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4,137
A Critical Point For Random Graphs With A Given Degree Sequence
, 2000
"... Given a sequence of nonnegative real numbers 0 ; 1 ; : : : which sum to 1, we consider random graphs having approximately i n vertices of degree i. Essentially, we show that if P i(i \Gamma 2) i ? 0 then such graphs almost surely have a giant component, while if P i(i \Gamma 2) i ! 0 the ..."
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Cited by 507 (8 self)
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Given a sequence of nonnegative real numbers 0 ; 1 ; : : : which sum to 1, we consider random graphs having approximately i n vertices of degree i. Essentially, we show that if P i(i \Gamma 2) i ? 0 then such graphs almost surely have a giant component, while if P i(i \Gamma 2) i ! 0
On estimating the expected return on the market  an exploratory investigation
 JOURNAL OF FINANCIAL ECONOMICS
, 1980
"... The expected market return is a number frequently required for the solution of many investment and corporate tinance problems, but by comparison with other tinancial variables, there has been little research on estimating this expected return. Current practice for estimating the expected market retu ..."
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Cited by 490 (3 self)
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from this exploratory investigation are: (1) in estimating models of the expected market return, the nonnegativity restriction of the expected excess return should be explicitly included as part of the specification; (2) estimators which use realized returns should be adjusted for heteroscedasticity.
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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with loops (undirected cycles). The algorithm is an exact inference algorithm for singly connected networks the beliefs converge to the cor rect marginals in a number of iterations equal to the diameter of the graph.1 However, as Pearl noted, the same algorithm will not give the correct beliefs for mul
Turing Compute Model for Nonnegative Binary Numbers
, 2009
"... Turingcomputable issue is important in research of Turing Machine and has significant value in both theory and practice. The paper analyzes Turingcomputable issue of nonnegative numbers by relational operations(includes greater than, less than and equal) and arithmetic operations(includes add op ..."
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Turingcomputable issue is important in research of Turing Machine and has significant value in both theory and practice. The paper analyzes Turingcomputable issue of nonnegative numbers by relational operations(includes greater than, less than and equal) and arithmetic operations(includes add
MOD M NORMALITY OF βEXPANSIONS YOUNGHO AHN
"... Abstract. If β> 1, then every nonnegative number x has a βexpansion, i.e., x = 0(x) + ..."
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Abstract. If β> 1, then every nonnegative number x has a βexpansion, i.e., x = 0(x) +
PROBABILISTIC NONNEGATIVE TENSOR FACTORIZATION USING MARKOV Chain Monte Carlo
, 2009
"... We present a probabilistic model for learning nonnegative tensor factorizations (NTF), in which the tensor factors are latent variables associated with each data dimension. The nonnegativity constraint for the latent factors is handled by choosing priors with support on the nonnegative numbers. T ..."
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Cited by 4 (0 self)
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We present a probabilistic model for learning nonnegative tensor factorizations (NTF), in which the tensor factors are latent variables associated with each data dimension. The nonnegativity constraint for the latent factors is handled by choosing priors with support on the nonnegative numbers
NONNEGATIVE TENSOR APPROXIMATIONS
"... Abstract. Necessary conditions are derived for a rankr tensor to be a best rankr approximation of a given tensor. It is shown that a positive tensor with rank> 1 has a unique rank one approximation, and that a non negative tensor generally has a unique lowrank nonnegative approximate. We discu ..."
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Abstract. Necessary conditions are derived for a rankr tensor to be a best rankr approximation of a given tensor. It is shown that a positive tensor with rank> 1 has a unique rank one approximation, and that a non negative tensor generally has a unique lowrank nonnegative approximate. We
Constructing Median Constrained Minimum Spanning Tree
"... 1 Introduction Consider a complete graph G(V; E) with node set V = fv1; v2; : : : ; vn g. In the problem which we consider, both edges and nodes are assigned numbers to reflect their lengths and weights. Associated with each edge (vi; vj) is a nonnegative number d(i; j) that represents its length. ..."
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1 Introduction Consider a complete graph G(V; E) with node set V = fv1; v2; : : : ; vn g. In the problem which we consider, both edges and nodes are assigned numbers to reflect their lengths and weights. Associated with each edge (vi; vj) is a nonnegative number d(i; j) that represents its length
Distinguished representations of nonnegative polynomials
"... Abstract. Let g1,..., gr ∈ R[x1,..., xn] such that the set K = {g1 ≥ 0,..., gr ≥ 0} in R n is compact. We study the problem of representing polynomials f with fK ≥ 0 in the form f = s0 + s1g1 + · · · + srgr with sums of squares si, with particular emphasis on the case where f has zeros in K. Assu ..."
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Cited by 18 (2 self)
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. Assuming that the quadratic module of all such sums is archimedean, we establish a localglobal condition for f to have such a representation, visàvis the zero set of f in K. This criterion is most useful when f has only finitely many zeros in K. We present a number of concrete situations where
Analysis on Nonnegative Factorizations and Applications
"... In this work we apply nonnegative matrix factorizations (NMF) to some imaging and inverse problems. We propose a sparse lowrank approximation of big data and images in terms of tensor products, and investigate its effectiveness in terms of the number of tensor products to be used in the approximat ..."
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In this work we apply nonnegative matrix factorizations (NMF) to some imaging and inverse problems. We propose a sparse lowrank approximation of big data and images in terms of tensor products, and investigate its effectiveness in terms of the number of tensor products to be used
Results 1  10
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4,137