Small Treatise on Spin-3/2 Fields and their Dual Spectral Functions

In this work we systematically study various aspects of spin-3/2 fields in a curved background. We mostly focus on a minimally coupled massive spin-3/2 field in arbitrary dimensions, and solve the equation of motion either explicitly or numerically in AdS, Schwarzschild-AdS and Reissner-Nordstr\"om-AdS backgrounds. Although not the main focus of this work, we also make a connection with the gravitino equation of motion in gauged supergravity. Motivated by the AdS/CFT correspondence, we emphasize calculational improvements and technical details of the dual spectral functions. We attempt to provide a coherent and comprehensive picture of the existing literature.Comment: 40 pages, 11 figures. V2: comments and references adde

3D printing: printing precision and application in food sector

Background: Three dimensional (3D) food printing is being widely investigated in food sector recent years due to its multiple advantages such as customized food designs, personalized nutrition, simplifying supply chain, and broadening of the available food material

FPGA-accelerated machine learning inference as a service for particle physics computing

New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.Comment: 16 pages, 14 figures, 2 table

Ion–Conducting Ceramic Membrane Reactors for the Conversion of Chemicals

Ion–conducting ceramic membranes, such as mixed oxygen ionic and electronic conducting (MIEC) membranes and mixed proton–electron conducting (MPEC) membranes, have the potential for absolute selectivity for specific gases at high temperatures. By utilizing these membranes in membrane reactors, it is possible to combine reaction and separation processes into one unit, leading to a reduction in by–product formation and enabling the use of thermal effects to achieve efficient and sustainable chemical production. As a result, membrane reactors show great promise in the production of various chemicals and fuels. This paper provides an overview of recent developments in dense ceramic catalytic membrane reactors and their potential for chemical production. This review covers different types of membrane reactors and their principles, advantages, disadvantages, and key issues. The paper also discusses the configuration and design of catalytic membrane reactors. Finally, the paper offers insights into the challenges of scaling up membrane reactors from experimental stages to practical applications

Fast convolutional neural networks on FPGAs with hls4ml

We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of $5\,\mu$s using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detectors. In particular, we discuss pruning and quantization-aware training, and demonstrate how resource utilization can be significantly reduced with little to no loss in model accuracy. We show that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.Comment: 18 pages, 18 figures, 4 table

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.Comment: 8 pages, 4 figures, To appear in Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020

Analysis of Cancer Mutation Signatures in Blood by a Novel Ultra-Sensitive Assay: Monitoring of Therapy or Recurrence in Non-Metastatic Breast Cancer

BACKGROUND: Tumor DNA has been shown to be present both in circulating tumor cells in blood and as fragments in the plasma of metastatic cancer patients. The identification of ultra-rare tumor-specific mutations in blood would be the ultimate marker to measure efficacy of cancer therapy and/or early recurrence. Herein we present a method for detecting microinsertions/deletions/indels (MIDIs) at ultra-high analytical selectivity. MIDIs comprise about 15% of mutations. METHODS AND FINDINGS: We describe MIDI-Activated Pyrophosphorolysis (MAP), a method of ultra-high analytical selectivity for detecting MIDIs. The high analytical selectivity of MAP is putatively due to serial coupling of two rare events: heteroduplex slippage and mis-pyrophosphorolysis. MAP generally has an analytical selectivity of one mutant molecule per >1 billion wild type molecules and an analytical sensitivity of one mutant molecule per reaction. The analytical selectivity of MAP is about 100,000-fold better than that of our previously described method of Pyrophosphorolysis Activated Polymerization-Allele specific amplification (PAP-A) for detecting MIDIs. The utility of this method is illustrated in two ways. 1) We demonstrate that two EGFR deletions commonly found in lung cancers are not present in tissue from four normal human lungs (10(7) copies of gDNA each) or in blood samples from 10 healthy individuals (10(7) copies of gDNA each). This is inconsistent, at least at an analytical sensitivity of 10(-7), with the hypotheses of (a) hypermutation or (b) strong selection of these growth factor-mutated cells during normal lung development leads to accumulation of pre-neoplastic cells with these EGFR mutations, which sometimes can lead to lung cancer in late adulthood. Moreover, MAP was used for large scale, high throughput "gene pool" analysis. No germline or early embryonic somatic mosaic mutation was detected (at a frequency of >0.3%) for the 15/18 bp EGFR deletion mutations in 6,400 individuals, suggesting that early embryonic EGFR somatic mutation is very rare, inconsistent with hypermutation or strong selection of these deletions in the embryo. 2) The second illustration of MAP utility is in personalized monitoring of therapy and early recurrence in cancer. Tumor-specific p53 mutations identified at diagnosis in the plasma of six patients with stage II and III breast cancer were undetectable after therapy in four women, consistent with clinical remission, and continued to be detected after treatment in two others, reflecting tumor progression. CONCLUSIONS: MAP has an analytical selectivity of one part per billion for detection of MIDIs and an analytical sensitivity of one molecule. MAP provides a general tool for monitoring ultra-rare mutations in tissues and blood. As an example, we show that the personalized cancer signature in six out of six patients with non-metastatic breast cancer can be detected and that levels over time are correlated with the clinical course of disease

Viral Aetiology in Adults with Acute Upper Respiratory Tract Infection in Jinan, Northern China

Our study investigated the epidemiology of respiratory viruses in adult patients with upper respiratory tract infection (URTI) between August 2009 and September 2010 in Jinan, northern China. Nasal and throat swabs (n=596) were collected from adult patients with URTIs. Nine respiratory-related viruses, including IFV, PIV, HRV, HMPV, HBoV, HCoV, ADV, RSV, and EV, were detected in all samples by conventional and reverse transcription polymerase chain reactions. Positive detection rate for respiratory virus was 38.76% and codetection rate was 4.70% in adults with acute respiratory tract infections. IFV (20.81%) was the dominant agent detected and IFVB had a higher incidence (12.58%) than IFVA (7.72%). Detection rates of 8.22%, 5.03%, 3.69%, and 2.52% were observed for HBoV, HRV, EV, and RSV, respectively. HCoV had the lowest detection rate of 0.50%. HBoV, HRV, EV, and ADV infection rates were higher in the 14–25-year-old group than in the 26–65-year-old group. Codetection rates were higher (7.52%) in the 14–25-year-old group than in the older age group (2.64%). The spectrum of respiratory virus infection in adult patients with URTIs was different in Jinan compared with other cities in China

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.Comment: 10 pages, 8 figures, TinyML Research Symposium 202