From single to multiple enrollment i-vectors: practical PLDA scoring variants for speaker verification The availability of multiple utterances (and hence, i-vectors) for speaker enrollment brings up several alternatives for their utilization with probabilistic linear discriminant analysis (PLDA). This paper provides an overview of their effective utilization, from a practical viewpoint. We derive expressions for the evaluation of the likelihood ratio for the multi-enrollment case, with details on the computation of the required matrix inversions and determinants. The performance of five different scoring methods, and the effect of i-vector length normalization is compared experimentally. We conclude that length normalization is a useful technique for all but one of the scoring methods considered, and averaging i-vectors is the most effective out of the methods compared. We also study the application of multicondition training on the PLDA model. Our experiments indicate that multicondition training is more effective in estimating PLDA hyperparameters than it is for likelihood computation. Finally, we look at the effect of the configuration of the enrollment data on PLDA scoring, studying the properties of conditional dependence and number-of-enrollment-utterances per target speaker. Our experiments indicate that these properties affect the performance of the PLDA model. These results further support the conclusion that i-vector averaging is a simple and effective way to process multiple enrollment utterances. The i-vector representation [1] followed by probabilistic linear discriminant analysis (PLDA) [2] has become state-of-the-art in speaker verification systems over the past few years. In a typical speaker verification trial, there are two i-vectors; one represents the enrollment utterance of a given speaker, and the other a test utterance. When speech utterances are represented as i-vectors, the speaker verification problem is simply to determine if the i-vectors share the same speaker information or not.. The most recent 2012 NIST speaker recognition evaluation (SRE) allows for multiple enrollment utterances (and hence i-vectors) for a given target speaker. In principle, the availability of more enrollment data can help in enhancing system performance, but it is not obvious how this can be achieved in practice. Although mul-In [11] , the authors propose a multi-channel version of PLDA with channel-specific generative model for i-vectors. Varying utterance duration was compensated for by calibrating the PLDA score in [12] , and by exploiting the uncertainty in the i-vector in [13] . The effect of noise on PLDA-based systems is studied in [14] . The experimental protocol in most of the above mentioned works involved a single i-vector for enrollment. Other studies have looked at explicitly incorporating multiple enrollment i-vectors into the PLDA model (the so-called 'by-the-book' scoring or multi-session scoring). The study [15] looked at multi-session scoring in with a partially open set speaker population, in the context of the NIST 2012 SRE. Further, [16] incorporated utterance duration as observation noise in a supervector generative model, leading to an investigation of different scoring methods using multiple enrollment utterances. Performance of i-vector averaging and multi-session scoring is also studied in [4] , whereas [17] includes a comparison of score-averaging and multi-session scoring. In the context of face recognition, a scalable formulation of PLDA is described in detail in [18] .. Despite the recent advances in PLDA-based speaker verification, an insightful survey of the basic scoring techniques is missing. The present study, targeted for practitioners, is intended to be a selfcontained tutorial for PLDA scoring involving multiple enrollment utterances. It extends our preliminary study in [19] . The current study involves three major contributions. Firstly, we elaborate on the mathematical details involved in the practical computation of the determinants and inverses of the large matrices required by PLDA scoring. In particular, we concentrate our effort on a simplified version of PLDA, which is described in [20, 21, 11] . Our second contribution is a detailed experimental comparison of five straightforward scoring strategies. Three of them -multi-session scoring, i-vector averaging and score averaging, have been reported elsewhere but not compared within a single study. The two remaining methods, are maximum score and pooled-sessions scoring. We compare each scoring variant with and without i-vector length normalization [21] and provide a recommended choice for practitioners. Furthermore, we restrict our focus to PLDA scoring variants that require only the enrollment and test i-vectors. Alternatively, more advanced techniques may utilize either a priori known or estimated channel labels (for example, see [11] ) to tackle the conditional independence assumption of standard PLDA scoring. As such, these techniques require either supplementary metadata or estimated channel/microphone labels produced by another classifier, leading to more complex design with increased computations or added human effort.. We also address the question regarding multicondition training: should multicondition training be applied to the enrollment i-vectors, the PLDA hyperparameter training, or both? The last and most interesting contribution, extending a previously noted problem of PLDA scoring dependency on the number of enrollment utterances [15, 4] , proposes to partially overcome the conditional independence assumption of PLDA. We address scoring in the situation when the number of enrollment utterances is not fixed but a random variable, providing new insights on the preferred ways to prepare enrollment i-vectors.. The topics studied in this paper are illustrated in Fig. 1 . The experiments are carried out on two up-to-date sets of data: a subset of the I4U consortium dataset [22] and a subset of the NIST 2012 SRE data. We provided a review and an experimental evaluation of the i-vector PLDA framework in the context of multiple enrollment utterances. Our main findings, useful from a practical viewpoint, are:. 1. Applying multicondition training (Table 2) : Confirming the findings of [6] , multicondition training is a useful technique to improve noise robustness. Applying it to the enrollment utterances (i.e. for likelihood computation) provided relative decrease of 16% and 9% in EER for males and females, respectively. Applying it to PLDA hyperparameter training stage instead provided corresponding relative decrements of 37% and 40%. Combining the two only increased computations without major added benefits. We therefore recommend applying multicondition data to PLDA training stage only. (Table 3) : When the operating SNR is not known in advance, multicondition training of the PLDA model is an effective way to add noise robustness. A relatively minor degradation of 8% EER is obtained in noisy conditions when multicondition data is used, when compared to the matched-SNR case.

O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O B O O O O B I I I O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A NOTE ON AN ARBITRATED QUANTUM SIGNATURE SCHEME Zeng and Keitel proposed an arbitrated quantum signature scheme in 2002. Recently, Curty and Lütkenhaus pointed out that the protocol is not operationally specified. In a reply, Zeng gave more details of the scheme. The author also claimed that the scheme is suitable for unknown messages. In this letter, we remark that the invented scenario in the original scheme is artificial. This is because its security entirely depends on the presence of a trustworthy arbitrator. Moreover, the claim that the original scheme is suitable for unknown messages is not sound. A digital signature of a message is a number dependent on some secret known only to the signer, and, additionally, on the content of the message being signed. Signatures must be verifiable; if a dispute arises as to whether a party signed a document (caused either by a lying signer trying to repudiate a signature it did create, or a fraudulent claimant), an unbiased third party should be able to resolve the matter equitably, without requiring access to the signer's secret information (private key). The importance of digital signatures to modern electronic commerce has become overwhelming such that Rivest 2 has written that " [they] may prove to be one of the most fundamental and useful inventions of modern cryptography." The security of all public key digital signature schemes presently depends on the inability of a forger to solve certain difficult mathematical problems, such as factoring large numbers.. 3 Regretfully, with a quantum computer, factoring becomes pointed out that the protocol is not clearly operationally defined and several steps are ambiguous. Moreover, they argued that the security statements are incorrect.. In the reply, 9 the author gave more detailed presentations and proofs of the scheme.. He also claimed that the scheme is suitable for unknown messages.. In this letter, we revisit the scheme using a general technique and show that the invented scenario in Ref. 9 is artificial. The claim that the original scheme is suitable for unknown messages is not sound. This is because it is unreasonable that in a signature scheme, the final verifier cannot know the content of the signed message. In this letter, we have remarked that the Zeng-Keitel arbitrated quantum signature scheme is artificial. We have also clarified that in a blind signature scheme, the final verifier knows the content of the signed message.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O


