This work introduces CLIP-aware Domain-Adaptive Super-Resolution (CDASR), a novel framework that addresses the critical challenge of domain generalization in single image super-resolution. By leveraging the semantic capabilities of CLIP (Contrastive Language-Image Pre-training), CDASR achieves unprecedented performance across diverse domains and extreme scaling factors. The proposed method integrates CLIP-guided feature alignment mechanism with a meta-learning inspired few-shot adaptation strategy, enabling efficient knowledge transfer and rapid adaptation to target domains. A custom domain-adaptive module processes CLIP features alongside super-resolution features through a multi-stage transformation process, including CLIP feature processing, spatial feature generation, and feature fusion. This intricate process ensures effective incorporation of semantic information into the super-resolution pipeline. Additionally, CDASR employs a multi-component loss function that combines pixel-wise reconstruction, perceptual similarity, and semantic consistency. Extensive experiments on benchmark datasets demonstrate CDASR's superiority, particularly in challenging scenarios. On the Urban100 dataset at $\times$8 scaling, CDASR achieves a significant PSNR gain of 0.15dB over existing methods, with even larger improvements of up to 0.30dB observed at $\times$16 scaling.

Dynamical universality is the observation that the dynamical properties of different systems might exhibit universal behavior that are independent of the system details. In this paper, we study the long-time dynamics of an one-dimensional noisy quantum magnetic model, and find that even though the system are inevitably driven to an infinite temperature state, the relaxation dynamics towards such featureless state can be highly nontrivial and universal. The effect of various mode-coupling mechanisms (external potential, disorder, interaction, and the interplay between them) as well as the conservation law on the long-time dynamics of the systems have been studied, and their relevance with current ultracold atomic experiments have been discussed.

In today's complex and volatile financial market environment, risk management of multi-asset portfolios faces significant challenges. Traditional risk assessment methods, due to their limited ability to capture complex correlations between assets, find it difficult to effectively cope with dynamic market changes. This paper proposes a multi-asset portfolio risk prediction model based on Convolutional Neural Networks (CNN). By utilizing image processing techniques, financial time series data are converted into two-dimensional images to extract high-order features and enhance the accuracy of risk prediction. Through empirical analysis of data from multiple asset classes such as stocks, bonds, commodities, and foreign exchange, the results show that the proposed CNN model significantly outperforms traditional models in terms of prediction accuracy and robustness, especially under extreme market conditions. This research provides a new method for financial risk management, with important theoretical significance and practical value.

Accurate tumor segmentation in PET/CT images is crucial for computer-aided cancer diagnosis and treatment. The primary challenge lies in effectively integrating the complementary information from PET and CT images. In clinical settings, the quality of PET and CT images often varies significantly, leading to uncertainty in the modality information extracted by networks. To address this challenge, we propose a novel Multi-modal Evidential Fusion Network (MEFN), which consists of two core stages: Cross-Modal Feature Learning (CFL) and Multi-modal Trustworthy Fusion (MTF). The CFL stage aligns features across different modalities and learns more robust feature representations, thereby alleviating the negative effects of domain gap. The MTF stage utilizes mutual attention mechanisms and an uncertainty calibrator to fuse modality features based on modality uncertainty and then fuse the segmentation results under the guidance of Dempster-Shafer Theory. Besides, a new uncertainty perceptual loss is introduced to force the model focusing on uncertain features and hence improve its ability to extract trusted modality information. Extensive comparative experiments are conducted on two publicly available PET/CT datasets to evaluate the performance of our proposed method whose results demonstrate that our MEFN significantly outperforms state-of-the-art methods with improvements of 3.10% and 3.23% in DSC scores on the AutoPET dataset and the Hecktor dataset, respectively. More importantly, our model can provide radiologists with credible uncertainty of the segmentation results for their decision in accepting or rejecting the automatic segmentation results, which is particularly important for clinical applications. Our code will be available at this https URL

