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

The Cultural Reconstruction of Taboo Under Mama Uluk’s Leadership in Kampong Dukuh, a Sundanese Traditional Hamlet in Garut Regency West Java Indonesia

 Kampung Dukuh yang terletak di Desa Ciroyom, Kecamatan Cikelet Kabupaten Garut merupa- kan salah satu kampung adat yang ada di Jawa Barat yang memiliki banyak keunikan. Pamali sebagai salah satu sistem pengetahuan masyarakat adat Sunda. Pamali masih dipertahankan dalam kebu- dayaan masyarakat adat Kampung Dukuh. Walaupun tidak ada resiko yang tertulis ketika melaku- kan hal yang melanggar pamali, namun masyarakat kampung adat masih merasa takut durhaka atau dosa jika pamali tidak dilaksanakan dalam keseharian hidupnya. Sekaitan dengan hal ini, penelitian ini bertujuan menggambarkan berbagai larangan atau pamali yang telah direkonstruksi di masa kepemimpinan Mama Uluk di kampung Adat Dukuh Kabupaten Garut. Penelitian ini mengguna- kan metode kualitatif dengan teknik pengumpulan data wawancara dan pengamatan langsung. Hasil penelitian ini menjelaskan cara penyampaian larangan pada waktu yang telah ditentukan dan jenis larangan atau pamali atau pamali yang dipelihara dan terus diwariskankan secara turun temurun sampai saat ini dalam kehidupan sehari-hari seperti larangan di Makom Syech Jalil, Hutan Lindung dan bagaimana ketua adat (mama uluk) dalam kepemimpinannya merekonstruksi budaya tersebut dalam kehidupan keseharian mereka di kampung Dukuh kabupaten Garut.

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

Modeling pedestrian evacuation movement in a swaying ship

With the advance in living standard, cruise travel has been rapidly expanding around the world in recent years. The transportation of passengers in water has also made a rapid development. It is expected that ships will be more and more widely used. Unfortunately, ship disasters occurred in these years caused serious losses. It raised the concern on effectiveness of passenger evacuation on ships. The present study thus focuses on pedestrian evacuation features on ships. On ships, passenger movements are affected by the periodical water motion and thus are quite different from the characteristic when walking on static horizontal floor. Taking into consideration of this special feature, an agent-based pedestrian model is formulized and the effect of ship swaying on pedestrian evacuation efficiency is investigated. Results indicated that the proposed model can be used to quantify the special evacuation process on ships.Comment: Traffic and Granular Flow'15, At Delft, the Netherland

Electromagnetic Form Factors of the Nucleon and Compton Scattering

We review the experimental and theoretical status of elastic electron scattering and elastic low-energy photon scattering (with both real and virtual photons) from the nucleon. As a consequence of new experimental facilities and new theoretical insights, these subjects are advancing with unprecedented precision. These reactions provide many important insights into the spatial distributions and correlations of quarks in the nucleon.Comment: 47 pages, 18 figures, includes corrections and updates to published manuscrip

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 $\gamma$ in ${\mathbb R}^3$, and two analytic non-vanishing orthogonal vector fields $v$ and $w$ along $\gamma$, find an isothermic surface that is tangent to $\gamma$ and that has $v$ and $w$ 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 $\gamma$, 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 $$W_2=\sum_e \beta^2(e)\quad W_{2,w}=\sum_e (n_i+n_j)\beta^2(e),$$ where $\beta(e)$ is the extrinsic intersection angle of the circumcircles of the triangles of the mesh sharing the edge $e=(ij)$ and $n_i$ is the valence of vertex $i$. Besides minimizing the squared local conformal discrete Willmore energy $W$ this functional also minimizes local differences of the angles $\beta$. 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 $W_2$ and $W_{2,w}$ 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