Nationbuilding 101: Property, Liberty, and Corporate Governance

Nationbuilders in less developed countries need to understand how Western legal systems with "property" at their center have materially accounted for Western prosperity and liberty, but legal definitions of property are so abstruse that explication of this vital concept is made difficult. This paper finds an historical definitional essence to property in the right to exclude and maintains that liberty and property both share this essential meaning. The problems of corporate governance are then placed in the context of the exclusionary concept of property/liberty.http://deepblue.lib.umich.edu/bitstream/2027.42/39913/3/wp528.pd

Nationbuilding 101: Property, Liberty, and Corporate Governance

Nationbuilders in less developed countries need to understand how Western legal systems with "property" at their center have materially accounted for Western prosperity and liberty, but legal definitions of property are so abstruse that explication of this vital concept is made difficult. This paper finds an historical definitional essence to property in the right to exclude and maintains that liberty and property both share this essential meaning. The problems of corporate governance are then placed in the context of the exclusionary concept of property/liberty.property, property rights, development and property, liberty, and corporate governance

Evaluation of Output Embeddings for Fine-Grained Image Classification

Image classification has advanced significantly in recent years with the availability of large-scale image sets. However, fine-grained classification remains a major challenge due to the annotation cost of large numbers of fine-grained categories. This project shows that compelling classification performance can be achieved on such categories even without labeled training data. Given image and class embeddings, we learn a compatibility function such that matching embeddings are assigned a higher score than mismatching ones; zero-shot classification of an image proceeds by finding the label yielding the highest joint compatibility score. We use state-of-the-art image features and focus on different supervised attributes and unsupervised output embeddings either derived from hierarchies or learned from unlabeled text corpora. We establish a substantially improved state-of-the-art on the Animals with Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate that purely unsupervised output embeddings (learned from Wikipedia and improved with fine-grained text) achieve compelling results, even outperforming the previous supervised state-of-the-art. By combining different output embeddings, we further improve results.Comment: @inproceedings {ARWLS15, title = {Evaluation of Output Embeddings for Fine-Grained Image Classification}, booktitle = {IEEE Computer Vision and Pattern Recognition}, year = {2015}, author = {Zeynep Akata and Scott Reed and Daniel Walter and Honglak Lee and Bernt Schiele}

Assessing B2G Customer/Contractor Relationships Using Social Exchange Theory During the Search and Selection Stage

Social Exchange Theory (SET) is used widely to explain commercial business-to-business (B2B) relationship exchange. However, very little literature is dedicated to SET’s use in explaining business-to-government (B2G) relationship exchange. More specifically, little if any literature explores: How customer/contractor relationship is developed during the SET search and selection process The influence exerted by contractors to shape customer’s requirements and selection criteria (Positioning) Impact of contractors’ communication interchange on development of perceived customer relationship (trust and commitment) Success competing for contract award as measured by reputational trust and reputational performance satisfaction This ethnomethodology match pair study utilizes Wilson’s (1995) search and selection phase of the relationship development model of SET as a lens to evaluate Business Development (BD) personnel interaction with customers in the B2G business sector impact award decisions. This study looks at the development of perceived trust, perceived commitment, positioning, and communications interchange by the contractor\u27s BD personnel with government customers prior to the release of a Request for Proposal (RFP) and how the customer\u27s evaluation of reputational trust and reputational performance satisfaction impacted the contract award decision following formal proposal evaluation. In this way, the match pair approach looks at the 1) contractors’ evaluation of the customer at the point of the RFP’s release and 2) the customer’s evaluation of the contractor’s post-proposal submission allowing the researcher to contrast the two viewpoints as compared to the results—award decision. This research expands the use of SETs to predict future contract awards based on the customer/contractor relationship exchange during the search and selection phase. Additionally, this research improves the understanding of how contractors influence the B2G customer’s requirements during the development process. A key finding in this research was that Contractors who engage in active Communications Interchange to develop customer Perceived Trust and Perceived Commitment and Position themselves for upcoming contract opportunities prior to solicitation release indicated a trend showing a statistically significant, positive impact on the award decision. Perceived Trust and Perceived Commitment in the absence of Communications Interchange indicated a trend showing a statistically significant, negative impact on the award decision. Additional key findings from the customer debriefs indicated a trend showing Reputational Trust was a reliable predictor of the awardee. However, Reputational Performance Satisfaction consisting of the customer\u27s overall rating of the contractor\u27s past performance, was not a reliable predictor of the contract awardee

Evaluation of Tagging Techniques Gamma-decay Probabilities Using the Surrogate Method

A detailed analysis of the statistical and discrete [gamma]-decay tagging techniques was conducted using the absolute surrogate and surrogate ratio method (SRM) to obtain the 92Mo(n,[gamma]) cross section in an equivalent neutron energy range of 80 to 880 keV. Excited 93Mo and 95Mo nuclei were populated using (d,p) reactions on 92Mo and 94Mo targets, respectively. The absolute surrogate 92Mo(n,[gamma]) cross sections disagreed with evaluated neutron capture cross section data by as much as a factor of 4 using the statistical tagging approach, whereas the discrete [gamma]-decay tag absolute surrogate cross section disagreed with the evaluated neutron capture cross section by as much as a factor of 2, with both, statistical and discrete [gamma]-decay tagging techniques showing an unfavorable trend with the results. The surrogate cross sections obtained via the SRM, in comparison to the absolute surrogate method, offered a more favorable trend with the evaluated data as well as a more agreeable measurement with the evaluated 92Mo cross section. The experimental results suggest that the discrete and statistical [gamma]-decay channel tagging approaches may sample different contributions of the [gamma]-cascade from the residual nucleus for the near spherical nuclei probed in this experiment. This work serves as the first evaluation of the surrogate method in the determination of neutron capture cross sections on spherical and quasi-spherical nuclei in the mass-90 region and provides a possible pathway to extend the SRM to a broader mass range

Generative Adversarial Text to Image Synthesis

Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image model- ing, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions.Comment: ICML 201