Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement

Soil moisture is a crucial hydrological state variable that has significant importance to the global environment and agriculture. Precise monitoring of soil moisture in crop fields is critical to reducing agricultural drought and improving crop yield. In-situ soil moisture sensors, which are buried at pre-determined depths and distributed across the field, are promising solutions for monitoring soil moisture. However, high-density sensor deployment is neither economically feasible nor practical. Thus, to achieve a higher spatial resolution of soil moisture dynamics using a limited number of sensors, we integrate a physics-based agro-hydrological model based on Richards' equation in a physics-constrained deep learning framework to accurately predict soil moisture dynamics in the soil's root zone. This approach ensures that soil moisture estimates align well with sensor observations while obeying physical laws at the same time. Furthermore, to strategically identify the locations for sensor placement, we introduce a novel active learning framework that combines space-filling design and physics residual-based sampling to maximize data acquisition potential with limited sensors. Our numerical results demonstrate that integrating Physics-constrained Deep Learning (P-DL) with an active learning strategy within a unified framework--named the Physics-constrained Active Learning (P-DAL) framework--significantly improves the predictive accuracy and effectiveness of field-scale soil moisture monitoring using in-situ sensors

The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimation

Soil moisture is a key hydrological parameter that has significant importance to human society and the environment. Accurate modeling and monitoring of soil moisture in crop fields, especially in the root zone (top 100 cm of soil), is essential for improving agricultural production and crop yield with the help of precision irrigation and farming tools. Realizing the full sensor data potential depends greatly on advanced analytical and predictive domain-aware models. In this work, we propose a physics-constrained deep learning (P-DL) framework to integrate physics-based principles on water transport and water sensing signals for effective reconstruction of the soil moisture dynamics. We adopt three different optimizers, namely Adam, RMSprop, and GD, to minimize the loss function of P-DL during the training process. In the illustrative case study, we demonstrate the empirical convergence of Adam optimizers outperforms the other optimization methods in both mini-batch and full-batch training

AT2018dyk Revisited: a Tidal Disruption Event Candidate with Prominent Infrared Echo and Delayed X-ray Emission in a LINER Galaxy

The multiwavelength data of nuclear transient AT2018dyk, initially discovered as a changing-look low-ionization nuclear emission-line region (LINER) galaxy, has been revisited by us and found being in agreement with a tidal disruption event (TDE) scenario. The optical light curve of AT2018dyk declines as a power-law form approximately with index -5/3 yet its X-ray emission lags behind the optical peak by $\sim140$ days, both of which are typical characteristics for TDEs. The X-ray spectra are softer than normal active galactic nuclei (AGNs) although they show a slight trend of hardening. Interestingly, its rising time scale belongs to the longest among TDEs while it is nicely consistent with the theoretical prediction from its relatively large supermassive black hole (SMBH) mass ($\sim10^{7.38} M_{\odot}$). Moreover, a prominent infrared echo with peak luminosity $\sim7.4\times10^{42}~\text{erg}~\text{s}^{-1}$ has been also detected in AT2018dyk, implying an unusually dusty subparsec nuclear environment in contrast with other TDEs. In our sample, LINERs share similar covering factors with AGNs, which indicates the existence of the dusty torus in these objects. Our work suggests that the nature of nuclear transients in LINERs needs to be carefully identified and their infrared echoes offer us a unique opportunity for exploring the environment of SMBHs at low accretion rate, which has been so far poorly explored but is crucial for understanding the SMBH activity.Comment: 9 pages, 6figures, 1 table. Accepted for publication in MNRA

Efficient Quantized Constant Envelope Precoding for Multiuser Downlink Massive MIMO Systems