Owing to the versatility in their chemical and physical properties, transition metal perovskite oxides have emerged as a new category of highly efficient photocatalysts for photoelectrochemical water splitting. Here, to understand the underlying mechanism for the enhanced photoelectrochemical water splitting in mixed perovskites, we explore ideal epitaxial thin films of the BiFeO3-SrTiO3 system. The electronic struture and carrier dynamics are determined from both experiment and density-functional theory calculations. The intrinsic phenomena are measured in this ideal sytem, contrasting to commonly studied polycrstalline solid solutions where extrinsic structural features obscure the intrinsic phenomena. We determined that when SrTiO3 is added to BiFeO3 the conduction band minimum position is raised and an exponential tail of trap states from hybridized Ti 3d and Fe 3d orbitals emerges near the conduction band edge. The presence of these trap states strongly suppresses the fast electron-hole recombination and improves the photocurrent density in the visible-light region, up to 16 times at 0 VRHE compared to the pure end member compositions. Our work provides a new design approach for optimising the photoelectrochemical performance in mixed perovksite oxides.

Sensor management in multi-object stochastic systems is a theoretically and computationally challenging problem. This paper presents a novel approach to the multi-target multi-sensor control problem within the partially observed Markov decision process (POMDP) framework. We model the multi-object state as a labeled multi-Bernoulli random finite set (RFS), and use the labeled multi-Bernoulli filter in conjunction with minimizing a task-driven control objective function: posterior expected error of cardinality and state (PEECS). A major contribution is a guided search for multi-dimensional optimization in the multi-sensor control command space, using coordinate descent method. In conjunction with the Generalized Covariance Intersection method for multi-sensor fusion, a fast multi-sensor algorithm is achieved. Numerical studies are presented in several scenarios where numerous controllable (mobile) sensors track multiple moving targets with different levels of observability. The results show that our method works significantly faster than the approach taken by a state of art method, with similar tracking errors.

In this paper, we study Hausdorff operator $\mathcal{H}_\mu$ on a large class of weighted mixed norm Fock spaces $F_\phi^{p,q}$ for $1\leq p,q\leq\infty$. The boundedness and compactness of $\mathcal{H}_\mu$ on $F_\phi^{p,q}$ are characterized. As applications, we give when Hausdorff operator on $F_\phi^{p,q}$ is power bounded or uniformly mean ergodic.

We study the phase diagram of spin-1 antiferromagnetic chain with isotropic antiferromagnetic interactions decaying with a power-law $\propto r^{-\alpha}$ ($\alpha\ge 1$) accompanied by modulated single-ion anisotropy. Employing the techniques of the density-matrix renormalization group, effects of long-range interactions and single-ion anisotropy on a variety of correlations are investigated. In order to check the consistency, the fidelity susceptibilities are evaluated across quantum phase transitions. The quantum critical points are faithfully detected and orders of phase transitions are determined. The correlation-length critical exponent is extracted from scaling functions of the fidelity susceptibility. The presence of long-range interactions leads to quantitative change of the phase boundaries and reduces the order of phase transition under certain conditions. A direct first-order transition between the periodic N\'eel phase and the large-$D$ phase occurs for slowly decaying antiferromagnetic interactions.

For an irreducible character $\chi$ of a finite group $G$, let $\mathrm{cod}(\chi):=|G: \ker(\chi)|/\chi(1)$ denote the codegree of $\chi$, and let $\mathrm{cod}(G)$ be the set of irreducible character codegrees of $G$. In this note, we prove that if $\ker(\chi)$ is not nilpotent, then there exists an irreducible character $\xi$ of $G$ such that \ker(\xi)<\ker(\chi) and \mathrm{cod}(\xi)> \mathrm{cod}(\chi). This provides a character codegree analogue of a classical theorem of Broline and Garrison. As a consequence, we obtain that for a nonidentity solvable group $G$, its Fitting height $\ell_{\mathbf{F}}(G)$ does not exceed $|\mathrm{cod}(G)|-1$. Additionally, we provide two other upper bounds for the Fitting height of a solvable group $G$ as follows: $\ell_{\mathbf{F}}(G)\leq \frac{1}{2}(|\mathrm{cod}(G)|+2)$, and $\ell_{\mathbf{F}}(G)\leq 8\log_2(|\mathrm{cod}(G)|)+80$.

Superconductivity beyond the conventional Bardeen-Cooper-Schrieffer (BCS) framework often emerges out of a normal state that is accompanied by exotic magnetism and thereby displays many exceptional transport and thermodynamic properties. Here we report that the normal state of the heavy $d$-electron superconductor Rh$_{17}$S$_{15}$ is characterized by a weak \textit{ferromagnetism} that persists up to room temperature. We show that the broad hump in its resistivity likely results from the Kondo interaction of the conduction electrons with this novel magnetism. By applying pressure, superconductivity is fully suppressed first. In the high-pressure regime, however, we observe a second dome of superconductivity with its maximum $T_c$ greater than the ambient pressure value, highlighting the possible \textit{unconventional} superconductivity in this heavy $d$-electron sulfide.

