Weighted Modal Transition Systems

Specification theories as a tool in model-driven development processes of component-based software systems have recently attracted a considerable attention. Current specification theories are however qualitative in nature, and therefore fragile in the sense that the inevitable approximation of systems by models, combined with the fundamental unpredictability of hardware platforms, makes it difficult to transfer conclusions about the behavior, based on models, to the actual system. Hence this approach is arguably unsuited for modern software systems. We propose here the first specification theory which allows to capture quantitative aspects during the refinement and implementation process, thus leveraging the problems of the qualitative setting. Our proposed quantitative specification framework uses weighted modal transition systems as a formal model of specifications. These are labeled transition systems with the additional feature that they can model optional behavior which may or may not be implemented by the system. Satisfaction and refinement is lifted from the well-known qualitative to our quantitative setting, by introducing a notion of distances between weighted modal transition systems. We show that quantitative versions of parallel composition as well as quotient (the dual to parallel composition) inherit the properties from the Boolean setting.Comment: Submitted to Formal Methods in System Desig

Probabilistic Opacity in Refinement-Based Modeling

Given a probabilistic transition system (PTS) $\cal A$ partially observed by an attacker, and an $\omega$-regular predicate $\varphi$over the traces of $\cal A$, measuring the disclosure of the secret $\varphi$ in $\cal A$ means computing the probability that an attacker who observes a run of $\cal A$ can ascertain that its trace belongs to $\varphi$. In the context of refinement, we consider specifications given as Interval-valued Discrete Time Markov Chains (IDTMCs), which are underspecified Markov chains where probabilities on edges are only required to belong to intervals. Scheduling an IDTMC $\cal S$ produces a concrete implementation as a PTS and we define the worst case disclosure of secret $\varphi$ in ${\cal S}$ as the maximal disclosure of $\varphi$ over all PTSs thus produced. We compute this value for a subclass of IDTMCs and we prove that refinement can only improve the opacity of implementations

Compositionality for Quantitative Specifications

We provide a framework for compositional and iterative design and verification of systems with quantitative information, such as rewards, time or energy. It is based on disjunctive modal transition systems where we allow actions to bear various types of quantitative information. Throughout the design process the actions can be further refined and the information made more precise. We show how to compute the results of standard operations on the systems, including the quotient (residual), which has not been previously considered for quantitative non-deterministic systems. Our quantitative framework has close connections to the modal nu-calculus and is compositional with respect to general notions of distances between systems and the standard operations

Arriving on time: estimating travel time distributions on large-scale road networks

Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a `stop-and-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area

L and T Dwarf Models and the L to T Transition

Using a model for refractory clouds, a novel algorithm for handling them, and the latest gas-phase molecular opacities, we have produced a new series of L and T dwarf spectral and atmosphere models as a function of gravity and metallicity, spanning the \teff range from 2200 K to 700 K. The correspondence with observed spectra and infrared colors for early- and mid-L dwarfs and for mid- to late-T dwarfs is good. We find that the width in infrared color-magnitude diagrams of both the T and L dwarf branches is naturally explained by reasonable variations in gravity and, therefore, that gravity is the "second parameter" of the L/T dwarf sequence. We investigate the dependence of theoretical dwarf spectra and color-magnitude diagrams upon various cloud properties, such as particle size and cloud spatial distribution. In the region of the L$\to$T transition, we find that no one cloud-particle-size and gravity combination can be made to fit all the observed data. Furthermore, we note that the new, lower solar oxygen abundances of Allende-Prieto, Lambert, & Asplund (2002) produce better fits to brown dwarf data than do the older values. Finally, we discuss various issues in cloud physics and modeling and speculate on how a better correspondence between theory and observation in the problematic L$\to$T transition region might be achieved.Comment: accepted to the Astrophysical Journal, 21 figures (20 in color); spectral models in electronic form available at http://zenith.as.arizona.edu/~burrow

Worm Algorithm for Problems of Quantum and Classical Statistics

This is a chapter of the multi-author book "Understanding Quantum Phase Transitions," edited by Lincoln Carr and published by Taylor and Francis. In this chapter, we give a general introduction to the worm algorithm and present important results highlighting the power of the approachComment: 27 pages, 15 figures, chapter in a boo