Photophysical and structural characterisation of in situ formed quantum dots

Conjugated polymer–semiconductor quantum dot (QD) composites are attracting increasing attention due to the complementary properties of the two classes of materials. We report a convenient method for in situ formation of QDs, and explore the conditions required for light emission of nanocomposite blends. In particular we explore the properties of nanocomposites of the blue emitting polymer poly[9,9-bis(3,5-di-tert-butylphenyl)-9H-fluorene] together with cadmium sulphide (CdS) and cadmium selenide (CdSe) precursors. We show the formation of emissive quantum dots of CdSe from thermally decomposed precursor. The dots are formed inside the polymer matrix and have a photoluminescence quantum yield of 7.5%. Our results show the importance of appropriate energy level alignment, and are relevant to the application of organic–inorganic systems in optoelectronic devices

Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

Deep generative modeling for single-cell transcriptomics.

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Radiation monitoring with CVD Diamonds and PIN Diodes at BaBar

The BaBar experiment at the Stanford Linear Accelerator Center has been using two polycrystalline chemical vapor deposition (pCVD) diamonds and 12 silicon PIN diodes for radiation monitoring and protection of the Silicon Vertex Tracker (SVT). We have used the pCVD diamonds for more than 3 years, and the PIN diodes for 7 years. We will describe the SVT and SVT radiation monitoring system as well as the operational difficulties and radiation damage effects on the PIN diodes and pCVD diamonds in a high-energy physics environment

Resonant Structure of τ→3ππ0ντ\tau\to 3\pi\pi^{0}\nu_{\tau} and τ→ωπντ\tau\to \omega\pi\nu_{\tau} Decays

The resonant structure of the four pion final state in the decay $\tau \to 3\pi\pi^0\nu_\tau$ is analyzed using 4.27 million $\tau^+\tau^-$ pairs collected by the CLEO II experiment. We search for second class currents in the decay $\tau \to \omega\pi\nu_\tau$ using spin-parity analysis and establish an upper limit on the non-vector current contribution. The mass and width of the $\rho'$ resonance are extracted from a fit to the $\tau \to \omega\pi\nu_\tau$ spectral function. A partial wave analysis of the resonant structure of the $\tau \to 3\pi\pi^0\nu_\tau$ decay is performed; the spectral decomposition of the four pion system is dominated by the $\omega\pi$ and $a_1 \pi$ final states.Comment: 34 pages postscript, also available through http://w4.lns.cornell.edu/public/CLN

Measurement of ISR-FSR interference in the processes e+ e- --> mu+ mu- gamma and e+ e- --> pi+ pi- gamma

Charge asymmetry in processes e+ e- --> mu+ mu- gamma and e+ e- --> pi+ pi- gamma is measured using 232 fb-1 of data collected with the BABAR detector at center-of-mass energies near 10.58 GeV. An observable is introduced and shown to be very robust against detector asymmetries while keeping a large sensitivity to the physical charge asymmetry that results from the interference between initial and final state radiation. The asymmetry is determined as afunction of the invariant mass of the final-state tracks from production threshold to a few GeV/c2. It is compared to the expectation from QED for e+ e- --> mu+ mu- gamma and from theoretical models for e+ e- --> pi+ pi- gamma. A clear interference pattern is observed in e+ e- --> pi+ pi- gamma, particularly in the vicinity of the f_2(1270) resonance. The inferred rate of lowest order FSR production is consistent with the QED expectation for e+ e- --> mu+ mu- gamma, and is negligibly small for e+ e- --> pi+ pi- gamma.Comment: 32 pages,29 figures, to be submitted to Phys. Rev.

Search for a Scalar Bottom Quark with Mass 3.5-4.5 GeV/c2c^{2}

We report on a search for a supersymmetric $\tilde{B}$ meson with mass between 3.5 and 4.5 GeV/$c^2$ using 4.52 ${\rm fb}^{-1}$ of integrated luminosity produced at $\sqrt{s}=10.52$ GeV, just below the $e^+e^-\to B\bar{B}$ threshold, and collected with the CLEO detector. We find no evidence for a light scalar bottom quark.Comment: 10 pages postscript, also available through http://w4.lns.cornell.edu/public/CLN