“…The resulting genome assemblies have high contiguity (contig NG50: 17.24–80.64 Mb), few assembly errors (contig NGA50: 11.8–59.59 Mb), good consensus quality (QV: 27.84–42.88) and high gene completeness (BUSCO complete: 94.6–95.2%), while consuming low computational resources (CPU hours: 187–1,200). In particular, the WENGAN assembly of the haploid CHM13 sample achieved a contig NG50 of 80.64 Mb (NGA50: 59.59 Mb), which surpasses the contiguity of the current human reference genome (GRCh38 contig NG50: 57.88 Mb).…”
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“…We use the first example as a benchmark on three different computational platforms: CPU, GPU, and mobile system-on-chip devices. We show that the PLS-framework speeds up computations without increasing the memory footprint and maintains high model fidelity comparable to the fully-computed model or with lookup-table approximation. …”
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“…We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. …”
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“…The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.…”
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“…Based on mIOU and Dice loss with cross entropy (CE), we conduct experiments in both CPU and GPU environments to verify the effectiveness of our model. …”
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“…Finally, we show how the MAbTope-determined epitopes for a series of antibodies targeting the same protein can be used to predict competitions, and demonstrate the accuracy with an experimentally validated example. 3D: three-dimensionalRMSD: root mean square deviationCDR: complementary-determining regionCPU: central processing unitsVH: heavy chain variable regionVL: light chain variable regionscFv: single-chain variable fragmentsVHH: single-chain antibody variable regionIL4Rα: Interleukin 4 receptor alpha chainSPR: surface plasmon resonancePDB: protein data bankHEK293: Human embryonic kidney 293 cellsEDTA: Ethylenediaminetetraacetic acidFBS: Fetal bovine serumANOVA: Analysis of varianceEGFR: Epidermal growth factor receptorPE: PhycoerythrinAPC: AllophycocyaninFSC: forward scatterSSC: side scatterWT: wild type Keywords: MAbTope, Epitope Mapping, Molecular docking, Antibody modeling, Antibody-antigen docking…”
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“…We observed an acceleration of [Formula: see text] times using our multi-GPU approach compared to the multi-core CPU implementation. Further multi-GPU based MLEM obtained superior reconstruction compared to traditional FDK algorithm.…”
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“…For mice injected with the ferritin‐encoding virus vector (rAAV2‐retro‐CAG‐Ferritin) in the caudate putamen (CPu), several regions showed significant changes in MRI contrasts, such as PFC (prefrontal cortex), HIP (hippocampus), Ins (insular cortex) and BLA (basolateral amygdala). …”
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“…RESULTS: The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations using 10 CPU). We test ARCHes on public data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP) as well as simulated examples of known admixture. …”
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“…We identified 82 SNPs for the seven traits by GEMMA-based and FarmCPU-based GWASs. Both methods detected two quantitative trait loci (QTL) on SSC8 and SSC17 for body conformation traits. …”
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“…We test the performance of our approach on a modern ARMv8.2 (A64FX) CPU and assess the different layers of our implementation by sorting/partitioning integers, double floating-point numbers, and key/value pairs of integers. …”
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“…The computations showed that the model and the solution methods are efficient and can obtain high-quality solutions in suitable CPU times. Other analyses and computations are done based on a real case study in the tire industry. …”
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“…In this study, we conducted a genome-wide association study on three traits—anterior teat position (ATP), posterior teat position (PTP), and front teat length (FTL)—in which the FarmCPU method was used for association analyses. Phenotypic data were collected from 1000 Chinese Holstein cattle, and the GeneSeek Genomic Profiler Bovine 100K single-nucleotide polymorphisms (SNP) chip was used for cattle genotyping data. …”
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“…The running time of GTX780Ti is about 15 times shorter than that of CPU. The accuracy of N detection based on the GPU-accelerated neural network model reached 98.06%. …”
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“…Then we used liquid chromatography-mass spectrometry (LC-MS) to simultaneously determine the contents of various neurotransmitters – dopamine (DA), norepinephrine (NE), 5-hydroxytryptamine (5-HT), 5-hydroxyindole acetic acid (5-HIAA), glutamic acid (Glu) and glutamine (Gln) – in different brain regions of the prefrontal cortex (PFc), nucleus accumbens (NAc), caudate-putamen (CPu) and hippocampus (Hip), which are believed to be relevant to the drug’s reward effect. …”
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“…Five SNPs were found to be significantly associated with gummosis disease resistance, of which SNPrs285957, located on chromosome6 across 28 Mb, was detected by both the BLINK and the FarmCPU model. Six candidate genes flanked by or harboring the significant SNPs, previously implicated in biotic stress tolerance, were significantly associated with this resistance. …”
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“…Genome-wide association study (GWAS) using a mixed linear FarmCPU model identified SNPs associated with the studied traits i.e., about seven and eight SNPs for the grain yield; 16 and 12 for anthesis date; seven and eight for anthesis silking interval; 14 and 5 for both ear and plant height; and 15 and 5 for moisture under both WW and WS environments, respectively. …”
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“…Our neural network solution decreases inference time on traditional CPU hardware by 1057× and on GPU hardware accelerators by 883× while maintaining 99% cosine similarity.…”
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“…Conclusions The supremacy of SDFL is corroborated in analysing motion data for human activity recognition requiring no GPU but only a CPU with a fast- learning rate.…”
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“…The terminal has short running time and delay time, small memory, and central processing unit (CPU) occupation and can meet the needs of users. …”
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