Solving local constraint conditions in slave particle theory

With the Becchi-Rouet-Stora-Tyutin (BRST) quantization of gauge theory, we solve the long-standing difficult problem of the local constraint conditions, i.e., the single occupation of a slave particle per site, in the slave particle theory. This difficulty is actually caused by inconsistently dealing with the local Lagrange multiplier $\lambda_i$ which ensures the constraint: In the Hamiltonian formalism of the theory, $\lambda_i$ is time-independent and commutes with the Hamiltonian while in the Lagrangian formalism, $\lambda_i(t)$ becomes time-dependent and plays a role of gauge field. This implies that the redundant degrees of freedom of $\lambda_i(t)$ are introduced and must be removed by the additional constraint, the gauge fixing condition $\partial_t \lambda_i(t)=0$. In literature, this gauge fixing condition was missed. We add this gauge fixing condition and use the BRST quantization of gauge theory for Dirac's first-class constraints in the slave particle theory. This gauge fixing condition endows $\lambda_i(t)$ with dynamics and leads to important physical results. As an example, we study the Hubbard model at half-filling and find that the spinon is gapped in the weak $U$ and the system is indeed a conventional metal, which resolves the paradox that the weak coupling state is a superconductor in the previous slave boson mean field theory. For the $t$-$J$ model, we find that the dynamic effect of $\lambda_i(t)$ substantially suppresses the $d$-wave pairing gap and then the superconducting critical temperature may be lowered at least a factor of one-fifth of the mean field value which is of the order of 1000 K. The renormalized $T_c$ is then close to that in cuprates.Comment: 9 pages, revised version, Commun. Theor. Phys. in pres

Mendelian randomization study of thyroid function and anti-Müllerian hormone levels

ObjectiveAlthough previous studies have reported an association between thyroid function and anti-Müllerian hormone (AMH) levels, which is considered a reliable marker of ovarian reserve, the causal relationship between them remains uncertain. This study aims to investigate whether thyrotropin (TSH), free thyroxine (fT4), hypo- and hyperthyroidism are causally linked to AMH levels.MethodsWe obtained summary statistics from three sources: the ThyroidOmics Consortium (N = 54,288), HUNT + MGI + ThyroidOmics meta-analysis (N = 119,715), and the most recent AMH genome-wide association meta-analysis (N = 7,049). Two-sample MR analyses were conducted using instrumental variables representing TSH and fT4 levels within the normal range. Additionally, we conducted secondary analyses to explore the effects of hypo- and hyperthyroidism. Subgroup analyses for TSH were also performed.ResultsMR analyses did not show any causality relationship between thyroid function and AMH levels, using normal range TSH, normal range fT4, subclinical hypothyroidism, subclinical hyperthyroidism and overt hypothyroidism as exposure, respectively. In addition, neither full range TSH nor TSH with individuals <50 years old was causally associated with AMH levels. MR sensitivity analyses guaranteed the robustness of all MR results, except for the association between fT4 and AMH in the no-DIO1+DIO2 group.ConclusionOur findings suggest that there was no causal association between genetically predicted thyroid function and AMH levels in the European population

Stacking-induced magnetic frustration and spiral spin liquid

Like the twisting control in magic angle twisted bilayer graphenes, the stacking control is another mechanical approach to manipulate the fundamental properties of solids, especially the van der Waals materials. We explore the stacking-induced magnetic frustration and the spiral spin liquid on a multilayer triangular lattice antiferromagnet where the system is built from the ABC stacking with competing intralayer and interlayers couplings. By combining the nematic bond theory and the self-consistent Gaussian approximation, we establish the phase diagram for this ABC-stacked multilayer magnet. It is shown that, the system supports a wide regime of spiral spin liquid with multiple degenerate spiral lines in the reciprocal space, separating the low-temperature spiral order and the high-temperature featureless paramagnet. The transition to the spiral order from the spiral spin liquid regime is first order. We further show that the spiral-spin-liquid behavior persists even with small perturbations such as further neighbor intralayer exchanges. The connection to the ABC-stacked magnets, the effects of Ising or planar spin anisotropy, and the outlook on the stacking-engineered quantum magnets are discussed.Comment: main text: 7 pages + 4 figures; supplemental materials: 15 pages + 5 figures; update: fixed typos + adjusted the notation of action for consistency purpose

Efficient GPU Tree Walks for Effective Distributed N-Body Simulations

N-body problems, such as simulating the motion of stars in a galaxy, are popularly solved using tree codes like Barnes-Hut. ChaNGa is a best-of-breed n-body platform that uses an asymptotically-efficient tree traversal strategy known as a dual-tree walk to quickly determine which bodies need to interact with each other to provide an accurate simulation result. However, this strategy does not work well on GPUs, due to the highly-irregular nature of the dual-tree algorithm. On GPUs, ChaNGa uses a hybrid strategy where the CPU performs the tree walk to determine which bodies interact while the GPU performs the force computation. In this paper, we show that a highly-optimized single-tree walk approach is able to achieve better GPU performance by significantly accelerating the tree walk and reducing CPU/GPU communication. Our experiments show that this new design can achieve a 8.25× speedup over baseline ChaNGa using a one node, one process per node configuration

Abnormal traffic detection system in SDN based on deep learning hybrid models

Software defined network (SDN) provides technical support for network construction in smart cities, However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection methods have complex algorithms and find it difficult to detect abnormalities in the network promptly, which cannot meet the demand for abnormal detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection technique, which first achieves rough detection of abnormal traffic based on port information. Then it uses wavelet transform and deep learning techniques for fine detection of all traffic data flowing through suspicious switches. The experimental results show that the proposed detection method based on port information can quickly complete the approximate localization of the source of abnormal traffic. the accuracy, precision, and recall of the fine detection are significantly improved compared with the traditional method of abnormal traffic detection in SDN