AIREPAIR: A Repair Platform for Neural Networks

We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods on the same model, thus enabling the fair comparison of different repair techniques. We evaluate AIREPAIR with three state-of-the-art repair tools on popular deep-learning datasets and models. Our evaluation confirms the utility of AIREPAIR, by comparing and analyzing the results from different repair techniques. A demonstration is available at https://youtu.be/UkKw5neeWhw

QNNRepair: Quantized Neural Network Repair

We present QNNRepair, the first method in the literature for repairing quantized neural networks (QNNs). QNNRepair aims to improve the accuracy of a neural network model after quantization. It accepts the full-precision and weight-quantized neural networks and a repair dataset of passing and failing tests. At first, QNNRepair applies a software fault localization method to identify the neurons that cause performance degradation during neural network quantization. Then, it formulates the repair problem into a linear programming problem of solving neuron weights parameters, which corrects the QNN's performance on failing tests while not compromising its performance on passing tests. We evaluate QNNRepair with widely used neural network architectures such as MobileNetV2, ResNet, and VGGNet on popular datasets, including high-resolution images. We also compare QNNRepair with the state-of-the-art data-free quantization method SQuant. According to the experiment results, we conclude that QNNRepair is effective in improving the quantized model's performance in most cases. Its repaired models have 24% higher accuracy than SQuant's in the independent validation set, especially for the ImageNet dataset

An Improved Adaptive Simulated Annealing Particle Swarm Optimization Algorithm for ARAIM Availability

Civil aviation transportation equipment is more convenient and faster than other transportation tools and is an essential part of intelligent transportation. It is significant to study the reliability of positioning information and enhance traffic safety. Advanced receiver autonomous integrity monitoring (ARAIM) can provide vertical guidance during the different navigation stages in civil aviation fields. The traditional multiple hypothesis solution separation (MHSS) algorithm distributes the probability of hazardous misleading information (PHMI) and probability of false alarm (PFA) uniformly over all visible satellites resulting in reduced global availability of ARAIM. Aiming at this problem, we proposed an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm to redistribute integrity and continuity risks and establish a protection level optimization model. Based on the real BeiDou navigation satellite system/global positioning system (BDS/GPS) data, the experimental results show that the optimized algorithm can reduce the vertical protection level (VPL), and the ARAIM global availability of BDS/GPS is improved by 1.73%∼2.73%. The optimized algorithm can improve the availability of integrity monitoring at different stages of the navigation system and provide a basis for ensuring the reliability of the positioning results

Ligation-Dependent Cas14a1-Activated Biosensor for One-pot Pathogenic Diagnostic

Pathogenic identification requires nucleic acid diagnosis with simple equipment and fast manipulation. Our work established an all-in-one strategy assay with excellent sensitivity and high specificity, Transcription-Amplified Cas14a1-Activated Signal Biosensor (TACAS), for the fluorescence-based bacterial RNA detection. The DNA as a promoter probe and a reporter probe directly ligated via SplintR ligase once specifically hybridized to the single-stranded target RNA sequence, with the ligation product transcribed into Cas14a1 RNA activators by T7 RNA polymerase. This forming sustained isothermal one-pot ligation-transcription cascade produced RNA activators constantly and enabled Cas14a1/sgRNA complex to generate fluorescence signal, thus leading to a sensitive detection limit of 1 CFU/mL E.coli within 2-3 h of incubation time. TACAS was applied in contrived E.coli infected fish samples, and a significant signal differentiation between positive (infected) and negative (uninfected) samples was reached. Meanwhile, E.coli colonization and transmit time in vivo were explored and the TACAS assay promoted the understanding of the infection mechanisms of the E.coli infection, demonstrating an excellent detection capability