18,550 research outputs found
Learning a Deep Listwise Context Model for Ranking Refinement
Learning to rank has been intensively studied and widely applied in
information retrieval. Typically, a global ranking function is learned from a
set of labeled data, which can achieve good performance on average but may be
suboptimal for individual queries by ignoring the fact that relevant documents
for different queries may have different distributions in the feature space.
Inspired by the idea of pseudo relevance feedback where top ranked documents,
which we refer as the \textit{local ranking context}, can provide important
information about the query's characteristics, we propose to use the inherent
feature distributions of the top results to learn a Deep Listwise Context Model
that helps us fine tune the initial ranked list. Specifically, we employ a
recurrent neural network to sequentially encode the top results using their
feature vectors, learn a local context model and use it to re-rank the top
results. There are three merits with our model: (1) Our model can capture the
local ranking context based on the complex interactions between top results
using a deep neural network; (2) Our model can be built upon existing
learning-to-rank methods by directly using their extracted feature vectors; (3)
Our model is trained with an attention-based loss function, which is more
effective and efficient than many existing listwise methods. Experimental
results show that the proposed model can significantly improve the
state-of-the-art learning to rank methods on benchmark retrieval corpora
Unbiased Learning to Rank with Unbiased Propensity Estimation
Learning to rank with biased click data is a well-known challenge. A variety
of methods has been explored to debias click data for learning to rank such as
click models, result interleaving and, more recently, the unbiased
learning-to-rank framework based on inverse propensity weighting. Despite their
differences, most existing studies separate the estimation of click bias
(namely the \textit{propensity model}) from the learning of ranking algorithms.
To estimate click propensities, they either conduct online result
randomization, which can negatively affect the user experience, or offline
parameter estimation, which has special requirements for click data and is
optimized for objectives (e.g. click likelihood) that are not directly related
to the ranking performance of the system. In this work, we address those
problems by unifying the learning of propensity models and ranking models. We
find that the problem of estimating a propensity model from click data is a
dual problem of unbiased learning to rank. Based on this observation, we
propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker
and an \textit{unbiased propensity model}. DLA is an automatic unbiased
learning-to-rank framework as it directly learns unbiased ranking models from
biased click data without any preprocessing. It can adapt to the change of bias
distributions and is applicable to online learning. Our empirical experiments
with synthetic and real-world data show that the models trained with DLA
significantly outperformed the unbiased learning-to-rank algorithms based on
result randomization and the models trained with relevance signals extracted by
click models
Implementation of three-qubit Toffoli gate in a single step
Single-step implementations of multi-qubit gates are generally believed to
provide a simpler design, a faster operation, and a lower decoherence. For
coupled three qubits interacting with a photon field, a realizable scheme for a
single-step Toffoli gate is investigated. We find that the three qubit system
can be described by four effective modified Jaynes-Cummings models in the
states of two control qubits. Within the rotating wave approximation, the
modified Jaynes-Cummings models are shown to be reduced to the conventional
Jaynes-Cummings models with renormalized couplings between qubits and photon
fields. A single-step Toffoli gate is shown to be realizable with tuning the
four characteristic oscillation periods that satisfy a commensurate condition.
Possible values of system parameters are estimated for single-step Toffli gate.
