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Floating edge bands (FEB) have been identified in systems such as obstructed atomic insulators and layered nonsymmorphic semimetals, attracting considerable interest recently. Here we demonstrate that FEB can arise in a simplified model incorporating altermagnetism. By enhancing the Bernevig-Hughes-Zhang model on a square lattice with additional altermagnetic and Zeeman fields perpendicular to the 2D plane, we uncover the emergence of FEB that are distinct from the bulk bands across the entire Brillouin zone and over broad parameter regimes. We calculate topological phase diagrams, highlighting the strong topological properties characterized by the Chern number and the weak topological properties marked by the winding number. Furthermore, we provide analytical results of the energy spectrum and the wave functions of the FEB. We also study the robustness of the FEB, showcasing its resilience against various perturbations such as geometric rotation, energy spectrum asymmetry, and spin coupling. Our findings advance our understanding of FEB and may pave new avenues for further exploration of topological phases in quantum materials., Comment: 10 pages, 4 figures

The modeling and manipulation of 3D scenes captured from the real world are pivotal in various applications, attracting growing research interest. While previous works on editing have achieved interesting results through manipulating 3D meshes, they often require accurately reconstructed meshes to perform editing, which limits their application in 3D content generation. To address this gap, we introduce a novel single-image-driven 3D scene editing approach based on 3D Gaussian Splatting, enabling intuitive manipulation via directly editing the content on a 2D image plane. Our method learns to optimize the 3D Gaussians to align with an edited version of the image rendered from a user-specified viewpoint of the original scene. To capture long-range object deformation, we introduce positional loss into the optimization process of 3D Gaussian Splatting and enable gradient propagation through reparameterization. To handle occluded 3D Gaussians when rendering from the specified viewpoint, we build an anchor-based structure and employ a coarse-to-fine optimization strategy capable of handling long-range deformation while maintaining structural stability. Furthermore, we design a novel masking strategy to adaptively identify non-rigid deformation regions for fine-scale modeling. Extensive experiments show the effectiveness of our method in handling geometric details, long-range, and non-rigid deformation, demonstrating superior editing flexibility and quality compared to previous approaches., Comment: 10 pages, 12 figures

Currently, lung cancer is a leading cause of global cancer mortality, often necessitating minimally invasive interventions. Microwave ablation (MWA) is extensively utilized for both primary and secondary lung tumors. Although numerous clinical guidelines and standards for MWA have been established, the clinical evaluation of ablation surgery remains challenging and requires long-term patient follow-up for confirmation. In this paper, we propose a method termed respiratory subtraction to evaluate lung tumor ablation therapy performance based on pre- and post-operative image guidance. Initially, preoperative images undergo coarse rigid registration to their corresponding postoperative positions, followed by further non-rigid registration. Subsequently, subtraction images are generated by subtracting the registered preoperative images from the postoperative ones. Furthermore, to enhance the clinical assessment of MWA treatment performance, we devise a quantitative analysis metric to evaluate ablation efficacy by comparing differences between tumor areas and treatment areas. To the best of our knowledge, this is the pioneering work in the field to facilitate the assessment of MWA surgery performance on pulmonary tumors. Extensive experiments involving 35 clinical cases further validate the efficacy of the respiratory subtraction method. The experimental results confirm the effectiveness of the respiratory subtraction method and the proposed quantitative evaluation metric in assessing lung tumor treatment.

Jet-associated deuteron production in $pp$ collisions at $\sqrt{s}=13$ TeV and $p$-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV is studied in the coalescence model by using the phase-space information of proton and neutron pairs from a multiphase transport (AMPT) model at the kinetic freezeout. In the low transverse momentum ($p_T$) region $p_T/A < 1.5$ GeV/$c$, where $A$ is the mass number of a nucleus, the in-jet coalescence factor $B_2^\text{In-jet}$ for deuteron production, given by the ratio of the in-jet deuteron number to the square of the in-jet proton number, is found to be larger than the coalescence factor $B_2$ in the medium perpendicular to the jet by a factor of about 10 in $pp$ collisions and of 25 in $p-$Pb collisions, which are consistent with the ALICE measurements at the LHC. Such large low-momentum enhancements mainly come from coalescence of nucleons inside the jet with the medium nucleons. Coalescence of nucleons inside the jet dominates deuteron production only at the higher $p_T$ region of $p_T/A\gtrsim 4$ GeV/$c$, where both the yield ratio $d/p$ of deuteron to proton numbers and the $B_2$ are also significantly larger in the jet direction than in the direction perpendicular to the jet due to the strong collinear correlation among particles produced from jet fragmentation. Studying jet-associated deuteron production in relativistic nuclear collisions thus opens up a new window to probe the phase-space structure of nucleons inside jets.

