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.

Structure and deterioration of semantic memory: a neuropsychological and computational investigation.

Abstract: Wernicke (1900, as cited in G. H. Eggert, 1977) suggested that semantic knowledge arises from the interaction of perceptual representations of objects and words. The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual representations of objects and their verbal descriptions. To test the theory, they trained the model to associate names, verbal descriptions, and visual representations of objects. When its inputs and outputs are constructed to capture aspects of structure apparent in attribute-norming experiments, the model provides an intuitive account of semantic task performance. The authors then used the model to understand the structure of impaired performance in patients with selective and progressive impairments of conceptual knowledge. Data from 4 well-known semantic tasks revealed consistent patterns that find a ready explanation in the model. The relationship between the model and related theories of semantic representation is discussed.

Do Credit Spreads Reflect Stationary Leverage Ratios

Abstract: Most structural models of default preclude the firm from altering its capital structure. In practice, firms adjust outstanding debt levels in response to changes in firm value, thus generating mean-reverting leverage ratios. We propose a structural model of default with stochastic interest rates that captures this mean reversion. Our model generates credit spreads that are larger for low-leverage firms, and less sensitive to changes in firm value, both of which are more consistent with empirical findings than predictions of extant models. Further, the term structure of credit spreads can be upward sloping for speculative-grade debt, consistent with recent empirical findings.

Conceptual and Procedural Knowledge : The Case of Mathematics

Abstract: : According to a common folk model, students learn arithmetic by understanding the teacher's explanation of it. This folk model suggests that other, more complicated procedural skills are also acquired by being told. The evidence presented herein suggests that learning-by-being-told is an inaccurate model of the kind of arithmetic learning that actually occurs in classrooms. Rather, arithmetic is learned by induction: the generalization and integration of examples. Contents: 1) Schematic vs. teleological knowledge; 2) Three ways that arithmetic could be learned; 3) The conservative evaluation of the induction hypothesis; 4) A liberal evaluation of the induction hypothesis; 5) Learning by analogy; 6) Learning by being told; 7) Summary; 8) Concluding remarks; 9) Appendix.

SEAD: Secure efficient distance vector routing for mobile wireless ad hoc networks

Abstract: An ad hoc network is a collection of wireless computers (nodes), communicating among themselves over possibly multihop paths, without the help of any infrastructure such as base stations or access points. Although many previous ad hoc network routing protocols have been based in part on distance vector approaches, they have generally assumed a trusted environment. In this paper, we design and evaluate the Secure Efficient Ad hoc Distance vector routing protocol (SEAD), a secure ad hoc network routing protocol based on the design of the Destination-Sequenced Distance-Vector routing protocol. In order to support use with nodes of limited CPU processing capability, and to guard against Denial-of-Service attacks in which an attacker attempts to cause other nodes to consume excess network bandwidth or processing time, we use efficient one-way hash functions and do not use asymmetric cryptographic operations in the protocol. SEAD performs well over the range of scenarios we tested, and is robust against multiple uncoordinated attackers creating incorrect routing state in any other node, even in spite of any active attackers or compromised nodes in the network.