arXiv subjects

Wu Wang

Publications and source records attributed to Wu Wang.

25 records · Page 2Linked to original sources

arXiv2022

Nuclear excitation cross section of $^{229}$Th via inelastic electron scattering

Nuclear excitation cross section of $^{229}$Th from the ground state to the low-lying isomeric state via inelastic electron scattering is calculated, on the level of Dirac distorted wave Born approximation. With electron energies below 100 eV, inelastic scattering is very efficient in the isomeric excitation, yielding excitation cross sections on the order of 10$^{-27}$ to 10$^{-26}$ cm$^2$. Systematic analyses are presented on elements affecting the excitation cross section, including the ion-core potential, the relativistic effect, the knowledge of the reduced nuclear transition probabilities, etc.

nucl-th↗

arXiv2022

Underwater Image Enhancement via Learning Water Type Desensitized Representations

We present a novel underwater image enhancement method termed SCNet to improve the image quality meanwhile cope with the degradation diversity caused by the water. SCNet is based on normalization schemes across both spatial and channel dimensions with the key idea of learning water type desensitized features. Specifically, we apply whitening to de-correlate activations across spatial dimensions for each instance in a mini-batch. We also eliminate channel-wise correlation by standardizing and re-injecting the first two moments of the activations across channels. The normalization schemes of spatial and channel dimensions are performed at each scale of the U-Net to obtain multi-scale representations. With such water type irrelevant encodings, the decoder can easily reconstruct the clean signal and be unaffected by the distortion types. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance images with diverse water types, and achieves competitive performance in visual quality improvement.

cs.CV↗

arXiv2021

Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label

Hyperspectral image fusion (HIF) is critical to a wide range of applications in remote sensing and many computer vision applications. Most traditional HIF methods assume that the observation model is predefined or known. However, in real applications, the observation model involved are often complicated and unknown, which leads to the serious performance drop of many advanced HIF methods. Also, deep learning methods can achieve outstanding performance, but they generally require a large number of image pairs for model training, which are difficult to obtain in realistic scenarios. Towards these issues, we proposed a self-regression learning method that alternatively reconstructs hyperspectral image (HSI) and estimate the observation model. In particular, we adopt an invertible neural network (INN) for restoring the HSI, and two fully-connected network (FCN) for estimating the observation model. Moreover, \emph{SoftMax} nonlinearity is applied to the FCN for satisfying the non-negative, sparsity and equality constraints. Besides, we proposed a local consistency loss function to constrain the observation model by exploring domain specific knowledge. Finally, we proposed an angular loss function to improve spectral reconstruction accuracy. Extensive experiments on both synthetic and real-world dataset show that our model can outperform the state-of-the-art methods

cs.CV↗

arXiv2021

Quantum Zeno Effect in Heisenberg Picture and Critical Measurement Time

Quantum Zeno effect is conventionally interpreted by the assumption of the wave-packet collapse, in which does not involve the duration of measurement. However, we predict duration $τ_m$ of each measurement will appear in quantum Zeno effect by a dynamical approach. Moreover, there exists a model-free critical measurement time, which quantum Zeno effect does not occur when $τ_m$ takes some special values. In order to give these predictions, we first present a description of quantum Zeno effect in the Heisenberg picture, which is based on the expectation value of an observable and its fluctuation. Then we present a general proof for quantum Zeno effect in the Heisenberg picture, which is independent of the concrete systems. Finally, we calculate the average population and relative fluctuation after $N$ successive measurements in XX model, which agrees with our prediction about the critical measurement time.

quant-ph↗

arXiv2021

Magneto-electric Tuning of Pinning-Type Permanent Magnets through Atomic-Scale Engineering of Grain Boundaries

Pinning-type magnets maintaining high coercivity, i.e. the ability to sustain magnetization, at high temperature are at the core of thriving clean-energy technologies. Among these, Sm2Co17-based magnets are excellent candidates owing to their high-temperature stability. However, despite decades of efforts to optimize the intragranular microstructure, the coercivity currently only reaches 20~30% of the theoretical limits. Here, the roles of the grain-interior nanostructure and the grain boundaries in controlling coercivity are disentangled by an emerging magneto-electric approach. Through hydrogen charging/discharging by applying voltages of only ~ 1 V, the coercivity is reversibly tuned by an unprecedented value of ~ 1.3 T. In situ magneto-structural measurements and atomic-scale tracking of hydrogen atoms reveal that the segregation of hydrogen atoms at the grain boundaries, rather than the change of the crystal structure, dominates the reversible and substantial change of coercivity. Hydrogen lowers the local magnetocrystalline anisotropy and facilitates the magnetization reversal starting from the grain boundaries. Our study reveals the previously neglected critical role of grain boundaries in the conventional magnetisation-switching paradigm, suggesting a critical reconsideration of strategies to overcome the coercivity limits in permanent magnets, via for instance atomic-scale grain boundary engineering.

cond-mat.mtrl-sci↗

arXiv2020

Three-dimensional Insight on the Evolution of a Supramolecular Preorganization Complex to Hollow-Structure Carbon Nitride

The supramolecular preorganization approach can be applied to effectively fabricate various morphologies of graphitic carbon nitride(g-CN)with improved photocatalytic activity, while a comprehensive understanding for the morphology evolution from supramolecular aggregates to g-CNs is lacking. Herein, 3D characterizations from electron tomography provide a fundamental insight on the evolution from rod-like melamine-cyanuric acid(MC)complex to hollow-structure g-CN in the thermal polycondensation process. The internal region and a group of surfaces of the rod-like complex initially underwent polycondensation, while the other two groups of surfaces with ~100 nm thickness almost unchanged. With the temperature reached to 550 degree C, the hollow-structure g-CN eventually formed due to most of the internal matter vanishing, and voids arose in the previously unaffected surfaces(edges),resulting in a porous shell structure. Quantitative electron tomography indicates that the key of structure evolution is the differentiated condensation polymerization between edges and inner region of the rod-like MC complex, which is ascribed to a higher dense of surfaces and a lower dense of inner matter with loose, defective orignization.

cond-mat.mes-hall↗

arXiv2019

A New Anchor Word Selection Method for the Separable Topic Discovery

Separable Non-negative Matrix Factorization (SNMF) is an important method for topic modeling, where "separable" assumes every topic contains at least one anchor word, defined as a word that has non-zero probability only on that topic. SNMF focuses on the word co-occurrence patterns to reveal topics by two steps: anchor word selection and topic recovery. The quality of the anchor words strongly influences the quality of the extracted topics. Existing anchor word selection algorithm is to greedily find an approximate convex hull in a high-dimensional word co-occurrence space. In this work, we propose a new method for the anchor word selection by associating the word co-occurrence probability with the words similarity and assuming that the most different words on semantic are potential candidates for the anchor words. Therefore, if the similarity of a word-pair is very low, then the two words are very likely to be the anchor words. According to the statistical information of text corpora, we can get the similarity of all word-pairs. We build the word similarity graph where the nodes correspond to words and weights on edges stand for the word-pair similarity. Following this way, we design a greedy method to find a minimum edge-weight anchor clique of a given size in the graph for the anchor word selection. Extensive experiments on real-world corpus demonstrate the effectiveness of the proposed anchor word selection method that outperforms the common convex hull-based methods on the revealed topic quality. Meanwhile, our method is much faster than typical SNMF based method.

cs.IR↗