Secure information flow via stripping and fast simulation

Type systems for secure information flow aim to prevent a program from leaking information from H (high) to L (low) variables. Traditionally, bisimulation has been the prevalent technique for proving the soundness of such systems. This work introduces a new proof technique based on stripping and fast simulation, and shows that it can be applied in a number of cases where bisimulation fails. We present a progressive development of this technique over a representative sample of languages including a simple imperative language (core theory), a multiprocessing nondeterministic language, a probabilistic language, and a language with cryptographic primitives. In the core theory we illustrate the key concepts of this technique in a basic setting. A fast low simulation in the context of transition systems is a binary relation where simulating states can match the moves of simulated states while maintaining the equivalence of low variables; stripping is a function that removes high commands from programs. We show that we can prove secure information flow by arguing that the stripping relation is a fast low simulation. We then extend the core theory to an abstract distributed language under a nondeterministic scheduler. Next, we extend to a probabilistic language with a random assignment command; we generalize fast simulation to the setting of discrete time Markov Chains, and prove approximate probabilistic noninterference. Finally, we introduce cryptographic primitives into the probabilistic language and prove computational noninterference, provided that the underling encryption scheme is secure.

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Um controlador dual subótimo com fator de peso variável no tempo

This work concerns a refinement of a suboptimal dual controller for discrete time systems with stochastic parameters. The dual property means that the control signal is chosen so that estimation of the model parameters and regulation of the output signals are optimally balanced. The control signal is computed in such a way so as to minimize the variance of output around a reference value one step further, with the addition of terms in the loss function. The idea is add simple terms depending on the covariance matrix of the parameter estimates two steps ahead. An algorithm is used for the adaptive adjustment of the adjustable parameter lambda, for each step of the way. The actual performance of the proposed controller is evaluated through a Monte Carlo simulations method.

Preferência e competição alimentar em um grupo de Sapajus flavius em fragmento de Mata Atlântica em Caaporã – Paraíba – Brasil

Feeding is the primary selective pressure in all forms of animals. Nutritional ecological models predict consequences of preferred and non-preferred food consumption on behavioural, physiological and morphological adaptations. At same time, socioecological models infer socio-organizarion patterns based on feeding competition faced by animals. A list of preferred foods, and inferences regarding the intensity of feeding competition and its behavioural consequences are information of much importance for management of populations in fragments. In this work we observed the feeding behavior and spatial positioning of a group of more than 100 blond capuchin monkeys (Sapajus flavius) that inhabit a fragment of Atlantic forest, surrounded by sugarcane plantation. We compared the consumption of different food items with their monthly availability in the area to define the preferred and fallback food items. We recorded the vocalizations of aggression and the inter-individual distance (area of Minimum Convex Polygon/n individuals) to infer the type of food competition experienced by animals. In the year studied the fruit feeding time correlated with top consumed fruit productivity, indicating preference for fruits. Our data indicate that the species Elaeis sp., Cecropia palmata, Inga spp. and Simarouba amara are the preferred food items in the diet. Available all year round and uniformly distributed, sugarcane was a regular item in the diet and its was characterized as a staple fallback food for this group. Although fruits are preferential food items, direct competition rate did not correlate to fruit productivity in the area, maintaining the high rates throughout the year (2.45 events/ hour). The inter-individual distance index positively correlated with rain fall indicating scramble food competition. The number of neighbours of females carrying infants was smaller when fruit productivity is low, indicating that females carrying infants are suffering increased indirect competition. Our data indicates that blond capuchins in this fragment make use of sugar cane as a staple fallback food, which evidence the importance of sugar cane landscape for the survival of this critically endangered capuchin species in fragmented habitats in Northeast Brazil. A preliminary list of preferred and important foods is offered, and can assist in the choice of trees for reforestation, better fragments to be preserved and areas of release and translocation of animals. We did not observe an increase of contest competition while using preferred foods, but when using staple FBF. This may be due the altered environment, which results in high competition food throughout the year. Both the food preference as the social and behavioral consequences of high food competition experienced by animals in this fragment must be accompanied over the years to ensure the survival of this population.

