コンピュータ・サイエンス : ACM computing surveys

1. Random Variables and Stochastic Processes

Healthcare workers are at risk of infection during the severe acute respiratory syndrome coronavirus-2 pandemic. International guidance suggests direct droplet transmission is likely and airborne transmission occurs only with aerosol-generating procedures. Recommendations determining infection control measures to ensure healthcare worker safety follow these presumptions. Three mechanisms have been described for the production of smaller sized respiratory particles ('aerosols') that, if inhaled, can deposit in the distal airways. These include: laryngeal activity such as talking and coughing; high velocity gas flow; and cyclical opening and closure of terminal airways. Sneezing and coughing are effective aerosol generators, but all forms of expiration produce particles across a range of sizes. The 5-μm diameter threshold used to differentiate droplet from airborne is an over-simplification of multiple complex, poorly understood biological and physical variables. The evidence defining aerosol-generating procedures comes largely from low-quality case and cohort studies where the exact mode of transmission is unknown as aerosol production was never quantified. We propose that transmission is associated with time in proximity to severe acute respiratory syndrome coronavirus-1 patients with respiratory symptoms, rather than the procedures per se. There is no proven relation between any aerosol-generating procedure with airborne viral content with the exception of bronchoscopy and suctioning. The mechanism for severe acute respiratory syndrome coronavirus-2 transmission is unknown but the evidence suggestive of airborne spread is growing. We speculate that infected patients who cough, have high work of breathing, increased closing capacity and altered respiratory tract lining fluid will be significant producers of pathogenic aerosols. We suggest several aerosol-generating procedures may in fact result in less pathogen aerosolisation than a dyspnoeic and coughing patient. Healthcare workers should appraise the current evidence regarding transmission and apply this to the local infection prevalence. Measures to mitigate airborne transmission should be employed at times of risk. However, the mechanisms and risk factors for transmission are largely unconfirmed. Whilst awaiting robust evidence, a precautionary approach should be considered to assure healthcare worker safety.

Airborne transmission of severe acute respiratory syndrome coronavirus-2 to healthcare workers: a narrative review.

To increase productivity in designing digital hardware components, high-level synthesis (HLS) is seen as the next step in raising the design abstraction level. However, the quality of results (QoRs) of HLS tools has tended to be behind those of manual register-transfer level (RTL) flows. In this paper, we survey the scientific literature published since 2010 about the QoR and productivity differences between the HLS and RTL design flows. Altogether, our survey spans 46 papers and 118 associated applications. Our results show that on average, the QoR of RTL flow is still better than that of the state-of-the-art HLS tools. However, the average development time with HLS tools is only a third of that of the RTL flow, and a designer obtains over four times as high productivity with HLS. Based on our findings, we also present a model case study to sum up the best practices in comparative studies between HLS and RTL. The outcome of our case study is also in line with the survey results, as using an HLS tool is seen to increase the productivity by a factor of six. In addition, to help close the QoR gap, we present a survey of literature focused on improving HLS. Our results let us conclude that HLS is currently a viable option for fast prototyping and for designs with short time to market.

Are We There Yet? A Study on the State of High-Level Synthesis

Summary Exposure of healthcare providers to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a significant safety concern during the coronavirus disease 2019 (COVID-19) pandemic, requiring contact/droplet/airborne precautions. Because of global shortages, limited availability of personal protective equipment (PPE) has motivated the development of barrier-enclosure systems, such as aerosol boxes, plastic drapes, and similar protective systems. We examined the available evidence and scientific publications about barrier-enclosure systems for airway management in suspected/confirmed COVID-19 patients. MEDLINE/Embase/Google Scholar databases (from December 1, 2019 to May 27, 2020) were searched for all articles on barrier enclosures for airway management in COVID-19, including references and websites. All sources were reviewed by a panel of experts using a Delphi method with a modified nominal group technique. Fifty-two articles were reviewed for their results and level of evidence regarding barrier device feasibility, advantages, protection against droplets and aerosols, effectiveness, safety, ergonomics, and cleaning/disposal. The majority of analysed papers were expert opinions, small case series, technical descriptions, small-sample simulation studies, and pre-print proofs. The use of barrier-enclosure devices adds to the complexity of airway procedures with potential adverse consequences, especially during airway emergencies. Concerns include limitations on the ability to perform airway interventions and the aid that can be delivered by an assistant, patient injuries, compromise of PPE integrity, lack of evidence for added protection of healthcare providers (including secondary aerosolisation upon barrier removal), and lack of cleaning standards. Enclosure barriers for airway management are no substitute for adequate PPE, and their use should be avoided until adequate validation studies can be reported.

