Shape from Shading through Shape Evolution

In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images. Our approach consists of two synergistic processes: the evolution of complex shapes from simple primitives, and the training of a deep network for shape-from-shading. The evolution generates better shapes guided by the network training, while the training improves by using the evolved shapes. We show that our approach achieves state-of-the-art performance on a shape-from-shading benchmark

Learning to Prove Theorems via Interacting with Proof Assistants

Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics. However, human experts have to construct proofs manually by entering tactics into the proof assistant. In this paper, we study the problem of using machine learning to automate the interaction with proof assistants. We construct CoqGym, a large-scale dataset and learning environment containing 71K human-written proofs from 123 projects developed with the Coq proof assistant. We develop ASTactic, a deep learning-based model that generates tactics as programs in the form of abstract syntax trees (ASTs). Experiments show that ASTactic trained on CoqGym can generate effective tactics and can be used to prove new theorems not previously provable by automated methods. Code is available at https://github.com/princeton-vl/CoqGym.Comment: Accepted to ICML 201

Learning Single-Image Depth from Videos using Quality Assessment Networks

Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild

Modeling ammonia emissions from dairy production systems in the United States

Dairy production systems are hot spots of ammonia (NH3) emission. However, there remains large uncertainty in quantifying and mitigating NH3 emissions from dairy farms due to the lack of both long-term field measurements and reliable methods for extrapolating these measurements. In this study, a process-based biogeochemical model, Manure-DNDC, was tested against measurements of NH3 fluxes from five barns and one lagoon in four dairy farms over a range of environmental conditions and management practices in the United States. Results from the validation tests indicate that the magnitudes and seasonal patterns of NH3 fluxes simulated by Manure-DNDC were in agreement with the observations across the sites. The model was then applied to assess impacts of alternative management practices on NH3 emissions at the farm scale. The alternatives included reduction of crude protein content in feed, replacement of scraping with flushing for removal of manure from barn, lagoon coverage, increase in frequency for removal of slurry from lagoon, and replacement of surface spreading with incorporation for manure land application. The simulations demonstrate that: (a) all the tested alternative management practices decreased the NH3 emissions although the efficiency of mitigation varied; (b) a change of management in an upstream facility affected the NH3 emissions from all downstream facilities; and (c) an optimized strategy by combining the alternative practices on feed, manure removal, manure storage, and land application could reduce the farm-scale NH3 emission by up to 50%. The results from this study may provide useful information for mitigating NH3 emissions from dairy production systems and emphasize the necessity of whole-farm perspectives on the assessment of potential technical options for NH3 mitigation. This study also demonstrates the potential of utilizing process-based models, such as Manure-DNDC, to quantify and mitigate NH3 emissions from dairy farms

Modeling impacts of changes in temperature and water table on C gas fluxes in an Alaskan peatland

Northern peatlands have accumulated a large amount of organic carbon (C) in their thick peat profile. Climate change and associated variations in soil environments are expected to have significant impacts on the C balance of these ecosystems, but the magnitude is still highly uncertain. Verifying and understanding the influences of changes in environmental factors on C gas fluxes in biogeochemical models are essential for forecasting feedbacks between C gas fluxes and climate change. In this study, we applied a biogeochemical model, DeNitrification-DeComposition (DNDC), to assess impacts of air temperature (TA) and water table (WT) on C gas fluxes in an Alaskan peatland. DNDC was validated against field measurements of net ecosystem exchange of CO2 (NEE) and CH4 fluxes under manipulated surface soil temperature and WT conditions in a moderate rich fen. The validation demonstrates that DNDC was able to capture the observed impacts of the manipulations in soil environments on C gas fluxes. To investigate responses of C gas fluxes to changes in TA and soil water condition, we conducted a series of simulations with varying TA and WT. The results demonstrate that (1) uptake rates of CO2 at the site were reduced by either too colder or warmer temperatures and generally increased with increasing soil moisture; (2) CH4 emissions showed an increasing trend as TAincreased or WT rose toward the peat surface; and (3) the site could shift from a net greenhouse gas (GHG) sink into a net GHG source under some warm and/or dry conditions. A sensitivity analysis evaluated the relative importance of TA and WT to C gas fluxes. The results indicate that both TA and WT played important roles in regulating NEE and CH4 emissions and that within the investigated ranges of the variations in TA and WT, changes in WT showed a greater impact than changes in TA on NEE, CH4 fluxes, and net C gas fluxes at the study fen

Site-wise manipulations and Mott insulator-superfluid transition of interacting photons using superconducting circuit simulators

The Bose Hubbard model (BHM) of interacting bosons in a lattice has been a paradigm in many-body physics, and it exhibits a Mott insulator (MI)-superfluid (SF) transition at integer filling. Here a quantum simulator of the BHM using a superconducting circuit is proposed. Specifically, a superconducting transmission line resonator supporting microwave photons is coupled to a charge qubit to form one site of the BHM, and adjacent sites are connected by a tunable coupler. To obtain a mapping from the superconducting circuit to the BHM, we focus on the dispersive regime where the excitations remain photon-like. Standard perturbation theory is implemented to locate the parameter range where the MI-SF transition may be simulated. This simulator allows single-site manipulations and we illustrate this feature by considering two scenarios where a single-site manipulation can drive a MI-SF transition. The transition can be analyzed by mean-field analyses, and the exact diagonalization was implemented to provide accurate results. The variance of the photon density and the fidelity metric clearly show signatures of the transition. Experimental realizations and other possible applications of this simulator are also discussed.Comment: 13 pages, 9 figure