MUSE: robust surface fitting using unbiased scale estimates

Despite many successful applications of robust statistics, they have yet to be completely adapted to many computer vision problems. Range reconstruction, particularly in unstructured environments, requires a robust estimator that not only tolerates a large outlier percentage but also tolerates several discontinuities, extracting multiple surfaces in an image region. Observing that random outliers and/or points from across discontinuities increase a hypothesized fit's scale estimate (standard deviation of the noise), our new operator, called MUSE (Minimum Unbiased Scale Estimator), evaluates a hypothesized fit over potential inlier sets via an objective function of unbiased scale estimates. MUSE extracts the single best fit from the data by minimizing its objective function over a set of hypothesized fits and can sequentially extract multiple surfaces from an image region. We show MUSE to be effective on synthetic data modelling small scale discontinuities and in preliminary experiments..

Combining feature aggregation and geometric similarity for re-identification of patterned animals

Image-based re-identification of animal individuals allows gathering of information such as migration patterns of the animals over time. This, together with large image volumes collected using camera traps and crowdsourcing, opens novel possibilities to study animal populations. For many species, the re-identification can be done by analyzing the permanent fur, feather, or skin patterns that are unique to each individual. In this paper, we address the re-identification by combining two types of pattern similarity metrics: 1) pattern appearance similarity obtained by pattern feature aggregation and 2) geometric pattern similarity obtained by analyzing the geometric consistency of pattern similarities. The proposed combination allows to efficiently utilize both the local and global pattern features, providing a general re-identification approach that can be applied to a wide variety of different pattern types. In the experimental part of the work, we demonstrate that the method achieves promising re-identification accuracies for Saimaa ringed seals and whale sharks.Comment: Camera traps, AI, and Ecology, 3rd International Worksho

Wildbook: Crowdsourcing, computer vision, and data science for conservation

Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for whales (flukebook.org), sharks (whaleshark.org), two species of zebras (Grevy's and plains), and several others. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List: http://www.iucnredlist.org/details/7950/0. In 2016, Wildbook partnered up with WWF to build Wildbook for Sea Turtles, Internet of Turtles (IoT), as well as systems for seals and lynx. Most recently, we have demonstrated that we can now use publicly available social media images to count and track wild animals. In this paper we present and discuss both the impact and challenges that the use of crowdsourced images can have on wildlife conservation.Comment: Presented at the Data For Good Exchange 201

Sediment-Water Interactions Affecting Dissolved-Mercury Distributions in Camp Far West Reservoir, California

Field and laboratory studies were conducted in April and November 2002 to provide the first direct measurements of the benthic flux of dissolved (0.2-micrometer filtered) mercury species (total and methylated forms) between the bottom sediment and water column at three sampling locations within Camp Far West Reservoir, California: one near the Bear River inlet to the reservoir, a second at a mid-reservoir site of comparable depth to the inlet site, and the third at the deepest position in the reservoir near the dam (herein referred to as the inlet, midreservoir and near-dam sites, respectively; Background, Fig. 1). Because of interest in the effects of historic hydraulic mining and ore processing in the Sierra Nevada foothills just upstream of the reservoir, dissolved-mercury species and predominant ligands that often control the mercury speciation (represented by dissolved organic carbon, and sulfides) were the solutes of primary interest. Benthic flux, sometimes referred to as internal recycling, represents the transport of dissolved chemical species between the water column and the underlying sediment. Because of the affinity of mercury to adsorb onto particle surfaces and to form insoluble precipitates (particularly with sulfides), the mass transport of mercury in mining-affected watersheds is typically particle dominated. As these enriched particles accumulate at depositional sites such as reservoirs, benthic processes facilitate the repartitioning, transformation, and transport of mercury in dissolved, biologically reactive forms (dissolved methylmercury being the most bioavailable for trophic transfer). These are the forms of mercury examined in this study. In contrast to typical scientific manuscripts, this report is formatted in a pyramid-like structure to serve the needs of diverse groups who may be interested in reviewing or acquiring information at various levels of technical detail (Appendix 1). The report enables quick transitions between the initial summary information (figuratively at the top of the pyramid) and the later details of methods or results (figuratively towards the base of the pyramid) using hyperlinks to supporting figures and tables, and an electronically linked Table of Contents. During two sampling events, two replicate sediment cores (Coring methods; Fig. 2) from each of three reservoir locations (Fig. 1) were used in incubation experiments to provide flux estimates and benthic biological characterizations. Incubation of these cores provided “snapshots” of solute flux across the sediment-water interface in the reservoir, under benthic, environmental conditions representative of the time and place of collection. Ancillary data, including nutrient and ligand fluxes, were gathered to provide a water-quality framework from which to compare the results for mercury

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

Avelumab Alone or in Combination With Chemotherapy Versus Chemotherapy Alone in Platinum-Resistant or Platinum-Refractory Ovarian Cancer (JAVELIN Ovarian 200): An Open-Label, Three-Arm, Randomised, Phase 3 Study

The majority of patients with ovarian cancer will experience relapse and develop platinum-resistant disease after being treated with frontline platinum-based chemotherapy. Treatment options for platinum-resistance or platinum-refractory disease are very limited, usually involving nonplatinum chemotherapy, and they are associated with poor objective response rates and life expectancy