We have studied the electronic and magnetic structures of the ternary iron arsenides AFe$_2$As$_2$ (A = Ba, Ca, or Sr) using the first-principles density functional theory. The ground states of these compounds are in a collinear antiferromagnetic ord
er, resulting from the interplay between the nearest and the next-nearest neighbor superexchange antiferromagnetic interactions bridged by As $4p$ orbitals. The correction from the spin-orbit interaction to the band structure is small. The pressure can reduce dramatically the magnetic moment and diminish the collinear antiferromagnetic order. Based on the calculations, we propose that the low energy dynamics of these materials is described effectively by a $t-J_H-J_1-J_2$-type model.
A universal linear-temperature dependence of the uniform magnetic susceptibility has been observed in the nonmagnetic normal state of iron-pnictides. This non-Pauli and non-Curie-Weiss-like paramagnetic behavior cannot be understood within a pure iti
nerant picture. We argue that it results from the existence of a wide antiferromagnetic fluctuation window in which the local spin-density-wave correlations exist but the global directional order has not been established yet.
From first-principles calculations, we have studied the electronic and magnetic structures of the ground state of LaOFeAs. The Fe spins are found to be collinear antiferromagnetic ordered, resulting from the interplay between the strong nearest and n
ext-nearest neighbor superexchange antiferromagnetic interactions. The structure transition observed by neutron scattering is shown to be magnetically driven. Our study suggests that the antiferromagnetic fluctuation plays an important role in the Fe-based superconductors. This sheds light on the understanding of the pairing mechanism in these materials.
We have studied the newly found superconductor compound LaO$_{1-x}$F$_x$FeAs through the first-principles density functional theory calculations. We find that the parent compound LaOFeAs is a quasi-2-dimensional antiferromgnetic semimetal with most c
arriers being electrons and with a magnetic moment of $2.3mu_B$ located around each Fe atom on the Fe-Fe square lattice. Furthermore this is a commensurate antiferromagnetic spin density wave due to the Fermi surface nesting, which is robust against the F-doping. The observed superconduction happens on the Fe-Fe antiferromagnetic layer, suggesting a new superconductivity mechanism, mediated by the spin fluctuations. An abrupt change on the Hall measurement is further predicted for the parent compound LaOFeAs.
Measuring network flow sizes is important for tasks like accounting/billing, network forensics and security. Per-flow accounting is considered hard because it requires that many counters be updated at a very high speed; however, the large fast memori
es needed for storing the counters are prohibitively expensive. Therefore, current approaches aim to obtain approximate flow counts; that is, to detect large elephant flows and then measure their sizes. Recently the authors and their collaborators have developed [1] a novel method for per-flow traffic measurement that is fast, highly memory efficient and accurate. At the core of this method is a novel counter architecture called counter braids. In this paper, we analyze the performance of the counter braid architecture under a Maximum Likelihood (ML) flow size estimation algorithm and show that it is optimal; that is, the number of bits needed to store the size of a flow matches the entropy lower bound. While the ML algorithm is optimal, it is too complex to implement. In [1] we have developed an easy-to-implement and efficient message passing algorithm for estimating flow sizes.
`Tree pruning (TP) is an algorithm for probabilistic inference on binary Markov random fields. It has been recently derived by Dror Weitz and used to construct the first fully polynomial approximation scheme for counting independent sets up to the `t
ree uniqueness threshold. It can be regarded as a clever method for pruning the belief propagation computation tree, in such a way to exactly account for the effect of loops. In this paper we generalize the original algorithm to make it suitable for decoding linear codes, and discuss various schemes for pruning the computation tree. Further, we present the outcomes of numerical simulations on several linear codes, showing that tree pruning allows to interpolate continuously between belief propagation and maximum a posteriori decoding. Finally, we discuss theoretical implications of the new method.
