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Reconfigurable Unpatterned Metasurfaces via Acoustoelectric Gating of Graphene.
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Risk-Informed Decision-Making and Reconfiguration using OPRA
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Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware
This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the n oise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.
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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Assessment of a reconfiguration of the InterSpread Plus US national FMD model as a potential tool to analyze a foot-and-mouth disease outbreak on a single large cattle feedlot in the United States
An incursion of foot-and-mouth disease (FMD) into the United States remains a concern of high importance and would have devastating socioeconomic impacts to the livestock and associated industries. This highly transmissible and infectious disease poses continual risk for introduction into the United States (US), due to the legal and illegal global movement of people, animals, and animal products. While stamping out has been shown to effectively control FMD, depopulation of large cattle feedlots (>50,000 head) presents a number of challenges for responders due to the resources required to depopulate and dispose of large numbers of animals in a timely and effective manner. However, evaluating alternative strategies for FMD control on large feedlots requires a detailed within-farm modeling approach, which can account for the unique structure of these operations. To address this, we developed a single feedlot, within-farm spread model using a novel configuration within the InterSpread Plus (ISP) framework. As proof of concept we designed six scenarios: (i) depopulation - the complete depopulation of the feedlot, (ii) burn-through – a managed “burn-through” where the virus is allowed to spread through the feedlot and only movement restriction and biosecurity are implemented, (iii) firebreak-NV – targeted depopulation of infected pens and adjacent pens without vaccination; (iv) firebreak - targeted depopulation of infected pens and adjacent pens with vaccination of remaining pens; (v) harvest-NV - selective harvest of pens where a 100% movement restriction is applied for 28-30 days, then pens are set for selection to be sent to slaughter, while allowing a controlled “burn-through” without vaccination; and (vi) harvest - selective harvest of pens with vaccination. Overall, the burn-through scenario (ii) had the shortest epidemic duration (31d (30, 33)) median (25th, 75th percentiles), while the firebreak scenario (iv) had the longest (47d (38,55)). Additionally, we found that scenarios implementing depopulation delayed the peak day of infection and reduced the total number of pens infected compared to non-depopulation scenarios.
Dynamic Reconfiguration of Switchgrass Proteomes in Response to Rust ( Puccinia novopanici ) Infection
Switchgrass (Panicum virgatum L.) can be infected by the rust pathogen (Puccinia novopanici) and results in lowering biomass yields and quality. Label-free quantitative proteomics was conducted on leaf extracts harvested from non-infected and infected plants from a susceptible cultivar (Summer) at 7, 11, and 18 days after inoculation (DAI) to follow the progression of disease and evaluate any plant compensatory mechanisms to infection. Some pustules were evident at 7 DAI, and their numbers increased with time. However, fungal DNA loads did not appreciably change over the course of this experiment in the infected plants. In total, 3830 proteins were identified at 1% false discovery rate, with 3632 mapped to the switchgrass proteome and 198 proteins mapped to different Puccinia proteomes. Across all comparisons, 1825 differentially accumulated switchgrass proteins were identified and subjected to a STRING analysis using Arabidopsis (A. thaliana L.) orthologs to deduce switchgrass cellular pathways impacted by rust infection. Proteins associated with plastid functions and primary metabolism were diminished in infected Summer plants at all harvest dates, whereas proteins associated with immunity, chaperone functions, and phenylpropanoid biosynthesis were significantly enriched. At 18 DAI, 1105 and 151 proteins were significantly enriched or diminished, respectively. Many of the enriched proteins were associated with mitigation of cellular stress and defense.
Reconfiguring Plant Metabolism for Biodegradable Plastic Production
For decades, plants have been the subject of genetic engineering to synthesize novel, value-added compounds. Polyhydroxyalkanoates (PHAs), a large class of biodegradable biopolymers naturally synthesized in eubacteria, are among the novel products that have been introduced to make use of plant acetyl-CoA metabolic pathways. It was hoped that renewable PHA production would help address environmental issues associated with the accumulation of nondegradable plastic wastes. However, after three decades of effort synthesizing PHAs, and in particular the simplest form polyhydroxybutyrate (PHB), and seeking to improve their production in plants, it has proven very difficult to reach a commercially profitable rate in a normally growing plant. This seems to be due to the growth defects associated with PHA production and accumulation in plant cells. Here, we review major breakthroughs that have been made in plant-based PHA synthesis using traditional genetic engineering approaches and discuss challenges that have been encountered. Then, from the point of view of plant synthetic biology, we provide perspectives on reprograming plant acetyl-CoA pathways for PHA production, with the goal of maximizing PHA yield while minimizing growth inhibition. Specifically, we suggest genetic elements that can be considered in genetic circuit design, approaches for nuclear genome and plastome modification, and the use of multiomics and mathematical modeling in understanding and restructuring plant metabolic pathways.
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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