Novel insights into the γγ∗→π0\gamma\gamma^*\to \pi^0 transition form factor

BaBar's observation of significant deviations of the pion transition form factor (TFF) from the asymptotic expectation with $Q^2>9$ GeV$^2$ has brought a serious crisis to a fundamental picture established for such a simplest $q\bar{q}$ system by perturbative QCD, i.e. the dominance of collinear factorization at high momentum transfers for the pion TFF. We show that non-factorizable contributions due to open flavors in $\gamma\gamma^*\to\pi^0$ could be an important source that contaminates the pQCD asymptotic limit and causes such deviations with $Q^2>9$ GeV$^2$. Within an effective Lagrangian approach, the non-factorizable amplitudes can be related to intermediate hadron loops, i.e. $K^{(*)}$ and $D^{(*)}$ etc, and their corrections to the $\pi^0$ and $\eta$ TFFs can be estimated.Comment: Revtex, 6 pages and 4 eps figures; Extended version accepted by Eur. Phys. J.

Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data

This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently. By assuming that some aspects of driver drowsiness increase over time due to tiredness, we formulate an algorithm that can learn from such weakly labeled data. We derive a scalable stochastic optimization method as a way of implementing the algorithm. Numerical experiments on real driving datasets demonstrate the advantages of our algorithm against baseline methods.Comment: Accepted by ICASSP202