Children’s drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children’s drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and broad appeal of our approach by building and releasing the Animated Drawings Demo, a freely available public website that has been used by millions of people around the world. We present a set of experiments exploring the amount of training data needed for fine-tuning, as well as a perceptual study demonstrating the appeal of a novel twisted perspective retargeting technique. Finally, we introduce the Amateur Drawings Dataset, a first-of-its-kind annotated dataset, collected via the public demo, containing over 178,000 amateur drawings and corresponding user-accepted character bounding boxes, segmentation masks, and joint location annotations.

Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and AI researchers do not have access to the datasets and frameworks that enable fast iteration and development of ideas, and getting started requires a significant engineering investment. What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field.We introduce CompilerGym, a set of environments for real world compiler optimization tasks, and a toolkit for exposing new optimization tasks to compiler researchers. CompilerGym enables anyone to experiment on production compiler optimization problems through an easy-to-use package, regardless of their experience with compilers. We build upon the popular OpenAI Gym interface enabling researchers to interact with compilers using Python and a familiar API.We describe the CompilerGym architecture and implementation, characterize the optimization spaces and computational efficiencies of three included compiler environments, and provide extensive empirical evaluations. Compared to prior works, CompilerGym offers larger datasets and optimization spaces, is 27× more computationally efficient, is fault-tolerant, and capable of detecting reproducibility bugs in the underlying compilers.In making it easy for anyone to experiment with compilers - irrespective of their background - we aim to accelerate progress in the AI and compiler research domains.

Abstract The sharing of information is an inherent tendency of human beings that helps in content dissemination as it cascades to the larger networked audience. In this article, we have proposed a novel weighted cumulative measure for discovering influential users in such networked structures. Our method generically combines the impact of multiple structural features of the network, along with local and global information to produce weighted cumulative centrality (WCC) score. Local information internments nodes direct neighborhood influence whereas global information scrutinizes network structure as a whole via propagating shortest paths. To assess the performance of our innovated measure, a benchmark diffusion model is employed as an artificial stochastic model to track the rate of information spread. The resultant outcome is verified on five different networks including scale‐free and random, and real‐world networks, namely, Facebook, BlogCatalog, and Twitter network. Furthermore, a comparative study is conducted to justify the spreading ability of proposed multifeature based WCC against existing single‐feature based approaches using relative performance ratio. Simulation with single and multiple triggering nodes endorse the efficacy of our proposed method over existing counterparts in terms of the coverage rate, thus showing support for early adoption phenomena in proposed WCC to infest a social network.

We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform. Our platform evaluates NLP models directly instead of relying on self-reported metrics or predictions on a single dataset. Under this paradigm, models are submitted to be evaluated in the cloud, circumventing the issues of reproducibility, accessibility, and backwards compatibility that often hinder benchmarking in NLP. This allows users to interact with uploaded models in real time to assess their quality, and permits the collection of additional metrics such as memory use, throughput, and robustness, which -- despite their importance to practitioners -- have traditionally been absent from leaderboards. On each task, models are ranked according to the Dynascore, a novel utility-based aggregation of these statistics, which users can customize to better reflect their preferences, placing more/less weight on a particular axis of evaluation or dataset. As state-of-the-art NLP models push the limits of traditional benchmarks, Dynaboard offers a standardized solution for a more diverse and comprehensive evaluation of model quality.