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Second-order topological insulators can be characterized by their bulk polarization, which is believed to be intrinsically connected to the center of the Wannier function. In this study, we demonstrate the existence of second-order topological insulators that feature a pair of partially degenerate photonic bands. These arise from the nonsymmorphic glide symmetry in an all-dielectric photonic crystal. The center of the maximally localized Wannier function (MLWF) is consistently located at the origin but is not equivalent with respect to the sum of constituent polarizations. As a result, topological corner modes can be identified by the distinctly hybridized MLWFs that truncate at the sample boundary. Through full-wave numerical simulations paired with microwave experiments, the second-order topology is clearly confirmed and characterized. These topological corner states exhibit notably unique modal symmetries, which are made possible by the inversion of the Wannier bands. Our results provide an alternative approach to explore higher-order topological physics with significant potential for applications in integrated and quantum photonics.

We study the one-dimensional non-Hermitian lattices with linearly varying nonreciprocal hopping, where the non-Hermitian skin effect (NHSE) is found to be dissolved gradually as the strength of nonreciprocity increases. The energy spectrum under the open boundary condition is composed of real and imaginary eigenenergies when the nonreciprocal hopping is weak. Interestingly, the real eigenenergies form an equally spaced ladder, and the corresponding eigenstates are localized at the boundary with a Gaussian distribution due to NHSE. By increasing the nonreciprocity, the number of real eigenenergies will decrease while more and more eigenenergies become imaginary. Accompanied by the real-imaginary transition in the spectrum, the eigenstates are shifted from the boundary into the bulk of the lattice. When the nonreciprocity gets strong enough, the whole spectrum will be imaginary and the NHSE disappears completely in the system; i.e., all the eigenstates become Gaussian bound states localized inside the bulk. Our work unveils the exotic properties of non-Hermitian systems with spatially varying nonreciprocal hopping., Comment: 7 pages, 5 figures

We investigate the theory of extending structures by the unified product for perm algebras, and the factorization problem as well as the classifying complements problem in the setting of perm algebras. For a special extending structure, non-abelian extension, we study the inducibility of a pair of automorphisms associated to a non-abelian extension of perm algebras, and give the fundamental sequence of Wells in the context of perm algebras. For a special extending structure, bicrossed product, we introduce the concept of perm bialgebras, equivalently characterized by Manin triples of perm algebras and certain matched pairs of perm algebras. We introduce and study coboundary perm bialgebras, and our study leads to the ''$\mathcal{S}$-equation" in perm algebras, which is an analogue of the classical Yang-Baxter equation. A symmetric solution of $\mathcal{S}$-equation gives a perm bialgebra., Comment: Comments are welcome. arXiv admin note: text overlap with arXiv:1511.08571 by other authors

We study the Wannier-Stark (WS) localization in one-dimensional amplitude-chirped lattices with the $j$th onsite potential modulated by a function $Fj\cos(2\pi \alpha j)$, where $F$ is the external field with a period determined by $\alpha=p/q$ ($p$ and $q$ are coprime integers). In the Hermitian (or non-Hermitian) systems with real (or imaginary) fields, we can obtain real (or imaginary) WS ladders in the eigenenergy spectrum. In most cases with $q \geq 2$, there are multiple WS ladders with all the eigenstates localized in the strong field limit. However, in the lattices with $q=4$, the energy-dependent localization phenomenon emerges due to the presence of both spatially periodic and linearly increasing behaviors in the onsite potential. About half the number of eigenstates are gathered at the band center and can extend over a wide region or even the full range of the lattice, even when the field becomes very strong. Moreover, in the non-Hermitian lattices with odd $q$, some of the WS ladders become doubly degenerate, where the eigenstates are evenly distributed at two neighboring sites in a wide regime of field strength. Our work opens an avenue for exploring WS localization in both Hermitian and non-Hermitian amplitude-chirped lattices., Comment: 8 pages, 9 figures (including supplementary)

