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An algorithm for rapidly computing the complex Voigt function was published by Shippony and Read. Its claimed accuracy was 1 part in 10^8. It was brought to our attention by Wells that Shippony and Read was not meeting its claimed accuracy for extremely small but non zero y values. Although true, the fix to the code is so trivial to warrant this note for those who use this algorithm.
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Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a Probabilistic Bidirectional Recurrent Network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a Gated Recurrent Unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared to other data-driven statistical process monitoring schemes using real plant data from an industrial Air Separations Unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model’s ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.
Summary Quantum Monte Carlo (QMC) applications perform simulation with respect to an initial state of the quantum mechanical system, which is often captured by using a cubic B‐spline basis. This representation is stored as a read‐only table of coefficients and accesses to the table are generated at random as part of the Monte Carlo simulation. Current QMC applications, such as QWalk and QMCPACK, replicate this table at every process or node, which limits scalability because increasing the number of processors does not enable larger systems to be run. We present a partitioned global address space approach to transparently managing this data using Global Arrays in a manner that allows the memory of multiple nodes to be aggregated. We develop an automated data management system that significantly reduces communication overheads, enabling new capabilities for QMC codes. Experimental results with QWalk and QMCPACK demonstrate the effectiveness of the data management system. Copyright © 2016 John Wiley & Sons, Ltd.
Urban ecological studies have the potential to extend our understanding of socio-ecological systems beyond that of an individual city or region. Cross-comparative empirical work and synthesis are imperative to develop a general urban ecological theory. This can be achieved only if studies are replicable and generalizable. Transparency in methods reporting facilitates generalizability and replicability by documenting the decisions scientists make during the various steps of research design; this is particularly true for sampling design and selection because of their impact on both internal and external validity and the potential to unintentionally introduce bias. Three interdependent aspects of sample design are study sample selection (e.g., specific organisms, soils, or water), sample specification (measurement of specific variable of interest), and site selection (locations sampled). Of these, documentation of site selection—the where component of sample design—is underrepresented in the urban ecology literature. Using a stratified random sample of 158 papers from 12 major urban ecology journals, we investigated how researchers selected study sites in urban ecosystems and evaluated whether their site selection methods were transparent. We extracted data from these papers using a 50-question, theory-based questionnaire and a multiple-reviewer approach. Our sample represented almost 45 years of urban ecology research across 40 different countries. We found that more than 80% of the papers read were not transparent in their site selection methodology. We do not believe site selection methods are replicable for 70% of the papers read. Key weaknesses include incomplete descriptions of populations and sampling frames, urban gradients, sample selection methods, and property access. Low transparency in reporting the where methodology limits urban ecologists’ ability to assess the internal and external validity of studies’ findings and to replicate published studies; it also limits the generalizability of existing studies. The challenges of low transparency are particularly relevant in urban ecology, a field where standard protocols for site selection and delineation are still being developed. These limitations interfere with the fields’ ability to build theory and inform policy. We conclude by offering a set of recommendations to increase transparency, replicability, and generalizability.
The modern approach to m-form global symmetries in a d-dimensional quantum field theory (QFT) entails specifying dimension d–m–1 topological generalized symmetry operators which non-trivially link with m-dimensional defect operators. In QFTs engineered via string constructions on a non-compact geometry X, these defects descend from branes wrapped on non-compact cycles which extend from a localized source / singularity to the boundary ∂X. The generalized symmetry operators which link with these defects arise from magnetic dual branes wrapped on cycles in ∂X. This provides a systematic way to read off various properties of such topological operators, including their worldvolume topological field theories, and the resulting fusion rules. We illustrate these general features in the context of 6D superconformal field theories, where we use the F-theory realization of these theories to read off the worldvolume theory on the generalized symmetry operators. Defects of dimension 3 which are charged under a suitable 3-form symmetry detect a non-invertible fusion rule for these operators. We also sketch how similar considerations hold for related systems.
