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The Audio-Visual Question Answering (AVQA) task holds significant potential for applications. Compared to traditional unimodal approaches, the multi-modal input of AVQA makes feature extraction and fusion processes more challenging. Euclidean space is difficult to effectively represent multi-dimensional relationships of data. Especially when extracting and processing data with a tree structure or hierarchical structure, Euclidean space is not suitable as an embedding space. Additionally, the self-attention mechanism in Transformers is effective in capturing the dynamic relationships between elements in a sequence. However, the self-attention mechanism's limitations in window modeling and quadratic computational complexity reduce its effectiveness in modeling long sequences. To address these limitations, we propose SHMamba: Structured Hyperbolic State Space Model to integrate the advantages of hyperbolic geometry and state space models. Specifically, SHMamba leverages the intrinsic properties of hyperbolic space to represent hierarchical structures and complex relationships in audio-visual data. Meanwhile, the state space model captures dynamic changes over time by globally modeling the entire sequence. Furthermore, we introduce an adaptive curvature hyperbolic alignment module and a cross fusion block to enhance the understanding of hierarchical structures and the dynamic exchange of cross-modal information, respectively. Extensive experiments demonstrate that SHMamba outperforms previous methods with fewer parameters and computational costs. Our learnable parameters are reduced by 78.12\%, while the average performance improves by 2.53\%. Experiments show that our method demonstrates superiority among all current major methods and is more suitable for practical application scenarios.

We propose a novel and robust online function-on-scalar regression technique via geometric median to learn associations between functional responses and scalar covariates based on massive or streaming datasets. The online estimation procedure, developed using the average stochastic gradient descent algorithm, offers an efficient and cost-effective method for analyzing sequentially augmented datasets, eliminating the need to store large volumes of data in memory. We establish the almost sure consistency, $L_p$ convergence, and asymptotic normality of the online estimator. To enable efficient and fast inference of the parameters of interest, including the derivation of confidence intervals, we also develop an innovative two-step online bootstrap procedure to approximate the limiting error distribution of the robust online estimator. Numerical studies under a variety of scenarios demonstrate the effectiveness and efficiency of the proposed online learning method. A real application analyzing PM$_{2.5}$ air-quality data is also included to exemplify the proposed online approach.

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. Experimental results illustrate the effectiveness of DDPM in addressing data limitations and significantly improved classification model performance. The proposed DDPM-CNN method shows high reliability, with 100%classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, offering a promising avenue for rapid and automated spectral analysis.

Motivated by the widely used geometric median-of-means estimator in machine learning, this paper studies statistical inference for ultrahigh dimensionality location parameter based on the sample spatial median under a general multivariate model, including simultaneous confidence intervals construction, global tests, and multiple testing with false discovery rate control. To achieve these goals, we derive a novel Bahadur representation of the sample spatial median with a maximum-norm bound on the remainder term, and establish Gaussian approximation for the sample spatial median over the class of hyperrectangles. In addition, a multiplier bootstrap algorithm is proposed to approximate the distribution of the sample spatial median. The approximations are valid when the dimension diverges at an exponentially rate of the sample size, which facilitates the application of the spatial median in the ultrahigh dimensional region. The proposed approaches are further illustrated by simulations and analysis of a genomic dataset from a microarray study.

The introduction of moir\'e superlattices to electronic materials can dramatically alter electronic properties, promising emergent correlated and topological phenomena. Its first demonstration in van der Waals magnets exhibited noncollinear states and domain structures with, however, limited manipulation. Here, we fabricated twisted double antiferromagnetic bilayer CrI3, and by magneto-optical Kerr effect microscopy demonstrate the coexistence of antiferromagnetic and ferromagnetic orders with nonzero net magnetization, which is the hallmark of moir\'e magnetism. Such magnetic state exhibits nonmonotonic temperature dependence and extends over a wide range of twist angles with transitions at ~0{\deg} and above 20{\deg}. We further demonstrate voltage-assisted magnetic switching and the linear magnetoelectric effect. The observed nontrivial magnetic states and unprecedented control by twist angle, temperature and electrical gating are supported by the simulated phase diagram of the moir\'e magnetism. Our results illustrate the rich behaviors of twisted antiferromagnets and the control over them.

