Dartmouth College

About: Dartmouth College is a education organization based out in Hanover, New Hampshire, United States. It is known for research contribution in the topics: Population & Health care. The organization has 20740 authors who have published 51426 publications receiving 2796969 citations. The organization is also known as: Dartmouth.

Enhancing breast tumor detection with near-field imaging

Abstract: This article outlines the main features of active, passive, and hybrid systems under investigation for breast cancer detection. Our main focus is on active microwave systems, in particular microwave tomography and confocal microwave imaging.

Why It Pays to Get Inside the Head of Your Opponent The Differential Effects of Perspective Taking and Empathy in Negotiations

Abstract: The current research explored whether two re- lated yet distinct social competencies—perspective taking (the cognitive capacity to consider the world from another individual's viewpoint) and empathy (the ability to connect emotionally with another individual)—have differential effects in negotiations. Across three studies, using both individual difference measures and experimental manip- ulations, we found that perspective taking increased in- dividuals' ability to discover hidden agreements and to both create and claim resources at the bargaining table. However, empathy did not prove nearly as advantageous and at times was detrimental to discovering a possible deal and achieving individual profit. These results held re- gardless of whether the interaction was a negotiation in which a prima facie solution was not possible or a multiple- issue negotiation that required discovering mutually ben- eficial trade-offs. Although empathy is an essential tool in many aspects of social life, perspective taking appears to be a particularly critical ability in negotiations.

Kalman-Consensus Filter : Optimality, stability, and performance

Abstract: One of the fundamental problems in sensor networks is to estimate and track the state of targets (or dynamic processes) of interest that evolve in the sensing field. Kalman filtering has been an effective algorithm for tracking dynamic processes for over four decades. Distributed Kalman Filtering (DKF) involves design of the information processing algorithm of a network of estimator agents with a two-fold objective: 1) estimate the state of the target of interest and 2) reach a consensus with neighboring estimator agents on the state estimate. We refer to this DKF algorithm as Kalman-Consensus Filter (KCF). The main contributions of this paper are as follows: i) finding the optimal decentralized Kalman-Consensus filter and showing that its computational and communication costs are not scalable in n and ii) introducing a scalable suboptimal Kalman-Consensus Filter and providing a formal stability and performance analysis of this distributed and cooperative filtering algorithm. Kalman-Consensus Filtering algorithm is applicable to sensor networks with variable topology including mobile sensor networks and networks with packet-loss.

Credibility of Management Forecasts

Abstract: We examine how the market's ability to assess the truthfulness of management earnings forecasts affects how managers bias their forecasts, and we evaluate whether the market's response to management forecasts is consistent with it identifying predictable forecast bias. We find managers' willingness to misrepresent their forwardlooking information as a function of their incentives varies with the market's ability to detect misrepresentation. We examine incentives induced by the litigation environment, insider trading activities, firm financial distress, and industry concentration. With regard to the stock price response to forecasts, we find the market varies its response with the predictable bias in the forecast. The efficiency of the market's response, however, varies with the forecast news.

Decoding Neural Representational Spaces Using Multivariate Pattern Analysis

Abstract: A major challenge for systems neuroscience is to break the neural code. Computational algorithms for encoding information into neural activity and extracting information from measured activity afford understanding of how percepts, memories, thought, and knowledge are represented in patterns of brain activity. The past decade and a half has seen significant advances in the development of methods for decoding human neural activity, such as multivariate pattern classification, representational similarity analysis, hyperalignment, and stimulus-model-based encoding and decoding. This article reviews these advances and integrates neural decoding methods into a common framework organized around the concept of high-dimensional representational spaces.