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The structured light (SL)-based 3D measurement techniques with deep learning have been widely studied, among which speckle projection profilometry (SPP) and fringe projection profilometry (FPP) are two popular methods. However, they generally use a single projection pattern for reconstruction, resulting in fringe order ambiguity or poor reconstruction accuracy. To alleviate these problems, we propose a parallel dual-branch Convolutional Neural Network (CNN)-Transformer network (PDCNet), to take advantage of convolutional operations and self-attention mechanisms for processing different SL modalities. Within PDCNet, a Transformer branch is used to capture global perception in the fringe images, while a CNN branch is designed to collect local details in the speckle images. To fully integrate complementary features, we design a double-stream attention aggregation module (DAAM) that consist of a parallel attention subnetwork for aggregating multi-scale spatial structure information. This module can dynamically retain local and global representations to the maximum extent. Moreover, an adaptive mixture density head with bimodal Gaussian distribution is proposed for learning a representation that is precise near discontinuities. Compared to the standard disparity regression strategy, this adaptive mixture head can effectively improves performance at object boundaries. Extensive experiments demonstrate that our method can reduce fringe order ambiguity while producing high-accuracy results on a self-made dataset. We also show that the proposed architecture reveals the potential in infrared-visible image fusion task.

Azimuthal anisotropy has been ubiquitously observed in high-energy proton-proton, proton-nucleus, and nucleus-nucleus (heavy-ion) collisions, shaking the early belief that those anisotropies must stem from utterly strong interactions. This work reports a study of anisotropic expansion of cold $^{6}$Li Fermi gases, initially trapped in an anisotropic potential, as a function of the interaction strength that can be readily tuned by an external magnetic field. It is found that the expansion anisotropy builds up quickly at small interaction strength, without the need of utterly strong interactions. A universal behavior of the expansion anisotropy is quantitatively observed between cold-atom and heavy-ion systems, despite their vast differences in physics. This universality will potentially unify a variety of disciplines in nature, from the weakly interacting dilute systems of gases to the strongly interacting quark-gluon plasma of the early universe., Comment: 11 pages, 11 figures

First-principles calculations have been used to investigate the electronic and topological properties of the two-dimensional C6-2x(BN)x biphenylene network, a graphene-like structure composed of not only hexagonal ring but also octagonal and square rings. Nontrivial topological properties have been found in two of them, with a stoichiometry of C4BN and C2(BN)2. The former C4BN is predicted to be a type-II Dirac semimetal with a superconducting critical temperature Tc=0.38K, which is similar to the pure carbon biphenylene network (C-BPN). The latter shows a novel isolated edge state exists between the conduction and valence bands. By regulation of strains and virtual-crystal approximation calculations, we found the annihilation of two pairs of Dirac points (DPs) in the non-high symmetric region (non-HSR) causes the two corresponding edge states stick together to generate this isolated edge state. In addition, we found that one pair of DPs arises from the shift of DPs in the C-BPN, while another new pair of DPs emerges around the Time Reversal Invariant Momenta (TRIM) point X due to the doping of boron and nitrogen. We constructed a tight-binding (TB) model to reveal the mechanism of forming the isolated edge state from the C-BPN to C2(BN)2. This study not only demonstrates the existence and mechanism of forming the isolated edge state in semimetals, but also provides an example in which the DPs can move away from the high-symmetry region., Comment: 32 pages, 10 figures, with supplementary materials

