10,000 publications from this institution
We present a framework for learning single-view shape and pose prediction\nwithout using direct supervision for either. Our approach allows leveraging\nmulti-view observations from unknown poses as supervisory signal during\ntraining. Our proposed training setup enforces geometric consistency between\nthe independently predicted shape and pose from two views of the same instance.\nWe consequently learn to predict shape in an emergent canonical (view-agnostic)\nframe along with a corresponding pose predictor. We show empirical and\nqualitative results using the ShapeNet dataset and observe encouragingly\ncompetitive performance to previous techniques which rely on stronger forms of\nsupervision. We also demonstrate the applicability of our framework in a\nrealistic setting which is beyond the scope of existing techniques: using a\ntraining dataset comprised of online product images where the underlying shape\nand pose are unknown.\n
Metal ion cofactors are necessary for prenyltransferase enzymes. Magnesium and manganese can be used as metal ion cofactor by rubber transferase (a cis-prenyltransferase) associated with purified rubber particles. The rubber initiation rate, biosynthetic rate, and molecular weight produced in vitro from Hevea brasiliensis rubber transferase is regulated by metal ion concentration. In addition, varies significantly with [Mg(2+)]. decreases from 8000 +/- 600 microM at [Mg(2+)] = 4 mM to 68 +/- 10 microM at [Mg(2+)] = 8 mM and increases back to 970 +/- 70 microM at [Mg(2+)] = 30 mM. The highest affinity of rubber transferase for IPP.Mg occurred when [Mg(2+)] = A(max) (metal concentration that gives highest IPP incorporation rate). A metal ion is required for rubber biosynthesis, but an excess of metal ions interacts with the rubber transferase inhibiting its activity. The results suggest that H. brasiliensis could use [Mg(2+)] as a regulatory mechanism for rubber biosynthesis and molecular weight in vivo.
Heterogeneous catalysts, used in industry for the production of fuels and chemicals, are microporous solids characterized by a high internal surface area. The catalyticly active sites may occur at the surface of the bulk solid or of small crystallites deposited on a porous support. An example of the former case would be a zeolite, and of the latter, a supported metal catalyst. Since the activity and selectivity of a catalyst are known to be a function of surface composition and structure, it is highly desirable to characterize catalyst surfaces with atomic scale resolution. Where the active phase is dispersed on a support, it is also important to know the dispersion of the deposited phase, as well as its structural and compositional uniformity, the latter characteristics being particularly important in the case of multicomponent catalysts. Knowledge of the pore size and shape is also important, since these can influence the transport of reactants and products through a catalyst and the dynamics of catalyst deactivation.
Cluster expansions have proven to be a valuable tool in alloy theory and other problems in materials science but the generation of cluster expansions can be a computationally expensive and time-consuming process. We present a Bayesian framework for developing cluster expansions that explicitly incorporates physical insight into the fitting procedure. We demonstrate how existing methods fit within this framework and use the framework to develop methods that significantly improve the predictive power of cluster expansions for a given training set size. The key to the methods is to apply physical insight and cross validation to develop physically meaningful prior probability distributions for the cluster expansion coefficients. We use the Bayesian approach to develop an efficient method for generating cluster expansions for low-symmetry systems such as surfaces and nanoparticles.
A general plate model based on the peridynamic theory of solid mechanics is presented. The model is derived as a two-dimensional approximation of the three-dimensional bond-based theory of peridynamics via an asymptotic analysis. The resulting plate theory is demonstrated using a specially designed peridynamics code to simulate the fracture of a brittle plate with a central crack under tensile loading.
Abstract As a result of their unique architecture and structural as well as functional versatility, dendrimers have generated considerable interest in numerous areas of the physical sciences, engineering, as well as the biological sciences. Both their size ‐ in the 1‐10 nm range ‐ and their globular shape resemble those of many proteins suggesting a host of biomimetic and nanotechnological applications. This brief highlight describes some of our recent work with nascent applications of dendrimers as unimolecular nanoreactors, as nanoscale antennae for energy harvesting and transduction, and as nanosized carriers for diagnostic or therapeutic applications. While implementation of some of these applications may still be distant, the impatient critic might remember that new markets are not created overnight as demonstrated by the slow commercial acceptance of many promising molecules and technologies with development frequently extending decades after their initial discovery.