Van der Waals (vdW) magnet heterostructures have emerged as new platforms to explore exotic magnetic orders and quantum phenomena. Here, we study heterostructures of layered antiferromagnets, CrI3 and CrCl3, with perpendicular and in-plane magnetic anisotropy, respectively. Using magneto-optical Kerr effect microscopy, we demonstrate out-of-plane magnetic order in the CrCl3 layer proximal to CrI3, with ferromagnetic interfacial coupling between the two. Such an interlayer exchange field leads to higher critical temperature than that of either CrI3 or CrCl3 alone. We further demonstrate significant electric-field control of the coercivity, attributed to the naturally broken structural inversion symmetry of the heterostructure allowing unprecedented direct coupling between electric field and interfacial magnetism. These findings illustrate the opportunity to explore exotic magnetic phases and engineer spintronic devices in vdW heterostructures.

Strain in two-dimensional (2D) materials has attracted particular attention owing to the remarkable modification of electronic and optical properties. However, emergent electromechanical phenomena and hidden mechanisms remain under debate. Here, using scanning photocurrent microscopy, we observe significant photocurrent enhancement in hybrid vertical junction devices made of strained few-layer graphene and InGaN quantum dots. Optoelectronic response and photoluminescence measurements demonstrate a mechanism closely tied to the flexoelectric effect in few-layer graphene, where the strain can induce a lateral built-in electric field and assist the separation of electron-hole pairs. Photocurrent mapping reveals an unprecedentedly ordered hexagonal network, suggesting the potential to create a superlattice by strain engineering. Our work provides insights into optoelectronic phenomena in the presence of strain and paves the way for practical applications associated with strained 2D materials.

We propose a new unit-root test for a stationary null hypothesis $H_0$ against a unit-root alternative $H_1$. Our approach is nonparametric as $H_0$ only assumes that the process concerned is $I(0)$ without specifying any parametric forms. The new test is based on the fact that the sample autocovariance function (ACVF) converges to the finite population ACVF for an $I(0)$ process while it diverges to infinity for a process with unit-roots. Therefore the new test rejects $H_0$ for the large values of the sample ACVF. To address the technical challenge `how large is large', we split the sample and establish an appropriate normal approximation for the null-distribution of the test statistic. The substantial discriminative power of the new test statistic is rooted from the fact that it takes finite value under $H_0$ and diverges to infinity under $H_1$. This allows us to truncate the critical values of the test to make it with the asymptotic power one. It also alleviates the loss of power due to the sample-splitting. The test is implemented in a user-friendly R-function.

Van der Waals heterostructures usually formed using atomic thin transition metal dichalcogenides (TMDCs) with a direct bandgap in the near-infrared to the visible range are promising candidates for low-dimensional optoelectronic applications. The interlayer interaction or coupling between two-dimensional (2D) layer and the substrate or between adjacent 2D layers plays an important role in modifying properties of the individual 2D material or device performances through Coulomb interaction or forming interlayer excitons. Here, we report the realization of quasi zero-dimensional (0D) photon emission of WS2 in a coupled hybrid structure of monolayer WS2 and InGaN quantum dots (QDs). An interfacial bound exciton, i.e., coupling between excitons in WS2 and the electrons in QDs has been identified. The emission of this interfacial bound exciton inherits the 0-D confinement of QDs as well as the spin-valley physics of excitons in monolayer WS2. The effective coupling between 2D materials and conventional semiconductors observed in this work provides an effective way to realize 0D emission of 2D materials and opens the potential of compact on-chip integration of valleytronics and conventional electronics/optoelectronics.

In this paper, we consider the exact/approximate general joint block diagonalization (GJBD) problem of a matrix set $\{A_i\}_{i=0}^p$ ($p\ge 1$), where a nonsingular matrix $W$ (often referred to as diagonalizer) needs to be found such that the matrices $W^{H}A_iW$'s are all exactly/approximately block diagonal matrices with as many diagonal blocks as possible. We show that the diagonalizer of the exact GJBD problem can be given by $W=[x_1, x_2, \dots, x_n]\Pi$, where $\Pi$ is a permutation matrix, $x_i$'s are eigenvectors of the matrix polynomial $P(\lambda)=\sum_{i=0}^p\lambda^i A_i$, satisfying that $[x_1, x_2, \dots, x_n]$ is nonsingular, and the geometric multiplicity of each $\lambda_i$ corresponding with $x_i$ equals one. And the equivalence of all solutions to the exact GJBD problem is established. Moreover, theoretical proof is given to show why the approximate GJBD problem can be solved similarly to the exact GJBD problem. Based on the theoretical results, a three-stage method is proposed and numerical results show the merits of the method.