Quantifying biogenic bias in screening libraries.

In lead discovery, libraries of 10(6) molecules are screened for biological activity. Given the over 10(60) drug-like molecules thought possible, such screens might never succeed. The fact that they do, even occasionally, implies a biased selection of library molecules. We have developed a method to quantify the bias in screening libraries toward biogenic molecules. With this approach, we consider what is missing from screening libraries and how they can be optimized

Patent Validity and Litigation: Evidence from US Inter Partes Review

We analyze how new information about the validity of a patent impacts the settlement of patent infringement litigation. A party accused of patent infringement in the United States may—in parallel with defending itself in court—challenge the validity of the allegedly infringed patent by petitioning the Patent Trial and Appeal Board (PTAB), an administrative tribunal in the US Patent and Trademark Office. Review by PTAB generates new information about the validity of challenged patents, and we study empirically the resulting effect on settlement of an accused infringer’s decision to file a petition to challenge a patent’s validity and, conditional on the filing of a petition, the PTAB’s initial decision to grant or deny the petition on the basis of its assessment of a reasonable likelihood of invalidity. We find that both decision points have large, positive effects on the settlement of parallel court proceedings

Support Vector Machines for Business Applications

This chapter discusses the usage of Support Vector Machines (SVM) for business applications. It provides a brief historical background on inductive learning and pattern recognition, and then an intuitive motivation for SVM methods. The method is compared to other approaches, and the tools and background theory required to successfully apply SVMs to business applications are introduced. The authors hope that the chapter will help practitioners to understand when the SVM should be the method of choice, as well as how to achieve good results in minimal time

A numerical tidal model and its application to Cook Inlet, Alaska

A numerical scheme for predicting tides and tidal currents has been designed for rapid application to new situations with a minimum of effort. The model is two-dimensional and includes Coriolis and fric tional terms. An application to Cook Inlet, Alaska, is described for a tide having a period of 12.42 hours and an amplitude of one half the mean tidal range...

Kernel Based Algebraic Curve Fitting

An algebraic curve is defined as the zero set of a multivariate polynomial. We consider the problem of fitting an algebraic curve to a set of vectors given an additional set of vectors labelled as interior or exterior to the curve. The problem of fitting a linear curve in this way is shown to lend itself to a support vector representation, allowing non-linear curves and high dimensional surfaces to be estimated using kernel functions. The approach is attractive due to the stability of solutions obtained, the range of functional forms made possible (including polynomials), and the potential for applying well understood regularisation operators from the theory of Support Vector Machines

Towards a Maximum Entropy Method for Estimating HMM Parameters

Training a Hidden Markov Model (HMM) to maximise the probability of a given sequence can result in over-fitting. That is, the model represents the training sequence well, but fails to generalise. In this paper, we present a possible solution to this problem, which is to maximise a linear combination of the likelihood of the training data, and the entropy of the model. We derive the necessary equations for gradient based maximisation of this combined term. The performance of the system is then evaluated in comparison with three other algorithms, on a classification task using synthetic data. The results indicate that the method is potentially useful. The main problem with the method is the computational intractability of the entropy calculation

Algebraic Curve Fitting Support Vector Machines

An algebraic curve is defined as the zero set of a multivariate polynomial. We consider the problem of fitting an algebraic curve to a set of vectors given an additional set of vectors labelled as interior or exterior to the curve. The problem of fitting a linear curve in this way is shown to lend itself to a support vector representation, allowing non-linear curves and high dimensional surfaces to be estimated using kernel functions. The approach is attractive due to the stability of solutions obtained, the range of functional forms made ossible (including polynomials), and the potential for applying well understood regularisation operators from the theory of Support Vector Machines