Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs

The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention. The current methods for automatically evaluating the attribution, which are often based on Large Language Models (LLMs), are still inadequate, particularly in recognizing subtle differences between attributions, and complex relationships between citations and statements. To compare these attribution evaluation methods and develop new ones, we introduce a set of fine-grained categories (i.e., supportive, insufficient, contradictory and irrelevant) for measuring the attribution, and develop a Complex Attributed Question Answering (CAQA) benchmark by leveraging knowledge graphs (KGs) for automatically generating attributions of different categories to question-answer pairs. Our analysis reveals that existing evaluators perform poorly under fine-grained attribution settings and exhibit weaknesses in complex citation-statement reasoning. Our CAQA benchmark, validated with human annotations, emerges as a promising tool for selecting and developing LLM attribution evaluators.Comment: 13 pages, 5 figure

Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.Comment: Accepted by AAAI-202

Real-time Local Feature with Global Visual Information Enhancement

Local feature provides compact and invariant image representation for various visual tasks. Current deep learning-based local feature algorithms always utilize convolution neural network (CNN) architecture with limited receptive field. Besides, even with high-performance GPU devices, the computational efficiency of local features cannot be satisfactory. In this paper, we tackle such problems by proposing a CNN-based local feature algorithm. The proposed method introduces a global enhancement module to fuse global visual clues in a light-weight network, and then optimizes the network by novel deep reinforcement learning scheme from the perspective of local feature matching task. Experiments on the public benchmarks demonstrate that the proposal can achieve considerable robustness against visual interference and meanwhile run in real time.Comment: 6 pages, 5 figures, 2 tables. Accepted by ICIEA 202

Study on the biomechanical properties of 3D printed blended esophageal stents with different structural parameters based on patient CT

Introduction: Esophageal stenting is a widely used treatment for esophageal diseases, which can also be used for adjuvant therapy and feeding after chemotherapy for esophageal cancer. The structural parameters of the stent have a significant impact on its mechanical properties and patient comfort.Methods: In the present work, we reconstructed the esophagus model based on the patient’s computed tomography (CT) data, and designed stents with different structural parameters. We used 3D printing technology to achieve rapid production of the designed stents by using Thermoplastic polyurethane (TPU)/Poly-ε-caprolactone (PCL) blends as the materials. The mechanical properties and effects on the esophagus of polymer stents with four different structural parameters of diameter, wall thickness, length and flaring were investigated by in vitro tests of radial compression and migration of the stents, as well as by finite element simulations of the stent implantation process in the esophagus and of the stent migration process. An artificial neural network model was established to predict the radial force of the stent and the maximum equivalent stress of the esophagus during implantation based on these four structural parameters.Results: The results show that wall thickness was the structural parameter that had the greatest impact on the radial force of the stent (statistically significant, p < 0.01), and flaring was the structural parameter that had the greatest impact on the maximum equivalent stress of the esophageal wall after stent implantation (statistically significant, p < 0.01). No. 6 stent had a maximum radial force of 18.07 N, which exceeded that of commercial esophageal stents and had good mechanical properties. And the maximum equivalent force on the esophagus caused by its implantation was only 30.39 kPa, which can improve patient comfort. The predicted values of the constructed back propagation (BP) neural network model had an error of less than 10% from the true values, and the overall prediction accuracies were both above 97%, which can provide guidance for optimizing the design of the stent and for clinical research.Discussion: 3D printing technology presents a wide range of applications for the rapid fabrication of personalized TPU/PCL blend stents that are more suitable for individual patients

MLDT: Multi-Level Decomposition for Complex Long-Horizon Robotic Task Planning with Open-Source Large Language Model

In the realm of data-driven AI technology, the application of open-source large language models (LLMs) in robotic task planning represents a significant milestone. Recent robotic task planning methods based on open-source LLMs typically leverage vast task planning datasets to enhance models' planning abilities. While these methods show promise, they struggle with complex long-horizon tasks, which require comprehending more context and generating longer action sequences. This paper addresses this limitation by proposing MLDT, theMulti-Level Decomposition Task planning method. This method innovatively decomposes tasks at the goal-level, task-level, and action-level to mitigate the challenge of complex long-horizon tasks. In order to enhance open-source LLMs' planning abilities, we introduce a goal-sensitive corpus generation method to create high-quality training data and conduct instruction tuning on the generated corpus. Since the complexity of the existing datasets is not high enough, we construct a more challenging dataset, LongTasks, to specifically evaluate planning ability on complex long-horizon tasks. We evaluate our method using various LLMs on four datasets in VirtualHome. Our results demonstrate a significant performance enhancement in robotic task planning, showcasing MLDT's effectiveness in overcoming the limitations of existing methods based on open-source LLMs as well as its practicality in complex, real-world scenarios