A Characterization of Optimal FF Coding Rate Using a New Optimistically Optimal Code †a) , Hiroki KOGA † †b) , Senior Members, and Ken-ichi IWATA † † †c) , MemberIn this letter, we first introduce a stronger notion of the optimistic achievable coding rate and discuss a coding theorem. Next, we give a necessary and sufficient condition under which the coding rates of all the optimal FF codes asymptotically converge to a constant. key words: information spectrum, general source, FF code, optimistic achievable coding rateIn information spectrum methods [4] , the performance of a fixed-to-fixed length (FF) source code is measured by the limit superior of the coding rate and the error probability of the code. Since there is a trade-off between the coding rate and the error probability, the optimal coding rate is defined as the infimum of the limit superior of the coding rate subject to the condition that the error probability converges to zero. It is proved that this optimal coding rate coincides with the spectral sup-entropy rate H(X) [4] . Under this criterion, however, the coding rate does not converge to a constant in general.On the other hand, let us consider the situation where we construct an FF code with vanishing error probability for a stationary and memoryless source. Since almost all the typical sequences should be correctly decoded in such a code [3, Theorem 3.3.1], its coding rate is asymptotically lower-bounded by H(X) − γ, where H(X) is the entropy of the source and γ > 0 is an arbitrarily small constant. Hence, if we require that the FF code is optimal, i.e., its coding rate is upper-bounded by H(X) + γ for sufficiently large blocklength, the coding rate is almost equal to the entropy. That is, the coding rate of any optimal code is arbitrarily close to the entropy.The objective of this paper is clarifying the condition under which the coding rate of any optimal code asymptotically attaining H(X) converge to a constant that is independent of the optimal code. To this end, we define a and obtain a general formula of the infimum attainable coding rate. Then, by using this notion of the optimality, we obtain a necessary and sufficient condition under which the coding rate converges to H(X). This letter is organized as follows. Section 2 is devoted to definitions of a general source and an FF source code. In Sect. 3 we define the strongly optimistic achievable coding rate, and show that its infimum coincides with the quantity H * (X) introduced in [2], [7] . The necessary and sufficient condition is established in Sect. 4.In this section, we define FF coding of general sources.Let N be the set of all the positive integers. For each n ∈ N let X n = X 1 X 2 · · · X n be a random variable representing n outputs from a source, where each X i takes values in a finite or countable set X. The probability distribution of X n is denoted by P X n . The probability that X n = x n is expressed as P X n (x n ). We call X = {X n } n∈N a general source [5] . We do not impose the consistency condition on P X n , n ∈ N.For a general source, we define two limits concerning the probability distribution of the self information.Throughout this letter, the bases of logarithmic and exponential functions are assumed to be 2. It is known thatNext, we define an FF code for a general source. A source output x n of length n is encoded to a codeword ϕ n (x n ) by an encoder We call C = {(ϕ n , ψ n )} n∈N a code. The set of all x n which can be encoded and decoded correctly is denoted byThen the probability of decoding error (error probability) is written asHan and Verdú formulated the infimum achievable coding rate R(X) and obtained a general formula for R(X). The infimum of the achievable coding rate R is called the infimum achievable coding rate and is denoted by R(X).Based on this theorem, we introduce the optimality of a code C as follows.It is shown in [1] that there exists a H-optimal code for any general source X. We discuss a basic property on the Hoptimal codes in Sect. 4.In this section we introduce a new notion of optimality on the FF coding. First we review the following results on the optimistic FF coding.A rate R is called an optimistic achievable coding rate for X if for any δ > 0 and τ > 0 there exists a code C satisfying bothfor infinitely many n. The infimum of the optimistic achievable coding rate for X is called the optimistic infimum achievable coding rate and is denoted by T (X).Definition 3.1 means that, if R is optimistic achievable, then for arbitrarily given δ > 0 and τ > 0 a code C satisfies (4) with some subsequence {n i } i∈N . It is important to note that this subsequence {n i } i∈N depends on δ and τ. The following definition actually requires that C has a subsequence {n i } i∈N that is independent on δ and τ.A rate R is called a strongly optimistic achievable coding rate for X if there exists a code C = {(ϕ n , ψ n )} n∈N satisfying lim supfor some subsequence {n i } i∈N . The infimum of the strongly optimistic achievable coding rate for X is called the strongly optimistic infimum achievable coding rate and is denoted by T * (X).We have the following theorem for the strongly optimistic infimum achievable coding rate.Proof of the direct part of Theorem 3.2 is similar to [1, Theorem 4.4]. The scheme used here can be regarded as another version of the diagonal line argument [4] . Before giving the proof, we give lemmas used in the proof.From the definition of H * (X), for any constant γ > 0 and τ ∈ (0, 1) it holds thatWe arbitrarily fix a constant γ > 0 and a sequence {τ i } i∈N satisfying 1 > τ 1 , τ i > 0, τ i > τ i+1 , ∀i ∈ N, and limThen, it follows from (7) that the cardinality of N i is countably infinite for each i.Proof : This claim immediately follows from the definitions of N i and {τ i } i∈N .Q.E.D.There exists a strictly monotone increasing sequence {n i } i∈N of positive integers satisfying {n j : j ≥ i} ⊆ N i for all i ∈ N.Proof : Define n 0 = 0 and n i = min{n ∈ N : n ∈ N i and n > n i−1 } for i ≥ 1. (9) Note that, since N i is a countably infinite set, for each i ≥ 1 n i is well-defined. That is, if there is no n ∈ N i satisfying n > n i−1 , N i turns out to be a finite set, which is a contradiction. Therefore, the claim of this lemma follows because n i ∈ N i and N i+1 ⊆ N i for all i ∈ N. Q . E . D .The sequence {n i } i∈N defined by (9) satisfiesProof : Letting τ ∈ (0, 1) be an arbitrary constant, we provefor all sufficiently large i.In view of the definition of {τ i } i∈N , for any τ ∈ (0, 1) we can define i 0 as the minimum integer i ≥ 1 satisfying τ i ≤ τ. Then, it obviously holds thatWe also use the following lemma for proving the converse part of Theorem 3.2. it holds that lim infProof : Assume that (12) does not hold. Then, there exists a real number γ 0 > 0 satisfying 1 n log M n ≤ H * (X) − 2γ 0 for infinitely many n. (13) Recall here that any code C satisfiesfor all n ≥ 1 [4, Lemma 1.3.2]. Thus, the combination of (13) and (14) yieldsfor infinitely many n. (15) It is important to notice that lim infTherefore, (16) tells us that the right hand side of (15) is positive for all sufficiently large n. This contradicts (11). Q.E.D. Now, we are ready to prove Theorem 3.2. Proof of Theorem 3.2: Let γ > 0 be an arbitrary constant. First we prove T * (X) ≤ H * (X) by showing that R = H * (X) + γ is a strongly optimistic achievable coding rate. To this end, fix {τ i } i∈N satisfying (8) arbitrarily. Then, Lemma 3.2 guarantees the existence of a subsequence {n i } i∈N satisfying (10). Define N = {n i : i ≥ 1}.In order to prove the direct part, it suffices to construct a code only forIn addition, it follows from [4, Lemma 1.3.1] and Lemma 3.3 thatwhere the second inequality follows from the definition of M n i . This argument establishes that R = H * (X) + γ is a strongly optimistic achievable coding rate.Next, we prove T * (X) ≥ H * (X). Let C be an arbitrary code satisfying lim i→∞ ε n i = 0 for some subsequence {n i } i∈N . Since it holds that lim infNote that T * (X) ≥ H * (X) can be proved by T * (X) ≥ T (X) and Theorem 3.1. But we use Lemma 3.4 because this lemma is used also in the following section.Using the result of the preceding section, we can characterize the asymptotic behavior of the coding rate of H-optimal codes. The following theorem shows that H(X) = H * (X) is a necessary and sufficient condition such that coding rates of all the H-optimal FF codes converge to H(X). Theorem 4.1: All the H-optimal FF codes for X satisfy Since H(X) = H * (X) by assumption, (19) and (20) yieldNext, we prove that, if (18) holds for any H-optimal code for X, then X satisfies H(X) = H * (X). We prove this by a contradiction argument. Suppose that H * (X) < H(X). Then, there exists a real number γ 0 > 0 satisfying H * (X) + 2γ 0 ≤ H(X). Hereinafter, we construct an H-optimal code C by combining an arbitrary H-optimal code C with a code satisfying (5) and (6) with R = H * (X) + γ 0 .To be more precise, let C = {(ϕ n , ψ n )} n∈N be an arbitrary H-optimal FF code. From the definition of H-optimality, C satisfies lim supand lim n→∞ ε n = 0.On the other hand, Theorem 3.2 tells us that there exists a codefor some subsequence {n i } i∈N . Setting N = {n i : i ≥ 1}, we define a new code C = {(φ n ,ψ n )} n∈N from C and C as follows:ϕ n = ϕ n if n N, ϕ n if n ∈ N,ψ n = ψ n if n N, ψ n if n ∈ N.From (22) and (24), the error probability of C satisfies lim n→∞ ε n = 0. Therefore, C turns out to be H-optimal. However, by using (18) Q.E.D.In this letter, we have defined a stronger notion of the optimistic optimality of FF codes. It is shown that the infimum achievable strongly optimistic coding rates coincides with H * (X) as in the ordinary case. By using this property, we have proved that the rates of all the optimal FF codes converge to H(X) if and only if the source X satisfies H(X) = H * (X), which means that the right endpoint of the entropy spectrum converges. In information spectrum methods [4] , the performance of a fixed-to-fixed length (FF) source code is measured by the limit superior of the coding rate and the error probability of the code. Since there is a trade-off between the coding rate and the error probability, the optimal coding rate is defined as the infimum of the limit superior of the coding rate subject to the condition that the error probability converges to zero. It is proved that this optimal coding rate coincides with the spectral sup-entropy rate H(X) [4] . Under this criterion, however, the coding rate does not converge to a constant in general.. On the other hand, let us consider the situation where we construct an FF code with vanishing error probability for a stationary and memoryless source. Since almost all the typical sequences should be correctly decoded in such a code [3, Theorem 3.3.1] , its coding rate is asymptotically lower-bounded by H(X) − γ, where H(X) is the entropy of the source and γ > 0 is an arbitrarily small constant. Hence, if we require that the FF code is optimal, i.e., its coding rate is upper-bounded by H(X) + γ for sufficiently large blocklength, the coding rate is almost equal to the entropy. That is, the coding rate of any optimal code is arbitrarily close to the entropy.. The objective of this paper is clarifying the condition under which the coding rate of any optimal code asymptotically attaining H(X) converge to a constant that is independent of the optimal code. To this end, we define a new optimality of the source codes which is closely related to the optimistic source coding discussed in [2] , [6] , [8] , [9] and obtain a general formula of the infimum attainable coding rate. Then, by using this notion of the optimality, we obtain a necessary and sufficient condition under which the coding rate converges to H(X). This letter is organized as follows. Section 2 is devoted to definitions of a general source and an FF source code. In Sect. 3 we define the strongly optimistic achievable coding rate, and show that its infimum coincides with the quantity H * (X) introduced in [2] , [7] . The necessary and sufficient condition is established in Sect. 4.