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102,446
ON A WALD'S EQUATION AND AVERAGE SAMPLE NUMBER IN A SEQUENTIAL SIGNAL DETECTION By
, 1971
"... It is shown that in detecting sequentially a deterministic signal 0(0 in white noise 72(0 a similar identity (iii) in theorem 2.1, to the Wald's holds concerning a stopping time r determined by making use of a likelihood ratio. It is also shown that r has finite moments of any order under quite ..."
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It is shown that in detecting sequentially a deterministic signal 0(0 in white noise 72(0 a similar identity (iii) in theorem 2.1, to the Wald's holds concerning a stopping time r determined by making use of a likelihood ratio. It is also shown that r has finite moments of any order under quite weak conditions over the signal. The exact A. S. N. E{y} in a constant signal case has been obtained and given by (2, 8). It is also considered a detection problem of a constant signal OW a in a coloured noise based on a suboptimal statistic which become optimal when the noise were white. Similar properties of a stopping time r to those in the white noise case have been obtained in theorem 3.1. § 2. Detection of a deterministic signal in a white noise. We consider the following detection problem of a signal OW in the white noise 72(0; H; x(t) = W(t) H; x (t) = m(0+W(t),(2.1) where m(t) = 0(s)ds is the integrated signal and { W(t), 0 t < oo} is the Wiener 0 process which is considered to be the integrated form of the white noise 17(t). By Ho we mean that there is no signal in the (integrated) observationx(t) whose distribution is induced from the Wiener measure P0 and by Hl the observation x(t) is the sum of the signal m(t) and the noise W(t) whose distribution is induced by P1, e. a shift of Po by m(•). In order for the detection problem (2.1) to be nonsingular, we assume that 0(•) is square integrable on each finite interval [0, t], 0 t < oo. Let us put
Compressive sampling
, 2006
"... Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired res ..."
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Cited by 1441 (15 self)
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Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired
Feature selection: Evaluation, application, and small sample performance
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1997
"... Abstract—A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature s ..."
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Cited by 474 (13 self)
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Abstract—A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature
An extended set of Haarlike features for rapid objection detection
 IEEE ICIP
"... Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated haarlike features. These novel features significantly enrich the simple features of [5] and can also be calculated ef ..."
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Cited by 577 (4 self)
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efficiently. With these new rotated features our sample face detector shows off on average a 10 % lower false alarm rate at a given hit rate. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%. 1
On the Use of Windows for Harmonic Analysis With the Discrete Fourier Transform
 Proc. IEEE
, 1978
"... AhmwThis Pw!r mak = available a concise review of data win compromise consists of applying windows to the sampled daws pad the ^ affect On the Of in the data set, or equivalently, smoothing the spectral samples. '7 of aoise9 m the ptesence of sdroag bar The two operations to which we subject ..."
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Cited by 668 (0 self)
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subject the data are momc mterference. We dm call attention to a number of common = in be rp~crh of windows den used with the fd F ~ sampling and windowing. These operations can be performed transform. This paper includes a comprehensive catdog of data win in either order. Sampling is well understood
Understanding and using the Implicit Association Test: I. An improved scoring algorithm
 Journal of Personality and Social Psychology
, 2003
"... behavior relations Greenwald et al. Predictive validity of the IAT (Draft of 30 Dec 2008) 2 Abstract (131 words) This review of 122 research reports (184 independent samples, 14,900 subjects), found average r=.274 for prediction of behavioral, judgment, and physiological measures by Implic ..."
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Cited by 632 (94 self)
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behavior relations Greenwald et al. Predictive validity of the IAT (Draft of 30 Dec 2008) 2 Abstract (131 words) This review of 122 research reports (184 independent samples, 14,900 subjects), found average r=.274 for prediction of behavioral, judgment, and physiological measures
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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, loopy belief propagation and sampling. We found that loopy belief propagation always con verged in this case with the average number of iter ations equal to 10.2. The experimental protocol for the toyQMR network was similar to that of the PYRAMID network except that we randomized over structure as well
Tobins Q, corporate diversification and firm performance
, 1993
"... In this paper, we show that Tobin's q and firm diversification are negatively related. This negative relation holds for different diversification measures and when we control for other known determinants of q. We show further that diversified firms have lower q's than equivalent portfolios ..."
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Cited by 499 (26 self)
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portfolios of specialized firms. This negativerelation holds throughout the 1980s in our sample. Finally, it holds for firms that have kept their number of segments constant over a number of years as well as for firms that have not. In our sample, firms that increase their number of segments have lower q
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
, 2002
"... This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A ..."
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Cited by 784 (5 self)
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This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs
Muscle: multiple sequence alignment with high accuracy and high throughput
 NUCLEIC ACIDS RES
, 2004
"... We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the logexpectation score, and refinement using treedependent r ..."
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Cited by 2509 (7 self)
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, MUSCLE achieves average accuracy statistically indistinguishable from TCoffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data
Results 1  10
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102,446