Dimensionality reduction of clustered data sets

We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalisation of linear discriminant analysis. This provides a completely new approach to one of the most established and widely used classification algorithms. The performance of the model is then demonstrated on a number of real and artificial data sets

NLO QCD corrections to the production of a weak boson pair associated by a hard jet

In this talk we discuss recent progress concerning precise predictions for the LHC. We give a status report of an application of the GOLEM method to deal with multi-leg one-loop amplitudes, namely the next-to-leading order QCD corrections to the process pp to V V + jet, where V is a weak boson W,Z.Comment: Talk at 2008 Rencontres de Moriond, QCD session, La Thuile, March 2007. Four page

Adding Carbon to the Equation in Online Flight Search

This study explores the potential to promote lower-emissions air travel by providing consumers with information about the carbon emissions of alternative flight choices in the context of online flight search and booking. Researchers surveyed over 450 UC Davis faculty, researchers, and staff, asking them to choose among hypothetical flight options for university-related business trips. Emissions estimates for flight alternatives were prominently displayed alongside cost, layovers and airport, and the lowest-emissions flight was labeled “Greenest Flight”. The researchers found an impressive rate of willingness to pay for lower-emissions flights: around $200/ton of CO2E saved, a magnitude higher than that seen in carbon offsets programs. They also found that displaying carbon information encouraged Davis employees to choose nonstop (lower-emissions) flights, when available, from a more distant airport over indirect flights from their preferred airport for medium-distance flights. In a second step of analysis, they estimated the carbon and cost impacts for UC Davis business travel if the university were to adopt a flight-search interface that prioritizes carbon emissions information and displays alternatives from multiple regional airports in their employee travel-booking portal. Based on the choice models from the survey data, a year’s worth of actual employee air travel data, and data collected on flight alternatives with respect to the flights chosen by employees, they estimated potential annual savings of more than 79 tons of CO2E, and a more impressive$56,000 reduction in airfare costs, due to an increased willingness of travelers to take advantage of cheaper (often nonstop) flight options out of SFO. Broader university policies encouraging lower-emissions flights and enhanced public transportation within the multi-airport mega-region would likely support much greater carbon savings. Institutionalizing this “nudge” within organizations with large travel budgets, like the UC system, could have an industry-wide impact in aviation.View the NCST Project Webpag

Learning and Designing Stochastic Processes from Logical Constraints

Stochastic processes offer a flexible mathematical formalism to model and reason about systems. Most analysis tools, however, start from the premises that models are fully specified, so that any parameters controlling the system's dynamics must be known exactly. As this is seldom the case, many methods have been devised over the last decade to infer (learn) such parameters from observations of the state of the system. In this paper, we depart from this approach by assuming that our observations are {\it qualitative} properties encoded as satisfaction of linear temporal logic formulae, as opposed to quantitative observations of the state of the system. An important feature of this approach is that it unifies naturally the system identification and the system design problems, where the properties, instead of observations, represent requirements to be satisfied. We develop a principled statistical estimation procedure based on maximising the likelihood of the system's parameters, using recent ideas from statistical machine learning. We demonstrate the efficacy and broad applicability of our method on a range of simple but non-trivial examples, including rumour spreading in social networks and hybrid models of gene regulation

Factored expectation propagation for input-output FHMM models in systems biology

We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the classical free form structured variational mean field approach and use a expectation propagation to approximate the expectations needed in the variational loop. We show that this corresponds to a factored expectation constrained approximate inference. We validate our model through extensive simulations and demonstrate its applicability on a real world bacterial data set

Verdi’s six-fours and la parola scenica

Verdi’s operas display many non-normative six-four chords. The question for the opera analyst, however, is not only what occurs musically, but why it does. Is there a dramatic function being served by this mix of harmonic-intervallic instability? We discuss four types of non-normative six-fours in Verdi: the arrival, wonder, evasion, and dissolving. But, even as we individuate these types, we note that they are all similar in one regard: they are all linked to a crucial dramatic statement that Verdi termed la parola scenica, a textual-musical signal that makes a dramatic situation suddenly evident

The politics of intergovernmental deficits: Theory and evidence

The purpose of this paper is twofold. First, we present a model of decentralized fiscal policy-making where a "coordination failure" problem arises. Second, we make an effort in order to empirically test this approach by developing an empirical investigation based on the recent experience of two countries: Argentina and Great Britain.

Power Allocation in Two-Hop Amplify-and-Forward MIMO Relay Systems with QoS requirements

The problem of minimizing the total power consumption while satisfying different quality-of-service (QoS) requirements in a two-hop multiple-input multiple-output network with a single non-regenerative relay is considered. As shown by Y. Rong in [1], the optimal processing matrices for both linear and non-linear transceiver architectures lead to the diagonalization of the source-relay-destination channel so that the power minimization problem reduces to properly allocating the available power over the established links. Unfortunately, finding the solution of this problem is numerically difficult as it is not in a convex form. To overcome this difficulty, existing solutions rely on the computation of upper- and lower-bounds that are hard to obtain or require the relaxation of the QoS constraints. In this work, a novel approach is devised for both linear and non-linear transceiver architectures, which allows to closely approximate the solutions of the non-convex power allocation problems with those of convex ones easy to compute in closed-form by means of multi-step procedures of reduced complexity. Computer simulations are used to assess the performance of the proposed approach and to make comparisons with alternatives