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis Genome-wide association studies can help identify multi-gene contributions to disease. As the number of high-density genomic markers tested increases, however, so does the number of loci associated with disease by chance. Performing a brute-force test for the interaction of four or more high-density genomic loci is unfeasible given the current computational limitations. Heuristics must be employed to limit the number of statistical tests performed.In this paper we explore the use of biological domain knowledge to supplement statistical analysis and data mining methods to identify genes and pathways associated with disease. We describe Pathway/SNP, a software application designed to help evaluate the association between pathways and disease. Pathway/SNP integrates domain knowledge-SNP, gene and pathway annotation from multiple sources-with statistical and data mining algorithms into a tool that can be used to explore the etiology of complex diseases. This paper describes Pathway/SNP, a software application that allows its user to utilize pathway data in the analysis of high-density genomic SNP data derived from disease association studies. Our goal in building Pathway/SNP is to allow the user to explore flexibly the etiology of disease using different statistical and data mining algorithms. The task of utilizing pathway data to help in the interpretation of genomic SNP data is complex. There are many statistical and data mining approaches that can be applied, and new approaches are being developed. There is no single approach that is in some sense best. Different approaches may work better with different data sets and with different diseases. As a result, it is important to provide the user with a flexible set of tools that allow different approaches to be tried incrementally and interactively.. Indeed, the challenge of using pathway knowledge to assist in the interpretation of genomic SNP association data is one instance of a broader challenge. There are large amounts of very diverse biological knowledge being generated that can potentially be used integratively to help analyze high-throughput data. Examples include pathway information, tissue and cellular component localization, gene expression information, and regulatory networks. This knowledge can help drive the analysis of genomic data for many different purposes. In the case of Pathway/SNP, the purpose is to analyze the underlying etiology of disease through the integration of pathway information. At the same time, there is a wide range of useful statistical and data mining approaches that can be used when integrating biological knowledge in the analysis of such genomic data.. The goals of this paper are (1) to describe the current implementation of Pathway/SNP and its functionality, (2) to discuss some of the lessons learned during its implementation concerning the use of different statistical and data mining algorithms, and (3) to discuss some of the implications for building interactive tools in the future that allow such algorithms to be flexibly adapted to integrative genomic analysis. In this paper we introduced Pathway/SNP, a software program that can perform pathway-based association analysis between genome-wide high density SNPs and disease. The software tool integrates domain knowledge-pathway information, gene and SNP annotation-with statistical and data mining algorithms. Pathway/SNP can be used to explore the etiology of complex diseases in a flexible, interactive, incremental fashion. Over the past decade, many studies have used microarrays to analyze gene expression profiles associated with disease, most notably cancer [1] [2] [3] . Over the past 3 years, newly developed high-density single nucleotide polymorphism (SNP) microarray technology has brought within reach the promise of performing genome-wide association (GWA) studies to identify genomic mutations that are associated with a wide range of diseases. The results from several successful GWA studies have already been published, identifying mutations linked to age related macular degeneration (AMD) [4] and type 1 diabetes [5] . Other large-scale GWA studies are in progress, e.g., Welcome Trust Case Control Consortium's analysis of 19,000 DNA samples at 675,000 genome-wide SNPs [6] .. Complex diseases, e.g., diabetes, hypertension, Alzheimer's disease, and AMD, are believed to be caused by the interaction of multiple genes and environmental factors. The number of mathematical operations required to assess the association between multiple interacting genomic loci and disease grows exponentially with the number of interacting SNPs. Simple arithmetic calculations (Appendix A) show that even with the most powerful supercomputers available today it is computationally impossible to perform a comprehensive test of association for four or more interacting SNPs when analyzing data sets for several hundred individuals and several hundred thousands SNPs. As a result, a variety of statistical, computational, heuristic, and knowledge-based approaches (often combined) must be developed to compensate for the inability to perform all potentially desirable computations.. Various statistical approaches to addressing the issue of multiple genomic loci association with quantitative traits (e.g., diseases) have shown interesting, if sometimes inconclusive, results. Storey and colleagues recently proposed a computationally efficient stepwise algorithm to identify genetic loci associated with gene expression in yeast [7] . In this algorithm, at each step the genomic locus with the strongest association to the trait is selected from the entire set of loci. The authors found, to their surprise, that the stepwise method was more powerful than the exhaustive method performing the association test over all pairs of loci. The authors acknowledged, however, that their method could miss locus pairs with primarily epistatic (and little individual locus) effect on the trait [7] . In another recent study, Marchini and colleagues [8] concluded that the determination of one single best strategy in a multilocus disease association model might not be possible. Different strategies can perform better in different conditions based on varying parameters such as allele frequencies or locus interaction models (e.g., additive vs. multiplicative effects). Moreover, the authors determined that the same varying parameters can affect the ability to replicate the association of interacting loci across different studies. Using a set of simulated data, the authors found that in the largest fraction of configurations tested, the comprehensive testing of all paired loci interactions had the highest statistical power [8] .. Roeder and colleagues [9] recently proposed integrative analysis using genetic linkage data to weigh the association p-values. The authors proposed dividing the association pvalues by weights that are larger than 1 in regions previously linked with the disease and smaller than 1 in unlinked regions. Similarly, the use of biological knowledge, such as pathway information, can help researchers focus on genes involved in processes known to be related to disease. Such thinking underlies many candidate gene association analyses. For example, a recent investigation attempted to use pathway level information to model the association between various genes and disease and the interaction among genes, and between genes and environmental factors [10] . In another recent study, Schaid and colleagues proposed a non-parametric test of association between disease and a set of pathway genes using U-statistics [11] . The authors applied their method to investigate the association between prostate cancer and a group of several dozen SNPs selected from two metabolic pathways (androgen and estrogen response elements). In this example, the authors determined that their method could identify the combined disease association of multiple markers with small effects while a single locus association was not significant after Bonferroni correction for multiple testing. There are analogous efforts in functional genomic studies to use pathway information to better understand disease etiology [12, 13] .. A number of data mining approaches, such as dimensionality reduction [14, 15] , neural networks [16, 17] , random forests [18] , and support vector machines (SVM) [19, 20] , have also been proposed for multi-locus association with traits. Data mining approaches are well-suited for identifying trends in large dimensional data sets. Some, however, can be computationally intensive (e.g., SVM, neural networks) and some may be hard to interpret (e.g., neural networks).. One conclusion that can be drawn from these previous studies is that the ''one model fits all'' approach is likely not optimal when analyzing the association between genomic loci and disease. We developed Pathway/SNP, a software tool that allows an exploratory approach to integrative association analysis, combining the use of biological pathway information and different statistical and data mining algorithms. Pathway/SNP integrates pathway information, gene annotation, and SNP location to identify the pathways that are the most strongly associated with disease. The association between a pathway and disease can be quantified by z-scores generated by U-statistics, or by the percent of correct disease class assignments using data mining algorithms. The users can further drill down on specific pathways to explore which genomic loci contributed most to the pathway association score by performing association tests solely on one pathway's SNPs. The statistical significance can be assessed via permutationbased false discovery rate (FDR).

