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: Population & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

Does Positive Affect Influence Health

Abstract: This review highlights consistent patterns in the literature associating positive affect (PA) and physical health. However, it also raises serious conceptual and methodological reservations. Evidence suggests an association of trait PA and lower morbidity and of state and trait PA and decreased symptoms and pain. Trait PA is also associated with increased longevity among older community-dwelling individuals. The literature on PA and surviving serious illness is inconsistent. Experimentally inducing intense bouts of activated state PA triggers short-term rises in physiological arousal and associated (potentially harmful) effects on immune, cardiovascular, and pulmonary function. However, arousing effects of state PA are not generally found in naturalistic ambulatory studies in which bouts of PA are typically less intense and often associated with health protective responses. A theoretical framework to guide further study is proposed.

Imagined communities: awareness, information sharing, and privacy on the facebook

Abstract: Online social networks such as Friendster, MySpace, or the Facebook have experienced exponential growth in membership in recent years. These networks offer attractive means for interaction and communication, but also raise privacy and security concerns. In this study we survey a representative sample of the members of the Facebook (a social network for colleges and high schools) at a US academic institution, and compare the survey data to information retrieved from the network itself. We look for underlying demographic or behavioral differences between the communities of the network's members and non-members; we analyze the impact of privacy concerns on members' behavior; we compare members' stated attitudes with actual behavior; and we document the changes in behavior subsequent to privacy-related information exposure. We find that an individual's privacy concerns are only a weak predictor of his membership to the network. Also privacy concerned individuals join the network and reveal great amounts of personal information. Some manage their privacy concerns by trusting their ability to control the information they provide and the external access to it. However, we also find evidence of members' misconceptions about the online community's actual size and composition, and about the visibility of members' profiles.

The CMU pose, illumination, and expression database

Abstract: In the Fall of 2000, we collected a database of more than 40,000 facial images of 68 people. Using the Carnegie Mellon University 3D Room, we imaged each person across 13 different poses, under 43 different illumination conditions, and with four different expressions. We call this the CMU pose, illumination, and expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.

LOAM: Lidar Odometry and Mapping in Real-time

Abstract: We propose a real-time method for odometry and mapping using range measurements from a 2-axis lidar moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation can cause mis-registration of the resulting point cloud. To date, coherent 3D maps can be built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements. The key idea in obtaining this level of performance is the division of the complex problem of simultaneous localization and mapping, which seeks to optimize a large number of variables simultaneously, by two algorithms. One algorithm performs odometry at a high frequency but low fidelity to estimate velocity of the lidar. Another algorithm runs at a frequency of an order of magnitude lower for fine matching and registration of the point cloud. Combination of the two algorithms allows the method to map in real-time. The method has been evaluated by a large set of experiments as well as on the KITTI odometry benchmark. The results indicate that the method can achieve accuracy at the level of state of the art offline batch methods.

Locally Weighted Learning

Abstract: This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.