In this paper, we classify the simple Harish-Chandra modules over the superconformal current algebra $\widehat{\frak g}$, which is the semi-direct sum of the $N=1$ superconformal algebra with the affine Lie superalgebra $\dot{\frak g} \otimes \mathcal{A}\oplus \mathbb CC_1$, where $\dot{\frak g}$ is a finite-dimensional simple Lie algebra, and $\mathcal{A}$ is the tensor product of the Laurent polynomial algebra and the Grassmann algebra. As an application, we can directly get the classification of the simple Harish-Chandra modules over the $N=1$ Heisenberg-Virasoro algebra.

The van der Waals, pseudo-binary chalcogenides (ACh)m(Pn2Ch3)n (A = Ge, Mn, Pb, etc.; Pn = Sb or Bi; Ch = Te, Se) have recently been reported to host a vast landscape of topological phases of matter, including the quantum anomalous Hall state and topological axion state with quantized magnetoelectric effect. A subgroup in this series, like MnSb4Te7 and GeSb4Te7, can be driven to a superconducting state by applying a physical pressure, making them viable candidates to realize so-called topological superconductivity. However, the role of magnetic fluctuations in this pressure-induced superconductivity remains unclear. Here, we report the pressure-induced multiple superconducting phases in the nonmagnetic GeBi4Te7, accompanied by corresponding structural transitions evidenced from the high-pressure Raman scattering. In comparison with other members in this family, we find the superconducting transition temperature of the nonmagnetic subgroup is significantly higher than their magnetic homologues, possibly hinting at the detrimental role played by the magnetic fluctuations in the superconductivity formation, at least in this pseudo-binary chalcogenide family.

Clustering is an efficient and essential technique for exploring latent knowledge of data. However, limited attention has been given to the interpretability of the clusters detected by most clustering algorithms. In addition, due to the homogeneity of data, different groups of data have their own homogeneous styles. In this paper, the above two aspects are considered, and an interpretable style Takagi-Sugeno-Kang (TSK) fuzzy clustering (IS-TSK-FC) algorithm is proposed. The clustering behavior of IS-TSK-FC is fully guided by the TSK fuzzy inference on fuzzy rules. In particular, samples are grouped into clusters represented by the corresponding consequent vectors of all fuzzy rules learned in an unsupervised manner. This can explain how the clusters are generated in detail, thus making the underlying decision-making process of the IS-TSK-FC interpretable. Moreover, a series of style matrices are introduced to facilitate the consequents of fuzzy rules in IS-TSK-FC by capturing the styles of clusters as well as the nuances between different styles. Consequently, all the fuzzy rules in IS-TSK-FC have powerful data representation capability. After determining the antecedents of all the fuzzy rules, the optimization problem of IS-TSK-FC can be iteratively solved in an alternation manner. The effectiveness of IS-TSK-FC as an interpretable clustering tool is validated through extensive experiments on benchmark datasets with unknown implicit/explicit styles. Specially, the superior clustering performance of IS-TSK-FC is demonstrated on case studies where different groups of data present explicit styles. The source code of IS-TSK-FC can be downloaded from this https URL

We report a detailed inelastic neutron scattering (INS) and muon spin relaxation (muSR) investigation of a trimer Ruthenate Ba5Ru3O12 system, which undergoes long-range antiferromagnetic ordering at TN = 60 K. The INS reveals two distinct spin wave excitations below TN: one at 5.6 meV and the other at 10-15 meV. By accompanying the INS spectra based on a linear spin wave theory using SpinW software and machine learning force fields (MLFFs), we show that Ba5Ru3O12 exhibits spin frustration due to competing exchange interactions between neighboring and next-neighboring Ru-moments, exchange anisotropy, and strong spin-orbit coupling, which yields a non-collinear spin structure, in contrast to other ruthenate trimers in this series. Interestingly, these magnetic excitations do not completely vanish even at high temperatures above TN, evidencing short-range magnetic correlations in this trimer system. This is further supported by muSR spectroscopy, which exhibits a gradual drop in the initial asymmetry around the magnetic phase transition and is further verified through maximum entropy analysis. The results of muSR spectroscopy indicate a dynamic nature of magnetic order, attributed to local magnetic anisotropy within the trimer as a result of local structural distortion and different hybridization, consistent with canted spin-structure. We predict the ground state of Ru3O12-isolated trimer through theoretical calculations, which agree with the experimentally observed spin excitation

