A new epistemic model

Meier (2012) gave a "mathematical logic foundation" of the purely measurable universal type space (Heifetz and Samet, 1998). The mathematical logic foundation, however, discloses an inconsistency in the type space literature: a finitary language is used for the belief hierarchies and an infinitary language is used for the beliefs. In this paper we propose an epistemic model to fix the inconsistency above. We show that in this new model the universal knowledgebelief space exists, is complete and encompasses all belief hierarchies. Moreover, by examples we demonstrate that in this model the players can agree to disagree Aumann (1976)'s result does not hold, and Aumann and Brandenburger (1995)'s conditions are not sufficient for Nash equilibrium. However, we show that if we substitute selfevidence (Osborne and Rubinstein, 1994) for common knowledge, then we get at that both Aumann (1976)'s and Aumann and Brandenburger (1995)'s results hold.

Temporal analysis of land use and transportation investments with geographic information system

Land use and transportation interaction has been a research topic for several decades. There have been efforts to identify impacts of transportation on land use from several different perspectives. One focus has been the role of transportation improvements in encouraging new land developments or relocation of activities due to improved accessibility. The impacts studied have included property values and increased development. Another focus has been on the changes in travel behavior due to better mobility and accessibility. Most studies to date have been conducted in metropolitan level, thus unable to account for interactions spatially and temporally at smaller geographic scales. ^ In this study, a framework for studying the temporal interactions between transportation and land use was proposed and applied to three selected corridor areas in Miami-Dade County, Florida. The framework consists of two parts: one is developing of temporal data and the other is applying time series analysis to this temporal data to identify their dynamic interactions. Temporal GIS databases were constructed and used to compile building permit data and transportation improvement projects. Two types of time series analysis approaches were utilized: univariate models and multivariate models. Time series analysis is designed to describe the dynamic consequences of time series by developing models and forecasting the future of the system based on historical trends. Model estimation results from the selected corridors were then compared. ^ It was found that the time series models predicted residential development better than commercial development. It was also found that results from three study corridors varied in terms of the magnitude of impacts, length of lags, significance of the variables, and the model structure. Long-run effect or cumulated impact of transportation improvement on land developments was also measured with time series techniques. The study offered evidence that congestion negatively impacted development and transportation investments encouraged land development. ^

Refinement methods for software architecture design using the software architecture model

Modern software systems are often large and complicated. To better understand, develop, and manage large software systems, researchers have studied software architectures that provide the top level overall structural design of software systems for the last decade. One major research focus on software architectures is formal architecture description languages, but most existing research focuses primarily on the descriptive capability and puts less emphasis on software architecture design methods and formal analysis techniques, which are necessary to develop correct software architecture design. ^ Refinement is a general approach of adding details to a software design. A formal refinement method can further ensure certain design properties. This dissertation proposes refinement methods, including a set of formal refinement patterns and complementary verification techniques, for software architecture design using Software Architecture Model (SAM), which was developed at Florida International University. First, a general guideline for software architecture design in SAM is proposed. Second, specification construction through property-preserving refinement patterns is discussed. The refinement patterns are categorized into connector refinement, component refinement and high-level Petri nets refinement. These three levels of refinement patterns are applicable to overall system interaction, architectural components, and underlying formal language, respectively. Third, verification after modeling as a complementary technique to specification refinement is discussed. Two formal verification tools, the Stanford Temporal Prover (STeP) and the Simple Promela Interpreter (SPIN), are adopted into SAM to develop the initial models. Fourth, formalization and refinement of security issues are studied. A method for security enforcement in SAM is proposed. The Role-Based Access Control model is formalized using predicate transition nets and Z notation. The patterns of enforcing access control and auditing are proposed. Finally, modeling and refining a life insurance system is used to demonstrate how to apply the refinement patterns for software architecture design using SAM and how to integrate the access control model. ^ The results of this dissertation demonstrate that a refinement method is an effective way to develop a high assurance system. The method developed in this dissertation extends existing work on modeling software architectures using SAM and makes SAM a more usable and valuable formal tool for software architecture design. ^