Geochemical characteristics of dissolved heavy metals in Zhujiang River, Southwest China: spatial-temporal distribution, source, export flux estimation, and a water quality assessment

To investigate the sources and spatial-temporal distribution of dissolved heavy metals in river water, and to evaluate the water quality, a total of 162 water samples were collected from 81 key sampling points in high and low flow seasons separately in the Zhujiang River, Southwest China. Ten dissolved heavy metals (V, Cr, Mn, Co, Ni, Cu, Mo, Cd, Ba, and Pb) in the Zhujiang River water exhibit little variation at temporal scale, but vary with a significant spatial heterogeneity. Furthermore, different metals present different variation trends along the main channel of the Zhujiang River. Our results suggest that Ba (14.72 μg L−1 in low flow season and 12.50 μg L−1 in high flow season) and Cr (6.85 μg L−1 in low flow season and 7.52 μg L−1 in high flow season) are consistently the most abundant metals in the two sampling periods. According to the water quality index (WQI values ranged from 1.3 to 43.9) and health risk assessment, metals investigated in Zhujiang River are below the hazard level (all hazard index (HI) < 1). Application of statistical approaches, including correlation matrix and principal component analysis (PCA), identify three principal components that account for 61.74% of the total variance, the results conclude that the anthropogenic heavy metals (V, Cr, Ni, and Cu) are greatly impacted by the dilution effect, and the heavy metals in Zhujiang River are mainly presented a natural sources signature from the perspective of entire basin. Moreover, our results reveal that the estimated export budget of several heavy metals including V (735.6 t year−1), Cr (1,561.1 t year−1), Ni (498.2 t year−1), and Mo (118.9 t year−1) to the ocean are higher than the world average

Direct observation of nucleation in the bulk of an opaque sample

Remarkably little is known about the physical phenomena leading to nucleation of new perfect crystals within deformed metals during annealing, in particular how and where volumes with nearly perfect lattices evolve from structures filled with dislocations, and how local variations at the micrometer length scale affect this nucleation process. We present here the first experimental measurements that relate directly nucleation of recrystallization to the local deformation microstructure in the bulk of a sample of cold rolled aluminum, further deformed locally by a hardness indentation. White beam differential aperture X-ray microscopy is used for the measurements, allowing us to map a selected gauge volume in the bulk of the sample in the deformed state, then anneal the sample and map the exact same gauge volume in the annealed state. It is found that nuclei develop at sites of high stored energy and they have crystallographic orientations from those present in the deformed state. Accordingly we suggest that for each nucleus the embryonic volume arises from a structural element contained within the voxels identified with the same orientation. Possible nucleation mechanisms are discussed and the growth potentials of the nuclei are also analyzed and discussed

Boron neutron capture therapy induces cell cycle arrest and cell apoptosis of glioma stem/progenitor cells in vitro

BACKGROUND: Glioma stem cells in the quiescent state are resistant to clinical radiation therapy. An almost inevitable glioma recurrence is due to the persistence of these cells. The high linear energy transfer associated with boron neutron capture therapy (BNCT) could kill quiescent and proliferative cells. METHODS: The present study aimed to evaluate the effects of BNCT on glioma stem/progenitor cells in vitro. The damage induced by BNCT was assessed using cell cycle progression, apoptotic cell ratio and apoptosis-associated proteins expression. RESULTS: The surviving fraction and cell viability of glioma stem/progenitor cells were decreased compared with differentiated glioma cells using the same boronophenylalanine pretreatment and the same dose of neutron flux. BNCT induced cell cycle arrest in the G2/M phase and cell apoptosis via the mitochondrial pathway, with changes in the expression of associated proteins. CONCLUSIONS: Glioma stem/progenitor cells, which are resistant to current clinical radiotherapy, could be effectively killed by BNCT in vitro via cell cycle arrest and apoptosis using a prolonged neutron irradiation, although radiosensitivity of glioma stem/progenitor cells was decreased compared with differentiated glioma cells when using the same dose of thermal neutron exposure and boronophenylalanine pretreatment. Thus, BNCT could offer an appreciable therapeutic advantage to prevent tumor recurrence, and may become a promising treatment in recurrent glioma