Neural Belief Reasoner

This paper proposes a new generative model called neural belief reasoner (NBR). It differs from previous models in that it specifies a belief function rather than a probability distribution. Its implementation consists of neural networks, fuzzy-set operations and belief-function operations, and query-answering, sample-generation and training algorithms are presented. This paper studies NBR in two tasks. The first is a synthetic unsupervised-learning task, which demonstrates NBR's ability to perform multi-hop reasoning, reasoning with uncertainty and reasoning about conflicting information. The second is supervised learning: a robust MNIST classifier for 4 and 9, which is the most challenging pair of digits. This classifier needs no adversarial training, and it substantially exceeds the state of the art in adversarial robustness as measured by the L2 metric, while at the same time maintains 99.1% accuracy on natural images

Incubation Push or Business Pull? Investigating the Geography of US Business Incubators

The primary purposes of this paper are to present the geographic distribution of US business incubators and to explore geographically bounded factors that influence the location of business incubators. Our data show that US business incubators are unevenly distributed across the urban/rural division, states, as well as counties. Factor analysis identifies three common factors from 27 demographic, social, and economic variables drawn from publicly available data at the county level. These factors include agglomeration, welfare, and business/entrepreneurship. The results of binominal logistic regressions suggest that incubators are more likely to be found in counties with high levels of agglomeration but low levels of existing business development. Our findings support the “incubation push” model over the “business pull” model on the location of business incubators, which reflects the policy strategy of incubator creation