Strong dopant dependence of electric transport in ion-gated MoS2

We report modifications of the temperature-dependent transport properties of $\mathrm{MoS_2}$ thin flakes via field-driven ion intercalation in an electric double layer transistor. We find that intercalation with $\mathrm{Li^+}$ ions induces the onset of an inhomogeneous superconducting state. Intercalation with $\mathrm{K^+}$ leads instead to a disorder-induced incipient metal-to-insulator transition. These findings suggest that similar ionic species can provide access to different electronic phases in the same material.Comment: 5 pages, 3 figure

The choice of exchange rate system for developing countries

Call number: LD2668 .R4 ECON 1988 R83Master of ArtsEconomic

Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction

Dimensionality reduction is an essential technique for multi-way large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when facing large-scale data. In this paper, taking advantages of the recently proposed tensor random projection method, we propose two TR decomposition algorithms. By employing random projection on every mode of the large-scale tensor, the TR decomposition can be processed at a much smaller scale. The simulation experiment shows that the proposed algorithms are $4-25$ times faster than traditional algorithms without loss of accuracy, and our algorithms show superior performance in deep learning dataset compression and hyperspectral image reconstruction experiments compared to other randomized algorithms.Comment: ICASSP submissio

Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model possibilities grows exponentially with the tensor order, which makes it rather challenging to find the optimal TR decomposition. In this paper, by exploiting the low-rank structure of the TR latent space, we propose a novel tensor completion method which is robust to model selection. In contrast to imposing the low-rank constraint on the data space, we introduce nuclear norm regularization on the latent TR factors, resulting in the optimization step using singular value decomposition (SVD) being performed at a much smaller scale. By leveraging the alternating direction method of multipliers (ADMM) scheme, the latent TR factors with optimal rank and the recovered tensor can be obtained simultaneously. Our proposed algorithm is shown to effectively alleviate the burden of TR-rank selection, thereby greatly reducing the computational cost. The extensive experimental results on both synthetic and real-world data demonstrate the superior performance and efficiency of the proposed approach against the state-of-the-art algorithms

Theoretical and experimental studies on manipulation of fluorescence by gold nanoparticle : application for molecular imaging.

Gold nanoparticles (GNPs) have shown beneficial properties for biomedical use, e.g., their non-toxic nature and surface properties for easy modification. Upon receiving light, they generate a strong surface plasmon field, which can alter the fluorescence of fluorophores. The level and type of the fluorescence alteration depend on the GNP size and shape, excitation (Ex)/emission (Em) wavelengths and quantum yield of the fluorophore, as well as the distance between the fluorophore and GNP. In this dissertation, the effect of the properties listed above on the fluorescence output was theoretically analyzed for the fluorophores frequently used in biomedical studies. For fluorescence quenching, fluorophores with the Em wavelength near the GNP plasmon resonance peak (520 nm) are better suited. As the Em wavelength increases, a shorter distance is required for achieving the same level of quenching. A bigger GNP requires shorter distance for quenching. To obtain fluorescence enhancement, the Em wavelength of the fluorophore needs to be longer than the GNP plasmon resonance peak (e.g., \u3e 650 nm). The fluorophore with lower intrinsic quantum yield tends to be enhanced more. The GNP needs to be sufficiently large (\u3e 5 nm), and a bigger GNP provides a higher maximum enhancement. Utilizing the quenching/enhancement ability of GNPs, a near-infrared (NIR) contrast agent that emits fluorescence at a higher level only at the particular cancer site was developed. Cypate, a safe NIR fluorophore, was selected as the fluorophore because NIR penetrates deeper into tissue and because Cypate is non-toxic. Cypate was conjugated to a GNP via two spacers. One is short for the quenching and with a substrate for a breast cancer-specific enzyme, urokinase-type plasminogen activator (uPA). The other is a long, biocompatible polymer chain for fluorescence enhancement. The fluorescence of the contrast agent was quenched by GNP by 93%. In the presence of uPA, the short spacer was cleaved and the remaining long spacer enhanced fluorescence 1.8 times. The study results are beneficial for developing efficacious optical contrast agents. This novel contrast agent can detect and diagnose breast cancer with high specificity and sensitivity, as FRET or molecular beacon but with a higher sensitivity and without the restriction of using DNA/RNA segments