Showing papers by "University of Texas at Arlington published in 2009"

Reinforcement learning and adaptive dynamic programming for feedback control

Abstract: Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based or reinforcement learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.

Entrepreneurial Self-Efficacy: Refining the Measure

Abstract: A growing number of studies on entrepreneurial motivation, intentions, and behavior include entrepreneurial self-efficacy (ESE) as an explanatory variable. While there is broad consensus among researchers on the importance of including ESE in an intentionality model, there remain inconsistencies in the definition, dimensionality, and measurement of ESE. This study takes an important step toward refinement and standardization of ESE measurement. Within a new venture creation process framework, a multi-dimensional ESE instrument is developed and tested on a diverse sample that includes nascent entrepreneurs. Implications for entrepreneurship theory and entrepreneurship education are discussed.

Online actor critic algorithm to solve the continuous-time infinite horizon optimal control problem

Abstract: In this paper we discuss an online algorithm based on policy iteration for learning the continuous-time (CT) optimal control solution with infinite horizon cost for nonlinear systems with known dynamics. We present an online adaptive algorithm implemented as an actor/critic structure which involves simultaneous continuous-time adaptation of both actor and critic neural networks. We call this ‘synchronous’ policy iteration. A persistence of excitation condition is shown to guarantee convergence of the critic to the actual optimal value function. Novel tuning algorithms are given for both critic and actor networks, with extra terms in the actor tuning law being required to guarantee closed-loop dynamical stability. The convergence to the optimal controller is proven, and stability of the system is also guaranteed. Simulation examples show the effectiveness of the new algorithm.

The Debate over Doing Good: Corporate Social Performance, Strategic Marketing Levers, and Firm-Idiosyncratic Risk

Abstract: Marketers and investors face a heated, provocative debate over whether excelling in social responsibility initiatives hurts or benefits firms financially. This study develops a theoretical framework that predicts (1) the impact of corporate social performance (CSP) on firm-idiosyncratic risk and (2) the role of two strategic marketing levers, advertising and research and development (R&D), in explaining the variability of this impact among different firms. The results show that higher CSP lowers undesirable firm-idiosyncratic risk. Notably, although the salutary impact of CSP is greater in firms with higher (versus lower) advertising, a simultaneous pursuit for CSP, advertising, and R&D is harmful with increased firm-idiosyncratic risk. For theory, the authors advance the literature on the marketing–finance interface by drawing attention to the risk-reduction potential of CSP and by shedding new light on some critical but neglected roles of strategic marketing levers. They also extend CSP researc...

Overcoming the J-shaped distribution of product reviews

Abstract: Introduction While product review systems that collect and disseminate opinions about products from recent buyers (Table 1) are valuable forms of word-of-mouth communication, evidence suggests that they are overwhelmingly positive. Kadet notes that most products receive almost five stars. Chevalier and Mayzlin also show that book reviews on Amazon and Barnes & Noble are overwhelmingly positive. Is this because all products are simply outstanding? However, a graphical representation of product reviews reveals a J-shaped distribution (Figure 1) with mostly 5-star ratings, some 1-star ratings, and hardly any ratings in between. What explains this J-shaped distribution? If products are indeed outstanding, why do we also see many 1-star ratings? Why aren't there any product ratings in between? Is it because there are no "average" products? Or, is it because there are biases in product review systems? If so, how can we overcome them? The J-shaped distribution also creates some fundamental statistical problems. Conventional wisdom assumes that the average of the product ratings is a sufficient proxy of product quality and product sales. Many studies used the average of product ratings to predict sales. However, these studies showed inconsistent results: some found product reviews to influence product sales, while others did not. The average is statistically meaningful only when it is based on a unimodal distribution, or when it is based on a symmetric bimodal distribution. However, since product review systems have an asymmetric bimodal (J-shaped) distribution, the average is a poor proxy of product quality. This report aims to first demonstrate the existence of a J-shaped distribution, second to identify the sources of bias that cause the J-shaped distribution, third to propose ways to overcome these biases, and finally to show that overcoming these biases helps product review systems better predict future product sales. We tested the distribution of product ratings for three product categories (books, DVDs, videos) with data from Amazon collected between February--July 2005: 78%, 73%, and 72% of the product ratings for books, DVDs, and videos are greater or equal to four stars (Figure 1), confirming our proposition that product reviews are overwhelmingly positive. Figure 1 (left graph) shows a J-shaped distribution of all products. This contradicts the law of "large numbers" that would imply a normal distribution. Figure 1 (middle graph) shows the distribution of three randomly-selected products in each category with over 2,000 reviews. The results show that these reviews still have a J-shaped distribution, implying that the J-shaped distribution is not due to a "small number" problem. Figure 1 (right graph) shows that even products with a median average review (around 3-stars) follow the same pattern.

