An upper limit on CP violation in the Bs0−Bˉs0B^0_s-\bar{B}^0_s system

In a previous publication we noted that the time dependence of an incoherent $B^0-\bar{B}^0$ mixture undergoes a qualitative change when the magnitude of CP violation $\delta$ exceeds a critical value. Requiring, on physical grounds, that the system evolve from an initial incoherent state to a final pure state in a monotonic way, yields a new upper limit for $\delta$. The recent measurement of the wrong charge semileptonic asymmetry of $B_s^0$ mesons presented by the D0 collaboration is outside this bound by one standard deviation. If this result is confirmed it implies the existence of a new quantum mechanical oscillation phenomenon.Comment: 7 pages, 2 figures, version submitted for publication (Physical Review

Magnetic and axial vector form factors as probes of orbital angular momentum in the proton

We have recently examined the static properties of the baryon octet (magnetic moments and axial vector coupling constants) in a generalized quark model in which the angular momentum of a polarized nucleon is partly spin $\langle S_z \rangle$ and partly orbital $\langle L_z \rangle$. The orbital momentum was represented by the rotation of a flux-tube connecting the three constituent quarks. The best fit is obtained with $\langle S_z \rangle = 0.08\pm 0.15$, $\langle L_z \rangle = 0.42\pm 0.14$. We now consider the consequences of this idea for the $q^2$-dependence of the magnetic and axial vector form factors. It is found that the isovector magnetic form factor $G_M^{\mathrm{isovec}}(q^2)$ differs in shape from the axial form factor $F_A(q^2)$ by an amount that depends on the spatial distribution of orbital angular momentum. The model of a rigidly rotating flux-tube leads to a relation between the magnetic, axial vector and matter radii, $\langle r^2 \rangle_{\mathrm{mag}} = f_{\mathrm{spin}} \langle r^2 \rangle_{\mathrm{axial}} + \frac{5}{2} f_{\mathrm{orb}} \langle r^2 \rangle_{\mathrm{matt}}$, where $f_{\mathrm{orb}}/ f_{\mathrm{spin}} = \frac{1}{3}\langle L_z \rangle / G_A$, $f_{\mathrm{spin}} + f_{\mathrm{orb}} = 1$. The shape of $F_A(q^2)$ is found to be close to a dipole with $M_A = 0.92\pm 0.06$ GeV.Comment: 18 pages, 5 ps-figures, uses RevTe

Baryon Magnetic Moments and Proton Spin: A Model with Collective Quark Rotation

We analyse the baryon magnetic moments in a model that relates them to the parton spins $\Delta u$, $\Delta d$, $\Delta s$, and includes a contribution from orbital angular momentum. The specific assumption is the existence of a 3-quark correlation (such as a flux string) that rotates with angular momentum $\langle L_z \rangle$ around the proton spin axis. A fit to the baryon magnetic moments, constrained by the measured values of the axial vector coupling constants $a^{(3)}=F+D$, $a^{(8)}=3F-D$, yields $\langle S_z \rangle = 0.08 \pm 0.13$, $\langle L_z \rangle = 0.39 \pm 0.09$, where the error is a theoretical estimate. A second fit, under slightly different assumptions, gives $\langle L_z \rangle = 0.37 \pm 0.09$, with no constraint on $\langle S_z \rangle$. The model provides a consistent description of axial vector couplings, magnetic moments and the quark polarization $\langle S_z \rangle$ measured in deep inelastic scattering. The fits suggest that a significant part of the angular momentum of the proton may reside in a collective rotation of the constituent quarks.Comment: 16 pages, 3 ps-figures, uses RevTeX. Abstract, Sec. II, III and IV have been expande

Avian malaria co-infections confound infectivity and vector competence assays of Plasmodium homopolare.

