Automatic Differentiation Variational Inference

Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this process. Deriving algorithms for new models can be both mathematically and computationally challenging, which makes it difficult to efficiently cycle through the steps. To this end, we develop automatic differentiation variational inference (ADVI). Using our method, the scientist only provides a probabilistic model and a dataset, nothing else. ADVI automatically derives an efficient variational inference algorithm, freeing the scientist to refine and explore many models. ADVI supports a broad class of models-no conjugacy assumptions are required. We study ADVI across ten different models and apply it to a dataset with millions of observations. ADVI is integrated into Stan, a probabilistic programming system; it is available for immediate use

An expert system shell for inferring vegetation characteristics: Interface for the addition of techniques (Task H)

All the NASA VEGetation Workbench (VEG) goals except the Learning System provide the scientist with several different techniques. When VEG is run, rules assist the scientist in selecting the best of the available techniques to apply to the sample of cover type data being studied. The techniques are stored in the VEG knowledge base. The design and implementation of an interface that allows the scientist to add new techniques to VEG without assistance from the developer were completed. A new interface that enables the scientist to add techniques to VEG without assistance from the developer was designed and implemented. This interface does not require the scientist to have a thorough knowledge of Knowledge Engineering Environment (KEE) by Intellicorp or a detailed knowledge of the structure of VEG. The interface prompts the scientist to enter the required information about the new technique. It prompts the scientist to enter the required Common Lisp functions for executing the technique and the left hand side of the rule that causes the technique to be selected. A template for each function and rule and detailed instructions about the arguments of the functions, the values they should return, and the format of the rule are displayed. Checks are made to ensure that the required data were entered, the functions compiled correctly, and the rule parsed correctly before the new technique is stored. The additional techniques are stored separately from the VEG knowledge base. When the VEG knowledge base is loaded, the additional techniques are not normally loaded. The interface allows the scientist the option of adding all the previously defined new techniques before running VEG. When the techniques are added, the required units to store the additional techniques are created automatically in the correct places in the VEG knowledge base. The methods file containing the functions required by the additional techniques is loaded. New rule units are created to store the new rules. The interface that allow the scientist to select which techniques to use is updated automatically to include the new techniques. Task H was completed. The interface that allows the scientist to add techniques to VEG was implemented and comprehensively tested. The Common Lisp code for the Add Techniques system is listed in Appendix A

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;

Astrophysics science operations in the Great Observatories era

Plans for Astrophysics science operations during the decade of the nineties are described from the point of view of a scientist who wishes to make a space-borne astronomical observation or to use archival astronomical data. 'Science Operations' include the following: proposal preparation, observation planning and execution, data collection, data processing and analysis, and dissemination of results. For each of these areas of science operations, we derive technology requirements for the next ten to twenty years. The scientist will be able to use a variety of services and infrastructure, including the 'Astrophysics Data System.' The current status and plans for these science operations services are described

Co-morbidity burden in Parkinson’s disease : Comparison with controls and its influence on prognosis

Financial support This study was funded by Parkinson’s UK, the Scottish Chief Scientist Office, NHS Grampian endowments, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, and SPRING. The funders had no involvement in the study. Acknowledgements We acknowledge funding for the PINE study from Parkinson’s UK (G-0502, G-0914, G-1302), the Scottish Chief Scientist Office(CAF/12/05), the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, NHS Grampian endowments and SPRING. We thank the patients and controls for their participation and the research staff who collected data and supported the study database.Peer reviewedPostprintPostprintPublisher PD

Biomedical Academic Entrepreneurship Through the SBIR Program

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 follow-on venture capital funding, SBIR program completion, and patenting.

Biomedical Academic Entrepreneurship Through the SBIR Program

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,star scientists,SBIR,Venture Capital,innovation