On a coalgebraic view on Logic

In this paper we present methods of transition from one perspective on logic to others, and apply this in particular to obtain a coalgebraic presentation of logic. The central ingredient in this process is to view consequence relations as morphisms in a category

Is there a connection between Broad Absorption Line Quasars and Narrow Line Seyfert 1 galaxies?

We consider whether Broad Absorption Line Quasars (BAL QSOs) and Narrow Line Seyfert 1 galaxies (NLS1s) are similar, as suggested by Brandt & Gallagher (2000) and Boroson (2002). For this purpose we constructed a sample of 11 BAL QSOs from existing Chandra and Swift observations. We found that BAL QSOs and NLS1s both operate at high Eddington ratios L/Ledd, although BAL QSOs have slightly lower L/Ledd. BAL QSOs and NLS1s in general have high FeII/H$\beta$ and low [OIII]/H$\beta$ ratios following the classic 'Boroson \& Green' eigenvector 1 relation. We also found that the mass accretion rates $\dot{M}$ of BAL QSOs and NLS1s are more similar than previously thought, although some BAL QSOs exhibit extreme mass accretion rates of more than 10 \msun/year. These extreme mass accretion rates may suggest that the black holes in BAL QSOs are relativistically spinning. Black hole masses in BAL QSOs are a factor of 100 larger than NLS1s. From their location on a M-$\sigma$ plot, we find that BAL QSOs contain fully developed black holes. Applying a principal component analysis to our sample we find eigenvector 1 to correspond to the Eddington ratio L/Ledd, and eigenvector 2 to black hole mass.Comment: Accepted for publication in the Astronomical Journal; 27 pages, 6 figure

Biomedical academic entrepreneurship through the SBIR program2.

This paper considers the U.S. Small Business Innovation Research (SBIR) program as a policy fostering academic entrepreneurship. We highlight two main characteristics of the program that make it attractive as an entrepreneurship policy: early-stage financing and scientist involvement in commercialization. Using unique data on NIH supported biomedical researchers, we trace the incidence of biomedical entrepreneurship through SBIR and describe some of the characteristics of these individuals. To explore the importance of early-stage financing and scientist involvement, we complement our individual level data with information on scientist-linked and non-linked SBIR firms. Our results show that the SBIR program is being used as a commercialization channel by academic scientists. Moreover, we find that the firms associated with these scientists perform significantly better than other non-linked SBIR firms in terms of followon venture capital funding, SBIR program completion, and patenting.Academic entrepreneurship; Characteristics; Data; Firms; Information; Innovation;

The geometry of L^p-spaces over atomless measure spaces and the Daugavet property

We show that $L^p$-spaces over atomless measure spaces can be characterized in terms of a $p$-concavity type geometric property that is related with the Daugavet property

Is there a trade-off between academic research and faculty entrepreneurship? Evidence from U.S. NIH supported biomedical researchers

Is there a trade-off of scholarly research productivity when faculty members found or join for-profit firms? This paper offers an empirical examination of this question for a subpopulation of biomedical academic scientists who received research funding from the U.S. National Institutes of Health (NIH). In this study, we are able to distinguish between permanent versus temporary employment transitions by entrepreneurial faculty members and examine how their journal article publication rates change using individual-level panel data. We find that the biomedical scientists who eventually choose to found or join a for-profit firm were more productive during their careers in academe than a randomly selected control group of their NIH peers. When they pursue entrepreneurship in the private sector, however, their scholarly productivity falls. Those entrepreneurial faculty members who return to academe are not as productive as they were before their entrepreneurial experience in terms of journal publications. --academic entrepreneurship,SBIR,NIH,biomedical research,life scientist productivity

Patent Protection, Market Uncertainty, and R&D Investment

Real options investment theory predicts current investment falls as uncertainty about market returns increases. In the case of R&D investment, which is usually considered an irreversible form of investment, this effect should be quite pronounced. This paper tests the real options prediction about the R&D investment-uncertainty relationship and further considers how patent protection influences this relationship. Patent protection, by limiting the threat of market rivalry, should mitigate firm-specific uncertainty and stimulate current R&D investment. Our empirical results support both the prediction of real options theory and the mitigating effect of patent protection. --Real Options Theory,Uncertainty,R&D,Intellectual Property Protection,Censored Regression

Business R&D and the Interplay of R&D Subsidies and Market Uncertainty

The literature suggests that public research and development (R&D) subsidies may reduce market failures affecting private R&D investment caused by incomplete appropriability of knowledge and financial constraints due capital market imperfections. Drawing on the theory of investment under uncertainty, this paper argues that public R&D subsidies increase business R&D investment through an additional mechanism – mitigating the effects of market uncertainty on R&D investment in markets for new products. Using a sample of German manufacturing firms, we show that market uncertainty indeed reduces R&D investment, and that R&D subsidies mitigate the effect of uncertainty. Our findings suggest that public policies aimed at increasing business R&D investment can achieve this objective by reducing the degree of uncertainty in the demand for innovative products. --Real Options Theory,Uncertainty,R&D,Censored Regression

Exploring the relationship between scientist human capital and firm performance: The case of biomedical academic entrepreneurs in the SBIR program

Do academic scientists bring valuable human capital to the companies they found or join? If so, what are the particular skills that compose their human capital and how are these skills related to firm performance? This paper examines these questions using a particular group of academic entrepreneurs – biomedical research scientists who choose to commercialize their knowledge through the U.S. Small Business Innovation Research Program. Our conceptual framework assumes the nature of an academic entrepreneurs? prior research reflects the development of their human capital. We highlight differences in firm performance that correlate with differences in the scientists? research orientations developed during their academic careers. We find that biomedical academic entrepreneurs with human capital oriented toward exploring scientific opportunities significantly improve their firms? performance of research tasks such as ?proof of concept? studies. Biomedical academic entrepreneurs with human capital oriented toward exploring commercial opportunities significantly improve their firms? performance of invention oriented tasks such as patenting. Consistent with prior evidence, there also appears to be a form of diminishing returns to scientifically oriented human capital in a commercialization environment. Holding the commercial orientation of the scientists? human capital constant, we find that increasing their human capital for identifying and exploring scientific opportunities significantly detracts from their firms? patenting performance. --Academic Entrepreneurship,SBIR Program,Human Capital,Biotechnology

Constructing Wildebeest Density Distributions by Spatio-temporal Smoothing of Ordinal Categorical Data Using GAMs

Spatio-temporal smoothing of large ecological datasets describing species distributions can be made challenging by high computational costs and deficiencies in the available data. We present an application of a GAM-based smoothing method to a large ordinal categorical dataset on the distribution of wildebeest in the Serengeti ecosystem