Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) are the most common infectious bacteria in our daily life, and seriously affect human's health. Because of the frequent and extensive use of antibiotics, the microbial strains forming drug resistance have become more and more difficult to deal with. Herein, we utilized bovine serum albumin (BSA) as the template to synthesize uniform copper sulfide (CuS) nanoparticles via a biomineralization method. The as-prepared BSA-CuS nanocomposites showed good biocompatibility and strong near-infrared absorbance performance and can be used as an efficient photothermal conversion agent for pathogenic bacteria ablation with a 980 nm laser at a low power density of 1.59 W/cm. The cytotoxicity of BSA-CuS nanocomposite was investigated using skin fibroblast cells and displayed good biocompatibility. Furthermore, the antibacterial tests indicated that BSA-CuS nanocomposite showed no antibacterial activity without NIR irradiation. In contrast, they demonstrated satisfying killing bacterial ability in the presence of NIR irradiation. Interestingly, S. aureus and E. coli showed various antibacterial mechanisms, possibly because of the different architectures of bacterial walls. Considering the low cost, easy preparation, excellent biocompatibility and strong photothermal convention efficiency (24.68%), the BSA-CuS nanocomposites combined with NIR irradiation will shed bright light on the treatment of antibiotic-resistant pathogenic bacteria.

For the universal hypothesis testing problem, where the goal is to decide between the known null hypothesis distribution and some other unknown distribution, Hoeffding proposed a universal test in the nineteen sixties. Hoeffding's universal test statistic can be written in terms of Kullback-Leibler (K-L) divergence between the empirical distribution of the observations and the null hypothesis distribution. In this paper a modification of Hoeffding's test is considered based on a relaxation of the K-L divergence test statistic, referred to as the mismatched divergence. The resulting mismatched test is shown to be a generalized likelihood-ratio test (GLRT) for the case where the alternate distribution lies in a parametric family of the distributions characterized by a finite dimensional parameter, i.e., it is a solution to the corresponding composite hypothesis testing problem. For certain choices of the alternate distribution, it is shown that both the Hoeffding test and the mismatched test have the same asymptotic performance in terms of error exponents. A consequence of this result is that the GLRT is optimal in differentiating a particular distribution from others in an exponential family. It is also shown that the mismatched test has a significant advantage over the Hoeffding test in terms of finite sample size performance. This advantage is due to the difference in the asymptotic variances of the two test statistics under the null hypothesis. In particular, the variance of the K-L divergence grows linearly with the alphabet size, making the test impractical for applications involving large alphabet distributions. The variance of the mismatched divergence on the other hand grows linearly with the dimension of the parameter space, and can hence be controlled through a prudent choice of the function class defining the mismatched divergence.Comment: Accepted to IEEE Transactions on Information Theory, July 201

Neuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations. In their most computationally attractive formulations, these techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? The goal of this paper is to propose an approach using the solutions to associated fluid and diffusion approximations. In order to illustrate this approach, the paper focuses on an application to dynamic speed scaling for power management in computer processors.

Global positioning system (GPS) multipath disturbance is a bottleneck problem that limits the accuracy of precise GPS positioning applications. A method based on the technique of cross-validation for automatically identifying wavelet signal layers is developed and used for separating noise from signals in data series, and applied to mitigate GPS multipath effects. Experiments with both simulated data series and real GPS observations show that the method is a powerful signal decomposer, which can successfully separate noise from signals as long as the noise level is lower than about half of the magnitude of the signals. A multipath correction model is derived based on the proposed method and the sidereal day-to-day repeating property of GPS multipath signals to remove multipath effects on GPS observations and to improve the quality of the GPS measurements.

No abstract

No abstract

No abstract

In recent years solutions to various hypothesis testing problems in the asymptotic setting have been proposed using results from large deviations theory. Such tests are optimal in terms of appropriately defined error-exponents. For the practitioner, however, error probabilities in the finite sample size setting are more important. In this paper we show how results on weak convergence of the test statistic can be used to obtain better approximations for the error probabilities in the finite sample size setting. While this technique is popular among statisticians for common tests, we demonstrate its applicability for several recently proposed asymptotically optimal tests, including tests for robust goodness of fit, homogeneity tests, outlier hypothesis testing, and graphical model estimation.