A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape

Large Deviation Approach to the Randomly Forced Navier-Stokes Equation

The random forced Navier-Stokes equation can be obtained as a variational problem of a proper action. By virtue of incompressibility, the integration over transverse components of the fields allows to cast the action in the form of a large deviation functional. Since the hydrodynamic operator is nonlinear, the functional integral yielding the statistics of fluctuations can be practically computed by linearizing around a physical solution of the hydrodynamic equation. We show that this procedure yields the dimensional scaling predicted by K41 theory at the lowest perturbative order, where the perturbation parameter is the inverse Reynolds number. Moreover, an explicit expression of the prefactor of the scaling law is obtained.Comment: 24 page

Magnetic properties of GdT2T_2Zn20_{20} (T = Fe, Co) investigated by X-ray diffraction and spectroscopy

We investigate the magnetic and electronic properties of the Gd$T_2$Zn$_{20}$ ($T$ = Fe and Co) compounds using X-ray resonant magnetic scattering (XRMS), X-ray absorption near-edge structure (XANES) and X-ray magnetic circular dichroism (XMCD) techniques. The XRMS measurements reveal that the GdCo$_2$Zn$_{20}$ compound has a commensurate antiferromagnetic spin structure with a magnetic propagation vector $\vec{\tau}$ = $(\frac{1}{2},\frac{1}{2},\frac{1}{2})$ below the N\'eel temperature ($T_N \sim$ 5.7 K). Only the Gd ions carry a magnetic moment forming an antiferromagnetic structure with magnetic representation $\Gamma_6$. For the ferromagnetic GdFe$_2$Zn$_{20}$ compound, an extensive investigation was performed at low temperature and under magnetic field using XANES and XMCD techniques. A strong XMCD signal of about 12.5 $\%$ and 9.7 $\%$ is observed below the Curie temperature ($T_C \sim$ 85 K) at the Gd-$L_2$ and $L_3$ edges, respectively. In addition, a small magnetic signal of about 0.06 $\%$ of the jump is recorded at the Zn $K$-edge suggesting that the Zn 4$p$ states are spin polarized by the Gd 5$d$ extended orbitals