226,513 research outputs found
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
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