The Experts below are selected from a list of 30015 Experts worldwide ranked by ideXlab platform
Roy E Welsch - One of the best experts on this subject based on the ideXlab platform.
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growing semantic Vines for robust asset allocation
Social Science Research Network, 2019Co-Authors: Frank Z Xing, Erik Cambria, Roy E WelschAbstract:The vine structure has been widely studied as a graphical representation for high-dimensional dependence modeling, depiction of complicated probability density functions, and robust correlation estimation. However, the number of candidate vine structures grows exponentially as the number of elements increases, making the specification of the best vine structure a challenging issue. In this article, we propose to leverage semantic prior knowledge of assets extracted from their descriptive documents to find a suitable vine structure for financial portfolio optimization. A vine growing algorithm is provided and the robust covariance matrix estimation process is performed on this vine structure. The experiments show that our construction of a semantic vine is superior to the state-of-the-art arbitrary vine-growing method. The effectiveness of using semantic Vines for robust correlation estimation for the classic asset allocation model on a large scale is also demonstrated.
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growing semantic Vines for robust asset allocation
Knowledge Based Systems, 2019Co-Authors: Frank Z Xing, Erik Cambria, Roy E WelschAbstract:Abstract The vine structure has been widely studied as a graphical representation for high-dimensional dependence modeling, depicting complicated probability density functions, and robust correlation estimation. However, specification of the best vine structure is challenging as the number of candidate vine structures grows combinatorially when the number of elements increases. In this article, we propose to leverage semantic prior knowledge of assets extracted from their descriptive documents to find a suitable vine structure for financial portfolio optimization. A vine growing algorithm is provided and the robust covariance matrix estimation process is performed on this vine structure. Our construction of a semantic vine improves the state-of-the-art arbitrary vine-growing method in the context of robust correlation estimation and multi-period asset allocation. The effectiveness of our methods on a large scale is also demonstrated by experiments.
Frank Z Xing - One of the best experts on this subject based on the ideXlab platform.
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growing semantic Vines for robust asset allocation
Social Science Research Network, 2019Co-Authors: Frank Z Xing, Erik Cambria, Roy E WelschAbstract:The vine structure has been widely studied as a graphical representation for high-dimensional dependence modeling, depiction of complicated probability density functions, and robust correlation estimation. However, the number of candidate vine structures grows exponentially as the number of elements increases, making the specification of the best vine structure a challenging issue. In this article, we propose to leverage semantic prior knowledge of assets extracted from their descriptive documents to find a suitable vine structure for financial portfolio optimization. A vine growing algorithm is provided and the robust covariance matrix estimation process is performed on this vine structure. The experiments show that our construction of a semantic vine is superior to the state-of-the-art arbitrary vine-growing method. The effectiveness of using semantic Vines for robust correlation estimation for the classic asset allocation model on a large scale is also demonstrated.
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growing semantic Vines for robust asset allocation
Knowledge Based Systems, 2019Co-Authors: Frank Z Xing, Erik Cambria, Roy E WelschAbstract:Abstract The vine structure has been widely studied as a graphical representation for high-dimensional dependence modeling, depicting complicated probability density functions, and robust correlation estimation. However, specification of the best vine structure is challenging as the number of candidate vine structures grows combinatorially when the number of elements increases. In this article, we propose to leverage semantic prior knowledge of assets extracted from their descriptive documents to find a suitable vine structure for financial portfolio optimization. A vine growing algorithm is provided and the robust covariance matrix estimation process is performed on this vine structure. Our construction of a semantic vine improves the state-of-the-art arbitrary vine-growing method in the context of robust correlation estimation and multi-period asset allocation. The effectiveness of our methods on a large scale is also demonstrated by experiments.
Robert J White - One of the best experts on this subject based on the ideXlab platform.
