Geographic variation in thermal physiological traits: the role of thermal stress on telomere length in a polymorphic ectotherm

Geographic variation in thermal adaption is common across ectothermic organisms due to their intrinsic metabolic link with ambient temperatures. However, in the face of altered thermal regimes, ectotherms may incur an array of biomolecular costs associated with suboptimal temperatures. As such, this thesis aimed to investigate the effect of altered thermal regimes on the thermal physiology of two populations of a polymorphic lizard Ctenophorus pictus. Lizards from both a warm adapted and cool adapted population were acclimated in either a warm or cool treatment for three-months, and various metrics of thermal physiology were assessed pre-and post-acclimation. Chapter 2: Populations were found to differ significantly in several traits such as metabolic rate, body size, basking behaviour and reproductive investment, potentially indicating thermal adaption between the populations. Surprisingly, temperature treatment only influenced post-maturity skeletal growth, with individuals in the cool treatment growing considerably longer, suggesting equivalent levels of phenotypic plasticity across both populations. Chapter 3: Telomere length as a potential biomolecular cost of sub-optimal temperatures was quantified via qPCR from blood samples in the field, pre-and post-acclimation. Surprisingly, telomere length was only significantly shorter for the cold-adapted population in the cool room, contrasting with the majority of theoretical predictions. Increased basking activity due to the decreased temperatures may account for such a result; however, no such trend was seen in the warm adapted population. Population-specific telomere response to altered thermal regimes found in this experiment is the first of its kind and represents a tantalizing new ecological research area. Overall, the results of this thesis demonstrate levels of thermal adaptation within both populations of C. pictus as well as levels of thermal plasticity to altered thermal regimes. The potential telomeric cost of thermal acclimation in ectotherms is an underexplored phenomenon, with more research needed to delineate the complex and counterintuitive relationship between temperature and telomere response

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