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

Effect of home exercise rehabilitation on cardiopulmonary function in patients with varying degrees of coronary revascularization

Objective To investigate the effect of home exercise rehabilitation on cardiopulmonary function in patients with varying degrees of coronary revascularization. Methods A total of 93 patients who were diagnosed with acute coronary syndrome and underwent percutaneous coronary intervention from September 2020 to September 2022 and received home exercise rehabilitation were selected from the database of Cardiac Rehabilitation Center. According to the residual syntax score (rSS), the patients were divided into rSS<8 group with 51 patients and rSS≥8 group with 42 patients. The cardiopulmonary function exercise test (CPET) was used to evaluate cardiopulmonary function, and the two groups were compared in terms of the changes in CPET parameters after 6 months of home exercise rehabilitation. Results After 6 months of home exercise rehabilitation, both groups had significant increases in oxygen uptake at anaerobic threshold, peak oxygen uptake, oxygen pulse rate at anaerobic threshold, and peak oxygen pulse rate (t=-2.953--5.483,P<0.05). There were significant differences in the changes of carbon dioxide ventilation efficiency at anaerobic threshold and peak carbon dioxide ventilation efficiency after home exercise rehabilitation between the two groups (Z=-2.046,-2.206,P<0.05). Conclusion Home exercise rehabilitation can improve the cardiopulmonary function of patients with acute coronary syndrome after percutaneous coronary intervention and bring more benefits for cardiac function in patients with rSS≥8

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

SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior

Novel View Synthesis (NVS) for street scenes play a critical role in the autonomous driving simulation. The current mainstream technique to achieve it is neural rendering, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Although thrilling progress has been made, when handling street scenes, current methods struggle to maintain rendering quality at the viewpoint that deviates significantly from the training viewpoints. This issue stems from the sparse training views captured by a fixed camera on a moving vehicle. To tackle this problem, we propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data. Specifically, we first fine-tune a Diffusion Model by adding images from adjacent frames as condition, meanwhile exploiting depth data from LiDAR point clouds to supply additional spatial information. Then we apply the Diffusion Model to regularize the 3DGS at unseen views during training. Experimental results validate the effectiveness of our method compared with current state-of-the-art models, and demonstrate its advance in rendering images from broader views