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sine transcription by rna polymerase iii is suppressed by histone methylation but not by dna methylation
Nature Communications, 2015Co-Authors: Dhaval Varshney, Jana Vavrovaanderson, Andrew J Oler, Victoria H Cowling, Bradley R Cairns, Robert J WhiteAbstract:Short interspersed nuclear elements (SINEs), such as Alu, spread by retrotransposition, which requires their transcripts to be copied into DNA and then inserted into new chromosomal sites. This can lead to genetic damage through insertional mutagenesis and chromosomal rearrangements between non-allelic SINEs at distinct loci. SINE DNA is heavily methylated and this was thought to suppress its accessibility and transcription, thereby protecting against retrotransposition. Here we provide several lines of evidence that methylated SINE DNA is occupied by RNA polymerase III, including the use of high-throughput bisulphite sequencing of ChIP DNA. We find that loss of DNA methylation has little effect on accessibility of SINEs to transcription machinery or their expression in vivo. In contrast, a histone methyltransferase inhibitor selectively promotes SINE expression and occupancy by RNA polymerase III. The data suggest that methylation of histones rather than DNA plays a dominant role in suppressing SINE transcription.
Sam Townsend - One of the best experts on this subject based on the ideXlab platform.
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the influence of control vanes on pneumatic conveying of pulverised fuel at a trifurcator
Powder Technology, 2020Co-Authors: Ismail Abubakar, Benardos Panorios, David T Branson, Donald Giddings, Sam TownsendAbstract:Abstract Distribution of pulverised fuels in pneumatic conveying to conventional boilers is an ongoing research area with the intention of improving the performance of power-generating plants. One of the major challenges is the issue of obtaining an even fuel distribution across the fuel carrying lines to the burners or a customised distribution that satisfies the boiler load demand. A 1/3rd scale pneumatic conveying test rig was tested with inert cenosphere powder in a 3-way split configuration. Flow control vanes, similar to those applied in power plant pulverised fuel conveying lines were fitted into the junction and controlled using pneumatic proportional control actuators to alter the distribution of the powder in the three downstream branch pipes extending from the trifurcator. The measurement of the powder mass flux in each stream was carried out and the sensitivity of the particulate stream was assessed with respect to interference from the vane positions at the trifurcator.
Erik Cambria - One of the best experts on this subject based on the ideXlab platform.
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growing semantic Vines for robust asset allocation
Social Science Research Network, 2019Co-Authors: Frank Z Xing, Erik Cambria, Roy E WelschAbstract:The vine structure has been widely studied as a graphical representation for high-dimensional dependence modeling, depiction of complicated probability density functions, and robust correlation estimation. However, the number of candidate vine structures grows exponentially as the number of elements increases, making the specification of the best vine structure a challenging issue. In this article, we propose to leverage semantic prior knowledge of assets extracted from their descriptive documents to find a suitable vine structure for financial portfolio optimization. A vine growing algorithm is provided and the robust covariance matrix estimation process is performed on this vine structure. The experiments show that our construction of a semantic vine is superior to the state-of-the-art arbitrary vine-growing method. The effectiveness of using semantic Vines for robust correlation estimation for the classic asset allocation model on a large scale is also demonstrated.
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growing semantic Vines for robust asset allocation
Knowledge Based Systems, 2019Co-Authors: Frank Z Xing, Erik Cambria, Roy E WelschAbstract:Abstract The vine structure has been widely studied as a graphical representation for high-dimensional dependence modeling, depicting complicated probability density functions, and robust correlation estimation. However, specification of the best vine structure is challenging as the number of candidate vine structures grows combinatorially when the number of elements increases. In this article, we propose to leverage semantic prior knowledge of assets extracted from their descriptive documents to find a suitable vine structure for financial portfolio optimization. A vine growing algorithm is provided and the robust covariance matrix estimation process is performed on this vine structure. Our construction of a semantic vine improves the state-of-the-art arbitrary vine-growing method in the context of robust correlation estimation and multi-period asset allocation. The effectiveness of our methods on a large scale is also demonstrated by experiments.
