skip to main content

Search for: All records

Creators/Authors contains: "Xie, S."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available August 1, 2022
  2. Boeri, L. ; Hennig, R. ; Hirschfeld, P. ; Profeta, G. ; Sanna, A. ; Zurek, E. (Ed.)
    Last year, the report of Room-Temperature Superconductivity in high-pressure carbonaceous sulfur hydride marked a major milestone in the history of physics: one of the holy grails of condensed matter research was reached after more than one century of continuing efforts. This long path started with Neil Ashcroft’s and Vitaly Ginzburg’s visionary insights on high-temperature superconductivity in metallic hydrogen in the 60’s and 70’s, and has led to the current hydride fever, following the report of high-Tc high-pressure superconductivity in H3S in 2014. This Roadmap collects selected contributions from many of the main actors in this exciting chapter of condensed mattermore »history. Key for the rapid progress of this field has been a new course for materials discovery, where experimental and theoretical discoveries proceed hand in hand. The aim of this Roadmap is not only to offer a snapshot of the current status of superconductor materials research, but also to define the theoretical and experimental obstacles that must be overcome for us to realize fully exploitable room temperature superconductors, and foresee future strategies and research directions. This means improving synthesis techniques, extending first-principles methods for superconductors and structural search algorithms for crystal structure predictions, but also identifying new approaches to material discovery based on artificial intelligence.« less
    Free, publicly-accessible full text available October 1, 2022
  3. Abstract The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computationmore »and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.« less
  4. Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the graph structure of neural networks affect their predictive performance. To this end, we develop a novel graph-based representation of neural networks called relational graph, where layers of neural network computation correspond to rounds of message exchange along the graph structure. Using this representation we show that: (1) a “sweet spot” of relational graphs leads to neural networksmore »with significantly improved predictive performance; (2) neural network’s performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph; (3) our findings are consistent across many different tasks and datasets; (4) the sweet spot can be identified efficiently; (5) topperforming neural networks have graph structure surprisingly similar to those of real biological neural networks. Our work opens new directions for the design of neural architectures and the understanding on neural networks in general.« less
  5. This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph- based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problemmore »as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm.« less
  6. Free, publicly-accessible full text available July 1, 2022
  7. Free, publicly-accessible full text available September 1, 2022