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Microgrids (MGs) have been equipped with large-scale distributed energy sources (DESs), and become more vulnerable due to the low inertia characteristic. In particular, high-density misbehaving DESs caused by cascading faults bring a great challenge to frequency synchronization and active power sharing among DESs. To tackle the problem, we propose a fully distributed resilient consensus protocol, which utilizes confidence weights to evaluate the level of trust among agents with a first-order filter and a softmax-type function. We pioneer the analysis of this nonlinear control system from the system operating range and the graph structure perspectives. Both necessary and sufficient conditions are provided to ensure DACC to be uniformly ultimately bounded, even in a robust network with low connectivity. Simulations on a modified IEEE33-bus microgrid testbed with 17 DESs validate that DACC outperforms existing methods in the presence of 8 misbehaving DESs.

Koopman operator theory has proven to be highly significant in system identification, even for challenging scenarios involving nonlinear time-varying systems (NTVS). In this context, we examine a network of connected agents, each with limited observation capabilities, aiming to estimate the dynamics of an NTVS collaboratively. Drawing inspiration from Koopman operator theory, deep neural networks, and distributed consensus, we introduce a distributed algorithm for deep Koopman learning of the dynamics of an NTVS. This approach enables individual agents to approximate the entire dynamics despite having access to only partial state observations. We guarantee consensus not only on the estimated dynamics but also on its structure, i.e., the matrices encountered in the linear equation of the lifted Koopman system. We provide theoretical insights into the convergence of the learning process and accompanying numerical simulations.

Harmonizing serology measurements is critical for identifying reference materials that permit standardization and comparison of results across different diagnostic platforms. However, the theoretical foundations of such tasks have yet to be fully explored in the context of antibody thermodynamics and uncertainty quantification (UQ). This has restricted the usefulness of standards currently deployed and limited the scope of materials considered as viable reference material. To address these problems, we develop rigorous theories of antibody normalization and harmonization, as well as formulate a probabilistic framework for defining correlates of protection. We begin by proposing a mathematical definition of harmonization equipped with structure needed to quantify uncertainty associated with the choice of standard, assay, etc. We then show how a thermodynamic description of serology measurements (i) relates this structure to the Gibbs free-energy of antibody binding, and thereby (ii) induces a regression analysis that directly harmonizes measurements. We supplement this with a novel, optimization-based normalization (not harmonization!) method that checks for consistency between reference and sample dilution curves. Last, we relate these analyses to uncertainty propagation techniques to estimate correlates of protection. A key result of these analyses is that under physically reasonable conditions, the choice of reference material does not increase uncertainty associated with harmonization or correlates of protection. We provide examples and validate main ideas in the context of an interlab study that lays the foundation for using monoclonal antibodies as a reference for SARS-CoV-2 serology measurements.

We propose a novel method to construct large-scale boundary-identical microstructure datasets with high attribute coverage for highly efficient multiscale design. Central to our technique is using a deep generative model to generate microstructures under the two conditions, including the specified boundary and homogenized elastic tensor. We achieve the desired dataset by alternately adding microstructures with diverse attributes constructed by the deep generative model into the dataset and retraining the deep generative model. Sixteen compatible metamaterial datasets with high attribute coverage are constructed by our method. We demonstrate the effectiveness and practicability of the obtained datasets over various multiscale design examples. Specifically, in the design of a mechanical cloak, we utilize macrostructures with $30 \times 30$ elements and microstructures filled with $256 \times 256$ elements. The entire reverse design process is completed within one minute, significantly enhancing the efficiency of the multiscale optimization.

