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Articles 1 - 7 of 7
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The Amulet Wearable Platform: Demo Abstract, Josiah Hester, Travis Peters, Tianlong Yun, Ronald Peterson, Joseph Skinner, Bhargav Golla, Kevin Storer, Steven Hearndon, Sarah Lord, Ryan Halter, David Kotz, Jacob Sorber
The Amulet Wearable Platform: Demo Abstract, Josiah Hester, Travis Peters, Tianlong Yun, Ronald Peterson, Joseph Skinner, Bhargav Golla, Kevin Storer, Steven Hearndon, Sarah Lord, Ryan Halter, David Kotz, Jacob Sorber
Dartmouth Scholarship
In this demonstration we present the Amulet Platform; a hardware and software platform for developing energy- and resource-efficient applications on multi-application wearable devices. This platform, which includes the Amulet Firmware Toolchain, the Amulet Runtime, the ARP-View graphical tool, and open reference hardware, efficiently protects applications from each other without MMU support, allows developers to interactively explore how their implementation decisions impact battery life without the need for hardware modeling and additional software development, and represents a new approach to developing long-lived wearable applications. We envision the Amulet Platform enabling long-duration experiments on human subjects in a wide variety of studies.
Artificial Intelligence And Amikacin Exposures Predictive Of Outcomes In Multidrug-Resistant Tuberculosis Patients, Chawangwa Modongo, Jotam G. Pasipanodya, Shashikant Srivastava, Nicola Zetola, Scott Williams, Giorgio Sirugo, Tawanda Gumbo
Artificial Intelligence And Amikacin Exposures Predictive Of Outcomes In Multidrug-Resistant Tuberculosis Patients, Chawangwa Modongo, Jotam G. Pasipanodya, Shashikant Srivastava, Nicola Zetola, Scott Williams, Giorgio Sirugo, Tawanda Gumbo
Dartmouth Scholarship
Aminoglycosides such as amikacin continue to be part of the backbone of treatment of multidrug-resistant tuberculosis (MDR- TB). We measured amikacin concentrations in 28 MDR-TB patients in Botswana receiving amikacin therapy together with oral levofloxacin, ethionamide, cycloserine, and pyrazinamide and calculated areas under the concentration-time curves from 0 to 24 h (AUC0 –24). The patients were followed monthly for sputum culture conversion based on liquid cultures. The median duration of amikacin therapy was 184 (range, 28 to 866) days, at a median dose of 17.30 (range 11.11 to 19.23) mg/kg. Only 11 (39%) pa- tients had sputum culture conversion during …
Demo: Wanda, Securely Introducing Mobile Devices, Timothy J. Pierson, Xiaohui Liang, Ronald Peterson, David Kotz
Demo: Wanda, Securely Introducing Mobile Devices, Timothy J. Pierson, Xiaohui Liang, Ronald Peterson, David Kotz
Dartmouth Scholarship
Nearly every setting is increasingly populated with wireless and mobile devices – whether appliances in a home, medical devices in a health clinic, sensors in an industrial setting, or devices in an office or school. There are three fundamental operations when bringing a new device into any of these settings: (1) to configure the device to join the wireless local-area network, (2) to partner the device with other nearby devices so they can work together, and (3) to configure the device so it connects to the relevant individual or organizational account in the cloud. The challenge is to accomplish all …
Privacy And Security In Mobile Health – A Research Agenda, David Kotz, Carl A. Gunter, Santosh Kumar, Jonathan P. Weiner
Privacy And Security In Mobile Health – A Research Agenda, David Kotz, Carl A. Gunter, Santosh Kumar, Jonathan P. Weiner
Dartmouth Scholarship
Mobile health technology has great potential to increase healthcare quality, expand access to services, reduce costs, and improve personal wellness and public health. However, mHealth also raises significant privacy and security challenges.
A Unifying Framework In Vector-Valued Reproducing Kernel Hilbert Spaces For Manifold Regularization And Co-Regularized Multi-View Learning, Hà Quang Minh, Loris Bazzani, Vittorio Murino
A Unifying Framework In Vector-Valued Reproducing Kernel Hilbert Spaces For Manifold Regularization And Co-Regularized Multi-View Learning, Hà Quang Minh, Loris Bazzani, Vittorio Murino
Dartmouth Scholarship
This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a struc- tured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to …
Improving Structure Mcmc For Bayesian Networks Through Markov Blanket Resampling, Chengwei Su, Mark E. Borsuk
Improving Structure Mcmc For Bayesian Networks Through Markov Blanket Resampling, Chengwei Su, Mark E. Borsuk
Dartmouth Scholarship
Algorithms for inferring the structure of Bayesian networks from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian approach to structure learning uses posterior probabilities to quantify the strength with which the data and prior knowledge jointly support each possible graph feature. Existing Markov Chain Monte Carlo (MCMC) algorithms for estimating these posterior probabilities are slow in mixing and convergence, especially for large networks. We present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the Markov blanket of nodes, thus allowing the sampler to more effectively …
Data Publication With The Structural Biology Data Grid Supports Live Analysis, Peter A. Meyer, Stephanie Socias, Jason Key, Elizabeth Ransey, Emily C. Tjon, Alejandro Buschiazzo, Ming Lei, Chris Botka, James Withrow, David Neau, Kanagalaghatta Rajashankar, Karen S. Anderson, Chung-I Chang, Walter J. Chazin, Kevin D. Corbett, Michael S. Cosgrove, Sean Crosson, Sirano Dhe-Paganon, Enrico Di Cera, Catherine L. Drennan, Michael J. Eck, Brandt F. Eichman, Qing R. Fan, Adrian R. Ferre-D’Amare, J. Christopher Fromme, K. Christopher Garcia, Rachelle Gaudet, Peng Gong, Stephen C. Harrison, Ekaterina E. Heldwein, Zongchao Jia, Robert J. Keenan, Andrew C. Kruse, Marc Kvansaku, Jason S. Mclellan
Data Publication With The Structural Biology Data Grid Supports Live Analysis, Peter A. Meyer, Stephanie Socias, Jason Key, Elizabeth Ransey, Emily C. Tjon, Alejandro Buschiazzo, Ming Lei, Chris Botka, James Withrow, David Neau, Kanagalaghatta Rajashankar, Karen S. Anderson, Chung-I Chang, Walter J. Chazin, Kevin D. Corbett, Michael S. Cosgrove, Sean Crosson, Sirano Dhe-Paganon, Enrico Di Cera, Catherine L. Drennan, Michael J. Eck, Brandt F. Eichman, Qing R. Fan, Adrian R. Ferre-D’Amare, J. Christopher Fromme, K. Christopher Garcia, Rachelle Gaudet, Peng Gong, Stephen C. Harrison, Ekaterina E. Heldwein, Zongchao Jia, Robert J. Keenan, Andrew C. Kruse, Marc Kvansaku, Jason S. Mclellan
Dartmouth Scholarship
Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the …