Presidential Decision Making: Comparing the Personality Profiles of Barack Obama and Franklin D. Roosevelt

This paper presents the results of indirect assessments of the personalities of U.S. presidents Franklin D. Roosevelt and Barack H. Obama, from the conceptual perspective of personologist Theodore Millon. Information concerning Roosevelt and Obama was collected from biographical sources and published reports and synthesized into personality profiles using the second edition of the Millon Inventory of Diagnostic Criteria (MIDC), which yields 34 normal and maladaptive personality classifications congruent with Axis II of DSM-IV. The personality profiles yielded by the MIDC were analyzed on the basis of interpretive guidelines provided in the MIDC manual. Roosevelt’s primary personality pattern was found to be Dominant/controlling, with secondary features of the Ambitious/self-serving and Conscientious/dutiful patterns. Obama’s primary personality pattern was found to be Ambitious/self-serving, with secondary features of the Conscientious/respectful and Retiring/reserved patterns. Roosevelt’s and Obama’s personalities are compared and contrasted and the influence of their personality patterns on presidential decision making discussed in the context of parallel political and economic challenges faced by these two presidents

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Reusable Container System Optimization for Smart Cities

Federal and local governments are investing in methods to discourage use of disposable containers in order to reduce waste generation and protect the environment. In this project we propose the use of reusable takeout food containers as a replacement for disposable takeout food containers. Reusable takeout container systems may use barcode or RFID (radio frequency identification) technology to track and manage the distribution, collection, cleaning, and end-of-life recycling of reusable takeout food containers. Such systems will require the use of container collection bins. The design and optimization of a network of container collection bins is the topic of this project. We propose a method to optimize the location network of collection bins at a Smart City. As a case study we use data collected in the city of San Luis Obispo, CA. The reusable container use cycle can be described as follows. A company provides the reusable takeout food containers to restaurants. The restaurants distribute these containers to their customers. After the container is used a customer drops it off in a convenient location for the company to pick it up and wash it. Since convenience of container drop off is crucial to customer participation, strategically placing the drop off bins around the city such that they are highly visible and easily accessible will maximize user satisfaction and benefit to the city. Determining the optimal set of container collection bin locations was performed using a linear programming model that optimized the bin network visibility and accessibility. Visibility and accessibility were measured by traffic volume, pedestrian volume, and population density. The optimization model included varying the quantities of drop-off bins, as well as varying bin sizes and costs. An economic analysis was used to determine the optimal combination of quantity of bins, bin size, and bin cost that maximized the benefit to the city. We simulated the potential container collection routes in order to estimate collection and transportation times and determine the optimal set of collection routes. Similar to the linear programming model, the simulation model also had variable input capabilities. The flexibility of our models may prove useful for future efforts to plan reusable container systems for Smart Cities

Substitutes for nitrogen fertilizers in orcharding

Caption title.At head of title: A wartime publication.Digitized 2006 AES MoU

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem

The Divine Clockwork: Bohr's correspondence principle and Nelson's stochastic mechanics for the atomic elliptic state

We consider the Bohr correspondence limit of the Schrodinger wave function for an atomic elliptic state. We analyse this limit in the context of Nelson's stochastic mechanics, exposing an underlying deterministic dynamical system in which trajectories converge to Keplerian motion on an ellipse. This solves the long standing problem of obtaining Kepler's laws of planetary motion in a quantum mechanical setting. In this quantum mechanical setting, local mild instabilities occur in the Kelperian orbit for eccentricities greater than 1/\sqrt{2} which do not occur classically.Comment: 42 pages, 18 figures, with typos corrected, updated abstract and updated section 6.