U-based binary alloys have been widely adopted in fast nuclear reactors, but their stability under extreme conditions of high-pressure is almost unknown, mounting up to latent risk in applications. Here, possible ordered phases in U-Zr system up to 200 GPa are comprehensively investigated by unbiased first-principles structure prediction. Stable U2Zr, metastable U3Zr and U4Zr phases are discovered for the first time, which exhibit strong stability under compression. They all are metallic, with 5f electrons of uranium dominating the electronic density of states near the Fermi level. Prominent ionic interactions between U and Zr atoms, as well as covalent interactions between adjacent uranium atoms, are found. The same strategy is applied to explore the stability of ordered phases in other U-based binary transition metal alloys, U-X (X= Sc, Ti, V, Cr, Y, Nb, Mo, Hf, Ta, W). Stable and metastable ordered phases similar to U-Zr alloy are unveiled, all with similar electronic structures. For these alloys, we find that the structure of U2X (X=Zr, Ti, Hf) hosts a unique hybrid phase transition similar to U2Nb, which is a superposition of a first-order transition and a second-order transition. The prediction of these novel phases not only refutes the stability of the long-believed ordered phase I4/mmm-U2Mo, but also rewrites the phase diagrams of U-X (X= Zr, Sc, Ti, V, Cr, Nb, Mo, Hf, Ta) alloys under high pressure. All of these findings promote our understanding of the high-pressure behavior of the broad category of U-based binary alloys with transition metals., Comment: 27 pages, 12 figures, with supplementary material

The boundary critical behavior of the two-dimensional (2D) quantum antiferromagnetic(AF) XXZ model coupled with either a dangling spin-1/2 XXZ chain or a dangling two-leg ladder on the boundary is studied with the bosonization and renormalization group analysis. A rich boundary phase diagram is obtained in each case. In the dangling chain case, the boundary either develops a long-range AF order in the easy-plane or the easy-axis direction with extraordinary critical behavior, or has a valence bond solid order with ordinary boundary critical behavior. In the case of a dangling two-leg ladder, besides the possible easy-plane or easy-axis AF ordered phases on the boundary with extraordinary critical behavior, the boundary can form a singlet phase with ordinary critical behavior without breaking any symmetry.

Detecting players from sports broadcast videos is essential for intelligent event analysis. However, existing methods assume fixed player categories, incapably accommodating the real-world scenarios where categories continue to evolve. Directly fine-tuning these methods on newly emerging categories also exist the catastrophic forgetting due to the non-stationary distribution. Inspired by recent research on incremental object detection (IOD), we propose a Refined Response Distillation (R^2D) method to effectively mitigate catastrophic forgetting for IOD tasks of the players. Firstly, we design a progressive coarse-to-fine distillation region dividing scheme, separating high-value and low-value regions from classification and regression responses for precise and fine-grained regional knowledge distillation. Subsequently, a tailored refined distillation strategy is developed on regions with varying significance to address the performance limitations posed by pronounced feature homogeneity in the IOD tasks of the players. Furthermore, we present the NBA-IOD and Volleyball-IOD datasets as the benchmark and investigate the IOD tasks of the players systematically. Extensive experiments conducted on benchmarks demonstrate that our method achieves state-of-the-art results.The code and datasets are available at https://github.com/beiyan1911/Players-IOD., Comment: 13 pages, 10 figures

As correlation strength has a key influence on the simulation of strongly correlated materials, many approaches have been proposed to obtain the parameter using first-principles calculations. However, the comparison of the different Coulomb strengths obtained using these approaches and an investigation of the mechanisms behind them are still needed. Taking lanthanide metals as an example, we research the factors that affect the effective Coulomb interaction strength, $U_{\rm eff}$, by local screened Coulomb correction (LSCC), linear response (LR) and constrained random-phase approximation (cRPA) in VASP. The $U^{\rm LSCC}_{\rm eff}$ value increases from 4.75 eV to 7.78 eV, $U^{\rm LR}_{\rm eff}$ is almost stable at about 6.0 eV (except for Eu, Er and Lu), and $U^{\rm cRPA}_{\rm eff}$ shows a two-stage decreasing trend in both light and heavy lanthanides. To investigate these differences, we established a scheme to analyze coexistence and competition between the orbital localization and the screening effect. We find that LSCC and cRPA are dominated by the orbital localization and the screening effect, respectively, whereas LR shows a balance of the competition between the two factors. Additionally, the performance of these approaches is influenced by different starting points from PBE and PBE+$U$, especially for cRPA. Our results provide useful knowledge for understanding the $U_{\rm eff}$ of lanthanide materials, and similar analyses can also be used in the research of other correlation strength simulation approaches., Comment: 10 pages, 8 figures, 1 table

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component.