ABSTRACT Background In recent years, machine learning (ML) APIs have emerged as a valuable resource for addressing complex problems, such as image recognition. However, developers should use ML APIs carefully, as they have their own characteristics different from traditional ones: an ML API has its own training data set, a concrete target task, and its output is often the probability. As a result, developers may use an inappropriate API, and the program can still run without reporting errors, especially as there are many similar ML APIs provided by different platforms. Methods This paper proposes an approach called DAOR to help developers use ML APIs properly in their tasks. First, a comparative analysis of ML APIs is conducted, leveraging information from documentation and user reviews to identify comparable APIs. This involves extracting differences from the documentation, categorized into inputs, functions, and outputs, and summarizing key information from user reviews using GPT‐driven prompts. Finally, a visualization framework is designed to summarize and show the results. Evaluation and Results To evaluate the approach, a series of experiments is conducted based on the ML APIs from two famous platforms, Amazon Web Service AI and IBM Watson. The results show that useful information for distinguishing similar ML APIs can be gained, and it is helpful for developers to use the ML APIs correctly.

Ocean surface wind speed is important for numerical prediction of the marine environment, marine disaster monitoring, sea–steam interaction, meteorological prediction, climate research, and so on. At present, ocean surface wind speed retrieval models extract DDM features from the delay-Doppler domain, the angle of which is single, where certain detail information cannot be effectively extracted from the image. To improve the accuracy of wind speed retrieval, starting from the feature extraction, this letter proposes a frequency-informed neural network (FINN)-based wind speed retrieval. First, the wind speed retrieval model based on the delay-Doppler domain and frequency domain is constructed, based on the extraction of features from the DDM delay-Doppler domain, the features from the DDM frequency domain are also extracted, so the wind speed retrieval is performed separately by extracting features from different angles. Then, a multimodel fusion scheme based on dynamic weights is designed, which can dynamically weight submodels according to different input samples, and realize the dynamic fusion of delay-Doppler-domain retrieval results and frequency-domain retrieval results. Finally, the improved gradient loss (IG Loss) function is proposed. Contrast experiments and ablation studies prove that the present algorithm has excellent retrieval performance.

Since the first identified case of COVID-19 in December 2019, a plethora of pharmaceuticals and therapeutics have been tested for COVID-19 treatment. While medical advancements and breakthroughs are well underway, the sheer number of studies, treatments, and associated reports makes it extremely challenging to keep track of the rapidly growing COVID-19 research landscape. While existing scientific literature search systems provide basic document retrieval, they fundamentally lack the ability to explore data, and in addition, do not help develop a deeper understanding of COVID-19 related clinical experiments and findings. As research expands, results do so as well, resulting in a position that is complicated and overwhelming. To address this issue, we present a named entity recognition based framework that accurately extracts COVID-19 related information from clinical test results articles, and generates an efficient and interactive visual knowledge graph. This knowledge graph platform is user friendly, and provides intuitive and convenient tools to explore and analyze COVID-19 research data and results including medicinal performances, side effects and target populations.

Edge Computing has emerged as a new computing paradigm dedicated for mobile performance enhancement and energy efficiency purposes. Specifically, it benefits today’s interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes in close proximity. Meanwhile, FPGA is well known for its excellence in accelerating (domain-specific) compute-intensive tasks such as deep learning algorithms in a high performance and energy-efficient manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based mobile interactive applications, and implement their back-end computation engines on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.

Since the first cased of COVID-19 was identified in December 2019, a plethora of different drugs have been tested for COVID-19 treatment, making it a daunting task to keep track of the rapid growth of COVID-19 research landscape. Using the existing scientific literature search systems to develop a deeper understanding of COVID-19 related clinical experiments and results turns to be increasingly complicated. In this paper, we build a named entity recognition-based framework to extract information accurately and generate knowledge graph efficiently from a myriad of clinical test results articles. Of the tested drugs to treat COVID-19, we also develop a question answering system answers to medical questions regarding COVID-19 related symptoms using Wikipedia articles. We combine the state-of-the-art question answering model - Bidirectional Encoder Representations from Transformers (BERT), with Knowledge Graph to answer patients' questions about treatment options for their symptoms. This generated knowledge graph is user-friendly with intuitive and convenient tools to find the supporting and/or contradictory references of certain drugs with properties such as side effects, target population, etc. The trained question answering platform provides a straightforward and error-tolerant way to query for treatment suggestions given uses' input symptoms.