From numerical calculation, further, our single-step Toffoli gate operation
errors are discussed due to imperfections in system parameters, which shows
that a Toffoli gate with high fidelity can be obtained by adjusting pairs of
the photon-qubit and the qubit-qubit coupling strengthes. In addition, a
decoherence effect on the Toffoli gate operation is discussed due to a thermal
reservoir.Comment: 8 pages, 4 figures, to appear in PR
Constructing solutions to the Bj\"orling problem for isothermic surfaces by structure preserving discretization
In this article, we study an analog of the Bj\"orling problem for isothermic
surfaces (that are more general than minimal surfaces): given a real analytic
curve in , and two analytic non-vanishing orthogonal
vector fields and along , find an isothermic surface that is
tangent to and that has and as principal directions of
curvature. We prove that solutions to that problem can be obtained by
constructing a family of discrete isothermic surfaces (in the sense of Bobenko
and Pinkall) from data that is sampled along , and passing to the limit
of vanishing mesh size. The proof relies on a rephrasing of the
Gauss-Codazzi-system as analytic Cauchy problem and an in-depth-analysis of its
discretization which is induced from the geometry of discrete isothermic
surfaces. The discrete-to-continuous limit is carried out for the Christoffel
and the Darboux transformations as well.Comment: 29 pages, some figure
On a new conformal functional for simplicial surfaces
We introduce a smooth quadratic conformal functional and its weighted version
where
is the extrinsic intersection angle of the circumcircles of the
triangles of the mesh sharing the edge and is the valence of
vertex . Besides minimizing the squared local conformal discrete Willmore
energy this functional also minimizes local differences of the angles
. We investigate the minimizers of this functionals for simplicial
spheres and simplicial surfaces of nontrivial topology. Several remarkable
facts are observed. In particular for most of randomly generated simplicial
polyhedra the minimizers of and are inscribed polyhedra. We
demonstrate also some applications in geometry processing, for example, a
conformal deformation of surfaces to the round sphere. A partial theoretical
explanation through quadratic optimization theory of some observed phenomena is
presented.Comment: 14 pages, 8 figures, to appear in the proceedings of "Curves and
Surfaces, 8th International Conference", June 201
TALKING TO ME? CREATING NETWORKS FROM ONLINE COMMUNITY LOGS
Online communities offer many potential sources of value to individuals and organisations. However, the effectiveness of online communities in delivering benefits such as knowledge sharing depends on the network of social relations within a community. Research in this area aims to understand and op-timize such networks. Researchers in this area employ diverse network creation methods, with little focus on the selection process, the fit of the selected method, or its relative accuracy. In this study we evaluate and compare the performance of four network creation methods. First we review the litera-ture to identify four network creation methods (algorithms) and their underlying assumptions. Using several data sets from an online community we test and compare the accuracy of each method against a baseline (‘actual’) network determined by content analysis. We use visual inspection, network cor-relation analysis and sensitivity analysis to highlight similarities and differences between the methods, and find some differences significant enough to impact study results. Based on our observations we argue for more careful selection of network creation methods. We propose two key guidelines for re-search into social networks that uses unstructured data from online communities. The study contrib-utes to the rigour of methodological decisions underpinning research in this area
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P2-type Na2/3Ni1/3Mn2/3O2 Cathode Material with Excellent Rate and Cycling Performance for Sodium-Ion Batteries
P2-type Na2/3Ni1/3Mn2/3O2 is an air-stable cathode material for sodium-ion batteries. However, it suffers irreversible P2-O2 phase transition in 4.2-V plateau and shows poor cycling stability and rate capability within this plateau. To evaluate the practicability of this material in 2.3–4.1 V voltage range, single-crystal micro-sized P2-type Na2/3Ni1/3Mn2/3O2 with high rate capability and cycling stability is synthesized via polyvinylpyrrolidone (PVP)-combustion method. The electrochemical performance is evaluated by galvanostatic charge-discharge tests. The kinetics of Na+ intercalation/deintercalation is studied detailly with potential intermittent titration technique (PITT), galvanostatic intermittent titration technique (GITT) and cyclic voltammetry (CV). The discharge capacity at 0.1 C in 2.3–4.1 V is 87.6 mAh g−1. It can deliver 91.5% capacity at 40 C rate and keep 89% after 650 cycles at 5C. The calculated theoretical energy density of full cell with hard carbon anode is 210 Wh kg−1. The moderate energy density associated with high power density and long cycle life is acceptable for load adjustment of new-energy power, showing the prospect of practical application
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