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Non-Hermitian skin effects (NHSEs) have recently been investigated extensively at the single-particle level. When many-body interactions become dominant, novel non-Hermitian physical phenomena can emerge. In this work, we theoretically study NHSEs controlled by dissipation and interaction. We consider a 1D zigzag Bose-Hubbard lattice, subject to magnetic flux, staggered onsite single-particle loss, and uniform onsite two-particle loss. When the two-particle loss is small, two-body bound eigenstates (i.e., doublons) are all localized at the same boundary due to the interplay of the magnetic flux and staggered single-particle loss. While, for strong two-particle loss, the localization direction of doublons is unexpectedly reversed. This is attributed to the effective strong nonreciprocal hopping of doublons contributing from the virtual second-order and third-order hopping processes of particle pairs in combination with the magnetic flux, the strong two-particle loss, and the many-body interaction. Moreover, a two-particle gain can induce the same skin-localization of doublons, which can be utilized to dynamically observe the NHSE and its reversal of doublons controlled by interactions. Our results open up a new avenue for exploring novel non-Hermitian phenomena in many-body systems., Comment: 16 pages, 9 figures; Comments are welcome

The interplay of quantum emitters and non-Hermitian structured baths has received increasing attention in recent years. Here, we predict unconventional quantum optical behaviors of quantum emitters coupled to a non-Hermitian topological bath, which is realized in a 1D Su-Schrieffer-Heeger photonic chain subjected to nonlocal dissipation. In addition to the Hermitian-like chiral bound states in the middle line gap and skin-mode-like hidden bound states inside the point gap, we identify peculiar in-gap chiral and extended photon-emitter dressed states. This is due to the competition of topological-edge localization and non-Hermitian skin-mode localization in combination with the non-Bloch bulk-boundary correspondence. Furthermore, when two emitters are coupled to the same bath, such in-gap dressed states can mediate the nonreciprocal long-range emitter-emitter interactions, with the interaction range limited only by the dissipation of the bath. Our work opens the door to further study rich quantum optical phenomena and exotic many-body physics utilizing quantum emitters coupled to non-Hermitian topological baths., Comment: 22 pages, 10 figures; Comments are welcome

Let $G$ be a graph. For $x\in V(G)$, let $N(x)=\{y\in V(G)\colon xy\in E(G)\}$. The minimum common degree of $G$, denoted by $\delta_{2}(G)$, is defined as the minimum of $|N(x)\cap N(y)|$ over all non-edges $xy$ of $G$. In 1982, H\"{a}ggkvist showed that every triangle-free graph with minimum degree greater than $\lfloor\frac{3n}{8}\rfloor$ is homomorphic to a cycle of length 5. In this paper, we prove that every triangle-free graph with minimum common degree greater than $\lfloor\frac{n}{8}\rfloor$ is homomorphic to a cycle of length 5, which implies H\"{a}ggkvist's result. The balanced blow-up of the M\"{o}bius ladder graph shows that it is best possible., Comment: 11 pages, 9 figures

Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current monitoring technologies for detecting underwater litter face limitations in survey efficiency, cost, and environmental conditions, highlighting the need for efficient, consumer-grade technologies for automatic detection. This research introduces the Aerial-Aquatic Speedy Scanner (AASS) combined with Super-Resolution Reconstruction (SRR) and an improved YOLOv8 detection network. AASS enhances data acquisition efficiency over traditional methods, capturing high-quality images that accurately identify underwater waste. SRR improves image-resolution by mitigating motion blur and insufficient resolution, thereby enhancing detection tasks. Specifically, the RCAN model achieved the highest mean average precision (mAP) of 78.6% for detection accuracy on reconstructed images among the tested SRR models. With a magnification factor of 4, the SRR test set shows an improved mAP compared to the conventional bicubic set. These results demonstrate the effectiveness of the proposed method in detecting underwater litter., Comment: The earlier version of this conference paper was accepted at OCEANS 2024-Halifax, Canada and was selected for inclusion in the Student Poster Competition (SPC) Program