O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I


B-Queue: Efficient and Practical Queuing for Fast Core-to-Core Communication Abstract Core-to-core communication is critical to the effective use of multi-core processors. A number of software based concurrent lock-free queues have been proposed to address this problem. Existing solutions, however, suffer from performance degradation in real testbeds, or rely on auxiliary hardware or software timers to handle the deadlock problem when batching is used, making those solutions good in theory but difficult to use in practice. This paper describes the pros and cons of existing concurrent lock-free queues in both dummy and real testbeds and proposes B-Queue, an efficient and practical single-producer-single-consumer concurrent lock-free queue that solves the deadlock problem gracefully by introducing a self-adaptive backtracking mechanism. Experiments show that in real massively-parallel applications, B-Queue is faster than FastForward and MCRingBuffer, the two state-of-the-art concurrent lock-free queues, by up to 10x and 5x, respectively. Moreover, B-Queue outperforms FastForward and MCRingBuffer in terms of stability and scalability, making it a good candidate for fast core-to-core communication on multi-core architectures. As multi-core architectures become ubiquitous, great efforts have been made to parallelize single-threaded applications [1, 17, 28, 34, 35] . One example is that a great deal of work applies pipeline parallelism to sequential network applications [5, 34, 35] . Harnessing abundant CPU resources, however, is still a big challenge. One problem is the lack of a fast and efficient core-to-core communication mechanism on existing commodity multi-core platforms, and threads have to rely on shared memory to exchange information [4] . Take 10 Gbps network as an example, a minimal 64-byte Ethernet frame must be processed in amortized time of 67 ns, which is about one DRAM access time. If there is no efficient core-to-core communication, parallelizing such network applications cannot take effect because a single memory based core-to-core communication might offset the benefit from parallelization.. A large body of work focuses on providing core-to-core communication with FirstIn-First-Out (FIFO) queues [5, 10, 13, 16, 17, 27, 35] . It is generally accepted that a lock-based solution is inappropriate for applications featuring fine-grained parallelism, and a lock-free design is a promising approach. Unfortunately, general purpose lock-free FIFOs still do not perform adequately. For example, cache-unaware lockfree FIFOs [12, 14] take more than 1,000 CPU cycles to insert or extract an element. To tackle this problem, FastForward [5] and subsequent research efforts [16, 35] provide single-producer-single-consumer concurrent lock-free FIFOs (abbreviated as CLF queues from here on) to support fast core-to-core communication; all of them try to avoid cache thrashing as much as possible. Experiments on dummy testbeds [5, 16] show that these solutions take 20 cycles on worst-case for each insert/extract operation, making CLF queues a promising candidate for fast core-to-core communication on multi-core architectures.. When employing existing CLF queues in practice, we encountered the following problems. (1) Peak performance in dummy testbed sometimes is misleading, and in real applications performance of these CLF queues decreases dramatically. Experimentally we found that the claimed peak performance is achieved only when CLF queues stores in L1 cache (Up to 97 % memory accesses must be served by L1 cache), and no cache thrashing occurs. However, in real applications, L1 cache miss and cache thrashing are unavoidable [19, 32] . (2) Performance of existing CLF queues drops dramatically when the number of queues increases. This performance degradation has been observed and reported in [16, 35] , but not carefully studied in the literature. (3) Existing CLF queues are hard to use. FastForward [5] relies on predefined thresholds to avoid cache thrashing, but these thresholds vary from system to system and are hard to tune in practice. Other work [10, 16, 35] relies on batching to achieve maximum performance. However, to avoid the deadlock problem inherent in batching (Sect. 2.5), auxiliary timer and threads must be used to periodically check the state of a CLF queue to keep the consumer alive. The auxiliary timer and threads disturb CLF queue's cache behavior, and complicate the system as well (Sect. 2.6) .. This paper studies CLF queues in building up a real multi-10 Gbps network processing system where parallelism is widely used [4, 5, 16, 35] , and both the hardware and software capabilities are stressed [2, 6] . The major contributions of this paper are as follows:. -Existing CLF queues are evaluated on both dummy and real applications; their strengths on dummy testbeds and weaknesses on real applications are studied. The evaluation comes to the conclusion that for CLF queues, no further effort should be made to pursue higher performance on dummy testbeds. In contrast, great attention must be paid to putting them into practice. -A fast yet practical CLF queue, named B-Queue, is proposed. B-Queue outperforms existing CLF queues in terms of scalability and stability and requires no parameter tuning. All these advantages come from a novel backtracking mechanism that can adaptively adjust consumption to production without the need of any auxiliary mechanism or manually tuned parameters.. The remainder of this paper proceeds as follows. Section 2 provides the background and motivation of this paper. Section 3 describes B-Queue's design, and proves its correctness in Sect. 4. Section 5 evaluates the performance of CLF queues on dummy and real testbeds. Section 6 discusses related work and Sect. 7 concludes. This paper presents B-Queue, a cache-aware CLF queue for fast core-to-core communication. Batch operation and backtracking are incorporated elegantly, where batching allows B-Queue to get high performance by avoiding cache thrashing, and backtracking prevents deadlock by adaptively adjust the batching distance according to the production speed. No running system parameter is used, and no system complexity is added. B-Queue improves the performance of existing CLF queues in terms of stability, scalability and it is simpler to use. The efficiency of B-Queue is demonstrated on a real testbed where multiple B-Queues are used.. Real applications featuring fine-grained parallelism require practical and efficient lock-free data structures. However, most lock-free data structures are very good in theory but difficult to use in practice and hard to tune for high-performance. All existing solutions including B-queue are only partial solutions to this important research topic. This is an open area that requires continuing attention. Since queues are widely used in multithreaded programs for communication, there are a wide array of studies on concurrent lock-free queues [12, 14, 20, 22, 33] . Most of these studies focus on multiple-producer and/or multiple-consumer queues. However, these queues often have limited performance, as a large portion of work has to be done to avoid ABA problem [21] . For example, a single enqueue operation may take more than one microsecond [12] .. Being a special case, single-producer-single-consumer (SPSC) CLF queue is a promising candidate for high speed core-to-core communication [5, 10, 15, 16, 27, 31, 35] , as it fundamentally avoids the ABA problem. So far, cache-aware SPSC CLF queues are studied and reported in [5, 16, 35] , and the ILP optimization is reported in [10] .. General purpose CLF queues commonly use linked list that requires dynamic memory management and may result in poor cache locality and extra synchronization [12, 14, 20, 31] . By exchanging data between producer and consumer through a statically allocated array, ring-buffer based queues [5, 16, 35] can exploit cache locality and facilitate the hardware cache prefetching.. Another approach is hardware queues [13, 17, 26] that provide instructions for enqueue and dequeue operations to reduce the overhead of software queues. However, hardware queues require to modify processors, and also impose challenges to the operating system as the queue state must be preserved across context switch. To date, hardware queues mainly exist in simulators, and none of the general purpose processors supports this feature yet.