Aerosol boxes and barrier enclosures for airway management in COVID-19 patients: a scoping review and narrative synthesis

We report the rapid evolution of the Aerosol Box, originally designed by Dr Hsien Yung Lai, Mennonite Christian Hospital, Taiwan [1]. The Aerosol Box was intended to protect healthcare workers performing aerosol generating procedures (AGPs), specifically tracheal intubation, by providing a physical barrier to droplet and/or aerosol exposure [2]. An increased infection rate has been reported in healthcare workers internationally, particularly when the level of personal protective equipment (PPE) has been inadequate or when the supply of PPE has been depleted [3]. The Aerosol Box can be made of transparent acrylic or polycarbonate sheeting, and is re-usable after careful decontamination with an appropriate cleansing agent. The original model was based on a simple cuboid design, with two access ports for arms.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/anae.15109

Maximising application of the aerosol box in protecting healthcare workers during the COVID-19 pandemic.

Some excellent surveys of the Gaussian random number generators (GRNGs) from the algorithmic perspective exist in the published literature to date (e.g., Thomas et al. l2007r). In the last decade, however, advancements in digital hardware have resulted in an ever-decreasing hardware cost and increased design flexibility. Additionally, recent advances in applications like gaming, weather forecasting, and simulations in physics and astronomy require faster, cheaper, and statistically accurate GRNGs. These two trends have contributed toward the development of a number of novel GRNG architectures optimized for hardware design. A detailed comparative study of these hardware architectures has been somewhat missing in the published literature. This work provides the potential user a capsulization of the published hardware GRNG architectures. We have provided the method and theory, pros and cons, and a comparative summary of the speed, statistical accuracy, and hardware resource utilization of these architectures. Finally, we have complemented this work by describing two novel hardware GRNG architectures, namely, the CLT-inversion and the multihat algorithm, respectively. These new architectures provide high tail accuracy (6σ and 8σ, respectively) at a low hardware cost.

Gaussian Random Number Generation: A Survey on Hardware Architectures

An efficient hardware implementation of Gaussian Random Number (GRN) generator based on Central Limit Theorem (CLT) is presented. CLT, although very simple to implement, is never used to generate high quality Gaussian numbers. This is due to the fact that direct implementation of CLT provides very poor accuracy in tail regions of the probability density function. In this work, we have shown that it is possible to achieve high tail accuracy by empirically computing the error in CLT, which can be compensated with a simple correction algorithm. The error has been modeled as first degree piece-wise polynomial approximation, using a novel non-uniform segmentation algorithm to compute the coefficients of polynomial segments. A novel hardware architecture of GRN generator is presented which requires only 420 slices and 1 DSP block of Xilinx Virtex-4 XC4VLX15 operating at 220 MHz. This resource utilization is better than any of the previously reported designs. Demonstrated for the tail accuracy of 6σ, the GRN generator design is scalable to achieve even higher accuracy with minimal increase in hardware resources. The accuracy of GRN generator is validated using statistical goodness of fit tests.

Generating high tail accuracy Gaussian Random Numbers in hardware using central limit theorem

Box Muller (BM) algorithm is extensively used for generation of high quality Gaussian Random Numbers (GRNs) in hardware. Most efficient published implementation of BM method utilizes transformation of 32-bit data path to 16 bits and use of first degree piece-wise polynomial approximation to compute logarithmic and square root functions. In this work, we have performed extensive error analysis to show that coefficient memory for polynomial approximation can be reduced by more than 35 percent without compromising on quality of generated Gaussian samples. This also reduces complexity of corresponding address generator, which requires most hardware resources. We have also used more efficient and statistically accurate skip-ahead Linear Feedback Shift Registers to generate uniformly distributed numbers for the BM algorithm. Complete hardware implementation utilizes only 407 slices, 03 DSP blocks and 1.5 memory blocks on Xilinx Virtex-4 XC4VLX15 operating at 230 MHz while providing a tail accuracy of 6.6σ. This is better in terms of accuracy and hardware utilization than any of the previously reported architecture.

An efficient hardware implementation of high quality AWGN generator using Box-Muller method

An efficient hardware implementation of Gaussian Random Number (GRN) generator based upon Box-Muller (BM) and CORDIC algorithms is presented. We will illustrate a novel hardware architecture with flexible design space that unifies the two algorithms. A major advantage of this work is that unlike any of the previously reported architectures, it is possible to eliminate hardware multipliers and memory blocks in the synthesized hardware. This is achieved without compromising on statistical accuracy of GRN generators which is proved both through error analysis and standard tests. We will also demonstrate two different hardware implementations that vary in terms of speed, tail accuracy (4.7σ to 9.4σ), and utilization of hardware resources such as DSP blocks, logic slices and memory blocks on FPGAs. Finally, we will present a comparison of designed architectures with previously published hardware GRN generators.

Jamshaid Sarwar Malik

Papers

Maximising application of the aerosol box in protecting healthcare workers during the COVID-19 pandemic.

Gaussian Random Number Generation: A Survey on Hardware Architectures

Generating high tail accuracy Gaussian Random Numbers in hardware using central limit theorem

An efficient hardware implementation of high quality AWGN generator using Box-Muller method

Unifying CORDIC and Box-Muller algorithms: An accurate and efficient Gaussian Random Number generator