Power-quality standards provide limited guidance on frequency quality for short time scales, such as less than one hour. Capturing frequency variations and events requires high time resolutions, e.g., 0.1 seconds or l...
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Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for...
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Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.
A pure state of fixed Hamming weight is a superposition of computational basis states such that each bitstring in the superposition has the same number of ones. Given a Hilbert space of the form H=(C2)⊗n, or an n-qubi...
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A pure state of fixed Hamming weight is a superposition of computational basis states such that each bitstring in the superposition has the same number of ones. Given a Hilbert space of the form H=(C2)⊗n, or an n-qubit system, the identity operator can be decomposed as a sum of projectors onto subspaces of fixed Hamming weight. In this work, we propose several quantum algorithms that realize a coherent Hamming weight projective measurement on an input pure state, meaning that the post-measurement state of the algorithm is the projection of the input state onto the corresponding subspace of fixed Hamming weight. We analyze a depth-width tradeoff for the corresponding quantum circuits, allowing for a depth reduction of the circuits at the cost of more control qubits. For an n-qubit input, the depth-optimal algorithm uses O(n) control qubits and the corresponding circuit has depth O(log(n)), assuming that we have the ability to perform qubit resets. Furthermore, the proposed algorithm construction uses only one- and two-qubit gates.
Understanding the impact of electronic correlations and disorder is essential for an accurate description of solids. Here, we study the role of correlations, disorder, and multimagnon processes in THz spin dynamics. W...
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Understanding the impact of electronic correlations and disorder is essential for an accurate description of solids. Here, we study the role of correlations, disorder, and multimagnon processes in THz spin dynamics. We reveal that a significant part of the electron self-energy, which goes beyond the adiabatic local spin density approximation, arises from the interaction between electrons and a virtual magnon gas. This interaction leads to a substantial modification of the exchange splitting and a renormalization of magnon energies, in agreement with the experimental data. Finally, we establish a quantitative hierarchy of magnon relaxation processes based on first principles.
Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form,by analyzing their x-ray diffraction(XRD)*** many materials are readily available as cry...
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Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form,by analyzing their x-ray diffraction(XRD)*** many materials are readily available as crystalline powder,powder crystallography is of growing usefulness to many ***,powder crystallography does not have an analytically known solution,and therefore the structural inference typically involves a laborious process of iterative design,structural refinement,and domain knowledge of skilled experts.A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning,while capturing the ambiguities around molecule orientation,symmetries,and reconstruction *** we present an ML approach for structure determination from powder diffraction *** works by estimating the electron density in a unit cell using a variational coordinate-based deep neural *** demonstrate the approach on computed powder x-ray diffraction(PXRD),along with partial chemical composition information,as *** evaluated on theoretically simulated data for the cubic and trigonal crystal systems,the system achieves up to 93.4%average similarity(as measured by structural similarity index)with the ground truth on unseen materials,both with known and partially-known chemical composition information,showing great promise for successful structure solution even from degraded and incomplete input *** approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples,paving the way to reconstruction of yet unresolved nanostructures.
Cellular automata are a class of computational models based on simple rules and algorithms that can simulate a wide range of complex ***,when using conventional computers,these'simple'rules are only encapsulat...
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Cellular automata are a class of computational models based on simple rules and algorithms that can simulate a wide range of complex ***,when using conventional computers,these'simple'rules are only encapsulated at the level of *** can be taken one step further by simplifying the underlying physical ***,we propose and implement a simple photonic hardware platform for simulating complex phenomena based on cellular *** this special-purpose computer,we experimentally demonstrate complex phenomena,including fractals,chaos,and solitons,which are typically associated with much more complex physical *** flexibility and programmability of our photonic computer present new opportunities to simulate and harness complexity for effcient,robust,and decentralized information processing using light.
Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low stor...
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Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth.
Integrating renewable energy sources, electric vehicles, and storage systems into power grids demands advanced control and monitoring systems. Precise current sensors are a critical component of these systems, essenti...
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Recurrent neural networks (RNN) are ubiquitous computing systems for sequences and multivariate time-series data. While several robust RNN architectures are known, it is unclear how to relate RNN initialization, archi...
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An innovative double-ejection micro-cathode arc thruster(AC_(d-μ)CAT),which consists of a cylindrical inner anode,a cylindrical outer cathode and two insulating sleeves(an inner insulating sleeve and an outer insulat...
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An innovative double-ejection micro-cathode arc thruster(AC_(d-μ)CAT),which consists of a cylindrical inner anode,a cylindrical outer cathode and two insulating sleeves(an inner insulating sleeve and an outer insulating sleeve),was *** differences in electrical characteristics,plasma parameters and propulsion performance between the newly proposed AC_(d-μ)CAT and a traditionalμCAT were *** results showed that compared to the traditionalμCAT structure,by using the AC_(d-μ)CAT,the peak value of produced thrust was increased 9.3 times,while the amplitude of the ion current and the ion-to-arc ratio were increased 5.4 and 5.9 times(from 1.2%to 7.1%),*** addition,data from Langmuir probe experiments indicated that peak values of the directional propagation speed and density of plasma plume were increased 3.1 times and 4.2 times,***,plasma plume directional ejection performance was also significantly *** study result will provide support for the development of a new-generationμCAT.
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