O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Linear Multiuser Receivers: Effective Interference, Effective Bandwidth and User Capacity Abstract-Multiuser receivers improve the performance of spread-spectrum and antenna-array systems by exploiting the structure of the multiaccess interference when demodulating the signal of a user. Much of the previous work on the performance analysis of multiuser receivers has focused on their ability to reject worst case interference. Their performance in a powercontrolled network and the resulting user capacity are less wellunderstood. In this paper, we show that in a large system with each user using random spreading sequences, the limiting interference effects under several linear multiuser receivers can be decoupled, such that each interferer can be ascribed a level of effective interference that it provides to the user to be demodulated. Applying these results to the uplink of a single power-controlled cell, we derive an effective bandwidth characterization of the user capacity: the signal-to-interference requirements of all the users can be met if and only if the sum of the effective bandwidths of the users is less than the total number of degrees of freedom in the system. The effective bandwidth of a user depends only on its own SIR requirement, and simple expressions are derived for three linear receivers: the conventional matched filter, the decorrelator, and the MMSE receiver. The effective bandwidths under the three receivers serve as a basis for performance comparison.Index Terms-Decorrelator, effective bandwidth, effective interference, MMSE receiver, multiuser detection, power control, user capacity, random spreading sequences.

O O O B I B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O B I B I B I B I B I B I B I I


