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

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

The choice of exchange rate system for developing countries

Call number: LD2668 .R4 ECON 1988 R83Master of ArtsEconomic

Applying Polyacrylamide (PAM) to Reduce Seepage Loss of Water Through Unlined Canals

High molecular weight, linear, anionic polyacrylamide (PAM) is under investigation as a means of sealing unlined water delivery canals, thus potentially increasing the amount of water for downstream users. This study uses a two-layer conceptual model to explore the mechanism of reducing water loss from seepage

Multidrug resistance-associated protein 1 (MRP1/ABCC1) polymorphism: from discovery to clinical application

Multidrug resistance-associated protein 1 (MRP1/ABCC1) is the first identified member of ABCC subfamily which belongs to ATP-binding cassette (ABC) transporter superfamily. It is ubiquitously expressed in almost all human tissues and transports a wide spectrum of substrates including drugs, heavy metal anions, toxicants, and conjugates of glutathione, glucuronide and sulfate. With the advance of sequence technology, many MRP1/ABCC1 polymorphisms have been identified. Accumulating evidences show that some polymorphisms are significantly associated with drug resistance and disease susceptibility. In vitro reconstitution studies have also unveiled the mechanism for some polymorphisms. In this review, we present recent advances in understanding the role and mechanism of MRP1/ABCC1 polymorphisms in drug resistance, toxicity, disease susceptibility and severity, prognosis prediction, and methods to select and predict functional polymorphisms