Jingwei Zhuo

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PURPOSE To propose and evaluate P-LORAKS a new calibrationless parallel imaging reconstruction framework. THEORY AND METHODS LORAKS is a flexible and powerful framework that was recently proposed for constrained MRI reconstruction. LORAKS was based on the observation that certain matrices constructed from fully sampled k-space data should have low rank(More)
BACKGROUND ABCG2 is a member of the ATP binding cassette (ABC) transporter superfamily and identified to play an important role in multidrug resistance in many studies. But the expression of ABCG2 is controversial in many kinds of tumors including colorectal cancer (CRC). OBJECTIVE To clarify the expression patterns of ABCG2 and elucidate the prognostic(More)
Supervised topic models leverage label information to learn discriminative latent topic representations. As collecting a fully labeled dataset is often time-consuming, semi-supervised learning is of high interest. In this paper, we present an effective semi-supervised max-margin topic model by naturally introducing manifold posterior regularization to a(More)
The Lagrange dual function is: g(u, v) = min x L(x, u, v) The corresponding dual problem is: maxu,v g(u, v) subject to u ≥ 0 The Lagrange dual function can be viewd as a pointwise maximization of some affine functions so it is always concave. The dual problem is always convex even if the primal problem is not convex. For any primal problem and dual problem,(More)
Thompson sampling has impressive empirical performance for many multi-armed bandit problems. But current algorithms for Thompson sampling only work for the case of conjugate priors since these algorithms require to infer the posterior, which is often computationally intractable when the prior is not conjugate. In this paper, we propose a novel algorithm for(More)
Overlapping community detection plays a key role in statistical network modeling. Despite the importance, popular models such as mixed membership stochastic blockmodels (MMSB) [2] are often not applicable to real world massive networks due to limited speed and memory of a single computing node. In this project, we develop distributed inference for models(More)
Most of the sequence tagging tasks in natural language processing require to recognize segments with certain syntactic role or semantic meaning in a sentence. They are usually tackled with Conditional Random Fields (CRFs), which do indirect word-level modeling over word-level features and thus cannot make full use of segment-level information. Semi-Markov(More)
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