Self-intercalated chromium tellurides Cr1+xTe2 have garnered growing attention due to their high-temperature ferromagnetism, tunable spin structures and air stability, all of which are vital for versatile applications in next-generation memory and information technology. Here, we report strong magnetic anisotropy and a large topological Hall effect (THE) in self-intercalated Cr1.61Te2 single crystals, which are both highly desirable properties for future spintronic applications. Our results demonstrate that Cr1.61Te2 is a soft ferromagnet with strong in-plane magnetic anisotropy. Remarkably, distinct THE behaviors are observed in different temperature regimes, reflecting the intricate spin structures and competing exchange interactions. More interestingly, a large topological Hall resistivity, induced by microscopic non-coplanar spin structures, emerges in the temperature range 70-240 K, reaching a maximum value of 0.93 {\mu}{\Omega} cm at 150 K. Moreover, a sign-reversed and weak THE is observed at low temperatures below ~70 K, indicating the emergence of an additional topological spin structure with opposite topological charges. This work not only offers valuable insights into the correlation between magnetocrystalline anisotropy and topological phenomena in Cr1+xTe2 systems, but also provides a robust platform for engineering the evolution of complex spin textures that can be leveraged in diverse spintronic device applications.

To provide a physical example of quantum scars, we study the many-body scars in the spin-1 Kitaev chain where the so-called PXP Hamiltonian is exactly embedded in the spectra. Regarding the conserved quantities, the Hilbert space is fragmented into disconnected subspaces and we explore the associated constrained dynamics. The continuous revivals of the fidelity and the entanglement entropy when the initial state is prepared in $\vert\mathbb{Z}_k\rangle$ ($k=2,3$) state illustrate the essential physics of the PXP model. We study the quantum phase transitions in the one-dimensional spin-1 Kitaev-Heisenberg model using the density-matrix renormalization group and Lanczos exact diagonalization methods, and determine the phase diagram. We parametrize the two terms in the Hamiltonian by the angle $\phi$, where the Kitaev term is $K\equiv\sin(\phi)$ and competes with the Heisenberg $J\equiv\cos(\phi)$ term. One finds a rich ground state phase diagram as a function of the angle $\phi$. Depending on the ratio $K/J\equiv\tan(\phi)$, the system either breaks the symmetry to one of distinct symmetry broken phases, or preserves the symmetry in a quantum spin liquid phase with frustrated interactions. We find that the scarred state is stable for perturbations which obey $\mathbb{Z}_2$-symmetry, while it becomes unstable against Heisenberg-type perturbations.\\ \textit{Accepted for publication in Physical Review Research}

This paper presents a novel statistical information fusion method to integrate multiple-view sensor data in multi-object tracking applications. The proposed method overcomes the drawbacks of the commonly used Generalized Covariance Intersection method, which considers constant weights allocated for sensors. Our method is based on enhancing the Generalized Covariance Intersection with adaptive weights that are automatically tuned based on the amount of information carried by the measurements from each sensor. To quantify information content, Cauchy-Schwarz divergence is used. Another distinguished characteristic of our method lies in the usage of the Labeled Multi-Bernoulli filter for multi-object tracking, in which the weight of each sensor can be separately adapted for each Bernoulli component of the filter. The results of numerical experiments show that our proposed method can successfully integrate information provided by multiple sensors with different fields of view. In such scenarios, our method significantly outperforms the state of art in terms of inclusion of all existing objects and tracking accuracy.

In this paper, under the generalized curvature-dimension inequality recently introduced by F. Baudoin and N. Garofalo, we obtain differential Harnack inequalities for the positive solutions to the Schödinger equation associated to subelliptic operator with potential. As applications of the differential Harnack inequality, we derive the corresponding parabolic Harnack inequality. Also we define the Perelman type entropy associated to subelliptic operators and derive its monotonicity.