Enhancing Angular Sensitivity of Segmented Antineutrino Detectors for Reactor Monitoring Applications
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We present Gaia, a monolithic array of 96 high-purity germanium pixel detectors integrated with a custom low-noise application-specific integrated circuit (ASIC) and a field-programmable gate array (FPGA)-based data acquisition system. The sensor operates at ∼100 K using a commercial closed-cycle cryocooler, with the in-vacuum electronics thermally isolated from the cold finger to ensure thermal stability. The system demonstrates an average energy resolution of 711 eV at 122 keV, measured using a 57 Co source, and 253 eV at 5.89 keV, measured with 55 Fe across all channels. The readout architecture incorporates a high-performance FPGA paired with a dual-core ARM processor, forming a complete embedded Linux-based computing platform. Communication between the processor and FPGA is handled via memory-mapped I/O, and data are streamed over high-speed gigabit Ethernet. A full-scale 384-pixel Gaia detector, based on this 96-element module, is currently under fabrication.
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The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.
The rapid growth of global positioning system (GPS) devices has led to a corresponding increase in the size of GPS datasets. While these large GPS datasets contain a wealth of information about the behaviors of the moving objects in them, manual classification and anomaly detection are prohibitively time consuming. We utilize unsupervised machine learning techniques to first identify the behaviors for individual moving objects and then cluster those objects by their behavioral sequences. In this way, trajectories behaving unusually as well as common patterns of behavior are both detectable in large datasets without requiring an a priori definition of "unusual" or "common."
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.
This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.