PurposeTo assess the feasibility and diagnostic performance of contrast-enhanced, C-arm computed tomography (CACT) of the pulmonary arteries compared to digital subtraction angiography (DSA) in patients suffering from chronic thromboembolic pulmonary hypertension (CTEPH).MaterialsFifty-two patients with CTEPH underwent ECG-gated DSA and contrast-enhanced CACT. Two readers (R1, R2) independently evaluated pulmonary artery segments and their sub-segmental branching using DSA and CACT for optimal image quality. Afterwards, the diagnostic findings, i.e., intraluminal filling defects, stenosis, and occlusion, were compared. Inter-modality and inter-observer agreement was calculated, and subsequently consensus reading was done and correlated to a reference standard representing the overall consensus of both modalities. Fisher’s exact test and Cohen’s Kappa were applied.ResultsA total of 1352 pulmonary segments were evaluated, of which 1255 (92.8 %) on DSA and 1256 (92.9 %) on CACT were rated to be fully diagnostic. The main causes of the non-diagnostic image quality were motion artifacts on CACT (R1:37, R2:78) and insufficient contrast enhancement on DSA (R1:59, R2:38). Inter-observer agreement was good for DSA (κ = 0.74) and CACT (κ = 0.75), while inter-modality agreement was moderate (R1: κ = 0.46, R2: κ = 0.47). Compared to the reference standard, the inter-modality agreement for CACT was excellent (κ = 0.96), whereas it was inferior for DSA (κ = 0.61) due to the higher number of abnormal consensus findings read as normal on DSA.ConclusionCACT of the pulmonary arteries is feasible and provides additional information to DSA. CACT has the potential to improve the diagnostic work-up of patients with CTEPH and may be particularly useful prior to surgical or interventional treatment.
Efforts to study the microbial communities associated with corals can be limited by inefficiencies in the sequencing process due to high levels of host amplification by universal bacterial 16S rRNA gene primers. Here, we develop an inexpensive peptide nucleic acid (PNA) clamp that binds to a target sequence of host DNA during PCR and blocks amplification. We then test the ability of this PNA clamp to mitigate host contamination and increase overall microbial sequence coverage on samples from three coral species: the gorgonians Eunicea flexuosa and Gorgonia ventalina, and the scleractinian Porites panamensis. The 20-bp PNA clamp was designed using DNA from E. flexuosa. Adding the PNA clamp during PCR increased the percentage of microbial reads in E. flexuosa samples more than 11-fold. Microbial community diversity was similar without- and with-PNA clamps, as were the relative frequencies of the ten most abundant ASVs (amplicon sequence variants), indicating that the clamps successfully blocked host DNA amplification while simultaneously increasing microbial DNA amplification proportionally across the most abundant taxa. The reduction of E. flexuosa DNA correlated with an increase in the abundance of rarer taxa. The clamp also increased the average percentage of microbial reads in another gorgonian, G. ventalina, by 8.6-fold without altering the microbial community beta diversity, and in a distantly related scleractinian coral, P. panamensis, by nearly double. The reduction of host contamination correlated with the number of nucleotide mismatches between the host amplicon and the PNA clamp. The PNA clamp costs as little as $0.48 per sample, making it an efficient and cost-effective solution to increase microbial sequence coverage for high-throughput sequencing of coral microbial communities.
The influence of the local terrestrial environment on nocturnal atmospheric CO 2 measurements at a 329-m television transmitter tower (and a component of a CO 2 monitoring network) was estimated in this paper with a tracer release experiment and a subsequent simulation of the releases. This was done to characterize the vertical transport of emissions from the surface to the uppermost tower level and how it is affected by atmospheric stability. The tracer release experiment was conducted over two nights in May of 2009 near the Department of Energy’s Savannah River Site (SRS) in South Carolina. Tracer was released on two contrasting nights—slightly stable and moderately stable—from several upwind surface locations. Measurements at the 329-m level on both nights indicate that tracer was able to mix vertically within a relatively short (~24 km) distance, implying that nocturnal stable conditions do not necessarily prevent vertical dispersion in the boundary layer and that CO 2 measurements at the tower are at least partly influenced by nearby emissions. A simulation of the tracer release is used to calculate the tower footprint on the two nights to estimate the degree to which the local domain affects the tower readings. The effect of the nocturnal boundary layer on the area sampled by the tower can be seen clearly, as the footprints were affected by changes in stability. Finally, the contribution of local sources to the measurements at the tower was minimal, however, suggesting that nocturnal concentrations at upper levels are contributed mostly by regional sources.
Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view. The BI-RADS-based breast density assessment is a qualitative process made by visual observation of both the MLO and CC views by radiologists, where there is a notable inter- and intra-reader variability. In order to maintain consistency and accuracy in BI-RADS-based breast density assessment, gaining understanding on radiologists’ reading behaviors will be educational. In this study, we proposed to leverage the newly emerged deep learning approach to investigate how the MLO and CC view images of a mammogram examination may have been clinically used by radiologists in coming up with a BI-RADS density category. We implemented a convolutional neural network (CNN)-based deep learning model, aimed at distinguishing the breast density categories using a large (15,415 images) set of real-world clinical mammogram images. Our results showed that the classification of density categories (in terms of area under the receiver operating characteristic curve) using MLO view images is significantly higher than that using the CC view. This indicates that most likely it is the MLO view that the radiologists have predominately used to determine the breast density BI-RADS categories. Our study holds a potential to further interpret radiologists’ reading characteristics, enhance personalized clinical training to radiologists, and ultimately reduce reader variations in breast density assessment.
Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269–1274, 2016; Arch Dis Child 81.2:172–173, 1999) atlas. The evaluation of BAA can be a tedious and time-consuming process for the radiologist. As such, several computer-assisted detection/diagnosis (CAD) methods have been proposed for automation of BAA. Classical CAD tools have traditionally relied on hard-coded algorithmic features for BAA which suffer from a variety of drawbacks. Recently, the advent and proliferation of convolutional neural networks (CNNs) has shown promise in a variety of medical imaging applications. There have been at least two published applications of using deep learning for evaluation of bone age (Med Image Anal 36:41–51, 2017; JDI 1–5, 2017). However, current implementations are limited by a combination of both architecture design and relatively small datasets. The purpose of this study is to demonstrate the benefits of a customized neural network algorithm carefully calibrated to the evaluation of bone age utilizing a relatively large institutional dataset. In doing so, this study will aim to show that advanced architectures can be successfully trained from scratch in the medical imaging domain and can generate results that outperform any existing proposed algorithm. The training data consisted of 10,289 images of different skeletal age examinations, 8909 from the hospital Picture Archiving and Communication System at our institution and 1383 from the public Digital Hand Atlas Database. The data was separated into four cohorts, one each for male and female children above the age of 8, and one each for male and female children below the age of 10. The testing set consisted of 20 radiographs of each 1-year-age cohort from 0 to 1 years to 14–15+ years, half male and half female. The testing set included left-hand radiographs done for bone age assessment, trauma evaluation without significant findings, and skeletal surveys. A 14 hidden layer-customized neural network was designed for this study. The network included several state of the art techniques including residual-style connections, inception layers, and spatial transformer layers. Data augmentation was applied to the network inputs to prevent overfitting. A linear regression output was utilized. Mean square error was used as the network loss function and mean absolute error (MAE) was utilized as the primary performance metric. MAE accuracies on the validation and test sets for young females were 0.654 and 0.561 respectively. For older females, validation and test accuracies were 0.662 and 0.497 respectively. For young males, validation and test accuracies were 0.649 and 0.585 respectively. Finally, for older males, validation and test set accuracies were 0.581 and 0.501 respectively. The female cohorts were trained for 900 epochs each and the male cohorts were trained for 600 epochs. An eightfold cross-validation set was employed for hyperparameter tuning. Test error was obtained after training on a full data set with the selected hyperparameters. Using our proposed customized neural network architecture on our large available data, we achieved an aggregate validation and test set mean absolute errors of 0.637 and 0.536 respectively. To date, this is the best published performance on utilizing deep learning for bone age assessment. Our results support our initial hypothesis that customized, purpose-built neural networks provide improved performance over networks derived from pre-trained imaging data sets. We build on that initial work by showing that the addition of state-of-the-art techniques such as residual connections and inception architecture further improves prediction accuracy. This is important because the current assumption for use of residual and/or inception architectures is that a large pre-trained network is required for successful implementation given the relatively small datasets in medical imaging. Instead we show that a small, customized architecture incorporating advanced CNN strategies can indeed be trained from scratch, yielding significant improvements in algorithm accuracy. It should be noted that for all four cohorts, testing error outperformed validation error. One reason for this is that our ground truth for our test set was obtained by averaging two pediatric radiologist reads compared to our training data for which only a single read was used. This suggests that despite relatively noisy training data, the algorithm could successfully model the variation between observers and generate estimates that are close to the expected ground truth.
