Separable graph Hamiltonian network: A graph deep learning model for lattice systems

Addressing the challenges posed by nonlinear lattice models, which are vital across diverse scientific disciplines, we present a new deep learning approach that harnesses the power of graph neural networks. By representing the lattice system as a graph and leveraging the graph structures to identify complex nonlinear relationships, we have developed a flexible solution that outperforms traditional techniques. Our model not only offers precise trajectory predictions and energy conservation properties by incorporating separable Hamiltonians but also proves superior to existing top-tier models when tested on classic nonlinear oscillator lattice problems: a mixed Fermi-Pasta-Ulam Klein-Gordon, a Klein-Gordon system with long-range interactions, and a twodimensional Frenkel-Kontorova, highlighting its potential for wide-reaching applications

The origins of the anomalous warming in the California coastal ocean and San Francisco Bay during 2014-2016

During 2014 exceptionally warm water temperatures developed across a wide area off the California coast and within San Francisco Bay (SFB) and persisted into 2016. Observations and numerical model output are used to document this warming and determine its origins. The coastal warming was mostly confined to the upper 100 m of the ocean and was manifested strongly in the two leading modes of upper ocean (0-100 m) temperature variability in the extratropical eastern Pacific. Observations suggest that the coastal warming in 2014 propagated into nearshore regions from the west while later indicating a warming influence that propagated from south to north into the region associated with the 2015-2016 El Nino event. An analysis of the upper ocean (0-100 m) heat budget in a Regional Ocean Modeling System (ROMS) simulation confirmed this scenario. The results from a set of sensitivity runs with the model in which the lateral boundary conditions varied supported the conclusions drawn from the heat budget analysis. Concerning the warming in the SFB, an examination of the observations and the heat budget in an unstructured-grid numerical model simulation suggested that the warming during the second half of 2014 and early 2016 originated in the adjacent California coastal ocean and propagated through the Golden Gate into the Bay. The finding that the coastal and Bay warming are due to the relatively slow propagation of signals from remote sources raises the possibility that such warming events may be predictable many months or even several seasons in advance. Plain Language Summary The origins of the exceptionally warm water temperatures that developed off the California coast and in San Francisco Bay were studied using observations and computer model experiments. The coastal warming was mostly confined to the upper ocean. The coastal warming in 2014 was found to have moved into coastal waters from further offshore in the northeastern Pacific. Warming persisted into 2015-2016 as a warming influence from the south associated with the 2015-16 El Nino event in the tropical Pacific Ocean. The model experiments suggested confirmed that propagation of the warming signals from the west and north into the California coastal ocean and suggested that the warming in San Francisco Bay was found to have originated primarily in the adjacent California coastal ocean. The finding that the coastal and Bay warming are due to the relatively slow propagation of signals from remote sources raises the possibility that such warming events may be predictable many months or even several seasons in advance

Human total, basal and activity energy expenditures are independent of ambient environmental temperature

Lower ambient temperature (

Hold that pose: Capturing cervical dystonia\u27s head deviation severity from video

OBJECTIVE: Deviated head posture is a defining characteristic of cervical dystonia (CD). Head posture severity is typically quantified with clinical rating scales such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Because clinical rating scales are inherently subjective, they are susceptible to variability that reduces their sensitivity as outcome measures. The variability could be circumvented with methods to measure CD head posture objectively. However, previously used objective methods require specialized equipment and have been limited to studies with a small number of cases. The objective of this study was to evaluate a novel software system-the Computational Motor Objective Rater (CMOR)-to quantify multi-axis directionality and severity of head posture in CD using only conventional video camera recordings. METHODS: CMOR is based on computer vision and machine learning technology that captures 3D head angle from video. We used CMOR to quantify the axial patterns and severity of predominant head posture in a retrospective, cross-sectional study of 185 patients with isolated CD recruited from 10 sites in the Dystonia Coalition. RESULTS: The predominant head posture involved more than one axis in 80.5% of patients and all three axes in 44.4%. CMOR\u27s metrics for head posture severity correlated with severity ratings from movement disorders neurologists using both the TWSTRS-2 and an adapted version of the Global Dystonia Rating Scale (rho = 0.59-0.68, all p \u3c0.001). CONCLUSIONS: CMOR\u27s convergent validity with clinical rating scales and reliance upon only conventional video recordings supports its future potential for large scale multisite clinical trials

Adaptive analysis using scaled boundary finite element method in 3D

In this paper, an adaptive refinement technique using the scaled boundary finite element method (SBFEM) is proposed. The salient feature of this technique is that it is not required to regenerate the mesh for the whole model during the iterations. To this end, a local mesh refinement strategy is implemented based on a polytree algorithm in three dimensions, which can be applied to polyhedral elements with arbitrary number of nodes, edges and faces. These elements constructed by the SBFEM can be used in analysis with their boundaries discretized only, which reduce the difficulty to connect elements with different sizes. An explicit residual based error indicator is developed using the discontinuity of the stress field to guide the adaptive mesh refinement. The accuracy and efficiency of the proposed method are demonstrated using five numerical examples, including complex geometry and stress singularity

A global analysis of the spatial and temporal variability of usable Landsat observations at the pixel scale

The Landsat program has the longest collection of moderate-resolution satellite imagery, and the data are free to everyone. With the improvements of standardized image products, the flexibility of cloud computing platforms, and the development of time series approaches, it is now possible to conduct global-scale analyses of time series using Landsat data over multiple decades. Efforts in this regard are limited by the density of usable observations. The availability of usable Landsat Tier 1 observations at the scale of individual pixels from the perspective of time series analysis for land change monitoring is remarkably variable both in space (globally) and time (1985–2020), depending most immediately on which sensors were in operation, the technical capabilities of the mission, and the acquisition strategies and objectives of the satellite operators (e.g., USGS, commercial company) and the international ground receiving stations. Additionally, analysis of data density at the pixel scale allows for the integration of quality control data on clouds, cloud shadows, and snow as well as other properties returned from the atmospheric correction process. Maps for different time periods show the effect of excluding observations based on the presence of clouds, cloud shadows, snow, sensor saturation, hazy observations (based on atmospheric opacity), and lack of aerosol optical depth information. Two major discoveries are: 1) that filtering saturated and hazy pixels is helpful to reduce noise in the time series, although the impact may vary across different continents; 2) the atmospheric opacity band needs to be used with caution because many images are removed when no value is given in this band, when many of those observations are usable. The results provide guidance on when and where time series analysis is feasible, which will benefit many users of Landsat data.University of Connecticut; National Aeronautics and Space Administration; 80NSSC20K0022 - NASA; 20-DG-11132762-017 - Department of Agriculture/Forest Service; G12PC00070 - Department of the Interior/U.S. Geological SurveyPublished versio