Plasmonic FET Terahertz Spectrometer

We show that Si MOSFETs, AlGaN/GaN HEMTs, AlGaAs/InGaAs HEMTs, and p-diamond FETs with feature sizes ranging from 20 nm to 130 nm could operate at room temperature as THz spectrometers in the frequency range from 120 GHz to 9.3 THz with different subranges corresponding to the transistors with different features sizes and tunable by the gate bias. The spectrometer uses a symmetrical FET with interchangeable source and drain with the rectified THz voltage between the source and drain being proportional to the sine of the phase shift between the voltages induced by the THz signal between gate-to-drain and gate-to-source. This phase difference could be created by using different antennas for the source-to-gate and drain-to gate contacts or by using a delay line introducing a phase shift or even by manipulating the impinging angle of the two antennas. The spectrometers are simulated using the multi-segment unified charge control model implemented in SPICE and ADS and accounting for the electron inertia effect and the distributed channel resistances, capacitances and Drude inductances.Comment: 5 pages, 10 figures, submission to IEEE Acces

Contextual Bandits with Budgeted Information Reveal

Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as pro-treatment actions. In practice, clinicians have a limited budget to encourage patients to take these actions and collect additional information. We introduce a novel optimization and learning algorithm to address this problem. This algorithm effectively combines the strengths of two algorithmic approaches in a seamless manner, including 1) an online primal-dual algorithm for deciding the optimal timing to reach out to patients, and 2) a contextual bandit learning algorithm to deliver personalized treatment to the patient. We prove that this algorithm admits a sub-linear regret bound. We illustrate the usefulness of this algorithm on both synthetic and real-world data

Agricultural carbon emission efficiency and agricultural practices: implications for balancing carbon emissions reduction and agricultural productivity increment

The current Ukraine War underlines the importance of grain self-sufficiency. After the adoption of the Paris Agreement, two major challenges developing countries are facing in the coming decades are increasing agricultural production to ensure food security and reducing carbon emissions (CE). The key to such an “environment-development dilemma” is to improve agricultural carbon emission efficiency (CEE). Using China as the study site, we systematically analyze the impacts of agricultural management activities on agricultural CEE from 1997 to 2019. Global and local Moran's I index tests provide evidence of a positive spatial dependence of agricultural CEE. Using the LISA cluster map, we observe that high CEE regions tend to be distributed together, dominated by environmental conditions. However, with the promotion of agricultural management activities, such a clustering pattern vanished. Our spatial Durbin model (SDM) estimation results indicate that there are significant nonlinear relationships between agricultural practices and agricultural CEE. While the consumption of fertilizers and pesticides has economies of scale effects, the deployment of agricultural machinery and irrigation have diseconomies of scale effects on local CEE. Based on the SDM results, the direct and indirect effect estimation results suggest that the significant direct and spillover effects of many practices on agricultural CEE have opposite nonlinear shapes, implying a more complicated situation in promoting these activities, as the positive regional effect of an agricultural activity might have a negative impact on adjacent regions. All the results indicate that local policymakers should carefully tailor agricultural development policies based on local environmental conditions

A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics

Autonomous vehicles and driving technologies have received notable attention in the past decades. In autonomous driving systems, \textcolor{black}{the} information of vehicle dynamics is required in most cases for designing of motion planning and control algorithms. However, it is nontrivial for identifying a global model of vehicle dynamics due to the existence of strong non-linearity and uncertainty. Many efforts have resorted to machine learning techniques for building data-driven models, but it may suffer from interpretability and result in a complex nonlinear representation. In this paper, we propose a deep learning framework relying on an interpretable Koopman operator to build a data-driven predictor of the vehicle dynamics. The main idea is to use the Koopman operator for representing the nonlinear dynamics in a linear lifted feature space. The approach results in a global model that integrates the dynamics in both longitudinal and lateral directions. As the core contribution, we propose a deep learning-based extended dynamic mode decomposition (Deep EDMD) algorithm to learn a finite approximation of the Koopman operator. Different from other machine learning-based approaches, deep neural networks play the role of learning feature representations for EDMD in the framework of the Koopman operator. Simulation results in a high-fidelity CarSim environment are reported, which show the capability of the Deep EDMD approach in multi-step prediction of vehicle dynamics at a wide operating range. Also, the proposed approach outperforms the EDMD method, the multi-layer perception (MLP) method, and the Extreme Learning Machines-based EDMD (ELM-EDMD) method in terms of modeling performance. Finally, we design a linear MPC with Deep EDMD (DE-MPC) for realizing reference tracking and test the controller in the CarSim environment.Comment: 12 pages, 10 figures, 1 table, and 2 algorithm