228 research outputs found
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
Full text linkDespite the progress in deep learning networks, efficient learning at the
edge (enabling adaptable, low-complexity machine learning solutions) remains a
critical need for defense and commercial applications. We envision a pipeline
to utilize large neuroimaging datasets, including maps of the brain which
capture neuron and synapse connectivity, to improve machine learning
approaches. We have pursued different approaches within this pipeline
structure. First, as a demonstration of data-driven discovery, the team has
developed a technique for discovery of repeated subcircuits, or motifs. These
were incorporated into a neural architecture search approach to evolve network
architectures. Second, we have conducted analysis of the heading direction
circuit in the fruit fly, which performs fusion of visual and angular velocity
features, to explore augmenting existing computational models with new insight.
Our team discovered a novel pattern of connectivity, implemented a new model,
and demonstrated sensor fusion on a robotic platform. Third, the team analyzed
circuitry for memory formation in the fruit fly connectome, enabling the design
of a novel generative replay approach. Finally, the team has begun analysis of
connectivity in mammalian cortex to explore potential improvements to
transformer networks. These constraints increased network robustness on the
most challenging examples in the CIFAR-10-C computer vision robustness
benchmark task, while reducing learnable attention parameters by over an order
of magnitude. Taken together, these results demonstrate multiple potential
approaches to utilize insight from neural systems for developing robust and
efficient machine learning techniques.Comment: 11 pages, 4 figure
Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project
Get PDFThe Numerical INJection Analysis (NINJA) project is a collaborative effort
between members of the numerical relativity and gravitational-wave data
analysis communities. The purpose of NINJA is to study the sensitivity of
existing gravitational-wave search algorithms using numerically generated
waveforms and to foster closer collaboration between the numerical relativity
and data analysis communities. We describe the results of the first NINJA
analysis which focused on gravitational waveforms from binary black hole
coalescence. Ten numerical relativity groups contributed numerical data which
were used to generate a set of gravitational-wave signals. These signals were
injected into a simulated data set, designed to mimic the response of the
Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this
data using search and parameter-estimation pipelines. Matched filter
algorithms, un-modelled-burst searches and Bayesian parameter-estimation and
model-selection algorithms were applied to the data. We report the efficiency
of these search methods in detecting the numerical waveforms and measuring
their parameters. We describe preliminary comparisons between the different
search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ
Social isolation and incident heart failure hospitalization in older women: Women\u27s health initiative study findings
Get PDFBackground The association of social isolation or lack of social network ties in older adults is unknown. This knowledge gap is important since the risk of heart failure (HF) and social isolation increase with age. The study examines whether social isolation is associated with incident HF in older women, and examines depressive symptoms as a potential mediator and age and race and ethnicity as effect modifiers. Methods and Results This study included 44 174 postmenopausal women of diverse race and ethnicity from the WHI (Women\u27s Health Initiative) study who underwent annual assessment for HF adjudication from baseline enrollment (1993-1998) through 2018. We conducted a mediation analysis to examine depressive symptoms as a potential mediator and further examined effect modification by age and race and ethnicity. Incident HF requiring hospitalization was the main outcome. Social isolation was a composite variable based on marital/partner status, religious ties, and community ties. Depressive symptoms were assessed using CES-D (Center for Epidemiology Studies-Depression). Over a median follow-up of 15.0 years, we analyzed data from 36 457 women, and 2364 (6.5%) incident HF cases occurred; 2510 (6.9%) participants were socially isolated. In multivariable analyses adjusted for sociodemographic, behavioral, clinical, and general health/functioning; socially isolated women had a higher risk of incident HF than nonisolated women (HR, 1.23; 95% CI, 1.08-1.41). Adding depressive symptoms in the model did not change this association (HR, 1.22; 95% CI, 1.07-1.40). Neither race and ethnicity nor age moderated the association between social isolation and incident HF. Conclusions Socially isolated older women are at increased risk for developing HF, independent of traditional HF risk factors. Registration URL: http://www.clinicaltrials.gov; Unique identifier: NCT00000611
Undocumented Worker Employment and Firm Survivability
Full text linkDo firms employing undocumented workers have a competitive advantage? Using administrative data from the state of Georgia, this paper investigates the incidence of undocumented worker employment across firms and how it affects firm survival. Firms are found to engage in herding behavior, being more likely to employ undocumented workers if competitors do. Rivals' undocumented employment harms firms' ability to survive while firms' own undocumented employment strongly enhances their survival prospects. This finding suggests that firms enjoy cost savings from employing lower-paid undocumented at workers wages less than their marginal revenue product. The herding behavior and competitive effects are found to be much weaker in geographically broad product markets, where firms have the option to shift labor-intensive production out of state or abroad
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Get PDFLong noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
Get PDFAlthough theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN
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