Social networks often provide group features to help users with similar interests associate and consume content together. Recommending groups to users poses challenges due to their complex relationship: user-group affinity is typically measured implicitly and varies with time; similarly, group characteristics change as users join and leave. To tackle these challenges, we adapt existing matrix f...

We used QCD factorization for the hadronic matrix elements to show that the existing data, in particular the branching ratios BR ( ?J/?K) and BR ( ?J/??), can be accounted for this approach. We analyzed the decay within the framework of QCD factorization. We have complete calculation of the relevant hard-scattering kernels for twist-2 and twist-3. We calculated this decays in a special scale ...

Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative data with the sparseness constraint. The degree of sparseness plays an important role for model regularization. This paper presents Bayesian group sparse learning for NMF and applies it for single-channel source separation. This method establishes the common bases and individual bases to characteri...

Z[ √ −5] = {a+ b √ −5 : a, b ∈ Z}, because 2, 3, and 1± √ −5 are irreducible and nonassociate. These notes present a formula that in some sense measures the extent to which unique factorization fails in environments such as Z[ √ −5]. The large-scale methodology deserves immediate note, before the reader is immersed in a long succession of smaller attention-filling specifics: • algebra lets us d...

Deep convolutional neural networks (Deep ConvNets or CNNs) have exhibited their promise as a universal image representation for recognition. In this work we explore how the transferability of such deep ConvNet representations trained on large-scale annotated object-centric datasets (ImageNet) can be further enhanced for other visual recognition tasks with limited amount of unlabeled training da...

Many observable data sets such as images, videos and speech can be modeled by a mixture of manifolds which are the result of multiple factors (latent variables). In this paper, we propose a novel algorithm to learn multiple linear manifolds for face recognition, called Group Sparse Non-negative Matrix Factorization (GSNMF). Via the group sparsity constraint imposed on the column vectors of the ...

LetF be a 2-factorization of the complete graphKv admitting an automorphism groupG acting primitively on the set of vertices. IfF consists of Hamiltonian cycles, thenF is the unique, up to isomorphisms, 2-factorization ofKpn admitting an automorphism group which acts 2-transitively on the vertex-set, see [A. Bonisoli, M. Buratti, G. Mazzuoccolo, Doubly transitive 2-factorizations, J. Combin.Des...

We propose a generative model of a group EEG analysis, based on appropriate kernel assumptions on EEG data. We derive the variational inference update rule using various approximation techniques. The proposed model outperforms the current state-of-the-art algorithms in terms of common pattern extraction. The validity of the proposed model is tested on the BCI competition dataset.

User groups on photo sharing websites, such as Flickr, are self-organized communities to share photos and conversations with similar interest and have gained massive popularity. However, the huge volume of groups brings troubles for users to decide which group to choose. Further, directly applying collaborative filtering techniques to group recommendation will suffer from cold start problem sin...