The South Pole Telescope third-generation (SPT-3G) receiver was installed during the austral summer of 2016–2017. It is designed to measure the cosmic microwave background across three frequency bands centered at 95, 150, and 220 GHz. The SPT-3G receiver has ten focal plane modules, each with 269 pixels. Each pixel features a broadband sinuous antenna coupled to a niobium microstrip transmission line. In-line filters define the desired band-passes before the signal is coupled to six bolometers with Ti/Au/Ti/Au transition edge sensors (three bands × two polarizations). In total, the SPT-3G receiver is composed of 16,000 detectors, which are read out using a 68 × frequency-domain multiplexing scheme. In this paper, we present the process employed in fabricating the detector arrays.
Transition edge sensors (TESs) have proven to be highly sensitive and versatile X-ray spectrometers. Upcoming missions, including Athena X-IFU, will rely on highly multiplexed focal planes where more than 32 TES pixels are read out using a single SQUID amplifier channel. We have implemented an error correction algorithm for code-division multiplexed TES signals that can compensate for potential failures of individual SQUID readout channels and that is both scalable and easily implemented in hardware. We present this algorithm for error correction and show results of laboratory tests to assess algorithm performance at recovering TES channels after a SQUID failure.
Frequency-division multiplexing is the baseline read-out system for large arrays of superconducting transition-edge sensors (TES’s) under development for the X-ray and infrared instruments like X-IFU (Athena) and SAFARI, respectively. In this multiplexing scheme, the sensors are ac-biased at different frequencies from 1 to 5 MHz and operate as amplitude modulators. Weak superconductivity is responsible for the complex TES resistive transition, experimentally explored in great detail so far, both with dc- and ac-biased read-out schemes. In this paper, we will review the current status of our understanding of the physics of the TES’s and their interaction with the ac bias circuit. In particular, we will compare the behaviour of the TES nonlinear impedance, across the superconducting transition, for several detector families, namely: high-normal-resistance TiAu TES bolometers, low-normal-resistance MoAu TES microcalorimeters and high-normal-resistance TiAu TES microcalorimeters.
The readout requirements for instruments based on transition-edge sensors (TESs) have dramatically increased over the last decade as demand for systems with larger arrays and faster sensors has grown. Emerging systems are expected to contain many thousands of sensors and/or sensors with time constants as short as 100 ms. These requirements must be satisfied while maintaining low noise, high dynamic range, and low crosstalk. A promising readout candidate for future TES arrays is the microwave SQUID multiplexer, which offers several gigahertz of readout bandwidth per pair of coaxial cables. In microwave SQUID multiplexing, sensor signals are coupled to RF-SQUIDs embedded in superconducting microwave resonators, which are probed via a common microwave feedline and read out using gigahertz signals. This form of SQUID multiplexing moves complexity from the cryogenic stages to room temperature hardware and digital signal processing firmware which must synthesize the microwave tones and process the information contained within them. To demultiplex signals from the microwave SQUID multiplexer, we have implemented an FPGA-based firmware architecture that is flexible enough to read out a variety of differently optimized TESs. A gamma-ray spectrometer targeted at nuclear materials accounting applications, known as SLEDGEHAMMER, is an early adopter of microwave SQUID multiplexing and is driving our current firmware development effort. This instrument utilizes 300 kHz full-width half-maximum resonators with 256 channels in a one gigahertz wide band. We have recently demonstrated undegraded readout of 128 channels using two ROACH2s on a single pair of coaxial cables. This manuscript describes the firmware implementation for the readout electronics of these early array-scale demonstrations.
