Diffusion-based clock synchronization for molecular communication under inverse Gaussian distribution

Nanonetworks are expected to expand the capabilities of individual nanomachines by allowing them to cooperate and share information by molecular communication. The information molecules are released by the transmitter nanomachine and diffuse across the aqueous channel as a Brownian motion holding the feature of a strong random movement with a large propagation delay. In order to ensure an effective real-time cooperation, it is necessary to keep the clock synchronized among the nanomachines in the nanonetwork. This paper proposes a model on a two-way message exchange mechanism with the molecular propagation delay based on the inverse Gaussian distribution. The clock offset and clock skew are estimated by the maximum likelihood estimation (MLE). Simulation results by MATLAB show that the mean square errors (MSE) of the estimated clock offsets and clock skews can be reduced and converge with a number of rounds of message exchanges. The comparison of the proposed scheme with a clock synchronization method based on symmetrical propagation delay demonstrates that our proposed scheme can achieve a better performance in terms of accuracy

Pairing, Charge, and Spin Correlations in the Three-Band Hubbard Model

Using the Constrained Path Monte Carlo (CPMC) method, we simulated the two-dimensional, three-band Hubbard model to study pairing, charge, and spin correlations as a function of electron and hole doping and the Coulomb repulsion $V_{pd}$ between charges on neighboring Cu and O lattice sites. As a function of distance, both the $d_{x^2 - y^2}$-wave and extended s-wave pairing correlations decayed quickly. In the charge-transfer regime, increasing $V_{pd}$ decreased the long-range part of the correlation functions in both channels, while in the mixed-valent regime, it increased the long-range part of the s-wave behavior but decreased that of the d-wave behavior. Still the d-wave behavior dominated. At a given doping, increasing $V_{pd}$ increased the spin-spin correlations in the charge-transfer regime but decreased them in the mixed-valent regime. Also increasing $V_{pd}$ suppressed the charge-charge correlations between neighboring Cu and O sites. Electron and hole doping away from half-filling was accompanied by a rapid suppression of anti-ferromagnetic correlations.Comment: Revtex, 8 pages with 15 figure

d-Wave Pairing Correlation in the Two-Dimensional t-J Model

The pair-pair correlation function of the two-dimensional t-J model is studied by using the power-Lanczos method and an assumption of monotonic behavior. In comparison with the results of the ideal Fermi gas, we conclude that the 2D t-J model does not have long range d-wave superconducting correlation in the interesting parameter range of $J/t \leq 0.5$. Implications of this result will also be discussed.Comment: 4 pages, 6 figures, accepted by PR

Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) is a challenging task that requires simultaneously learn object classifiers and estimate object locations under the supervision of image category labels. A major line of WSOD methods roots in multiple instance learning which regards images as bags of instances and selects positive instances from each bag to learn the detector. However, a grand challenge emerges when the detector inclines to converge to discriminative parts of objects rather than the whole objects. In this paper, under the hypothesis that optimal solutions are included in local minima, we propose a discovery-and-selection approach fused with multiple instance learning (DS-MIL), which finds rich local minima and select optimal solution from multiple local minima. To implement DS-MIL, an attention module is proposed so that more context information can be captured by feature maps and more valuable proposals can be collected during training. With proposal candidates, a selection module is proposed to select informative instances for object detector. Experimental results on commonly used benchmarks show that our proposed DS-MIL approach can consistently improve the baselines, reporting state-of-the-art performance

Intelligent Hybrid Approaches for Mobile Robots Path Planning

University of Technology Sydney. Faculty of Engineering and Information Technology.The practical applications of mobile robots are widely implemented in various areas, such as education, industry, environment, and civil applications. The requirements of robots' navigation are one of the primary considerations for autonomous operation. Path planning is essential for the successful application of mobile robots. Considering all available information, it aims to generate the robot's optimal path from the start to the target location. Depending on the operation scenarios, various factors are counted in the cost function for path planning. Meeting the flexibility, robustness, and efficiency requirements for real-time mobile robot path planning implementation is challenging. A review of multi-robot path planning is published to compare the path planning approaches and decision-making strategies, listing the challenges. This thesis aims to tackle major challenges by developing intelligent hybrid approaches, including 1) trapping in local optimal, 2) slow convergence of path generation, and 3) robots' fault tolerance. It also provides the path planning algorithms for single-robot and multi-robot systems in three-dimensional and two-dimensional space. For single mobile robot path planning, the bio-inspired approaches have gained more attention recently with high robustness and flexibility. In contrast, it is highly possible to trap in a local optimal. The proposed Harmony-particle swarm optimization algorithm significantly reduces the iterations during planning to solve the aerial path planning problem in a multi-building environment. Also, a hybrid approach of particle swarm optimization and simulated annealing is proposed for single-vehicle path planning in the industrial warehouse scenario. It updates the personal best value to jump out of the locally optimal. Compared with other evolutionary approaches, it shows excellent performance. Moreover, fast convergence is a significant challenge for multiple robots' path planning. A dual-layer Weight-Leader-Vicsek-Model is proposed that generates the path for the virtual leaders first for each group of robots, and then all the robots will move by following their leaders. This dual-layer approach can achieve fast convergence, generating vehicle paths in one calculation step. Fault tolerance is also an essential issue for the real-time implementation of path planning, but it is lacking in previous studies. The Cultural-Particle Swarm Optimization algorithm is proposed to offer a backup plan in case of system failures. It updates the inertial weight to enhance the search abilities, balancing the global and local search abilities. The experiments and validated results are presented for each proposed approach