We study the influence of external electric and Zeeman fields on the Josephson response of a planar superconductor/altermagnet/superconductor junction. We show that the critical Josephson current across the junction oscillates as the altermagnetic field strength increases. The current-phase relation can be forward- or backward-skewed due to significant second-order Josephson response. It decisively depends on the altermagnetic field strength. We discover that the skewness can be manipulated by electric gating. We reveal two additional impacts of external fields on the Josephson current: (i) Fields can induce 0-$\pi$ transitions. (ii) Fields can substantially enhance the critical current., Comment: 6 pages + 2 pages

In underwater image enhancement (UIE), convolutional neural networks (CNN) have inherent limitations in modeling long-range dependencies and are less effective in recovering global features. While Transformers excel at modeling long-range dependencies, their quadratic computational complexity with increasing image resolution presents significant efficiency challenges. Additionally, most supervised learning methods lack effective physical model constraint, which can lead to insufficient realism and overfitting in generated images. To address these issues, we propose a physical model constraint-based underwater image enhancement framework, Mamba-UIE. Specifically, we decompose the input image into four components: underwater scene radiance, direct transmission map, backscatter transmission map, and global background light. These components are reassembled according to the revised underwater image formation model, and the reconstruction consistency constraint is applied between the reconstructed image and the original image, thereby achieving effective physical constraint on the underwater image enhancement process. To tackle the quadratic computational complexity of Transformers when handling long sequences, we introduce the Mamba-UIE network based on linear complexity state space models. By incorporating the Mamba in Convolution block, long-range dependencies are modeled at both the channel and spatial levels, while the CNN backbone is retained to recover local features and details. Extensive experiments on three public datasets demonstrate that our proposed Mamba-UIE outperforms existing state-of-the-art methods, achieving a PSNR of 27.13 and an SSIM of 0.93 on the UIEB dataset. Our method is available at https://github.com/zhangsong1213/Mamba-UIE.

In the fast-growing field of Remote Sensing (RS) image analysis, the gap between massive unlabeled datasets and the ability to fully utilize these datasets for advanced RS analytics presents a significant challenge. To fill the gap, our work introduces an innovative auto-labeling framework named ALPS (Automatic Labeling for Pre-training in Segmentation), leveraging the Segment Anything Model (SAM) to predict precise pseudo-labels for RS images without necessitating prior annotations or additional prompts. The proposed pipeline significantly reduces the labor and resource demands traditionally associated with annotating RS datasets. By constructing two comprehensive pseudo-labeled RS datasets via ALPS for pre-training purposes, our approach enhances the performance of downstream tasks across various benchmarks, including iSAID and ISPRS Potsdam. Experiments demonstrate the effectiveness of our framework, showcasing its ability to generalize well across multiple tasks even under the scarcity of extensively annotated datasets, offering a scalable solution to automatic segmentation and annotation challenges in the field. In addition, the proposed a pipeline is flexible and can be applied to medical image segmentation, remarkably boosting the performance. Note that ALPS utilizes pre-trained SAM to semi-automatically annotate RS images without additional manual annotations. Though every component in the pipeline has bee well explored, integrating clustering algorithms with SAM and novel pseudo-label alignment significantly enhances RS segmentation, as an off-the-shelf tool for pre-training data preparation. Our source code is available at: https://github.com/StriveZs/ALPS.

