Time and frequency localized pulse shape for resolution enhancement in STFT-BOTDR

Short Time Fourier Transform-Brillouin Optical Time Domain Reflectometry (STFT-BOTDR) implements STFT over the full frequency spectrum to measure the distributed temperature and strain along the optic fiber, providing new research advances in dynamic distributed sensing. The spatial and frequency resolution of the dynamic sensing is limited by the Signal to Noise Ratio (SNR) and the Time-Frequency (T-F) localization of the input pulse shape. T-F localization is fundamentally important for the communication system, which suppresses interchannel interference (ICI) and intersymbol interference (ISI) to improve the transmission quality in multi-carrier modulation (MCM). This paper demonstrates that the T-F localized input pulse shape can enhance the SNR, the spatial and frequency resolution in STFT-BOTDR. Simulation and experiments of T-F localized different pulses shapes are conducted to compare the limitation of the system resolution. The result indicates that rectangular pulse should be selected to optimize the spatial resolution, Lorentzian pulse could be chosen to optimize the frequency resolution, while Gaussian shape pulse can be used in general applications for its balanced performance in both spatial and frequency resolution. Meanwhile, T-F localization is proved to be useful in the pulse shape selection for system resolution optimization

Effect of conjugated linoleic acid on inhibition of prolyl hydroxylase 1 in hearts of mice

<p>Abstract</p> <p>Background</p> <p>Results from different trails have provided evidence of protective effects of <it>cis-</it>9,<it>trans</it>-11-conjugated linoleic acid (CLA) on cardiovascular diseases. But the inhibition of prolyl hydroxylase 1 (PHD1) associated with induction of hypoxia inducible factors (HIFs) by CLA in these protective effects has never been reported before. The objective of this study was to evaluate if the two predominant <it>cis-</it>9,<it>trans</it>-11 (c9, t11), <it>trans</it>-10,<it>cis</it>-12 (t10, c12) CLA isomers and mixture of these two isomers can inhibit PHD1 with induction of HIFs in myocardium in mice and subsequent effects on myocardium metabolism.</p> <p>Results</p> <p>CLA mixture and c9, t11 CLA inhibited PHD1 protein expression and increased the levels of protein and mRNA in HIF-2α in myocardium in mice. Meanwhile, CLA mixture and c9, t11 CLA also elevated the expression of HIF related transcriptional factors like PDK4 and PPARα. The reprogramming of basal metabolism in myocardium in mice was shown on increasing of GLUT4 gene expression by c9, t11 CLA supplemented group. UCP2 was increased by CLA mixture and c9, t11 CLA for attenuating production of ROS.</p> <p>Conclusion</p> <p>CLA mixture and c9, t11 CLA could inhibit PHD1 and induce HIF-2α in myocardium in mice, which is associated with upregulation of PDK4 by activation of PPARα. This process also implies a reprogramming of basal metabolism and oxidative damage protection in myocardium in mice. All the effects shown in hearts of mice are due to c9, t11 CLA but not t10, c12 CLA.</p

Chiral spin state and nematic ferromagnet in the spin-1 Kitaev-Γ\Gamma model

The higher-spin Kitaev magnets, in which the Kitaev interaction and off-diagonal exchange couplings are overwhelmingly large, have emerged as a fertile avenue to explore exotic phases and unusual excitations. In this work, we study the quantum phase diagram of the spin-1 Kitaev-$\Gamma$ model on the honeycomb lattice using density-matrix renormalization group. It harbours six distinct phases and the intriguing findings are three magnetically ordered phases in which both time-reversal symmetry and lattice symmetry albeit of different sort are broken spontaneously. The chiral spin state originates from the order-by-disorder effect and exhibits an almost saturated scalar spin chirality at the quantum level. Depending on the relative strength of the two interactions, it also features columnar-like or plaquette-like dimer pattern as a consequence of the translational symmetry breaking. In parallel, the nematic ferromagnets are situated at ferromagnetic Kitaev side and possess small but finite ferromagnetic ordering. The lattice-rotational symmetry breaking enforces nonequivalent bond energy along one of the three bonds. Although the intrinsic difference between the two nematic ferromagnets remains elusive, the discontinuities in the von Neumann entropy, hexagonal plaquette operator, and Wilson loop operator convincingly suggest that they are separated via a first-order phase transition.Comment: 12 pages, 8 figures. Phys. Rev. B to appea

