Strong Electron-Phonon Interaction and Colossal Magnetoresistance in EuTiO3_3

At low temperatures, EuTiO$_3$ system has very large resistivities and exhibits colossal magnetoresistance. Based on a first principle calculation and the dynamical mean-field theory for small polaron we have calculated the transport properties of EuTiO$_3$. It is found that due to electron-phonon interaction the conduction band may form a tiny subband which is close to the Fermi level. The tiny subband is responsible for the large resistivity. Besides, EuTiO$_3$ is a weak antiferromagnetic material and its magnetization would slightly shift the subband via exchange interaction between conduction electrons and magnetic atoms. Since the subband is close to the Fermi level, a slight shift of its position gives colossal magnetoresistance.Comment: 6 pages, 5 figure

Grassmann Time-Evolving Matrix Product Operators for Quantum Impurity Models

The time-evolving matrix product operators (TEMPO) method, which makes full use of the Feynman-Vernon influence functional, is the state-of-the-art tensor network method for bosonic impurity problems. However, for fermionic impurity problems the Grassmann path integral prohibits application of this method. We develop Grassmann time-evolving matrix product operators, a full fermionic analog of TEMPO, that can directly manipulates Grassmann path integrals with similar numerical cost as the bosonic counterpart. We further propose a zipup algorithm to compute expectation values on the fly without explicitly building a single large augmented density tensor, which boosts the efficiency of our method on top of the vanilla TEMPO. We demonstrate our method on the non-equilibrium dynamics of the single impurity Anderson models, and find a favorable performance against existing tensor network influence functional methods. Our method could significantly change the application landscape of tensor network based impurity solvers.Comment: 4 pages, 3 figure

Large adiabatic temperature and magnetic entropy changes in EuTiO3

We have investigated the magnetocaloric effect in single and polycrystalline samples of quantum paraelectric EuTiO3 by magnetization and heat capacity measurements. Single crystalline EuTiO3 shows antiferromagnetic ordering due to Eu2+ magnetic moments below TN = 5.6 K. This compound shows a giant magnetocaloric effect around its Neel temperature. The isothermal magnetic entropy change is 49 Jkg-1K-1, the adiabatic temperature change is 21 K and the refrigeration capacity is 500 JKg-1 for a field change of 7 T at TN. The single crystal and polycrystalline samples show similar values of the magnetic entropy change and adiabatic temperature changes. The large magnetocaloric effect is due to suppression of the spin entropy associated with localized 4f moment of Eu2+ ions. The giant magnetocaloric effect together with negligible hysteresis, suggest that EuTiO3 could be a potential material for magnetic refrigeration below 20 K.Comment: 12 pages, 4 figure

Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution

Super-resolution (SR) techniques designed for real-world applications commonly encounter two primary challenges: generalization performance and restoration accuracy. We demonstrate that when methods are trained using complex, large-range degradations to enhance generalization, a decline in accuracy is inevitable. However, since the degradation in a certain real-world applications typically exhibits a limited variation range, it becomes feasible to strike a trade-off between generalization performance and testing accuracy within this scope. In this work, we introduce a novel approach to craft training degradation distributions using a small set of reference images. Our strategy is founded upon the binned representation of the degradation space and the Fr\'echet distance between degradation distributions. Our results indicate that the proposed technique significantly improves the performance of test images while preserving generalization capabilities in real-world applications.Comment: This paper has been accepted to ICML 202

Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning

Oversmoothing is a common phenomenon in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance. Graph contrastive learning (GCL) is emerging as a promising way of leveraging vast unlabeled graph data. As a marriage between GNNs and contrastive learning, it remains unclear whether GCL inherits the same oversmoothing defect from GNNs. This work undertakes a fundamental analysis of GCL from the perspective of oversmoothing on the first hand. We demonstrate empirically that increasing network depth in GCL also leads to oversmoothing in their deep representations, and surprisingly, the shallow ones. We refer to this phenomenon in GCL as long-range starvation', wherein lower layers in deep networks suffer from degradation due to the lack of sufficient guidance from supervision (e.g., loss computing). Based on our findings, we present BlockGCL, a remarkably simple yet effective blockwise training framework to prevent GCL from notorious oversmoothing. Without bells and whistles, BlockGCL consistently improves robustness and stability for well-established GCL methods with increasing numbers of layers on real-world graph benchmarks. We believe our work will provide insights for future improvements of scalable and deep GCL frameworks.Comment: Preprint; Code is available at https://github.com/EdisonLeeeee/BlockGC