Ablation and Plasma Effects during Nanosecond Laser Matter Interaction in Air and Water

Despite extensive research work, a clear understanding of laser matter interaction i

On the spectrum of quasi-periodic Schr\"odinger operators on Zd\mathbb{Z}^d with C2C^2-cosine type potentials

In this paper, we establish the Anderson localization, strong dynamical localization and the $(\frac 12-)$-H\"older continuity of the integrated density of states (IDS) for some multi-dimensional discrete quasi-periodic (QP) Schr\"odinger operators with asymmetric $C^2$-cosine type potentials. To this end, we develop an approach based on Green's function estimates to handle asymmetric Rellich functions with collapsed gaps.Comment: Comments welcome. 75 page

Localization and Regularity of the Integrated Density of States for Schr\"odinger Operators on Zd\mathbb{Z}^d with C2C^2-cosine Like Quasi-periodic Potential

In this paper, we study the multidimensional lattice Schr\"odinger operators with $C^2$-cosine like quasi-periodic (QP) potential. We establish quantitative Green's function estimates, the arithmetic version of Anderson (and dynamical) localization, and the finite volume version of $(\frac 12-)$-H\"older continuity of the integrated density of states (IDS) for such QP Schr\"odinger operators. Our proof is based on an extension of the fundamental multi-scale analysis (MSA) type method of Fr\"ohlich-Spencer-Wittwer [\textit{Comm. Math. Phys.} 132 (1990): 5--25] to the higher lattice dimensions. We resolve the level crossing issue on eigenvalues parameterizations in the case of both higher lattice dimension and $C^2$ regular potential.Comment: 63 pages, to appear in CM

Rank-Aware Negative Training for Semi-Supervised Text Classification

Semi-supervised text classification-based paradigms (SSTC) typically employ the spirit of self-training. The key idea is to train a deep classifier on limited labeled texts and then iteratively predict the unlabeled texts as their pseudo-labels for further training. However, the performance is largely affected by the accuracy of pseudo-labels, which may not be significant in real-world scenarios. This paper presents a Rank-aware Negative Training (RNT) framework to address SSTC in learning with noisy label manner. To alleviate the noisy information, we adapt a reasoning with uncertainty-based approach to rank the unlabeled texts based on the evidential support received from the labeled texts. Moreover, we propose the use of negative training to train RNT based on the concept that ``the input instance does not belong to the complementary label''. A complementary label is randomly selected from all labels except the label on-target. Intuitively, the probability of a true label serving as a complementary label is low and thus provides less noisy information during the training, resulting in better performance on the test data. Finally, we evaluate the proposed solution on various text classification benchmark datasets. Our extensive experiments show that it consistently overcomes the state-of-the-art alternatives in most scenarios and achieves competitive performance in the others. The code of RNT is publicly available at:https://github.com/amurtadha/RNT.Comment: TACL 202

Multi-focus image fusion based on non-negative sparse representation and patch-level consistency rectification

Most existing sparse representation-based (SR) fusion methods consider the local information of each image patch independently during fusion. Some spatial artifacts are easily introduced to the fused image. A sliding window technology is often employed by these methods to overcome this issue. However, this comes at the cost of high computational complexity. Alternatively, we come up with a novel multi-focus image fusion method that takes full consideration of the strong correlations among spatially adjacent image patches with NO need for a sliding window. To this end, a non-negative SR model with local consistency constraint (CNNSR) on the representation coefficients is first constructed to encode each image patch. Then a patch-level consistency rectification strategy is presented to merge the input image patches, by which the spatial artifacts in the fused images are greatly reduced. As well, a compact non-negative dictionary is constructed for the CNNSR model. Experimental results demonstrate that the proposed fusion method outperforms some state-of-the art methods. Moreover, the proposed method is computationally efficient, thereby facilitating real-world applications

