Non-equilibrium properties of trapped ions under sudden application of a laser

Coherent quantum-state manipulation of trapped ions using classical laser fields is a trademark of modern quantum technologies. In this work, we study aspects of work statistics and irreversibility in a single trapped ion due to sudden interaction with the impinging laser. This is clearly an out-of-equilibrium process where work is performed through illumination of an ion by the laser. Starting with the explicit evaluation of the first moments of the work distribution, we proceed to a careful analysis of irreversibility as quantified by the nonequilibrium lag. The treatment employed here is not restricted to the Lamb-Dicke limit, what allows us to investigate the interplay between nonlinearities and irreversibility. We show, for instance, that in the resolved carrier and sideband regimes, variation of the Lamb-Dicke parameter may cause a non-monotonic behavior of the irreversibility indicator. Counterintuitively, we find a working point where nonlinearity helps reversibility, making the sudden quench of the Hamiltonian closer to what would have been obtained quasistatically and isothermally.

Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.

Pathways of the Photocatalytic Reaction of Acetate in H2O and D2O Combined: An EPR and ATR-FTIR Study

The adsorption and photocatalytic degradation of acetate on TiO2 surfaces was investigated in H2O and D2O by ATR-FTIR and EPR Spectroscopy respectively. These studies were carried out in the dark and under UV(A) illumination to gain additional insights into the adsorption behaviour with the identification of paramagnetic species formed during the oxidation of acetate. Isotopic exchange during the adsorption of D2O on TiO2 surface led to different interactions between the adsorbate and OD groups. At different pH levels, several surface complexes of acetate can be formed such as monodentate, or bidentates. Under UV(A) irradiation of TiO2 aqueous suspensions, the formation of hydroxyl and methoxy radicals evidenced as the corresponding spin-adducts, were found to dominate in alkaline and acidic suspensions respectively. Two possible pathways for the oxidation of acetate have been suggested at different pH levels in solution in terms of the source of the spin adduct formed. These proposed pathways were found to be in good agreement with ATR-FTIR and EPR results.

Insight into determinants of substrate binding and transport in a multidrug efflux protein

Multidrug resistance arising from the activity of integral membrane transporter proteins presents a global public health threat. In bacteria such as Escherichia coli, transporter proteins belonging to the major facilitator superfamily make a considerable contribution to multidrug resistance by catalysing efflux of myriad structurally and chemically different antimicrobial compounds. Despite their clinical relevance, questions pertaining to mechanistic details of how these promiscuous proteins function remain outstanding, and the role(s) played by individual amino acid residues in recognition, binding and subsequent transport of different antimicrobial substrates by multidrug efflux members of the major facilitator superfamily requires illumination. Using in silico homology modelling, molecular docking and mutagenesis studies in combination with substrate binding and transport assays, we identified several amino acid residues that play important roles in antimicrobial substrate recognition, binding and transport by Escherichia coli MdtM, a representative multidrug efflux protein of the major facilitator superfamily. Furthermore, our studies suggested that 'aromatic clamps' formed by tyrosine and phenylalanine residues located within the substrate binding pocket of MdtM may be important for antimicrobial substrate recognition and transport by the protein. Such 'clamps' may be a structurally and functionally important feature of all major facilitator multidrug efflux proteins.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.