9,149 research outputs found
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 GdZn (T = Fe, Co) investigated by X-ray diffraction and spectroscopy
We investigate the magnetic and electronic properties of the GdZn
( = 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
GdCoZn compound has a commensurate antiferromagnetic spin structure
with a magnetic propagation vector =
below the N\'eel temperature ( 5.7 K). Only the Gd ions carry a magnetic moment forming an
antiferromagnetic structure with magnetic representation . For the
ferromagnetic GdFeZn 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 ( 85 K) at the Gd- and edges,
respectively. In addition, a small magnetic signal of about 0.06 of the
jump is recorded at the Zn -edge suggesting that the Zn 4 states are spin
polarized by the Gd 5 extended orbitals
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