The Experts below are selected from a list of 6408 Experts worldwide ranked by ideXlab platform
V. A. Soroka - One of the best experts on this subject based on the ideXlab platform.
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degenerate odd poisson bracket on grassmann variables
Physics of Atomic Nuclei, 2000Co-Authors: V. A. SorokaAbstract:A linear degenerate odd Poisson bracket (antibracket) realized solely on Grassmann variables is proposed. It is revealed that this bracket has at once three Grassmann-odd nilpotent Δ-like differential operators of the first, second and third orders with respect to the Grassmann derivatives. It is shown that these Δ-like operators, together with the Grassmann-odd nilpotent Casimir function of this bracket, form a finite-dimensional Lie superalgebra.
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linear odd poisson bracket on grassmann variables
Physics Letters B, 1999Co-Authors: V. A. SorokaAbstract:Abstract A linear odd Poisson bracket (antibracket) realized solely in terms of Grassmann variables is suggested. It is revealed that the bracket, which corresponds to a semi-simple Lie group, has at once three Grassmann-odd nilpotent Δ -like differential operators of the first, the second and the third orders with respect to Grassmann derivatives, in contrast with the canonical odd Poisson bracket having the only Grassmann-odd nilpotent differential Δ -operator of the second order. It is shown that these Δ -like operators together with a Grassmann-odd nilpotent Casimir function of this bracket form a finite-dimensional Lie superalgebra.
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linear odd poisson bracket on grassmann variables
arXiv: High Energy Physics - Theory, 1998Co-Authors: V. A. SorokaAbstract:A linear odd Poisson bracket (antibracket) realized solely in terms of Grassmann variables is suggested. It is revealed that the bracket, which corresponds to a semi-simple Lie group, has at once three Grassmann-odd nilpotent $\Delta$-like differential operators of the first, the second and the third orders with respect to Grassmann derivatives, in contrast with the canonical odd Poisson bracket having the only Grassmann-odd nilpotent differential $\Delta$-operator of the second order. It is shown that these $\Delta$-like operators together with a Grassmann-odd nilpotent Casimir function of this bracket form a finite-dimensional Lie superalgebra.
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degenerate odd poisson bracket on grassmann variables
arXiv: High Energy Physics - Theory, 1998Co-Authors: V. A. SorokaAbstract:A linear degenerate odd Poisson bracket (antibracket) realized solely on Grassmann variables is presented. It is revealed that this bracket has at once three nilpotent $\Delta$-like differential operators of the first, the second and the third orders with respect to the Grassmann derivatives. It is shown that these $\Delta$-like operators together with the Grassmann-odd nilpotent Casimir function of this bracket form a finite-dimensional Lie superalgebra.
Giovanni Franco Sepulveda - One of the best experts on this subject based on the ideXlab platform.
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implementacion bajo incertidumbre de mercado del algoritmo de optimizacion lerchs Grossmann en depositos de carbon implementation under uncertainty market optimization algorithm lerchs Grossmann in coal deposits
2013Co-Authors: Willy Jose Corzo Linares, Giovanni Franco SepulvedaAbstract:This paper shows a methodology for implementing the optimization algorithm Lerchs - Grossmann in a coal deposit under uncertainty in the price in the market. The methodology consists in make different simulations with optimization algorithm Lerchs - Grossmann, varied ore price in each of these simulations, choosing the price needed to perform the simulations in the current cycle before. Economic analysis was performed for each of the simulations with the aim of fi nding exploitable reserves and the net present value (NPV).
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implementacion bajo incertidumbre de mercado del algoritmo de optimizacion lerchs Grossmann en depositos de carbon
Boletín de Ciencias de la Tierra (Colombia) Num.33, 2013Co-Authors: Willy Jose Corzo Linares, Giovanni Franco SepulvedaAbstract:En el presente trabajo se muestra una metodologia para la implementacion del algoritmo de optimizacion de Lerchs – Grossmann en un deposito de carbon bajo incertidumbre del precio en el mercado. La metodologia consiste en realizar diferentes simulaciones con el algoritmo de optimizacion de Lerchs – Grossmann, variando el precio del mineral en cada una de estas simulaciones, escogiendo los precios necesarios para realizar las simulaciones en el ciclo economico anterior al actual. Se realizo un analisis economico para cada una de las simulaciones con el objetivo de encontrar las reservas explotables y el valor presente neto (VPN).
Willy Jose Corzo Linares - One of the best experts on this subject based on the ideXlab platform.
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implementacion bajo incertidumbre de mercado del algoritmo de optimizacion lerchs Grossmann en depositos de carbon implementation under uncertainty market optimization algorithm lerchs Grossmann in coal deposits
2013Co-Authors: Willy Jose Corzo Linares, Giovanni Franco SepulvedaAbstract:This paper shows a methodology for implementing the optimization algorithm Lerchs - Grossmann in a coal deposit under uncertainty in the price in the market. The methodology consists in make different simulations with optimization algorithm Lerchs - Grossmann, varied ore price in each of these simulations, choosing the price needed to perform the simulations in the current cycle before. Economic analysis was performed for each of the simulations with the aim of fi nding exploitable reserves and the net present value (NPV).
