The Correlates of Panethnic Identification: Assessing Similarities and Differences among Latinos and Asians in the United States

Latinos and Asian-Americans constitute the largest recent immigrant groups in the United States. Upon arrival, immigrants from these groups generally identify with their national origin despite being categorized as “Asian” or “Latino” for state enumeration. While both are racialized and excluded from mainstream identities, they differ in their internal linguistic and religious diversities, socioeconomic status, and immigration experiences. Sociologists theorized that Asian-American panethnicity is based on structural commonalities while Latino panethnicity is built upon cultural commonalities. We elaborate the theoretical understanding of contexts associated with this identification and find alternative underpinnings that shape both groups’ panethnic identification. We find generation since immigration is a common basis for elevated likelihood of panethnic identification for both groups. However, among Asian-Americans, we find English proficiency and age increase people’s odds of identifying with a panethnic identity over a national origin term, whereas for Latinos, political affiliation and religiosity increase these odds.

Reliability Assessment of Power Generation System Using an Optimized State Enumeration Method

Background: The single state enumeration method cannot meet the requirement of accuracy and high efficiency in the reliability assessment of complex power systems because of many uncertain factors and the large scale of the power grid. Methods: A new method of generating system reliability assessment based on self-organizing map (SOM) neural network and state enumeration is presented. First, the input parameters of the state enumeration method are optimized by using the feature of the SOM neural network algorithm that can automatically, quickly, and accurately classify the sample parameters in this method. Second, combining with Markov Model, the optimized system state samples are divided into fault state and normal state, and then the reliability indexes are enumerated. Finally, this method is used to calculate the reliability indexes of IEEE-RTS single-stage power units under different operation conditions. Results: The results show that this method is superior to the single state enumeration method in calculation time; it can be used to evaluate the reliability of modern complex power systems. Conclusion: The optimized state enumeration method is more suitable for the reliability evaluation of the system with a large network scale, and its reliability index is more accurate; while retaining the higher calculation accuracy of the state enumeration method, it can promote the safe, reliable, and economical operation of the power system.

Applying DTGolog to Large-scale Domains

While decision theoretic planning (DTP) offers great potential benefits to elicit purposeful behavior of the agent operating in uncertain environments, state-based approaches to DTP are known to be computationally intractable in large-scale domains. DTGolog is a decision-theoretic extension of a logic-based high level programming language Golog that completes a given partial Golog program using a form of directed value iteration. DTGolog has been proposed to alleviate some of the computational difficulties associated with DTP. The main advantages of DTGolog are that a DTP problem can be formulated using a logical representation to avoid explicit state enumeration, and the programmer can encode domain-specific knowledge in terms of high-level procedural templates to partially specify behavior of an agent. These templates constrain the search space to manageable size. Despite these clear advantages, there are few studies that investigate the applicability of DTGolog to very large-scale practical domains. In this thesis, we conduct two studies. First, we apply DTGolog to the well known case-study of the London Ambulance Service to demonstrate advantages and potentials of DTGolog as a quantitative evaluation tool for designing decision making agents. Second, we develop a software interface that allows to control the well-known Sony's AIBO robotics platform using DTGolog. We show that DTGolog can be used on this platform with a minimal amount of software customization. We run experiments to test functionality of our interface. The main contribution of this thesis is demonstration of applicability of DTGolog to two different large scale domains that are both practical and interesting.

