55 research outputs found
Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge
3D object detection plays a pivotal role in many applications, most notably
autonomous driving and robotics. These applications are commonly deployed on
edge devices to promptly interact with the environment, and often require near
real-time response. With limited computation power, it is challenging to
execute 3D detection on the edge using highly complex neural networks. Common
approaches such as offloading to the cloud induce significant latency overheads
due to the large amount of point cloud data during transmission. To resolve the
tension between wimpy edge devices and compute-intensive inference workloads,
we explore the possibility of empowering fast 2D detection to extrapolate 3D
bounding boxes. To this end, we present Moby, a novel system that demonstrates
the feasibility and potential of our approach. We design a transformation
pipeline for Moby that generates 3D bounding boxes efficiently and accurately
based on 2D detection results without running 3D detectors. Further, we devise
a frame offloading scheduler that decides when to launch the 3D detector
judiciously in the cloud to avoid the errors from accumulating. Extensive
evaluations on NVIDIA Jetson TX2 with real-world autonomous driving datasets
demonstrate that Moby offers up to 91.9% latency improvement with modest
accuracy loss over state of the art.Comment: Accepted to ACM International Conference on Multimedia (MM) 202
The Drosophila 7SK snRNP and the essential role of dHEXIM in development
Regulation of the positive transcription elongation factor, P-TEFb, plays a major role in controlling mammalian transcription and this is accomplished in part by controlled release of P-TEFb from the 7SK snRNP that sequesters the kinase in an inactive state. We demonstrate here that a similar P-TEFb control system exists in Drosophila. We show that an RNA previously suggested to be a 7SK homolog is, in fact, associated with P-TEFb, through the action of a homolog of the human HEXIM1/2 proteins (dHEXIM). In addition, a Drosophila La related protein (now called dLARP7) is shown to be the functional homolog of human LARP7. The Drosophila 7SK snRNP (d7SK snRNP) responded to treatment of cells with P-TEFb inhibitors and to nuclease treatment of cell lysates by releasing P-TEFb. Supporting a critical role for the d7SK snRNP in Drosophila development, dLARP7 and dHEXIM were found to be ubiquitously expressed throughout embryos and tissues at all stages. Importantly, knockdown of dHEXIM was embryonic lethal, and reduction of dHEXIM in specific tissues led to serious developmental defects. Our results suggest that regulation of P-TEFb by the d7SK snRNP is essential for the growth and differentiation of tissues required during Drosophila development
NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing
computing efficiency and capabilities by following brain-inspired principles.
However, the rich diversity of techniques employed in neuromorphic research has
resulted in a lack of clear standards for benchmarking, hindering effective
evaluation of the advantages and strengths of neuromorphic methods compared to
traditional deep-learning-based methods. This paper presents a collaborative
effort, bringing together members from academia and the industry, to define
benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are
to be a collaborative, fair, and representative benchmark suite developed by
the community, for the community. In this paper, we discuss the challenges
associated with benchmarking neuromorphic solutions, and outline the key
features of NeuroBench. We believe that NeuroBench will be a significant step
towards defining standards that can unify the goals of neuromorphic computing
and drive its technological progress. Please visit neurobench.ai for the latest
updates on the benchmark tasks and metrics
NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics
Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees
Significance
Contributions of rare variants to common and complex traits such as type 2 diabetes (T2D) are difficult to measure. This paper describes our results from deep whole-genome analysis of large Mexican-American pedigrees to understand the role of rare-sequence variations in T2D and related traits through enriched allele counts in pedigrees. Our study design was well-powered to detect association of rare variants if rare variants with large effects collectively accounted for large portions of risk variability, but our results did not identify such variants in this sample. We further quantified the contributions of common and rare variants in gene expression profiles and concluded that rare expression quantitative trait loci explain a substantive, but minor, portion of expression heritability.</jats:p
NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community
A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data
Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants
Loss-of-function mutations in SLC30A8 protect against type 2 diabetes.