The pairing mechanism behind the monolayer FeSe is one essential question for iron-based superconductors. In this work, we show the sublattice degree of freedoms of monolayer FeSe plays a special role in its pairing properties, namely the sublattice dichotomy. The high-quality monolayer FeSe samples with atomic flat $1\times1$ topography on the SrTiO$_3$(001) substrates are grown by molecular beam epitaxy. By comparing the tunneling spectra at $\alpha$ and $\beta$ Fe sublattices, we find the coherence peak of $\alpha$-Fe at the inner gap $+V_i$ is higher than $\beta$-Fe while the coherence peak of $\beta$-Fe at $-V_i$ is higher than $\alpha$-Fe with a similar amount. We also observed a reversed effect at the outer gap $\pm V_o$. We propose the $\eta$-pairing mechanism between $k$ and $-k+Q$ is the key mechanism for this unconventional sublattice dichotomy effect., Comment: 6 pages, 4 figures

The intrinsic magnetic order, large topological-magnetic gap and rich topological phases make MnBi2Te4 a wonderful platform to study exotic topological quantum states such as axion insulator and Chern insulator. To realize and manipulate these topological phases in a MnBi2Te4 thin film, precise manipulation of the electric field across the film is essential, which requires a dual-gate structure. In this work, we achieve dual-gate tuning of MnBi2Te4 thin films grown with molecular beam epitaxy on SrTiO3(111) substrates by applying the substrate and an AlOx layer as the gate dielectrics of bottom and top gates, respectively. Under magnetic field of 9T and temperature of 20 mK, the Hall and longitudinal resistivities of the films show inversed gate-voltage dependence, for both top- and bottom-gates, signifying the existence of the dissipationless edge state contributed by Chern insulator phase in the ferromagnetic configuration. The maximum of the Hall resistivity only reaches 0.8 h/e2, even with dual-gate tuning, probably due to the high density of bulk carriers introduced by secondary phases. In the antiferromagnetic state under zero magnetic field, the films show normal insulator behavior. The dual-gated MnBi2Te4 thin films lay the foundation for developing devices based on electrically tunable topological quantum states., Comment: 24 pages, 4 figures

The discovery of high-mobility two-dimensional electron gas and low carrier density superconductivity in multiple SrTiO$_3$-based heterostructures has stimulated intense interest in the surface properties of SrTiO$_3$. The recent discovery of high-T$_c$ superconductivity in the monolayer FeSe/SrTiO$_3$ aroused the upsurge and underscored the atomic precision probe of the surface structure. By performing atomically resolved cryogenic scanning tunneling microscopy/spectroscopy characterization on dual-TiO$_{2}$-${\delta}$-terminated SrTiO$_3$(001) surfaces with ($\sqrt{13}$ $\times$ $\sqrt{13}$), c(4 $\times$ 2), mixed (2 $\times$ 1), and (2 $\times$ 2) reconstructions, we disclosed universally broken rotational symmetry and contrasting bias- and temperature-dependent electronic states for apical and equatorial oxygen sites. With the sequentially evolved surface reconstructions and simultaneously increasing equatorial oxygen vacancies, the surface anisotropy reduces, and the work function lowers. Intriguingly, unidirectional stripe orders appear on the c(4 $\times$ 2) surface, whereas local (4 $\times$ 4) order emerges and eventually forms long-range unidirectional c(4 $\times$ 4) charge order on the (2 $\times$ 2) surface. This work reveals robust unidirectional charge orders induced by oxygen vacancies due to strong and delicate electronic-lattice interaction under broken rotational symmetry, providing insights into understanding the complex behaviors in perovskite oxide-based heterostructures.

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount of data poses a significant challenge. Astronomers are turning to deep learning techniques to address this, but the methods are limited by their specific training sets, leading to considerable duplicate workloads too. Hence, as an example to present how to overcome the issue, we built a framework for general analysis of galaxy images, based on a large vision model (LVM) plus downstream tasks (DST), including galaxy morphological classification, image restoration, object detection, parameter extraction, and more. Considering the low signal-to-noise ratio of galaxy images and the imbalanced distribution of galaxy categories, we have incorporated a Human-in-the-loop (HITL) module into our large vision model, which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively. The proposed framework exhibits notable few-shot learning capabilities and versatile adaptability to all the abovementioned tasks on galaxy images in the DESI legacy imaging surveys. Expressly, for object detection, trained by 1000 data points, our DST upon the LVM achieves an accuracy of 96.7%, while ResNet50 plus Mask R-CNN gives an accuracy of 93.1%; for morphology classification, to obtain AUC ~0.9, LVM plus DST and HITL only requests 1/50 training sets compared to ResNet18. Expectedly, multimodal data can be integrated similarly, which opens up possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-message astronomy., Comment: 26 pages, 10 figures, to be published on Chinese Physics C