Applying DTGolog to Large-scale Domains

While decision theoretic planning (DTP) offers great potential benefits to elicit purposeful behavior of the agent operating in uncertain environments, state-based approaches to DTP are known to be computationally intractable in large-scale domains. DTGolog is a decision-theoretic extension of a logic-based high level programming language Golog that completes a given partial Golog program using a form of directed value iteration. DTGolog has been proposed to alleviate some of the computational difficulties associated with DTP. The main advantages of DTGolog are that a DTP problem can be formulated using a logical representation to avoid explicit state enumeration, and the programmer can encode domain-specific knowledge in terms of high-level procedural templates to partially specify behavior of an agent. These templates constrain the search space to manageable size. Despite these clear advantages, there are few studies that investigate the applicability of DTGolog to very large-scale practical domains. In this thesis, we conduct two studies. First, we apply DTGolog to the well known case-study of the London Ambulance Service to demonstrate advantages and potentials of DTGolog as a quantitative evaluation tool for designing decision making agents. Second, we develop a software interface that allows to control the well-known Sony's AIBO robotics platform using DTGolog. We show that DTGolog can be used on this platform with a minimal amount of software customization. We run experiments to test functionality of our interface. The main contribution of this thesis is demonstration of applicability of DTGolog to two different large scale domains that are both practical and interesting.

An Artificial Bee Colony Algorithm for Detecting Distributed Predicates

Developing correct dependable distributed applications is a difficult task. The techniques developed to detect distributed predicates can be used to verify the satisfaction of specific properties (represented as predicates) in a given run of a distributed application. These techniques have several applications including the verification of the correctness of a particular implementation of a distributed system (runtime verification). Due to concurrency, the number of states to be considered in detecting a distributed predicate is exponential. This article investigates the effectiveness of using a meta-heuristic algorithm in solving the problem of detecting distributed predicates. Namely, the authors have used the Artificial Bee Colony algorithm to develop an algorithm to detect distributed predicates. The conducted experiments demonstrate that the ABC-based detection algorithm has a better performance compared with the traditional state enumeration-based detection algorithm.

A literature review on network reliability analysis and its engineering applications

The applications of networks can be observed in numerous engineering realms mainly computers and communication, transportation, electric transmission and oil and gas distribution. Estimating the reliability of such networks is a critical task for the well-being of society as well as a nation. The paradigm of network reliability has evolved considerably over the past few decades. The purpose of this article is to present the developments in network reliability domain in a laconic manner. This paper discusses the various metrics used to evaluate network connectivity along with their mathematical backgrounds. The various algorithms namely State Enumeration, Sum of disjoint product, Minimal Cut Set, Factoring theorem, Cellular Automata, Subset Simulation, Percolation theory, Binary Decision Diagrams, and Universal Generating Functions are enumerated in this review study. The application part of algorithms with their advantages, limitations and scope are presented. Finally, the methodologies used for assessment of network reliability are discussed. This article aims at providing a holistic view of the network reliability practices, which may prove to be helpful for researchers across the globe.

A Tutorial Exposition of Various Methods for Analyzing Capacitated Networks

In order to assess the performance indexes of some practical systems having fixed channel capacities, such as telecommunication networks, power transmission systems or commodity pipeline systems, we propose various types of techniques for analyzing a capacitated network. These include Karnaugh maps, capacity-preserving network reduction rules associated with delta-star transformations, and a generalization of the max-flow min-cut theorem. All methods rely on recognizing the network capacity function as a random pseudo-Boolean function of link successes; a fact that allows the expected value of this function to be easily obtainable from its sum-of-products expression. This network capacity has certain advantages for representation of nonbinary discrete random functions, mostly employed in the analysis of flow networks. Five tutorial examples demonstrate the afore-mentioned methods and illustrate their computational advantages over the exhaustive state enumeration method.

Multiracial Categorization, Identity, and Policy in (Mixed) Racial Formations

This article examines recent developments in the literature on multiracial categorization, policy, and identity—one that has grown as data on multiracial populations have become widely available, particularly in the United States since the enumeration of multiple race responses was instituted in Census 2000. Significant new research takes advantage of the data generated by the Census providing new insights to questions and claims about the meanings of mixedness and racial boundaries in the United States that were largely speculative even a decade ago. Though this review focuses primarily on issues related to how state enumeration of mixed race populations reflects and engenders particular identity and group configurations, I also discuss emerging research on interracial intimacy—intermarriage and interracial births—the phenomena from which contemporary attention to multiracial categorization and identity arise. An increasingly internationalist discussion is challenging long-held interpretations of the meaning of intermarriage and multiracial identification for understanding emergent racial formations.