With intrinsic giant anomalous Hall and Nernst effects, Weyl ferromagnet C02MnGa stands as an ideal candidate for exploring efficient charge-to-spin conversions. In this work, by utilizing the high-quality epitaxial C...
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ISBN:
(数字)9798350362213
ISBN:
(纸本)9798350362220
With intrinsic giant anomalous Hall and Nernst effects, Weyl ferromagnet C02MnGa stands as an ideal candidate for exploring efficient charge-to-spin conversions. In this work, by utilizing the high-quality epitaxial C02MnGa single films, we observed a sizable unidirectional magnetoresistance. Surprisingly, the isotropic crystallographic axis dependence of unidirectional magnetoresistance, together with its linear scaling to the C02MnGa thickness, neither can be explained by the conventional spin current drift-diffusion process. By employing heat equation, we analytically derived the current induced vertical temperature gradient, and unambiguously show the thickness dependence of unidirectional magnetoresistance induced by anomalous Nernst effects. Our work provides direct evidences of thermoelectric voltages in the nonlinear transport that may be extended to other material systems as well.
Neural Networks (NN) have been proven to be powerful tools to analyze Big Data. However, traditional CPUs cannot achieve the desired performance and/or energy efficiency for NN applications. Therefore, numerous NN acc...
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Neural Networks (NN) have been proven to be powerful tools to analyze Big Data. However, traditional CPUs cannot achieve the desired performance and/or energy efficiency for NN applications. Therefore, numerous NN accelerators have been used or designed to meet these goals. These accelerators all fall into three categories: GPGPUs, ASIC NN Accelerators and CISC NN Accelerators. GPGPUs achieve general purpose and high computing throughput, but cannot provide desired energy efficiency because their stream architecture cannot achieve efficient data reuse required by NN applications. ASIC NN Accelerators achieve best performance or energy efficiency through advanced data reuse optimization, however, they only support limited NN use cases. CISC NN Accelerators aim to achieve both general purpose and high energy efficiency by decomposing NN applications into multiple relatively simple matrix or vector CISC instructions. Though CISC NN Accelerators can achieve considerable smaller memory footprint than GPGPU thus improve energy efficiency;they still fail to provide same level of data reuse optimization achieved by ASIC NN Accelerators because of the inherited poor pragrammability of their CISC architecture. We argue that, for NN Accelerators, RISC is a better design choice than CISC, as is the case with general purpose processors. We propose RISC-NN, a novel many-core RISC-based NN accelerator that achieves high expressiveness and high parallelism and features strong programmability and low control-hardware costs. We show that, RISC-NN can implement all the necessary instructions of state-of-the-art CISC NN Accelerators;in the meantime, RISC-NN manages to achieve advanced optimization such as multiple-level data reuse and support for Sparse NN applications which previously only existed in ASIC NN Accelerators. Experiment results show that, RISC-NN achieves on average 11.88× performance efficiency compared with state-of-the-art Nvidia TITAN Xp GPGPU for various NN applicati
Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, quantum resource is too sc...
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With the increasing emphasis on embedding advanced technology into system controls, the Direct Power control (DPC) approach has garnered significant attention, owing to its straightforward and highly adaptable algorit...
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The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melan...
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Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** s...
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Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.
This paper presents the static output feedback control synthesis problem for a class of positive polynomial fuzzy systems with time delay using a sum of squares (SOS) approach. To study the considered analysis and des...
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ISBN:
(纸本)9781665407830
This paper presents the static output feedback control synthesis problem for a class of positive polynomial fuzzy systems with time delay using a sum of squares (SOS) approach. To study the considered analysis and design problems, the polynomial Lyapunov-Krasovskii function is proposed. Based on The sum of squares technique, the polynomial conditions can be directly solved. Finally, a simulation example is given to testify the validity of the analysis result.
This paper proposes a new method to estimate the unknown parameters for stochastic nonlinear systems that can be described by Wiener-Hammerstein (W-H) mathematical models (L-N-L cascade). The key of this technique is ...
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ISBN:
(数字)9781728110806
ISBN:
(纸本)9781728110813
This paper proposes a new method to estimate the unknown parameters for stochastic nonlinear systems that can be described by Wiener-Hammerstein (W-H) mathematical models (L-N-L cascade). The key of this technique is to decompose the W-H structure into three sub-blocks. Afterwards, we apply the Recursive Extended Least Squares (RELS) estimator at each discrete-time in order to estimate the unknown parameters. Next, we put the output system under two matrix forms and from that, we determine the others parameters using the predicted intermediate variables. The convergence analysis of the W-H-RHPE algorithm is made using the differential equation approach and its performance is validated by treating a simulation example.
Federated linear regressions have been developed and applied in various domains, where multiparties collaboratively and securely perform optimization algorithms, e.g., Gradient Descent, to learn a set of optimal model...
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This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several a...
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