Search

Palm oil production has been identified as one of the major drivers of deforestation for tropical countries. To meet supply chain objectives, commodity producers and other stakeholders need timely information of land cover dynamics in their supply shed. However, such data are difficult to obtain from suppliers who may lack digital geographic representations of their supply sheds and production locations. Here we present a "community model," a machine learning model trained on pooled data sourced from many different stakeholders, to develop a specific land cover probability map, in this case a semi-global oil palm map. An advantage of this method is the inclusion of varied inputs, the ability to easily update the model as new training data becomes available and run the model on any year that input imagery is available. Inclusion of diverse data sources into one probability map can help establish a shared understanding across stakeholders on the presence and absence of a land cover or commodity (in this case oil palm). The model predictors are annual composites built from publicly available satellite imagery provided by Sentinel-1, Sentinel-2, and ALOS DSM. We provide map outputs as the probability of palm in a given pixel, to reflect the uncertainty of the underlying state (palm or not palm). The initial version of this model provides global accuracy estimated to be approximately 90% (at 0.5 probability threshold) from spatially partitioned test data. This model, and resulting oil palm probability map products are useful for accurately identifying the geographic footprint of palm cultivation. Used in conjunction with timely deforestation information, this palm model is useful for understanding the risk of continued oil palm plantation expansion in sensitive forest areas., Comment: v0

The public education landscape has changed dramatically since the start of the COVID-19 pandemic. Communities and school systems throughout the country have faced unimaginable consequences from this public health crisis and the disruption to K-12 public schools. The pandemic's disproportionate impact on low-income communities of color, along with growing national awareness around racial injustice, have heightened concerns about inequity and spurred well-publicized and politicized debates around how schools should respond to these challenges. Yet parents' perspectives are often missing from debates on these issues. This report examines how parents experienced school and school system responses to the pandemic and heightened attention to racial injustice. We also explore how the pandemic has changed parents' attitudes and preferences related to school choice. Using online opt-in survey data from 3,654 parents across five states (Colorado, Florida, Louisiana, Michigan, and Oregon) during spring 2021, we draw six core findings: (1) Parents on average were generally satisfied with their schools during COVID-19, with some groups of parents expressing particularly high levels of satisfaction: parents in private schools, those with higher incomes, those with kids learning in person, and those residing in Florida; (2) Yet, many parents felt their child was receiving less instruction than they would in a typical year; (3) A majority of parents wanted to see an emphasis on race, equity, and diversity in the school curriculum; (4) Many parents were considering remote schooling options for the coming year (2021-22); (5) When considering where to enroll their child, parents reported that health and safety protocols, a caring environment, and social-emotional learning increased in importance since the start of the pandemic; and (6) Lower income parents, Black parents, and to a lesser extent, Latinx parents were more likely to indicate concerns about their children's education relative to other groups.

How do international crises unfold? We conceptualize of international relations as a strategic chess game between adversaries and develop a systematic way to measure pieces, moves, and gambits accurately and consistently over a hundred years of history. We introduce a new ontology and dataset of international events called ICBe based on a very high-quality corpus of narratives from the International Crisis Behavior (ICB) Project. We demonstrate that ICBe has higher coverage, recall, and precision than existing state of the art datasets and conduct two detailed case studies of the Cuban Missile Crisis (1962) and Crimea-Donbas Crisis (2014). We further introduce two new event visualizations (event icongraphy and crisis maps), an automated benchmark for measuring event recall using natural language processing (sythnetic narratives), and an ontology reconstruction task for objectively measuring event precision. We make the data, online appendix, replication material, and visualizations of every historical episode available at a companion website www.crisisevents.org and the github repository., Comment: v1.1

We use data from the 2020 PACE/USC Rossier annual voter poll to report on California voters' attitudes towards educational equity policy initiatives, specifically: (a) increasing the number of public school teachers of color in California and (b) requiring all high school students in California to complete an ethnic studies course. A majority of voters supported these initiatives. Respondents showed higher levels of support for increasing the number of teachers of color when informed about the positive academic impact this would likely have for students of color. Support varied depending on voters' race/ethnicity and political party. White respondents were less likely to support the initiatives as compared to African American and Latinx respondents; Republicans were less likely to show support as compared to Democrats and Independents.

Here we report the development of high-efficiency microscale GaAs laser power converters, and their successful transfer printing onto silicon substrates, presenting a unique, high power, low-cost and integrated power supply solution for implantable electronics, autonomous systems and internet of things applications. We present 300 {\mu}m diameter single-junction GaAs laser power converters and successfully demonstrate the transfer printing of these devices to silicon using a PDMS stamp, achieving optical power conversion efficiencies of 48% and 49% under 35 and 71 W/cm2 808 nm laser illumination respectively. The transferred devices are coated with ITO to increase current spreading and are shown to be capable of handling very high short-circuit current densities up to 70 A/cm2 under 141 W/cm2 illumination intensity (~1400 Suns), while their open circuit voltage reaches 1235 mV, exceeding the values of pre-transfer devices indicating the presence of photon-recycling. These optical power sources could deliver Watts of power to sensors and systems in locations where wired power is not an option, while using a massively parallel, scalable, and low-cost fabrication method for the integration of dissimilar materials and devices., Comment: 16 pages, 6 figures