During the austral summer of 2016-2017, the third-generation camera, SPT-3G, was installed on the South Pole Telescope, increasing the detector count in the focal plane by an order of magnitude relative to the previous generation. Designed to map the polarization of the cosmic microwave background, SPT-3G contains ten 6 ''-hexagonal modules of detectors, each with 269 trichroic and dual-polarization pixels, read out using 68x frequency-domain multiplexing. Here we discuss design, assembly, and layout of the modules, as well as early performance characterization of the first-year array, including yield and detector properties.
We describe the construction and characterization of the 280 GHz bolometric focal plane units (FPUs) to be deployed on the second flight of the balloon-borne Spider instrument. These FPUs are vital to Spider’s primary science goal of detecting or placing an upper limit on the amplitude of the primordial gravitational wave signature in the cosmic microwave background (CMB) by constraining the B-mode contamination in the CMB from Galactic dust emission. Each 280 GHz focal plane contains a $16 \times 16$ grid of corrugated silicon feedhorns coupled to an array of aluminum–manganese transition-edge sensor (TES) bolometers fabricated on 150 mm diameter substrates. In total, the three 280 GHz FPUs contain 1530 polarization-sensitive bolometers (765 spatial pixels) optimized for the low loading environment in flight and read out by time-division SQUID multiplexing. In this paper, we describe the mechanical, thermal, and magnetic shielding architecture of the focal planes and present cryogenic measurements which characterize yield and the uniformity of several bolometer parameters. The assembled FPUs have high yields, with one array as high as 95% including defects from wiring and readout. We demonstrate high uniformity in device parameters, finding the median saturation power for each TES array to be $\sim $ 3 pW at 300 mK with a less than 6% variation across each array at $1\sigma $ . These focal planes will be deployed alongside the 95 and 150 GHz telescopes in the Spider-2 instrument, slated to fly from McMurdo Station in Antarctica in December 2018.
The Background Imaging of Cosmic Extragalactic Polarization (BICEP)/Keck (BK) collaboration is currently leading the quest for the highest-sensitivity measurements of the polarized cosmic microwave background (CMB) anisotropies on a degree scale with a series of cryogenic telescopes, of which BICEP Array (BA) is the latest Stage-3 upgrade with a total of ~ 32,000 detectors. The instrument comprises 4 receivers spanning 30–270 GHz, with the low-frequency 30/40 GHz deployed to the South Pole Station in late 2019. The full complement of receivers is forecast to set the most stringent constraints on the tensor-to-scalar ratio r. Building on these advances, the overarching small-aperture telescope concept is already being used as the reference for further Stage-4 experiment design. This paper describes the development of the BICEP Array 150 GHz detector module and its fabrication requirements, with highlights on the high-density time division multiplexing (TDM) design of the cryogenic circuit boards. The low-impedance wiring required between the detectors and the frst stage of superconducting quantum interference device amplifers is crucial to maintaining a stable bias current on the detectors. Here, a novel multilayer FR4 Printed Circuit Board with superconducting traces, capable of reading out up to 648 detectors, is detailed along with its validation tests. An ultra-high-density TDM detector module concept we developed for a CMB-S4-like experiment that allows up to 1920 detectors to be read out is also presented. TDM has been chosen as the detector readout technology for the Cosmic Microwave Background Stage-4 (CMB-S4) experiment based on its proven low-noise performance, predictable costs, and overall maturity of the architecture. The heritage for TDM is rooted in mm- and sub-mm-wave experiments dating back 20 years and has since evolved to support a multiplexing factor of 64x in Stage-3 experiments.