Clustering cliques for graph-based summarization of the biomedical research literature Background: Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts). Automatic summarization is emerging as a viable information processing mechanism to help users effectively access the large amount of textual data available online, especially in the biomedical domain. Such processing distils the most important information from source documents to produce an abridged condensate that serves as an informative and indicative summary of a given topic [1, 2] . Summarization is often thought of as a natural language processing task due to the need for in-depth understanding of text to provide a useful summary. The analysis of source text may take various forms. In earlier work this was often limited to textual cues that identify salient information, while more recent research may involve concepts in a domain ontology [3] and semantic relation extraction [4, 5] .. To facilitate access to the biomedical research literature, Fiszman et al. [6] developed an automatic abstractive summarization method based on semantic predications identified in biomedical text by the SemRep natural language processing system [5, 7] . Both semantic predications and the abstractive summarization method are exploited in the Semantic MEDLINE Web application [8] , which produces graphical summaries by condensing semantic predications found in MEDLINE citations (titles and abstracts) retrieved with PubMed queries. Relying on four manually predefined schemas, the user directs Semantic MEDLINE to generate summaries focused on several points of view, either treatment of disease, substance interactions, diagnosis, or pharmacogenomics. Nodes in the summary are concepts from the Unified Medical Language System (UMLS) [9] Metathesaurus, and arcs are semantic relations from the Semantic Network. By clicking on an arc, a user can obtain the sentence from which the relation was generated ( Figure 1 ).. Although Semantic MEDLINE shows promise in managing the results of PubMed searches [10] , it produces graphs that are too large and dense when generating summaries from more than a few hundred citations. This characteristic does not accommodate the thousands of citations that may be returned by a PubMed query (for example, nearly 150,000 for "breast cancer"). In earlier work [11] , we exploited the graph theoretic notion of degree centrality to reduce large graphs by retaining only highly connected concepts. The method is effective in presenting readable, focused information to the user. However, it relies on predefined schemas, which must be devised for each topic point of view, and thus limit the applicability of this summarization methodology in covering the thematic diversity seen in the biomedical research literature.. In this paper, we explore a graph-based method to make automatic summarization more robust when confronted with large numbers of MEDLINE citations, without using predefined schemas. This multidocument method is based on a network representation of the semantic predications extracted from citations (titles and abstracts) returned by a PubMed query. Cliques are first identified in this graph and then clustered and labeled to identify several points of view represented in the summary. Since schemas are not used, the method is applicable to any biomedical topic.. The primary contribution of this paper is the application of graph theoretic constructs to semantic predications for automatic summarization in the biomedical domain. We also introduce a novel semantics-based criterion for determining final clusters, which is compared to a silhouette coefficient method. Finally, evaluations for cluster validity and accuracy, as well as the quality of the summary are also provided. Our method for automatic summarization includes two major parts: (1) applying graphical metrics to help produce a summary and (2) using a clustering technique as well as semantics to identify themes contained in the summary (work flow shown in Figure 3 ). In the first part, the citations from a PubMed search are represented as semantic predications with SemRep. Then predications are converted into a graph with arguments as nodes and predicates as arcs. Degree centrality and frequency of occurrence of predications are used to eliminate nonsalient predications from the graph, thus identifying relationships important for the summary. Finally, cliques are identified in the summarized graph. In the second part, theme identification, a hierarchical clustering algorithm is applied to cliques to group them into several clusters, and a semantic theme label is assigned to each cluster. We exploited graph theoretical methods to summarize biomedical documents; using hierarchical clustering, we then grouped the summary into several themes for a given topic based on the semantics contained in the summary. The system summary was compared to a reference standard produced by selecting the same number of the most frequent major MeSH descriptors as the number of concepts in the summary. The result showed that recall, precision and F-score were 0.64, 0.65 and 0.65 respectively. The validity of the clusters was compared to a baseline computed with the Silhouette Coefficient method for cohesion, separation and overall validity. The overall validity of the system output clusters was better than that of the Silhouette Coefficient clusters. Generally, results showed that our method, based on graph theory as well as semantic predications, can produce satisfying summaries of large numbers of biomedical documents. The validity of clusters determined by semantics was better than that determined by the Silhouette Coefficient, and, further, the summary represented the major salient content of topics. Analysis of the overall validity of clusters showed that system output is 10% better than the baseline (43% versus 33%). Although the cohesion of the baseline is slightly higher than that of the system summary, the separation of the system summary is significantly better than that of the baseline. The number of clusters determined by the Silhouette Coefficient is greater than the number determined by semantic information, which results in a relatively higher cohesion and lower separation in the baseline. We used metapredications to calculate cohesion and separation; such predications were constructed from semantic information pertinent to the core meaning of the themes. For example, the drug therapy theme (<Chemicals & Drugs > <Therapy > <Disorders>) expresses predications asserting specific drug therapies (TREATS) and comparison of such therapies (COMPARED_WITH).. Predications that do not belong to these two metapredications are counted as false positives when computing cohesion and separation. A problem arose with the predicate CAUSES, which SemRep uses to expresses both side effect of drug (which would be reasonable to include in the drug therapy theme) and etiology of disease (which is not in the scope of this theme). We chose not to include CAUSES in this theme, which caused some legitimate side-effect predications to be considered false positives when evaluating this theme. This decreased cohesion and separation, as well as overall validity for clusters containing the drug therapy theme. An example of the final summary Figure 8 illustrates the final graphical summary (produced with Pajek [46] ) for the topic schizophrenia, which appears as the central node of the graph. Four themes were identified and are highlighted in color. Notably in this summary, delusions and hallucinations are seen as comorbidities of schizophrenia, while dopamine, glutamate and neurotransmitters are associated with its etiology. Drug treatment constitutes the largest cluster; in addition to representing major drugs for schizophrenia (linked by blue TREATS arcs), it shows comparison between two drugs (purple arcs, COMPARED_WITH), and some adverse effects resulting from the drugs, such as weight gain and tardive dyskinesia (red arcs, CAUSES). It should be noted that the characteristics of this graph reflect the condensing aspects of a summary, and do not necessarily accommodate other information management tasks, such as discovery. For an example of processing semantic predications for discovery see [47] . Table 4 shows cohesion, separation and overall validity of both the system summary (SS) clusters and the baseline (BL, Silhouette clusters) for the testing data. Out of 11 topics, one, interleukin-6, produced only one cluster, so we did not compute cohesion and separation for this topic. Results for the other 10 topics appear in Table 4 .

O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Onboard monocular pedestrian detection by combining spatio-temporal hog with structure from motion algorithm In this paper, we brought out a novel pedestrian detection framework for the advanced driver assistance system of mobile platform under the normal urban street environment. Different from the conventional systems that focus on the pedestrian detection at near distance by interfusing multiple sensors (such as radar, laser and infrared camera), our system has achieved the pedestrian detection at all (near, middle and long) distance on a normally driven vehicle (1-40 km/h) with monocular camera under the street scenes. Since pedestrians typically exhibit not only their humanlike shape but also the unique human movements generated by their legs and arms, we use the spatio-temporal histogram of oriented gradient (STHOG) to describe the pedestrian appearance and motion features. The shape and movement of a pedestrian will be described by a unique feature produced by concatenating the spatial and temporal his- (1) ground constraint with monocular camera to reduce the computational cost and false alarms; (2) preprocessing by stabilizing the successive images captured from mobile camera with the SfM algorithm; (3) long-distance (maximum 100 m) pedestrian detection at various velocities (1-40 km/h). Through the extensive experiments under different city scenes, the effectiveness of our algorithm has been proved. Due to the explosive increase of automobiles over the world in the last two decades, the requirement for protecting both the drivers and pedestrians has been kept on rising. Correspondingly, many protection systems have been proposed to improve the traffic safety, such as the seat belt, electronic stabilization program and air bag. However, most of those systems only focus on protecting the drivers. The most serious traffic accident vehicle-to-pedestrian accident still remains as a challenging task.. To avoid the vehicle-to-pedestrian accidents, more intelligent protection systems are required to anticipate accidents. Such systems are usually called as the advanced driver assistant systems (ADAS) which include the auto control, driver monitoring and pedestrian protection, etc. In this paper, we focus on the kernel task of ADAS-"pedestrian detection" by applying the visible spectrum (VS) monocular camera to locate the pedestrians so as to avoid the vehicle-to-pedestrian accidents.. With years of research and great efforts from both the academic researcher and manufactures, many onboard pedestrian detection systems have been brought out. Most of those proposed systems mainly rely on combining the multiple sensors (such as the radar, laser range finder, far/near infrared camera and stereo camera) to locate the pedestrians. In 2008-2009, the BMW 7 Series and Mercedes-Benz E-Class vehicles were implemented with the pedestrian protection system that could work at night. By combining the monocular camera with radar, the Volvo S60 has achieved the collision warning system that can detect the people and stop the vehicle, automatically. Although, all these systems could work efficiently under some conditions, detecting pedestrians at long distance still remains as a difficult task. This is because although the depth sensors could find an obstacle at long distance, the classification of detected obstacle still relies on the pedestrian detection algorithms with VS camera. As mentioned in [1] , most of the current detectors are designed for near distance (which means that pedestrians are higher than 80 pixels in the image) and perform poor even at medium distance (pedestrian height varies from 30-80 pixels).. The difficulties of onboard pedestrian detection with VS monocular camera include:. • The detection must be performed under the highly dynamic urban city scene. Here, the image quality from VS camera is usually poorer than the surveillance scenes. This is because both the illumination and weather condition are unpredictable, where the strong shadow or poor contrast in the visible spectrum may dramatically degrade the image quality.. • The appearance of both the pedestrian and background are highly variable due to the various viewpoints. Since both the pedestrian and camera are in motion, the pedestrian detection at different view angles (front, lateral, rear) and distance becomes extremely difficult, because the pedestrian appearance feature will become unclear or blurred at the long distance. Since most current pedestrian detectors require the clear pedestrian features, people at long distance remains as a challenging task for those algorithms (detailed discussion could be found in [1] ).. Since the conventional onboard pedestrian detection algorithms mainly apply the appearance feature to describe the human and require the clear target feature for discriminating a pedestrian from background, they tend to suffer from the huge human appearance variation (generated by the different clothes, camera viewpoints, articulated pose, baggage, etc.) and the blurred human image at long distance.. In this paper, to overcome such problems, the temporal (or motion) features are introduced to improve the detection performance based on the fact that not only human shape but also human motion makes a pedestrian different from the surrounding environment. The pedestrian feature is described by a spatio-temporal histogram of oriented gradients which corresponds to his/her appearance and motion features. To achieve the pedestrian detection at long distance (which means the pedestrian height will be smaller than 30 pixels in the image), the images captured from the monocular VS camera are stabilized according to the SfM algorithm, through which the appearance and motion features of pedestrians could be smoothly extracted from two adjacent frames. The main contributions of our work include:. • Describing the pedestrian feature with a spatio-temporal histogram of oriented gradients (STHOG). Since pedestrians will exhibit not only their human-like shapes but also the unique human-like movements caused by their arms/legs, a pedestrian's appearance is described by the HOG feature, and his/her motion is represented by another histogram of temporal gradients computed from successive frames. Compared with the appearance features, the STHOG feature is more invariant to the changes of clothes and particularly powerful in discriminating pedestrians from cluttered background, where some background objects happen to contain the human-like shape but their motion is different from that of human.. • Geometric ground constraint according to the SfM algorithm. Since it is reasonable to assume that a pedestrian will only walking on the ground but not in the sky, the geometric constraint of a planar ground will reduce not only the computational cost but also the false alarms as the region higher than the ground (like road mark or the house roof). Here, the depth of each image pixel could be computed from the SfM by estimating the camera motion with RANSAC method. A constraint planar ground is constructed by the pixels whose depth varies 10-100 m (explanation for selecting such values could be found in Sec.3.2).. • Preprocessing for stabilizing the images captured from the monocular camera. Since the camera on a vehicle may move fast, the location of a pedestrian in two adjacent images may be discontinuous. We brought a stabilization system to estimate the pixel correspondence between two images according to the vehicle velocity and camera parameters. With the estimated pixel correspondence, we could not only smoothly compute the temporal motion from two frames but also predict the pedestrian position in the next frame which can help us to reduce the computational cost and false alarms.. This paper is organized as follows: in Sect. 2, we provide a brief review for related work about the onboard pedestrian detection; in Sect. 3, there is a detailed introduction for the proposed system; Sect. 4 shows the comparative experimental results among the proposed system and other pedestrian detectors.

