affecting capital flow. Given that impact investing expertise is spread among dozens if not hundreds of practitioners and academics, the report is a curation of some -- but certainly not all -- of those leading voices. The 15 articles are meant to provide investors, intermediaries and policy-makers with actionable insights on how to incorporate impact investing into their work.The report's goals are to show how mainstream investors and intermediaries have overcome the challenges in the impact investment sector, and to democratize the insights and expertise for anyone and everyone interested in the field. Divided into four main sections, the report contains lessons learned from practitioner's experience, and showcases best practices, organizational structures and innovative instruments that asset owners, asset managers, financial institutions and impact investors have successfully implemented

Laparoscopic supracervical hysterectomy versus endometrial ablation for women with heavy menstrual bleeding (HEALTH) : a parallel-group, open-label, randomised controlled trial

UK National Institute for Health Research Health Technology Assessment Programme. Acknowledgments We thank the women who participated in the HEALTH study. We also thank Angela Hyde (Vice Chair of the Royal College of Obstetricians and Gynaecologists Women's Network until September, 2015, and co-applicant on the original grant application until October, 2016) for her contribution to the design of the participant facing documents and participation in trial meetings from the perspective of a patient, Jonathan Cook (statistician and co-applicant on the original grant application until April, 2014) for his contributions to the study design, Rebecca Bruce for her secretarial support and data management, members of the project management group for their ongoing advice and support of the trial, plus the independent members of the trial steering committee (Henry Kitchener [Chair], Patrick Chien, Barbara Farrell, and Isobel Montgomery) and data monitoring committee (Jane Norman [Chair], Peter O'Donovan, and Andy Vail), and the staff at the recruitment sites who facilitated recruitment, treatment, and follow-up of trial participants. The project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment Programme (project number 12/35/23). The Health Services Research Unit and the Health Economics Research Unit are funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. The views expressed herein are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care.Peer reviewedPublisher PD

Modelling Automation–Human Driver Handovers Using Operator Event Sequence Diagrams

This research aims to show the effectiveness of Operator Event Sequence Diagrams (OESDs) in the normative modelling of vehicle automation to human drivers’ handovers and validate the models with observations from a study in a driving simulator. The handover of control from automation to human operators has proved problematic, and in the most extreme circumstances catastrophic. This is currently a topic of much concern in the design of automated vehicles. OESDs were used to inform the design of the interaction, which was then tested in a driving simulator. This test provided, for the first time, the opportunity to validate OESDs with data gathered from videoing the handover processes. The findings show that the normative predictions of driver activity determined during the handover from vehicle automation in a driving simulator performed well, and similar to other Human Factors methods. It is concluded that OESDs provided a useful method for the human-centred automation design and, as the predictive validity shows, can continue to be used with some confidence. The research in this paper has shown that OESDs can be used to anticipate normative behaviour of drivers engaged in handover activities with vehicle automation in a driving simulator. Therefore, OESDs offer a useful modelling tool for the Human Factors profession and could be applied to a wide range of applications and domains.</jats:p

Ground and In-Flight Calibration of the OSIRIS-REx Camera Suite

The OSIRIS-REx Camera Suite (OCAMS) onboard the OSIRIS-REx spacecraft is used to study the shape and surface of the mission’s target, asteroid (101955) Bennu, in support of the selection of a sampling site. We present calibration methods and results for the three OCAMS cameras—MapCam, PolyCam, and SamCam—using data from pre-flight and in-flight calibration campaigns. Pre-flight calibrations established a baseline for a variety of camera properties, including bias and dark behavior, flat fields, stray light, and radiometric calibration. In-flight activities updated these calibrations where possible, allowing us to confidently measure Bennu’s surface. Accurate calibration is critical not only for establishing a global understanding of Bennu, but also for enabling analyses of potential sampling locations and for providing scientific context for the returned sample

Laparoscopic supracervical hysterectomy compared with second-generation endometrial ablation for heavy menstrual bleeding:the HEALTH RCT

Peer reviewedPublisher PD

The ocean sampling day consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201