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Federated Learning (FL) trains a shared model using data and computation power on distributed agents coordinated by a central server. Decentralized FL (DFL) utilizes local model exchange and aggregation between agents to reduce the communication and computation overheads on the central server. However, when agents are mobile, the communication opportunity between agents can be sporadic, largely hindering the convergence and accuracy of DFL. In this paper, we propose Cached Decentralized Federated Learning (Cached-DFL) to investigate delay-tolerant model spreading and aggregation enabled by model caching on mobile agents. Each agent stores not only its own model, but also models of agents encountered in the recent past. When two agents meet, they exchange their own models as well as the cached models. Local model aggregation utilizes all models stored in the cache. We theoretically analyze the convergence of Cached-DFL,explicitly taking into account the model staleness introduced by caching. We design and compare different model caching algorithms for different DFL and mobility scenarios. We conduct detailed case studies in a vehicular network to systematically investigate the interplay between agent mobility, cache staleness, and model convergence. In our experiments, Cached-DFL converges quickly, and significantly outperforms DFL without caching. 

Federated Learning (FL) aims to train a shared model using data and computation power on distributed agents coordinated by a central server. Decentralized FL (DFL) utilizes local model exchange and aggregation between agents to reduce the communication and computation overheads on the central server. However, when agents are mobile, the communication opportunity between agents can be sporadic, largely hindering the convergence and accuracy of DFL. In this paper, we study delay-tolerant model spreading and aggregation enabled by model caching on mobile agents. Each agent stores not only its own model, but also models of agents encountered in the recent past. When two agents meet, they exchange their own models as well as the cached models. Local model aggregation works on all models in the cache. We theoretically analyze the convergence of DFL with cached models, explicitly taking into account the model staleness introduced by caching. We design and compare different model caching algorithms for different DFL and mobility scenarios. We conduct detailed case studies in a vehicular network to systematically investigate the interplay between agent mobility, cache staleness, and model convergence. In our experiments, cached DFL converges quickly, and significantly outperforms DFL without caching. 

Through laser-heated diamond anvil cell experiments, we synthesize a series of rubidium superhydrides and explore their properties with synchrotron x-ray powder diffraction and Raman spectroscopy measurements, combined with density functional theory calculations. Upon heating rubidium monohydride embedded in ${\mathrm{H}}_2$at a pressure of 18 GPa, we form ${\mathrm{RbH}}_9\text{−}\mathrm{I}$, which is stable upon decompression down to 8.7 GPa, the lowest stability pressure of any known superhydride. At 22 GPa, another polymorph, ${\mathrm{RbH}}_9\text{−}\mathrm{II}$is synthesised at high temperature. Unique to the Rb-H system among binary metal hydrides is that further compression does not promote the formation of polyhydrides with higher hydrogen content. Instead, heating above 87 GPa yields ${\mathrm{RbH}}_5$, which exhibits two polymorphs ( ${\mathrm{RbH}}_5\text{−}\mathrm{I}$and ${\mathrm{RbH}}_5\text{−}\mathrm{II}$). All of the crystal structures comprise a complex network of quasimolecular ${\mathrm{H}}_2$units and ${\mathrm{H}}^{-}$anions, with ${\mathrm{RbH}}_5$providing the first experimental evidence of linear ${\mathrm{H}}_3^{-}$anions. Published by the American Physical Society2025 

Abstract The X₂MH₆family, consisting of an electropositive cation Xⁿ⁺and a main group metal M octahedrally coordinated by hydrogen, have been identified as promising templates for high‐temperature conventional superconductivity. Herein, we analyze the electronic structure of two members of this family, Mg₂IrH₆and Ca₂IrH₆, showing why the former may possess superconducting properties rivaling those of the cuprates, whereas the latter does not. Within Mg₂IrH₆the vibrations of the anions IrH₆⁴⁻anions are key for the superconducting mechanism, and they induce coupling in the set of orbitals, which are antibonding between the H 1sand the Ir or orbitals. Because calcium possesses low‐lyingd‐orbitals, →Cadback‐donation is preferred, quenching the superconductivity. Our analysis explains why high critical temperatures were only predicted for second or third row X metal atoms, and may provide rules for identifying likely high‐temperature superconductors in other systems where the antibonding anionic states are filled. 

Abstract Cardiac microtissues provide a promising platform for disease modeling and developmental studies, which require the close monitoring of the multimodal excitation-contraction dynamics. However, no existing assessing tool can track these multimodal dynamics across the live tissue. We develop a tissue-like mesh bioelectronic system to track these multimodal dynamics. The mesh system has tissue-level softness and cell-level dimensions to enable stable embedment in the tissue. It is integrated with an array of graphene sensors, which uniquely converges both bioelectrical and biomechanical sensing functionalities in one device. The system achieves stable tracking of the excitation-contraction dynamics across the tissue and throughout the developmental process, offering comprehensive assessments for tissue maturation, drug effects, and disease modeling. It holds the promise to provide more accurate quantification of the functional, developmental, and pathophysiological states in cardiac tissues, creating an instrumental tool for improving tissue engineering and studies.