Currently, there are very few studies of avian malaria that investigate relationships among the host-vector-parasite triad concomitantly. In the current study, we experimentally measured the vector competence of several Culex mosquitoes for a newly described avian malaria parasite, Plasmodium homopolare. Song sparrow (Melospiza melodia) blood infected with a low P. homopolare parasitemia was inoculated into a naïve domestic canary (Serinus canaria forma domestica). Within 5 to 10 days post infection (dpi), the canary unexpectedly developed a simultaneous high parasitemic infection of Plasmodium cathemerium (Pcat6) and a low parasitemic infection of P. homopolare, both of which were detected in blood smears. During this infection period, PCR detected Pcat6, but not P. homopolare in the canary. Between 10 and 60 dpi, Pcat6 blood stages were no longer visible and PCR no longer amplified Pcat6 parasite DNA from canary blood. However, P. homopolare blood stages remained visible, albeit still at very low parasitemias, and PCR was able to amplify P. homopolare DNA. This pattern of mixed Pcat6 and P. homopolare infection was repeated in three secondary infected canaries that were injected with blood from the first infected canary. Mosquitoes that blood-fed on the secondary infected canaries developed infections with Pcat6 as well as another P. cathemerium lineage (Pcat8); none developed PCR detectable P. homopolare infections. These observations suggest that the original P. homopolare-infected songbird also had two un-detectable P. cathemerium lineages/strains. The vector and host infectivity trials in this study demonstrated that current molecular assays may significantly underreport the extent of mixed avian malaria infections in vectors and hosts

A hybrid neural network based speech recognition system for pervasive environments

One of the major drawbacks to using speech as the input to any pervasive environment is the requirement to balance accuracy with the high processing overheads involved. This paper presents an Arabic speech recognition system (called UbiqRec), which address this issue by providing a natural and intuitive way of communicating within ubiquitous environments, while balancing processing time, memory and recognition accuracy. A hybrid approach has been used which incorporates spectrographic information, singular value decomposition, concurrent self-organizing maps (CSOM) and pitch contours for Arabic phoneme recognition. The approach employs separate self-organizing maps (SOM) for each Arabic phoneme joined in parallel to form a CSOM. The performance results confirm that with suitable preprocessing of data, including extraction of distinct power spectral densities (PSD) and singular value decomposition, the training time for CSOM was reduced by 89%. The empirical results also proved that overall recognition accuracy did not fall below 91%

Stacked regression ensemble for cancer class prediction

Design of a machine learning algorithm as a robust class predictor for various DNA microarray datasets is a challenging task, as the number of samples are very small as compared to the thousands of genes (feature set). For such datasets, a class prediction model could be very successful in classifying one type of dataset but may fail to perform in a similar fashion for other datasets. This paper presents a stacked regression ensemble (SRE) model for cancer class prediction. Results indicate that SRE has provided performance stability for various microarray datasets. The performance of SRE has been cross validated using the k-fold cross validation method (leave one out) technique for BRCA1, BRCA2 and sporadic classes for ovarian and breast cancer microarray datasets. The paper also presents comparative results of SRE with most commonly used SVM and GRNN. Empirical results confirmed that SRE has demonstrated better performance stability as compared to SVM and GRNN for the classification of assorted cancer data

A collateral missing value estimation algorithm for DNA microarrays

Genetic microarray expression data often contains multiple missing values that can significantly affect the performance of statistical and machine learning algorithms. This paper presents an innovative missing value estimation technique, called collateral missing value estimation (CMVE) which has demonstrated superior estimation performance compared with the K-nearest neighbour (KNN) imputation algorithm, the least square impute (LSImpute) and Bayesian principal component analysis (BPCA) techniques. Experimental results confirm that CMVE provides an improvement of 89%, 12% and 10% for the BRCA1, BRCA2 and sporadic ovarian cancer mutations, respectively, compared to the average error rate of KNN, LSImpute and BPCA imputation methods, over a range of randomly selected missing values. The underlying theory behind CMVE also means that it is not restricted to bioinformatics data, but can be successfully applied to any correlated data set