2385031
Semi-Supervised Multi-Task Regression
Labeled data are needed for many machine learning applications but the amount available in some applications is scarce. Semi-supervised learning and multi-task learning are two of the approaches that have been proposed to alleviate this problem. In this paper, we seek to integrate these two approaches for regression applications. We first propose a new supervised multi-task regression method called SMTR, which is based on Gaussian processes (GP) with the assumption that the kernel parameters for all tasks share a common prior. We then incorporate unlabeled data into SMTR by changing the kernel function of the GP prior to a data-dependent kernel function, resulting in a semi-supervised extension of SMTR, called SSMTR. Moreover, we incorporate pairwise information into SSMTR to further boost the learning performance for applications in which such information is available. Experiments conducted on two commonly used data sets for multi-task regression demonstrate the effectiveness of our methods.
4846997
Semi-supervised Discriminant Analysis Via CCCP
Linear discriminant analysis (LDA) is commonly used for dimensionality reduction. In real-world applications where labeled data are scarce, LDA does not work very well. However, unlabeled data are often available in large quantities. We propose a novel semi-supervised discriminant analysis algorithm called SSDA$_{\mathit{CCCP}}$. We utilize unlabeled data to maximize an optimality criterion of LDA and use the constrained concave-convex procedure to solve the optimization problem. The optimization procedure leads to estimation of the class labels for the unlabeled data. We propose a novel confidence measure for selecting those unlabeled data points with high confidence. The selected unlabeled data can then be used to augment the original labeled data set for performing LDA. We also propose a variant of SSDA$_{\mathit{CCCP}}$, called M-SSDA$_{\mathit{CCCP}}$, which adopts the manifold assumption to utilize the unlabeled data. Extensive experiments on many benchmark data sets demonstrate the effectiveness of our proposed methods.
16356479
A multi-agent simulation for social agents
Agent-based modeling of human social behavior is an increasingly important research area. Many fully functional multi-agent systems have been developed and have been put to use over the years. But efficient scalable and robust social systems are difficult to engineer, both from the modeling perspective and the implementation perspective. There exist three important difficulties. First, the system needs to have an adaptable agent framework that can successfully make intuitive and deliberative decisions much like a real social participant would. Secondly, the system must have a robust architecture that not only ensures its functioning no matter the simulation, but also provides an easily understood interface that researchers can interact with while running their simulations. Finally, the system must be effectively distributed to handle the necessary number of agents that social research requires to obtain meaningful results. This paper presents our work on creating a multi-agent simulation for social agents that would overcome these three difficulties. Lastly, we present a simulation to test the system and provide the results of that simulation.
18262600
Parameters optimization of support vector regression based on immune particle swarm optimization algorithm
A novel Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as antibodies. Therefore, evaluated the fitness of particles by the Leave-One-Out Cross-Validation (LOOCV) standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters of SVR. It can construct high accuracy and generalization performance regression model rapidly by optimizing the combination of three SVR parameters at the same time. Under the datasets generated from sinx function with additive noise and spectra dataset, simulation results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.
18862565
Inferring Gene Regulatory Networks from Multiple Data Sources Via a Dynamic Bayesian Network with Structural EM
Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone.
9713230
A Direction to Avoid Re-encryption in Cryptographic File Sharing
Almost all cryptographic file sharing systems need reencryption when the sharing was revoked. These systems differ from each other only in the timing of re-encryption. As re-encryption is an expensive operation, it is significant to avoid re-encryption. The purpose of this paper is to advise a direction to avoid re-encryption and facilitate file sharing in cryptographic file sharing system. A Black-box model is set up to achieve this objective. In the model, FPGA or ASIC chips are used to act as the black-box as they have been extensively researched and applied in cryptography. Some applications of FPGA and ASIC in cryptography are detailed in this paper. Their feasibility to be functioned as the black-box is discussed. Also a software implementation on FPGA is attached with tested and effective performance.
16272826
Analyzing the impact of on-chip network traffic on program phases for CMPs
It is known that the execution of programs exhibits repetitive phases; in other words, the execution of programs can be partitioned into segments of execution, during which the application exhibits unique architectural properties. This property has been used for various optimization goals. In addition, phase information is utilized to reduce the run time of the architectural simulation. Conventionally, an application is examined in an architecture-independent manner (such as the number of times a basic block is executed) to extract information about the phases and then only the representative execution intervals are executed to analyze architectural choices. We claim that such approaches are becoming inadequate in the many-core era as application execution is not dominated by the instructions only, but instead the communication structure of the application is becoming as important as the instruction behavior. Hence, we propose to utilize communication behavior to determine the phases of an application. Our results reveal that the inclusion of the communication information can increase the accuracy of the phase detection significantly. Specifically, for SPLASH2 and MineBench applications, the average (geometric mean) CPI error rate with the instruction-based phase detection is 11.01%, while our phase detection scheme has an average error rate of 3.41% when compared to the simulations that run the applications to completion.