791 research outputs found
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 which ensures the constraint: In the
Hamiltonian formalism of the theory, is time-independent and
commutes with the Hamiltonian while in the Lagrangian formalism,
becomes time-dependent and plays a role of gauge field. This implies that the
redundant degrees of freedom of are introduced and must be
removed by the additional constraint, the gauge fixing condition . 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 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 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 -
model, we find that the dynamic effect of substantially
suppresses the -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 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
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