A statistical learning strategy for closed-loop control of fluid flows

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system’s dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz’63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities

Search for CP Violation in the Decay Z -> b (b bar) g

About three million hadronic decays of the Z collected by ALEPH in the years 1991-1994 are used to search for anomalous CP violation beyond the Standard Model in the decay Z -> b \bar{b} g. The study is performed by analyzing angular correlations between the two quarks and the gluon in three-jet events and by measuring the differential two-jet rate. No signal of CP violation is found. For the combinations of anomalous CP violating couplings, ${\hat{h}}_b = {\hat{h}}_{Ab}g_{Vb}-{\hat{h}}_{Vb}g_{Ab}$ and $h^{\ast}_b = \sqrt{\hat{h}_{Vb}^{2}+\hat{h}_{Ab}^{2}}$, limits of \hat{h}_b < 0.59$and$h^{\ast}_{b} < 3.02$ are given at 95\% CL.Comment: 8 pages, 1 postscript figure, uses here.sty, epsfig.st

Production of excited beauty states in Z decays

A data sample of about 3.0 million hadronic Z decays collected by the ALEPH experiment at LEP in the years 1991 through 1994, is used to make an inclusive selection of B~hadron events. In this event sample 4227 \pm 140 \pm 252 B^* mesons in the decay B^* \to B \gamma and 1944 \pm 108 \pm 161 B^{**} mesons decaying into a B~meson and a charged pion are reconstructed. For the well established B^* meson the following quantities areobtained: \Delta M = M_{B^*} - M_{B} = (45.30\pm 0.35\pm 0.87)~\mathrm{MeV}/c^2 and N_{B^*}/(N_B+N_{B^*}) = (77.1 \pm 2.6 \pm 7.0)\%. The angular distribution of the photons in the B^* rest frame is used to measure the relative contribution of longitudinal B^* polarization states to be \sigma_L/(\sigma_L + \sigma_T)= (33 \pm 6 \pm 5)\%. \\ Resonance structure in the M(B\pi)-M(B) mass difference is observed at (424 \pm 4 \pm 10)~\mathrm{MeV}/c^2. Its shape and position is in agreement with the expectation for B^{**}_{u,d} states decaying into B_{u,d}^{(*)} \pi^\pm. The signal is therefore interpreted as arising from them. The relative production rate is determined to be \frac{BR(Z \to b \to B_{u,d}^{**})}{BR(Z \to b \to B_{u,d})} = [27.9 \pm 1.6(stat) \pm 5.9(syst) \phantom{a}^{+3.9}_{-5.6}(model)]\%. where the third error reflects the uncertainty due to different production and decay models for the broad B_{u,d}^{**} states

Tau hadronic branching ratios

From 64492 selected \tau-pair events, produced at the Z^0 resonance, the measurement of the tau decays into hadrons from a global analysis using 1991, 1992 and 1993 ALEPH data is presented. Special emphasis is given to the reconstruction of photons and \pi^0's, and the removal of fake photons. A detailed study of the systematics entering the \pi^0 reconstruction is also given. A complete and consistent set of tau hadronic branching ratios is presented for 18 exclusive modes. Most measurements are more precise than the present world average. The new level of precision reached allows a stringent test of \tau-\mu universality in hadronic decays, g_\tau/g_\mu \ = \ 1.0013 \ \pm \ 0.0095, and the first measurement of the vector and axial-vector contributions to the non-strange hadronic \tau decay width: R_{\tau ,V} \ = \ 1.788 \ \pm \ 0.025 and R_{\tau ,A} \ = \ 1.694 \ \pm \ 0.027. The ratio (R_{\tau ,V} - R_{\tau ,A}) / (R_{\tau ,V} + R_{\tau ,A}), equal to (2.7 \pm 1.3) \ \%, is a measure of the importance of QCD non-perturbative contributions to the hadronic \tau decay widt