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Efficient computation using spatial-photonic Ising machines with low-rank and circulant matrix constraints
Spatial-photonic Ising machines (SPIMs) have shown promise as an energy-efficient Ising machine, but currently can only solve a limited set of Ising problems. There is currently limited understanding on what experimental constraints may impact the performance of SPIM, and what computationally intensive problems can be efficiently solved by SPIM. Our results indicate that the performance of SPIMs is critically affected by the rank and precision of the coupling matrices. By developing and assessing advanced decomposition techniques, we expand the range of problems SPIMs can solve, overcoming the limitations of traditional Mattis-type matrices. Our approach accommodates a diverse array of coupling matrices, including those with inherently low ranks, applicable to complex NP-complete problems. We explore the practical benefits of the low-rank approximation in optimisation tasks, particularly in financial optimisation, to demonstrate the real-world applications of SPIMs. Finally, we evaluate the computational limitations imposed by SPIM hardware precision and suggest strategies to optimise the performance of these systems within these constraints.
Human-structure and human-structure-human interaction in electro-quasistatic regime
Augmented living equipped with electronic devices requires widespread connectivity and a low-loss communication medium for humans to interact with ambient technologies. However, traditional radiative radio frequency-based communications require wireless pairing to ensure specificity during information exchange, and with their broadcasting nature, these incur energy absorption from the surroundings. Recent advancements in electroquasistatic body-coupled communication have shown great promise by utilizing conductive objects like the human body as a communication medium. Here we propose a fundamental set of modalities of non-radiative interaction by guiding electroquasistatic signals through conductive structures between humans and surrounding electronic devices. Our approach offers pairing-free communication specificity and lower path loss during touch. Here, we propose two modalities: Human-Structure Interaction and Human-Structure Human Interaction with wearable devices. We validate our theoretical understanding with numerical electromagnetic simulations and experiments to show the feasibility of the proposed approach. A demonstration of the real-time transfer of an audio signal employing an human body communications-based Human-Structure Interaction link is presented to highlight the practical impact of this work. The proposed techniques can potentially influence Human-Machine Interaction research, including the development of assistive technology for augmented living and personalized healthcare.
A 50-spin surface acoustic wave Ising machine
Time-multiplexed spinwave Ising Machines have unveiled a route towards miniaturized and low-cost combinatorial optimization solvers but are constrained in the number of spins by nonlinear spinwave dispersion. In contrast, surface acoustic waves offer an intrinsically linear dispersion and high thermal stability. Here, we demonstrate an all-to-all, fully programmable, 50-spin Ising machine using a surface acoustic wave delay line and off-the-shelf microwave components. Our device solves random 50-spin MAX-CUT problems with a single run compute time of 10 ms and a figure of merit of 55 solutions s−1 W−1 reaching success probability of 84% for 99%-accurate solutions on 0.5-density matrices. Moreover, it demonstrates 4–5 orders of magnitude better thermal stability than optical Coherent Ising Machines while having similar scalability potential. Our results illustrate the general merits of wave-based time-multiplexed Ising machines operating in the microwave domain as compact, energy-efficient, and high-performance platforms for commercially feasible combinatorial optimization solvers.
DeepPrep: an accelerated, scalable and robust pipeline for neuroimaging preprocessing empowered by deep learning
Neuroimaging has entered the era of big data. However, the advancement of preprocessing pipelines falls behind the rapid expansion of data volume, causing substantial computational challenges. Here we present DeepPrep, a pipeline empowered by deep learning and a workflow manager. Evaluated on over 55,000 scans, DeepPrep demonstrates tenfold acceleration, scalability and robustness compared to the state-of-the-art pipeline, thereby meeting the scalability requirements of neuroimaging.
C(sp3)–heteroatom bond formation by iron-catalyzed soft couplings
Carbon–heteroatom bonds are of great importance due to their prevalence in pharmaceuticals, agrochemicals, materials, and natural products. Despite the effective use of metal-catalyzed cross-coupling reactions between sp2-hybridized organohalides and soft heteroatomic nucleophiles for carbon–heteroatom bond formation, the use of sp3-hybridized organohalides remain limited and the coupling with thiols remains elusive. Here, we report the coupling of sp3-hybridized benzyl or tertiary halides with soft thiol nucleophiles catalyzed by iron and extend the utility to alcohol and amine nucleophiles. The reaction is broad in substrate scope for both coupling partners and applicable in the construction of congested tri- and tetrasubstituted carbon centers as well as β-quaternary heteroatomic products. The synthetic utility is further emphasized by gram-scale synthesis and rapid herbicide library synthesis. Overall, we provide an efficient method to prepare pharmaceutically and materially relevant carbon–heteroatom bonds by expanding iron-catalyzed cross-coupling reactions to the coupling of sp3-hybridized organohalides with soft nucleophiles.
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