Kernel Cross-Correlator

Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking. Beyond the linear cross-correlator, this paper proposes a kernel cross-correlator (KCC) that breaks traditional limitations. First, by introducing the kernel trick, the KCC extends the linear cross-correlation to non-linear space, which is more robust to signal noises and distortions. Second, the connection to the existing works shows that KCC provides a unified solution for correlation filters. Third, KCC is applicable to any kernel function and is not limited to circulant structure on training data, thus it is able to predict affine transformations with customized properties. Last, by leveraging the fast Fourier transform (FFT), KCC eliminates direct calculation of kernel vectors, thus achieves better performance yet still with a reasonable computational cost. Comprehensive experiments on visual tracking and human activity recognition using wearable devices demonstrate its robustness, flexibility, and efficiency. The source codes of both experiments are released at https://github.com/wang-chen/KCCComment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18

Quantum discord amplification induced by quantum phase transition via a cavity-Bose-Einstein-condensate system

We propose a theoretical scheme to realize a sensitive amplification of quantum discord (QD) between two atomic qubits via a cavity-Bose-Einstein condensate (BEC) system which was used to firstly realize the Dicke quantum phase transition (QPT) [Nature 464, 1301 (2010)]. It is shown that the influence of the cavity-BEC system upon the two qubits is equivalent to a phase decoherence environment. It is found that QPT in the cavity-BEC system is the physical mechanism of the sensitive QD amplification.Comment: 5 pages, 3 figure

Signaling Characterization of Up-Regulation of Mouse and Human Bile Salt Export Pump (Bsep) By Berberine

Bile salt export pump (Bsep) is primarily responsible for biliary excretion of bile salts in the liver. Genetic mutation or drug-induced dysfunction of Bsep often leads to disruption of enterohepatic circulation of bile acids and consequently cholestatic liver injury. Berberine (BBR), a traditional herbal medicine, promotes bile flow and has been suggested to treat liver diseases, including cholestasis. We recently reported that BBR induces Bsep expression in mouse liver. However, the underlying mechanism by which BBR induces Bsep expression is unknown. My dissertation project showed that BBR induced mouse and human Bsep/BSEP mRNA and protein expression. In addition, BBR increased Bsep/BSEP transport activity, evidenced by increased cellular efflux of dichlorofluorescin diacetate, a selective Bsep substrate. BBR activated NRF2 signaling in human hepatoma cells, which contributed to BBR-induced human BSEP expression. However, activation of Nrf2 signaling was not essential for induction of mouse Bsep by BBR because BBR continued to increase Bsep expression in Nrf2-null mouse liver and Nrf2-silenced mouse hepatoma cells. In addition, BBR reversed LPS-decreased mouse Bsep expression in both mouse liver and cultured mouse hepatoma cells. Mechanistically, BBR attenuated LPS-activated TLR4-NF-кB signaling, which may contribute to BBR-induced mouse Bsep expression. In conclusion, BBR induced human BSEP expression through NRF2 activation; whereas BBR induced mouse Bsep expression most likely through TLR4 inhibition

Advancing systems biology of yeast through machine learning and comparative genomics

Synthetic biology has played a pivotal role in accomplishing the production of high value commodities, pharmaceuticals, and bulk chemicals. Fueled by the breakthrough of synthetic biology and metabolic engineering, Saccharomyces cerevisiae and various other yeasts (such as Yarrowia lipolytica, Pichia pastoris) have been proven to be promising microbial cell factories and are frequently used in scientific studies. However, the cellular metabolism and physiological properties for most of the yeast species have not been characterized in detail. To address these knowledge gaps, this thesis aims to leverage the large amounts of data available for yeast species and use state-of-the-art machine learning techniques and comparative genomic analysis to gain a deeper insight into yeast traits and metabolism.In this thesis, machine learning was applied to various unresolved biological problems on yeasts, i.e., gene essentiality, enzyme turnover number (kcat), and protein production. In the first part of the work, machine learning approaches were employed to predict gene essentiality based on sequence features and evolutionary features. It was demonstrated that the essential gene prediction could be substantially improved by integrating evolution-based features. Secondly, a high-quality deep learning model DLKcat was developed to predict kcat\ua0values by combining a graph neural network for substrates and a convolutional neural network for proteins. By predicting kcat profiles for 343 yeast/fungi species, enzyme-constrained models were reconstructed and used to further elucidate the cellular metabolism on a large scale. Lastly, a random forest algorithm was adopted to investigate feature importance analysis on protein production, it was found that post-translational modifications (PTMs) have a relatively higher impact on protein production compared with amino acid composition. In comparative genomics, a comprehensive toolbox HGTphyloDetect was developed to facilitate the identification of horizontal gene transfer (HGT) events. Case studies on some yeast species demonstrated the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, through systematic evolution analysis (e.g., HGT, gene family expansion) and genome-scale metabolic model simulation, the underlying mechanisms for substrate utilization were further probed across large-scale yeast species

Lepton Flavor Violating Radiative Decays in EW-Scale νR\nu_R Model: An Update

We perform an updated analysis for the one-loop induced lepton flavor violating radiative decays $l_i \to l_j \gamma$ in an extended mirror model. Mixing effects of the neutrinos and charged leptons constructed with a horizontal $A_4$ symmetry are also taken into account. Current experimental limit and projected sensitivity on the branching ratio of $\mu \to e \gamma$ are used to constrain the parameter space of the model. Calculations of two related observables, the electric and magnetic dipole moments of the leptons, are included. Implications concerning the possible detection of mirror leptons at the LHC and the ILC are also discussed.Comment: 9 figures, 36 single-side pages. Updated email addresses and referenc