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Partitions and constantvalue codes
"... Abstract. We study the relationship between partitions of some integer a in GF (p) in unequal parts of size at most (p − 1)/2, and binary vectors with socalled value a. In particular we investigate a group of transformations acting on the family A = {A,A,..., A}, where A stands for the set of all v ..."
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Abstract. We study the relationship between partitions of some integer a in GF (p) in unequal parts of size at most (p − 1)/2, and binary vectors with socalled value a. In particular we investigate a group of transformations acting on the family A = {A,A,..., A}, where A stands for the set of all
Abbreviation Parameter Units Constant Value
"... Acap capacitive area cm2 0.0001534 [1] aaAE transition rate (AE) N/A N/A aaCHE transition rate (CHE) N/A N/A aaNBC transition rate (NBC) N/A N/A aaNHE transition rate (NHE) N/A N/A [ADP] ADP concentration mM N/A αh opening rate constant for h gate ms1 N/A αj opening rate constant for j gate ms1 N/ ..."
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Acap capacitive area cm2 0.0001534 [1] aaAE transition rate (AE) N/A N/A aaCHE transition rate (CHE) N/A N/A aaNBC transition rate (NBC) N/A N/A aaNHE transition rate (NHE) N/A N/A [ADP] ADP concentration mM N/A αh opening rate constant for h gate ms1 N/A αj opening rate constant for j gate ms1 N
Depth first search and linear graph algorithms
 SIAM JOURNAL ON COMPUTING
, 1972
"... The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect ..."
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Cited by 1406 (19 self)
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The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components
Constant propagation with conditional branches
 ACM Transactions on Programming Languages and Systems
, 1991
"... Constant propagation is a wellknown global flow analysis problem. The goal of constant propagation is to discover values that are constant on all possible executions of a program and to propagate these constant values as far forward through the program as possible. Expressions whose operands are al ..."
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Cited by 355 (1 self)
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Constant propagation is a wellknown global flow analysis problem. The goal of constant propagation is to discover values that are constant on all possible executions of a program and to propagate these constant values as far forward through the program as possible. Expressions whose operands
Bayesian Interpolation
 NEURAL COMPUTATION
, 1991
"... Although Bayesian analysis has been in use since Laplace, the Bayesian method of modelcomparison has only recently been developed in depth. In this paper, the Bayesian approach to regularisation and modelcomparison is demonstrated by studying the inference problem of interpolating noisy data. T ..."
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Cited by 728 (17 self)
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. `Occam's razor' is automatically embodied by this framework. The way in which Bayes infers the values of regularising constants and noise levels has an elegant interpretation in terms of the effective number of parameters determined by the data set. This framework is due to Gull and Skilling.
Perspectives on Program Analysis
, 1996
"... eing analysed. On the negative side, the semantic correctness of the analysis is seldom established and therefore there is often no formal justification for the program transformations for which the information is used. The semantics based approach [1; 5] is often based on domain theory in the form ..."
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Cited by 685 (35 self)
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in the form of abstract domains modelling sets of values, projections, or partial equivalence relations. The approach tends to focus more directly on discovering the extensional properties of interest: for constant propagation it might operate on sets of values with constancy corresponding to singletons
House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle
, 2002
"... I develop a general equilibrium model with sticky prices, credit constraints, nominal loans and asset prices. Changes in asset prices modify agents ’ borrowing capacity through collateral value; changes in nominal prices affect real repayments through debt deflation. Monetary policy shocks move asse ..."
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Cited by 512 (10 self)
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I develop a general equilibrium model with sticky prices, credit constraints, nominal loans and asset prices. Changes in asset prices modify agents ’ borrowing capacity through collateral value; changes in nominal prices affect real repayments through debt deflation. Monetary policy shocks move
Regression Shrinkage and Selection Via the Lasso
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1994
"... We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactl ..."
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Cited by 4212 (49 self)
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We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients
Implementing data cubes efficiently
 In SIGMOD
, 1996
"... Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total ..."
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Cited by 548 (1 self)
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total sales. The values of many of these cells are dependent on the values of other cells in the data cube..A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach
Exact Matrix Completion via Convex Optimization
, 2008
"... We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfe ..."
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Cited by 873 (26 self)
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perfectly recover most lowrank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys m ≥ C n 1.2 r log n for some positive numerical constant C, then with very high probability, most n × n matrices of rank r can be perfectly recovered
Results 1  10
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