The Impact of Data Sovereignty on American Indian Self-Determination: A Framework Proof of Concept Using Data Science

The Data Sovereignty Initiative is a collection of ideas that was designed to create SMART solutions for tribal communities. This concept was to develop a horizontal governance framework to create a strategic act of sovereignty using data science. The core concept of this idea was to present data sovereignty as a way for tribal communities to take ownership of data in order to affect policy and strategic decisions that are data driven in nature. The case studies in this manuscript were developed around statistical theories of spatial statistics, exploratory data analysis, and machine learning. And although these case studies are first, scientific in nature, the data sovereignty framework was designed around these concepts to leverage nation building, cultural capital, and citizen science for economic development and planning. The data sovereignty framework is a flexible way to create data domains, around developed key indicators to integrate appropriate cultural capital when working with Native nations. This design is intended to put scientific theory into practice to affect everyday outcomes using data driven decision making. This framework is a proof concept and represents both applied and theoretical metrics in design strength

The IceCube Neutrino Observatory: Instrumentation and Online Systems

The IceCube Neutrino Observatory is a cubic-kilometer-scale high-energy neutrino detector built into the ice at the South Pole. Construction of IceCube, the largest neutrino detector built to date, was completed in 2011 and enabled the discovery of high-energy astrophysical neutrinos. We describe here the design, production, and calibration of the IceCube digital optical module (DOM), the cable systems, computing hardware, and our methodology for drilling and deployment. We also describe the online triggering and data filtering systems that select candidate neutrino and cosmic ray events for analysis. Due to a rigorous pre-deployment protocol, 98.4% of the DOMs in the deep ice are operating and collecting data. IceCube routinely achieves a detector uptime of 99% by emphasizing software stability and monitoring. Detector operations have been stable since construction was completed, and the detector is expected to operate at least until the end of the next decade.Comment: 83 pages, 50 figures; updated with minor changes from journal review and proofin

A Comparative Verfication Study of Seasonal Outlooks Using Neural Networking and Historical Analog Strength Tools in South Dakota and Neighboring Climate Divisions

The Climate Prediction Center (CPC) issues seasonal 30-60-90 day forecasts in the United States and are used for planning purposes on a nation-wide scale. Categorical forecasting using the concept of equal chance (EC) determines whether a forecast is above normal (A) or below normal (B) for future monthly outlooks. CPC forecasting is too broad in scope for local forecasting systems. Thus, the need for more effective small scale local forecasting is the subject of this analysis. The purpose of this study is to predict and compare a neural and analog network model relative to the CPC seasonal forecasts. The tools generated from the model are referenced against historical sixty-year half standard deviation values to determine categorical forecasts. The results are paired with actual observed categorical forecasts to populate a 3x3 contingency table for each climate division in the study. Measures of the skill of forecasting are initially measured using the Heidke skill score. Furthermore, due to the ordinal nature of the forecast variables, the marginal probability scores are weighted giving penalties to forecasts that are farther away from the actual observed values to test the validity of the prediction measures

The Molecular Taxonomy of Primary Prostate Cancer

There is substantial heterogeneity among primary prostate cancers, evident in the spectrum of molecular abnormalities and its variable clinical course. As part of The Cancer Genome Atlas (TCGA), we present a comprehensive molecular analysis of 333 primary prostate carcinomas. Our results revealed a molecular taxonomy in which 74% of these tumors fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, and FLI1) or mutations (SPOP, FOXA1, and IDH1). Epigenetic profiles showed substantial heterogeneity, including an IDH1 mutant subset with a methylator phenotype. Androgen receptor (AR) activity varied widely and in a subtype-specific manner, with SPOP and FOXA1 mutant tumors having the highest levels of AR-induced transcripts. 25% of the prostate cancers had a presumed actionable lesion in the PI3K or MAPK signaling pathways, and DNA repair genes were inactivated in 19%. Our analysis reveals molecular heterogeneity among primary prostate cancers, as well as potentially actionable molecular defectsclose

Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo

International audienceIntermediate-mass black holes (IMBHs) span the approximate mass range 100−105 M⊙, between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150 M⊙ providing direct evidence of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200 M⊙ and effective aligned spin 0.8 at 0.056 Gpc−3 yr−1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc−3 yr−1.Key words: gravitational waves / stars: black holes / black hole physicsCorresponding author: W. Del Pozzo, e-mail: [email protected]† Deceased, August 2020

Search for gravitational waves from Scorpius X-1 with a hidden Markov model in O3 LIGO data

Results are presented for a semicoherent search for continuous gravitational waves from the low-mass x-ray binary Scorpius X-1, using a hidden Markov model (HMM) to allow for spin wandering. This search improves on previous HMM-based searches of Laser Interferometer Gravitational-Wave Observatory data by including the orbital period in the search template grid, and by analyzing data from the latest (third) observing run. In the frequency range searched, from 60 to 500 Hz, we find no evidence of gravitational radiation. This is the most sensitive search for Scorpius X-1 using a HMM to date. For the most sensitive subband, starting at 256.06 Hz, we report an upper limit on gravitational wave strain (at 95% confidence) of h 95 % 0 = 6.16 × 10 − 26 , assuming the orbital inclination angle takes its electromagnetically restricted value ι = 4 4 ° . The upper limits on gravitational wave strain reported here are on average a factor of ∼ 3 lower than in the second observing run HMM search. This is the first Scorpius X-1 HMM search with upper limits that reach below the indirect torque-balance limit for certain subbands, assuming ι = 4 4 °