Magnetization of the spin-1/2 Heisenberg antiferromagnet on the triangular lattice

After decades of debate, now there is a rough consensus that at zero temperature the spin-$1/2$ Heisenberg antiferromagnet on the triangular lattice is three-sublattice $120^\circ$ magnetically ordered, in contrast to a quantum spin liquid as originally proposed. However, there remains considerable discrepancy in the magnetization reported among various methods. To resolve this issue, in this work we revisit this model by the tensor-network state algorithm. The ground-state energy per bond $E_b$ and magnetization per spin $M_0$ in the thermodynamic limit are obtained with high precision. The former is estimated to be $E_b = -0.18334(10)$. This value agrees well with that from the series expansion. The three-sublattice magnetic order is firmly confirmed and the magnetization is determined as $M_0 = 0.161(5)$. It is about $32\%$ of its classical value and slightly below the lower bound from the series expansion. In comparison with the best estimated value by Monte Carlo and density-matrix renormalization group, our result is about $20\%$ smaller. This magnetic order is consistent with further analysis of the three-body correlation. Our work thus provides new benchmark results for this prototypical model.Comment: 7 pages, 6 figures, 2 table

Online Clustering of Bandits with Misspecified User Models

The contextual linear bandit is an important online learning problem where given arm features, a learning agent selects an arm at each round to maximize the cumulative rewards in the long run. A line of works, called the clustering of bandits (CB), utilize the collaborative effect over user preferences and have shown significant improvements over classic linear bandit algorithms. However, existing CB algorithms require well-specified linear user models and can fail when this critical assumption does not hold. Whether robust CB algorithms can be designed for more practical scenarios with misspecified user models remains an open problem. In this paper, we are the first to present the important problem of clustering of bandits with misspecified user models (CBMUM), where the expected rewards in user models can be perturbed away from perfect linear models. We devise two robust CB algorithms, RCLUMB and RSCLUMB (representing the learned clustering structure with dynamic graph and sets, respectively), that can accommodate the inaccurate user preference estimations and erroneous clustering caused by model misspecifications. We prove regret upper bounds of $O(\epsilon_*T\sqrt{md\log T} + d\sqrt{mT}\log T)$ for our algorithms under milder assumptions than previous CB works (notably, we move past a restrictive technical assumption on the distribution of the arms), which match the lower bound asymptotically in $T$ up to logarithmic factors, and also match the state-of-the-art results in several degenerate cases. The techniques in proving the regret caused by misclustering users are quite general and may be of independent interest. Experiments on both synthetic and real-world data show our outperformance over previous algorithms

Online Corrupted User Detection and Regret Minimization

In real-world online web systems, multiple users usually arrive sequentially into the system. For applications like click fraud and fake reviews, some users can maliciously perform corrupted (disrupted) behaviors to trick the system. Therefore, it is crucial to design efficient online learning algorithms to robustly learn from potentially corrupted user behaviors and accurately identify the corrupted users in an online manner. Existing works propose bandit algorithms robust to adversarial corruption. However, these algorithms are designed for a single user, and cannot leverage the implicit social relations among multiple users for more efficient learning. Moreover, none of them consider how to detect corrupted users online in the multiple-user scenario. In this paper, we present an important online learning problem named LOCUD to learn and utilize unknown user relations from disrupted behaviors to speed up learning, and identify the corrupted users in an online setting. To robustly learn and utilize the unknown relations among potentially corrupted users, we propose a novel bandit algorithm RCLUB-WCU. To detect the corrupted users, we devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred user relations. We prove a regret upper bound for RCLUB-WCU, which asymptotically matches the lower bound with respect to $T$ up to logarithmic factors, and matches the state-of-the-art results in degenerate cases. We also give a theoretical guarantee for the detection accuracy of OCCUD. With extensive experiments, our methods achieve superior performance over previous bandit algorithms and high corrupted user detection accuracy