Preconditioned NonSymmetric/Symmetric Discontinuous Galerkin Method for Elliptic Problem with Reconstructed Discontinuous Approximation

In this paper, we propose and analyze an efficient preconditioning method for the elliptic problem based on the reconstructed discontinuous approximation method. We reconstruct a high-order piecewise polynomial space that arbitrary order can be achieved with one degree of freedom per element. This space can be directly used with the symmetric/nonsymmetric interior penalty discontinuous Galerkin method. Compared with the standard DG method, we can enjoy the advantage on the efficiency of the approximation. Besides, we establish an norm equivalence result between the reconstructed high-order space and the piecewise constant space. This property further allows us to construct an optimal preconditioner from the piecewise constant space. The upper bound of the condition number to the preconditioned symmetric/nonsymmetric system is shown to be independent of the mesh size. Numerical experiments are provided to demonstrate the validity of the theory and the efficiency of the proposed method

Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study

The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can also apply to the medical image domain. This paper thoroughly studies the knowledge transferability of pre-trained VLMs to the medical domain, where we show that well-designed medical prompts are the key to elicit knowledge from pre-trained VLMs. We demonstrate that by prompting with expressive attributes that are shared between domains, the VLM can carry the knowledge across domains and improve its generalization. This mechanism empowers VLMs to recognize novel objects with fewer or without image samples. Furthermore, to avoid the laborious manual designing process, we develop three approaches for automatic generation of medical prompts, which can inject expert-level medical knowledge and image-specific information into the prompts for fine-grained grounding. We conduct extensive experiments on thirteen different medical datasets across various modalities, showing that our well-designed prompts greatly improve the zero-shot performance compared to the default prompts, and our fine-tuned models surpass the supervised models by a significant margin.Comment: 14 pages, 4 figures

Acoustic realization of projective mirror Chern insulators

Symmetry plays a key role in classifying topological phases. Recent theory shows that in the presence of gauge fields, the algebraic structure of crystalline symmetries needs to be projectively represented, which enables unprecedented topological band physics. Here, we report a concrete acoustic realization of mirror Chern insulators by exploiting the concept of projective symmetry. More specifically, we introduce a simple but universal recipe for constructing projective mirror symmetry, and conceive a minimal model for achieving the projective symmetry-enriched mirror Chern insulators. Based on our selective-excitation measurements, we demonstrate unambiguously the projective mirror eigenvalue-locked topological nature of the bulk states and associated chiral edge states. More importantly, we extract the non-abelian Berry curvature and identify the mirror Chern number directly, as conclusive experimental evidence for this exotic topological phase. All experimental results agree well with the theoretical predictions. Our findings will shine new light on the topological systems equipped with gauge fields.Comment: 5 figure

CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention

Predicting the State-of-Health (SoH) of lithium-ion batteries is a fundamental task of battery management systems on electric vehicles. It aims at estimating future SoH based on historical aging data. Most existing deep learning methods rely on filter-based feature extractors (e.g., CNN or Kalman filters) and recurrent time sequence models. Though efficient, they generally ignore cyclic features and the domain gap between training and testing batteries. To address this problem, we present CyFormer, a transformer-based cyclic time sequence model for SoH prediction. Instead of the conventional CNN-RNN structure, we adopt an encoder-decoder architecture. In the encoder, row-wise and column-wise attention blocks effectively capture intra-cycle and inter-cycle connections and extract cyclic features. In the decoder, the SoH queries cross-attend to these features to form the final predictions. We further utilize a transfer learning strategy to narrow the domain gap between the training and testing set. To be specific, we use fine-tuning to shift the model to a target working condition. Finally, we made our model more efficient by pruning. The experiment shows that our method attains an MAE of 0.75\% with only 10\% data for fine-tuning on a testing battery, surpassing prior methods by a large margin. Effective and robust, our method provides a potential solution for all cyclic time sequence prediction tasks

