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Two Improved RangeEfficient Algorithms for F0 Estimation ⋆
"... Abstract. We present two new algorithms for rangeefficient F0 estimating problem and improve the previously best known result, proposed by Pavan and Tirthapura in [15]. Furthermore, these algorithms presented in our paper also improve the previously best known result for MaxDominance Norm Problem. ..."
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Abstract. We present two new algorithms for rangeefficient F0 estimating problem and improve the previously best known result, proposed by Pavan and Tirthapura in [15]. Furthermore, these algorithms presented in our paper also improve the previously best known result for MaxDominance Norm Problem
Estimating dominance norms of multiple data streams
 in Proceedings of the 11th European Symposium on Algorithms (ESA
, 2003
"... Abstract. There is much focus in the algorithms and database communities on designing tools to manage and mine data streams. Typically, data streams consist of multiple signals. Formally, a stream of multiple signals is (i, ai,j) where i’s correspond to the domain, j’s index the different signals an ..."
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Cited by 29 (8 self)
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and ai,j ≥ 0 give the value of the jth signal at point i. We study the problem of finding norms that are cumulative of the multiple signals in the data stream. For example, consider the maxdominance norm, defined as i maxj{ai,j}. It may be thought as estimating the norm of the “upper envelope
and S.Tirthapura, “Rangeefficient computation of F0 over massive data streams
 in Proceedings of 21st International Conference on Data Engineering, 2005
"... Efficient onepass computation of F0, the number of distinct elements in a data stream, is a fundamental problem arising in various contexts in databases and networking. We consider the problem of efficiently estimating F0 of a data stream where each element of the stream is an interval of integer ..."
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Cited by 7 (2 self)
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( 12 log 1 log n) bits. Our algorithm improves upon a previous algorithm by BarYossef, Kumar and Sivakumar [5], which requires O ( 15 log 1 log 5 n) processing time per item. Our algorithm can be used to compute the maxdominance norm of a stream of multiple signals, and significantly im
Very sparse stable random projections, estimators and tail bounds for stable random projections
, 2006
"... [36] proposed stable random projections, now a popular tool for data streaming computations, data mining, and machine learning. For example, in data streaming, stable random projections offer a unified, efficient, and elegant methodology for approximating the lα norm of a single data stream, or the ..."
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Cited by 5 (2 self)
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, or the lα distance between a pair of streams, for any 0 < α ≤ 2. [16] and [18] applied stable random projections for approximating the Hamming norm and the maxdominance norm, respectively, using very small α. Another application is to approximate all pairwise lα distances in a data matrix to speed up
Max{stable sketches: estimation of `®¡norms, dominance norms and point queries for non{negative signals
, 2006
"... Let f: f1; 2; : : : ; Ng! [0;1) be a non{negative signal, de¯ned over a very large domain and suppose that we want to be able to address approximate aggregate queries or point queries about f. To answer queries about f, we introduce a new type of random sketches called max{stable sketches. The (idea ..."
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. Max{stable sketches are particularly natural when dealing with maximally updated data streams, logs of record events and dominance norms or relations between large signals. By using only max{stable sketches of relatively small size K << N, we can compute in small space and time: (i) the `®¡norm
Norm, Point, and Distance Estimation Over Multiple Signals Using Max–Stable Distributions
"... Consider a set of signals fs: {1,..., N} → [0,..., M] appearing as a stream of tuples (i, fs(i)) in arbitrary order of i and s. We would like to devise one pass approximate algorithms for estimating various functionals on the dominant signal fmax, defined as fmax = {(i, maxs fs(i)), ∀i}. For exampl ..."
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Cited by 1 (1 self)
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Consider a set of signals fs: {1,..., N} → [0,..., M] appearing as a stream of tuples (i, fs(i)) in arbitrary order of i and s. We would like to devise one pass approximate algorithms for estimating various functionals on the dominant signal fmax, defined as fmax = {(i, maxs fs(i)), ∀i
NearOptimal Private Approximation Protocols via a Black Box Transformation
"... We show the following transformation: any twoparty protocol for outputting a (1 + ε)approximation to f(x, y) = n j=1 g(xj, yj) with probability at least 2/3, for any nonnegative efficienty computable function g, can be transformed into a twoparty private approximation protocol with only a polylo ..."
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Cited by 2 (1 self)
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distance for every p ≥ 0, for the heavy hitters and importance sampling problems with respect to any ℓpnorm, for the maxdominance and other dominant ℓpnorms, for the distinct summation problem, for entropy, for cascaded frequency moments, for subspace approximation and block sampling, and for measuring
Vertical Handovers in Multiple Heterogeneous Wireless Networks: A Measurement Study for the Future Internet
"... As the access patterns of mobile users are diverse and their traffic demand is growing, multiple wireless access networks become dominant and their coexistence will be the norm in the future Internet infrastructure. To evaluate protocols and algorithms in these heterogeneous wireless networking envi ..."
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Cited by 3 (0 self)
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As the access patterns of mobile users are diverse and their traffic demand is growing, multiple wireless access networks become dominant and their coexistence will be the norm in the future Internet infrastructure. To evaluate protocols and algorithms in these heterogeneous wireless networking
• Optimization reformulations • Algorithm and complexity analysis • Numerical experiments2. Sparse PCA (sPCA)
"... p ≤ n • Goal: Find unitnorm vector z ∗ ∈ R n which simultaneously 1. maximizes variance z T A T Az 2. is sparse If sparsity is not required, z ∗ is the dominant right singular vector of A: max zT z z≤1 T A T Az = λmax(A T A) = (σmax(A)) 2. Extracting more components: Discussion above is about the ..."
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p ≤ n • Goal: Find unitnorm vector z ∗ ∈ R n which simultaneously 1. maximizes variance z T A T Az 2. is sparse If sparsity is not required, z ∗ is the dominant right singular vector of A: max zT z z≤1 T A T Az = λmax(A T A) = (σmax(A)) 2. Extracting more components: Discussion above is about
Max Weber and the Reinvention of Popular Power
, 2008
"... Abstract Political scientists are not generally accustomed to treating Max Weber's unusual account of democracyplebiscitary leader democracyas a genuine democratic theory. The typical objection is that Weber's account of democracy in terms of the generation of charismatic leadership is ..."
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Abstract Political scientists are not generally accustomed to treating Max Weber's unusual account of democracyplebiscitary leader democracyas a genuine democratic theory. The typical objection is that Weber's account of democracy in terms of the generation of charismatic leadership
Results 1  10
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