Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects

With the widespread deployment of deep neural networks (DNNs), ensuring the reliability of DNN-based systems is of great importance. Serious reliability issues such as system failures can be caused by numerical defects, one of the most frequent defects in DNNs. To assure high reliability against numerical defects, in this paper, we propose the RANUM approach including novel techniques for three reliability assurance tasks: detection of potential numerical defects, confirmation of potential-defect feasibility, and suggestion of defect fixes. To the best of our knowledge, RANUM is the first approach that confirms potential-defect feasibility with failure-exhibiting tests and suggests fixes automatically. Extensive experiments on the benchmarks of 63 real-world DNN architectures show that RANUM outperforms state-of-the-art approaches across the three reliability assurance tasks. In addition, when the RANUM-generated fixes are compared with developers' fixes on open-source projects, in 37 out of 40 cases, RANUM-generated fixes are equivalent to or even better than human fixes.Comment: To appear at 45th International Conference on Software Engineering (ICSE 2023), camera-ready versio

SSLRec: A Self-Supervised Learning Framework for Recommendation

Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despite the growing number of SSL algorithms designed to provide state-of-the-art performance in various recommendation scenarios (e.g., graph collaborative filtering, sequential recommendation, social recommendation, KG-enhanced recommendation), there is still a lack of unified frameworks that integrate recommendation algorithms across different domains. Such a framework could serve as the cornerstone for self-supervised recommendation algorithms, unifying the validation of existing methods and driving the design of new ones. To address this gap, we introduce SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders. The SSLRec framework features a modular architecture that allows users to easily evaluate state-of-the-art models and a complete set of data augmentation and self-supervised toolkits to help create SSL recommendation models with specific needs. Furthermore, SSLRec simplifies the process of training and evaluating different recommendation models with consistent and fair settings. Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field. Our implemented SSLRec framework is available at the source code repository https://github.com/HKUDS/SSLRec.Comment: Published as a WSDM'24 full paper (oral presentation

Analysis of air quality changes and causes in the Liaoning region from 2017 to 2022

Using the air quality monitoring data from the Department of Ecology and Environment of Liaoning Province for the period from 2017 to 2022, the temporal and spatial changes in the concentrations of various air pollutants in the Liaoning region for the periods from 2017 to 2019 and 2020 to 2022 were analyzed by using the Evaluation on the meteorological condition index of PM2.5 pollution (EMI) and the ArcGIS Kriging Interpolation Method, and the contributions of pollution reduction to the changes in the air quality of the Liaoning region were assessed. The results show that after the implementation of emission reduction measures, the quality of the atmospheric environment in the Liaoning region has significantly improved, and the mean concentrations of PM2.5, PM10, SO2, NO2, CO and O3 are all reduced by a certain magnitude in the period 2020 to 2022 compared with the period 2017 to 2019; Based on the EMI index calculation, the average EMI index in Liaoning during the period 2020 to 2022 is about 1.7% lower than the average value of the region during the period 2017 to 2019, and the atmospheric dispersion conditions are relatively good; From the perspective of daily changes in pollutant concentrations, the trend of PM2.5 and PM10 concentrations changed from double peaks and single valleys to single peaks and single valleys, and there were no significant changes in the types of valleys for CO, SO2, NO2, and O3, whereas the peaks of O3 concentrations during the daytime were basically the same as in previous years, and the concentrations during the nighttime were slightly higher than in previous years. Classification by topographic areas revealed that the mean pollutant concentration for the period from 2017 to 2019 was more significant than the mean value for the period from 2020 to 2022, except for O3, where the air quality in the mountainous areas of Liaodong and Liaoxi was better than that of the Liaohe Plain, and regional classification by coastal and inland, where the air quality in the coastal areas was better than that of the inland areas

