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.

Food-Miles and the Relative Climate Impacts of Food Choices in the United States

Abstract: Despite significant recent public concern and media attention to the environmental impacts of food, few studies in the United States have systematically compared the life-cycle greenhouse gas (GHG) emissions associated with food production against long-distance distribution, aka “food-miles.” We find that although food is transported long distances in general (1640 km delivery and 6760 km life-cycle supply chain on average) the GHG emissions associated with food are dominated by the production phase, contributing 83% of the average U.S. household’s 8.1 t CO2e/yr footprint for food consumption. Transportation as a whole represents only 11% of life-cycle GHG emissions, and final delivery from producer to retail contributes only 4%. Different food groups exhibit a large range in GHG-intensity; on average, red meat is around 150% more GHG-intensive than chicken or fish. Thus, we suggest that dietary shift can be a more effective means of lowering an average household’s food-related climate footprint than “buy...

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Abstract: Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss function that enables PoseCNN to handle symmetric objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input. When using depth data to further refine the poses, our approach achieves state-of-the-art results on the challenging OccludedLINEMOD dataset. Our code and dataset are available at this https URL.

Counterexample-guided abstraction refinement for symbolic model checking

Abstract: The state explosion problem remains a major hurdle in applying symbolic model checking to large hardware designs. State space abstraction, having been essential for verifying designs of industrial complexity, is typically a manual process, requiring considerable creativity and insight.In this article, we present an automatic iterative abstraction-refinement methodology that extends symbolic model checking. In our method, the initial abstract model is generated by an automatic analysis of the control structures in the program to be verified. Abstract models may admit erroneous (or "spurious") counterexamples. We devise new symbolic techniques that analyze such counterexamples and refine the abstract model correspondingly. We describe aSMV, a prototype implementation of our methodology in NuSMV. Practical experiments including a large Fujitsu IP core design with about 500 latches and 10000 lines of SMV code confirm the effectiveness of our approach.

The K-D-B-tree: a search structure for large multidimensional dynamic indexes

Abstract: The problem of retrieving multikey records via range queries from a large, dynamic index is considered. By large it is meant that most of the index must be stored on secondary memory. By dynamic it is meant that insertions and deletions are intermixed with queries, so that the index cannot be built beforehand. A new data structure, the K-D-B-tree, is presented as a solution to this problem. K-D-B-trees combine properties of K-D-trees and B-trees. It is expected that the multidimensional search effieciency of balanced K-D-trees and the I/O efficiency of B-trees should both be approximated in the K-D-B-tree. Preliminary experimental results that tend to support this are reported.

Understanding the Motivations, Participation, and Performance of Open Source Software Developers: A Longitudinal Study of the Apache Projects

Abstract: Understanding what motivates participation is a central theme in the research on open source software (OSS) development. Our study contributes by revealing how the different motivations of OSS developers are interrelated, how these motivations influence participation leading to performance, and how past performance influences subsequent motivations. Drawing on theories of intrinsic and extrinsic motivation, we develop a theoretical model relating the motivations, participation, and performance of OSS developers. We evaluate our model using survey and archival data collected from a longitudinal field study of software developers in the Apache projects. Our results reveal several important findings. First, we find that developers' motivations are not independent but rather are related in complex ways. Being paid to contribute to Apache projects is positively related to developers' status motivations but negatively related to their use-value motivations. Perhaps surprisingly, we find no evidence of diminished intrinsic motivation in the presence of extrinsic motivations; rather, status motivations enhance intrinsic motivations. Second, we find that different motivations have an impact on participation in different ways. Developers' paid participation and status motivations lead to above-average contribution levels, but use-value motivations lead to below-average contribution levels, and intrinsic motivations do not significantly impact average contribution levels. Third, we find that developers' contribution levels positively impact their performance rankings. Finally, our results suggest that past-performance rankings enhance developers' subsequent status motivations.