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  1. Abstract Interstellar pickup ions are an ubiquitous and thermodynamically important component of the solar wind plasma in the heliosphere. These PUIs are born from the ionization of the interstellar neutral gas, consisting of hydrogen, helium, and trace amounts of heavier elements, in the solar wind as the heliosphere moves through the local interstellar medium. As cold interstellar neutral atoms become ionized, they form an energetic ring beam distribution comoving with the solar wind. Subsequent scattering in pitch angle by intrinsic and self-generated turbulence and their advection with the radially expanding solar wind leads to the formation of a filled-shell PUImore »distribution, whose density and pressure relative to the thermal solar wind ions grows with distance from the Sun. This paper reviews the history of in situ measurements of interstellar PUIs in the heliosphere. Starting with the first detection in the 1980s, interstellar PUIs were identified by their highly nonthermal distribution with a cutoff at twice the solar wind speed. Measurements of the PUI distribution shell cutoff and the He focusing cone, a downwind region of increased density formed by the solar gravity, have helped characterize the properties of the interstellar gas from near-Earth vantage points. The preferential heating of interstellar PUIs compared to the core solar wind has become evident in the existence of suprathermal PUI tails, the nonadiabatic cooling index of the PUI distribution, and PUIs’ mediation of interplanetary shocks. Unlike the Voyager and Pioneer spacecraft, New Horizon’s Solar Wind Around Pluto (SWAP) instrument is taking the only direct measurements of interstellar PUIs in the outer heliosphere, currently out to $\sim47~\text{au}$ ∼ 47 au from the Sun or halfway to the heliospheric termination shock.« less
    Free, publicly-accessible full text available June 1, 2023
  2. Co-location of processing infrastructure and IoT devices at the edge is used to reduce response latency and long-haul network use for IoT applications. As a result, edge clouds for many applications (e.g. agriculture, ecology, and smart city deployments) must operate in remote, unattended, and environmentally harsh settings, introducing new challenges. One key challenge is heat exposure, which can degrade the performance, reliability, and longevity of electronics. For edge clouds, these problems are exacerbated because they increasingly perform complex workloads, such as machine learning, to affect data-driven actuation and control of devices and systems in the environment. The goal of ourmore »work is to protect edge clouds from overheating. To enable this, we develop a heat-budget-based scheduling system, called Sparta, which leverages dynamic voltage and frequency scaling (DVFS) to adaptively control CPU temperature. Sparta takes machine learning applications, datasets, and a temperature threshold as input. It sets the initial frequency of the CPU based on historical data and then dynamically updates it, according to the applications’ execution profile and ambient temperature, to safeguard edge devices. We find that for a suite of machine learning applications and deployment temperatures, Sparta is able to maintain CPU temperature below the threshold 94% of the time while facilitating improvements in execution time by 1.04x − 1.32x over competitive approaches.« less
    Free, publicly-accessible full text available December 1, 2022
  3. We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature. Recently, Hsu et al. (2019a) and Jiang et al. (2020) applied machine learning techniques in other data stream problems, using a trained oracle that can predict certain properties of the stream elements to improve on prior “classical” algorithms that did not use oracles. In this paper, we explore the power of a “heavy edge” oracle in multiple graph edge streaming models. In the adjacency list model, we present amore »one-pass triangle counting algorithm improving upon the previous space upper bounds without such an oracle. In the arbitrary order model, we present algorithms for both triangle and four cycle estimation with fewer passes and the same space complexity as in previous algorithms, and we show several of these bounds are optimal. We analyze our algorithms under several noise models, showing that the algorithms perform well even when the oracle errs. Our methodology expands upon prior work on “classical” streaming algorithms, as previous multi-pass and random order streaming algorithms can be seen as special cases of our algorithms, where the first pass or random order was used to implement the heavy edge oracle. Lastly, our experiments demonstrate advantages of the proposed method compared to state-of-the-art streaming algorithms.« less
    Free, publicly-accessible full text available January 1, 2023
  4. Free, publicly-accessible full text available September 1, 2022
  5. Serverless computing is an emerging event-driven programming model that accelerates the development and deployment of scalable web services on cloud computing systems. Though widely integrated with the public cloud, serverless computing use is nascent for edge-based, IoT deployments. In this work, we design and develop STOIC (Serverless TeleOperable HybrId Cloud), an IoT application deployment and offloading system that extends the serverless model in three ways. First, STOIC adopts a dynamic feedback control mechanism to precisely predict latency and dispatch workloads uniformly across edge and cloud systems using a distributed serverless framework. Second, STOIC leverages hardware acceleration (e.g. GPU resources) formore »serverless function execution when available from the underlying cloud system. Third, STOIC can be configured in multiple ways to overcome deployment variability associated with public cloud use. Finally, we empirically evaluate STOIC using real-world machine learning applications and multi-tier IoT deployments (edge and cloud). We show that STOIC can be used for training image processing workloads (for object recognition) – once thought too resource intensive for edge deployments. We find that STOIC reduces overall execution time (response latency) and achieves placement accuracy that ranges from 92% to 97%.« less