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Heterogeneity in computing systems is clearly increasing, especially as “accelerators” burrow deeper and deeper into different parts of an architecture. What is new, however, is a rapid change in not only the number of such heterogeneous processors, but in their connectivity to other structures, such as cores with different ISAs or smart memory interfaces. Technologies such as chiplets are accelerating this trend. This paper is focused on the problem of how to architect efficient systems that combine multiple heterogeneous concurrent threads, especially when the underlying heterogeneous cores are separated by networks or have no shared-memory access paths. The goal is to eliminate today’s need to invoke significant software stacks to cross any of these boundaries. A suggestion is made of using migrating threads as the glue. Two experiments are described: using a heterogeneous platform where all threads share the same memory to solve a rich ML problem, and a fast PageRank approximation that mirrors the kind of computation for which thread migration may be useful. Architectural “lessons learned” are developed that should help guide future development of such systems.} 

We developed a multi-frequency, multi-Global Navigation Satellite System (GNSS) positioning instrument optimized for autonomous applications in the cryosphere. At lower power requirements and a fraction of the cost and weight compared to commercially available options, this instrument simplifies field usage and associated logistics. In this paper, we assess several baseline aspects of performance in a polar environment relative to geodetic receivers commonly used for glaciological applications. Evaluations of precision and relative accuracy of positioning show millimeter to centimeter-level (‘geodetic-grade’) quality of this instrument, making it a competitive alternative for GNSS glaciological and geophysical applications such as monitoring surface elevation change and ice flow. An array of these instruments, tested in the field on the Greenland Ice Sheet, also demonstrated robustness throughout the polar winter and met power and reliability requirements.

 

The past six decades have seen major advances in our understanding of endocytosis, ranging from descriptive studies based on electron microscopy to biochemical and genetic characterization of factors required for vesicle formation. Most studies focus on clathrin as the major coat protein; indeed, clathrin-mediated endocytosis (CME) is the primary pathway for internalization. Clathrin-independent (CIE) pathways also exist, although mechanistic understanding of these pathways remains comparatively elusive. Here, we discuss how early studies of CME shaped our understanding of endocytosis and describe recent advances in CIE, including pathways in model organisms that are poised to provide key insights into endocytic regulation.

 

Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here, we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for the first time attitudinal, intrinsic, rewarded, and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 real data disclosures. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win–win for all: remarkable privacy recovery for people with evident costs reduction for service providers.