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.

A survey of exact algorithms for the simple assembly line balancing problem

Abstract: In this survey paper we discuss the development of the simple assembly line balancing problem SALBP; modifications and generalizations over time; present alternate 0-1 programming formulations and a general integer programming formulation of the problem; discuss other well-known problems related to SALBP; describe and comment on a number of exact i.e., optimum-seeking methods; and present a summary of the reported computational experiences. All models discussed here are deterministic i.e., all input parameters are assumed to be known with certainty and all the algorithms discussed are exact. The problem is termed "simple" in the sense that no "mixed-models," "subassembly lines," "zoning restrictions," etc. are considered. Due to the richness of the literature, we exclude from discussion here a the inexact i.e., heuristic/approximate algorithms for SALPB and b the algorithms for the general assembly line balancing problem including the stochastic models.

Multitask learning: a knowledge-based source of inductive bias

Abstract: This paper suggests that it may be easier to learn several hard tasks at one time than to learn these same tasks separately. In effect, the information provided by the training signal for each task serves as a domain-specific inductive bias for the other tasks. Frequently the world gives us clusters of related tasks to learn. When it does not, it is often straightforward to create additional tasks. For many domains, acquiring inductive bias by collecting additional teaching signal may be more practical than the traditional approach of codifying domain-specific biases acquired from human expertise. We call this approach Multitask Learning (MTL). Since much of the power of an inductive learner follows directly from its inductive bias, multitask learning may yield more powerful learning. An empirical example of multitask connectionist learning is presented where learning improves by training one network on several related tasks at the same time. Multitask decision tree induction is also outlined.

Atomically Precise Gold Nanoclusters as New Model Catalysts

Abstract: Many industrial catalysts involve nanoscale metal particles (typically 1–100 nm), and understanding their behavior at the molecular level is a major goal in heterogeneous catalyst research. However, conventional nanocatalysts have a nonuniform particle size distribution, while catalytic activity of nanoparticles is size dependent. This makes it difficult to relate the observed catalytic performance, which represents the average of all particle sizes, to the structure and intrinsic properties of individual catalyst particles. To overcome this obstacle, catalysts with well-defined particle size are highly desirable.In recent years, researchers have made remarkable advances in solution-phase synthesis of atomically precise nanoclusters, notably thiolate-protected gold nanoclusters. Such nanoclusters are composed of a precise number of metal atoms (n) and of ligands (m), denoted as Aun(SR)m, with n ranging up to a few hundred atoms (equivalent size up to 2–3 nm). These protected nanoclusters are well-defined ...

Projection Bias in Predicting Future Utility

Abstract: People exaggerate the degree to which their future tastes will resemble their current tastes. We present evidence from a variety of domains which demonstrates the prevalence of such projection bias, develop a formal model of it, and use this model to demonstrate its importance in economic environments. We show that, when people exhibit habit formation, projection bias leads people to consume too much early in life, and to decide, as time passes, to consume more— and save less— than originally planned. Projection bias can also lead to misguided purchases of durable goods. We discuss a number of additional applications and implications.