Transverse mode control and switching in gas laser arrays

Theoretical and experimental investigations of multiple transverse mode laser oscillation involving spatially varying gain and loss are carried out. The effect of gain and loss distribution on mode competition is analyzed. Numerical examples are given for a CO2 waveguide laser array. Experimental results of CO2 laser arrays are found to be consistent with the theory, and robust in-phase coupled mode array operation has been achieved

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

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

Visual Object Networks: Image Generation with Disentangled 3D Representation

Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model, Visual Object Networks (VON), synthesizing natural images of objects with a disentangled 3D representation. Inspired by classic graphics rendering pipelines, we unravel our image formation process into three conditionally independent factors---shape, viewpoint, and texture---and present an end-to-end adversarial learning framework that jointly models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes. It then renders the object's 2.5D sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches to generate natural images. The VON not only generates images that are more realistic than state-of-the-art 2D image synthesis methods, but also enables many 3D operations such as changing the viewpoint of a generated image, editing of shape and texture, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.Comment: NeurIPS 2018. Code: https://github.com/junyanz/VON Website: http://von.csail.mit.edu

Predicting all-cause mortality from basic physiology in the Framingham Heart Study

Using longitudinal data from a cohort of 1349 participants in the Framingham Heart Study, we show that as early as 28–38 years of age, almost 10% of variation in future lifespan can be predicted from simple clinical parameters. Specifically, we found diastolic and systolic blood pressure, blood glucose, weight, and body mass index (BMI) to be relevant to lifespan. These and similar parameters have been well‐characterized as risk factors in the relatively narrow context of cardiovascular disease and mortality in middle to old age. In contrast, we demonstrate here that such measures can be used to predict all‐cause mortality from mid‐adulthood onward. Further, we find that different clinical measurements are predictive of lifespan in different age regimes. Specifically, blood pressure and BMI are predictive of all‐cause mortality from ages 35 to 60, while blood glucose is predictive from ages 57 to 73. Moreover, we find that several of these parameters are best considered as measures of a rate of ‘damage accrual’, such that total historical exposure, rather than current measurement values, is the most relevant risk factor (as with pack‐years of cigarette smoking). In short, we show that simple physiological measurements have broader lifespan‐predictive value than indicated by previous work and that incorporating information from multiple time points can significantly increase that predictive capacity. In general, our results apply equally to both men and women, although some differences exist

Direct Evidence for the Source of Reported Magnetic Behavior in "CoTe"

In order to unambiguously identify the source of magnetism reported in recent studies of the Co-Te system, two sets of high-quality, epitaxial CoTe$_x$ films (thickness $\simeq$ 300 nm) were prepared by pulse laser deposition (PLD). X-ray diffraction (XRD) shows that all of the films are epitaxial along the [001] direction and have the hexagonal NiAs structure. There is no indication of any second phase metallic Co peaks (either $fcc$ or $hcp$) in the XRD patterns. The two sets of CoTe$_x$ films were grown on various substrates with PLD targets having Co:Te in the atomic ratio of 50:50 and 35:65. From the measured lattice parameters $c = 5.396 \AA$ for the former and $c = 5.402\AA$ for the latter, the compositions CoTe$_{1.71}$ (63.1% Te) and CoTe$_{1.76}$ (63.8% Te), respectively, are assigned to the principal phase. Although XRD shows no trace of metallic Co second phase, the magnetic measurements do show a ferromagnetic contribution for both sets of films with the saturation magnetization values for the CoTe$_{1.71}$ films being approximately four times the values for the CoTe$_{1.76}$ films. $^{59}$Co spin-echo nuclear magnetic resonance (NMR) clearly shows the existence of metallic Co inclusions in the films. The source of weak ferromagnetism reported in several recent studies is due to the presence of metallic Co, since the stoichiometric composition "CoTe" does not exist.Comment: 19 pages, 7 figure

Neutron Diffraction and Magnetic Studies of RFe₁₂₋ₓTₓC\u3csub\u3ey\u3c/sub\u3e (R=Y,Er; T=V,Ti,Mo) Alloys

RFe12-xTxCy, (R=Y,Er; T=V,Ti,Mo) alloys were prepared by rf induction melting and analyzed using neutron powder diffraction and superconducting quantum interference device (SQUID) measurements. Rietveld analysis of the neutron diffraction data indicates that V, Ti, and Mo atoms all prefer the 8i sites. The refined amount of carbon atoms found in the interstitial sites from neutron diffraction data is significantly less than the nominal carbon content. All samples have the easy direction along the c axis. The Er sublattice couples to the Fe sublattice antiferromagnetically. The average Fe site moments range from 1.3 to 2.8 μB. The anisotropies of the crystal structures are found to relate to both the rare earth atoms and the stabilizing transition metal atoms. The SQUID measurements show that all samples have a Curie temperature near 600 K