“Tell Me Who You Hang with and I’ll Tell You What You Are:” Gang Identity, Organization, and Desistance

Thesis (Master's)--University of Washington, 2016-06

Toy model for a relational formulation of quantum theory

In the absence of an external frame of reference-i.e., in background independent theories such as general relativity-physical degrees of freedom must describe relations between systems. Using a simple model, we investigate how such a relational quantum theory naturally arises by promoting reference systems to the status of dynamical entities. Our goal is twofold. First, we demonstrate using elementary quantum theory how any quantum mechanical experiment admits a purely relational description at a fundamental. Second, we describe how the original non-relational theory approximately emerges from the fully relational theory when reference systems become semi-classical. Our technique is motivated by a Bayesian approach to quantum mechanics, and relies on the noiseless subsystem method of quantum information science used to protect quantum states against undesired noise. The relational theory naturally predicts a fundamental decoherence mechanism, so an arrow of time emerges from a time-symmetric theory. Moreover, our model circumvents the problem of the collapse of the wave packet as the probability interpretation is only ever applied to diagonal density operators. Finally, the physical states of the relational theory can be described in terms of spin networks introduced by Penrose as a combinatorial description of geometry, and widely studied in the loop formulation of quantum gravity. Thus, our simple bottom-up approach (starting from the semiclassical limit to derive the fully relational quantum theory) may offer interesting insights on the low energy limit of quantum gravity.

A class of self-stabilizing MCA learning algorithms

In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results.

Physiologically based synthetic models of hepatic disposition

Current Physiologically based pharmacokinetic (PBPK) models are inductive. We present an additional, different approach that is based on the synthetic rather than the inductive approach to modeling and simulation. It relies on object-oriented programming A model of the referent system in its experimental context is synthesized by assembling objects that represent components such as molecules, cells, aspects of tissue architecture, catheters, etc. The single pass perfused rat liver has been well described in evaluating hepatic drug pharmacokinetics (PK) and is the system on which we focus. In silico experiments begin with administration of objects representing actual compounds. Data are collected in a manner analogous to that in the referent PK experiments. The synthetic modeling method allows for recognition and representation of discrete event and discrete time processes, as well as heterogeneity in organization, function, and spatial effects. An application is developed for sucrose and antipyrine, administered separately and together PBPK modeling has made extensive progress in characterizing abstracted PK properties but this has also been its limitation. Now, other important questions and possible extensions emerge. How are these PK properties and the observed behaviors generated? The inherent heuristic limitations of traditional models have hindered getting meaningful, detailed answers to such questions. Synthetic models of the type described here are specifically intended to help answer such questions. Analogous to wet-lab experimental models, they retain their applicability even when broken apart into sub-components. Having and applying this new class of models along with traditional PK modeling methods is expected to increase the productivity of pharmaceutical research at all levels that make use of modeling and simulation.

Continuous action system refinement

Action systems are a framework for reasoning about discrete reactive systems. Back, Petre and Porres have extended these action systems to continuous action systems, which can be. used to model hybrid systems. In this paper we define a refinement relation, and develop practical data refinement rules for continuous action systems. The meaning of continuous action systems is expressed in terms of a mapping from continuous action systems to action systems. First, we present a new mapping from continuous act ion systems to action systems, such that Back's definition of trace refinement is correct with respect to it. Second, we present a stream semantics that is compatible with the trace semantics, but is preferable to it because it is more general. Although action system trace refinement rules are applicable to continuous action systems with a stream semantics, they are not complete. Finally, we introduce a new data refinement rule that is valid with respect to the stream semantics and can be used to prove refinements that are not possible in the trace semantics, and we analyse the completeness of our new rule in conjunction with the existing trace refinement rules.

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

The 'moving targets' training algorithm

A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The method resembles back-propagation in that it is a least-squares, gradient-based optimization method, but the optimization is carried out in the hidden part of state space instead of weight space. A straightforward adaptation of this method to feedforward networks offers an alternative to training by conventional back-propagation. Computational results are presented for simple dynamical training problems, with varied success. The failures appear to arise when the method converges to a chaotic attractor. A patch-up for this problem is proposed. The patch-up involves a technique for implementing inequality constraints which may be of interest in its own right.

Study of Queueing Behaviour in IP Buffers

It is unquestioned that the importance of IP network will further increase and that it will serve as a platform for more and more services, requiring different types and degrees of service quality. Modern architectures and protocols are being standardized, which aims at guaranteeing the quality of service delivered to users. In this paper, we investigate the queueing behaviour found in IP output buffers. This queueing increases because multiple streams of packets with different length are being multiplexed together. We develop balance equations for the state of the system, from which we derive packet loss and delay results. To analyze these types of behaviour, we study the discrete-time version of the “classical” queue model M/M/1/k called Geo/Gx/1/k, where Gx denotes a different packet length distribution defined on a range between a minimum and maximum value.

