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Nonreciprocal superconducting devices have attracted growing interest in recent years as they potentially enable directional charge transport for applications in superconducting quantum circuits. Specifically, the superconducting diode effect has been explored in two-terminal devices that exhibit superconducting transport in one current direction while showing dissipative transport in the opposite direction. Here, we exploit multiterminal Josephson junctions (MTJJs) to engineer magnetic-field-free nonreciprocity in multiport networks. We show that when treated as a two-port electrical network, a three terminal Josephson junction (JJ) with an asymmetric graphene region exhibits reconfigurable two-port nonreciprocity. We observe nonreciprocal (reciprocal) transport between superconducting terminals with broken (preserved) spatial mirror symmetry. We explain our observations by considering a circuit network of JJs with different critical currents. 

Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.

 

Charging processes are the key to promoting electric taxis and improving their operational efficiency due to frequent charging activities and long charging time. Nevertheless, optimizing charging resource allocation in real time is extremely challenging because of uneven charging demand/supply distributions, heuristic-based charging behaviors of drivers, and city-scale of the fleets. The existing solutions have utilized real-time contextual information for charging recommendation, but they do not consider the much-richer fleet information, leading to the suboptimal individual-based charging recommendation. In this paper, we design a data-driven fleet-oriented charging recommendation system for charging resource allocation called ForETaxi for electric taxis , which aims to minimize the overall charging overhead for the entire fleet, instead of individual vehicles. ForETaxi considers not only current charging requests but also possible charging requests of other nearby electric taxis in the near future by inferring their status in real time. More importantly, we implement ForETaxi with multiple types of sensor data from the Chinese Shenzhen city including GPS data, and taxi transaction data from more than 13,000 electric taxis, combined with road network data and charging station data. The data-driven evaluation results show that compared to the state-of-the-art individual-based recommendation methods, our fleet-oriented ForETaxi outperforms them by 16% in the total charging time reduction and 82% in the queuing time reduction. 

Two-dimensional (2D) materials have drawn immense interests in scientific and technological communities, owing to their extraordinary properties and their tunability by gating, proximity, strain and external fields. For electronic applications, an ideal 2D material would have high mobility, air stability, sizable band gap, and be compatible with large scale synthesis. Here we demonstrate air stable field effect transistors using atomically thin few-layer PdSe₂sheets that are sandwiched between hexagonal BN (hBN), with large saturation current > 350 μA/μm, and high field effect mobilities of ~ 700 and 10,000 cm²/Vs at 300 K and 2 K, respectively. At low temperatures, magnetotransport studies reveal unique octets in quantum oscillations that persist at all densities, arising from 2-fold spin and 4-fold valley degeneracies, which can be broken by in-plane and out-of-plane magnetic fields toward quantum Hall spin and orbital ferromagnetism.

 

In genomic analysis, the major computation bottle- neck is the memory- and compute-intensive DNA short reads alignment due to memory-wall challenge. This work presents the first Resistive RAM (RRAM) based Compute-in-Memory (CIM) macro design for accelerating state-of-the-art BWT based genome sequencing alignment. Our design could support all the core instructions, i.e., XNOR based match, count, and addition, required by alignment algorithm. The proposed CIM macro implemented in integration of HfO2 RRAM and 65nm CMOS demonstrates the best energy efficiency to date with 2.07 TOPS/W and 2.12G suffixes/J at 1.0V. 

Channel decoders are key computing modules in wired/wireless communication systems. Recently neural network (NN)-based decoders have shown their promising error-correcting performance because of their end-to-end learning capability. However, compared with the traditional approaches, the emerging neural belief propagation (NBP) solution suffers higher storage and computational complexity, limiting its hardware performance. To address this challenge and develop a channel decoder that can achieve high decoding performance and hardware performance simultaneously, in this paper we take a first step towards exploring SRAM-based in-memory computing for efficient NBP channel decoding. We first analyze the unique sparsity pattern in the NBP processing, and then propose an efficient and fully Digital Sparse In-Memory Matrix vector Multiplier (DSPIMM) computing platform. Extensive experiments demonstrate that our proposed DSPIMM achieves significantly higher energy efficiency and throughput than the state-of-the-art counterparts. 

Due to the often limited communication bandwidth of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training at each communication round. Compared with engaging all the available clients, such a random-selection mechanism could lead to significant performance degradation on non-IID (independent and identically distributed) data. In this paper, we present our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from randomly selected clients. Based on this observation, we design an efficient heterogeneity-aware client sampling mechanism, namely, Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the grouped dataset from the intentionally selected clients. We first propose a measure of class-imbalance which can be derived in a privacy-preserving way. Based on this measure, we design a computationefficient client sampling strategy such that the actively selected clients will generate a more classbalanced grouped dataset with theoretical guarantees. Experimental results show that Fed-CBS outperforms the status quo approaches in terms of test accuracy and the rate of convergence while achieving comparable or even better performance than the ideal setting where all the available clients participate in the FL training.