Hermit crabs play a crucial role in coastal ecosystems by dispersing seeds, cleaning up debris, and disturbing soil. They serve as vital indicators of marine environmental health, responding to climate change and pollution. Traditional survey methods, like quadrat sampling, are labor-intensive, time-consuming, and environmentally dependent. This study presents an innovative approach combining UAV-based remote sensing with Super-Resolution Reconstruction (SRR) and the CRAB-YOLO detection network, a modification of YOLOv8s, to monitor hermit crabs. SRR enhances image quality by addressing issues such as motion blur and insufficient resolution, significantly improving detection accuracy over conventional low-resolution fuzzy images. The CRAB-YOLO network integrates three improvements for detection accuracy, hermit crab characteristics, and computational efficiency, achieving state-of-the-art (SOTA) performance compared to other mainstream detection models. The RDN networks demonstrated the best image reconstruction performance, and CRAB-YOLO achieved a mean average precision (mAP) of 69.5% on the SRR test set, a 40% improvement over the conventional Bicubic method with a magnification factor of 4. These results indicate that the proposed method is effective in detecting hermit crabs, offering a cost-effective and automated solution for extensive hermit crab monitoring, thereby aiding coastal benthos conservation., Comment: The earlier version of this conference paper was presented at OCEANS 2024-Singapore and was selected for inclusion in the Student Poster Competition (SPC) Program

Deep learning-based methods for Time Series Classification (TSC) typically utilize deep networks to extract features, which are then processed through a combination of a Fully Connected (FC) layer and a SoftMax function. However, we have observed the phenomenon of inter-class similarity and intra-class inconsistency in the datasets from the UCR archive and further analyzed how this phenomenon adversely affects the "FC+SoftMax" paradigm. To address the issue, we introduce ECR, which, for the first time to our knowledge, applies deep learning-based retrieval algorithm to the TSC problem and integrates classification and retrieval models. Experimental results on 112 UCR datasets demonstrate that ECR is state-of-the-art(sota) compared to existing deep learning-based methods. Furthermore, we have developed a more precise classifier, ECRTime, which is an ensemble of ECR. ECRTime surpasses the currently most accurate deep learning classifier, InceptionTime, in terms of accuracy, achieving this with reduced training time and comparable scalability.

The non-Hermitian skin effect (NHSE), featured by the collapse of bulk-band eigenstates into the localized boundary modes of the systems, is one of most striking properties in the fields of non-Hermitian physics. Unique physical phenomena related to the NHSE have attracted a lot of interest, however, their experimental realizations usually require nonreciprocal hopping, which faces a great challenge in ultracold-atom systems. In this work, we propose to realize the NHSE in a 1D optical lattice by periodically-driven ultracold atoms in the presence of staggered atomic loss. By studying the effective Floquet Hamiltonian in the high-frequency approximation, we reveal the underlying mechanism for the periodic-driving-induced the NHSE. We found that the robust NHSE can be tuned by driving phase, which is manifested by the dynamical localization. Most remarkably, we uncover the periodic-driving-induced critical skin effect for two coupled chains with different driving phases, accompanied by the appearance of size-dependent topological in-gap modes. Our studies provide a feasible way for observing the NHSE and exploring corresponding unique physical phenomena due to the interplay of non-Hermiticity and many-body statistics in ultracold-atom systems., Comment: 10 pages, 9 figures

The interplay of non-Hermiticity and disorder dramatically influences system's localization properties, giving rise to intriguing quantum phenomena. Although the intrinsic non-Hermitian skin effect (NHSE) is robust against the weak disorder even in a one-dimensional system, it becomes Anderson localization under strong disorder. Here, we study a localization-delocalization transition by coupling a strongly disordered Hatano-Nelson (HN) chain to a disordered Hermitian chain with its disorder correlated to that of HN chain. Regardless of the disorder strength, as the coupling strength between HN and Hermitian chain increases, a delocalization transition occurs. This leads to a reentrant NHSE due to the interplay of non-Hermiticity and correlated disorder. We reveal the underlying mechanism for the reentrant NHSE under correlated disorder. Moreover, the localization-delocalization transition is well captured by the real-space winding number., Comment: 7 pages, 5 figures