Continuous mobile vision is becoming increasingly important as it finds compelling applications which substantially improve our everyday life. However, meeting the requirements of quality of experience (QoE) diversity, energy efficiency and multi-tenancy simultaneously represents a significant challenge. In this paper, we present SCYLLA, an FPGA-based framework that enables QoE-aware continuous mobile vision with dynamic reconfiguration to effectively address this challenge. SCYLLA pre-generates a pool of FPGA design and DNN models, and dynamically applies the optimal software-hardware configuration to achieve the maximum overall performance on QoE for concurrent tasks. We implement SCYLLA on state-of-the-art FPGA platform and evaluate SCYLLA using drone-based traffic surveillance application on three datasets. Our evaluation shows that SCYLLA provides much better design flexibility and achieves superior QoE trade-offs than status-quo CPU-based solution that existing continuous mobile vision applications are built upon.

SUMMARY: Here, we present a highly efficient R-package seqminer2 for querying and retrieving sequence variants from biobank scale datasets of millions of individuals and hundreds of millions of genetic variants. Seqminer2 implements a novel variant-based index for querying VCF/BCF files. It improves the speed of query and retrieval by several magnitudes compared to the state-of-the-art tools based upon tabix. It also reimplements support for BGEN and PLINK format, which improves speed over alternative implementations. The improved efficiency and comprehensive support for popular file formats will facilitate method development, software prototyping and data analysis of biobank scale sequence datasets in R. AVAILABILITY AND IMPLEMENTATION: The seqminer2 R package is available from https://github.com/zhanxw/seqminer. Scripts used for the benchmarks are available in https://github.com/yang-lina/seqminer/blob/master/seqminer2%20benchmark%20script.txt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in availability of high-density genomic data for a diverse array of bacteria, development of algorithms that could utilize genomic information to predict phenotype could both be useful clinically and assist with discovery of heretofore unrecognized AMR pathways. To facilitate understanding of the connections between DNA variation and phenotypic AMR, we developed a new bioinformatics tool, variant mapping and prediction of antibiotic resistance (VAMPr), to (1) derive gene ortholog-based sequence features for protein variants; (2) interrogate these explainable gene-level variants for their known or novel associations with AMR; and (3) build accurate models to predict AMR based on whole genome sequencing data. We curated the publicly available sequencing data for 3,393 bacterial isolates from 9 species that contained AMR phenotypes for 29 antibiotics. We detected 14,615 variant genotypes and built 93 association and prediction models. The association models confirmed known genetic antibiotic resistance mechanisms, such as blaKPC and carbapenem resistance consistent with the accurate nature of our approach. The prediction models achieved high accuracies (mean accuracy of 91.1% for all antibiotic-pathogen combinations) internally through nested cross validation and were also validated using external clinical datasets. The VAMPr variant detection method, association and prediction models will be valuable tools for AMR research for basic scientists with potential for clinical applicability.

Client mobility is a fundamental challenge when accessing the current Internet, especially in the context of vehicular networking because of its intermittent connectivity nature. Meanwhile, today's network applications are evolving from host-to-host communication to content retrieval, and fostering new designs of Information-centric networking (ICN) protocol and system optimized towards this end. In this paper, we present SoftStage, a client instructed ICN-based network layer function that effectively manages the edge caching to perform reactive content staging to improve vehicular content delivery without any assumption about the client mobility pattern. Experimental results based on an implementation in eXpressive Internet Architecture (XIA) shows that SoftStage achieves up to 10x throughput gain in vehicular networking environments.

Recently, Edge Computing has emerged as a new computing paradigm dedicated for mobile applications for performance enhancement and energy efficiency purposes. Specifically, it benefits today's interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes which is in close proximity. Meanwhile, Field Programmable Gate Array (FPGA) is well known for its excellence in accelerating compute-intensive tasks such as deep learning algorithms in a high performance and energy efficiency manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based interactive mobile applications, and implement their backend computation parts on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.

Automatic anaphora resolution is useful for many natural language process tasks, including automatic summarization, information extraction and machine translation. This paper took the novel Life as the original corpus, and then annotated the anaphora relations in this corpus. Based on this corpus, we analyzed the distribution of different types of anaphora phenomena, and then developed a set of rules for automatic anaphora resolution, which might be useful for future automatic processing.