An indexing structure and application model for vehicles moving on road networks

Moving objects database systems are the most challenging sub-category among Spatio-Temporal database systems. A database system that updates in real-time the location information of GPS-equipped moving vehicles has to meet even stricter requirements. Currently existing data storage models and indexing mechanisms work well only when the number of moving objects in the system is relatively small. This dissertation research aimed at the real-time tracking and history retrieval of massive numbers of vehicles moving on road networks. A total solution has been provided for the real-time update of the vehicles' location and motion information, range queries on current and history data, and prediction of vehicles' movement in the near future. ^ To achieve these goals, a new approach called Segmented Time Associated to Partitioned Space (STAPS) was first proposed in this dissertation for building and manipulating the indexing structures for moving objects databases. ^ Applying the STAPS approach, an indexing structure of associating a time interval tree to each road segment was developed for real-time database systems of vehicles moving on road networks. The indexing structure uses affordable storage to support real-time data updates and efficient query processing. The data update and query processing performance it provides is consistent without restrictions such as a time window or assuming linear moving trajectories. ^ An application system design based on distributed system architecture with centralized organization was developed to maximally support the proposed data and indexing structures. The suggested system architecture is highly scalable and flexible. Finally, based on a real-world application model of vehicles moving in region-wide, main issues on the implementation of such a system were addressed. ^

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Endothelin 3 induces skin pigmentation in a keratin-driven inducible mouse model

Endothelin 3 (Edn3) is a ligand important to developing neural crest cells (NCC). Some NCC eventually migrate into the skin and give rise to the pigment-forming melanocytes found in hair follicles. Edn3's effects on NCC have been largely explored through spontaneous mutants and cell culture experiments. These studies have shown the Endothelin receptor B/Edn3 signaling pathway to be important in the proliferation/survival and differentiation of developing melanocytes. To supplement these investigations I have created doxycycline-responsive transgenic mice which conditionally over-express Edn3. These mice will help us clarify Edn3's role during the development of early embryonic melanoblasts, differentiating melanocyte precursors in the skin, and fully differentiated melanocytes in the hair follicle. The transgene mediated expression of Edn3 was predominantly confined to the roof plate of the neural tube and surface ectoderm in embryos and postnatally in the epidermal keratinocytes of the skin. Relative to littermate controls, transgenics develop increased pigmentation on most areas of the skin. My doxycycline-based temporal studies have shown that both embryonic and postnatal events are important for establishing and maintaining pigmented skin. The study of my Edn3 transgenic mice may offer some insight into the genetics behind benign dermal pigmentation and offer clues about the time periods important in establishing these conditions. This apparently abnormal development is echoed in a benign condition of human skin. Cases of dermal melanocytosis, such as common freckles, Mongolian spotting, and nevus of Ito demonstrate histological and etiological characteristics similar to those of the transgenic mice generated in this study.

The role of temporal synchrony in the facilitation of perceptual learning during prenatal development

This study explored the critical features of temporal synchrony for the facilitation of prenatal perceptual learning with respect to unimodal stimulation using an animal model, the bobwhite quail. The following related hypotheses were examined: (1) the availability of temporal synchrony is a critical feature to facilitate prenatal perceptual learning, (2) a single temporally synchronous note is sufficient to facilitate prenatal perceptual learning, with respect to unimodal stimulation, and (3) in situations where embryos are exposed to a single temporally synchronous note, facilitated perceptual learning, with respect to unimodal stimulation, will be optimal when the temporally synchronous note occurs at the onset of the stimulation bout. To assess these hypotheses, two experiments were conducted in which quail embryos were exposed to various audio-visual configurations of a bobwhite maternal call and tested at 24 hr after hatching for evidence of facilitated prenatal perceptual learning with respect to unimodal stimulation. Experiment 1 explored if intermodal equivalence was sufficient to facilitate prenatal perceptual learning with respect to unimodal stimulation. A Bimodal Sequential Temporal Equivalence (BSTE) condition was created that provided embryos with sequential auditory and visual stimulation in which the same amodal properties (rate, duration, rhythm) were made available across modalities. Experiment 2 assessed: (a) whether a limited number of temporally synchronous notes are sufficient for facilitated prenatal perceptual learning with respect to unimodal stimulation, and (b) whether there is a relationship between timing of occurrence of a temporally synchronous note and the facilitation of prenatal perceptual learning. Results revealed that prenatal exposure to BSTE was not sufficient to facilitate perceptual learning. In contrast, a maternal call that contained a single temporally synchronous note was sufficient to facilitate embryos’ prenatal perceptual learning with respect to unimodal stimulation. Furthermore, the most salient prenatal condition was that which contained the synchronous note at the onset of the call burst. Embryos’ prenatal perceptual learning of the call was four times faster in this condition than when exposed to a unimodal call. Taken together, bobwhite quail embryos’ remarkable sensitivity to temporal synchrony suggests that this amodal property plays a key role in attention and learning during prenatal development.

