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.

Proof-carrying code

Abstract: This paper describes proof-carrying code (PCC), a mechanism by which a host system can determine with certainty that it is safe to execute a program supplied (possibly in binary form) by an untrusted source. For this to be possible, the untrusted code producer must supply with the code a safety proof that attests to the code's adherence to a previously defined safety policy. The host can then easily and quickly validate the proof without using cryptography and without consulting any external agents.In order to gain preliminary experience with PCC, we have performed several case studies. We show in this paper how proof-carrying code might be used to develop safe assembly-language extensions of ML programs. In the context of this case study, we present and prove the adequacy of concrete representations for the safety policy, the safety proofs, and the proof validation. Finally, we briefly discuss how we use proof-carrying code to develop network packet filters that are faster than similar filters developed using other techniques and are formally guaranteed to be safe with respect to a given operating system safety policy.

Current issues in recrystallization: a review

Abstract: The current understanding of the fundamentals of recrystallization is summarized. This includes understanding the as-deformed state. Several aspects of recrystallization are described: nucleation and growth, the development of misorientation during deformation, continuous, dynamic, and geometric dynamic recrystallization, particle effects, and texture. This article is authored by the leading experts in these areas. The subjects are discussed individually and recommendations for further study are listed in the final section.

ALVINN: An Autonomous Land Vehicle in a Neural Network

Abstract: ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following. Currently ALVINN takes images from a camera and a laser range finder as input and produces as output the direction the vehicle should travel in order to follow the road. Training has been conducted using simulated road images. Successful tests on the Carnegie Mellon autonomous navigation test vehicle indicate that the network can effectively follow real roads under certain field conditions. The representation developed to perform the task differs dramatically when the network is trained under various conditions, suggesting the possibility of a novel adaptive autonomous navigation system capable of tailoring its processing to the conditions at hand.

How coping mediates the effect of optimism on distress : a study of women with early stage breast cancer

Abstract: At diagnosis, 59 breast cancer patients reported on their overall optimism about life; 1 day presurgery, 10 days postsurgery, and at 3-, 6-, and 12-month follow-ups, they reported their recent coping responses and distress levels. Optimism related inversely to distress at each point, even controlling for prior distress. Acceptance, positive reframing, and use of religion were the most common coping reactions; denial and behavioral disengagement were the least common reactions. Acceptance and the use of humor prospectively predicted lower distress; denial and disengagement predicted more distress. Path analyses suggested that several coping reactions played mediating roles in the effect of optimism on distress. Discussion centers on the role of various coping reactions in the process of adjustment, the mechanisms by which dispositional optimism versus pessimism appears to operate, third variable issues, and applied implications.

Large steps in cloth simulation

Abstract: The bottle-neck in most cloth simulation systems is that time steps must be small to avoid numerical instability. This paper describes a cloth simulation system that can stably take large time steps. The simulation system couples a new technique for enforcing constraints on individual cloth particles with an implicit integration method. The simulator models cloth as a triangular mesh, with internal cloth forces derived using a simple continuum formulation that supports modeling operations such as local anisotropic stretch or compression; a unified treatment of damping forces is included as well. The implicit integration method generates a large, unbanded sparse linear system at each time step which is solved using a modified conjugate gradient method that simultaneously enforces particles’ constraints. The constraints are always maintained exactly, independent of the number of conjugate gradient iterations, which is typically small. The resulting simulation system is significantly faster than previous accounts of cloth simulation systems in the literature.