University of Massachusetts Amherst

About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.

The ATLAS Simulation Infrastructure

Abstract: The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes.

Cloned transgenic calves produced from nonquiescent fetal fibroblasts

Abstract: An efficient system for genetic modification and large-scale cloning of cattle is of importance for agriculture, biotechnology, and human medicine. Here, actively dividing fetal fibroblasts were genetically modified with a marker gene, a clonal line was selected, and the cells were fused to enucleated mature oocytes. Out of 28 embryos transferred to 11 recipient cows, three healthy, identical, transgenic calves were generated. Furthermore, the life-span of near senescent fibroblasts could be extended by nuclear transfer, as indicated by population doublings in fibroblast lines derived from a 40-day-old fetal clone. With the ability to extend the life-span of these primary cultured cells, this system would be useful for inducing complex genetic modifications in cattle.

Multi-view Convolutional Neural Networks for 3D Shape Recognition

Abstract: A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors. Recognition rates further increase when multiple views of the shapes are provided. In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance. The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes. We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives.