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Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework
Maoguo GONG, Xiangming JIANG, Hao LI
Front. Comput. Sci.. 2017, 11 (3): 362-391.
https://doi.org/10.1007/s11704-016-5552-0
Ill-posed problems are widely existed in signal processing. In this paper, we review popular regularization models such as truncated singular value decomposition regularization, iterative regularization, variational regularization. Meanwhile, we also retrospect popular optimization approaches and regularization parameter choice methods. In fact, the regularization problem is inherently a multiobjective problem. The traditional methods usually combine the fidelity term and the regularization term into a singleobjective with regularization parameters, which are difficult to tune. Therefore, we propose a multi-objective framework for ill-posed problems, which can handle complex features of problem such as non-convexity, discontinuity. In this framework, the fidelity term and regularization term are optimized simultaneously to gain more insights into the ill-posed problems. A case study on signal recovery shows the effectiveness of the multi-objective framework for ill-posed problems.
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Integrative cancer genomics: models, algorithms and analysis
Jinyu CHEN, Shihua ZHANG
Front. Comput. Sci.. 2017, 11 (3): 392-406.
https://doi.org/10.1007/s11704-016-5568-5
In the past decade, the remarkable development of high-throughput sequencing technology accelerates the generation of large amount of multiple dimensional data such as genomic, epigenomic, transcriptomic and proteomic data. The comprehensive data make it possible to understand the underlying mechanisms of biology and disease such as cancer systematically. It also provides great challenges for computational cancer genomics due to the complexity, scale and noise of data. In this article, we aim to review the recent developments and progresses of computational models, algorithms and analysis of complex data in cancer genomics. These topics of this paper include the identification of driver mutations, the genetic heterogeneity analysis, genomic markers discovery of drug response, pan-cancer scale analysis and so on.
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Sound image externalization for headphone based real-time 3D audio
Yougen YUAN, Lei XIE, Zhong-Hua FU, Ming XU, Qi CONG
Front. Comput. Sci.. 2017, 11 (3): 419-428.
https://doi.org/10.1007/s11704-016-6182-2
3D audio effects can provide immersive auditory experience, but we often face the so-called in-head localization (IHL) problem in headphone sound reproduction. To address this problem, we propose an effective sound image externalization approach. Specifically, we consider several important factors related to sound propagation, which include image-source model based early reflections with distance decay, wall absorption and air absorption, late reverberation and other dynamic factors like head movement. We apply our sound image externalization approach to a headphone based real-time 3D audio system. Subjective listening tests show that the sound image externalization performance is significantly improved and the sound source direction is preserved as well. A/B preference test further shows that, as compared with a recent popular approach, the proposed approach is mostly preferred by the listeners.
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Tenant-based access control model for multi-tenancy and sub-tenancy architecture in Software-as-a-Service
Qiong ZUO, Meiyi XIE, Guanqiu QI, Hong ZHU
Front. Comput. Sci.. 2017, 11 (3): 465-484.
https://doi.org/10.1007/s11704-016-5081-x
Software-as-a-Service (SaaS) introduces multitenancy architecture (MTA). Sub-tenancy architecture (STA), is an extension of MTA, allows tenants to offer services for subtenant developers to customize their applications in the SaaS infrastructure. In a STA system, tenants can create subtenants, and grant their resources (including private services and data) to their subtenants. The isolation and sharing relations between parent-child tenants, sibling tenants or two non-related tenants are more complicated than those between tenants in MTA. It is important to keep service components or data private, and at the same time, allow them to be shared, and support application customizations for tenants. To address this problem, this paper provides a formal definition of a new tenant-based access control model based on administrative role-based access control (ARBAC) forMTA and STA in service-oriented SaaS (called TMS-ARBAC). Autonomous areas (AA) and AA-tree are proposed to describe the autonomy of tenants, including their isolation and sharing relationships. Authorization operations on AA and different resource sharing strategies are defined to create and deploy the access control scheme in STA models. TMS-ARBAC model is applied to design a geographic e-Science platform.
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An approach to improve the quality of object-oriented models from novice modelers through project practice
Zhiyi MA
Front. Comput. Sci.. 2017, 11 (3): 485-498.
https://doi.org/10.1007/s11704-016-5164-8
The defects in object-oriented models will result in poor quality of applications based on the models, and thus it is necessary to know which defects often occur in practice, to what extent they occur, why they occur, and how they can be prevented. To gain deeper insights into these problems, this paper discusses how to improve the quality of objectoriented models from novice modelers through project practice. This paper summarizes a set of typical quality defect types from a large number of the defects, and confirms them through our project practice. Moreover, the paper analyzes the improvement of the quality of object-oriented models by quantifying the level of occurrence for the defect types in different phases of the project practice, and presents preventive measures by analyzing the causes for the defects to occur in object-oriented models in the aspects of syntax, semantics, and pragmatics.
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Reasoning about knowledge, belief and certainty in hierarchical multi-agent systems
Lijun WU, Kaile SU, Yabiao HAN, Jingyu CHEN, Xiangyu LU
Front. Comput. Sci.. 2017, 11 (3): 499-510.
https://doi.org/10.1007/s11704-016-5100-y
Multi-agent systems (MAS) have received extensive studies in the last decade. However, little attention is paid to investigation on reasoning about logics in MAS with hierarchical structures. This paper proposes a complete quantified temporal KBC (knowledge, belief and certainty) logic and corresponding reasoning in hierarchical multi-agent systems (HMAS). The key point is that internal beliefs and certainty, and external belief and certainty are considered in our logic. The internal beliefs and certainty show every agent is autonomous, while the external belief and certainty indicate the mutual influence of mental attitudes between two different agents on different layers in HMAS. To interpret this logic, we propose four classes of corresponding quantified interpreted systems, and define first-order KBC axiomatisations over HMAS, which are sound and complete with respect to the corresponding semantical classes. Finally, we give a case study to show the advantages in terms of expressiveness of our logic.
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Hybrid immunizing solution for job recommender system
Shaha AL-OTAIBI, Mourad YKHLEF
Front. Comput. Sci.. 2017, 11 (3): 511-527.
https://doi.org/10.1007/s11704-016-5241-z
Two traditional recommendation techniques, content-based and collaborative filtering (CF), have been widely used in a broad range of domain areas. Both methods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommendation problem from applicant’s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system (AIS), which has a powerful exploration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors’ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.
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Finding susceptible and protective interaction patterns in large-scale genetic association study
Yuan LI, Yuhai ZHAO, Guoren WANG, Xiaofeng ZHU, Xiang ZHANG, Zhanghui WANG, Jun PANG
Front. Comput. Sci.. 2017, 11 (3): 541-554.
https://doi.org/10.1007/s11704-016-5300-5
Interaction detection in large-scale genetic association studies has attracted intensive research interest, since many diseases have complex traits. Various approaches have been developed for finding significant genetic interactions. In this article, we propose a novel framework SRMiner to detect interacting susceptible and protective genotype patterns. SRMiner can discover not only probable combination of single nucleotide polymorphisms (SNPs) causing diseases but also the corresponding SNPs suppressing their pathogenic functions, which provides a better prospective to uncover the underlying relevance between genetic variants and complex diseases. We have performed extensive experiments on several real Wellcome Trust Case Control Consortium (WTCCC) datasets. We use the pathway-based and the protein-protein interaction (PPI) network-based evaluation methods to verify the discovered patterns. The results show that SRMiner successfully identifies many disease-related genes verified by the existing work. Furthermore, SRMiner can also infer some uncomfirmed but highly possible disease-related genes.
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14 articles
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