Fast Parallel Randomized Algorithm for Nonnegative Matrix Factorization with KL Divergence for Large Sparse Datasets

Nonnegative Matrix Factorization (NMF) with Kullback-Leibler Divergence (NMF-KL) is one of the most significant NMF problems and equivalent to Probabilistic Latent Semantic Indexing (PLSI), which has been successfully applied in many applications. For sparse count data, a Poisson distribution and KL divergence provide sparse models and sparse representation, which describe the random variation better than a normal distribution and Frobenius norm. Specially, sparse models provide more concise understanding of the appearance of attributes over latent components, while sparse representation provides concise interpretability of the contribution of latent components over instances. However, minimizing NMF with KL divergence is much more difficult than minimizing NMF with Frobenius norm; and sparse models, sparse representation and fast algorithms for large sparse datasets are still challenges for NMF with KL divergence. In this paper, we propose a fast parallel randomized coordinate descent algorithm having fast convergence for large sparse datasets to archive sparse models and sparse representation. The proposed algorithm's experimental results overperform the current studies' ones in this problem

Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting

Graph neural networks (GNNs) have been widely applied in multi-variate time-series forecasting (MTSF) tasks because of their capability in capturing the correlations among different time-series. These graph-based learning approaches improve the forecasting performance by discovering and understanding the underlying graph structures, which represent the data correlation. When the explicit prior graph structures are not available, most existing works cannot guarantee the sparsity of the generated graphs that make the overall model computational expensive and less interpretable. In this work, we propose a decoupled training method, which includes a graph generating module and a GNNs forecasting module. First, we use Graphical Lasso (or GraphLASSO) to directly exploit the sparsity pattern from data to build graph structures in both static and time-varying cases. Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model. The experimental results on three real-world datasets show that our novel approach has competitive performance against existing state-of-the-art forecasting algorithms while providing sparse, meaningful and explainable graph structures and reducing training time by approximately 40%. Our PyTorch implementation is publicly available at https://github.com/HySonLab/GraphLASS

Improving indigenous Vietnamese Black Rabbit frozen sperm quality: the role of glycine and sperm selection methods

[EN] Rabbit sperm are known to undergo damage during both cryopreservation and thawing, leading to decreased viability, motility and membrane integrity. Glycine can protect sperm and reduce damage during freezing. Swim-up is a simple semen processing method for selecting good motile sperm. The study evaluated the effect of the swim-up method and glycine with different concentrations supplemented to the frozen medium. Three indigenous black rabbits were selected for semen collection by artificial vagina. Next, semen was selected by swim-up method and diluted with glycine-added frozen medium. The samples were then transferred to 0.5 mL straws, cooled to 15°C and 5°C, placed in liquid nitrogen vapour, and finally placed directly into liquid nitrogen (-196°C). The samples were thawed and evaluated for sperm quality. The results showed that the medium supplemented with 10mM glycine in combination with swim-up method for 30 min gave the best results and was significantly different from the remaining concentrations (P<0.01), with viability rate, overall mobility and membrane integrity of 68.0%, 58.7% and 49.7%, respectively. In conclusion, 10 mM glycine concentration combined with swim-up for 30 min is the optimal choice for freezing local black rabbit semen. The study highlights the importance of optimising freezing protocols to improve the quality of frozen rabbit sperm, which can have important implications for animal breeding and conservation efforts.This study is funded in part by the Can Tho University, Code: T2022-133Tran, TTT.; Duy, NLK.; Hang, NT.; Ngoc, PK.; Tuyen, DND. (2023). Improving indigenous Vietnamese Black Rabbit frozen sperm quality: the role of glycine and sperm selection methods. World Rabbit Science. 31(4):229-236. https://doi.org/10.4995/wrs.2023.1969022923631

Improving Generative Flow Networks with Path Regularization

Generative Flow Networks (GFlowNets) are recently proposed models for learning stochastic policies that generate compositional objects by sequences of actions with the probability proportional to a given reward function. The central problem of GFlowNets is to improve their exploration and generalization. In this work, we propose a novel path regularization method based on optimal transport theory that places prior constraints on the underlying structure of the GFlowNets. The prior is designed to help the GFlowNets better discover the latent structure of the target distribution or enhance its ability to explore the environment in the context of active learning. The path regularization controls the flow in GFlowNets to generate more diverse and novel candidates via maximizing the optimal transport distances between two forward policies or to improve the generalization via minimizing the optimal transport distances. In addition, we derive an efficient implementation of the regularization by finding its closed form solutions in specific cases and a meaningful upper bound that can be used as an approximation to minimize the regularization term. We empirically demonstrate the advantage of our path regularization on a wide range of tasks, including synthetic hypergrid environment modeling, discrete probabilistic modeling, and biological sequence design.Comment: 28 pages, 2 figures, 5 tables. Anh Do, Duy Dinh, and Tan Nguyen contributed equally to this wor

Kognitivne perspektive, inovativnost i konkurentska prednost stratega: empirijska studija u Vijetnamu

The main aim of this study is to investigate the relationship between strategists’ intuitive and analytical thinking, innovation, and corporate competitive advantage. This study not only proposes the new model to the academic world but also provides the empirical investigation on the direct and indirect effect of a strategist’s analytic reasoning perspective and strategist’s generative reasoning perspective on innovation and competitive advantage as well as the mediating role of innovation between the strategist’s cognitive perspective of reasoning and corporate competitive advantage. This study conducted questionnaires of 382 samples in state-owned companies, FDI, and private companies in Vietnam. Structure equation modelling was applied through smart PLS to analyse the valid data. The results provide substantial evidence of significant relationships between strategists’ cognitive perspectives of reasoning, innovation, and competitive advantage in the context of State own, FDI, and private companies in Vietnam. Besides, the findings also show that there are non-relationships in the direct effect between product innovation and competitive advantage and between marketing innovation and competitive advantage. Moreover, the research results imply various managerial implications regarding how organizations successfully increase their competitive advantage by increasing their leader’s cognition in management.Glavni cilj ove studije je istražiti odnos između intuitivnog i analitičkog razmišljanja stratega, inovativnosti i konkurentske prednosti poduzeća. Ova studija ne samo da predlaže novi model akademskom svijetu, već također pruža empirijsko istraživanje izravnog i neizravnog učinka analitičke perspektive razmišljanja stratega i perspektive generativnog razmišljanja stratega o inovacijama i konkurentskoj prednosti, kao i o posredničkoj ulozi inovacije između kognitivne perspektive razmišljanja stratega i konkurentske prednosti poduzeća. Ovim istraživanjem provedena je anketa s 382 uzoraka u državnim tvrtkama, izravnim stranim ulaganjima i privatnim tvrtkama u Vijetnamu. Modeliranje strukturnih jednadžbi primijenjeno je putem pametnog PLS-a za analizu valjanih podataka. Rezultati pružaju bitne dokaze o značajnim odnosima između kognitivnih perspektiva razmišljanja, inovativnosti i konkurentske prednosti stratega u kontekstu državnih poduzeća, izravnih stranih ulaganja i privatnih tvrtki u Vijetnamu. Osim toga, nalazi također upućuju na ne postojanje veze s izravnom učinkom ni između inovacije proizvoda i konkurentske prednosti niti između marketinške inovacije i konkurentske prednosti. Štoviše, rezultati istraživanja upućuju na različite menadžerske implikacije o tome kako organizacije uspješno povećavaju svoju konkurentsku prednost povećanjem kognitivnih sposobnosti svog lidera u upravljanju