The production of light nuclei in isobaric $^{96}_{44}$Ru + $^{96}_{44}$Ru and $^{96}_{40}$Zr + $^{96}_{40}$Zr collisions, ranging from $\sqrt{s_{NN}}$ = 7.7 to 200 GeV, are studied using the string melting version of A Multi Phase Transport (AMPT) model in combination with a coalescence approach to light nuclei production. From the calculated yields, transverse momentum $p_{T}$ spectra, and rapidity dependences of light nuclei $p$, $n$, $d$, $t$, ${}^{3}$He, we find that the Ru+Ru/Zr+Zr ratios for the yields of these particles exceed unity with the inclusion of a quadrupole deformation $\beta_{ 2 }$ and octupole deformation $\beta_{ 3 }$ as well as the neutron skins. We also find that heavier particles have a larger deviation from unity. Furthermore, we find that as the collision energy increases, the influence of isospin effects on the production of light nuclei in isobar collisions gradually decreases, while the influence of nuclear structure becomes more significant, particularly evident from the energy dependence of the deuteron ratio, which is unaffected by isospin effects., Comment: 9 pages, 5 figures

Accurately reconstructing road surfaces is pivotal for various applications especially in autonomous driving. This paper introduces a position encoding Multi-Layer Perceptrons (MLPs) framework to reconstruct road surfaces, with input as world coordinates x and y, and output as height, color, and semantic information. The effectiveness of this method is demonstrated through its compatibility with a variety of road height sources like vehicle camera poses, LiDAR point clouds, and SFM point clouds, robust to the semantic noise of images like sparse labels and noise semantic prediction, and fast training speed, which indicates a promising application for rendering road surfaces with semantics, particularly in applications demanding visualization of road surface, 4D labeling, and semantic groupings.

Creating high-fidelity head avatars from multi-view videos is a core issue for many AR/VR applications. However, existing methods usually struggle to obtain high-quality renderings for all different head components simultaneously since they use one single representation to model components with drastically different characteristics (e.g., skin vs. hair). In this paper, we propose a Hybrid Mesh-Gaussian Head Avatar (MeGA) that models different head components with more suitable representations. Specifically, we select an enhanced FLAME mesh as our facial representation and predict a UV displacement map to provide per-vertex offsets for improved personalized geometric details. To achieve photorealistic renderings, we obtain facial colors using deferred neural rendering and disentangle neural textures into three meaningful parts. For hair modeling, we first build a static canonical hair using 3D Gaussian Splatting. A rigid transformation and an MLP-based deformation field are further applied to handle complex dynamic expressions. Combined with our occlusion-aware blending, MeGA generates higher-fidelity renderings for the whole head and naturally supports more downstream tasks. Experiments on the NeRSemble dataset demonstrate the effectiveness of our designs, outperforming previous state-of-the-art methods and supporting various editing functionalities, including hairstyle alteration and texture editing., Comment: Project page: https://conallwang.github.io/MeGA_Pages/

Recent research that combines implicit 3D representation with semantic information, like Semantic-NeRF, has proven that NeRF model could perform excellently in rendering 3D structures with semantic labels. This research aims to extend the Semantic Neural Radiance Fields (Semantic-NeRF) model by focusing solely on semantic output and removing the RGB output component. We reformulate the model and its training procedure to leverage only the cross-entropy loss between the model semantic output and the ground truth semantic images, removing the colour data traditionally used in the original Semantic-NeRF approach. We then conduct a series of identical experiments using the original and the modified Semantic-NeRF model. Our primary objective is to obverse the impact of this modification on the model performance by Semantic-NeRF, focusing on tasks such as scene understanding, object detection, and segmentation. The results offer valuable insights into the new way of rendering the scenes and provide an avenue for further research and development in semantic-focused 3D scene understanding.

