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The kagome spin ice can host frustrated magnetic excitations by flipping its local spin. Under an inelastic tunneling condition, the tip in a scanning tunneling microscope can flip the local spin, and we apply this technique to kagome metal HoAgGe with a long-range ordered spin ice ground state. Away from defects, we discover a pair of pronounced dips in the local tunneling spectrum at symmetrical bias voltages with negative intensity values, serving as a striking inelastic tunneling signal. This signal disappears above the spin ice formation temperature and has a dependence on the magnetic fields, demonstrating its intimate relation with the spin ice magnetism. We provide a two-level spin-flip model to explain the tunneling dips considering the spin ice magnetism under spin-orbit coupling. Our results uncover a local emergent excitation of spin ice magnetism in a kagome metal, suggesting that local electrical field induced spin flip climbs over a barrier caused by spin-orbital locking.

A general quantum mechanical theory is developed for the isomeric excitation of $^{229}$Th in strong femtosecond laser pulses. The theory describes the tripartite interaction between the nucleus, the atomic electrons, and the laser field. The nucleus can be excited both by the laser field and by laser-driven electronic transitions. Numerical results show that strong femtosecond laser pulses are very efficient in exciting the $^{229}$Th nucleus, yielding nuclear excitation probabilities on the order of $10^{-11}$ per nucleus per pulse. Laser-driven electronic excitations are found to be more efficient than direct optical excitations.

Monotone determined spaces are natural topological extensions of dcpo. Its main purpose is to build an extended framework for domain theory. In this paper, we study the one-step closure and ideal convergence on monotone determined space. Then we also introduce the equivalent characterizations of c-spaces and locally hypercompact space. The main results are:1.Every c-space has one-step closure and every locally hypercompact space has weak one-step closure;2.A monotone determined space has one-step closure if and only if it is d-meet continuous and has weak one-step closure. 3.IS-convergence(resp. IGS-convergence) is topological iff X is a c-space (resp. locally hypercompact space); 4.If X is a d-meet continuous space, then the following three conditions are equivalent to each other: (i) X is c-space; (ii) The net (xj ) ISL-converges to x iff (xj ) I-converges to x with respect to Lawson topology; (iii) The net (xj ) IGSL-converges to x iff (xj ) I-converges to x with respect to Lawson topology., Comment: 14pages

Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the subgroup structure is usually unknown to us. To this end, we develop a penalization-based approach which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. An effective computational algorithm is derived. We also establish the oracle properties and estimation consistency. Extensive numerical simulations demonstrate its superiority compared to several competing methods. The analysis of an air quality dataset leads to interesting findings and improved predictions., Comment: 24 pages, 9 figures

In this paper, the concept of quasi-finitely separating map and quasiapproximate identity are introduced. Based on these concepts, QFS-spaces and quasicontinuous maps are defined. Properties and characterizations of QFS-spaces are explored. Main results are: (1) Each QFS-space is quasicontinuous space; (2) Closed subspaces, quasicontinuous projection spaces of QFS-spaces are QFS-spaces; (3) Continuous retracts of QFS-spaces are QFS-spaces and a kind of retracts of QFS-spaces are constructed; (4) Upper powerspaces of continuous QFS-spaces are FS-spaces., Comment: 10 pages. arXiv admin note: substantial text overlap with arXiv:2211.08595

Powerspaces of directed spaces play an important role in modeling the semantics of nondeterministic functional programming languages. The notions of upper,lower and convex powerspace of a directed space are defined by the way of free algebras[25]. In this paper, We study the continuity of power structures of directed spaces and show that the directed lower powerspaces, directed upper powerspaces and directed convex powerspaces of continuous spaces are continuous spaces., Comment: the idea is studied by Hui kou

This study investigates clustered federated learning (FL), one of the formulations of FL with non-i.i.d. data, where the devices are partitioned into clusters and each cluster optimally fits its data with a localized model. We propose a clustered FL framework that incorporates a nonconvex penalty to pairwise differences of parameters. Without a priori knowledge of the set of devices in each cluster and the number of clusters, this framework can autonomously estimate cluster structures. To implement the proposed framework, we introduce a novel clustered FL method called Fusion Penalized Federated Clustering (FPFC). Building upon the standard alternating direction method of multipliers (ADMM), FPFC can perform partial updates at each communication round and allows parallel computation with variable workload. These strategies significantly reduce the communication cost while ensuring privacy, making it practical for FL. We also propose a new warmup strategy for hyperparameter tuning in FL settings and explore the asynchronous variant of FPFC (asyncFPFC). Theoretical analysis provides convergence guarantees for FPFC with general losses and establishes the statistical convergence rate under a linear model with squared loss. Extensive experiments have demonstrated the superiority of FPFC compared to current methods, including robustness and generalization capability., Comment: 46 pages, 9 figures

In this paper,the concepts of generalized ideal inf-limit and generalized ideal final lower bound limit are introduced in the directed complete poset,and their relations with Scott topology and Lawson topology are studied. The main results are as follows: (1) On directed complete posets,generalized ideal inf-limit topology is consistent with Scott topology; (2) Generalized ideal inf-limiti convergence is topological if and only if directed complete posets are quasi-continuous domains; (3) In quasi-continuous domain,generalized ideal final lower bound limit topology is consistent with Lawson topology;(4) In meet continuous directed complete posets,the generalized ideal final lower bound limit convergence is topological if and only if the directed complete poset is continuous., Comment: in Chinese language

A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to handle the ambiguity of the reference map. In this paper, we resolve UIE into distribution estimation and consensus process. We present a novel probabilistic network to learn the enhancement distribution of degraded underwater images. Specifically, we combine conditional variational autoencoder with adaptive instance normalization to construct the enhancement distribution. After that, we adopt a consensus process to predict a deterministic result based on a set of samples from the distribution. By learning the enhancement distribution, our method can cope with the bias introduced in the reference map labeling to some extent. Additionally, the consensus process is useful to capture a robust and stable result. We examined the proposed method on two widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our approach enables sampling possible enhancement predictions. Meanwhile, the consensus estimate yields competitive performance compared with state-of-the-art UIE methods. Code available at https://github.com/zhenqifu/PUIE-Net.

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., Comment: 9 pages, 5 figures

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

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 $\tau_m$ 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 $\tau_m$ 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., Comment: 8 pages, 3 figures

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.

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.

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.

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., Comment: 18 pages, 4 figures