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Self-Assembled Magnetic Nanoparticle Layers: Structural Control for Reconfigurable Magnetism and Functional Applications
We advance soft X-ray vector ptychographic tomography to map the 3D magnetization field in self-assembled superparamagnetic nanoparticles at a liquid–liquid interface, revealing how layered structures influence magnetic ordering. We observe that monolayers with low coordination numbers exhibit weak magnetic order, with magnetic vortices disrupting spin alignment. In contrast, bilayers and trilayers with higher coordination numbers display long-range magnetic order with strong spin correlations across larger distances and a suppression of magnetic vortices. We further quantify the average distance for vortex–antivortex pairs as 26.0 ± 2.0 nm, while vortex–vortex and antivortex–antivortex pairs exhibit larger separations, averaging 44.9 ± 5.2 and 54.1 ± 7.4 nm, respectively. These experimental results are supported by micromagnetic Monte Carlo simulations. Our findings illustrate how layered structures enhance magnetic order and spin correlation in superparamagnetic nanoparticle assemblies, providing a promising approach for tuning magnetic properties in applications such as data storage, microrobotics, and biomedicine.
Scalable photonic-phonoinc integrated circuitry for reconfigurable signal processing
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All-optical reconfiguration of single silicon-vacancy centers in diamond for non-volatile memories
Strain engineering is vital for tuning the optical and spin properties of solid-state color centers, enhancing spin coherence and compensating emission wavelength shift. Here, we develop an all-optical approach to directly modify the local strain of color centers at the nanoscale by migrating the nearby defect. High-power pulsed optical irradiation triggers defect migration, which subsequently leads to the redistribution of the local crystal lattice of the host material. This redistribution alters the strain experienced by nearby color centers. Using silicon-vacancy centers in diamond, we validate this method and demonstrate a ground state splitting enhancement of up to 1.8 THz. Unlike conventional methods, our approach requires no external fields or nanostructure modifications, enabling non-volatile strain control and optical memory functionality across wide temperature ranges. Its local, permanent nature offers a scalable path for enhancing spin coherence in large-scale quantum systems and has potential applications in photonic machine learning.
Reconfigurable Network Slicing Orchestration in Network Function Virtualization Compatible Operational Technology Environment
The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications’ requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.
Safe Reinforcement Learning-Based Transient Stability Control for Islanded Microgrids With Topology Reconfiguration
This paper proposes a safe reinforcement learning (RL)-based transient stability emergency control (TSEC) method for islanded microgrids. RL requires extensive interaction with the environment to learn control strategies, hence, a data-driven approach is used as a substitute for time-consuming time-domain simulation calculations. Deep sigma point processes (DSPP), which is a Gaussian process model, is utilized to predict the normal distribution of transient stability of microgrids and to construct a transient stability chance constraint. Reward-constrained policy optimization (RCPO) can simultaneously achieve objective prediction, policy learning, and constraint cost coefficient update across multiple timescales. RCPO interacts with the DSPP-based microgrid environment through a multi-process parallel manner, greatly increasing the training speed. Case studies on a real islanded microgrid demonstrate that the proposed method can efficiently and quickly obtain the optimal emergency control strategy while adhering to all hard constraints.
SPARTAN (Scalable Probabilistic Application Reconfigurable Tensor Autonomous Network)
The technical founder of Ludwig Computing Inc has been competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Ludwig Computing Inc, supported by the U.S. Department of Energy's Advanced Manufacturing Office through the Cyclotron Road program, has investigated the advantages of probabilistic computing for real-world compute-intensive applications. This research adds to the understanding of alternative computing paradigms by exploring a unique hardware-software co-design that integrates quantum computing methods with nature-inspired problem-solving techniques. The project's focus on areas such as combinatorial optimization, graph analytics, and machine learning demonstrates the potential for significant advancements in computational efficiency and performance. By harnessing natural randomness to streamline large circuits into fewer devices, Ludwig's approach enables massive parallelism, potentially offering higher throughput, speed, and energy efficiency compared to conventional hardware solutions. This work benefits the public by paving the way for more efficient computing solutions that could address complex real-world problems while potentially reducing energy consumption in data-intensive industries.
Neural-Inspired Dendritic Multiplication Using a Reconfigurable Analog Integrated Circuit
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High-speed electrical reconfigurability of liquid-crystal semiconductor metasurfaces at visible wavelengths
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Dynamically reconfigurable topological routing in nonlinear photonic systems
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Reconfigurable Optical Response of Colloidal Discoid Liquid Crystals
Presentation at APS annual meeting
Dynamically reconfigurable topological routing in nonlinear photonic systems
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On thermodynamics of reconfigurable architected materials
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Magnetic Liquid Metal Foam Emulsions for Reconfigurable and Printable Soft Electronics
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Electrochemical Transformation of Phases on Vanadium Oxide for Reconfigurable Computing
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hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.
All-optical reconfigurable chiral meta-molecules
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