A finite-strain hyperviscoplastic model and undrained triaxial tests of peat

This paper presents a finite-strain hyperviscoplastic constitutive model within a thermodynamically consistent framework for peat which was categorised as a material with both rate-dependent and thermodynamic equilibrium hysteresis based on the data reported in the literature. The model was implemented numerically using implicit time integration and verified against analytical solutions under simplified conditions. Experimental studies on the undrained relaxation and loading-unloading-reloading behaviour of an undisturbed fibrous peat were carried out to define the thermodynamic equilibrium state during deviatoric loading as a prerequisite for further modelling, to fit particularly those model parameters related to solid matrix properties, and to validate the proposed model under undrained conditions. This validation performed by comparison to experimental results showed that the hyperviscoplastic model could simulate undrained triaxial compression tests carried out at five different strain rates with loading/unloading relaxation steps.Comment: 30 pages, 16 figures, 4 tables. This is a pre-peer reviewed version of manuscript submitted to the International Journal of Numerical and Analytical Methods in Geomechanic

A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding

Humans demonstrate remarkable abilities to predict physical events in complex scenes. Two classes of models for physical scene understanding have recently been proposed: "Intuitive Physics Engines", or IPEs, which posit that people make predictions by running approximate probabilistic simulations in causal mental models similar in nature to video-game physics engines, and memory-based models, which make judgments based on analogies to stored experiences of previously encountered scenes and physical outcomes. Versions of the latter have recently been instantiated in convolutional neural network (CNN) architectures. Here we report four experiments that, to our knowledge, are the first rigorous comparisons of simulation-based and CNN-based models, where both approaches are concretely instantiated in algorithms that can run on raw image inputs and produce as outputs physical judgments such as whether a stack of blocks will fall. Both approaches can achieve super-human accuracy levels and can quantitatively predict human judgments to a similar degree, but only the simulation-based models generalize to novel situations in ways that people do, and are qualitatively consistent with systematic perceptual illusions and judgment asymmetries that people show.Comment: Accepted to CogSci 2016 as an oral presentatio

Learning to Reconstruct Shapes from Unseen Classes

From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction algorithms aim to solve this task in a similar fashion, but often end up with priors that are highly biased by training classes. Here we present an algorithm, Generalizable Reconstruction (GenRe), designed to capture more generic, class-agnostic shape priors. We achieve this with an inference network and training procedure that combine 2.5D representations of visible surfaces (depth and silhouette), spherical shape representations of both visible and non-visible surfaces, and 3D voxel-based representations, in a principled manner that exploits the causal structure of how 3D shapes give rise to 2D images. Experiments demonstrate that GenRe performs well on single-view shape reconstruction, and generalizes to diverse novel objects from categories not seen during training.Comment: NeurIPS 2018 (Oral). The first two authors contributed equally to this paper. Project page: http://genre.csail.mit.edu

Non-Extensive Quantum Statistics with Particle - Hole Symmetry

Based on Tsallis entropy and the corresponding deformed exponential function, generalized distribution functions for bosons and fermions have been used since a while. However, aiming at a non-extensive quantum statistics further requirements arise from the symmetric handling of particles and holes (excitations above and below the Fermi level). Naive replacements of the exponential function or cut and paste solutions fail to satisfy this symmetry and to be smooth at the Fermi level at the same time. We solve this problem by a general ansatz dividing the deformed exponential to odd and even terms and demonstrate that how earlier suggestions, like the kappa- and q-exponential behave in this respect

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cutting-edge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.Comment: CVPR 2018. The first two authors contributed equally to this work. Project page: http://pix3d.csail.mit.ed

Design of magnetic traps for neutral atoms with vortices in type-II superconducting micro-structures

We design magnetic traps for atoms based on the average magnetic field of vortices induced in a type-II superconducting thin film. This magnetic field is the critical ingredient of the demonstrated vortex-based atom traps, which operate without transport current. We use Bean's critical-state method to model the vortex field through mesoscopic supercurrents induced in the thin strip. The resulting inhomogeneous magnetic fields are studied in detail and compared to those generated by multiple normally-conducting wires with transport currents. Various vortex patterns can be obtained by programming different loading-field and transport current sequences. These variable magnetic fields are employed to make versatile trapping potentials.Comment: 11 pages, 14 figure