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implementacion bajo incertidumbre de mercado del algoritmo de optimizacion lerchs Grossmann en depositos de carbon
Boletín de Ciencias de la Tierra (Colombia) Num.33, 2013Co-Authors: Willy Jose Corzo Linares, Giovanni Franco SepulvedaAbstract:En el presente trabajo se muestra una metodologia para la implementacion del algoritmo de optimizacion de Lerchs – Grossmann en un deposito de carbon bajo incertidumbre del precio en el mercado. La metodologia consiste en realizar diferentes simulaciones con el algoritmo de optimizacion de Lerchs – Grossmann, variando el precio del mineral en cada una de estas simulaciones, escogiendo los precios necesarios para realizar las simulaciones en el ciclo economico anterior al actual. Se realizo un analisis economico para cada una de las simulaciones con el objetivo de encontrar las reservas explotables y el valor presente neto (VPN).
Kenneth Lapides - One of the best experts on this subject based on the ideXlab platform.
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henryk Grossmann on marx s wage theory and the increasing misery controversy
History of Political Economy, 1994Co-Authors: Kenneth LapidesAbstract:L'A. donne la premiere traduction en anglais d'un texte de Grossmann sur la theorie marxiste du salaire minimum. L'A. presente Grossmann ainsi que la controverse suscitee par la theorie de Marx et ce, en relation avec l'idee de «misere croissante» du proletariat
Kaibin Huang - One of the best experts on this subject based on the ideXlab platform.
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automatic recognition of space time constellations by learning on the grassmann manifold
Global Communications Conference, 2018Co-Authors: Guangxu Zhu, Jiayao Zhang, Kaibin HuangAbstract:Recent breakthroughs in machine learning especially artificial intelligence shift the paradigm of wireless communication towards intelligence radios. One of their core operations is automatic modulation recognition (AMR). Existing research focuses on coherent modulation schemes such as QAM, PSK and FSK. The AMR of (non- coherent) space-time modulation remains an uncharted area despite its wide deployment in modern multiple-input-multiple-output (MIMO) systems. The scheme using a so called Grassmann constellation (comprising unitary matrices) enables rate- enhancement using multi-antennas and blind detection. In this work, we propose an AMR approach for Grassmann constellation based on data clustering, which differs from traditional AMR based on classification using a modulation database. The approach allows algorithms for clustering on the Grassmann manifold (or the Grassmannian), such as Grassmann K-means, originally developed for computer vision to be applied to AMR. In this paper, the maximum- likelihood (ML) Grassmann constellation detection is proved to be equivalent to clustering on the Grassmannian. Thereby, a well-known machine-learning result that was originally established only for the Euclidean space is rediscovered for the Grassmannian.
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automatic recognition of space time constellations by learning on the grassmann manifold
2018Co-Authors: Guangxu Zhu, Jiayao Zhang, Kaibin HuangAbstract:Recent breakthroughs in machine learning especially artificial intelligence shift the paradigm of wireless communication towards intelligence radios. One of their core operations is automatic modulation recognition (AMR). Existing research focuses on coherent modulation schemes such as QAM, PSK and FSK. The AMR of (non-coherent) space-time modulation remains an uncharted area despite its wide deployment in modern multiple-input-multiple-output (MIMO) systems. The scheme using a so called Grassmann constellation enables rate-enhancement using multi-antennas and blind detection. In this work, we propose an AMR approach for Grassmann constellation based on data clustering, which differs from traditional AMR based on classification using a modulation database. The approach allows algorithms for clustering on the Grassmann manifold, such as Grassmann K-means and depth-first search, originally developed for computer vision to be applied to AMR. We further develop an analytical framework for studying and designing these algorithms in the context of AMR. First, the maximum-likelihood Grassmann constellation detection is proved to be equivalent to clustering on the Grassmannian. Thereby, a well-known machine-learning result that was originally established only for the Euclidean space is rediscovered for the Grassmannian. Next, despite a rich literature on algorithmic design, theoretical analysis of data clustering is largely overlooked due to the lack of tractable techniques. We tackle the challenge by introducing probabilistic metrics for measuring the inter-cluster separability and intra-cluster connectivity of received space-time symbols and deriving them using tools from differential geometry and Grassmannian packing. The results provide useful insights into the effects of various parameters ranging from the signal-to-noise ratio to constellation size, facilitating algorithmic design.
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Automatic Recognition of Space-Time Constellations by Learning on the Grassmann Manifold
IEEE Transactions on Signal Processing, 2018Co-Authors: Guangxu Zhu, Jiayao Zhang, Kaibin HuangAbstract:Recent breakthroughs in machine learning shift the paradigm of wireless communication towards intelligence radios. One of their core operations is automatic modulation recognition (AMR). Existing research focuses on coherent modulation schemes such as QAM and FSK. The AMR of (noncoherent) space-time modulation remains an uncharted area despite its deployment in modern multiple-input-multiple-output (MIMO) systems. The scheme using a so-called Grassmann constellation enables rate enhancement. In this paper, we propose an AMR approach for Grassmann constellation based on data clustering, which differs from traditional AMR based on classification using a modulation database. The approach allows algorithms for clustering on the Grassmann manifold (or the Grassmannian), such as Grassmann K-means and depth-first search, to be applied to AMR. We further develop an analytical framework for studying and designing these algorithms in the context of AMR. First, the expectation-maximization algorithm for Grassmann constellation detection is proved to be equivalent to clustering (K-means) on the Grassmannian for a high SNR. Thereby, a well-known machine-learning result that was originally established only for the Euclidean space is rediscovered for the Grassmannian. Next, we tackle the challenge on theoretical analysis of data clustering by introducing probabilistic metrics for measuring the inter-cluster separability and intra-cluster connectivity of received space-time symbols and deriving them using tools from differential geometry. The results provide useful insights into the effects of various parameters ranging from the signal-to-noise ratio to constellation size, facilitating algorithmic design.
