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Electroencephalography (EEG) provides reliable indications of human cognition and mental states. Accurate emotion recognition from EEG remains challenging due to signal variations among individuals and across measurement sessions. To address these challenges, we introduce a multi-source dynamic contrastive domain adaptation method (MS-DCDA), which models coarse-grained inter-domain and fine-grained intra-class adaptations through a multi-branch contrastive neural network and contrastive sub-domain discrepancy learning. Our model leverages domain knowledge from each individual source and a complementary source ensemble and uses dynamically weighted learning to achieve an optimal tradeoff between domain transferability and discriminability. The proposed MS-DCDA model was evaluated using the SEED and SEED-IV datasets, achieving respectively the highest mean accuracies of $90.84\%$ and $78.49\%$ in cross-subject experiments as well as $95.82\%$ and $82.25\%$ in cross-session experiments. Our model outperforms several alternative domain adaptation methods in recognition accuracy, inter-class margin, and intra-class compactness. Our study also suggests greater emotional sensitivity in the frontal and parietal brain lobes, providing insights for mental health interventions, personalized medicine, and development of preventive strategies.

Motivated by recent experimental observations of Kondo resonances in cobalt atoms on single layer 1T-TaSe$_{2}$, we theoretically investigate the effect of coupling a U(1) quantum spin liquid with a spinon Fermi surface to a lattice of Anderson impurities. Within the slave-rotor formalism, we find that above a critical coupling strength between the spin liquid and impurity lattice, the spinons hybridize to form heavy quasiparticles near the Fermi level, realizing a {\it spinon Kondo lattice phase} analogous to heavy fermion materials. Using the Bethe-Salpeter equation and accounting for emergent gauge fluctuations, we compute the spectral density and density of states, revealing the formation of spinon-chargon bound states in the spinon Kondo lattice phase. We characterize the thermodynamic and spectroscopic signatures of this phase, demonstrating specific heat and neutron scattering responses distinct from a pure quantum spin liquid. Our findings establish the spinon Kondo lattice as a framework to study the rich physics of spin liquids., Comment: 12 pages, 7 figures

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates patients' molecular profiles with patients' survival. With complex relationships between survival and high-dimensional molecular predictors, it is challenging to conduct non-parametric modeling and irrelevant predictors removing simultaneously. In this paper, we build a kernel Cox proportional hazards semi-parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and non-parametric predictors through a LASSO penalty. An efficient high-dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes.

Topological magnons in a one-dimensional (1D) ferromagnetic (FM) Su-Schrieffer-Heeger (SSH) model with anisotropic exchange interactions are investigated. Apart from the inter-cellular isotropic Heisenberg interaction, the intercellular anisotropic exchange interactions, i.e. Dzyaloshinskii-Moriya interaction (DMI) and pseudo-dipolar interaction (PDI), also can induce the emergence of the non-trivial phase with two degenerate in-gap edge states separately localized at the two ends of the 1D chain, while the intracellular interactions instead unfavors the topological phase. The interplay among them has synergistic effects on the topological phase transition, very different from that in the two-dimensional (2D) ferromagnet. These results demonstrate that the 1D magnons possess rich topological phase diagrams distinctly different from the electronic version of the SSH model and even the 2D magnons. Due to the lower dimensional structural characteristics of this 1D topological magnonic system, the magnonic crystals can be constructed from bottom to top, which has important potential applications in the design of novel magnonic devices., Comment: 22 pages, 11 figures

A recently developed constitutive model is implemented into LS-DYNA as a user defined material model (UMAT) to characterize the nonlinear strain rate dependent behavior of polymers. By utilizing this model within a micromechanics technique based on a laminate analogy, an algorithm to analyze the strain rate dependent, nonlinear deformation of a fiber reinforced polymer matrix composite is then developed as a UMAT to simulate the response of these composites under high strain rate impact. The models are designed for shell elements in order to ensure computational efficiency. Experimental and numerical stress-strain curves are compared for two representative polymers and a representative polymer matrix composite, with the analytical model predicting the experimental response reasonably well.