O O B I O O B I O B I I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O


Detection

and

correction

of

probe-level

artefacts

on

microarrays

Background:

A

recent

large-scale

analysis

of

Gene

Expression

Omnibus

(GEO)

data

found

frequent

evidence

for

spatial

defects

in

a

substantial

fraction

of

Affymetrix

microarrays

in

the

GEO.

Nevertheless,

in

contrast

to

quality

assessment,

artefact

detection

is

not

widely

used

in

standard

gene

expression

analysis

pipelines.

Furthermore,

although

approaches

have

been

proposed

to

detect

diverse

types

of

spatial

noise

on

arrays,

the

correction

of

these

artefacts

is

mostly

left

to

either

summarization

methods

or

the

corresponding

arrays

are

completely

discarded.We

show

that

state-of-the-art

robust

summarization

procedures

are

vulnerable

to

artefacts

on

arrays

and

cannot

appropriately

correct

for

these.

To

address

this

problem,

we

present

a

simple

approach

to

detect

artefacts

with

high

recall

and

precision,

which

we

further

improve

by

taking

into

account

the

spatial

layout

of

arrays.

Finally,

we

propose

two

correction

methods

for

these

artefacts

that

either

substitute

values

of

defective

probes

using

probeset

information

or

filter

corrupted

probes.

We

show

that

our

approach

can

identify

and

correct

defective

probe

measurements

appropriately

and

outperforms

existing

tools.While

summarization

is

insufficient

to

correct

for

defective

probes,

this

problem

can

be

addressed

in

a

straightforward

way

by

the

methods

we

present

for

identification

and

correction

of

defective

probes.

As

these

methods

output

CEL

files

with

corrected

probe

values

that

serve

as

input

to

standard

normalization

and

summarization

procedures,

they

can

be

easily

integrated

into

existing

microarray

analysis

pipelines

as

an

additional

pre-processing

step.

An

R

package

is

freely

available

from

In

this

article,

we

illustrate

the

necessity

of

integrating

artefact

detection

and

correction

into

standard

gene

expression

analysis

pipelines

as

state-of-the-art

normalization

and

summarization

procedures

were

found

to

be

vulnerable

even

to

small

spatial

defects.

We

propose

a

general

and

simple

approach

for

identification

and

correction

of

these

artefacts

that

relies

only

on

the

availability

of

controls

in

the

form

of

technical

or

biological

replicates.

By

additionally

taking

the

probe

neighborhood

into

account,

we

can

furthermore

improve

detection

accuracy

also

compared

to

more

complex

approaches.

Thus,

even

if

a

substantial

amount

of

probes

is

defective

on

an

array,

the

measurements

of

the

remaining

probes

can

be

still

be

used.

A

recent

study

showed

that

an

alarming

number

of

arrays

deposited

in

the

Gene

Expression

Omnibus

(GEO)

contain

spatial

defects.

As

deposition

in

the

GEO

generally

occurs

only

prior

to

publication,

this

does

not

even

include

the

array

measurements

that

were

discarded

before

analysis

due

to

larger

artefacts.

If

artefacts

are

not

considered

substantial

enough

to

discard

the

array,

normalization

and

summarization

procedures

are

routinely

relied

upon

to

correct

for

these

smaller

defects..

In

this

study,

we

show

that

this

reliance

is

risky

as

even

small

artefacts

can

have

serious

impacts

on

the

results

of

normalization

and

summarization

procedures.

For

this

purpose,

we

used

a

set

of

exon

array

measurements

which

contained

several

artefacts

of

various

sizes

in

the

form

of

stains

in

the

center

of

the

arrays.

In

this

case,

replacement

of

the

corresponding

arrays

by

measurements

of

the

same

samples

was

not

an

option

as

for

some

samples

all

RNA

material

had

been

used

up.

Thus,

the

only

alternatives

were

to

either

repeat

the

whole

experiments

and

all

array

measurements

which

would

be

highly

cost-intensive

or

try

to

use

at

least

the

measurements

of

the

probes

not

affected

by

the

artefacts..

However,

the

comparison

of

replicates

containing

artefacts

with

corresponding

artefact-free

measurements

clearly

showed

that

even

a

robust

summarization

procedure

such

as

RMA

could

not

appropriately

correct

for

the

defective

probes

as

considerable

non-linear

deviations

were

observed

between

replicates.

Interestingly,

the

degree

of

the

deviations

between

replicates

did

not

necessarily

depend

on

the

number

of

probes

affected

in

total

on

the

array.

One

of

the

arrays

showed

only

a

minor

stain

which

would

normally

be

considered

acceptable.

Yet,

deviations

compared

to

the

control

were

substantial,

in

particular

for

high

expression

values.

Although

this

effect

could

be

traced

partially

to

quantile

normalization,

it

did

not

disappear

when

omitting

normalization..