It is known that finite-time blow-up in the 3D Patlak-Keller-Segel system may occur for arbitrarily small values of the initial mass. It's interesting whether one can prevent the finite-time blow-up via the stabilizing effect of the moving fluid. Consider the three-dimensional Patlak-Keller-Segel system coupled with the linearized Navier-Stokes equations near the Couette flow $(\ Ay, 0, 0 \ )$ in a finite channel $\mathbb{T}\times\mathbb{I}\times\mathbb{T}$ with $ \mathbb{T}=[0,2\pi) $ and $ \mathbb{I}=[-1,1] $, with the non-slip boundary condition, and we show that if the shear flow is sufficiently strong (A is large enough), then the solutions to Patlak-Keller-Segel-Navier-Stokes system are global in time as long as the initial cell mass is sufficiently small (for example, $M<\frac49$) and $ A\left(\|u_{2,0}(0)\|_{L^{2}}+\|u_{3,0}(0)\|_{L^{2}} \right)\leq C_{0} $, which seems to be the first result of considering the suppression effect of Couette flow in the 3D Patlak-Keller-Segel-Navier-Stokes model, and also the first time considering the non-slip boundary condition., Comment: arXiv admin note: substantial text overlap with arXiv:2401.15982

We establish the map between symmetries and orbital rules to realize tunable band gap in quantum anomalous Hall effect material. This band gap is determined by the SOC between local orbitals associated with band crossing, which is constrained by at least one of lattice symmetries. The band gap could be turned on/off by breaking or keeping corresponding lattice symmetry through rotation of magnetization direction. The components of local orbital related to band crossing is required to match the symmetry, and to produce non-zero SOC when symmetry is broken. Following this map, the TiSb monolayer is predicted to be a quantum anomalous Hall effect material with a band gap adjusted in the range of 0 to 209 meV through magnetization direction rotation.

The interface-enhanced superconductivity in monolayer iron selenide (FeSe) films on SrTiO3 has been actively pursued in the past decade. Although a synergistic effect between interfacial charge transfer and interfacial electron-phonon coupling (EPC) is proposed to be responsible for the mechanism, the microscopic nature of the interfacial EPC in the enhancement of superconductivity remains highly controversial. Herein we experimentally reveal that a coherent optical phonon mode at 4.2 THz from the SrTiO3 substrate couples to FeSe electrons and modulates the quasiparticle relaxations using ultrafast pump-probe spectroscopy. This mode originates from the antiferrodistortive (AFD) transition in SrTiO3 and is significantly stronger in the presence of monolayer FeSe than that in purely FeTe-capped Nb-doped SrTiO3. Pump fluence and temperature-dependent spectroscopy measurements suggest that SrTiO3 substrate facilitates the stabilization of FeSe structure and possibly prevents the occurrence of nematic phase transition, supporting that SrTiO3 substrate modifies the electronic structure of monolayer FeSe through a strong interfacial EPC strength as large as 0.77. Our results provide unprecedented direct evidence that the strong coupling of SrTiO3 coherent phonon to FeSe electrons is indeed responsible for the high-temperature superconductivity in monolayer FeSe and SrTiO3 heterostructure.

In this paper, we consider the three-dimensional Patlak-Keller-Segel system coupled with the Navier-Stokes equations near the non-parallel shear flow $( Ay, 0, Ay )$ in $\mathbb{T}\times\mathbb{R}\times\mathbb{T}$. We show that if the shear flow is sufficiently strong (A is large enough), then the solutions to Patlak-Keller-Segel-Navier-Stokes system are global in time without any smallness restriction on the initial cell mass as long as the initial velocity satisfies $A^{\frac{2}{3}}\|u_{\rm in}\|_{H^2}\leq C_0$, which seems to be the first result of studying the suppression effect of shear flows for the 3D Patlak-Keller-Segel-Navier-Stokes system. Moreover, it implies that the solutions of the 3D Navier-Stokes equations are global in time if the initial velocity satisfies $A^{\frac{2}{3}}\|v_{\rm in}-(Ay,0,Ay)\|_{H^2}\leq C_0,$ which also shows the transition threshold for the shear flow $(Ay,0,Ay)$ in $\mathbb{T}\times\mathbb{R}\times\mathbb{T}$.