The Virtuous Influence of Ethical Leadership Behavior: Evidence from the Field

Abstract: This study examines a moderated/mediated model of ethical leadership on follower job satisfaction and affective organizational commitment. We proposed that managers have the potential to be agents of virtue or vice within organizations. Specifically, through ethical leadership behavior we argued that managers can virtuously influence perceptions of ethical climate, which in turn will positively impact organizational members’ flourishing as measured by job satisfaction and affective commitment to the organization. We also hypothesized that perceptions of interactional justice would moderate the ethical leadership-to-climate relationship. Our results indicate that ethical leadership has both a direct and indirect influence on follower job satisfaction and affective commitment. The indirect effect of ethical leadership involves shaping perceptions of ethical climate, which in turn, engender greater job satisfaction and affective organizational commitment. Furthermore, when interactional justice is perceived to be high, this strengthens the ethical leadership-to-climate relationship.

Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems

Abstract: In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided.

A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation

Abstract: Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American sign language (ASL).

Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change

Abstract: Global environmental change, related to climate change and the deposition of airborne N-containing contaminants, has already resulted in shifts in plant community composition among plant functional types in Arctic and temperate alpine regions. In this paper, we review how key ecosystem processes will be altered by these transformations, the complex biological cascades and feedbacks that might result, and some of the potential broader consequences for the earth system. Firstly, we consider how patterns of growth and allocation, and nutrient uptake, will be altered by the shifts in plant dominance. The ways in which these changes may disproportionately affect the consumer communities, and rates of decomposition, are then discussed. We show that the occurrence of a broad spectrum of plant growth forms in these regions (from cryptogams to deciduous and evergreen dwarf shrubs, graminoids and forbs), together with hypothesized low functional redundancy, will mean that shifts in plant dominance result in a complex series of biotic cascades, couplings and feedbacks which are supplemental to the direct responses of ecosystem components to the primary global change drivers. The nature of these complex interactions is highlighted using the example of the climate-driven increase in shrub cover in low-Arctic tundra, and the contrasting transformations in plant functional composition in mid-latitude alpine systems. Finally, the potential effects of the transformations on ecosystem properties and processes that link with the earth system are reviewed. We conclude that the effects of global change on these ecosystems, and potential climate-change feedbacks, cannot be predicted from simple empirical relationships between processes and driving variables. Rather, the effects of changes in species distributions and dominances on key ecosystem processes and properties must also be considered, based upon best estimates of the trajectories of key transformations, their magnitude and rates of change.

Dynamic inversion with zero-dynamics stabilisation for quadrotor control

Abstract: For a quadrotor, one can identify the two well-known inherent rotorcraft characteristics: underactuation and strong coupling in pitch-yaw-roll. To confront these problems and design a station-keeping and tracking controller, dynamic inversion is used. Typical applications of dynamic inversion require the selection of the output control variables to render the internal dynamics stable. This means that in many cases, perfect tracking cannot be guaranteed for the actual desired outputs. Instead, the internal dynamics of the feedback linearised system is stabilised using a robust control term. Unlike standard dynamic inversion, the linear controller gains are chosen uniquely to satisfy the tracking performance. Stability and tracking performance are guaranteed using a Lyapunov-type proof. Simulation with a typical nonlinear quadrotor dynamic model is performed to show the effectiveness of the designed control law in the presence of input disturbances.

Backstepping Approach for Controlling a Quadrotor Using Lagrange Form Dynamics

Abstract: The dynamics of a quadrotor are a simplified form of helicopter dynamics that exhibit the same basic problems of underactuation, strong coupling, multi-input/multi-output design, and unknown nonlinearities. Control design for the quadrotor is more tractable yet reveals corresponding approaches for helicopter and UAV control design. In this paper, a backstepping approach is used for quadrotor controller design. In contrast to most other approaches, we apply backstepping on the Lagrangian form of the dynamics, not the state space form. This is complicated by the fact that the Lagrangian form for the position dynamics is bilinear in the controls. We confront this problem by using an inverse kinematics solution akin to that used in robotics. In addition, two neural nets are introduced to estimate the aerodynamic components, one for aerodynamic forces and one for aerodynamic moments. The result is a controller of intuitively appealing structure having an outer kinematics loop for position control and an inner dynamics loop for attitude control. The control approach described in this paper is robust since it explicitly deals with unmodeled state-dependent disturbances and forces without needing any prior knowledge of the same. A simulation study validates the results obtained in the paper.

