A Feature Selection Method for Multivariate Performance Measures

Feature selection with specific multivariate performance measures is the key to the success of many applications, such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. Based on the proposed regularizer, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed to solve this problem, and the convergence is presented. In addition, we adapt the proposed method to optimize multivariate measures for multiple instance learning problems. The analyses by comparing with the state-of-the-art feature selection methods show that the proposed method is superior to others. Extensive experiments on large-scale and high-dimensional real world datasets show that the proposed method outperforms $l_1$-SVM and SVM-RFE when choosing a small subset of features, and achieves significantly improved performances over SVM$^{perf}$ in terms of $F_1$-score

Efficient Multi-Template Learning for Structured Prediction

Conditional random field (CRF) and Structural Support Vector Machine (Structural SVM) are two state-of-the-art methods for structured prediction which captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on the whole input observations. These features are usually generated by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate this issue, in this paper, we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution. This paradigm can be formulated as a special multiple kernel learning problem with exponential number of constraints. Then we introduce an efficient cutting plane algorithm to solve this problem in the primal, and its convergence is presented. We also evaluate the proposed learning paradigm on two widely-studied structured prediction tasks, \emph{i.e.} sequence labeling and dependency parsing. Extensive experimental results show that the proposed method outperforms CRFs and Structural SVMs due to exploiting the importance of each template. Our complexity analysis and empirical results also show that our proposed method is more efficient than OnlineMKL on very sparse and high-dimensional data. We further extend this paradigm for structured prediction using generalized $p$-block norm regularization with $p>1$, and experiments show competitive performances when $p \in [1,2)$

Relativistic Hartree approach including both positive- and negative-energy bound states

We develop a relativistic model to describe the bound states of positive energy and negative energy in finite nuclei at the same time. Instead of searching for the negative-energy solution of the nucleon's Dirac equation, we solve the Dirac equations for the nucleon and the anti-nucleon simultaneously. The single-particle energies of negative-energy nucleons are obtained through changing the sign of the single-particle energies of positive-energy anti-nucleons. The contributions of the Dirac sea to the source terms of the meson fields are evaluated by means of the derivative expansion up to the leading derivative order for the one-meson loop and one-nucleon loop. After refitting the parameters of the model to the properties of spherical nuclei, the results of positive-energy sector are similar to that calculated within the commonly used relativistic mean field theory under the no-sea approximation. However, the bound levels of negative-energy nucleons vary drastically when the vacuum contributions are taken into account. It implies that the negative-energy spectra deserve a sensitive probe to the effective interactions in addition to the positive-energy spectra.Comment: 38 pages, Latex, 8 figures included; Int. J. Mod. Phys. E, in pres

Empirical Study of Simulated Two-planet Microlensing Event

We undertake the first study of two-planet microlensing models recovered from simulations of microlensing events generated by realistic multi-planet systems in which 292 planetary events including 16 two-planet events were detected from 6690 simulated light curves. We find that when two planets are recovered, their parameters are usually close to those of the two planets in the system most responsible for the perturbations. However, in one of the 16 examples, the apparent mass of both detected planets was more than doubled by the unmodeled influence of a third, massive planet. This fraction is larger than, but statistically consistent with, the roughly 1.5% rate of serious mass errors due to unmodeled planetary companions for the 274 cases from the same simulation in which a single planet is recovered. We conjecture that an analogous effect due to unmodeled stellar companions may occur more frequently. For seven out of 23 cases in which two planets in the system would have been detected separately, only one planet was recovered because the perturbations due to the two planets had similar forms. This is a small fraction (7/274) of all recovered single-planet models, but almost a third of all events that might plausibly have led to two-planet models. Still, in these cases, the recovered planet tends to have parameters similar to one of the two real planets most responsible for the anomaly.Comment: 21 pages, 9 figures, 2 tables; submitted to ApJ; for a short video introducing the key results, see https://www.youtube.com/watch?v=qhK4a6sbfO

Bound states of anti-nucleons in finite nuclei

We study the bound states of anti-nucleons emerging from the lower continuum in finite nuclei within the relativistic Hartree approach including the contributions of the Dirac sea to the source terms of the meson fields. The Dirac equation is reduced to two Schr\"{o}dinger-equivalent equations for the nucleon and the anti-nucleon respectively. These two equations are solved simultaneously in an iteration procedure. Numerical results show that the bound levels of anti-nucleons vary drastically when the vacuum contributions are taken into account.Comment: 8 pages, no figures. Proceedings of International Conference on Nonequilibrium and Nonlinear Dynamics in Nuclear and Other Finite Systems, Beijing, China 2001; AIP conference proceedings 597, edited by Zhuxia Li, Ke Wu, Xizhen Wu, Enguang Zhao, and F. Sakata (Melville, New York, 2001) page 112-11

Two particle correlations: a probe of the LHC QCD medium

The properties of $\gamma$--jet pairs emitted in heavy-ion collisions provide an accurate mean to perform a tomographic measurement of the medium created in the collision through the study of the medium modified jet properties. The idea is to measure the distribution of hadrons emitted on the opposite side of the %oppositely by tagging the direct photon. The feasibility of such measurements is studied by applying the approach on the simulation data, we have demonstrated that this method allows us to measure, with a good approximation, both the jet fragmentation and the back-to-back azimuthal alignment of the direct photon and the jet. Comparing these two observables measured in pp collisions with the ones measured in AA collisions reveals the modifications induced by the medium on the jet structure and consequently allows us to infer the medium properties. In this contribution, we discuss a first attempt of such measurements applied to real proton-proton data from the ALICE experiment.Comment: 4 pages, 4 figures, Proceedings for Hot Quark 2010 Conferenc

PRODUCTIVITY GROWTH, TECHNOLOGY PROGRESS, AND EFFICIENCY CHANGE IN CHINESE AGRICULTURAL PRODUCTION FROM 1984 TO 1993

This study applies a Data Envelopment Analysis (DEA) approach to analyze total factor productivity, technology, and efficiency changes in Chinese agricultural production from 1984 to 1993. Twenty- nine provinces in China were classified into advanced-technology and low-technology categories. The Malmquist productivity measures were decomposed into two components: technical change index and efficiency change index. The results showed that total factor productivity has risen in most provinces for both technology categories. Technical progress has been the most important factor to Chinese agricultural productivity growth since 1984 and will remain crucial to productivity growth in low-technology provinces. Low efficiency in many important agricultural provinces indicates a great potential for China to increase productivity through improving technical efficiency. Continuously expanding market economy and enhancing rural education may also help farmers to improve technical efficiency and productivity in agricultural production.Chinese agriculture, Total Factor Productivity (TFP), technology, technical efficiency, Data Envelopment Analysis (DEA)., Productivity Analysis, Research and Development/Tech Change/Emerging Technologies,