2,293 research outputs found
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
Anomalous Thermal Transport of SrTiO Driven by Anharmonic Phonon Renormalization
SrTiO 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
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
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