A tree species classification model based on improved YOLOv7 for shelterbelts

Tree species classification within shelterbelts is crucial for shelterbelt management. The large-scale satellite-based and low-altitude drone-based approaches serve as powerful tools for forest monitoring, especially in tree species classification. However, these methods face challenges in distinguishing individual tree species within complex backgrounds. Additionally, the mixed growth of trees within protective forest suffers from similar crown size among different tree species. The complex background of the shelterbelts negatively impacts the accuracy of tree species classification. The You Only Look Once (YOLO) algorithm is widely used in the field of agriculture and forestry, ie., plant and fruit identification, pest and disease detection, and tree species classification in forestry. We proposed a YOLOv7-Kmeans++_CoordConv_CBAM (YOLOv7-KCC) model for tree species classification based on drone RGB remote sensing images. Firstly, we constructed a dataset for tree species in shelterbelts and adopted data augmentation methods to mitigate overfitting due to limited training data. Secondly, the K-means++ algorithm was employed to cluster anchor boxes in the dataset. Furthermore, to enhance the YOLOv7 backbone network’s Efficient Layer Aggregation Network (ELAN) module, we used Coordinate Convolution (CoordConv) replaced the ordinary 1×1 convolution. The Convolutional Block Attention Module (CBAM) was integrated into the Path Aggregation Network (PANet) structure to facilitate multiscale feature extraction and fusion, allowing the network to better capture and utilize crucial feature information. Experimental results showed that the YOLOv7-KCC model achieves a mean average [email protected] of 98.91%, outperforming the Faster RCNN-VGG16, Faster RCNN-Resnet50, SSD, YOLOv4, and YOLOv7 models by 5.71%, 11.75%, 5.97%, 7.86%, and 3.69%, respectively. The GFlops and Parameter values of the YOLOv7-KCC model stand at 105.07G and 143.7MB, representing an almost 5.6% increase in F1 metrics compared to YOLOv7. Therefore, the proposed YOLOv7-KCC model can effectively classify shelterbelt tree species, providing a scientific theoretical basis for shelterbelt management in Northwest China focusing on Xinjiang

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation

Knowledge-intensive tasks (e.g., open-domain question answering (QA)) require a substantial amount of factual knowledge and often rely on external information for assistance. Recently, large language models (LLMs) (e.g., ChatGPT), have demonstrated impressive prowess in solving a wide range of tasks with world knowledge, including knowledge-intensive tasks. However, it remains unclear how well LLMs are able to perceive their factual knowledge boundaries, particularly how they behave when incorporating retrieval augmentation. In this study, we present an initial analysis of the factual knowledge boundaries of LLMs and how retrieval augmentation affects LLMs on open-domain QA. Specially, we focus on three primary research questions and analyze them by examining QA performance, priori judgement and posteriori judgement of LLMs. We show evidence that LLMs possess unwavering confidence in their capabilities to respond to questions and the accuracy of their responses. Furthermore, retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries, thereby improving their judgemental abilities. Additionally, we also find that LLMs have a propensity to rely on the provided retrieval results when formulating answers, while the quality of these results significantly impacts their reliance. The code to reproduce this work is available at https://github.com/RUCAIBox/LLM-Knowledge-Boundary

LbKAT3 may assist in mycorrhizal potassium uptake, and overexpression of LbKAT3 may promote potassium, phosphorus, and water transport from arbuscular mycorrhizal fungi to the host plant

Potassium plays important roles in most plant physiological processes. Arbuscular mycorrhizal (AM) fungi promote plant water and mineral nutrient acquisition to promote plant growth. However, few studies have focused on the effect of AM colonization on potassium uptake by the host plant. In this study, the effects of an AM fungus (Rhizophagus irregularis) and potassium concentration (0, 3, or 10 mM K+) on Lycium barbarum were evaluated. A split-root test with L. barbarum seedlings was conducted, and the potassium uptake capacity of LbKAT3 was verified in yeast. A tobacco line overexpressing LbKAT3 was generated and mycorrhizal functions under two potassium concentrations (0.2 and 2 mM K+) were studied. Inoculation of R. irregularis and application of potassium increased the dry weight, and potassium and phosphorus contents of L. barbarum, and increased the colonization rate and arbuscule abundance of R. irregularis. In addition, the expression of LbKAT3 and AQP genes in L. barbarum was upregulated. Inoculation of R. irregularis induced LbPT4, Rir-AQP1, and Rir-AQP2 expression, and application of potassium upregulated the expression of these genes. Inoculation with the AM fungus locally regulated the expression of LbKAT3. Inoculation of R. irregularis improved the growth, and potassium and phosphorus contents, and induced NtPT4, Rir-AQP1, and Rir-AQP2 expression in tobacco overexpressing LbKAT3 under both potassium concentrations. Overexpression of LbKAT3 in tobacco improved the growth, potassium accumulation, and AM colonization, and upregulated the expression of NtPT4 and Rir-AQP1 in mycorrhizal tobacco. The results suggest that LbKAT3 may assist in mycorrhizal potassium uptake, and overexpression of LbKAT3 may promote potassium, phosphorus, and water transport from the AM fungus to tobacco