In the data-driven artificial intelligence paradigm, models heavily rely on large amounts of training data. However, factors like sampling distribution imbalance can lead to issues of bias and unfairness in healthcare data. Sensitive attributes, such as race, gender, age, and medical condition, are characteristics of individuals that are commonly associated with discrimination or bias. In healthcare AI, these attributes can play a significant role in determining the quality of care that individuals receive. For example, minority groups often receive fewer procedures and poorer-quality medical care than white individuals in US. Therefore, detecting and mitigating bias in data is crucial to enhancing health equity. Bias mitigation methods include pre-processing, in-processing, and post-processing. Among them, Reweighting (RW) is a widely used pre-processing method that performs well in balancing machine learning performance and fairness performance. RW adjusts the weights for samples within each (group, label) combination, where these weights are utilized in loss functions. However, RW is limited to considering only a single sensitive attribute when mitigating bias and assumes that each sensitive attribute is equally important. This may result in potential inaccuracies when addressing intersectional bias. To address these limitations, we propose M3Fair, a multi-level and multi-sensitive-attribute reweighting method by extending the RW method to multiple sensitive attributes at multiple levels. Our experiments on real-world datasets show that the approach is effective, straightforward, and generalizable in addressing the healthcare fairness issues., Comment: 4 pages, 1 table, Beijing Health Data Science Summit 2023

In conventional plasmonic media and plasmonic metamaterials, such as metallic wire mesh, longitudinal mode of electromagnetic wave manifests itself in frequency overlapping transverse modes, which impedes clear observation of longitudinal-mode-specific physical effects. Through interlacing two sets of wire meshes, an ideal band for longitudinal mode is achieved ranging from zero frequency to plasma frequency where transverse modes are completely forbidden. The unique spectral separation of modes facilitates the observation of pure longitudinal mode and related plasmonic effects in bulk medium. We report the first experiment of anomalous optical transmission, induced solely by electromagnetic longitudinal mode resonance, below plasma frequency in the wire mesh medium.

In this paper, we introduce the notions of crossed module of associative conformal algebras, 2-term strongly homotopy associative conformal algebras, and discuss the relationship between them and the 3-th Hochschild cohomology of associative conformal algebras. We classify the non-abelian extensions by introducing the non-abelian cohomology. We show that non-abelian extensions of an associative conformal algebra can be viewed as Maurer-Cartan elements of a suitable differential graded Lie algebra, and prove that the Deligne groupoid of this differential graded Lie algebra corresponds one to one with the non-abelian cohomology. Based on this classification, we study the inducibility of a pair of automorphisms about a non-abelian extension of associative conformal algebras, and give the fundamental sequence of Wells in the context of associative conformal algebras. Finally, we consider the extensibility of a pair of derivations about an abelian extension of associative conformal algebras, and give an exact sequence of Wells type., Comment: 35 pages, comments are welcome

Magnetic resonance imaging plays an essential role in clinical diagnosis by acquiring the structural information of biological tissue. Recently, many multi-contrast MRI super-resolution networks achieve good effects. However, most studies ignore the impact of the inappropriate foreground scale and patch size of multi-contrast MRI, which probably leads to inappropriate feature alignment. To tackle this problem, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution. The Flexible Alignment module of FASR-Net consists of two modules for feature alignment. (1) The Single-Multi Pyramid Alignment(S-A) module solves the situation where low-resolution (LR) images and reference (Ref) images have different scales. (2) The Multi-Multi Pyramid Alignment(M-A) module solves the situation where LR and Ref images have the same scale. Besides, we propose the Cross-Hierarchical Progressive Fusion (CHPF) module aiming at fusing the features effectively, further improving the image quality. Compared with other state-of-the-art methods, FASR-net achieves the most competitive results on FastMRI and IXI datasets. Our code will be available at \href{https://github.com/yimingliu123/FASR-Net}{https://github.com/yimingliu123/FASR-Net}.