A Novel Cross-layer Communication Protocol for Vehicular Sensor Networks

Communication protocols in Vehicular Sensor Networks (VSNs) in urban areas play an important role in intelligent transport systems applications. Many cross layer communication protocols studies are originated from topology-based algorithms, which is not suitable for the frequently-changing computational scenario. In addition, the influence factors that have been considered for VSNs routing are not enough. With these aspects in mind, this paper proposes a multi-factor cross layer position-based routing (MCLPR) protocol for VSNs to improve reliability and efficiency in message delivery. Considering the complex intersection environment, the algorithm for vehicles selection at intersections (called AVSI) is further proposed, in which comprehensive factors are taken into account including the position and direction of vehicle, the vehicle density, the signal-to-noise-plus-interference ratio (SNIR), as well as the frame error rate (FER) in MAC layer. Meanwhile, the dynamic HELLO STREAM broadcasting system with the various vehicle speeds is proposed to increase the decisions accuracy. Experimental results in Network Simulator 3 (NS-3) show the advantage of MCLPR protocol over traditional state-of the-art algorithms in terms of packet delivery ratio (PDR), overhead and the mean end-to-end delay

Toxicology and efficacy of tumor-targeting Salmonella typhimurium A1-R compared to VNP 20009 in a syngeneic mouse tumor model in immunocompetent mice.

Salmonella typhimurium A1-R (S. typhimurium A1-R) attenuated by leu and arg auxotrophy has been shown to target multiple types of cancer in mouse models. In the present study, toxicologic and biodistribution studies of tumor-targeting S. typhimurium A1-R and S. typhimurium VNP20009 (VNP 20009) were performed in a syngeneic tumor model growing in immunocompetent BALB/c mice. Single or multiple doses of S. typhimurium A1-R of 2.5 × 105 and 5 × 105 were tolerated. A single dose of 1 × 106 resulted in mouse death. S. typhimurium A1-R (5 × 105 CFU) was eliminated from the circulation, liver and spleen approximately 3-5 days after bacterial administration via the tail vein, but remained in the tumor in high amounts. S. typhimurium A1-R was cleared from other organs much more rapidly. S. typhimurium A1-R and VNP 20009 toxicity to the spleen and liver was minimal. S. typhimurium A1-R showed higher selective targeting to the necrotic areas of the tumors than VNP20009. S. typhimurium A1-R inhibited the growth of CT26 colon carcinoma to a greater extent at the same dose of VNP20009. In conclusion, we have determined a safe dose and schedule of S. typhimurium A1-R administration in BALB/c mice, which is also efficacious against tumor growth. The results of the present report indicate similar toxicity of S. typhimurium A1-R and VNP20009, but greater antitumor efficacy of S. typhimurium A1-R in an immunocompetent animal. Since VNP2009 has already proven safe in a Phase I clinical trial, the present results indicate the high clinical potential of S. typhimurium A1-R

Tuning electrochemical catalytic activity of defective 2D terrace MoSe2 heterogeneous catalyst via Co doping

This study presents successful growth of defective 2D terrace MoSe2/CoMoSe lateral heterostructures (LH), bilayer and multilayer MoSe2/CoMoSe LH, and vertical heterostructures (VH) nanolayers by doping metal Co (cobalt) element into MoSe2 atomic layers to form a CoMoSe alloy at the high temperature (~900 °C). After the successful introduction of metal Co heterogeneity in the MoSe2 thin layers, more active sites can be created to enhance hydrogen evolution reaction (HER) activities combining with metal Co catalysis, through the mechanisms including (1) atomic arrangement distortion in CoMoSe alloy nanolayers, (2) atomic level coarsening in LH interfaces and terrace edge layer architecture in VH, (3) formation of defective 2D terrace MoSe2 nanolayers heterogeneous catalyst via metal Co doping. The HER investigations indicated that the obtained products with LH and VH exhibited an improved HER activity in comparison with those from the pristine 2D MoSe2 electrocatalyst and LH type MoSe2/CoMoSe. The present work shows a facile yet reliable route to introduce metal ions into ultrathin 2D transition metal dichalcogenides (TMDCS) and produce defective 2D alloy atomic layers for exposing active sites, and thus eventually improve their electrocatalytic performance