Effect of soil particle-size distribution (PSD) on soil-subsoiler interactions in the discrete element model

Aim of study: This work investigated the significance and mechanism for the effect of particle-size distribution (PSD) under different nominal radii using the discrete element method (DEM) and validated using the laboratory soil-bin results to accurately determine PSD.Area of study: Yangling, ChinaMaterial and methods: The experimental soil was Lou soil. Soil disturbance characteristics (soil rupture distance ratio, height of accumulated soil, soil density change rate) and cutting forces (draft and vertical) under different treatments were predicted and measured respectively.Main results: The ANOVA outputs showed that PSD significantly affected draft and vertical forces (

Changes in clinical and CT manifestations related to liver abscesses in patients with vs. without basic diabetes mellitus before and after CT-guided interventional therapy: An observational study

Purpose: To explore differences in the changes of clinical and CT manifestations related to liver abscess before and after CT-guided interventional therapy between patients with and without Diabetes Mellitus (DM). Materials and methods: Fifty-eight consecutive patients with liver abscesses were retrospectively enrolled in this study. All patients underwent upper abdominal contrast-enhanced CT scans before and after CT-guided interventional therapy. They were divided into two groups including the DM group (n=30) and the Non-DM group (n=28) if the liver abscess occurred in patients with and without DM, respectively. The changes in the clinical and CT manifestations related to liver abscess after CT-guided interventional therapy in both groups were statistically analyzed. Results: After CT-guided interventional therapy, the length of hospital stay, white blood cell recovery time and drainage tube removal time in the DM group were longer than in the Non-DM group (all p-values < 0.05). The incidence of postoperative complications in the DM group was higher than in the Non-DM group (p < 0.05). As shown on CT, the postoperative reduced percentage of maximum diameter of abscess cavity and the reduction rate of edema band surrounding the liver abscess in the DM group were smaller than in the Non-DM group (both p-values < 0.05). The time intervals of the previous characteristic changes on CT before and after interventional therapy in the DM group were longer than in the Non-DM group (all p-values < 0.05). Conclusions: The liver abscesses patients with DM could not have a faster recovery and better therapeutic effect than those without DM after the CT-guided interventional therapy

GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds

Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset learning in VoteNet prevents high-quality 3D proposals from being generated. Moreover, seed points with different importance are treated equally in the voting process, aggravating this defect. To address these issues, we propose a novel global-local transformer voting scheme to provide more informative cues and guide the model pay more attention on potential seed points, promoting the generation of high-quality 3D proposals. Technically, a global-local transformer (GLT) module is employed to integrate object- and patch-aware prior into seed point features to effectively form strong feature representation for geometric positions of the seed points, thus providing more robust and accurate cues for offset learning. Subsequently, a simple yet effective training strategy is designed to train the GLT module. We develop an importance prediction branch to learn the potential importance of the seed points and treat the output weights vector as a training constraint term. By incorporating the above components together, we exhibit a superior tracking method GLT-T. Extensive experiments on challenging KITTI and NuScenes benchmarks demonstrate that GLT-T achieves state-of-the-art performance in the 3D single object tracking task. Besides, further ablation studies show the advantages of the proposed global-local transformer voting scheme over the original VoteNet. Code and models will be available at https://github.com/haooozi/GLT-T.Comment: Accepted to AAAI 2023. The source code and models will be available at https://github.com/haooozi/GLT-

Using Natural Language Explanations to Improve Robustness of In-context Learning for Natural Language Inference

Recent studies have demonstrated that large language models (LLMs) excel in diverse tasks through in-context learning (ICL) facilitated by task-specific prompts and examples. However, the existing literature shows that ICL encounters performance deterioration when exposed to adversarial inputs. Enhanced performance has been observed when ICL is augmented with natural language explanations (NLEs) (we refer to it as X-ICL). Thus, this work investigates whether X-ICL can improve the robustness of LLMs on a suite of seven adversarial and challenging natural language inference datasets. Moreover, we introduce a new approach to X-ICL by prompting an LLM (ChatGPT in our case) with few human-generated NLEs to produce further NLEs (we call it ChatGPT few-shot), which we show superior to both ChatGPT zero-shot and human-generated NLEs alone. We evaluate five popular LLMs (GPT3.5-turbo, LLaMa2, Vicuna, Zephyr, Mistral) and show that X-ICL with ChatGPT few-shot yields over 6% improvement over ICL. Furthermore, while prompt selection strategies were previously shown to significantly improve ICL on in-distribution test sets, we show that these strategies do not match the efficacy of the X-ICL paradigm in robustness-oriented evaluations.Comment: pre-prin

Distilling ChatGPT for Explainable Automated Student Answer Assessment

Providing explainable and faithful feedback is crucial for automated student answer assessment. In this paper, we introduce a novel framework that explores using ChatGPT, a cutting-edge large language model, for the concurrent tasks of student answer scoring and rationale generation. We identify the appropriate instructions by prompting ChatGPT with different templates to collect the rationales, where inconsistent rationales are refined to align with marking standards. The refined ChatGPT outputs enable us to fine-tune a smaller language model that simultaneously assesses student answers and provides rationales. Extensive experiments on the benchmark dataset show that the proposed method improves the overall QWK score by 11% compared to ChatGPT. Furthermore, our thorough analysis and human evaluation demonstrate that the rationales generated by our proposed method are comparable to those of ChatGPT. Our approach provides a viable solution to achieve explainable automated assessment in education. Code available at https://github.com/lijiazheng99/aera.Comment: Accepted EMNLP 202

Heat Transfer Analysis of MgB₂ Coil in Heat Treatment Process for Future Fusion Reactor

State of the art MgB2 is reviewed as a potential material for the poloidal field (PF) coils of the future fusion reactor due to its high critical temperature and low material cost. The heat treatment process is a crucial step in the development of MgB2 magnets. The temperature lag in heat treatment of large magnets can lead to insufficient thermal reaction time. It may be infeasible to control the temperature of a magnet according to the heat treatment scheme recommended for the MgB2 wire. Hence, the heat treatment process of a large magnet needs to be evaluated. Therefore, the dynamic temperature distribution of a MgB2 PF coil is obtained by simulating the heat transfer in heat treatment process. A suitable heat treatment schedule for a large magnet is proposed and the experimental results of a sub-size Cable-In-Conduit Conductor manufactured with MgB2 strand confirmed the feasibility of the newly proposed heat treatment process. The results provide a reference for the heat treatment method of a future larger MgB2 coil.</p