The taxonomical structure of microbial community sample is highly habitat-specific, making it possible for source tracking niches where samples are originated. Current methods face challenges when the number of samples and niches are magnitudes more than current in use, under which circumstances they are unable to accurately source track samples in a timely manner, rendering them difficult in knowledge discovery from sub-million heterogeneous samples. Here, we introduce a deep learning method based on Ontology-aware Neural Network approach, ONN4MST (https://github.com/HUST-NingKang-Lab/ONN4MST), which takes into consideration the ontology structure of niches and the relationship of samples from these ontologically-organized niches. ONN4MST's superiority in accuracy, speed and robustness have been proven, for example with an accuracy of 0.99 and AUC of 0.97 in a microbial source tracking experiment that 125,823 samples and 114 niches were involved. Moreover, ONN4MST has been utilized on several source tracking applications, showing that it could provide highly-interpretable results from samples with previously less-studied niches, detect microbial contaminants, and identify similar samples from ontologically-remote niches, with high fidelity.

The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method based on the Ontology-aware Neural Network approach, ONN4MST, for large-scale source tracking. ONN4MST outperformed other methods with near-optimal accuracy when source tracking among 125,823 samples from 114 niches. ONN4MST also has a broad spectrum of applications. Overall, this study represents the first model-based method for source tracking among sub-million microbial community samples from hundreds of niches, with superior speed, accuracy, and interpretability. ONN4MST is available at https://github.com/HUST-NingKang-Lab/ONN4MST.

Product tolerance is one of the key factors which can determine the good or bad performance of mechanical products. Its size not only affects the manufacturing and assembly process, but also affects product features [1]. Thus tolerance optimization design gets more and more attention. In this paper, an improved physical programming method is used to make mathematical modeling for tolerance allocation problem of assembly dimensional chain, and PSO algorithm is also used to improve solving ability. And the effective solution for tolerance optimization is designed.

No abstract

In the last decade, deep neural networks have proven to be very powerful in computer vision tasks, starting a revolution in the computer vision and machine learning fields. However, deep neural networks, usually, are not robust to perturbations of the input data. In fact, several studies showed that slightly changing the content of the images can cause a dramatic decrease in the accuracy of the attacked neural network. Several methods able to generate adversarial samples make use of gradients, which usually are not available to an attacker in real-world scenarios. As opposed to this class of attacks, another class of adversarial attacks, called black-box adversarial attacks, emerged, which does not make use of information on the gradients, being more suitable for real-world attack scenarios. In this work, we compare three well-known evolution strategies on the generation of black-box adversarial attacks for image classification tasks. While our results show that the attacked neural networks can be, in most cases, easily fooled by all the algorithms under comparison, they also show that some black-box optimization algorithms may be better in "harder" setups, both in terms of attack success rate and efficiency (i.e., number of queries). CCS CONCEPTS• Computing methodologies → Machine learning; Computer vision; Search methodologies.