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

Characterizing Internet worm spatial-temporal infection structures

Since the Morris worm was released in 1988, Internet worms continue to be one of top security threats. For example, the Conficker worm infected 9 to 15 million machines in early 2009 and shut down the service of some critical government and medical networks. Moreover, it constructed a massive peer-to-peer (P2P) botnet. Botnets are zombie networks controlled by attackers setting out coordinated attacks. In recent years, botnets have become the number one threat to the Internet. The objective of this research is to characterize spatial-temporal infection structures of Internet worms, and apply the observations to study P2P-based botnets formed by worm infection. First, we infer temporal characteristics of the Internet worm infection structure, i.e., the host infection time and the worm infection sequence, and thus pinpoint patient zero or initially infected hosts. Specifically, we apply statistical estimation techniques on Darknet observations. We show analytically and empirically that our proposed estimators can significantly improve the inference accuracy. Second, we reveal two key spatial characteristics of the Internet worm infection structure, i.e., the number of children and the generation of the underlying tree topology formed by worm infection. Specifically, we apply probabilistic modeling methods and a sequential growth model. We show analytically and empirically that the number of children has asymptotically a geometric distribution with parameter 0.5, and the generation follows closely a Poisson distribution. Finally, we evaluate bot detection strategies and effects of user defenses in P2P-based botnets formed by worm infection. Specifically, we apply the observations of the number of children and demonstrate analytically and empirically that targeted detection that focuses on the nodes with the largest number of children is an efficient way to expose bots. However, we also point out that future botnets may self-stop scanning to weaken targeted detection, without greatly slowing down the speed of worm infection. We then extend the worm spatial infection structure and show empirically that user defenses, e.g. , patching or cleaning, can significantly mitigate the robustness and the effectiveness of P2P-based botnets. To counterattack, we evaluate a simple measure by future botnets that enhances topology robustness through worm re-infection.

A conceptual model of ecological interactions in the mangrove estuaries of the Florida Everglades

A brackish water ecotone of coastal bays and lakes, mangrove forests, salt marshes, tidal creeks, and upland hammocks separates Florida Bay, Biscayne Bay, and the Gulf of Mexico from the freshwater Everglades. The Everglades mangrove estuaries are characterized by salinity gradients that vary spatially with topography and vary seasonally and inter-annually with rainfall, tide, and freshwater flow from the Everglades. Because of their location at the lower end of the Everglades drainage basin, Everglades mangrove estuaries have been affected by upstream water management practices that have altered the freshwater heads and flows and that affect salinity gradients. Additionally, interannual variation in precipitation patterns, particularly those caused to El Nin˜o events, control freshwater inputs and salinity dynamics in these estuaries. Two major external drivers on this system are water management activities and global climate change. These drivers lead to two major ecosystem stressors: reduced freshwater flow volume and duration, and sea-level rise. Major ecological attributes include mangrove forest production, soil accretion, and resilience; coastal lake submerged aquatic vegetation; resident mangrove fish populations; wood stork (Mycteria americana) and roseate spoonbill (Platelea ajaja) nesting colonies; and estuarine crocodilian populations. Causal linkages between stressors and attributes include coastal transgression, hydroperiods, salinity gradients, and the ‘‘white zone’’ freshwater/estuarine interface. The functional estuary and its ecological attributes, as influenced by sea level and freshwater flow, must be viewed as spatially dynamic, with a possible near-term balancing of transgression but ultimately a long-term continuation of inland movement. Regardless of the spatio-temporal timing of this transgression, a salinity gradient supportive of ecologically functional Everglades mangrove estuaries will be required to maintain the integrity of the South Florida ecosystem.

A conceptual ecological model to facilitate understanding the role of invasive species in large-scale ecosystem restoration

We developed a conceptual ecological model (CEM) for invasive species to help understand the role invasive exotics have in ecosystem ecology and their impacts on restoration activities. Our model, which can be applied to any invasive species, grew from the eco-regional conceptual models developed for Everglades restoration. These models identify ecological drivers, stressors, effects and attributes; we integrated the unique aspects of exotic species invasions and effects into this conceptual hierarchy. We used the model to help identify important aspects of invasion in the development of an invasive exotic plant ecological indicator, which is described a companion paper in this special issue journal. A key aspect of the CEM is that it is a general ecological model that can be tailored to specific cases and species, as the details of any invasion are unique to that invasive species. Our model encompasses the temporal and spatial changes that characterize invasion, identifying the general conditions that allow a species to become invasive in a de novo environment; it then enumerates the possible effects exotic species may have collectively and individually at varying scales and for different ecosystem properties, once a species becomes invasive. The model provides suites of characteristics and processes, as well as hypothesized causal relationships to consider when thinking about the effects or potential effects of an invasive exotic and how restoration efforts will affect these characteristics and processes. In order to illustrate how to use the model as a blueprint for applying a similar approach to other invasive species and ecosystems, we give two examples of using this conceptual model to evaluate the status of two south Florida invasive exotic plant species (melaleuca and Old World climbing fern) and consider potential impacts of these invasive species on restoration.

