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

Street recovery in the age of COVID-19: Simultaneous design for mobility, customer traffic and physical distancing

This paper explores the relationship between urban traffic, retail location and disease control during the COVID-19 pandemic crisis and tries to find a way to simultaneously address these issues for the purpose of street recovery. Drawing on the concept of the 15 min city, the study also aims at seeking COVID-19 exit paths and next-normal operating models to support long-term business prosperity using a case study of Royal Street, East Perth in Western Australia. Nearly half of the shops became vacant or closed at the end of 2020 along the east section of Royal Street, demonstrating the fragility of small business in a car-oriented street milieu that is inadequately supported by proper physical, digital and social infrastructure. A key finding from the analysis is the formulation of the concept of the Minute City. This describes a truly proximity-centred and socially driven hyper-local city, where residents and retailers work together on the local street as a walkable public open space (other than movement space), and benefit from ameliorated traffic flow, improved business location and a safer, connected community

Protein subcellular location pattern classification in cellular images using latent discriminative models

Motivation: Knowledge of the subcellular location of a protein is crucial for understanding its functions. The subcellular pattern of a protein is typically represented as the set of cellular components in which it is located, and an important task is to determine this set from microscope images. In this article, we address this classification problem using confocal immunofluorescence images from the Human Protein Atlas (HPA) project. The HPA contains images of cells stained for many proteins; each is also stained for three reference components, but there are many other components that are invisible. Given one such cell, the task is to classify the pattern type of the stained protein. We first randomly select local image regions within the cells, and then extract various carefully designed features from these regions. This region-based approach enables us to explicitly study the relationship between proteins and different cell components, as well as the interactions between these components. To achieve these two goals, we propose two discriminative models that extend logistic regression with structured latent variables. The first model allows the same protein pattern class to be expressed differently according to the underlying components in different regions. The second model further captures the spatial dependencies between the components within the same cell so that we can better infer these components. To learn these models, we propose a fast approximate algorithm for inference, and then use gradient-based methods to maximize the data likelihood

Classification of Stellar Spectra with LLE

We investigate the use of dimensionality reduction techniques for the classification of stellar spectra selected from the SDSS. Using local linear embedding (LLE), a technique that preserves the local (and possibly non-linear) structure within high dimensional data sets, we show that the majority of stellar spectra can be represented as a one dimensional sequence within a three dimensional space. The position along this sequence is highly correlated with spectral temperature. Deviations from this "stellar locus" are indicative of spectra with strong emission lines (including misclassified galaxies) or broad absorption lines (e.g. Carbon stars). Based on this analysis, we propose a hierarchical classification scheme using LLE that progressively identifies and classifies stellar spectra in a manner that requires no feature extraction and that can reproduce the classic MK classifications to an accuracy of one type.Comment: 15 pages, 13 figures; accepted for publication in The Astronomical Journa