Finding hidden order within disorder is a common interest in material science, wave physics, and mathematics. The Riemann hypothesis, stating the locations of nontrivial zeros of the Riemann zeta function, tentatively...
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Finding hidden order within disorder is a common interest in material science, wave physics, and mathematics. The Riemann hypothesis, stating the locations of nontrivial zeros of the Riemann zeta function, tentatively characterizes statistical order in the seemingly random distribution of prime numbers. This famous conjecture has inspired various connections with different branches of physics, recently with non-Hermitian physics, quantum field theory, trapped-ion qubits, and hyperuniformity. Here we develop the computing platform for the Riemann zeta function by employing classical scattering of light. We show that the Riemann hypothesis suggests the landscape of semi-infinite optical scatterers for the perfect reflectionless condition under the Born approximation. To examine the validity of the scattering-based computation, we investigate the asymptotic behaviors of suppressed reflections with the increasing number of scatterers and the emergence of multiple scattering. The result provides another bridge between classical physics and the Riemann zeros, exhibiting the design of wave devices inspired by number theory.
The COVID-19 pandemic has profoundly affected various facets of society. This research explores the discourse on social media, specifically "X"(Twitter), to understand public sentiment and needs regarding he...
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Smart grids are at risk of cyber-attacks due to more connected devices being introduced. Different kinds of attacks may incur different consequences in the operation of smart grids. Attackers have identified different...
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Autonomous driving has been significantly advanced in todays society, which revolutionized daily routines and facilitated the development of the Internet of Vehicles (IoV). A crucial aspect of this system is understan...
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Autonomous driving has been significantly advanced in todays society, which revolutionized daily routines and facilitated the development of the Internet of Vehicles (IoV). A crucial aspect of this system is understanding traffic density to enable intelligent traffic management. With the rapid improvement in deep neural networks (DNNs), the accuracy of density estimation has markedly improved. However, there are two main issues that remain unsolved. Firstly, current DNN-based models are excessively heavy, characterized by an overwhelming number of training parameters (millions or even billions) and substantial computational complexity, indicated by a high number of FLOPs. These requirements for storage and computation severely limit the practical application of these models, especially on edge devices with limited capacity and computational power. Secondly, despite the superior performance of DNN models, their effectiveness largely depends on the availability of large-scale data for training. Growing privacy concerns have made individuals increasingly hesitant to allow their data to be publicly used for model training, particularly in vehicle-related applications that might reveal personal movements, which leads to data isolation issues. In this paper, we address these two problems at once with a systematic framework. Specifically, we introduce the Proxy Model Distributed Learning (PMDL) model for traffic density estimation. PMDL model is composed of two main components. First, we introduce a proxy model learning strategy that transfers fine-grained knowledge from a larger master model to a lightweight proxy model, i.e., a proxy model. Second, we design a distributed learning strategy that trains multiple proxy models with privacy-aware local data and seamlessly aggregates these models via a global parameter server. This ensures privacy protection while significantly improving estimation performance compared to training models with limited, isolated data. We tested
Programmable photonic circuits (PPCs) have garnered substantial interest for their potential in facilitating deep learning accelerations and universal quantum computations. Although photonic computation using PPCs off...
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Programmable photonic circuits (PPCs) have garnered substantial interest for their potential in facilitating deep learning accelerations and universal quantum computations. Although photonic computation using PPCs offers ultrafast operation, energy-efficient matrix calculations, and room-temperature quantum states, its poor scalability hinders integration. This challenge arises from the temporally one-shot operation of propagating light in conventional PPCs, resulting in a light-speed increase in device footprints. Here we propose the concept of programmable photonic time circuits, utilizing time-cycle-based computations analogous to gate cycling in the von Neumann architecture and quantum computation. Our building block is a reconfigurable SU(2) time gate, consisting of two resonators with tunable resonances, and coupled via time-coded dual-channel gauge fields. We demonstrate universal U(N) operations with high fidelity using an assembly of the SU(2) time gates, substantially improving scalability from O(N2) to O(N) in terms of both the footprint and the number of gates. This result paves the way for PPC implementation in very large-scale integration.
In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However...
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A Network Slice (NS) is a set of network resources deployed to deliver premium service to a group of users as described in a Service Level Agreement (SLA). We consider that premium service entails per user Quality of ...
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We propose dynamic modulations of a photonic molecule to achieve topological properties of light. We investigate the Hall transport in synthetic dimensions and the system modulation strategy to demonstrate the pumping...
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Power electronic switching devices and pulse width modulation (PWM) not only improves the performance of motor drive systems, but also brings about common-mode voltage (CMV) issues, that challenging the normal operati...
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We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resource...
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