3D Gaussian splatting, emerging as a groundbreaking approach, has drawn increasing attention for its capabilities of high-fidelity reconstruction and real-time rendering. However, it couples the appearance and geometry of the scene within the Gaussian attributes, which hinders the flexibility of editing operations, such as texture swapping. To address this issue, we propose a novel approach, namely Texture-GS, to disentangle the appearance from the geometry by representing it as a 2D texture mapped onto the 3D surface, thereby facilitating appearance editing. Technically, the disentanglement is achieved by our proposed texture mapping module, which consists of a UV mapping MLP to learn the UV coordinates for the 3D Gaussian centers, a local Taylor expansion of the MLP to efficiently approximate the UV coordinates for the ray-Gaussian intersections, and a learnable texture to capture the fine-grained appearance. Extensive experiments on the DTU dataset demonstrate that our method not only facilitates high-fidelity appearance editing but also achieves real-time rendering on consumer-level devices, e.g. a single RTX 2080 Ti GPU.

It is important to understand whether $\alpha$-clustering structures can leave traces in ultra-relativistic heavy ion collisions. Using the modified AMPT model, we simulate three $\alpha$ + core configurations of $^{44}$Ti in $^{44}$Ti+$^{44}$Ti collisions at $\sqrt{s_{NN}}=5.02$ TeV as well as other systems with Woods-Saxon structures. One of these configurations has no additional constraint, but the other two have the Mott density edge $r_{\mathrm{Mott}}$ set as either a lower or upper bound on the cluster position $r_{\alpha}$ to check the influence of $\alpha$ dissolution. This is the first time that the initial stage of the geometric properties in heavy-ion collisions has been configured using the traditional treatment of the nuclear structure. We compare the radial nucleon density, multiplicity distribution, transverse momentum spectra, eccentricity, triangularity, elliptic flow and triangular flow of these six systems. $\alpha$ + core structures can alter all these observations especially in the most-central collisions, among which elliptic flow is the most hopeful as a probe of such structures., Comment: 11 pages, 8 figures; Ero. Phys. J. A, in press (2024)

Depth perception is crucial for a wide range of robotic applications. Multi-frame self-supervised depth estimation methods have gained research interest due to their ability to leverage large-scale, unlabeled real-world data. However, the self-supervised methods often rely on the assumption of a static scene and their performance tends to degrade in dynamic environments. To address this issue, we present Motion-Aware Loss, which leverages the temporal relation among consecutive input frames and a novel distillation scheme between the teacher and student networks in the multi-frame self-supervised depth estimation methods. Specifically, we associate the spatial locations of moving objects with the temporal order of input frames to eliminate errors induced by object motion. Meanwhile, we enhance the original distillation scheme in multi-frame methods to better exploit the knowledge from a teacher network. MAL is a novel, plug-and-play module designed for seamless integration into multi-frame self-supervised monocular depth estimation methods. Adding MAL into previous state-of-the-art methods leads to a reduction in depth estimation errors by up to 4.2% and 10.8% on KITTI and CityScapes benchmarks, respectively., Comment: Accepted by ICRA 2024; Project homepage: https://yuejiangdong.github.io/MotionAwareLoss/

Axions or axion-like particles (ALPs) are one of the most promising candidates of dark matter (DM). A prevalent method to detect axion-like DM is to seek the periodic oscillation feature in the polarization angles of linearly polarized light emitted from astrophysical sources. In this work, we use the time-resolved polarization measurements of the hyperactive repeating fast radio burst, FRB 20220912A, detected by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for extragalactic axion-like DM for the first time. Given a DM density profile of FRB 20220912A's host, we obtain upper limits on the ALP-photon coupling constant of $g_{a \gamma}<(2.9 \times 10^{-11}-1.1\times 10^{-9})\,\mathrm{GeV}^{-1}$ for the ALP masses $m_a \sim (1.4\times10^{-21}-5.2\times10^{-20})$ eV. Persistent polarimetric observations with FAST would further improve the constraints. We prove that FRBs offer an alternative way to detect axion-like DM on extragalactic distance scales, complementary to other galactic DM probes., Comment: 7 pages, 2 figures

