High-precision Density Mapping of Marine Debris and Floating Plastics via Satellite Imagery

Combining multi-spectral satellite data and machine learning has been suggested as a method for monitoring plastic pollutants in the ocean environment. Recent studies have made theoretical progress regarding the identification of marine plastic via machine learning. However, no study has assessed the application of these methods for mapping and monitoring marine-plastic density. As such, this paper comprised of three main components: (1) the development of a machine learning model, (2) the construction of the MAP-Mapper, an automated tool for mapping marine-plastic density, and finally (3) an evaluation of the whole system for out-of-distribution test locations. The findings from this paper leverage the fact that machine learning models need to be high-precision to reduce the impact of false positives on results. The developed MAP-Mapper architectures provide users choices to reach high-precision ($\textit{abbv.}$ -HP) or optimum precision-recall ($\textit{abbv.}$ -Opt) values in terms of the training/test data set. Our MAP-Mapper-HP model greatly increased the precision of plastic detection to 95\%, whilst MAP-Mapper-Opt reaches precision-recall pair of 87\%-88\%. The MAP-Mapper contributes to the literature with the first tool to exploit advanced deep/machine learning and multi-spectral imagery to map marine-plastic density in automated software. The proposed data pipeline has taken a novel approach to map plastic density in ocean regions. As such, this enables an initial assessment of the challenges and opportunities of this method to help guide future work and scientific study.Comment: 14 pages, 4 tables, 4 figure

Excited State Calculations In Solids By Auxiliary-Field Quantum Monte Carlo

We present an approach for ab initio many-body calculations of excited states in solids. Using auxiliary-field quantum Monte Carlo, we introduce an orthogonalization constraint with virtual orbitals to prevent collapse of the stochastic Slater determinants in the imaginary-time propagation. Trial wave functions from density-functional calculations are used for the constraints. Detailed band structures can be calculated. Results for standard semiconductors are in good agreement with experiments; comparisons are also made with GW calculations and the connections and differences are discussed. For the challenging ZnO wurtzite structure, we obtain a fundamental band gap of 3.26(16) eV, consistent with experiments

Information Disclosure and the Equivalence of Prospective Payment and Cost Reimbursement

A health care provider chooses unobservable service-quality and cost-reduction efforts. The efforts produce quality and cost efficiency. An insurer observes quality and cost, and chooses how to disclose this information to consumers. The insurer also decides how to pay the provider. In prospective payment, the insurer fully discloses quality, and sets a prospective payment price. In cost reimbursement, the insurer discloses a value index, a weighted average of quality and cost efficiency, and pays a margin above cost. The first-best quality and cost efforts can be implemented by prospective payment and by cost reimbursement. Cost reimbursement with value index eliminates dumping and cream skimming. Prospective payment with quality index eliminates cream skimming

Auxiliary-field quantum Monte Carlo calculations with multiple-projector pseudopotentials

We have implemented recently developed multiple-projector pseudopotentials into the plane-wave-based auxiliary-field quantum Monte Carlo (pw-AFQMC) method. Multiple-projector pseudopotentials can yield smaller plane-wave cutoffs while maintaining or improving transferability. This reduces the computational cost of pw-AFQMC, increasing its reach to larger and more complicated systems. We discuss the use of nonlocal pseudopotentials in the separable Kleinman-Bylander form, and the implementation in pw-AFQMC of the multiple-projector optimized norm-conserving pseudopotential ONCVPSP of Hamann. The accuracy of the method is first demonstrated by equation-of-state calculations of the ionic insulator NaCl and more strongly correlated metal Cu. The method is then applied to calibrate the accuracy of density-functional theory (DFT) predictions of the phase stability of recently discovered high temperature and pressure superconducting sulfur hydride systems. We find that DFT results are in good agreement with pw-AFQMC, due to the near cancellation of electron-electron correlation effects between different structures

Activation of G proteins by GIV-GEF is a pivot point for insulin resistance and sensitivity.

