Current status of sentinel lymph node biopsy in solid malignancies

Lymphatic mapping and sentinel lymph node biopsy were first reported in 1977 by Cabanas for penile cancer. Since that time, the technique has become rapidly assimilated into clinical practice. The sentinel node concept has been validated in cutaneous melanoma and breast cancer. However, follow-up data of patients from randomised trials is needed to establish the clinical significance of sentinel lymph node biopsy before accepting the procedure as a standard of care. This technique has the potential to be utilised in all solid tumours like colon, gastric, oesophageal, lung, gynaecologic, and head and neck cancer. This paper reviews the current status of sentinel lymph node biopsy in solid tumours

Predicting the Equity Premium With Dividend Ratios

Our paper reexamines the forecasting regressions which predict annual aggregate stock market returns net of the risk-free rate with lagged aggregate dividend-yield ratios and dividend-price ratios. Prior to 1990, the conditional dividend yield could reliably outperform the historical equity premium mean in predicting future equity premia *in-sample*. But our paper shows that the dividend ratios could not outperform the prevailing unconditional mean *out-of-sample*, plus any residual power was directly related to only two years, 1974 and 1975. As of 2000, even this in-sample predictive ability has disappeared. Our paper also documents changes in the time-series processes of the dividends themselves and shows that an increasing persistence of dividend-price ratio is largely responsible for weak stock return predictability.

Empirical cross-sectional asset pricing: a survey

I review the state of empirical asset pricing devoted to understanding cross-sectional differences in average rates of return. Both methodologies and empirical evidence are surveyed. Tremendous progress has been made in understanding return patterns. At the same time, there is a need to synthesize the huge amount of collected evidenc

Synchronized exchange of material and information

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2003.Includes bibliographical references (leaves 39-41).Commerce is all about the carefully managed exchange of material, money, and information. Traditionally, the connection between material and information has been tenuous, with humans acting as the intermediaries. This has made the supply chain inefficient and expensive. The Auto-lID Center has created a stronger, automatic link between inanimate objects and computers. This thesis completes the information exchange, or feedback loop, which makes commerce possible. Specifically, it identifies a framework for information exchange alongside material exchange using Savant-to-Savant communication. Messaging standards will need to support the Auto-ID Center's technology, and this thesis suggests how to augment existing and emerging communication standards to accomplish this feat. Finally, to address the issue of increasing information management, this thesis analyzes the aggregation database, an IT infrastructure component that might be of value to organizations. The outcome of this thesis is an understanding of the various issues necessary to develop a secure, efficient and robust system for tracking and automatically confirming material exchange.by Amit Goyal.M.Eng

A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.

Validating Network Value of Influencers by means of Explanations

Recently, there has been significant interest in social influence analysis. One of the central problems in this area is the problem of identifying influencers, such that by convincing these users to perform a certain action (like buying a new product), a large number of other users get influenced to follow the action. The client of such an application is a marketer who would target these influencers for marketing a given new product, say by providing free samples or discounts. It is natural that before committing resources for targeting an influencer the marketer would be interested in validating the influence (or network value) of influencers returned. This requires digging deeper into such analytical questions as: who are their followers, on what actions (or products) they are influential, etc. However, the current approaches to identifying influencers largely work as a black box in this respect. The goal of this paper is to open up the black box, address these questions and provide informative and crisp explanations for validating the network value of influencers. We formulate the problem of providing explanations (called PROXI) as a discrete optimization problem of feature selection. We show that PROXI is not only NP-hard to solve exactly, it is NP-hard to approximate within any reasonable factor. Nevertheless, we show interesting properties of the objective function and develop an intuitive greedy heuristic. We perform detailed experimental analysis on two real world datasets - Twitter and Flixster, and show that our approach is useful in generating concise and insightful explanations of the influence distribution of users and that our greedy algorithm is effective and efficient with respect to several baselines