Study on Mechanical Properties Subjected to Monotonic and Dynamic Loads of Loessal Soil in Songyuan City

In this paper taking loessal soil of Songyuan in Jilin province as the research object，static and dynamic triaxial tests were conducted to investigate the effects of water content and confining pressure on dynamic characteristics of loessal soil. The test results showed that: (1) Under monotonic loading, the deformation of loessal soil exhibits two situations: strain hardening and strain softening, and the deformation behavior is influenced by water content and confining pressure. The static strength decreases with the increase of water content and increases with the increase of confining pressure. (2) The cumulative plastic strain of loessal soil under dynamic load exhibits three modes: plastic shakedown, plastic creep, and incremental collapse. Existence of critical dynamic stress results in significant differences in the cumulative plastic strain of soil. (3) The cumulative plastic strain of soil samples is influenced by water content, confining pressure, and dynamic stress amplitude, low confining pressure, high water content, and high dynamic stress amplitude are adverse to the plastic stability of loessal soil. Based on experimental results, predict the range of critical dynamic stress. The research results have reference value for the evaluation of dynamic deformation stability of settlement of subgrade constructed in the loessal soil

De l’université au monde du travail : l’insertion professionnelle des jeunes diplômés chinois du Français Langue Étrangère

RÉSUMÉ.  La politique chinoise en matière d’enseignement supérieur des langues étrangères a évolué rapidement au cours des vingt dernières années pour répondre au développement du pays et aux besoins sociétaux. Dans ce contexte, l’enseignement du Français Langue Étrangère (FLE) dans le milieu universitaire répond-il aux besoins de l’insertion professionnelle des jeunes diplômés chinois ? Une étude empirique sur 292 sujets, provenant de 148 universités, travaillant dans les quatre métropoles chinoises (Pékin, Shanghai, Guangzhou et Shenzhen), présente les difficultés rencontrées, les types de compétences demandées dans l’insertion professionnelle et des avis sur l’adéquation entre la formation suivie et l’exigence du marché de l’emploi. Cette étude apporte sa contribution à l’amélioration des pratiques pédagogiques de l’enseignant du FLE et la professionnalisation de la formation en milieu universitaire. Mots-clés : FLE, insertion professionnelle, jeunes diplômés, université chinoise  ABSTRACT.  China’s foreign language higher education policy has evolved rapidly over the past two decades to accommodate the country’s development and societal demand. The aim of this paper is to discover whether the teaching of French as a foreign language (FLE) in the Chinese universities meet the needs of the professional integration of young Chinese graduates. An empirical study on 292 subjects from 148 universities in four Chinese metropolitan cities namely Beijing, Shanghai, Guangzhou and Shenzhen were conducted and the difficulties encountered by the students have been revealed. The investigation found that the French major undergraduates confronted three main difficulties in the job market: the type the skills acquired for occupational integration, the selection of the required educational training provided by the universities and the demand of the labor market. This study contributes to the improvement of the FLE teachers’ pedagogy and the professionalization of training in the university. Keywords: Chinese university, FLE, professional integration, young graduates

Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach

Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence. Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains. However, cognitive research on the overall knowledge structure of LLMs is still lacking. In this paper, based on educational diagnostic assessment method, we conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom Taxonomy. We aim to reveal the knowledge structures of LLMs and gain insights of their cognitive capabilities. This research emphasizes the significance of investigating LLMs' knowledge and understanding the disparate cognitive patterns of LLMs. By shedding light on models' knowledge, researchers can advance development and utilization of LLMs in a more informed and effective manner.Comment: Findings of EMNLP 2023 (Short Paper

Advancing Urban Renewal: An Automated Approach to Generating Historical Arcade Facades with Stable Diffusion Models

Urban renewal and transformation processes necessitate the preservation of the historical urban fabric, particularly in districts known for their architectural and historical significance. These regions, with their diverse architectural styles, have traditionally required extensive preliminary research, often leading to subjective results. However, the advent of machine learning models has opened up new avenues for generating building facade images. Despite this, creating high-quality images for historical district renovations remains challenging, due to the complexity and diversity inherent in such districts. In response to these challenges, our study introduces a new methodology for automatically generating images of historical arcade facades, utilizing Stable Diffusion models conditioned on textual descriptions. By classifying and tagging a variety of arcade styles, we have constructed several realistic arcade facade image datasets. We trained multiple low-rank adaptation (LoRA) models to control the stylistic aspects of the generated images, supplemented by ControlNet models for improved precision and authenticity. Our approach has demonstrated high levels of precision, authenticity, and diversity in the generated images, showing promising potential for real-world urban renewal projects. This new methodology offers a more efficient and accurate alternative to conventional design processes in urban renewal, bypassing issues of unconvincing image details, lack of precision, and limited stylistic variety. Future research could focus on integrating this two-dimensional image generation with three-dimensional modeling techniques, providing a more comprehensive solution for renovating architectural facades in historical districts.Comment: HABITS OF THE ANTHROPOCENE - Proceedings of the 43rd ACADIA Conference - Volume II: Proceedings book one, University of Colorado Denver, Denver, Colorado, USA, 26-28 October 2023, pp. 616-625, CUMINCAD, 202

