Orbital Selective Phase Transition

We review theoretical investigations on the origin of the orbital selective phase where localized and itinerant electrons coexist in the d shell at intermediate strength of the on-site Coulomb interactions between electrons. In particular, the effect of spatial fluctuations on the phase diagram of the two-orbital Hubbard model with unequal bandwidths is discussed. And different band dispersions in different orbitals as well as different magnetically ordered states in different orbitals which are responsible for orbital selective phase transitions are emphasized. This is due to the fact that these two mechanisms are independent of the Hund's rule coupling, and are completely distinct from other well-known mechanisms like orbitals of unequal bandwidths and orbitals with different degeneracies. Moreover, crystal field splitting is not required in these two recently proposed mechanisms.Comment: 25 pages, 9 figure

Bacterial swarming reduces Proteus mirabilis and Vibrio parahaemolyticus cell stiffness and increases β-lactam susceptibility

Swarmer cells of the gram-negative pathogenic bacteria Proteus mirabilis and Vibrio parahaemolyticus become long (>10-100 microns) and multinucleate during their growth and motility on polymer surfaces. We demonstrate increasing cell length is accompanied by a large increase in flexibility. Using a microfluidic assay to measure single-cell mechanics, we identified large differences in swarmer cell stiffness of (bending rigidity of P. mirabilis, 9.6 x 10^(-22) N m^2; V. parahaemolyticus, 9.7 x 10^(-23) N m^2) compared to vegetative cells (1.4 x 10^(-20) N m^2 and 3.2 x 10^(-22) N m^2, respectively). The reduction in bending rigidity (~3-15 fold) was accompanied by a decrease in the average polysaccharide strand length of the peptidoglycan layer of the cell wall from 28-30 to 19-22 disaccharides. Atomic force microscopy revealed a reduction in P. mirabilis peptidoglycan thickness from 1.5 nm (vegetative) to 1.0 nm (swarmer) and electron cryotomography indicated changes in swarmer cell wall morphology. P. mirabilis and V. parahaemolyticus swarmer cells became increasingly sensitive to osmotic pressure and susceptible to cell wall-modifying antibiotics (compared to vegetative cells)--they were ~30% more likely to die after 3 h of treatment with minimum inhibitory concentrations of the beta-lactams cephalexin and penicillin G. Long, flexible swarmer cells enables these pathogenic bacteria to form multicellular structures and promotes community motility. The adaptive cost of swarming is offset by a fitness cost in which cells are more susceptible to physical and chemical changes in their environment, thereby suggesting the development of new chemotherapies for bacteria that leverage swarming for survival

Exploring interfacial exchange coupling and sublattice effect in heavy metal/ferrimagnetic insulator heterostructures using Hall measurements, x-ray magnetic circular dichroism, and neutron reflectometry

We use temperature-dependent Hall measurements to identify contributions of spin Hall, magnetic proximity, and sublattice effects to the anomalous Hall signal in heavy metal/ferrimagnetic insulator heterostructures with perpendicular magnetic anisotropy. This approach enables detection of both the magnetic proximity effect onset temperature and the magnetization compensation temperature and provides essential information regarding the interfacial exchange coupling. Onset of a magnetic proximity effect yields a local extremum in the temperature-dependent anomalous Hall signal, which occurs at higher temperature as magnetic insulator thickness increases. This magnetic proximity effect onset occurs at much higher temperature in Pt than W. The magnetization compensation point is identified by a sharp anomalous Hall sign change and divergent coercive field. We directly probe the magnetic proximity effect using x-ray magnetic circular dichroism and polarized neutron reflectometry, which reveal an antiferromagnetic coupling between W and the magnetic insulator. Finally, we summarize the exchange-coupling configurations and the anomalous Hall-effect sign of the magnetized heavy metal in various heavy metal/magnetic insulator heterostructures

A simulation study on the measurement of D0-D0bar mixing parameter y at BES-III

We established a method on measuring the \dzdzb mixing parameter $y$ for BESIII experiment at the BEPCII $e^+e^-$ collider. In this method, the doubly tagged $\psi(3770) \to D^0 \overline{D^0}$ events, with one $D$ decays to CP-eigenstates and the other $D$ decays semileptonically, are used to reconstruct the signals. Since this analysis requires good $e/\pi$ separation, a likelihood approach, which combines the $dE/dx$, time of flight and the electromagnetic shower detectors information, is used for particle identification. We estimate the sensitivity of the measurement of $y$ to be 0.007 based on a $20fb^{-1}$ fully simulated MC sample.Comment: 6 pages, 7 figure

Fast Graph Condensation with Structure-based Neural Tangent Kernel

The rapid development of Internet technology has given rise to a vast amount of graph-structured data. Graph Neural Networks (GNNs), as an effective method for various graph mining tasks, incurs substantial computational resource costs when dealing with large-scale graph data. A data-centric manner solution is proposed to condense the large graph dataset into a smaller one without sacrificing the predictive performance of GNNs. However, existing efforts condense graph-structured data through a computational intensive bi-level optimization architecture also suffer from massive computation costs. In this paper, we propose reforming the graph condensation problem as a Kernel Ridge Regression (KRR) task instead of iteratively training GNNs in the inner loop of bi-level optimization. More specifically, We propose a novel dataset condensation framework (GC-SNTK) for graph-structured data, where a Structure-based Neural Tangent Kernel (SNTK) is developed to capture the topology of graph and serves as the kernel function in KRR paradigm. Comprehensive experiments demonstrate the effectiveness of our proposed model in accelerating graph condensation while maintaining high prediction performance. The source code is available on https://github.com/WANGLin0126/GCSNTK.Comment: 10 pages, 6 figures, 5 table