An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification.

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-scene classification, samples between source and target scenes are not drawn from the independent and identical distribution, resulting in significant performance degradation. To tackle this issue, a novel unsupervised domain adaptation (UDA) framework toward multilevel features and decision boundaries (ToMF-B) is proposed for the cross-scene HSIC, which can align task-related features and learn task-specific decision boundaries in parallel. Based on the maximum classifier discrepancy, a two-stage alignment scheme is proposed to bridge the interdomain gap and generate discriminative decision boundaries. In addition, to fully learn task-related and domain-confusing features, a convolutional neural network (CNN) and Transformer-based multilevel features extractor (generator) is developed to enrich the feature representation of two domains. Furthermore, to alleviate the harm even the negative transfer to UDA caused by task-irrelevant features, a task-oriented feature decomposition method is leveraged to enhance the task-related features while suppressing task-irrelevant features, and enabling the aligned domain-invariant features can be contributed to the classification task explicitly. Extensive experiments on three cross-scene HSI benchmarks have validated the effectiveness of the proposed framework

Identification of important charged residues for alkali cation exchange or pH regulation of NhaH, a Na+/H+ antiporter of Halobacillus dabanensis

AbstractNhaH is a novel Na+/H+ antiporter identified from the moderate halophile Halobacillus dabanensis. In this study, six conserved charged residues located in the putative transmembrane segments (TMS) including TMSV, TMSVI, TMSVIII and TMSXI of NhaH as well as two His residues in Loop III were replaced by site-directed mutagenesis for the identification of their potential roles in the antiport activity and pH regulation. Substitutions D137A, D166A and R325A caused a complete loss of Na+(Li+)/H+ antiport activity, revealing that D137, D166 and R325 are indispensable for the antiport activity. Substitution D137E led to a significant increase of the apparent Km values for Na+ and Li+ without affecting the changes of pH profile, confirming that D137 plays vital roles in alkali cation binding/translocation. Substitution D166E resulted in not only a significant increase of the apparent Km values for Na+ and Li+ but also an alkaline shift of pH profile, suggesting that D166 is involved in alkali cation binding/translocation as well as H+ binding or pH regulation. Substitutions E161N, D224A and D224E caused a significant increase of Km for Na+ and Li+, indicating that E161 and D224 partly contribute to alkali cation binding/translocation. Substitution E229K caused an over 50% elevation of the apparent Km for Li+, without affecting that for Na+, suggesting that E229 may be mainly responsible for Li+ binding/translocation. Substitutions H87A and H88A resulted in an acidic shift of pH profile without an effect on Km for Na+ and Li+, indicating that H87 and H88 are involved in H+ binding or pH regulation

NaoXinTong Inhibits the Development of Diabetic Retinopathy in d

Buchang NaoXinTong capsule (NXT) is a Chinese Materia Medica standardized product extracted from 16 Chinese traditional medical herbs and widely used for treatment of patients with cerebrovascular and cardiovascular diseases in China. Formation of microaneurysms plays an important role in the development of diabetic retinopathy. In this study, we investigated if  NXT can protect diabetic mice against the development of diabetic retinopathy. The db/db mice (~6 weeks old), a diabetic animal model, were divided into two groups and fed normal chow or plus NXT for 14 weeks. During the treatment, fasting blood glucose levels were monthly determined. After treatment, retinas were collected to determine retinal thickness, accumulation of carbohydrate macromolecules, and caspase-3 (CAS-3) expression. Our results demonstrate that administration of NXT decreased fasting blood glucose levels. Associated with the decreased glucose levels, NXT blocked the diabetes-induced shrink of multiple layers, such as photoreceptor layer and outer nuclear/plexiform layers, in the retina. NXT also inhibited the diabetes-induced expression of CAS-3 protein and mRNA, MMP-2/9 and TNFα mRNA, accumulation of carbohydrate macromolecules, and formation of acellular capillaries in the retina. Taken together, our study shows that NXT can inhibit the development of diabetic retinopathy and suggests a new potential application of NXT in clinic

CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model

Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding non-English inputs for multi-lingual code-related tasks is still far from well studied. This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset that is carefully filtered by program analyzers and optimized during the training process. Extensive experiments are conducted using real-world usage scenarios, the industry-standard benchmark HumanEval-x, and the specially designed CodeFuseEval for Chinese prompts. To assess the effectiveness of CodeFuse, we actively collected valuable human feedback from the AntGroup's software development process where CodeFuse has been successfully deployed. The results demonstrate that CodeFuse-13B achieves a HumanEval pass@1 score of 37.10%, positioning it as one of the top multi-lingual code LLMs with similar parameter sizes. In practical scenarios, such as code generation, code translation, code comments, and testcase generation, CodeFuse performs better than other models when confronted with Chinese prompts.Comment: 10 pages with 2 pages for reference

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe

Can Early Stage Vision Detect Topology

The apparent motion reveals what in an image that human vision detects first. Chen's assumption that early stage vision can percept global topology is proved incorrect in theory and experiments. Based upon psychological studies on human vision, a new theory, the blurred matching, was introduced into visual computation which well fits the results of all experiments about apparent motion, Chen's included. An algorithm was constructed accordingly which predicts apparent motion by comparison of distances.

Controllability and Optimization of Complex Networks Based on Bridges

In a complex network, each edge has different functions on controllability of the whole network. A network may be out of control due to failure or attack of some specific edges. Bridges are a kind of key edges whose removal will disconnect a network and increase connected components. Here, we investigate the effects of removing bridges on controllability of network. Various strategies, including random deletion of edges, deletion based on betweenness centrality, and deletion based on degree of source or target nodes, are used to compare with the effect of removing bridges. It is found that the removing bridges strategy is more efficient on reducing controllability than the other strategies of removing edges for ER networks and scale-free networks. In addition, we also found the controllability robustness under edge attack is related to the average degree of complex networks. Therefore, we propose two optimization strategies based on bridges to improve the controllability robustness of complex networks against attacks. The effectiveness of the proposed strategies is demonstrated by simulation results of some model networks. These results are helpful for people to understand and control spreading processes of epidemic across different paths