Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance

Homologous recombination is unlikely to play a major role in influenza B virus evolution

Influenza B viruses cause a significant amount of morbidity and mortality. The occurrence of homologous recombination in influenza viruses is controversial. To determine the extent of homologous recombination in influenza B viruses, recombination analyses of 2,650 sequences representing all eight segments of the influenza B viruses were carried out. Only four sequences were indentified as putative recombinants, which were verified using phylogenetic methods. However, the mosaics detected here were much likely to represent cases of laboratory-generated artificial recombinants. As in other myxoviruses, it is unlikely that homologous recombination plays a major role in influenza B virus evolution

A Dynamic Linear Hashing Method for Redundancy Management in Train Ethernet Consist Network

Massive transportation systems like trains are considered critical systems because they use the communication network to control essential subsystems on board. Critical system requires zero recovery time when a failure occurs in a communication network. The newly published IEC62439-3 defines the high-availability seamless redundancy protocol, which fulfills this requirement and ensures no frame loss in the presence of an error. This paper adopts these for train Ethernet consist network. The challenge is management of the circulating frames, capable of dealing with real-time processing requirements, fast switching times, high throughout, and deterministic behavior. The main contribution of this paper is the in-depth analysis it makes of network parameters imposed by the application of the protocols to train control and monitoring system (TCMS) and the redundant circulating frames discarding method based on a dynamic linear hashing, using the fastest method in order to resolve all the issues that are dealt with

Contrastive Attention for Automatic Chest X-ray Report Generation

Recently, chest X-ray report generation, which aims to automatically generate descriptions of given chest X-ray images, has received growing research interests. The key challenge of chest X-ray report generation is to accurately capture and describe the abnormal regions. In most cases, the normal regions dominate the entire chest X-ray image, and the corresponding descriptions of these normal regions dominate the final report. Due to such data bias, learning-based models may fail to attend to abnormal regions. In this work, to effectively capture and describe abnormal regions, we propose the Contrastive Attention (CA) model. Instead of solely focusing on the current input image, the CA model compares the current input image with normal images to distill the contrastive information. The acquired contrastive information can better represent the visual features of abnormal regions. According to the experiments on the public IU-X-ray and MIMIC-CXR datasets, incorporating our CA into several existing models can boost their performance across most metrics. In addition, according to the analysis, the CA model can help existing models better attend to the abnormal regions and provide more accurate descriptions which are crucial for an interpretable diagnosis. Specifically, we achieve the state-of-the-art results on the two public datasets.Comment: Appear in Findings of ACL 2021 (The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)

Developing Dipole-scheme Heterojunction Photocatalysts

The high recombination rate of photogenerated carriers is the bottleneck of photocatalysis, severely limiting the photocatalytic efficiency. Here, we develop a dipole-scheme (D-scheme for short) photocatalytic model and materials realization. The D-scheme heterojunction not only can effectively separate electrons and holes by a large polarization field, but also boosts photocatalytic redox reactions with large driving photovoltages and without any carrier loss. By means of first-principles and GW calculations, we propose a D-scheme heterojunction prototype with two real polar materials, PtSeTe/LiGaS2. This D-scheme photocatalyst exhibits a high capability of the photogenerated carrier separation and near-infrared light absorption. Moreover, our calculations of the Gibbs free energy imply a high ability of the hydrogen and oxygen evolution reaction by a large driving force. The proposed D-scheme photocatalytic model is generalized and paves a valuable route of significantly improving the photocatalytic efficiency.Comment: 10 pages, 5 figure

Adaptive Multi-Modality Prompt Learning

Although current prompt learning methods have successfully been designed to effectively reuse the large pre-trained models without fine-tuning their large number of parameters, they still have limitations to be addressed, i.e., without considering the adverse impact of meaningless patches in every image and without simultaneously considering in-sample generalization and out-of-sample generalization. In this paper, we propose an adaptive multi-modality prompt learning to address the above issues. To do this, we employ previous text prompt learning and propose a new image prompt learning. The image prompt learning achieves in-sample and out-of-sample generalization, by first masking meaningless patches and then padding them with the learnable parameters and the information from texts. Moreover, each of the prompts provides auxiliary information to each other, further strengthening these two kinds of generalization. Experimental results on real datasets demonstrate that our method outperforms SOTA methods, in terms of different downstream tasks

Carbon-Nanotubes-Supported Pd Nanoparticles for Alcohol Oxidations in Fuel Cells: Effect of Number of Nanotube Walls on Activity

Carbon nanotubes (CNTs) are well known electrocatalyst supports due to their high electrical conductivity, structural stability, and high surface area. Here, we demonstrate that the number of inner tubes or walls of CNTs also have a significant promotion effect on the activity of supported Pd nanoparticles (NPs) for alcohol oxidation reactions of direct alcohol fuel cells (DAFCs). Pd NPs with similar particle size (2.1–2.8 nm) were uniformly assembled on CNTs with different number of walls. The results indicate that Pd NPs supported on triple-walled CNTs (TWNTs) have the highest mass activity and stability for methanol, ethanol, and ethylene glycol oxidation reactions, as compared to Pd NPs supported on single-walled and multi-walled CNTs. Such a specific promotion effect of TWNTs on the electrocatalytic activity of Pd NPs is not related to the contribution of metal impurities in CNTs, oxygen-functional groups of CNTs or surface area of CNTs and Pd NPs. A facile charge transfer mechanism via electron tunneling between the outer wall and inner tubes of CNTs under electrochemical driving force is proposed for the significant promotion effect of TWNTs for the alcohol oxidation reactions in alkaline solutions