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on the granular ball (GB). However, the stability and efficiency of existing GBG methods need to be further improved due to their strong dependence on $k$-means or $k$-division. In addition, GB-based classifiers only unilaterally consider the GB's geometric characteristics to construct classification rules, but the GB's quality is ignored. Therefore, in this paper, based on the attention mechanism, a fast and stable GBG (GBG++) method is proposed first. Specifically, the proposed GBG++ method only needs to calculate the distances from the data-driven center to the undivided samples when splitting each GB instead of randomly selecting the center and calculating the distances between it and all samples. Moreover, an outlier detection method is introduced to identify local outliers. Consequently, the GBG++ method can significantly improve effectiveness, robustness, and efficiency while being absolutely stable. Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary. Finally, the experimental results indicate that the proposed method outperforms several existing GB-based classifiers and classical machine learning classifiers on $24$ public benchmark datasets.

We investigate the effects of random onsite disorder on higher-order Thouless pumping of noninteracting fermionic Benalcazar-Bernevig-Hughes (BBH) model. The interplay of disorderinduced topological phase transition and delocalization-localization transition is extensively explored. The higher-order Thouless pumping is characterized by the quantized corner-to-corner charge transport and nonzero Chern number, and the delocalization-localization transition is analyzed by utilizing both inverse participation ratio and energy-level statistics. The results show that the quantized corner-to-corner charge transport is broken in the strong disorder, where the instantaneous bulk energy gap is closed due to effects of disorder. While, although the instantaneous eigenstates are localized, the charge transport remains quantized. This is attributed to delocalized Floquet states caused by the periodic driving. Furthermore, the phase transition from the quantized charge transport to topologically trivial pumping is accompanied by the disorder-induced delocalization-localization transition of Floquet states., Comment: 8 pages, 7figures

General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well designed loss function, which cannot guarantee that all vital information of source images is sufficiently extracted. In this work, we propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of self-supervised learning framework, hierarchical representations of source images can be efficiently extracted. In particular, interactive feature embedding models are tactfully designed to build a bridge between the self-supervised learning and infrared and visible image fusion learning, achieving vital information retention. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods.

Few-shot learning can find the latent structure information between the prior knowledge and the queried data by the similarity metric of meta-learning to construct the discriminative model for recognizing the new categories with the rare labeled samples. Most existing methods try to model the similarity relationship of the samples in the intra tasks, and generalize the model to identify the new categories. However, the relationship of samples between the separated tasks is difficultly considered because of the different metric criterion in the respective tasks. In contrast, the proposed high-order structure preserving graph neural network(HOSP-GNN) can further explore the rich structure of the samples to predict the label of the queried data on graph that enables the structure evolution to explicitly discriminate the categories by iteratively updating the high-order structure relationship (the relative metric in multi-samples,instead of pairwise sample metric) with the manifold structure constraints. HOSP-GNN can not only mine the high-order structure for complementing the relevance between samples that may be divided into the different task in meta-learning, and but also generate the rule of the structure updating by manifold constraint. Furthermore, HOSP-GNN doesn't need retrain the learning model for recognizing the new classes, and HOSP-GNN has the well-generalizable high-order structure for model adaptability. Experiments show that HOSP-GNN outperforms the state-of-the-art methods on supervised and semi-supervised few-shot learning in three benchmark datasets that are miniImageNet, tieredImageNet and FC100.

Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph structure directly influences GCN for semi-supervised classification. Existing methods mostly combine the computational layer and the related losses into GCN for exploring the global graph(measuring graph structure from all data samples) or local graph (measuring graph structure from local data samples). Global graph emphasises on the whole structure description of the inter-class data, while local graph trend to the neighborhood structure representation of intra-class data. However, it is difficult to simultaneously balance these graphs of the learning process for semi-supervised classification because of the interdependence of these graphs. To simulate the interdependence, deep graph learning(DGL) is proposed to find the better graph representation for semi-supervised classification. DGL can not only learn the global structure by the previous layer metric computation updating, but also mine the local structure by next layer local weight reassignment. Furthermore, DGL can fuse the different structures by dynamically encoding the interdependence of these structures, and deeply mine the relationship of the different structures by the hierarchical progressive learning for improving the performance of semi-supervised classification. Experiments demonstrate the DGL outperforms state-of-the-art methods on three benchmark datasets (Citeseer,Cora, and Pubmed) for citation networks and two benchmark datasets (MNIST and Cifar10) for images.

Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks.

Existing zero-shot learning (ZSL) methods usually learn a projection function between a feature space and a semantic embedding space(text or attribute space) in the training seen classes or testing unseen classes. However, the projection function cannot be used between the feature space and multi-semantic embedding spaces, which have the diversity characteristic for describing the different semantic information of the same class. To deal with this issue, we present a novel method to ZSL based on learning class label autoencoder (CLA). CLA can not only build a uniform framework for adapting to multi-semantic embedding spaces, but also construct the encoder-decoder mechanism for constraining the bidirectional projection between the feature space and the class label space. Moreover, CLA can jointly consider the relationship of feature classes and the relevance of the semantic classes for improving zero-shot classification. The CLA solution can provide both unseen class labels and the relation of the different classes representation(feature or semantic information) that can encode the intrinsic structure of classes. Extensive experiments demonstrate the CLA outperforms state-of-art methods on four benchmark datasets, which are AwA, CUB, Dogs and ImNet-2.

The key of zero-shot learning (ZSL) is how to find the information transfer model for bridging the gap between images and semantic information (texts or attributes). Existing ZSL methods usually construct the compatibility function between images and class labels with the consideration of the relevance on the semantic classes (the manifold structure of semantic classes). However, the relationship of image classes (the manifold structure of image classes) is also very important for the compatibility model construction. It is difficult to capture the relationship among image classes due to unseen classes, so that the manifold structure of image classes often is ignored in ZSL. To complement each other between the manifold structure of image classes and that of semantic classes information, we propose structure propagation (SP) for improving the performance of ZSL for classification. SP can jointly consider the manifold structure of image classes and that of semantic classes for approximating to the intrinsic structure of object classes. Moreover, the SP can describe the constrain condition between the compatibility function and these manifold structures for balancing the influence of the structure propagation iteration. The SP solution provides not only unseen class labels but also the relationship of two manifold structures that encode the positive transfer in structure propagation. Experimental results demonstrate that SP can attain the promising results on the AwA, CUB, Dogs and SUN databases.

We define the star transform as a generalization of the broken ray transform introduced by us in previous work. The advantages of using the star transform include the possibility to reconstruct the absorption and the scattering coefficients of the medium separately and simultaneously (from the same data) and the possibility to utilize scattered radiation which, in the case of the conventional X-ray tomography, is discarded. In this paper, we derive the star transform from physical principles, discuss its mathematical properties and analyze numerical stability of inversion. In particular, it is shown that stable inversion of the star transform can be obtained only for configurations involving odd number of rays. Several computationally-efficient inversion algorithms are derived and tested numerically., Comment: Accepted to Inverse Problems in this form

Magnetic measurements up to 1000 K have been performed on hydrothermally synthesized $\alpha$-Fe$_{2}$O$_{3}$ nanoparticles (60 nm) using a Quantum Design vibrating sample magnetometer. A high vacuum environment (1$\times$10$^{-5}$ torr) during the magnetic measurement up to 1000 K leads to a complete reduction of $\alpha$-Fe$_{2}$O$_{3}$ to Fe$_{3}$O$_{4}$. This precludes the determination of the N\'eel temperature for the $\alpha$-Fe$_{2}$O$_{3}$ nanoparticles. In contrast, coating $\alpha$-Fe$_{2}$O$_{3}$ nanoparticles with SiO$_{2}$ stabilizes the $\alpha$-Fe$_{2}$O$_{3}$ phase up to 930 K, which allows us to determine the N\'eel temperature of the $\alpha$-Fe$_{2}$O$_{3}$ nanoparticles for the first time. The N\'eel temperature of the 60-nm $\alpha$-Fe$_{2}$O$_{3}$ nanoparticles is found to be 945 K, about 15 K below the bulk value. The small reduction of the N\'eel temperature of the $\alpha$-Fe$_{2}$O$_{3}$ nanoparticles is consistent with a finite-size scaling theory. Our current results also show that coating nanoparticles with SiO$_{2}$ can effectively protect nanoparticles from oxidation or reduction, which is important to technological applications., Comment: 5 pages, 5 figures