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Numerical modeling of ice sheet motion and hence projections of global sea level rise require information about the evolving subglacial environment, which unfortunately remains largely unknown due to its difficulty of access. Here we advance such subglacial observations by reporting multi‐year observations of seismic tremor likely associated with glacier sliding at Helheim Glacier. This association is confirmed by correlation analysis between tremor power and multiple environmental forcings on different timescales. Variations of the observed tremor power indicate that different factors affect glacial sliding on different timescales. Effective pressure may control glacial sliding on long (seasonal/annual) timescales, while tidal forcing modulates the sliding rate and tremor power on short (hourly/daily) timescales. Polarization results suggest that the tremor source comes from an upstream subglacial ridge. This observation provides insights on how different factors should be included in ice sheet modeling and how their timescales of variability play an essential role.

 

We report a general and practical palladium-catalyzed intramolecular decarbonylative coupling of thioesters via C–S bond cleavage, decarbonylation and C–S bond reformation. This robust approach shows excellent functional group tolerance and broad substrate scope using a commercially available, cheap, and practical Pd(OAc) 2 catalyst and phosphine ligands. This strategy operates under base-free conditions. The catalytic system represents the simplest method for intramolecular decarbonylation of thioesters by palladium catalysis reported to date. This versatile protocol is readily performed on a gram scale and applied in late-stage drug derivatization. 

We report conversion of esters to thioesters via selective C–O bond cleavage/weak C–S bond formation under transition-metal-free conditions. The method is notable for a general and practical transition-metal-free system, broad substrate scope and excellent functional group tolerance. The strategy was successfully deployed in late-stage thioesterification, site-selective cross-coupling/thioesterification/decarbonylation and easy-to-handle gram scale thioesterification. Selectivity and computational studies were performed to gain insight into the formation of weak C–S bonds by C–O bond cleavage, which contrasts with the traditional trend of nucleophilic additions to carboxylic acid derivatives. 

The increasing interests in analog computing nowadays call for multipurpose analog computing platforms with reconfigurability. The advancement of analog computing, enabled by novel electronic elements like memristors, has shown its potential to sustain the exponential growth of computing demand in the new era of analog data deluge. Here, a platform of a memristive field‐programmable analog array (memFPAA) is experimentally demonstrated with memristive devices serving as a variety of core analog elements and CMOS components as peripheral circuits. The memFPAA is reconfigured to implement a first‐order band pass filter, an audio equalizer, and an acoustic mixed frequency classifier, as application examples. The memFPAA, featured with programmable analog memristors, memristive routing networks, and memristive vector‐matrix multipliers, opens opportunities for fast prototyping analog designs as well as efficient analog applications in signal processing and neuromorphic computing.

 

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.