Triplet superconductivity in quasi one-dimensional systems

We study a Hubbard hamiltonian, including a quite general nearest-neighbor interaction, parametrized by repulsion V, exchange interactions Jz, Jperp, bond-charge interaction X and hopping of pairs W. The case of correlated hopping, in which the hopping between nearest neighbors depends upon the occupation of the two sites involved, is also described by the model for sufficiently weak interactions. We study the model in one dimension with usual continuum-limit field theory techniques, and determine the phase diagram. For arbitrary filling, we find a very simple necessary condition for the existence of dominant triplet superconducting correlations at large distance in the spin SU(2) symmetric case: 4V+J<0. In the correlated hopping model, the three-body interaction should be negative for positive V. We also compare the predictions of this weak-coupling treatment with numerical exact results for the correlated-hopping model obtained by diagonalizing small chains, and using novel techniques to determine the opening of the spin gap.Comment: 8 pages, 3 figure

An Artificial Liposome Compartment with Size Exclusion Molecular Transport

The cellular compartment plays an essential role in organizing the complex and diverse biochemical reactions within the cell. By mimicking the function of such cellular compartments, the challenge of constructing artificial compartments has been taken up to develop new biochemical tools for efficient material production and diagnostics. The important features required for the artificial compartment are that it isolates the interior from the external environment and is further functionalized to control the transport of target chemicals to regulate the interior concentration of both substrate and reaction products. In this study, an artificial compartment with size-selective molecular transport function was constructed by using a DNA origami-guided liposome prepared by modifying the method reported by Perrault et al. This completely isolates the liposome interior, including the DNA origami skeleton, from the external environment and allows the assembly of a defined number of molecules of interest inside and/or outside the compartment. By incorporating a bacterial membrane protein, OmpF, into the liposome, the resulting artificial compartment was shown to transport only the molecule of interest with a molecular weight below 600 Da from the external environment into the interior of the compartment

The crystal facet-dependent electrochemical performance of TiO2 nanocrystals for heavy metal detection: Theoretical prediction and experimental proof

Tailored design/fabrication of electroanalytical materials with highly-active exposed crystal planes is of great importance for the development of electrochemical sensing. In this work, combining experimental and theoretical efforts, we reported a facile strategy to fabricate TiO2 nanocrystals with tunable electrochemical performance for heavy metal detection. Density functional theory (DFT) calculations indicated that TiO2 (001) facet showed relative larger adsorption energy and lower diffusion energy barrier toward heavy metal ions, which is favorable for obtaining better electrochemical stripping behaviors. Based on this prediction, a series of TiO2 nanocrystals with different ratios of exposed (001) and (101) facets were synthesized. Electrochemical stripping experiments further demonstrated that with the increase of the percentage of exposed (001) facet, the sensitivity toward Pb(II) and Cd(II) was increased accordingly. When the percentage of exposed (001) facet was increased from 7% to 80%, the sensitivity increased by 190% and 93% for Pb(II) and Cd(II), respectively. Our work provides an effective route to construct advanced electroanalytical materials for sensing

Phase separation in the 2D Hubbard model: a fixed-node quantum Monte Carlo study

Fixed-node Green's function Monte Carlo calculations have been performed for very large 16x6 2D Hubbard lattices, large interaction strengths U=10,20, and 40, and many (15-20) densities between empty and half filling. The nodes were fixed by a simple Slater-Gutzwiller trial wavefunction. For each value of U we obtained a sequence of ground-state energies which is consistent with the possibility of a phase separation close to half-filling, with a hole density in the hole-rich phase which is a decreasing function of U. The energies suffer, however, from a fixed-node bias: more accurate nodes are needed to confirm this picture. Our extensive numerical results and their test against size, shell, shape and boundary condition effects also suggest that phase separation is quite a delicate issue, on which simulations based on smaller lattices than considered here are unlikely to give reliable predictions.Comment: 4 pages, 1 figure; revised version, more data point