Insulin resistance (IR) is a metabolic disorder characterized by impaired insulin signaling and cellular glucose uptake. The current paradigm for insulin signaling centers upon the insulin receptor (InsR) and its substrate IRS1; the latter is believed to be the sole conduit for postreceptor signaling. Here we challenge that paradigm and show that GIV/Girdin, a guanidine exchange factor (GEF) for the trimeric G protein Gαi, is another major hierarchical conduit for the metabolic insulin response. By virtue of its ability to directly bind InsR, IRS1, and phosphoinositide 3-kinase, GIV serves as a key hub in the immediate postreceptor level, which coordinately enhances the metabolic insulin response and glucose uptake in myotubes via its GEF function. Site-directed mutagenesis or phosphoinhibition of GIV-GEF by the fatty acid/protein kinase C-theta pathway triggers IR. Insulin sensitizers reverse phosphoinhibition of GIV and reinstate insulin sensitivity. We also provide evidence for such reversible regulation of GIV-GEF in skeletal muscles from patients with IR. Thus GIV is an essential upstream component that couples InsR to G-protein signaling to enhance the metabolic insulin response, and impairment of such coupling triggers IR. We also provide evidence that GIV-GEF serves as therapeutic target for exogenous manipulation of physiological insulin response and reversal of IR in skeletal muscles

Quantum Monte Carlo Calculations in Solids with Downfolded Hamiltonians

We present a combination of a downfolding many-body approach with auxiliary-field quantum Monte Carlo (AFQMC) calculations for extended systems. Many-body calculations operate on a simpler Hamiltonian which retains material-specific properties. The Hamiltonian is systematically improvable and allows one to dial, in principle, between the simplest model and the original Hamiltonian. As a by-product, pseudopotential errors are essentially eliminated using frozen orbitals constructed adaptively from the solid environment. The computational cost of the many-body calculation is dramatically reduced without sacrificing accuracy. Excellent accuracy is achieved for a range of solids, including semiconductors, ionic insulators, and metals. We apply the method to calculate the equation of state of cubic BN under ultrahigh pressure, and determine the spin gap in NiO, a challenging prototypical material with strong electron correlation effects

A Hybrid Labeled Multi-Bernoulli Filter With Amplitude For Tracking Fluctuating Targets

The amplitude information of target returns has been incorporated into many tracking algorithms for performance improvements. One of the limitations of employing amplitude feature is that the signal-to-noise ratio (SNR) of the target, i.e., the parameter of amplitude likelihood, is usually assumed to be known and constant. In practice, the target SNR is always unknown, and is dependent on aspect angle hence it will fluctuate. In this paper we propose a hybrid labeled multi-Bernoulli (LMB) filter that introduces the signal amplitude into the LMB filter for tracking targets with unknown and fluctuating SNR. The fluctuation of target SNR is modeled by an autoregressive gamma process and amplitude likelihoods for Swerling 1 and 3 targets are considered. Under Rao-Blackwell decomposition, an approximate Gamma estimator based on Laplace transform and Markov Chain Monte Carlo method is proposed to estimate the target SNR, and the kinematic state is estimated by a Gaussian mixture filter conditioned on the target SNR. The performance of the proposed hybrid filter is analyzed via a tracking scenario including three crossing targets. Simulation results verify the efficacy of the proposed SNR estimator and quantify the benefits of incorporating amplitude information for multi-target tracking

Investigation of the natural history of Equine Encephalitis Viruses with radiofrequency telemetry for detection of subclinical disease patterns

Neither licensed vaccines nor antiviral therapeutics with proven efficacy exist to protect against the equine encephalitis viruses (EEVs), specifically Eastern, Western, and Venezuelan Equine Encephalitis Viruses (EEEV, WEEV, and VEEV, respectively). Due to rigorous ethical, regulatory, and scientific considerations, animal models that can faithfully demonstrate aspects of clinical disease must be established for testing of countermeasure candidates. Such models must satisfy the Animal Rule promulgated and enforced by the United States Food and Drug Administration and capture key aspects of equine encephalitis virus presentation in humans, especially with respect to encephalitic disease, whose manifestations include fever and neurological signs. This study seeks to establish and study a model of human equine encephalitis virus infection via the aerosol route in the nonhuman primate, the cynomolgus macaque (Macaca fascicularis) with a focus on the natural history of disease through examination of radiofrequency electrocardiography data, and evaluates the feasibility of the use of such data to prognosticate severe disease courses which include encephalitis. Twelve nonhuman primate subjects, grouped four per type of equine encephalitis virus, were challenged with aerosol exposures of the alphaviruses in various doses. The following electrocardiography metrics compose a core set of variables suited to the characterization of disease rendered by EEVs: HR, PCt, P-Width, PR-I, QRS, QRSA, QT-I, R-H, and RR-I. Frequency spectrum analysis conducted on these metrics can be used to distinguish different periods of disease, if not distinguish between diseases, and Poincare plots of heart rate variability data can be used to track the progression of illness. The public health significance of this work rests in its contributions to disease detection to aid in vaccine and therapeutic development for both the prevention of infectious disease and the mitigation of risk posed by potential biological weapons attacks. Finally, improved clinical disease detection through RF telemetry and other markers will abet the surveillance function of public health