Enabling Real-Time Communications in Multi-Agent Systems: A Graph Neural Network Based Approach
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We use a reduced order model (ROM) for Rayleigh waveforms sourced by large atmospheric explosions along with data collected from a single seismic station (TLG) to estimate the yields of three atmospheric nuclear tests with order-megaton (MT) yields. These historical tests were conducted by China between 1973 and 1980 (CHIC 15, CHIC 16, and CHIC 26). We use our ROM to estimate yields of 2.34 MT (CHIC 15), 0.51 MT (CHIC 16), and 1.15 (CHIC 26) in the 20.5 s period band that are consistent with publicly accepted values of 3 MT (CHIC 15), 0.50 MT (CHIC 16), and 0.730 MT (CHIC 26).
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Historically, neuroscience principles have heavily influenced artificial intelligence (AI), for example the influence of the perceptron model, essentially a simple model of a biological neuron, on artificial neural networks. More recently, notable recent AI advances, for example the growing popularity of reinforcement learning, often appear more aligned with cognitive neuroscience or psychology, focusing on function at a relatively abstract level. At the same time, neuroscience stands poised to enter a new era of large-scale high-resolution data and appears more focused on underlying neural mechanisms or architectures that can, at times, seem rather removed from functional descriptions. While this might seem to foretell a new generation of AI approaches arising from a deeper exploration of neuroscience specifically for AI, the most direct path for achieving this is unclear. Here we discuss cultural differences between the two fields, including divergent priorities that should be considered when leveraging modern-day neuroscience for AI. For example, the two fields feed two very different applications that at times require potentially conflicting perspectives. We highlight small but significant cultural shifts that we feel would greatly facilitate increased synergy between the two fields.
Due to the difficulty of decentralized inference with conditional dependent observations, and motivated by large-scale heterogeneous networks, we formulate a framework for decentralized detection with coupled observations. Each agent has a state, and the empirical distribution of all agents’ states or the type of network dictates the individual agents’ behavior. In particular, agents’ observations depend on both the underlying hypothesis as well as the empirical distribution of the agents’ states. Hence, our framework captures a high degree of coupling, in that an individual agent’s behavior depends on both the underlying hypothesis and the behavior of all other agents in the network. Considering this framework, the method of types, and a series of equicontinuity arguments, we derive the error exponent for the case in which all agents are identical and show that this error exponent depends on only a single empirical distribution. The analysis is extended to the multi-class case, and numerical results with state-dependent agent signaling and state-dependent channels highlight the utility of the proposed framework for analysis of highly coupled environments.
A pervasive challenge in noble gas geochemistry is to ensure that analytical techniques do not modify the composition of the noble gases in the samples. Noble gases are present in the atmosphere and are used in a number of manufacturing procedures and by laboratory equipment. Of particular concern is the introduction of atmospheric or laboratory noble gases to samples during preparation before samples are placed in a vacuum chamber for analysis. Recent work has shown the potential for contamination of crushed samples with air-derived He that is not released by placing the samples under vacuum at room temperature. Using pure He gas as a tracer, we show that the act of crushing samples to a fine powder itself can introduce He contamination but that this is easily avoided by crushing under liquid or in an inert atmosphere. Because the He is trapped during crushing, the same concern does not extend to samples that are naturally fine-grained when collected. We also show model results that demonstrate when this effect might significantly impact samples for (U–Th) / He geochronology or 3 He cosmogenic nuclide dating. The degree of He contamination from crushing samples to sizes smaller than the > 63 µm range typically used for geochronology is insignificant for samples with a date of at least 1 Ma and 1 ppm U, and the degree of He contamination from crushing samples to sizes smaller than the 100–500 µm range typically used for cosmogenic nuclide dating is also insignificant for samples with a date of at least 10 ka with typical 3 He production rates.
Measurements of cosmic-ray-produced beryllium-10, neon-21, and helium-3 in quartz in a soil profile from a forested landscape in the Oregon Coast Range show that the cosmogenic noble gases 21 Ne and 3 He are depleted relative to 10Be in the shallow subsurface. The noble gases are mobile in mineral grains via thermally activated diffusion and 10 Be is not, implying that noble gas depletion is the result of surface heating by wildfires and subsequent mixing of partially degassed quartz downward into the soil. Cosmogenic noble gas depletion by wildfire heating of soils is a potential means of estimating wildfire intensity and/or frequency over pre-observational timescales.
Abstract. Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. The model performed well on Alaska's Seward Peninsula where it was trained and at Arctic evaluation sites (RMSE ≤ 0.15 m). It performed poorly at temperate sites with deeper snowpacks, partially due to training data limitations. Small temperature sensors are cheap and easy to deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring at high latitudes to an extent previously infeasible.
Abstract. In ice-rich polygonal tundra, spatiotemporal heterogeneity in ground-ice melt reshapes topography, impacting infrastructure, water and carbon cycles. This study evaluates changes in topography and subsurface structure at a coastal Arctic site by comparing data from two surveys conducted a decade apart. Each survey includes electrical resistivity tomography, active layer thickness, photogrammetry, and topographic data. Results reveal subsidence and decrease in permafrost table elevation with varying intensity and spatial distribution across polygons, alongside diverse thermal-hydrological responses, such as thermokarst pool formation in high-centered-polygons and more even subsidence in flat-centered-polygons. The study also underscores the value and limitations of sporadic surveys.
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