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As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices' communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy. 

In the current noisy intermediate-scale quantum (NISQ) Era, Quantum Computing faces significant challenges due to noise, which severely restricts the application of computing complex algorithms. Superconducting quantum chips, one of the pioneer quantum computation technologies, introduce additional noise when moving qubits to adjacent locations for operation on designated two-qubit gates. The current compilers rely on decision models that either count the swap gates or multiply the gate errors when choosing swap paths at the routing stage. Our research has unveiled the overlooked situations for error propagations through the circuit, leading to accumulations that may affect the final output. In this paper, we propose Error Propagation-Aware Routing (EPAR), designed to enhance the compilation performance by considering accumulated errors in routing. EPAR’s effectiveness is validated through benchmarks on a 27-qubit machine and two simulated systems with different topologies. The results indicate an average success rate improvement of 10% on both real and simulated heavy hex lattice topologies, along with a 16% enhancement in a mesh topology simulation. These findings underscore the potential of EPAR to advance quantum computing in the NISQ era substantially. 

Abstract The conduction efficiency of ions in excitable tissues and of charged species in organic conjugated materials both benefit from having ordered domains and anisotropic pathways. In this study, a photocurrent‐generating cardiac biointerface is presented, particularly for investigating the sensitivity of cardiomyocytes to geometrically comply to biomacromolecular cues differentially assembled on a conductive nanogrooved substrate. Through a polymeric surface‐templated approach, photoconductive substrates with symmetric peptide‐quaterthiophene (4T)‐peptide units assembled as 1D nanostructures on nanoimprinted polyalkylthiophene (P3HT) surface are developed. The 4T‐based peptides studied here can form 1D nanostructures on prepatterned polyalkylthiophene substrates, as directed by hydrogen bonding, aromatic interactions between 4T and P3HT, and physical confinement on the nanogrooves. It is observed that smaller 4T‐peptide units that can achieve a higher degree of assembly order within the polymeric templates serve as a more efficient driver of cardiac cytoskeletal anisotropy than merely presenting aligned ‐RGD bioadhesive epitopes on a nanotopographic surface. These results unravel some insights on how cardiomyocytes perceive submicrometer dimensionality, local molecular order, and characteristics of surface cues in their immediate environment. Overall, the work offers a cardiac patterning platform that presents the possibility of a gene modification‐free cardiac photostimulation approach while controlling the conduction directionality of the biotic and abiotic components. 

Abstract We construct Kähler–Einstein metrics with negative scalar curvature near an isolated log canonical (non-log terminal) singularity.Such metrics are complete near the singularity if the underlying space has complex dimension 2. We also establish a stability result for Kähler–Einstein metrics near certain types of isolated log canonical singularity. As application, for complex dimension 2 log canonical singularity, we show that any complete Kähler–Einstein metric of negative scalar curvature near an isolated log canonical (non-log terminal) singularity is smoothly asymptotically close to model Kähler–Einstein metrics from hyperbolic geometry.