141 research outputs found
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
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
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
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
fficient n-context earning on
gocentric ideos (), which elicits
in-context learning in VLMs for egocentric videos without requiring massive,
naturalistic egocentric video datasets. 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 -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
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Machine Unlearning (MU) algorithms have become increasingly critical due to
the imperative adherence to data privacy regulations. The primary objective of
MU is to erase the influence of specific data samples on a given model without
the need to retrain it from scratch. Accordingly, existing methods focus on
maximizing user privacy protection. However, there are different degrees of
privacy regulations for each real-world web-based application. Exploring the
full spectrum of trade-offs between privacy, model utility, and runtime
efficiency is critical for practical unlearning scenarios. Furthermore,
designing the MU algorithm with simple control of the aforementioned trade-off
is desirable but challenging due to the inherent complex interaction. To
address the challenges, we present Controllable Machine Unlearning (ConMU), a
novel framework designed to facilitate the calibration of MU. The ConMU
framework contains three integral modules: an important data selection module
that reconciles the runtime efficiency and model generalization, a progressive
Gaussian mechanism module that balances privacy and model generalization, and
an unlearning proxy that controls the trade-offs between privacy and runtime
efficiency. Comprehensive experiments on various benchmark datasets have
demonstrated the robust adaptability of our control mechanism and its
superiority over established unlearning methods. ConMU explores the full
spectrum of the Privacy-Utility-Efficiency trade-off and allows practitioners
to account for different real-world regulations. Source code available at:
https://github.com/guangyaodou/ConMU
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