Auctioning the NFL overtime possession

In an National Football League overtime, a coin is tossed to determine which team will receive the kick off. In the sudden death format starting on offense has a significantly higher chance of winning. This makes coin tossing one of the most climatic moments and immediately confers an advantage to one team. Proposals to improve the ex post fairness by guaranteeing each team one possession reduce the efficiency and excitement of the sudden-death format. We propose a simple approach motivated by economics: the coaches of opposing teams bid on the yardage from its end zone at which his team would begin offense, with the low bidder winning the offense right. For instance, if coaches of teams A and B bid 18 and 21 yards, respectively, team A would win and begin its offense at 18 from its end zone. This note analyzes the equilibrium outcome of such an auction and discusses its properties

Factors that influence muscle shear modulus during passive stretch

Although elastography has been increasingly used for evaluating muscle shear modulus associated with age, sex, musculoskeletal, and neurological conditions, its physiological meaning is largely unknown. This knowledge gap may hinder data interpretation, limiting the potential of using elastography to gain insights into muscle biomechanics in health and disease. We derived a mathematical model from a widely-accepted Hill-type passive force–length relationship to gain insight about the physiological meaning of resting shear modulus of skeletal muscles under passive stretching, and validated the model by comparing against the ex-vivo animal data reported in our recent work (Koo et al. 2013). The model suggested that resting shear modulus of a slack muscle is a function of specific tension and parameters that govern the normalized passive muscle force–length relationship as well as the degree of muscle anisotropy. The model also suggested that although the slope of the linear shear modulus–passive force relationship is primarily related to muscle anatomical cross-sectional area (i.e. the smaller the muscle cross-sectional area, the more the increase in shear modulus to result in the same passive muscle force), it is also governed by the normalized passive muscle force–length relationship and the degree of muscle anisotropy. Taken together, although muscle shear modulus under passive stretching has a strong linear relationship with passive muscle force, its actual value appears to be affected by muscle’s mechanical, material, and architectural properties. This should be taken into consideration when interpreting the muscle shear modulus values

Advances in discriminative dependency parsing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 167-176).Achieving a greater understanding of natural language syntax and parsing is a critical step in producing useful natural language processing systems. In this thesis, we focus on the formalism of dependency grammar as it allows one to model important head modifier relationships with a minimum of extraneous structure. Recent research in dependency parsing has highlighted the discriminative structured prediction framework (McDonald et al., 2005a; Carreras, 2007; Suzuki et al., 2009), which is characterized by two advantages: first, the availability of powerful discriminative learning algorithms like log-linear and max-margin models (Lafferty et al., 2001; Taskar et al., 2003), and second, the ability to use arbitrarily-defined feature representations. This thesis explores three advances in the field of discriminative dependency parsing. First, we show that the classic Matrix-Tree Theorem (Kirchhoff, 1847; Tutte, 1984) can be applied to the problem of non-projective dependency parsing, enabling both log-linear and max-margin parameter estimation in this setting. Second, we present novel third-order dependency parsing algorithms that extend the amount of context available to discriminative parsers while retaining computational complexity equivalent to existing second-order parsers. Finally, we describe a simple but effective method for augmenting the features of a dependency parser with information derived from standard clustering algorithms; our semi-supervised approach is able to deliver consistent benefits regardless of the amount of available training data.by Terry Koo.Ph.D

Simple semi-supervised dependency parsing

We present a simple and effective semisupervised method for training dependency parsers. We focus on the problem of lexical representation, introducing features that incorporate word clusters derived from a large unannotated corpus. We demonstrate the effectiveness of the approach in a series of dependency parsing experiments on the Penn Treebank and Prague Dependency Treebank, and we show that the cluster-based features yield substantial gains in performance across a wide range of conditions. For example, in the case of English unlabeled second-order parsing, we improve from a baseline accuracy of 92:02% to 93:16%, and in the case of Czech unlabeled second-order parsing, we improve from a baseline accuracy of 86:13% to 87:13%. In addition, we demonstrate that our method also improves performance when small amounts of training data are available, and can roughly halve the amount of supervised data required to reach a desired level of performance.Peer ReviewedPostprint (author’s final draft

Parse reranking with WordNet using a hidden variable model

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 79-80).We present a new parse reranking algorithm that extends work in (Michael Collins and Terry Koo 2004) by incorporating WordNet (Miller et al. 1993) word senses. Instead of attempting explicit word sense disambiguation, we retain word sense ambiguity in a hidden variable model. We define a probability distribution over candidate parses and word sense assignments with a feature-based log-linear model, and we employ belief propagation to obtain an efficient implementation. Our main results are a relative improvement of [approximately] 0.97% over the baseline parser in development testing, which translated into a [approximately] 0.5% improvement in final testing. We also performed experiments in which our reranker was appended to the (Michael Collins and Terry Koo 2004) boosting reranker. The cascaded system achieved a development set improvement of [approximately] 0.15% over the boosting reranker by itself, but this gain did not carry over into final testing.by Terry Koo.M.Eng

Structured prediction models via the matrix-tree theorem

This paper provides an algorithmic framework for learning statistical models involving directed spanning trees, or equivalently non-projective dependency structures. We show how partition functions and marginals for directed spanning trees can be computed by an adaptation of Kirchhoff’s Matrix-Tree Theorem. To demonstrate an application of the method, we perform experiments which use the algorithm in training both log-linear and max-margin dependency parsers. The new training methods give improvements in accuracy over perceptron-trained models.Peer ReviewedPostprint (author’s final draft

Rapid real-time PCR detection of Listeria monocytogenes in enriched food samples based on the ssrA gene, a novel diagnostic target

A real-time PCR assay was designed to detect a 162-bp fragment of the ssrA gene in Listeria monocytogenes. The specificity of the assay for L. monocytogenes was confirmed against a panel of 6 Listeria species and 26 other bacterial species. A detection limit of 1-10 genome equivalents was determined for the assay. Application of the assay in natural and artificially contaminated culture enriched foods, including soft cheese, meat, milk, vegetables and fish, enabled detection of 1-5 CFU L. monocytogenes per 25g/ml of food sample in 30h. The performance of the assay was compared with the Roche Diagnostics 'LightCycler foodproof Listeria monocytogenes Detection Kit'. Both methods detected L. monocytogenes in all artificially contaminated retail samples (n=27) and L. monocytogenes was not detected by either system in 27 natural retail food samples. The method developed in this study has the potential to enable the specific detection of L. monocytogenes in a variety of food types in a time-frame considerably faster than current standard methods. The potential of the ssrA gene as a nucleic acid diagnostic (NAD) target has been demonstrated in L. monocytogenes. We are currently developing NAD tests based on the ssrA gene for a range of common foodborne and clinically relevant bacterial pathogens