Tightly Coupled 3D Lidar Inertial Odometry and Mapping

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.Comment: Accepted by ICRA 201

An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment

The new energy industry development is affected by many factors. Among them, the resources utilization ratio is a major reason for the low productivity of enterprises. As the core problem of cloud computing, the resource allocation problem has been widely concerned by the people, and the resource allocation problem of the new energy industry as the key to energy innovation and transformation should be more paid attention to. In multi-resource cloud computing scenarios, requests made by users often involve multiple types of resources. Traditional resource allocation algorithms have a single optimization object, typically time efficiency. In order to achieve cluster load balancing, utilization of system resources and improvement of system work efficiency, this paper proposes a new cloud computing allocation algorithm based on improved ant colony algorithm. According to the limit conditions of cloud computing environment and computing resources, this paper finds the shortest response time of all resource nodes and gets a set of best available nodes. This method can meet the quality requirements of cloud computing, and the task completion time of the improved algorithm is shorter, the number of algorithm iterations is less, and the load balancing effect is better. Through MATLAB simulation experiments, the effectiveness of the proposed method is verified

RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL

One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton (i.e., SQL keywords). Such coupled targets increase the difficulty of parsing the correct SQL queries especially when they involve many schema items and logic operators. This paper proposes a ranking-enhanced encoding and skeleton-aware decoding framework to decouple the schema linking and the skeleton parsing. Specifically, for a seq2seq encoder-decode model, its encoder is injected by the most relevant schema items instead of the whole unordered ones, which could alleviate the schema linking effort during SQL parsing, and its decoder first generates the skeleton and then the actual SQL query, which could implicitly constrain the SQL parsing. We evaluate our proposed framework on Spider and its three robustness variants: Spider-DK, Spider-Syn, and Spider-Realistic. The experimental results show that our framework delivers promising performance and robustness. Our code is available at https://github.com/RUCKBReasoning/RESDSQL.Comment: Accepted to AAAI 2023 main conference (oral

Fuzzing Deep Learning Compilers with HirGen

Deep Learning (DL) compilers are widely adopted to optimize advanced DL models for efficient deployment on diverse hardware. Their quality has profound effect on the quality of compiled DL models. A recent bug study shows that the optimization of high-level intermediate representation (IR) is the most error-prone compilation stage. Bugs in this stage are accountable for 44.92% of the whole collected ones. However, existing testing techniques do not consider high-level optimization related features (e.g. high-level IR), and are therefore weak in exposing bugs at this stage. To bridge this gap, we propose HirGen, an automated testing technique that aims to effectively expose coding mistakes in the optimization of high-level IR. The design of HirGen includes 1) three coverage criteria to generate diverse and valid computational graphs; 2) full use of high-level IRs language features to generate diverse IRs; 3) three test oracles inspired from both differential testing and metamorphic testing. HirGen has successfully detected 21 bugs that occur at TVM, with 17 bugs confirmed and 12 fixed. Further, we construct four baselines using the state-of-the-art DL compiler fuzzers that can cover the high-level optimization stage. Our experiment results show that HirGen can detect 10 crashes and inconsistencies that cannot be detected by the baselines in 48 hours. We further validate the usefulness of our proposed coverage criteria and test oracles in evaluation

An Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs

Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20–50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local communities and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The Macro F1-score and accuracy of the periodontitis prediction task in our method reach 0.894 and 0.896, respectively, on Suzhou data set, and 0.820 and 0.824, respectively on Zhongshan data set. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well