Remotely sensed mid-channel bar dynamics in downstream of the Three Gorges Dam, China

The downstream reach of the Three Gorges Dam (TGD) along the Yangtze River (1560 km) hosts numerous mid-channel bars (MCBs). MCBs dynamics are crucial to the river’s hydrological processes and local ecological function. However, a systematic understanding of such dynamics and their linkage to TGD remains largely unknown. Using Landsat-image-extracted MCBs and several spatial-temporal analysis methods, this study presents a comprehensive understanding of MCB dynamics in terms of number, area, and shape, over downstream of TGD during the period 1985−2018. On average, a total of 140 MCBs were detected and grouped into four types representing small ( 2 km2), middle (2 km2 − 7 km2), large (7 km2 − 33 km2) and extra-large size (>33 km2) MCBs, respectively. MCBs number decreased after TGD closure but most of these happened in the lower reach. The area of total MCBs experienced an increasing trend (2.77 km2/yr, p-value 0.01) over the last three decades. The extra-large MCBs gained the largest area increasing rate than the other sizes of MCBs. Small MCBs tended to become relatively round, whereas the others became elongate in shape after TGD operation. Impacts of TGD operation generally diminished in the longitudinal direction from TGD to Hankou and from TGD to Jiujiang for shape and area dynamics, respectively. The quantified longitudinal and temporal dynamics of MCBs across the entire Yangtze River downstream of TGD provides a crucial monitoring basis for continuous investigation of the changing mechanisms affecting the morphology of the Yangtze River system

From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration

In this paper, we propose a novel approach to the rank minimization problem, termed rank residual constraint (RRC) model. Different from existing low-rank based approaches, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM), which estimate the underlying low-rank matrix directly from the corrupted observations, we progressively approximate the underlying low-rank matrix via minimizing the rank residual. Through integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we apply it to image restoration tasks, including image denoising and image compression artifacts reduction. Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image. In this manner, both the reference and the estimated image are updated gradually and jointly in each iteration. Based on the group-based sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art schemes in both the objective and perceptual quality

Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering

Differential privacy is a widely accepted measure of privacy in the context of deep learning algorithms, and achieving it relies on a noisy training approach known as differentially private stochastic gradient descent (DP-SGD). DP-SGD requires direct noise addition to every gradient in a dense neural network, the privacy is achieved at a significant utility cost. In this work, we present Spectral-DP, a new differentially private learning approach which combines gradient perturbation in the spectral domain with spectral filtering to achieve a desired privacy guarantee with a lower noise scale and thus better utility. We develop differentially private deep learning methods based on Spectral-DP for architectures that contain both convolution and fully connected layers. In particular, for fully connected layers, we combine a block-circulant based spatial restructuring with Spectral-DP to achieve better utility. Through comprehensive experiments, we study and provide guidelines to implement Spectral-DP deep learning on benchmark datasets. In comparison with state-of-the-art DP-SGD based approaches, Spectral-DP is shown to have uniformly better utility performance in both training from scratch and transfer learning settings.Comment: Accepted in 2023 IEEE Symposium on Security and Privacy (SP

Accelerating Split Federated Learning over Wireless Communication Networks

The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/splitting, in which DNN is divided into two parts which are deployed on device and server respectively for co-training or co-inference. In this paper, we consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL). We consider a practical scenario of heterogeneous devices with individual split points of DNN. We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. By using alternating optimization, we decompose the problem into two sub-problems and solve them optimally. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement

Universality of the surface magnetoelectric effect in half-metals

An electric field applied to a ferromagnetic metal produces a surface magnetoelectric effect originating from the spin-dependent screening of the electric field which results in a change in the surface magnetization of the ferromagnet

Nanoscale Bandgap Tuning across an Inhomogeneous Ferroelectric Interface

We report nanoscale bandgap engineering via a local strain across the inhomogeneous ferroelectric interface, which is controlled by the visible-light-excited probe voltage. Switchable photovolatic effects and the spectral response of the photocurrent were explore to illustrate the reversible bandgap variation (~0.3eV). This local-strain-engineered bandgap has been further revealed by in situ probe-voltage-assisted valence electron energy-loss spectroscopy (EELS). Phase-field simulations and first-principle calculations were also employed for illustration of the large local strain and the bandgap variation in ferroelectric perovskite oxides. This reversible bandgap tuning in complex oxides demonstrates a framework for the understanding of the opticallyrelated behaviors (photovoltaic, photoemission, and photocatalyst effects) affected by order parameters such as charge, orbital, and lattice parameters

Anomalous Thermal Transport of SrTiO3_3 Driven by Anharmonic Phonon Renormalization

SrTiO$_3$ has been extensively investigated owing to its abundant degrees of freedom for modulation. However, the microscopic mechanism of thermal transport especially the relationship between phonon scattering and lattice distortion during the phase transition are missing and unclear. Based on deep-potential molecular dynamics and self-consistent \textit{ab initio} lattice dynamics, we explore the lattice anharmonicity-induced tetragonal-to-cubic phase transition and explain this anomalous behavior during the phase transition. Our results indicate the significant role of the renormalization of third-order interatomic force constants to second-order terms. Our work provides a robust framework for evaluating the thermal transport properties during structural transformation, benefitting the future design of promising thermal and phononic materials and devices