Determining Spatial and Temporal Inputs of Freshwater, Including Submarine Groundwater Discharge, to a Subtropical Estuary Using Geochemical Tracers, Biscayne Bay, South Florida

Geochemical mixing models were used to decipher the dominant source of freshwater (rainfall, canal discharge, or groundwater discharge) to Biscayne Bay, an estuary in south Florida. Discrete samples of precipitation, canal water, groundwater, and bay surface water were collected monthly for 2 years and analyzed for salinity, stable isotopes of oxygen and hydrogen, and Sr2+/Ca2+ concentrations. These geochemical tracers were used in three separate mixing models and then combined to trace the magnitude and timing of the freshwater inputs to the estuary. Fresh groundwater had an isotopic signature (δ 18O = −2.66‰, δD −7.60‰) similar to rainfall (δ 18O = −2.86‰, δD = −4.78‰). Canal water had a heavy isotopic signature (δ 18O = −0.46‰, δD  = −2.48‰) due to evaporation. This made it possible to use stable isotopes of oxygen and hydrogen to separate canal water from precipitation and groundwater as a source of freshwater into the bay. A second model using Sr2+/Ca2+ ratios was developed to discern fresh groundwater inputs from precipitation inputs. Groundwater had a Sr2+/Ca2+ ratio of 0.07, while precipitation had a dissimilar ratio of 0.89. When combined, these models showed a freshwater input ratio of canal/precipitation/groundwater of 37%:53%:10% in the wet season and 40%:55%:5% in the dry season with an error of ±25%. For a bay-wide water budget that includes saltwater and freshwater mixing, fresh groundwater accounts for 1–2% of the total fresh and saline water input.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

A generic model of execution for synthesizing domain-specific models

Software engineering researchers are challenged to provide increasingly more powerful levels of abstractions to address the rising complexity inherent in software solutions. One new development paradigm that places models as abstraction at the forefront of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code.^ Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process.^ The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources.^ At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM's synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise.^ This dissertation investigates how to decouple the DSK from the MoE and subsequently producing a generic model of execution (GMoE) from the remaining application logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis component of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions.^ This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.^

The Role of Temporal Synchrony in the Facilitation of Perceptual Learning during Prenatal Development

This study explored the critical features of temporal synchrony for the facilitation of prenatal perceptual learning with respect to unimodal stimulation using an animal model, the bobwhite quail. The following related hypotheses were examined: (1) the availability of temporal synchrony is a critical feature to facilitate prenatal perceptual learning, (2) a single temporally synchronous note is sufficient to facilitate prenatal perceptual learning, with respect to unimodal stimulation, and (3) in situations where embryos are exposed to a single temporally synchronous note, facilitated perceptual learning, with respect to unimodal stimulation, will be optimal when the temporally synchronous note occurs at the onset of the stimulation bout. To assess these hypotheses, two experiments were conducted in which quail embryos were exposed to various audio-visual configurations of a bobwhite maternal call and tested at 24 hr after hatching for evidence of facilitated prenatal perceptual learning with respect to unimodal stimulation. Experiment 1 explored if intermodal equivalence was sufficient to facilitate prenatal perceptual learning with respect to unimodal stimulation. A Bimodal Sequential Temporal Equivalence (BSTE) condition was created that provided embryos with sequential auditory and visual stimulation in which the same amodal properties (rate, duration, rhythm) were made available across modalities. Experiment 2 assessed: (a) whether a limited number of temporally synchronous notes are sufficient for facilitated prenatal perceptual learning with respect to unimodal stimulation, and (b) whether there is a relationship between timing of occurrence of a temporally synchronous note and the facilitation of prenatal perceptual learning. Results revealed that prenatal exposure to BSTE was not sufficient to facilitate perceptual learning. In contrast, a maternal call that contained a single temporally synchronous note was sufficient to facilitate embryos’ prenatal perceptual learning with respect to unimodal stimulation. Furthermore, the most salient prenatal condition was that which contained the synchronous note at the onset of the call burst. Embryos’ prenatal perceptual learning of the call was four times faster in this condition than when exposed to a unimodal call. Taken together, bobwhite quail embryos’ remarkable sensitivity to temporal synchrony suggests that this amodal property plays a key role in attention and learning during prenatal development.