Robust Estimation in Multitype Branching Processes Based on their Asymptotic Properties

2000 Mathematics Subject Classification: 60J80.

Offspring Mean Estimators in Branching Processes with Immigration

2000 Mathematics Subject Classification: 60J80.

A Note on the Extinction Problem for Controlled Multitype Branching Processes

2000 Mathematics Subject Classification: 60J80, 60J10.

Contribuições para o processo de verificação de satisfatibilidade módulo teoria em Event-B

Event-B is a formal method for modeling and verification of discrete transition systems. Event-B development yields proof obligations that must be verified (i.e. proved valid) in order to keep the produced models consistent. Satisfiability Modulo Theory solvers are automated theorem provers used to verify the satisfiability of logic formulas considering a background theory (or combination of theories). SMT solvers not only handle large firstorder formulas, but can also generate models and proofs, as well as identify unsatisfiable subsets of hypotheses (unsat-cores). Tool support for Event-B is provided by the Rodin platform: an extensible Eclipse based IDE that combines modeling and proving features. A SMT plug-in for Rodin has been developed intending to integrate alternative, efficient verification techniques to the platform. We implemented a series of complements to the SMT solver plug-in for Rodin, namely improvements to the user interface for when proof obligations are reported as invalid by the plug-in. Additionally, we modified some of the plug-in features, such as support for proof generation and unsat-core extraction, to comply with the SMT-LIB standard for SMT solvers. We undertook tests using applicable proof obligations to demonstrate the new features. The contributions described can potentially affect productivity in a positive manner.

Sediment and ice characteristics at a buried ice-wedge system at Barrow

Barrow, the northernmost point in Alaska, is one of the most intensively studied areas in the Arctic. However, paleoenvironmental evidence is limited for northern Alaska for the Lateglacial-Holocene transition. For a regional paleoenvironmental reconstruction, we investigated a permafrost ice-wedge tunnel near Barrow, Alaska. The studied site was first excavated in the early 1960s and intercepts a buried ice-wedge system at 3-6 m depth below the surface. A multi-methodological approach was applied to this buried ice-wedge system and the enclosing sediments, which in their combination, give new insight into the Late Quaternary environmental and climate history. Results of geochronological, sedimentological, cryolithological, paleoecological, isotope geochemical and microbiological studies reflect different stages of mid to late Wisconsin (MW to LW), Allerod (AD), Younger Dryas (YD), Preboreal (PB), and Late Holocene paleoenvironmental evolution. The LW age of the site is indicated by AMS dates in the surrounding sediments of 21.7 kyr BP at the lateral contact of the ice-wedge system as well as 39.5 kyr BP below the ice-wedge system. It is only recently that in this region, stable isotope techniques have been employed, i.e. to characterize different types of ground ice. The stable isotope record (oxygen: d18O; hydrogen: dD) of two intersecting ice wedges suggests different phases of the northern Alaskan climate history from AD to PB, with radiocarbon dates from 12.4 to 9.9 kyr BP (ranging from 14.8 to 10.6 kyr cal BP). Stable isotope geochemistry of ice wedges reveals winter temperature variations of the Lateglacial-Holocene transition including a prominent YD cold period, clearly separated from the warmer AD and PB phases. YD is only weakly developed in summer temperature indicators (such as pollen) for the northern Alaska area, and by consequence, the YD cold stadial was here especially related to the winter season. This highlights that the combination of winter and summer indicators comprehensively describes the seasonality of climate-relevant processes in discrete time intervals. The stable isotope record for the Barrow buried ice-wedge system documents for the first time winter climate change at the Lateglacial-Holocene transition continuously and at relatively high (likely centennial) resolution.

A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the in influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.

Simulation of Distributed Contact in String Instruments: a Modal Expansion Approach

Impactive contact between a vibrating string and a barrier is a strongly nonlinear phenomenon that presents several challenges in the design of numerical models for simulation and sound synthesis of musical string instruments. These are addressed here by applying Hamiltonian methods to incorporate distributed contact forces into a modal framework for discrete-time simulation of the dynamics of a stiff, damped string. The resulting algorithms have spectral accuracy, are unconditionally stable, and require solving a multivariate nonlinear equation that is guaranteed to have a unique solution. Exemplifying results are presented and discussed in terms of accuracy, convergence, and spurious high-frequency oscillations.