Mechanical metamaterial is a synthetic material that can possess extraordinary physical characteristics, such as abnormal elasticity, stiffness, and stability, by carefully designing its internal structure. To make metamaterials contain delicate local structures with unique mechanical properties, it is a potential method to represent them through high-resolution voxels. However, it brings a substantial computational burden. To this end, this paper proposes a fast inverse design method, whose core is an advanced deep generative AI algorithm, to generate voxel-based mechanical metamaterials. Specifically, we use the self-conditioned diffusion model, capable of generating a microstructure with a resolution of $128^3$ to approach the specified homogenized tensor matrix in just 3 seconds. Accordingly, this rapid reverse design tool facilitates the exploration of extreme metamaterials, the sequence interpolation in metamaterials, and the generation of diverse microstructures for multi-scale design. This flexible and adaptive generative tool is of great value in structural engineering or other mechanical systems and can stimulate more subsequent research., Comment: 13 pages, 6 figures

In this paper, we investigate the complexity of an infinite family of Cayley graphs $\mathcal{D}_{n}=Cay(\mathbb{D}_{n}, b^{\pm\beta_1},b^{\pm\beta_2},\ldots,b^{\pm\beta_s}, a b^{\gamma_1}, a b^{\gamma_2},\ldots, a b^{\gamma_t} )$ on the dihedral group $\mathbb{D}_{n}=\langle a,b| a^2=1, b^n=1,(a\,b)^2=1\rangle$ of order $2n.$ We obtain a closed formula for the number $\tau(n)$ of spanning trees in $\mathcal{D}_{n}$ in terms of Chebyshev polynomials, investigate some arithmetical properties of this function, and find its asymptotics as $n\to\infty.$ Moreover, we show that the generating function $F(x)=\sum\limits_{n=1}^\infty\tau(n)x^n$ is a rational function with integer coefficients.

Suspensions of aerobic bacteria often develop flows from the interplay of chemotaxis and buoyancy, which is so-called the chemotaxis-Navier-Stokes flow. In 2004, Dombrowski et al. observed that Bacterial flow in a sessile drop related to those in the Boycott effect of sedimentation can carry bioconvective plumes, viewed from below through the bottom of a petri dish, and the horizontal "turbulence" white line near the top is the air-water-plastic contact line. In pendant drops such self-concentration occurs at the bottom. On scales much larger than a cell, concentrated regions exhibit transient, reconstituting, high-speed jets straddled by vortex streets. It's interesting to verify these turbulent phenomena mathematically. In this note, we investigate the Hausdorff dimension of these vortices (singular points) by considering partial regularity of weak solutions of the three dimensional chemotaxis-Navier-Stokes equations, and showed that the singular dimension is not larger than $1$, which seems to be consistent with the linear singularity in the experiment.

The interfacial FeSe/TiO$_{2-\delta}$ coupling induces high-temperature superconductivity in monolayer FeSe films. Using cryogenic atomically resolved scanning tunneling microscopy/spectroscopy, we obtained atomic-site dependent surface density of states, work function, and pairing gap in the monolayer FeSe on SrTiO$_3$(001)-($\sqrt{13} \times \sqrt{13}$)-33.7${\deg}$ surface. Our results disclosed the out-of-plane Se-Fe-Se triple layer gradient variation, switched DOS for Fe sites on and off TiO$_{5\square}$, and inequivalent Fe sublattices, which gives global spatial modulation of pairing gap contaminant with the ($\sqrt{13} \times \sqrt{13}$) pattern. Moreover, the coherent lattice coupling induces strong inversion asymmetry and in-plane anisotropy in the monolayer FeSe, which is demonstrated to correlate with the particle-hole asymmetry in coherence peaks. The strong atomic-scale correlations in lattice and electronic structure, and pairing gap in particular, put constraints on exploring the unconventional high-temperature superconductivity emerging from interface coupling, e.g., strong demand for atomic-scale interface engineering and characterization.