Neðst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn View/OpenLoss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets, but none have yet been described for type 2 diabetes (T2D). Through sequencing or genotyping of ~150,000 individuals across 5 ancestry groups, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8) and harbors a common variant (p.Trp325Arg) associated with T2D risk and glucose and proinsulin levels. Collectively, carriers of protein-truncating variants had 65% reduced T2D risk (P = 1.7 × 10(-6)), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34Serfs*50) demonstrated reduced glucose levels (-0.17 s.d., P = 4.6 × 10(-4)). The two most common protein-truncating variants (p.Arg138* and p.Lys34Serfs*50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested that reduced zinc transport increases T2D risk, and phenotypic heterogeneity was observed in mouse Slc30a8 knockouts. In contrast, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, suggesting ZnT8 inhibition as a therapeutic strategy in T2D prevention.US National Institutes of Health (NIH) Training
5-T32-GM007748-33
Doris Duke Charitable Foundation
2006087
Fulbright Diabetes UK Fellowship
BDA 11/0004348
Broad Institute from Pfizer, Inc.
NIH
U01 DK085501
U01 DK085524
U01 DK085545
U01 DK085584
Swedish Research Council
Dnr 521-2010-3490
Dnr 349-2006-237
European Research Council (ERC)
GENETARGET T2D
GA269045
ENGAGE
2007-201413
CEED3
2008-223211
Sigrid Juselius Foundation
Folkh lsan Research Foundation
ERC
AdG 293574
Research Council of Norway
197064/V50
KG Jebsen Foundation
University of Bergen
Western Norway Health Authority
Lundbeck Foundation
Novo Nordisk Foundation
Wellcome Trust
WT098017
WT064890
WT090532
WT090367
WT098381
Uppsala University
Swedish Research Council and the Swedish Heart- Lung Foundation
Academy of Finland
124243
102318
123885
139635
Finnish Heart Foundation
Finnish Diabetes Foundation, Tekes
1510/31/06
Commission of the European Community
HEALTH-F2-2007-201681
Ministry of Education and Culture of Finland
European Commission Framework Programme 6 Integrated Project
LSHM-CT-2004-005272
City of Kuopio and Social Insurance Institution of Finland
Finnish Foundation for Cardiovascular Disease
NIH/NIDDK
U01-DK085545
National Heart, Lung, and Blood Institute (NHLBI)
National Institute on Minority Health and Health Disparities
N01 HC-95170
N01 HC-95171
N01 HC-95172
European Union Seventh Framework Programme, DIAPREPP
Swedish Child Diabetes Foundation (Barndiabetesfonden)
5U01DK085526
DK088389
U54HG003067
R01DK072193
R01DK062370
Z01HG000024info:eu-repo/grantAgreement/EC/FP7/20201
Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes
Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants
Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation.
GWAS have identified a breast cancer susceptibility locus on 2q35. Here we report the fine mapping of this locus using data from 101,943 subjects from 50 case-control studies. We genotype 276 SNPs using the 'iCOGS' genotyping array and impute genotypes for a further 1,284 using 1000 Genomes Project data. All but two, strongly correlated SNPs (rs4442975 G/T and rs6721996 G/A) are excluded as candidate causal variants at odds against >100:1. The best functional candidate, rs4442975, is associated with oestrogen receptor positive (ER+) disease with an odds ratio (OR) in Europeans of 0.85 (95% confidence interval=0.84-0.87; P=1.7 × 10(-43)) per t-allele. This SNP flanks a transcriptional enhancer that physically interacts with the promoter of IGFBP5 (encoding insulin-like growth factor-binding protein 5) and displays allele-specific gene expression, FOXA1 binding and chromatin looping. Evidence suggests that the g-allele confers increased breast cancer susceptibility through relative downregulation of IGFBP5, a gene with known roles in breast cell biology
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