Group recommendation: semantics and efficiency

Abstract: We study the problem of group recommendation. Recommendation is an important information exploration paradigm that retrieves interesting items for users based on their profiles and past activities. Single user recommendation has received significant attention in the past due to its extensive use in Amazon and Netflix. How to recommend to a group of users who may or may not share similar tastes, however, is still an open problem. The need for group recommendation arises in many scenarios: a movie for friends to watch together, a travel destination for a family to spend a holiday break, and a good restaurant for colleagues to have a working lunch. Intuitively, items that are ideal for recommendation to a group may be quite different from those for individual members. In this paper, we analyze the desiderata of group recommendation and propose a formal semantics that accounts for both item relevance to a group and disagreements among group members. We design and implement algorithms for efficiently computing group recommendations. We evaluate our group recommendation method through a comprehensive user study conducted on Amazon Mechanical Turk and demonstrate that incorporating disagreements is critical to the effectiveness of group recommendation. We further evaluate the efficiency and scalability of our algorithms on the MovieLens data set with 10M ratings.

Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation

Abstract: Reduced-reference image quality assessment (RRIQA) methods estimate image quality degradations with partial information about the ldquoperfect-qualityrdquo reference image. In this paper, we propose an RRIQA algorithm based on a divisive normalization image representation. Divisive normalization has been recognized as a successful approach to model the perceptual sensitivity of biological vision. It also provides a useful image representation that significantly improves statistical independence for natural images. By using a Gaussian scale mixture statistical model of image wavelet coefficients, we compute a divisive normalization transformation (DNT) for images and evaluate the quality of a distorted image by comparing a set of reduced-reference statistical features extracted from DNT-domain representations of the reference and distorted images, respectively. This leads to a generic or general-purpose RRIQA method, in which no assumption is made about the types of distortions occurring in the image being evaluated. The proposed algorithm is cross-validated using two publicly-accessible subject-rated image databases (the UT-Austin LIVE database and the Cornell-VCL A57 database) and demonstrates good performance across a wide range of image distortions.

Association of enjoyable leisure activities with psychological and physical well-being.

Abstract: Objective:To examine whether engaging in multiple enjoyable activities was associated with better psychological and physiological functioning. Few studies have examined the health benefits of the enjoyable activities that individuals participate in voluntarily in their free time.Method:Participants

Privacy preservation in wireless sensor networks: A state-of-the-art survey

Abstract: Much of the existing work on wireless sensor networks (WSNs) has focused on addressing the power and computational resource constraints of WSNs by the design of specific routing, MAC, and cross-layer protocols. Recently, there have been heightened privacy concerns over the data collected by and transmitted through WSNs. The wireless transmission required by a WSN, and the self-organizing nature of its architecture, makes privacy protection for WSNs an especially challenging problem. This paper provides a state-of-the-art survey of privacy-preserving techniques for WSNs. In particular, we review two main categories of privacy-preserving techniques for protecting two types of private information, data-oriented and context-oriented privacy, respectively. We also discuss a number of important open challenges for future research. Our hope is that this paper sheds some light on a fruitful direction of future research for privacy preservation in WSNs.

Observation of single top-quark production

Abstract: We report observation of the electroweak production of single top quarks in pp collisions at s=1.96 TeV based on 2.3 fb(-1) of data collected by the D0 detector at the Fermilab Tevatron Collider. Using events containing an isolated electron or muon and missing transverse energy, together with jets originating from the fragmentation of b quarks, we measure a cross section of sigma(pp -> tb+X,tqb+X)=3.94 +/- 0.88 pb. The probability to measure a cross section at this value or higher in the absence of signal is 2.5x10(-7), corresponding to a 5.0 standard deviation significance for the observation.

Intergroup trust in Northern Ireland.