Preferência e competição alimentar em um grupo de Sapajus flavius em fragmento de Mata Atlântica em Caaporã – Paraíba – Brasil

Feeding is the primary selective pressure in all forms of animals. Nutritional ecological models predict consequences of preferred and non-preferred food consumption on behavioural, physiological and morphological adaptations. At same time, socioecological models infer socio-organizarion patterns based on feeding competition faced by animals. A list of preferred foods, and inferences regarding the intensity of feeding competition and its behavioural consequences are information of much importance for management of populations in fragments. In this work we observed the feeding behavior and spatial positioning of a group of more than 100 blond capuchin monkeys (Sapajus flavius) that inhabit a fragment of Atlantic forest, surrounded by sugarcane plantation. We compared the consumption of different food items with their monthly availability in the area to define the preferred and fallback food items. We recorded the vocalizations of aggression and the inter-individual distance (area of Minimum Convex Polygon/n individuals) to infer the type of food competition experienced by animals. In the year studied the fruit feeding time correlated with top consumed fruit productivity, indicating preference for fruits. Our data indicate that the species Elaeis sp., Cecropia palmata, Inga spp. and Simarouba amara are the preferred food items in the diet. Available all year round and uniformly distributed, sugarcane was a regular item in the diet and its was characterized as a staple fallback food for this group. Although fruits are preferential food items, direct competition rate did not correlate to fruit productivity in the area, maintaining the high rates throughout the year (2.45 events/ hour). The inter-individual distance index positively correlated with rain fall indicating scramble food competition. The number of neighbours of females carrying infants was smaller when fruit productivity is low, indicating that females carrying infants are suffering increased indirect competition. Our data indicates that blond capuchins in this fragment make use of sugar cane as a staple fallback food, which evidence the importance of sugar cane landscape for the survival of this critically endangered capuchin species in fragmented habitats in Northeast Brazil. A preliminary list of preferred and important foods is offered, and can assist in the choice of trees for reforestation, better fragments to be preserved and areas of release and translocation of animals. We did not observe an increase of contest competition while using preferred foods, but when using staple FBF. This may be due the altered environment, which results in high competition food throughout the year. Both the food preference as the social and behavioral consequences of high food competition experienced by animals in this fragment must be accompanied over the years to ensure the survival of this population.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

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 field experiment on a steel Gerber-truss bridge for damage detection utilizing vehicle-induced vibrations

A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This article summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including the identified modal parameters and their statistical patterns, Nair’s damage indicator and its statistical pattern and different sets of measurement points. The modal parameters are identified by autoregressive time-series models. The decision on bridge health condition is made and the sensitivity of variables is evaluated with the aid of the Mahalanobis–Taguchi system, a multivariate pattern recognition tool. Several observations are made as follows. For the modal parameters, although bridge damage detection can be achieved by performing Mahalanobis–Taguchi system on certain modal parameters of certain sets of measurement points, difficulties were faced in subjective selection of meaningful bridge modes and low sensitivity of the statistical pattern of the modal parameters to damage. For Nair’s damage indicator, bridge damage detection could be achieved by performing Mahalanobis–Taguchi system on Nair’s damage indicators of most sets of measurement points. As a damage indicator, Nair’s damage indicator was superior to the modal parameters. Three main advantages were observed: it does not require any subjective decision in calculating Nair’s damage indicator, thus potential human errors can be prevented and an automatic detection task can be achieved; its statistical pattern has high sensitivity to damage and, finally, it is flexible regarding the choice of sets of measurement points.

Modeling, Analysis and Performance Comparison of Two Direct Sampling DCSK Receivers under Frequency Nonselective Fading Channels

Two direct sampling correlator-type receivers for differential chaos shift keying (DCSK) communication systems under frequency non-selective fading channels are proposed. These receivers operate based on the same hardware platform with different architectures. In the first scheme, namely sum-delay-sum (SDS) receiver, the sum of all samples in a chip period is correlated with its delayed version. The correlation value obtained in each bit period is then compared with a fixed threshold to decide the binary value of recovered bit at the output. On the other hand, the second scheme, namely delay-sum-sum (DSS) receiver, calculates the correlation value of all samples with its delayed version in a chip period. The sum of correlation values in each bit period is then compared with the threshold to recover the data. The conventional DCSK transmitter, frequency non-selective Rayleigh fading channel, and two proposed receivers are mathematically modelled in discrete-time domain. The authors evaluated the bit error rate performance of the receivers by means of both theoretical analysis and numerical simulation. The performance comparison shows that the two proposed receivers can perform well under the studied channel, where the performances get better when the number of paths increases and the DSS receiver outperforms the SDS one.

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.

An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output broadcast channel, where the secondary system consisting of a secondary base-station (BS) and a group of secondary users (SUs) is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm. 

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 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.

Evolutionary dynamics under various modes of reproduction

Thesis (Ph.D.)--University of Washington, 2016-08

Age-Specific Fertility Dynamics: Sub-Saharan African Fertility in a Global Context

Thesis (Ph.D.)--University of Washington, 2016-08