Unsupervised learning and clustering using a random field approach

In this work we propose a random field approach to unsupervised machine learning, classifier training and pattern classification. The proposed method treats each sample as a random field and attempts to assign an optimal cluster label to it so as to partition the samples into clusters without a priori knowledge about the number of clusters and the initial centroids. To start with, the algorithm assigns each sample a unique cluster label, making it a singleton cluster. Subsequently, to update the cluster label, the similarity between the sample in question and the samples in a voting pool and their labels are involved. The clusters progressively form without the user specifying their initial centroids, as interaction among the samples continues. Due to its flexibility and adaptability, the proposed algorithm can be easily adjusted for on-line learning and is able to cope with the stability-plasticity dilemma

Partial mixture model for tight clustering of gene expression time-course

Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored. Results: In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms. Conclusion: For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the ombination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion

Scattering amplitudes for dark and bright excitons

Using the composite boson many-body formalism that takes single-exciton states rather than free carrier states as a basis, we derive the integral equation fulfilled by the exciton-exciton effective scattering from which the role of fermion exchanges can be unraveled. For excitons made of $(\pm1/2)$-spin electrons and $(\pm3/2)$-spin holes, as in GaAs heterostructures, one major result is that most spin configurations lead to brightness-conserving scatterings with equal amplitude $\Delta$, in spite of the fact that they involve different carrier exchanges. A brightness-changing channel also exists when two opposite-spin excitons scatter: dark excitons $(2,-2)$ can end either in the same dark states with an amplitude $\Delta_e$, or in opposite-spin bright states $(1,-1)$, with a different amplitude $\Delta_o$, the number of carrier exchanges being even or odd respectively. Another major result is that these amplitudes are linked by a striking relation, $\Delta_e+\Delta_o=\Delta$, which has decisive consequence for exciton Bose-Einstein condensation. Indeed, this relation leads to the conclusion that the exciton condensate can be optically observed through a bright part only when excitons have a large dipole, that is, when the electrons and holes are well separated in two adjacent layers.Comment: 8 pages, 4 figure

Influence of the Independence of Directors and the Combination of CEOs with Chairperson on Firm Value: Evidence from Cote d’Ivoire

Many empirical studies have been conducted to test the impact of the characteristics of board of directors on the performance of stock exchange listed companies in developed countries and emerging countries. There are no abundant literature on the impact of independence and Chief Executive Officer (CEO) duality on corporate performance in Cote d'Ivoire. Cote d'Ivoire is a developing country and according the International Monetary Fund (IFM), one of the three biggest economies in West Africa. Analyzes of developed economies are an example for developing economies countries and more a road map for poor countries to the development. However analyzes of the economies of developing or poor countries constitute a diagnostic and motivation to better lead these countries’ economies to the development. The aim of this study is to determine the effect of the board of directors’ characteristics on the performance of non-financial companies in Cote d'Ivoire. In particular, we focused on the analysis of three characteristics: board size, board independence and the duality of the CEO. Our empirical study has been conducted on a sample of 25 non-financial listing companies for a period from 2002 to 2016 using multiple regression analysis. The modeling was carried out after controlling multi-colinearity and correlation test by using the Hausman specific test, heteroskedasticity test. By controlling variables such as firm size, board meeting and leverage, our empirical results show a positive impact of board size on firm’s performance. It is also found that board independence has a negative effect, while CEO duality has a positive effect on financial performance proxied by ROA. However, when performance is measure by ROE, board independence has a positive effect, while CEO duality has a negative effect. Keywords: Board of Directors, Corporate Performance, Board Size, Board Independence, CEO Duality. DOI: 10.7176/JESD/12-12-04 Publication date:June 30th 2021

Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance.Comment: 17 pages, 14 figures. Submitted to in IEEE Transactions on Aerospace and Electronic System

Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance

ComPASS: a tool for distributed parallel finite volume discretizations on general unstructured polyhedral meshes

International audienceThe objective of the ComPASS project is to develop a parallel multiphase Darcy flow simulator adapted to general unstructured polyhedral meshes (in a general sense with possibly non planar faces) and to the parallelization of advanced finite volume discretizations with various choices of the degrees of freedom such as cell centres, vertices, or face centres. The main targeted applications are the simulation of CO2 geological storage, nuclear waste repository and reservoir simulations. The CEMRACS 2012 summer school devoted to high performance computing has been an ideal framework to start this collaborative project. This paper describes what has been achieved during the four weeks of the CEMRACS project which has been focusing on the implementation of basic features of the code such as the distributed unstructured polyhedral mesh, the synchronization of the degrees of freedom, and the connection to scientific libraries including the partitioner METIS, the visualization tool PARAVIEW, and the parallel linear solver library PETSc. The parallel efficiency of this first version of the ComPASS code has been validated on a toy parabolic problem using the Vertex Approximate Gradient finite volume spacial discretization with both cell and vertex degrees of freedom, combined with an Euler implicit time integration

Adsorption Behavior of Asymmetric Pd Pincer Complexes on a Cu(111) Surface

We address the adsorption of asymmetric Pd pincer complexes on a Cu(111) surface by scanning tunneling microscopy. The structural asymmetry is manifested in the observation of two chiral enantiomers. To enable an unambiguous identification of individual constituents, three closely related complexes with small modifications are investigated in parallel. Thereby, methyl substituents determine attractive molecule-molecule interaction. Depending on their distribution, dimerization and tetramerization can be observed