While

simulations

showed

a

clear

correlation

between

the

number

of

probes

affected

within

a

probeset

and

the

deviation

between

replicates

of

the

estimated

probeset

values,

this

was

not

quite

reflected

in

the

real-life

measurements.

Here,

probesets

were

similarly

affected

no

matter

whether

or

probes

were

corrupted.

Only

probesets

for

which

all

probes

were

defective

were

clear

outliers.

Furthermore,

even

probeset

measurements

with

no

corrupted

probes

were

biased.

To

some

degree

this

is

due

to

the

normalization

step

required

before

summarization,

which

is

based

on

the

global

intensity

distribution

on

the

arrays.

As

even

small

artefacts

on

the

arrays

can

have

a

substantial

influence

on

the

distribution

of

intensities,

all

normalized

probe

values

are

affected

to

some

degree.

Although

it

is

not

clear

why

this

is

effect

is

also

seen

without

normalization,

it

can

clearly

be

attributed

to

the

artefacts

as

it

disappears

after

correction

of

corrupted

probes..

Although

a

few

approaches

have

been

proposed

for

identifying

probe-level

artefacts,

they

are

usually

highly

complex

using

e.g.

image

analysis

as

in

the

case

of

Harshlight

or

singular

value

decomposition

in

case

of

Furthermore,

they

were

often

developed

for

previous

generations

of

Affymetrix

arrays

and

do

not

scale

well

to

the

new

Gene

and

Exon

ST

array

designs.

Here,

we

propose

a

much

simpler

approach

that

relies

only

on

the

availability

of

an

artefact-free

control

measurement,

which

is

usually

available

in

the

form

of

technical

or

biological

replicates.

Instead

of

replicates,

a

biological

control

can

alternatively

be

used

as

in

the

case

of

simultaneous

measurements

of

total,

newly

transcribed

and

pre-existing

RNA..

We

showed

that

even

a

simple

comparison

of

foldchanges

between

corrupted

and

control

measurements

was

sufficient

to

identify

the

corrupted

probes

on

our

exon

array

measurements

and

correct

them

by

either

replacing

their

values

or

removing

them

from

the

analysis.

While

noise

scores

calculated

only

from

the

probe

values

themselves

were

already

successful

for

this

purpose,

an

extension

of

this

approach

that

also

uses

the

neighborhood

of

the

probe

improved

the

performance

even

more.

In

this

case,

the

probe

noise

score

is

calculated

as

a

distance-weighted

average

of

all

noise

scores

in

a

window

around

the

probe

considered.

In

this

way,

the

layout

of

the

array

and

the

spatial

nature

of

most

artefacts

can

be

taken

into

account.

This

approach

was

also

superior

to

MBR

as

well

as

Harshlight,

which

in

general

appeared

to

be

too

stringent

on

probe

quality

as

it

also

flagged

many

probes

on

arrays

without

apparent

defects.

Although

not

commonly

included

in

standard

microarray

analysis

pipelines,

a

number

of

methods

have

been

previously

proposed

for

visualization

of

microarray

artefacts

as

well

as

identification

and/or

correction

of

corrupted

probe

measurements

(see

also

for

an

overview

of

methods

published

before

One

of

the

most

frequently

used

approaches

is

Harshlight

which

identifies

and

masks

local

artefacts

based

on

statistical

and

image

processing

methods.

Artefacts

are

grouped

into

three

classes

based

on

the

variation

from

the

median

array:

compact

defects

affecting

only

a

few

probes,

diffuse

defects

affecting

larger

areas

and

extended

defects

that

are

even

larger

and

may

thus

invalidate

the

whole

array.

Probes

within

defects

can

either

be

excluded

from

the

analysis

or

be

replaced

by

the

median

intensity

across

replicates..

An

alternative

method

for

identifying

artefacts

from

raw

intensity

values

is

Microarray

blob

remover

(MBR)

which

operates

in

two

steps.

First,

broad

areas

are

determined

in

which

more

than

half

of

the

probes

are

above

the

kth

percentile

of

probe

intensities,

where

k

may

be

in

the

range

of

to

These

candidate

areas

are

then

further

refined

and

probes

flagged

to

be

within

artefacts

are

added

to

the

'outlier

entries'

section

in

CEL

files..

In

addition,

several

other

methods

have

been

proposed

based

on

comparisons

to

reference

arrays

calculate

the

log

fold-change

of

the

probe

value

compared

to

the

trimmed

median

of

reference

arrays

to

visualize

spatial

artefacts

but

do

not

aim

to

identify

individual

defective

probes.

Arteaga-Salas

et

al.

use

a

modification

of

the

method

by

Upton

and

Lloyd

to

identify

areas

in

which

the

largest

foldchanges

compared

to

the

median

of

all

arrays

all

originate

from

the

same

array.

Having

identified

arrays

with

defects,

they

then

try

to

correct

the

original

values

using

the

values

for

the

probe

on

the

other

arrays.

A

similar

approach

is

also

pursued

by

Hulsman

et

al.

as

part

of

their

normalization

pipeline.

Hybridization-based

DNA

microarrays

are

a

key

technology

for

high-throughput

quantification

of

expression

levels

for

thousands

of

genes

State-of-the-art

microarrays

now

allow

the

genome-wide

analysis

of

transcript

abundance

not

only

for

entire

genes

but

also

individual

exons,

alternatively

spliced

transcripts

and

even

a

large

fraction

of

non-coding

genomic

regions

Thus,

despite

the

increasing

prevalence

of

alternative

methods

such

as

RNA-seq

RNA

microarrays

remain

important

for

the

analysis

of

many

biological

processes

such

as

miRNA-based

regulation

alternative

splicing

patterns

across

human

tissues

or

the

role

of

alternative

splicing

in

stem

cell

differentiation

and

cancer

The

latter

assumption

is

based

on

the

construction

of

microarrays

where

probes

of

the

same

probeset

are

not

contiguous

on

the

array.

Thus,

smaller

artefacts

due

to

uneven

hybridization

or

other

experimental

problems

may

only

affect

a

subset

of

probes

for

a

probeset.

It

is

usually

assumed

that

summarization

methods

-which

combine

the

values

for

individual

probes

to

a

probeset

value,

such

as

RMA

-can

estimate

the

probeset

value

correctly

despite

measurement

errors

for

some

probes..

In

this

study,

we

illustrate

that

this

assumption

is

often

invalid

by

showing

that

even

small

artefacts

on

the

array

can

have

a

significant

effect

on

the

overall

expression

values

of

many

probesets,

not

only

the

ones

affected

by

the

artefact.

Furthermore,

we

introduce

two

simple

but

effective

approaches

for

the

identification

of

corrupted

probes:

a

threshold-based

approach

and

an

extension

of

this

approach

that

takes

into

account

the

neighborhood

of

a

probe

i.e.

spatial

information

of

the

array.

We

show

that

the

use

of

spatial

information

improves

the

identification

of

defective

probes

as

well

as

reproducibility

of

probeset

intensities

after

summarization.

Finally,

two

strategies

are

proposed

to

either

correct

probe

values

using

probeset

information

or

filter

corrupted

probes,

both

of

which

improve

summarization

accuracy

as

well

as

reproducibility

between

replicates.

In

this

way,

we

can

recover

even

arrays

with

large

artefacts

for

downstream

analysis

that

otherwise

would

have

to

be

discarded.

O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O