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Impact of Artificial Reservoir Water Impoundment on Global Sea Level
By reconstructing the history of water impoundment in the world's artificial reservoirs, we show that a total of ∼10,800 cubic kilometers of water has been impounded on land to date, reducing theExpand
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Minimax Rates of Entropy Estimation on Large Alphabets via Best Polynomial Approximation
  • Y. Wu, P. Yang
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 1 July 2014
TLDR
We show that the minimax mean-square error is within the universal multiplicative constant factors of (k/n log k)2 t log2 k/n if n exceeds a constant factor of (K/log k). Expand
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Chebyshev polynomials, moment matching, and optimal estimation of the unseen
We consider the problem of estimating the support size of a discrete distribution whose minimum non-zero mass is at least $ \frac{1}{k}$. Under the independent sampling model, we show that the sampleExpand
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A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
TLDR
A deep learning based segmentation model with Flair, T1c, and T2 scans achieves competitive performance. Expand
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Optimal prediction of the number of unseen species
Significance Many scientific applications ranging from ecology to genetics use a small sample to estimate the number of distinct elements, known as ”species,” in a population. Classical results haveExpand
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Achieving Exact Cluster Recovery Threshold via Semidefinite Programming
TLDR
We show that the semidefinite programming relaxation of the maximum likelihood estimator achieves the optimal threshold for exactly recovering the partition from the graph with probability tending to one, resolving a conjecture of Abbe et al. Expand
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Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions
TLDR
Resolving a conjecture of Abbe, Bandeira, and Hall, the authors have recently shown that the semidefinite programming (SDP) relaxation of the maximum likelihood estimator achieves the sharp threshold for exactly recovering the community structure under the binary stochastic block model of two equal-sized clusters. Expand
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Near-Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices Via Sparse Power Factorization
  • K. Lee, Y. Wu, Y. Bresler
  • Computer Science, Mathematics
  • IEEE Transactions on Information Theory
  • 2 December 2013
TLDR
Compressed sensing of simultaneously sparse and low-rank matrices enables recovery of sparse signals from a few linear measurements of their bilinear form. Expand
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Strong data-processing inequalities for channels and Bayesian networks
TLDR
The data-processing inequality, that is, I(U; Y) for a Markov chain U → X → Y, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. Expand
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The Discrete Logarithm Problem in GL(n, q)
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