67,484 research outputs found
Adaptive Evolutionary Clustering
In many practical applications of clustering, the objects to be clustered
evolve over time, and a clustering result is desired at each time step. In such
applications, evolutionary clustering typically outperforms traditional static
clustering by producing clustering results that reflect long-term trends while
being robust to short-term variations. Several evolutionary clustering
algorithms have recently been proposed, often by adding a temporal smoothness
penalty to the cost function of a static clustering method. In this paper, we
introduce a different approach to evolutionary clustering by accurately
tracking the time-varying proximities between objects followed by static
clustering. We present an evolutionary clustering framework that adaptively
estimates the optimal smoothing parameter using shrinkage estimation, a
statistical approach that improves a naive estimate using additional
information. The proposed framework can be used to extend a variety of static
clustering algorithms, including hierarchical, k-means, and spectral
clustering, into evolutionary clustering algorithms. Experiments on synthetic
and real data sets indicate that the proposed framework outperforms static
clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox
available at http://tbayes.eecs.umich.edu/xukevin/affec
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Designing an e-commerce recommender system that serves hundreds of millions
of active users is a daunting challenge. From a human vision perspective,
there're two key factors that affect users' behaviors: items' attractiveness
and their matching degree with users' interests. This paper proposes Telepath,
a vision-based bionic recommender system model, which understands users from
such perspective. Telepath is a combination of a convolutional neural network
(CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its
CNN subnetwork simulates the human vision system to extract key visual signals
of items' attractiveness and generate corresponding activations. Its RNN and
DNN subnetworks simulate cerebral cortex to understand users' interest based on
the activations generated from browsed items. In practice, the Telepath model
has been launched to JD's recommender system and advertising system. For one of
the major item recommendation blocks on the JD app, click-through rate (CTR),
gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71%
respectively. For several major ads publishers of JD demand-side platform, CTR,
GMV and return on investment have increased 6.58%, 61.72% and 65.57%
respectively by the first launch, and further increased 2.95%, 41.75% and
41.37% respectively by the second launch.Comment: 8 pages, 11 figures, 1 tabl
A single extracellular amino acid in Free Fatty Acid Receptor 2 defines antagonist species selectivity and G protein selection bias
Free Fatty Acid Receptor 2 is a GPCR activated by short chain fatty acids produced in high levels in the lower gut by microbial fermentation of non-digestible carbohydrates. A major challenge in studying this receptor is that the mouse ortholog does not have significant affinity for antagonists that are able to block the human receptor. Docking of exemplar antagonists from two chemical series to homology models of both human and mouse Free Fatty Acid Receptor 2 suggested that a single lysine - arginine variation at the extracellular face of the receptor might provide the basis for antagonist selectivity and mutational swap studies confirmed this hypothesis. Extending these studies to agonist function indicated that although the lysine - arginine variation between human and mouse orthologs had limited effect on G protein-mediated signal transduction, removal of positive charge from this residue produced a signalling-biased variant of Free Fatty Acid Receptor 2 in which Gi-mediated signalling by both short chain fatty acids and synthetic agonists was maintained whilst there was marked loss of agonist potency for signalling via Gq/11 and G12/13 G proteins. A single residue at the extracellular face of the receptor thus plays key roles in both agonist and antagonist function
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