Abstract: Although prominent political agendas have placed a great deal of importance on building trust in postconflict areas, there has been a lack of empirical research on its role in areas of intergroup conflict. The authors conducted two studies to examine the relationship between trust and intergroup behavioral tendencies-and the potential for intergroup contact to build trust in Northern Ireland. Study 1 showed that outgroup trust mediates the impact of intergroup contact on behavioral tendencies toward the outgroup. Study 2 revealed the importance of trusting the outgroup over simply liking the outgroup; establishing outgroup trust is crucial, as trust is a stronger predictor of behavioral tendencies toward the outgroup than positive attitudes are. Results also demonstrated two mechanisms for increasing outgroup trust-through both direct and extended intergroup contact. These studies further our understanding of the psychological mechanisms underlying the formation of intergroup trust and behavior in areas of conflict.

Trust in Forecast Information Sharing

Abstract: This paper investigates the capacity investment decision of a supplier who solicits private forecast information from a manufacturer. To ensure abundant supply, the manufacturer has an incentive to inflate her forecast in a costless, non-binding, and non-verifiable type of communication known as "cheap talk.'' According to standard game theory, parties do not cooperate and the only equilibrium is uninformative -- the manufacturer's report is independent of her forecast and the supplier does not use the report to determine capacity. However, we observe in controlled laboratory experiments that parties cooperate even in the absence of reputation-building mechanisms and complex contracts. We argue that the underlying reason for cooperation is trust. The extant literature on forecast sharing and supply chain coordination implicitly assumes that supply chain members either absolutely trust each other and cooperate when sharing forecast information, or do not trust each other at all. Contrary to this all-or-nothing view, we determine that a continuum exists between these two extremes. In addition, we determine (i) when trust is important in forecast information sharing, (ii) how trust is affected by changes in the supply chain environment, and (iii) how trust affects related operational decisions. To explain and better understand the observed behavioral regularities, we also develop an analytical model of trust to incorporate both pecuniary and non-pecuniary incentives in the game-theoretic analysis of cheap-talk forecast communication. The model identifies and quantifies how trust and trustworthiness induce effective cheap-talk forecast sharing under the wholesale price contract. We also determine the impact of repeated interactions and information feedback on trust and cooperation in forecast sharing. We conclude with a discussion on the implications of our results for developing effective forecast management policies.

Informal Family Business in Africa

Abstract: Employing grounded theory based on comparative case studies of informal microfinanced businesses in East Africa, we build a theoretical foundation for studying the establishment and evolution of family firms in emerging markets. We show that East African entrepreneurs not only use both strong family and strong community ties to establish and grow businesses, but they also use strong community ties to counterbalance the obligations that strong extended family ties create. In addition, we show that economic informality presents opportunities for some entrepreneurial businesses but not others to cycle rapidly from opportunity to opportunity as they maneuver toward higher value-creating ventures.

Nanoscale magnetic materials and applications

Abstract: Spin Dynamics: Fast Switching of Macro-spins.- Core-Shell Magnetic Nanoclusters.- Designed Magnetic Nanostructures.- Superconductivity and Magnetism in Silicon and Germanium Clathrates.- Neutron Scattering of Magnetic Materials.- Tunable Exchange Bias Effects.- Dynamics of Domain Wall Motion in Wires with Perpendicular Anisotropy.- Magnetic Nanowires for Domain Wall Logic and Ultrahigh Density Data Storage.- Bit-Patterned Magnetic Recording: Nanoscale Magnetic Islands for Data Storage.- The Magnetic Microstructure of Nanostructured Materials.- Exchange-Coupled Nanocomposite Permanent Magnets.- High-Temperature Samarium Cobalt Permanent Magnets.- Nanostructured Soft Magnetic Materials.- Magnetic Shape Memory Phenomena.- Magnetocaloric Effect and Materials.- Spintronics and Novel Magnetic Materials for Advanced Spintronics.- Growth and Properties of Epitaxial Chromium Dioxide (CrO2) Thin Films and Heterostructures.- FePt and Related Nanoparticles.- Magnetic Manipulation of Colloidal Particles.- Applications of Magnetic Nanoparticles in Biomedicine.- Nano-Magnetophotonics.- Hard Magnetic Materials for MEMS Applications.- Solid-State Magnetic Sensors for Bioapplications.

Big Five predictors of behavior and perceptions in initial dyadic interactions: personality similarity helps extraverts and introverts, but hurts "disagreeables".