Efficient In-Context Learning in Vision-Language Models for Egocentric Videos

Recent advancements in text-only large language models (LLMs) have highlighted the benefit of in-context learning for adapting to new tasks with a few demonstrations. However, extending in-context learning to large vision-language models (VLMs) using a huge amount of naturalistic vision-language data has shown limited success, particularly for egocentric videos, due to high data collection costs. We propose a novel training method $\mathbb{E}$fficient $\mathbb{I}$n-context $\mathbb{L}$earning on $\mathbb{E}$gocentric $\mathbb{V}$ideos ($\mathbb{EILEV}$), which elicits in-context learning in VLMs for egocentric videos without requiring massive, naturalistic egocentric video datasets. $\mathbb{EILEV}$ involves architectural and training data adaptations to allow the model to process contexts interleaved with video clips and narrations, sampling of in-context examples with clusters of similar verbs and nouns, use of data with skewed marginal distributions with a long tail of infrequent verbs and nouns, as well as homonyms and synonyms. Our evaluations show that $\mathbb{EILEV}$-trained models outperform larger VLMs trained on a huge amount of naturalistic data in in-context learning. Furthermore, they can generalize to not only out-of-distribution, but also novel, rare egocentric videos and texts via in-context learning, demonstrating potential for applications requiring cost-effective training, and rapid post-deployment adaptability. Our code and demo are available at \url{https://github.com/yukw777/EILEV}.Comment: 10 pages, LaTeX; added acknowledgment

An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems

The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility, making it a promising tool for various related tasks. In this report, we explore the application of SAM in Weakly-Supervised Semantic Segmentation (WSSS). Particularly, we adapt SAM as the pseudo-label generation pipeline given only the image-level class labels. While we observed impressive results in most cases, we also identify certain limitations. Our study includes performance evaluations on PASCAL VOC and MS-COCO, where we achieved remarkable improvements over the latest state-of-the-art methods on both datasets. We anticipate that this report encourages further explorations of adopting SAM in WSSS, as well as wider real-world applications.Comment: Technique repor

SUIT: Learning Significance-guided Information for 3D Temporal Detection

3D object detection from LiDAR point cloud is of critical importance for autonomous driving and robotics. While sequential point cloud has the potential to enhance 3D perception through temporal information, utilizing these temporal features effectively and efficiently remains a challenging problem. Based on the observation that the foreground information is sparsely distributed in LiDAR scenes, we believe sufficient knowledge can be provided by sparse format rather than dense maps. To this end, we propose to learn Significance-gUided Information for 3D Temporal detection (SUIT), which simplifies temporal information as sparse features for information fusion across frames. Specifically, we first introduce a significant sampling mechanism that extracts information-rich yet sparse features based on predicted object centroids. On top of that, we present an explicit geometric transformation learning technique, which learns the object-centric transformations among sparse features across frames. We evaluate our method on large-scale nuScenes and Waymo dataset, where our SUIT not only significantly reduces the memory and computation cost of temporal fusion, but also performs well over the state-of-the-art baselines.Comment: Accepted to IROS 202

Towards a deep-learning-based framework of sentinel-2 imagery for automated active fire detection

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019-2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km(2) (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans

Frequency-Modulation Mode-Locked Laser with GHz Spectral Width Tunable in the 2-3 um Region

A narrow-bandwidth actively mode-locked laser using a Cr:ZnS gain medium has been successfully demonstrated. A free-space electro-optic phase modulator is employed in the solid-state laser resonator to achieve frequency-modulation (FM) mode-locking, which achieves a narrow spectral width of ~1 GHz and a pulse duration of ~500 ps over a wide tuning range of 1947-2445 nm. The operation frequency of the modulator determines the repetition rate of the mode-locked pulse train and can stabilize it to millihertz-level without any additional feedback loop systems. We also study the theoretical expression of pulse duration and spectral width in a FM mode-locking in a laser cavity that contains considerable group-delay dispersion. The results indicates that larger intracavity dispersion can only stabilize the laser operation by avoiding mode switching, but also narrow the spectral width and increase the pulse duration. The proposed laser features a narrow spectral width at a desired mid-infrared wavelength and a comb-like spectral structure with stabilized longitudinal mode spacing, providing a powerful tool for sensing and control of molecules.Comment: 9 pages, 4 figure