Experimental and Analytical Investigation on the Nonlinear Behaviors of Glulam Moment-Resisting Joints Composed of Inclined Self-Tapping Screws with Steel Side Plates

Glulam moment-resisting joint composed of inclined self-tapping-screws (STS) with steel side plates were designed and its nonlinear moment-rotational skeleton curve was predicted by taking nonlinear load(P)-deformation(u) relationships of all moment-resisting components into considerations within step-wise linear calculation process. P-u relationships of all moment-resisting components were estimated by the fundamental shear joint tests or appropriate empirical relationships and they were approximated by the tetra polygonal-line curves or bi-linear curves. The extended Normalized Characteristic Loop (NCL) model, which was originally developed for RC construction, was applied to describe the hysteresis loops. For predicting failure load, the design equations for a mechanical joint loaded with inclination to the grain direction were applied. Three replications of T-shaped beam-column joint specimens were fabricated using Canadian spruce glulam beam and column. Connections of steel plates to glulam members were all composed of full-threaded inclined-STS. Static push-pull cyclic loading tests were conducted and observed behaviors were compared with step-wise linear calculation results. Agreements between predicted nonlinear behaviors and observed ones were good on the whole

A Universal Mirror-stacking Approach for Constructing Topological Bound States in the Continuum

Bound states in the continuum (BICs) are counter-intuitive localized states with eigenvalues embedded in the continuum of extended states. Recently, nontrivial band topology is exploited to enrich the BIC physics, resulted in topological BICs (TBICs) with extraordinary robustness against perturbations or disorders. Here, we propose a simple but universal mirror-stacking approach to turn nontrivial bound states of any topological monolayer model into TBICs. Physically, the mirror-stacked bilayer Hamiltonian can be decoupled into two independent subspaces of opposite mirror parities, each of which directly inherits the energy spectrum information and band topology of the original monolayer. By tuning the interlayer couplings, the topological bound state of one subspace can move into and out of the continuum of the other subspace continuously without hybridization. As representative examples, we construct one-dimensional first-order and two-dimensional higher-order TBICs, and demonstrate them unambiguously by acoustic experiments. Our findings will expand the research implications of both topological materials and BICs.Comment: 5 figures,accepted by Phys.Rev.Let

Observation of Hybrid-Order Topological Pump in a Kekule-Textured Graphene Lattice

Thouless charge pumping protocol provides an effective route for realizing topological particle transport. To date, the first-order and higher-order topological pumps, exhibiting transitions of edge-bulk-edge and corner-bulk-corner states, respectively, are observed in a variety of experimental platforms. Here, we propose a concept of hybrid-order topological pump, which involves a transition of bulk, edge, and corner states simultaneously. More specifically, we consider a Kekul\'e-textured graphene lattice that features a tunable phase parameter. The finite sample of zigzag boundaries, where the corner configuration is abnormal and inaccessible by repeating unit cells, hosts topological responses at both the edges and corners. The former is protected by a nonzero winding number, while the latter can be explained by a nontrivial vector Chern number. Using our skillful acoustic experiments, we verify those nontrivial boundary landmarks and visualize the consequent hybrid-order topological pump process directly. This work deepens our understanding to higher-order topological phases and broadens the scope of topological pumps.Comment: 5 figure

Web service based Grid workflow application in quantitative remote sensing retrieval

Along with the unprecedented data-collecting capability, the higher algorithm accuracy and real-time application requirements, redundant spatial computing model had been implemented. Traditionally these spatial computing models are stored in different application centers. To avoid waste of resource, Grid workflow provides a powerful tool for sharing both remote sensing data and processing middleware. In order to enhance the interoperability of the heterogeneous quantitative remote sensing retrieval model in the Grid workflow environment, we propose a web service based Grid workflow framework to improve this situation. According to the Open Geospatial Consortium (OGC) and web service standards, we implement a prototype of this framework. Through the experiment, we can find that web service can work well with Grid workflow and provide a management ability of remote sensing model. Also this approach can separate the application logic and process logic, providing the interoperability ability both in application and process layers