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Indexing by latent semantic analysis
 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
, 1990
"... A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higherorder structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The p ..."
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Cited by 3779 (35 self)
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are represented as pseudodocument vectors formed from weighted combinations of terms, and documents with suprathreshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.
Temporal integration of nasal irritation from ammonia at threshold and suprathreshold
, 2005
"... Two experiments examined integration of perceived irritation over shortterm (~100–4000 ms) delivery of ammonia into the nasal cavity of human subjects. Experiment 1 examined tradeoffs between time and concentration at threshold level by means of nasal lateralization, a common measure of irritation ..."
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Cited by 6 (3 self)
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of irritation threshold. Within experimental sessions, the duration of a fixedconcentration stimulus varied to determine the shortest, detectable pulse. Subjects could lateralize increasingly weaker concentrations with longer stimulus presentations. Experiment 2 examined an analogous tradeoff for suprathreshold
ON THE COSINE PROBLEM
"... Abstract. Green’s review: This paper addresses one of the reviewer’s favourite questions, which has been referred to as ”Chowla’s cosine problem”. Let A ⊆ N be an arbitrary set of n positive integers, and let m(A): = − min X cos(ax). What is m(n) = minA m(A)? That m(n)> 0 follows by noting that ..."
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that the average value of the sum of cosines is zero. J. Bourgain??Acta Arith. 45 (1986), no. 4, 381–389; MR0847298 (87g:11096)] proved that (log n)c m(n)> e for some c> 0 and for n sufficiently large. The objective of this paper is to show that one can take c = 1/2. Indeed, the author obtains the precise
Clustering by Pattern Similarity in Large Data Sets
 In SIGMOD
"... Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, ..."
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Cited by 182 (19 self)
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Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words
Computing the Matrix Cosine
, 2003
"... la, rounding error analysis, SchurParlett method, MATLAB AMS subject classification: 65F30 1. Introduction Motivated by the need to solve numerically a certain class of second order ordinary differential equations, Serbin and Blalock [19] propose an algorithm for computing the cosine of a matri ..."
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Cited by 12 (7 self)
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la, rounding error analysis, SchurParlett method, MATLAB AMS subject classification: 65F30 1. Introduction Motivated by the need to solve numerically a certain class of second order ordinary differential equations, Serbin and Blalock [19] propose an algorithm for computing the cosine of a
KCosine Corner Detection
 Journal of Computers
, 2008
"... Abstract—This study presents a boundarybased corner detection method that achieves robust detection for digital objects containing wide angles and various curves using curvature. The boundary of an object is first represented into curvature measured by Kcosine. Then, by modifying the corner detect ..."
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detection error, this study proposes a suitable K value and curvature threshold for robust corner detection. Furthermore, the proposed Kcosine corner detection (KCD) was verified with several commonly employed digital objects. The experimental results reveal that the proposed method is free from
Discrete Cosine Transfonn
"... only the L values Ro through RL_1 are required, then it can be seen by comparing (15) and (11) that the new algorithm will require fewer multiplications than the FFT method if and will require fewer additions if L < 11.2 (1 + log2 N) (16a) L < 5.6 (1 + log2 N). (16b) Therefore, we conclude tha ..."
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only the L values Ro through RL_1 are required, then it can be seen by comparing (15) and (11) that the new algorithm will require fewer multiplications than the FFT method if and will require fewer additions if L < 11.2 (1 + log2 N) (16a) L < 5.6 (1 + log2 N). (16b) Therefore, we conclude
The Cosine Simplex Algorithm
"... An extension of the simplex algorithm is presented. For a given linear programming problem, a sequence of relaxed linear programming problems is solved until a solution to the original problem is reached. Each successive relaxed problem is obtained from the previous one by adding a single constrain ..."
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Cited by 3 (1 self)
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constraint chosen from the constraints violated by the solution to the previous relaxed problem. This added constraint maximizes the cosine of the angle that the gradient of any violated constraint forms with the gradient of the objective function. In other words, each successive relaxed problem is obtained
The DFT Magnitude of a Realvalued Cosine Sequence
"... This blog may seem a bit trivial to some readers here but, then again, it might be of some value to DSP beginners. It presents a mathematical proof of what is the magnitude of an Npoint discrete Fourier transform (DFT) when the DFT's input is a realvalued sinusoidal sequence. To be specific, ..."
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, if we perform an Npoint DFT on N realvalued timedomain samples of a discrete cosine wave, having exactly integer k cycles over N time samples, the peak magnitude of the cosine wave's positivefrequency spectral component will be Peak spectral magnitude X(k)  =
Results 1  10
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