Highly selective electrochemical hydrogenation of alkynes: Rapid construction of mechanochromic materials

Electrochemical hydrogenation has emerged as an environmentally benign and operationally simple alternative to traditional catalytic reduction of organic compounds. Here, we have disclosed for the first time the electrochemical hydrogenation of alkynes to a library of synthetically important Z-alkenes under mild conditions with great selectivity and efficiency. The deuterium and control experiments of electrochemical hydrogenation suggest that the hydrogen source comes from the solvent, supporting electrolyte, and base. The scanning electron microscopy and x-ray diffraction experiments demonstrate that palladium nanoparticles generated in the electrochemical reaction act as a chemisorbed hydrogen carrier. Moreover, complete reduction of alkynes to saturated alkanes can be achieved through slightly modified conditions. Furthermore, a series of novel mechanofluorochromic materials have been efficiently constructed with this protocol that showed blue-shifted mechanochromism. This discovery represents the first example of cis-olefins-based organic mechanochromic materials

Constraints on the CKM angle alpha in the B --> rho rho decays

Using a data sample of 122 million Upsilon(4S) -> BBbar decays collected with BaBar detector at the PEP-II asymmetric B factory at SLAC, we measure the time-dependent-asymmetry parameters of the longitudinally polarized component in the B0 -> rho^+ rho^- decay as C_L = -0.23 +/- 0.24 (stat) +/- 0.14 (syst) and S_L = -0.19 +/- 0.33 (stat) +/- 0.11 (syst). The B0 -> rho0 rho0 decay mode is also searched for in a data sample of about 227 million BBbar pairs. No significant signal is observed, and an upper limit of 1.1 * 10-6 (90% C.L.) on the branching fraction is set. The penguin contribution to the CKM angle $\alpha$ uncertainty is measured to be 11 degree. All results are preliminary.Comment: 3 pages, 1 postscript figues, submitted to DPF200

Face Attribute Prediction Using Off-the-Shelf CNN Features

Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks --- face localization, facial descriptor construction, and attribute classification --- in a pipeline. As a typical classification problem, face attribute prediction has been addressed using deep learning. Current state-of-the-art performance was achieved by using two cascaded Convolutional Neural Networks (CNNs), which were specifically trained to learn face localization and attribute description. In this paper, we experiment with an alternative way of employing the power of deep representations from CNNs. Combining with conventional face localization techniques, we use off-the-shelf architectures trained for face recognition to build facial descriptors. Recognizing that the describable face attributes are diverse, our face descriptors are constructed from different levels of the CNNs for different attributes to best facilitate face attribute prediction. Experiments on two large datasets, LFWA and CelebA, show that our approach is entirely comparable to the state-of-the-art. Our findings not only demonstrate an efficient face attribute prediction approach, but also raise an important question: how to leverage the power of off-the-shelf CNN representations for novel tasks.Comment: In proceeding of 2016 International Conference on Biometrics (ICB

Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild

Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature representations to cope with these unfavourable conditions. Given the success of Convolutional Neural Network (CNN) in image classification, the high-level CNN feature, as an intuitive and reasonable choice, has been widely utilized for this problem. In this paper, however, we consider the mid-level CNN features as an alternative to the high-level ones for attribute prediction. This is based on the observation that face attributes are different: some of them are locally oriented while others are globally defined. Our investigations reveal that the mid-level deep representations outperform the prediction accuracy achieved by the (fine-tuned) high-level abstractions. We empirically demonstrate that the midlevel representations achieve state-of-the-art prediction performance on CelebA and LFWA datasets. Our investigations also show that by utilizing the mid-level representations one can employ a single deep network to achieve both face recognition and attribute prediction.Comment: In proceedings of 2016 International Conference on Image Processing (ICIP

Subspace projection regularization for large-scale Bayesian linear inverse problems

The Bayesian statistical framework provides a systematic approach to enhance the regularization model by incorporating prior information about the desired solution. For the Bayesian linear inverse problems with Gaussian noise and Gaussian prior, we propose a new iterative regularization algorithm that belongs to subspace projection regularization (SPR) methods. By treating the forward model matrix as a linear operator between the two underlying finite dimensional Hilbert spaces with new introduced inner products, we first introduce an iterative process that can generate a series of valid solution subspaces. The SPR method then projects the original problem onto these solution subspaces to get a series of low dimensional linear least squares problems, where an efficient procedure is developed to update the solutions of them to approximate the desired solution of the original problem. With the new designed early stopping rules, this iterative algorithm can obtain a regularized solution with a satisfied accuracy. Several theoretical results about the algorithm are established to reveal the regularization properties of it. We use both small-scale and large-scale inverse problems to test the proposed algorithm and demonstrate its robustness and efficiency. The most computationally intensive operations in the proposed algorithm only involve matrix-vector products, making it highly efficient for large-scale problems