Recent empirical work has shown that human children are adept at learning and reasoning with probabilities. Here, we model a recent experiment investigating the development of school-age children's non-symbolic pr...
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Compute In Memory (CIM) has gained significant attention in recent years due to its potential to overcome the memory bottleneck in Von-Neumann computing architectures. While most CIM architectures use non-volatile mem...
Compute In Memory (CIM) has gained significant attention in recent years due to its potential to overcome the memory bottleneck in Von-Neumann computing architectures. While most CIM architectures use non-volatile memory elements in a NOR-based configuration, NAND-based configuration, and in particular, 3D-NAND flash memories are attractive because of their potential in achieving ultra-high memory density and ultra-low cost per bit storage. Unfortunately, the standard multiply-and-accumulate (MAC) CIM-paradigm can not be directly applied to NAND-flash memories. In this paper, we report a NAND-Flash-based CIM architecture by combining the conventional 3D-NAND flash with a Margin-Propagation (MP) based approximate computing technique. We show its application for implementing matrix-vector multipliers (MVMs) that do not require analog-to-digital converters (ADCs) for read-out. Using simulation results we show that this approach has the potential to provide a 100 x improvement in compute density, read speed, and computation efficiency compared to the current state-of-the-art.
False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid compone...
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False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5−100% and 99 − 100%, respectively, presenting improvements of 5 − 30% compared to benchmarks.
We present a deep-learning method based on Wiener filters and U-Nets that performs image reconstruction in systems with spatially-varying aberrations. We train on simulated microscopy measurements and test on experime...
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The increasing integration of Internet of Things (IoT) devices in Wireless Local Area Networks (WLANs) necessitates robust and efficient authentication mechanisms. While existing IoT authentication protocols address c...
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Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitati...
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Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative
Distinct selectivity to the spin angular momenta of photons has garnered significant attention in recent years, for its relevance in basic science and for imaging and sensing applications. While nonlocal metasurfaces ...
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Distinct selectivity to the spin angular momenta of photons has garnered significant attention in recent years, for its relevance in basic science and for imaging and sensing applications. While nonlocal metasurfaces with strong chiral responses to the incident light have been reported, these responses are typically limited to a narrow range of incident angles. In this study, we demonstrate a nonlocal metasurface that showcases strong chirality, characterized by circular dichroism (∼0.6), over a wide range of incident angles ±5°. Its quality factor, circular dichroism and resonant frequency can be optimized by design. These findings pave the way to further advance the development of valley-selective optical cavities and augmented reality applications.
In this work we evaluated the performance of a camera-based rigid body motion correction solution using a 2D checkerboard marker and a 3D encoded marker. The context of the results presented is in PET/MR imaging, but ...
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