Learning to Infer Graphics Programs from Hand-Drawn Images

We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input

ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels. The key contribution of our method is its ability to handle large scenes with varying spatial extent, managing the cubic growth in data size as scene size increases. To this end, we devise a fully-convolutional generative 3D CNN model whose filter kernels are invariant to the overall scene size. The model can be trained on scene subvolumes but deployed on arbitrarily large scenes at test time. In addition, we propose a coarse-to-fine inference strategy in order to produce high-resolution output while also leveraging large input context sizes. In an extensive series of experiments, we carefully evaluate different model design choices, considering both deterministic and probabilistic models for completion and semantic inference. Our results show that we outperform other methods not only in the size of the environments handled and processing efficiency, but also with regard to completion quality and semantic segmentation performance by a significant margin.Comment: Video: https://youtu.be/5s5s8iH0NF

Pathogenic, Molecular, and Immunological Properties of a Virus Associated with Sea Turtle Fibropapillomatosis. Phase II : Viral Pathogenesis and Development of Diagnostic Assays

Research conducted under this RWO from July 1, 1997 through June 30, 2000 has provided important new information about the pathogenesis, virology, and immunology of marine turtle fibropapillomatosis. In particular, we have provided strong evidence for the association of a herpesvirus with fibropapillomatosis of the green turtle,Chelonia mydas, and the loggerhead turtle, Caretta caretta, in Florida. In addition we have provided new evidence for the absence of papillomaviruses from sea turtle fibropapillomas. Although unsuccessful, important new attempts were made to cultivate the FP-associated herpesvirus in vitro in collaboration with the National Wildlife Health Center. During this period of time, we completed publication of the first comprehensive description of the comparative pathology and pathogenesis of experimentally induced and spontaneous fibropapillomas of green turtles (Chelonia mydas). We initiated innovative studies on the persistence of a Chelonian herpesviruses in the marine environment demonstrating for the first time that the environmental survivability of Chelonian herpesviruses makes them real threats to marine turtle health. Finally, we explored development of a serological assay for FP using synthetic herpesvirus peptides and developed methodologies for detection of antibodies to LETV [Iung-eye-trachea virus] a disease-associated herpesvirus of the green turtle, Chelonia mydas.. This last initiative is ongoing and will further our efforts to develop specific immunological assays for the FP-associated herpesvirus and FP. (17 page document

Scenic: A Language for Scenario Specification and Scene Generation

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.Comment: 41 pages, 36 figures. Full version of a PLDI 2019 paper (extending UC Berkeley EECS Department Tech Report No. UCB/EECS-2018-8

Alzheimer disease genetic risk factor APOE e4, and cognitive abilities in 111,739 UK Biobank participants

Background: the apolipoprotein (APOE) e4 locus is a genetic risk factor for dementia. Carriers of the e4 allele may be more vulnerable to conditions that are independent risk factors for cognitive decline, such as cardiometabolic diseases. Objective: we tested whether any association with APOE e4 status on cognitive ability was larger in older ages or in those with cardiometabolic diseases. Subjects: UK Biobank includes over 500,000 middle- and older aged adults who have undergone detailed medical and cognitive phenotypic assessment. Around 150,000 currently have genetic data. We examined 111,739 participants with complete genetic and cognitive data. Methods: baseline cognitive data relating to information processing speed, memory and reasoning were used. We tested for interactions with age and with the presence versus absence of type 2 diabetes (T2D), coronary artery disease (CAD) and hypertension. Results: in several instances, APOE e4 dosage interacted with older age and disease presence to affect cognitive scores. When adjusted for potentially confounding variables, there was no APOE e4 effect on the outcome variables. Conclusions: future research in large independent cohorts should continue to investigate this important question, which has potential implications for aetiology related to dementia and cognitive impairment