Momentum correlation functions between $\pi$, $K$ and $p$ are calculated for several heavy-ion collision systems, namely $_{5}^{10}\textrm{B}+_{5}^{10}\textrm{B}$, $_{8}^{16}\textrm{O}+_{8}^{16}\textrm{O}$, $_{20}^{40}\textrm{Ca}+_{20}^{40}\textrm{Ca}$ and $_{79}^{197}\textrm{Au}+_{79}^{197}\textrm{Au}$ in central collisions as well as $_{79}^{197}\textrm{Au}+_{79}^{197}\textrm{Au}$ collision in different centralities at center of mass energy $\sqrt{s_{NN}}$ = 39 GeV within the framework of A Multi-Phase Transport (AMPT) model complemented by the Lednick$\acute{y}$ and Lyuboshitz analytical method. The results present the centrality and system-size dependence of the momentum correlation functions among pairs of $\pi$, $K$ and $p$, from which the emission source-size can be deduced. It is found that the deduced source sizes increase with the decreasing of centrality for Au + Au system or with the increasing of system-size in central collisions with different nuclear size. In addition, through the momentum correlation functions of nonidentical particle pairs gated on velocity, the average emission sequence of non-identical particles can be indicated. The results illustrate that in the small relative momentum region, protons are emitted in average earlier than $\pi^+$ and $K^+$, and $K^+$ are emitted averagely earlier than $\pi^+$. Furthermore, it seems that larger interval of the average emission order among them is exhibited for smaller collision systems. The present study sheds light on the dynamics of light particle emission at RHIC energy., Comment: 11 pages, 14 figures; Physical Review C, accepted. arXiv admin note: substantial text overlap with arXiv:2301.02846

Self-supervised monocular depth estimation is of significant importance with applications spanning across autonomous driving and robotics. However, the reliance on self-supervision introduces a strong static-scene assumption, thereby posing challenges in achieving optimal performance in dynamic scenes, which are prevalent in most real-world situations. To address these issues, we propose PPEA-Depth, a Progressive Parameter-Efficient Adaptation approach to transfer a pre-trained image model for self-supervised depth estimation. The training comprises two sequential stages: an initial phase trained on a dataset primarily composed of static scenes, succeeded by an expansion to more intricate datasets involving dynamic scenes. To facilitate this process, we design compact encoder and decoder adapters to enable parameter-efficient tuning, allowing the network to adapt effectively. They not only uphold generalized patterns from pre-trained image models but also retain knowledge gained from the preceding phase into the subsequent one. Extensive experiments demonstrate that PPEA-Depth achieves state-of-the-art performance on KITTI, CityScapes and DDAD datasets., Comment: Accepted by AAAI 2024 Project homepage: https://yuejiangdong.github.io/PPEADepth/

Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despite their progress, these techniques often face limitations due to slow optimization or rendering processes, leading to extensive training and optimization times. In this paper, we introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes two transformer-based networks, namely a point decoder and a triplane decoder, to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation. This hybrid representation strikes a balance, achieving a faster rendering speed compared to implicit representations while simultaneously delivering superior rendering quality than explicit representations. The point decoder is designed for generating point clouds from single images, offering an explicit representation which is then utilized by the triplane decoder to query Gaussian features for each point. This design choice addresses the challenges associated with directly regressing explicit 3D Gaussian attributes characterized by their non-structural nature. Subsequently, the 3D Gaussians are decoded by an MLP to enable rapid rendering through splatting. Both decoders are built upon a scalable, transformer-based architecture and have been efficiently trained on large-scale 3D datasets. The evaluations conducted on both synthetic datasets and real-world images demonstrate that our method not only achieves higher quality but also ensures a faster runtime in comparison to previous state-of-the-art techniques. Please see our project page at https://zouzx.github.io/TriplaneGaussian/., Comment: Project Page: https://zouzx.github.io/TriplaneGaussian/

Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of controllable high-quality image generation from arbitrary text prompts. However, the generation quality and generalization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets. In this paper, we present PI3D, a framework that fully leverages the pre-trained text-to-image diffusion models' ability to generate high-quality 3D shapes from text prompts in minutes. The core idea is to connect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB Images. We fine-tune an existing text-to-image diffusion model to produce such pseudo-images using a small number of text-3D pairs. Surprisingly, we find that it can already generate meaningful and consistent 3D shapes given complex text descriptions. We further take the generated shapes as the starting point for a lightweight iterative refinement using score distillation sampling to achieve high-quality generation under a low budget. PI3D generates a single 3D shape from text in only 3 minutes and the quality is validated to outperform existing 3D generative models by a large margin., Comment: Accepted by CVPR 2024

Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of information from multiple views, these works encounter difficulties in generating controllable novel views. In this paper, we present DreamComposer, a flexible and scalable framework that can enhance existing view-aware diffusion models by injecting multi-view conditions. Specifically, DreamComposer first uses a view-aware 3D lifting module to obtain 3D representations of an object from multiple views. Then, it renders the latent features of the target view from 3D representations with the multi-view feature fusion module. Finally the target view features extracted from multi-view inputs are injected into a pre-trained diffusion model. Experiments show that DreamComposer is compatible with state-of-the-art diffusion models for zero-shot novel view synthesis, further enhancing them to generate high-fidelity novel view images with multi-view conditions, ready for controllable 3D object reconstruction and various other applications., Comment: Project Page: https://yhyang-myron.github.io/DreamComposer/

Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models. While the usage of classifier-free guidance is well acknowledged to be crucial for successful optimization, it is considered an auxiliary trick rather than the most essential component. In this paper, we re-evaluate the role of classifier-free guidance in score distillation and discover a surprising finding: the guidance alone is enough for effective text-to-3D generation tasks. We name this method Classifier Score Distillation (CSD), which can be interpreted as using an implicit classification model for generation. This new perspective reveals new insights for understanding existing techniques. We validate the effectiveness of CSD across a variety of text-to-3D tasks including shape generation, texture synthesis, and shape editing, achieving results superior to those of state-of-the-art methods. Our project page is https://xinyu-andy.github.io/Classifier-Score-Distillation, Comment: Our project page is https://xinyu-andy.github.io/Classifier-Score-Distillation

In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images.Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. To holistically improve the quality, consistency, and efficiency of image-to-3D tasks, we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure consistency, we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a geometry-aware normal fusion algorithm that extracts high-quality surfaces from the multi-view 2D representations. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and reasonably good efficiency compared to prior works., Comment: Project page: https://www.xxlong.site/Wonder3D/

While models derived from Vision Transformers (ViTs) have been phonemically surging, pre-trained models cannot seamlessly adapt to arbitrary resolution images without altering the architecture and configuration, such as sampling the positional encoding, limiting their flexibility for various vision tasks. For instance, the Segment Anything Model (SAM) based on ViT-Huge requires all input images to be resized to 1024$\times$1024. To overcome this limitation, we propose the Multi-Head Self-Attention Convolution (MSA-Conv) that incorporates Self-Attention within generalized convolutions, including standard, dilated, and depthwise ones. Enabling transformers to handle images of varying sizes without retraining or rescaling, the use of MSA-Conv further reduces computational costs compared to global attention in ViT, which grows costly as image size increases. Later, we present the Vision Transformer in Convolution (TiC) as a proof of concept for image classification with MSA-Conv, where two capacity enhancing strategies, namely Multi-Directional Cyclic Shifted Mechanism and Inter-Pooling Mechanism, have been proposed, through establishing long-distance connections between tokens and enlarging the effective receptive field. Extensive experiments have been carried out to validate the overall effectiveness of TiC. Additionally, ablation studies confirm the performance improvement made by MSA-Conv and the two capacity enhancing strategies separately. Note that our proposal aims at studying an alternative to the global attention used in ViT, while MSA-Conv meets our goal by making TiC comparable to state-of-the-art on ImageNet-1K. Code will be released at https://github.com/zs670980918/MSA-Conv.

