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 $p \in [1,2)$

Generalised theory on asymptotic stability and boundedness of stochastic functional differential equations

Asymptotic stability and boundedness have been two of most popular topics in the study of stochastic functional differential equations (SFDEs) (see e.g. Appleby and Reynolds (2008), Appleby and Rodkina (2009), Basin and Rodkina (2008), Khasminskii (1980), Mao (1995), Mao (1997), Mao (2007), Rodkina and Basin (2007), Shu, Lam, and Xu (2009), Yang, Gao, Lam, and Shi (2009), Yuan and Lygeros (2005) and Yuan and Lygeros (2006)). In general, the existing results on asymptotic stability and boundedness of SFDEs require (i) the coefficients of the SFDEs obey the local Lipschitz condition and the linear growth condition; (ii) the diffusion operator of the SFDEs acting on a C2,1-function be bounded by a polynomial with the same order as the C2,1-function. However, there are many SFDEs which do not obey the linear growth condition. Moreover, for such highly nonlinear SFDEs, the diffusion operator acting on a C2,1-function is generally bounded by a polynomial with a higher order than the C2,1-function. Hence the existing criteria on stability and boundedness for SFDEs are not applicable andwesee the necessity to develop new criteria. Our main aim in this paper is to establish new criteria where the linear growth condition is no longer needed while the up-bound for the diffusion operator may take a much more general form

Stochastic population dynamics under regime switching II

This is a continuation of our paper [Q. Luo, X. Mao, Stochastic population dynamics under regime switching, J. Math. Anal. Appl. 334 (2007) 69-84] on stochastic population dynamics under regime switching. In this paper we still take both white and color environmental noise into account. We show that a sufficient large white noise may make the underlying population extinct while for a relatively small noise we give both asymptotically upper and lower bound for the underlying population. In some special but important situations we precisely describe the limit of the average in time of the population

Stochastic population dynamics under regime switching II

AbstractThis is a continuation of our paper [Q. Luo, X. Mao, Stochastic population dynamics under regime switching, J. Math. Anal. Appl. 334 (2007) 69–84] on stochastic population dynamics under regime switching. In this paper we still take both white and color environmental noise into account. We show that a sufficient large white noise may make the underlying population extinct while for a relatively small noise we give both asymptotically upper and lower bound for the underlying population. In some special but important situations we precisely describe the limit of the average in time of the population

Towards Secure and Safe Appified Automated Vehicles

The advancement in Autonomous Vehicles (AVs) has created an enormous market for the development of self-driving functionalities,raising the question of how it will transform the traditional vehicle development process. One adventurous proposal is to open the AV platform to third-party developers, so that AV functionalities can be developed in a crowd-sourcing way, which could provide tangible benefits to both automakers and end users. Some pioneering companies in the automotive industry have made the move to open the platform so that developers are allowed to test their code on the road. Such openness, however, brings serious security and safety issues by allowing untrusted code to run on the vehicle. In this paper, we introduce the concept of an Appified AV platform that opens the development framework to third-party developers. To further address the safety challenges, we propose an enhanced appified AV design schema called AVGuard, which focuses primarily on mitigating the threats brought about by untrusted code, leveraging theory in the vehicle evaluation field, and conducting program analysis techniques in the cybersecurity area. Our study provides guidelines and suggested practice for the future design of open AV platforms

USE OF ADVANCED TRAFFIC SIGNAL STATUS WARNING SYSTEMS FOR

Signalized intersections are one of the most complicated and risky locations in the transportation network. If drivers make misjudgments and run a red light by mistake, it may put themselves and other road users at a great risk. To assist drivers in making the right decisions when passing through a signalized intersection, two Advanced Traffic Signal Status Warning Systems (ATSSWS), the Variable Message Sign (VMS) based warning system and the Vehicle to Infrastructure (V2I) based onboard driver warning system, were designed and tested by driving simulator-based experiments. The results revealed that both ATSSWSs can significantly reduce vehicles’ maximum deceleration rates, number of red light violations and other critical events when passing through a signalized intersection. Between these two ATSSWSs, the V2I based onboard driver warning system offers more remarkable improvements in most of cases. In addition, a post survey was conducted to the participated drivers for collecting their feedback on these two ATSSWSs. The survey results showed that most of the drivers believe that these two ATSSWSs are helpful and easy to accommodate