Probabilistic Independence Networks for Hidden Markov Probability Models

Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics.Formalisms for manipulating these models have been developedrelatively independently in these research communities. In this paper weexplore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independencenetworks (PINs). The paper contains a self-contained review of the basic principles of PINs.It is shown that the well-known forward-backward (F-B) and Viterbialgorithms for HMMs are special cases of more general inference algorithms forarbitrary PINs. Furthermore, the existence of inference and estimationalgorithms for more general graphical models provides a set of analysistools for HMM practitioners who wish to explore a richer class of HMMstructures.Examples of relatively complex models to handle sensorfusion and coarticulationin speech recognitionare introduced and treated within the graphical model framework toillustrate the advantages of the general approach

Binary Models for Marginal Independence

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions

On the computational complexity of sequence design problems

Inverse protein folding concerns the identification of an amino acid sequence that folds to a given structure. Sequence design problems attempt to avoid the apparent difficulty of inverse protein folding by defining an energy that can be minimized to find protein-like sequences. The authors evaluate the practical relevance of two sequence design problems by analyzing their computation complexity. They show that the canonical method of sequence design is intractable, and describe approximation algorithms for this problem. The authors also describe an efficient algorithm that exactly solves the grand canonical method. The analysis shows how sequence design problems can fail to reduce the difficulty of the inverse protein folding problem, and highlights the need to analyze these problems to evaluate their practical relevance

THE RAFAEL MULTI-TARGET HETEROGENEOUS SIGNAL-FLOW GRAPH COMPILER

This paper describes a signal-flow graph compiler which produces distributed code for heterogeneous target systems. The compiler is devoted for mainly Digital Signal Process- ing problems. The code generator features reprogrammable operation library, the static scheduler supports fully heterogeneous systems and the input graph may contain run-time decisions in a limited way. The system has been implemented on IBM PC compatibles under MS-VVindows so it does not require expansive host computer

Statistical circuit simulations - from ‘atomistic’ compact models to statistical standard cell characterisation

This thesis describes the development and application of statistical circuit simulation methodologies to analyse digital circuits subject to intrinsic parameter fluctuations. The specific nature of intrinsic parameter fluctuations are discussed, and we explain the crucial importance to the semiconductor industry of developing design tools which accurately account for their effects. Current work in the area is reviewed, and three important factors are made clear: any statistical circuit simulation methodology must be based on physically correct, predictive models of device variability; the statistical compact models describing device operation must be characterised for accurate transient analysis of circuits; analysis must be carried out on realistic circuit components. Improving on previous efforts in the field, we posit a statistical circuit simulation methodology which accounts for all three of these factors. The established 3-D Glasgow atomistic simulator is employed to predict electrical characteristics for devices aimed at digital circuit applications, with gate lengths from 35 nm to 13 nm. Using these electrical characteristics, extraction of BSIM4 compact models is carried out and their accuracy in performing transient analysis using SPICE is validated against well characterised mixed-mode TCAD simulation results for 35 nm devices. Static d.c. simulations are performed to test the methodology, and a useful analytic model to predict hard logic fault limitations on CMOS supply voltage scaling is derived as part of this work. Using our toolset, the effect of statistical variability introduced by random discrete dopants on the dynamic behaviour of inverters is studied in detail. As devices scaled, dynamic noise margin variation of an inverter is increased and higher output load or input slew rate improves the noise margins and its variation. Intrinsic delay variation based on CV/I delay metric is also compared using ION and IEFF definitions where the best estimate is obtained when considering ION and input transition time variations. Critical delay distribution of a path is also investigated where it is shown non-Gaussian. Finally, the impact of the cell input slew rate definition on the accuracy of the inverter cell timing characterisation in NLDM format is investigated

Macroscopic Quantum Dynamics of a Free Domain Wall in a Ferromagnet

We study macroscopic quantum dynamics of a free domain wall in a quasi-one-dimensional ferromagnet by use of the spin-coherent-state path integral in {\it discrete-time} formalism. Transition amplitudes between typical states are quantitatively discussed by use of {\it stationary-action approximation} with respect to collective degrees of freedom representing the center position and the chirality of the domain wall. It is shown that the chirality may be loosely said to be canonically conjugate to the center position; the latter moves with a speed depending on the former. It is clarified under what condition the center position can be regarded as an effective free-particle position, which exhibits the phenomenon of wave-packet spreading. We demonstrate, however, that in some case the non-linear character of the spin leads to such a dramatic phenomenon of a non-spreading wave packet as to completely invalidate the free-particle analogy. In the course of the discussion, we also point out various difficulties associated with the continuous-time formalism.Comment: 23 pages, REVTEX, 4 figures, submitted to Phys. Rev.

Transportation Systems Analysis and Assessment

The transportation system is the backbone of any social and economic system, and is also a very complex system in which users, transport means, technologies, services, and infrastructures have to cooperate with each other to achieve common and unique goals.The aim of this book is to present a general overview on some of the main challenges that transportation planners and decision makers are faced with. The book addresses different topics that range from user's behavior to travel demand simulation, from supply chain to the railway infrastructure capacity, from traffic safety issues to Life Cycle Assessment, and to strategies to make the transportation system more sustainable