We propose a novel type of skin effects in non-Hermitian quantum many-body systems which we dub a non-Hermitian Mott skin effect. This phenomenon is induced by the interplay between strong correlations and the non-Hermitian point-gap topology. The Mott skin effect induces extreme sensitivity to the boundary conditions only in the spin degree of freedom (i.e., the charge distribution is not sensitive to boundary conditions), which is in sharp contrast to the ordinary non-Hermitian skin effect in non-interacting systems. Concretely, we elucidate that a bosonic non-Hermitian chain exhibits the Mott skin effect in the strongly correlated regime by closely examining an effective Hamiltonian. The emergence of the Mott skin effect is also supported by numerical diagonalization of the bosonic chain. The difference between the ordinary non-Hermitian skin effect and the Mott skin effect is also reflected in the time-evolution of physical quantities; under the time-evolution spin accumulation is observed while the charge distribution remains spatially uniform., Comment: 8+17 pages, 3+16 figures, discussions of Liouvillian dynamics are revised

The Benalcazar-Bernevig-Hughes (BBH) quadrupole insulator model is a cornerstone model for higher-order topological phases. It requires \pi-flux threading through each plaquette of the two-dimensional Su-Schrieffer-Heeger model. Recent studies showed that particular \pi-flux patterns can modify the fundamental domain of momentum space from the shape of a torus to a Klein bottle with emerging topological phases. By designing different \pi-flux patterns, we propose two types of Klein-bottle BBH models. These models show rich topological phases, including Klein-bottle quadrupole insulators and Dirac semimetals. The phase with nontrivial Klein-bottle topology shows twined edge modes at open boundaries. These edge modes can further support second-order topology, yielding a quadrupole insulator. Remarkably, both models are robust against flux perturbations. Moreover, we show that different \pi-flux patterns dramatically affect the phase diagram of the Klein-bottle BBH models. Going beyond the original BBH model, Dirac semimetal phases emerge in Klein-bottle BBH models featured by the coexistence of twined edge modes and bulk Dirac points., Comment: 11 pages, 11 figures

The longitudinal asymmetry in relativistic heavy ion collisions arises from the fluctuation in the number of nucleons involved. This asymmetry causes a rapidity shift in the center of mass of the participating zone. Both the rapidity shift and the longitudinal asymmetry have been found to be significant at the top CERN Large Hadron Collider (LHC) energy for collisions of identical nuclei, and the longitudinal asymmetry is important for reconstructing the colliding vertex and correcting the rapidity shift. However, much discussion of the longitudinal asymmetry has treated the initial condition as a nonzero momentum contributed only by the number of participants, i.e., the asymmetry depends only on the number of participating nucleons. So we naturally raise a physical problem, can other initial conditions, such as two typical initial conditions for nuclei, geometric configuration, and momentum distribution, provide effects on the longitudinal asymmetry? Therefore, in this work we consider other effects on the longitudinal asymmetry other than the fluctuation in the number of participants, e.g., the {\alpha} clustering structure as well as the intrinsic momentum distribution in the target and projectile nuclei for the collisions in the framework of a multiphase transport (AMPT) model. By introducing systems with different {\alpha}-clustering structure and intrinsic momentum distribution, we calculated the ratio of the rapidity distributions of different systems and extracted expansion coefficients to analyze the difference contributed by these factors. ..., Comment: 13 pages, 7 figures

Reconstructing 3D objects from extremely sparse views is a long-standing and challenging problem. While recent techniques employ image diffusion models for generating plausible images at novel viewpoints or for distilling pre-trained diffusion priors into 3D representations using score distillation sampling (SDS), these methods often struggle to simultaneously achieve high-quality, consistent, and detailed results for both novel-view synthesis (NVS) and geometry. In this work, we present Sparse3D, a novel 3D reconstruction method tailored for sparse view inputs. Our approach distills robust priors from a multiview-consistent diffusion model to refine a neural radiance field. Specifically, we employ a controller that harnesses epipolar features from input views, guiding a pre-trained diffusion model, such as Stable Diffusion, to produce novel-view images that maintain 3D consistency with the input. By tapping into 2D priors from powerful image diffusion models, our integrated model consistently delivers high-quality results, even when faced with open-world objects. To address the blurriness introduced by conventional SDS, we introduce the category-score distillation sampling (C-SDS) to enhance detail. We conduct experiments on CO3DV2 which is a multi-view dataset of real-world objects. Both quantitative and qualitative evaluations demonstrate that our approach outperforms previous state-of-the-art works on the metrics regarding NVS and geometry reconstruction.