Scintillation detectors constructed with an optimized 2x2 silicon photomultiplier array

Silicon photomultipliers (SiPMs) are a good alternative to photomultiplier tubes (PMTs) because their gain and quantum efficiency are comparable to PMTs. However, the largest single-chip SiPM is still less than 1~cm$^2$. In order to use SiPMs with scintillators that have reasonable sensitivity, it is necessary to use multiple SiPMs. In this work, scintillation detectors are constructed and tested with a custom 2x2 SiPM array. The layout of the SiPMs and the geometry of the scintillator were determined by performing Geant4 simulations. Cubic NaI, CsI, and CLYC with 18~mm sides have been tested. The output of the scintillation detectors are stabilized over the temperature range between --20 and 50~$^{\circ}$C by matching the gain of the SiPMs in the array. The energy resolution for these detectors has been measured as a function of temperature. Furthermore, neutron detection for the CLYC detector was studied in the same temperature range. Using pulse-shape discrimination, neutrons can be cleanly identified without contribution from $\gamma$-photons. As a result, these detectors are suitable for deploying in spectroscopic personal radiation detectors (SPRD).Comment: IEEE Nuclear Science Symposium Conference Record (2016

Evolutionary neural architecture search for deep learning

Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. DNNs are often not used to their full potential because it is difficult to determine what architectures and hyperparameters should be used. While several approaches have been proposed, computational complexity of searching large design spaces makes them impractical for large modern DNNs. This dissertation introduces an efficient evolutionary algorithm (EA) for simultaneous optimization of DNN architecture and hyperparameters. It builds upon extensive past research of evolutionary optimization of neural network structure. Various improvements to the core algorithm are introduced, including: (1) discovering DNN architectures of arbitrary complexity; (1) generating modular, repetitive modules commonly seen in state-of-the-art DNNs; (3) extending to the multitask learning and multiobjective optimization domains; (4) maximizing performance and reducing wasted computation through asynchronous evaluations. Experimental results in image classification, image captioning, and multialphabet character recognition show that the approach is able to evolve networks that are competitive with or even exceed hand-designed networks. Thus, the method enables an automated and streamlined process to optimize DNN architectures for a given problem and can be widely applied to solve harder tasks.Computer Science

To Tube or Not to Tube? The Role of Intubation during Stroke Thrombectomy.

In the 10 years since the FDA first cleared the use of endovascular devices for the treatment of acute stroke, definitive evidence that such therapy improves outcomes remains lacking. The decision to intubate patients undergoing stroke thrombectomy impacts multiple variables that may influence outcomes after stroke. Three main areas where intubation may deleteriously affect acute stroke management include the introduction of delays in revascularization, fluctuations in peri-procedural blood pressure, and hypocapnia, resulting in cerebral vasoconstriction. In this mini-review, we discuss the evidence supporting these limitations of intubation during stroke thrombectomy and encourage neurohospitalists, neurocritical care specialists, and neurointerventionalists to carefully consider the decision to intubate during thrombectomy and provide strategies to avoid potential complications associated with its use in acute stroke

Applications of the Oriented Permission Role-Based Access Control Model

Role-based access control and role hierarchies have been the subject of considerable research in recent years. In this paper, we consider three useful applications of a new role-based access control model that contains a novel approach to permissions and permission inheritance: one is to illustrate that the new model provides a simpler and more natural way to implement BLP model using role-based techniques; a second application is to make it possible to define separation of duty constraints on two roles that have a common senior role and for a user to be assigned to or activate the senior role; finally, we describe how a single hierarchy in the new model can support the distinction between role activation and permission usage. In short, the oriented permission model provides ways of implementing a number of useful features that have previously required ad hoc and inelegant solutions