In this paper, we develop a cohomology theory of a left-symmetric conformal algebra and study its some applications. We define the cohomology of a left-symmetric conformal algebra, and then give an isomorphism between the cohomology spaces of the left-symmetric conformal algebra and its sub-adjacent Lie conformal algebra. As applications of the cohomology theory, we study linear deformations, formal $1$-parameter deformations, $T^*$-extensions of a left-symmetric conformal algebra respectively and obtain some properties., Comment: 16pages

We define a cup product on the Hochschild cohomology of an associative conformal algebra $A$, and show the cup product is graded commutative. We define a graded Lie bracket with the degree $-1$ on the Hochschild cohomology $\HH^{\ast}(A)$ of an associative conformal algebra $A$, and show that the Lie bracket together with the cup product is a Gerstenhaber algebra on the Hochschild cohomology of an associative conformal algebra. Moreover, we consider the Hochschild cohomology of split extension conformal algebra $A\hat{\oplus}M$ of $A$ with a conformal bimodule $M$, and show that there exist an algebra homomorphism from $\HH^{\ast}(A\hat{\oplus}M)$ to $\HH^{\ast}(A)$.

Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic function. Employing such a novel type of neurons offers a new perspective on developing deep learning. When analyzing quadratic neurons, we find that there exists a function such that a heterogeneous network can approximate it well with a polynomial number of neurons but a purely conventional or quadratic network needs an exponential number of neurons to achieve the same level of error. Encouraged by this inspiring theoretical result on heterogeneous networks, we directly integrate conventional and quadratic neurons in an autoencoder to make a new type of heterogeneous autoencoders. To our best knowledge, it is the first heterogeneous autoencoder that is made of different types of neurons. Next, we apply the proposed heterogeneous autoencoder to unsupervised anomaly detection for tabular data and bearing fault signals. The anomaly detection faces difficulties such as data unknownness, anomaly feature heterogeneity, and feature unnoticeability, which is suitable for the proposed heterogeneous autoencoder. Its high feature representation ability can characterize a variety of anomaly data (heterogeneity), discriminate the anomaly from the normal (unnoticeability), and accurately learn the distribution of normal samples (unknownness). Experiments show that heterogeneous autoencoders perform competitively compared to other state-of-the-art models.

In this paper, we study the Batalin-Vilkovisky structure on the Hochschild cohomology of quantum zigzag algebras $A_{q}$ of type $\widetilde{\mathbf{A}}_{1}$. We first calculate the dimensions of Hochschild homology groups and Hochschild cohomology groups of $A_{q}$. Based on these computations, we determine the Hochschild cohomology ring of $A_{q}$, and give the Batalin-Vilkovisky operator and the Gerstenhaber bracket on Hochschild cohomology ring of $A_{q}$ explicitly.

A neural network with the widely-used ReLU activation has been shown to partition the sample space into many convex polytopes for prediction. However, the parameterized way a neural network and other machine learning models use to partition the space has imperfections, \textit{e}.\textit{g}., the compromised interpretability for complex models, the inflexibility in decision boundary construction due to the generic character of the model, and the risk of being trapped into shortcut solutions. In contrast, although the non-parameterized models can adorably avoid or downplay these issues, they are usually insufficiently powerful either due to over-simplification or the failure to accommodate the manifold structures of data. In this context, we first propose a new type of machine learning models referred to as Manifoldron that directly derives decision boundaries from data and partitions the space via manifold structure discovery. Then, we systematically analyze the key characteristics of the Manifoldron such as manifold characterization capability and its link to neural networks. The experimental results on 4 synthetic examples, 20 public benchmark datasets, and 1 real-world application demonstrate that the proposed Manifoldron performs competitively compared to the mainstream machine learning models. We have shared our code in \url{https://github.com/wdayang/Manifoldron} for free download and evaluation.