Quantized constant envelope (QCE) precoding, a new transmission scheme that only discrete QCE transmit signals are allowed at each antenna, has gained growing research interests due to its ability of reducing the hardware cost and the energy consumption of massive multiple-input multiple-output (MIMO) systems. However, the discrete nature of QCE transmit signals greatly complicates the precoding design. In this paper, we consider the QCE precoding problem for a massive MIMO system with phase shift keying (PSK) modulation and develop an efficient approach for solving the constructive interference (CI) based problem formulation. Our approach is based on a custom-designed (continuous) penalty model that is equivalent to the original discrete problem. Specifically, the penalty model relaxes the discrete QCE constraint and penalizes it in the objective with a negative $\ell_2$-norm term, which leads to a non-smooth non-convex optimization problem. To tackle it, we resort to our recently proposed alternating optimization (AO) algorithm. We show that the AO algorithm admits closed-form updates at each iteration when applied to our problem and thus can be efficiently implemented. Simulation results demonstrate the superiority of the proposed approach over the existing algorithms.Comment: 5 pages, 5 figures, submitted for possible publicatio

CI-Based One-Bit Precoding for Multiuser Downlink Massive MIMO Systems with PSK Modulation: A Negative ℓ1\ell_1 Penalty Approach

In this paper, we consider the one-bit precoding problem for the multiuser downlink massive multiple-input multiple-output (MIMO) system with phase shift keying (PSK) modulation and focus on the celebrated constructive interference (CI)-based problem formulation. We first establish the NP-hardness of the problem (even in the single-user case), which reveals the intrinsic difficulty of globally solving the problem. Then, we propose a novel negative $\ell_1$ penalty model for the considered problem, which penalizes the one-bit constraint into the objective with a negative $\ell_1$-norm term, and show the equivalence between (global and local) solutions of the original problem and the penalty problem when the penalty parameter is sufficiently large. We further transform the penalty model into an equivalent min-max problem and propose an efficient alternating optimization (AO) algorithm for solving it. The AO algorithm enjoys low per-iteration complexity and is guaranteed to converge to a stationary point of the min-max problem and a local minimizer of the penalty problem. To further reduce the computational cost, we also propose a low-complexity implementation of the AO algorithm, where the values of the variables will be fixed in later iterations once they satisfy the one-bit constraint. Numerical results show that, compared against the state-of-the-art CI-based algorithms, both of the proposed algorithms generally achieve better bit-error-rate (BER) performance with lower computational cost, especially when the problem is difficult (e.g., high-order modulations, large number of antennas, or high user-antenna ratio).Comment: 13 pages, 8 figures, submitted for possible publication. arXiv admin note: text overlap with arXiv:2110.0476

AT 2023clx: the Faintest and Closest Optical Tidal Disruption Event Discovered in Nearby Star-forming Galaxy NGC 3799

We report the discovery of a faint optical tidal disruption event (TDE) in the nearby star-forming galaxy NGC 3799. Identification of the TDE is based on its position at the galaxy nucleus, a light curve declining as t^-5/3, a blue continuum with an almost constant blackbody temperature of ~12,000K, and broad (~15,000kms^-1) Balmer lines and characteristic He~II 4686A emission. The light curve of AT 2023clx peaked at an absolute magnitude of -17.16mag in the g-band and a maximum blackbody bolometric luminosity of 4.56*10^42 ergs^-1, making it the faintest TDE discovered to date. With a redshift of 0.01107 and a corresponding luminosity distance of 47.8Mpc, it is also the closest optical TDE ever discovered to our best knowledge. Furthermore, our analysis of Swift/XRT observations of AT 2023clx yields a very tight 3 sigma upper limit of 9.53*10^39 ergs^-1 in the range 0.3--10keV. AT 2023clx, together with very few other faint TDEs such as AT 2020wey, prove that there are probably a large number of faint TDEs yet to be discovered at higher redshifts, which is consistent with the prediction of luminosity functions (LFs). The upcoming deeper optical time-domain surveys, such as the Legacy Survey of Space and Time (LSST) and the Wide-Field Survey Telescope (WFST) will discover more TDEs at even lower luminosities, allowing for a more precise constraint of the low-end of the LF.Comment: 9 pages, 6 figures; Accepted for ApJL (July, 2023