In 2004, Dombrowski et al. showed that suspensions of aerobic bacteria often develop flows from the interplay of chemotaxis and buoyancy, which is described as the chemotaxis-Navier-Stokes model, and they observed self-concentration occurs as a turbulence by exhibiting transient, reconstituting, high-speed jets, which entrains nearby fluid to produce paired, oppositely signed vortices. In order to investigate the properties of these vortices (singular points), one approach is to follow the partial regularity theory of Caffarelli-Kohn-Nirenberg to study the singularity properties of suitable weak solutions. In this paper, we established the existence of suitable weak solution for the three dimensional chemotaxis-Navier-Stokes equations, where the main difficulty is to establish appropriate local energy inequalities.

Most previous approaches for analyzing food images have relied on extensively annotated datasets, resulting in significant human labeling expenses due to the varied and intricate nature of such images. Inspired by the effectiveness of contrastive self-supervised methods in utilizing unlabelled data, we explore leveraging these techniques on unlabelled food images. In contrastive self-supervised methods, two views are randomly generated from an image by data augmentations. However, regarding food images, the two views tend to contain similar informative contents, causing large mutual information, which impedes the efficacy of contrastive self-supervised learning. To address this problem, we propose Feature Suppressed Contrast (FeaSC) to reduce mutual information between views. As the similar contents of the two views are salient or highly responsive in the feature map, the proposed FeaSC uses a response-aware scheme to localize salient features in an unsupervised manner. By suppressing some salient features in one view while leaving another contrast view unchanged, the mutual information between the two views is reduced, thereby enhancing the effectiveness of contrast learning for self-supervised food pre-training. As a plug-and-play module, the proposed method consistently improves BYOL and SimSiam by 1.70\% $\sim$ 6.69\% classification accuracy on four publicly available food recognition datasets. Superior results have also been achieved on downstream segmentation tasks, demonstrating the effectiveness of the proposed method., Comment: Accepted by ACM MM 2023

Post-stratification is often used to estimate treatment effects with higher efficiency. However, the majority of existing post-stratification frameworks depend on prior knowledge of the distributions of covariates and assume that the units are classified into post-strata without error. We propose a novel method to determine a proper stratification rule by mapping the covariates into a post-stratification factor (PSF) using predictive regression models. Inspired by the bootstrap aggregating (bagging) method, we utilize the out-of-bag delete-D jackknife to estimate strata boundaries, strata weights, and the variance of the point estimate. Confidence intervals are constructed with these estimators to take into account the additional variability coming from uncertainty in the strata boundaries and weights. Extensive simulations show that our proposed method consistently improves the efficiency of the estimates when the regression models are predictive and tends to be more robust than the regression imputation method.

In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks. However, current relational stock methods are limited by their reliance on predefined stock relationships and the exclusive consideration of immediate effects. To address these limitations, we present a groundbreaking framework for financial market analysis. This approach, to our knowledge, is the first to jointly model investor expectations and automatically mine latent stock relationships. Comprehensive experiments conducted on China's CSI 300, one of the world's largest markets, demonstrate that our model consistently achieves an annual return exceeding 10%. This performance surpasses existing benchmarks, setting a new state-of-the-art standard in stock return prediction and multiyear trading simulations (i.e., backtesting)., Comment: In Advances in Knowledge Discovery and Data Mining 2023 (PAKDD 2023)

Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior work in this area focuses on node-level representation, limited research has been conducted on graph-level embedding, particularly for dynamic or temporal networks. However, learning low-dimensional graph-level representations for dynamic networks is critical for various downstream graph retrieval tasks such as temporal graph similarity ranking, temporal graph isomorphism, and anomaly detection. In this paper, we present a novel method for temporal graph-level embedding that addresses this gap. Our approach involves constructing a multilayer graph and using a modified random walk with temporal backtracking to generate temporal contexts for the graph's nodes. We then train a "document-level" language model on these contexts to generate graph-level embeddings. We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods. Our experimental results demonstrate the effectiveness of our method in generating graph-level embeddings for dynamic networks., Comment: In Companion Proceedings of the ACM Web Conference 2023