Déplacement d'un mannequin virtuel dans un environnement encombré : simulation de mouvement en intégrant les contraintes d'équilibre

This thesis was carried out in collaboration and co-funding of LSI of CEA/LIST and LBMC of IFSTTAR. The aim of the thesis was to study and develop a method for simulating the movement of a virtual manikin (VM) in a cluttered environment based on a priori knowledge. This thesis presents firstly motion capture (MoCap) experiments. The recorded data were analyzed to define some principles on human motion in cluttered environments. We then propose a general balance criterion and stability margin, based on a simplified model of VM. Then, we present a hierarchical framework that can generate and simulate dynamic movements of VM in a cluttered environment in three stages: a global trajectory of the center of mass (CoM) is generated at the global level to ensure balance in the VM's motion; then the trajectories of end-effectors (EE, ie feet, hands) and postures are generated locally under constraints of kinematics and collision avoidance; finally at the execution level, trajectories (CoM and EEs) and postures are used as references in a dynamic controller associated with VM so that the VM realizes the motion in a simulation. This framework is implemented in a car-ingress scenario in order to evaluate its performance and to suggest future improvementsCette thèse a été réalisée en collaboration et cofinancement impliquant le LSI du CEA/LIST et le LBMC de l'IFSTTAR. L'objectif de thèse était d'étudier et de développer une méthode pour simuler les mouvements d'un mannequin virtuel (MV) dans un environnement encombré en s'appuyant sur des connaissances a priori. L'étude présente, dans un premier temps, des expériences de capture de mouvement (MoCap). Les données enregistrées ont été analysées afin de définir quelques principes sur les mouvements humains dans des environnements encombrés. Nous proposons ensuite un critère général d'équilibre et une marge de stabilité, sur la base d'un modèle simplifié du MV. Puis, nous présentons un framework hiérarchique pouvant générer et simuler des mouvements dynamiques du MV dans un environnement encombré en trois étapes : une trajectoire globale du centre de masse (CoM) est générée au niveau global afin d'assurer l'équilibre du MV durant son mouvement; puis au niveau local, les trajectoires des organes terminaux (OT, i.e. pieds, mains) et les postures sont générées localement sous des contraintes cinématiques et d'évitement de collisions; enfin au niveau de l'exécution, les trajectoires (CoM et OTs) et les postures sont utilisées comme références dans un contrôleur dynamique associé au MV. Enfin, ce framework est mis en œuvre dans un scenario d'entrée dans un véhicule pour évaluer ses performances et proposer des améliorations future

Automatic Distractor Generation for Multiple Choice Questions in Standard Tests

To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains. In this paper, we propose a question and answer guided distractor generation (EDGE) framework to automate distractor generation. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility. Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art.Comment: accepted by COLING202

Qualifying Chinese Medical Licensing Examination with Knowledge Enhanced Generative Pre-training Model

Generative Pre-Training (GPT) models like ChatGPT have demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. Although ChatGPT has been integrated into the overall workflow to boost efficiency in many domains, the lack of flexibility in the finetuning process hinders its applications in areas that demand extensive domain expertise and semantic knowledge, such as healthcare. In this paper, we evaluate ChatGPT on the China National Medical Licensing Examination (CNMLE) and propose a novel approach to improve ChatGPT from two perspectives: integrating medical domain knowledge and enabling few-shot learning. By using a simple but effective retrieval method, medical background knowledge is extracted as semantic instructions to guide the inference of ChatGPT. Similarly, relevant medical questions are identified and fed as demonstrations to ChatGPT. Experimental results show that directly applying ChatGPT fails to qualify the CNMLE at a score of 51 (i.e., only 51\% of questions are answered correctly). While our knowledge-enhanced model achieves a high score of 70 on CNMLE-2022 which not only passes the qualification but also surpasses the average score of humans (61). This research demonstrates the potential of knowledge-enhanced ChatGPT to serve as versatile medical assistants, capable of analyzing real-world medical problems in a more accessible, user-friendly, and adaptable manner

Local path planning for mobile robots based on intermediate objectives

International audienceThis paper presents a path planning algorithm for autonomous navigation of non-holonomic mobile robots in complex environments. The irregular contour of obstacles is represented by segments. The goal of the robot is to move towards a known target while avoiding obstacles. The velocity constraints, robot kinematic model and non-holonomic constraint are considered in the problem. The optimal path planning problem is formulated as a constrained receding horizon planning problem and the trajectory is obtained by solving an optimal control problem with constraints. Local minima are avoided by choosing intermediate objectives based on the real time environment

DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically. Different from typical image captioning approaches that generate reports with an encoder and a decoder, DeltaNet applies a conditional generation process. In particular, given a medical image, DeltaNet employs three steps to generate a report: 1) first retrieving related medical reports, i.e., the historical reports from the same or similar patients; 2) then comparing retrieved images and current image to find the differences; 3) finally generating a new report to accommodate identified differences based on the conditional report. We evaluate DeltaNet on a COVID-19 dataset, where DeltaNet outperforms state-of-the-art approaches. Besides COVID-19, the proposed DeltaNet can be applied to other diseases as well. We validate its generalization capabilities on the public IU-Xray and MIMIC-CXR datasets for chest-related diseases. Code is available at \url{https://github.com/LX-doctorAI1/DeltaNet}

Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage

A Survey on Large Language Models for Recommendation

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec.Comment: 10 pages, 3 figure

Feasibility analysis of sentinel lymph node biopsy in breast cancer with axilla negative evaluation by physical examination but suspicious lymph nodes finding on preoperative imaging and metastasis confirmed with biopsy

Background and purpose: With the increasing sensitivity of axillary imaging and ultrasound guided biopsy, some clinically axillary negative patients were upstaged to axillary positive (cN1) and received axillary lymph node dissection (ALND). This study aimed to assess the feasibility of sentinel lymph node (SLN) biopsy (SLNB) in patients with axilla negative evaluation by physical examination but suspicious lymph nodes finding on preoperative imaging and metastasis confirmed with a fine-needle aspiration cytology/core-needle biopsy pathology (FNAC/CNBP). Methods: This retrospective cohort study included patients with early breast cancer who had axilla negative evaluation by physical examination but suspicious lymph nodes finding on preoperative imaging and metastasis confirmed with a FNAC/CNBP from October 2015 to December 2022 in the Breast Cancer Centre of Shandong Cancer Hospital and Institute. All patients received ALND. The data were analyzed by using SPSS version 27.0 statistical software, and P<0.05 was considered statistically significant. Results: A total of 158 patients were identified to have axilla negative evaluation by physical examination but one to two suspicious lymph nodes finding on preoperative imaging and metastasis confirmed with FNAC/CNBP, 43.7% (69/158) of them had only one ALN metastasis after ALND, 15.2% (24/158) had only two ALNs metastases after ALND, and 41.1% (65/158) had more than two ALNs metastases after ALND. Among these cases, 65 patients received SLNB followed by ALND, and the false negative rate of SLNB was 0%. Positive non-SLN metastasis rate was 0 in the 61.5% (40/65) patients with 1-2 positive SLNs metastasis. One, two, three and more than three non-SLN metastasis rates were 10.8% (7/65), 4.6% (3/65), 6.2% (4/65) and 16.9% (11/65), respectively (P=0.042). Conclusion: SLNB was safe and feasible in patients with axilla negative evaluation by physical examination but one to two suspicious lymph nodes finding on preoperative imaging and metastasis confirmed with a FNAC/CNBP. In these patients, the axillary lymph node tumor burden was high, and exhaustive radiotherapy and systematic treatment were needed to control reginal disease

Displacement of a virtual manikin in a cluttered environment (simulation of movment by integrating equilibrium constraints)

Cette thèse a été réalisée en collaboration et cofinancement impliquant le LSI du CEA/LIST et le LBMC de l'IFSTTAR. L'objectif de thèse était d étudier et de développer une méthode pour simuler les mouvements d un mannequin virtuel (MV) dans un environnement encombré en s appuyant sur des connaissances a priori. L étude présente, dans un premier temps, des expériences de capture de mouvement (MoCap). Les données enregistrées ont été analysées afin de définir quelques principes sur les mouvements humains dans des environnements encombrés. Nous proposons ensuite un critère général d'équilibre et une marge de stabilité, sur la base d un modèle simplifié du MV. Puis, nous présentons un framework hiérarchique pouvant générer et simuler des mouvements dynamiques du MV dans un environnement encombré en trois étapes : une trajectoire globale du centre de masse (CoM) est générée au niveau global afin d assurer l'équilibre du MV durant son mouvement; puis au niveau local, les trajectoires des organes terminaux (OT, i.e. pieds, mains) et les postures sont générées localement sous des contraintes cinématiques et d évitement de collisions; enfin au niveau de l'exécution, les trajectoires (CoM et OTs) et les postures sont utilisées comme références dans un contrôleur dynamique associé au MV. Enfin, ce framework est mis en œuvre dans un scenario d'entrée dans un véhicule pour évaluer ses performances et proposer des améliorations futuresPARIS-BIUSJ-Biologie recherche (751052107) / SudocSudocFranceF