Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Computer science & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

Haptic perception: a tutorial.

Abstract: This tutorial focuses on the sense of touch within the context of a fully active human observer. It is intended for graduate students and researchers outside the discipline who seek an introduction to the rapidly evolving field of human haptics. The tutorial begins with a review of peripheral sensory receptors in skin, muscles, tendons, and joints. We then describe an extensive body of research on “what” and “where” channels, the former dealing with haptic perception of objects, surfaces, and their properties, and the latter with perception of spatial layout on the skin and in external space relative to the perceiver. We conclude with a brief discussion of other significant issues in the field, including vision-touch interactions, affective touch, neural plasticity, and applications.

3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Abstract: Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera. Standard "dense" implementations of convolutional networks are very inefficient when applied on such sparse data. We introduce new sparse convolutional operations that are designed to process spatially-sparse data more efficiently, and use them to develop spatially-sparse convolutional networks. We demonstrate the strong performance of the resulting models, called submanifold sparse convolutional networks (SS-CNs), on two tasks involving semantic segmentation of 3D point clouds. In particular, our models outperform all prior state-of-the-art on the test set of a recent semantic segmentation competition.

A unified model for AGN feedback in cosmological simulations of structure formation

Abstract: We discuss a numerical model for black hole growth and its associated feedback processes that for the first time allows cosmological simulations of structure formation to self-consistently follow the build up of the cosmic population of galaxies and active galactic nuclei (AGNs). Our model assumes that seed black holes are present at early cosmic epochs at the centres of forming haloes. We then track their growth from gas accretion and mergers with other black holes in the course of cosmic time. For black holes that are active, we distinguish between two distinct modes of feedback, depending on the black hole accretion rate itself. Black holes that accrete at high rates are assumed to be in a ‘quasar regime’, where we model their feedback by thermally coupling a small fraction of their bolometric luminosity to the surrounding gas. The quasar activity requires high densities of relatively cold gas around the black hole, as it is achieved through large-scale inflows triggered by galaxy mergers. For black holes with low accretion rates, we conjecture that most of their feedback occurs in mechanical form, where AGN-driven bubbles are injected into a gaseous environment. This regime of activity, which is subdominant in terms of total black hole mass growth, can be identified with radio galaxies in clusters of galaxies, and can suppress cluster cooling flows without the requirement of a triggering by mergers. Using our new model, we carry out TREESPH cosmological simulations on the scales of individual galaxies to those of massive galaxy clusters, both for isolated systems and for cosmological boxes. We demonstrate that our model produces results for the black hole and stellar mass densities in broad agreement with observational constraints. We find that the black holes significantly influence the evolution of their host galaxies, changing their star formation history, their amount of cold gas and their colours. Furthermore, the properties of intracluster gas are affected strongly by the presence of massive black holes in the cores of galaxy clusters, leading to shallower metallicity and entropy profiles, and to a suppression of strong cooling flows. Our results support the notion that AGNs are a key ingredient in cosmological structure formation. They lead to a self-regulated growth of black holes and bring the simulated properties of their host galaxies into much better agreement with observations.

How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise

Abstract: In theory, the sum of squares of log returns sampled at high frequency estimates their variance. When market microstructure noise is present but unaccounted for, however, we show that the optimal sampling frequency is finite and derive its closed-form expression. But even with optimal sampling, using say five minute returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. We demonstrate that modelling the noise and using all the data is a better solution, even if one misspecifies the noise distribution. So the answer is: sample as often as possible.

Product familiarity and learning new information

Abstract: Does product familiarity improve shoppers' ability to learn new product information? We examine an earlier study which indicated that greater familiarity increased learning during a new purchase decision. Our reanalysis confirms that the effect depends strongly upon decision strategy. Familiarity facilitates learning when consumers rate each alternative, but when consumers are instructed to choose one alternative, an “inverted u” relationship between familiarity and learning results. Our new analyses also show that consumers familiar with the product category demonstrate stronger brand organization for the new information.