Abstract: The authors used the unstructured dyadic interaction paradigm to examine the effects of gender and the Big Five personality traits on dyad members' behaviors and perceptions in 87 initial, unstructured interactions. Most of the significant Big Five effects (84%) were associated with the traits of Extraversion and Agreeableness. There were several significant actor and partner effects for both of these traits. However, the most interesting and novel effects took the form of significant Actor x Partner interactions. Personality similarity resulted in relatively good initial interactions for dyads composed of 2 extraverts or 2 introverts, when compared with dissimilar (extravert-introvert) pairs. However, personality similarity resulted in uniquely poor initial interactions for dyads composed of 2 "disagreeables." In summary, the Big Five traits predict behavior and perceptions in initial dyadic interactions, not just in the form of actor and partner "main effects" but also in the form of Actor x Partner interactions.

Value of Strong Ties to Disconnected Others: Examining Knowledge Creation in Biomedicine

Abstract: Knowledge creation requires the combination and exchange of diverse and overlapping knowledge inputs as individuals interact with exchange partners to create new knowledge. In this study, we examine knowledge creation among university research scientists as a function of their professional (ego) networks---those others with whom they collaborate for the purpose of creating new knowledge. We propose that knowledge creation relies, in part, on two attributes of a researcher's professional network structure---average tie strength and ego network density---and we provide insights into how these attributes jointly affect knowledge creation. Our study of over 7,300 scientific publications by 177 research scientists working with more than 14,000 others over an 11-year period provides evidence that the relationship between a research scientist's professional network and knowledge creation depends on both ego network density and average tie strength. Our evidence suggests that both attributes affect knowledge creation. Moreover, average tie strength interacts with density to affect knowledge creation such that researchers who maintain mostly strong ties with research collaborators who themselves comprise a sparse network have the highest levels of new knowledge creation.

Commitment, Procedural Fairness, and Organizational Citizenship Behavior: A Multifoci Analysis

Abstract: Research on commitment, procedural fairness, and organizational citizenship behavior (OCB) suggests that employees maintain distinct beliefs about, and direct behaviors towards, multiple targets in the workplace (e.g., the organization as a whole, their supervisor, and fellow workgroup members). The present studies were designed to test for “target similarity effects,” in which the relationships between commitment, procedural fairness, and OCB were expected to be stronger when they referred to the same target than when they referred to different targets. As predicted, we found that: (1) the positive relationship between commitment and OCB, and (2) the mediating effect of commitment on the positive relationship between procedural fairness and OCB, was particularly likely to emerge when the constructs were in reference to the same target. Support for these target similarity effects was found among layoff survivors (Study 1) and student project teams (Study 2). Theoretical and practical implications are discussed, as are limitations of the studies and suggestions for future research. Copyright © 2008 John Wiley & Sons, Ltd.

The life and death of gene families

Abstract: One of the unique insights provided by the growing number of fully sequenced genomes is the pervasiveness of gene duplication and gene loss Indeed, several metrics now suggest that rates of gene birth and death per gene are only 10-40% lower than nucleotide substitutions per site, and that per nucleotide, the consequent lineage-specific expansion and contraction of gene families may play at least as large a role in adaptation as changes in orthologous sequences While gene family evolution is pervasive, it may be especially important in our own evolution since it appears that the "revolving door" of gene duplication and loss has undergone multiple accelerations in the lineage leading to humans In this paper, we review current understanding of gene family evolution including: methods for inferring copy number change, evidence for adaptive expansion and adaptive contraction of gene families, the origins of new families and deaths of previously established ones, and finally we conclude with a perspective on challenges and promising directions for future research

Quantifying the Long-Term Impact of Negative Word of Mouth on Cash Flows and Stock Prices

Abstract: This paper seeks to quantify the long-term financial impact of negative word of mouth (NWOM), an issue that has long challenged extant research. We do so with real-world data on firm security prices. The developed time-series models innovatively uncover (1) short-and long-term effects of NWOM on cash flows, stock returns, and stock volatilities, and (2) NWOM's “wear-in” effects (i.e., it takes a number of months before the stock price impact of NWOM reaches the peak point) and “wear-out” effects (i.e., it takes several months after the peak before the stock price impact of NWOM dies out completely). In addition, the results related to endogeneity and feedback effects from the stock market are also interesting, supporting the idea that historical underperformance in stock prices may breed more harmful future buzz in a “vicious” cycle of NWOM. After controlling for competition, NWOM's long-term financial harm becomes more destructive in magnitude, kicks in more quickly, and haunts investors longer. Overall, these findings offer some unique implications for buzz management, time-series models quantifying the financial impact of word of mouth, and the marketing-finance interface.