103 research outputs found
Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics
Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach
Towards Visual Saliency Explanations of Face Recognition
Deep convolutional neural networks have been pushing the frontier of face
recognition (FR) techniques in the past years. Despite the high accuracy, they
are often criticized for lacking explainability. There has been an increasing
demand for understanding the decision-making process of deep face recognition
systems. Recent studies have investigated using visual saliency maps as an
explanation, but they often lack a discussion and analysis in the context of
face recognition. This paper conceives a new explanation framework for face
recognition. It starts by providing a new definition of the saliency-based
explanation method, which focuses on the decisions made by the deep FR model.
Then, a novel correlation-based RISE algorithm (CorrRISE) is proposed to
produce saliency maps, which reveal both the similar and dissimilar regions of
any given pair of face images. Besides, two evaluation metrics are designed to
measure the performance of general visual saliency explanation methods in face
recognition. Consequently, substantial visual and quantitative results have
shown that the proposed method consistently outperforms other explainable face
recognition approaches
Discriminative Deep Feature Visualization for Explainable Face Recognition
Despite the huge success of deep convolutional neural networks in face
recognition (FR) tasks, current methods lack explainability for their
predictions because of their "black-box" nature. In recent years, studies have
been carried out to give an interpretation of the decision of a deep FR system.
However, the affinity between the input facial image and the extracted deep
features has not been explored. This paper contributes to the problem of
explainable face recognition by first conceiving a face reconstruction-based
explanation module, which reveals the correspondence between the deep feature
and the facial regions. To further interpret the decision of an FR model, a
novel visual saliency explanation algorithm has been proposed. It provides
insightful explanation by producing visual saliency maps that represent similar
and dissimilar regions between input faces. A detailed analysis has been
presented for the generated visual explanation to show the effectiveness of the
proposed method
Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism
The loss function for bounding box regression (BBR) is essential to object
detection. Its good definition will bring significant performance improvement
to the model. Most existing works assume that the examples in the training data
are high-quality and focus on strengthening the fitting ability of BBR loss. If
we blindly strengthen BBR on low-quality examples, it will jeopardize
localization performance. Focal-EIoU v1 was proposed to solve this problem, but
due to its static focusing mechanism (FM), the potential of non-monotonic FM
was not fully exploited. Based on this idea, we propose an IoU-based loss with
a dynamic non-monotonic FM named Wise-IoU (WIoU). The dynamic non-monotonic FM
uses the outlier degree instead of IoU to evaluate the quality of anchor boxes
and provides a wise gradient gain allocation strategy. This strategy reduces
the competitiveness of high-quality anchor boxes while also reducing the
harmful gradient generated by low-quality examples. This allows WIoU to focus
on ordinary-quality anchor boxes and improve the detector's overall
performance. When WIoU is applied to the state-of-the-art real-time detector
YOLOv7, the AP-75 on the MS-COCO dataset is improved from 53.03% to 54.50%.
Code is available at https://github.com/Instinct323/wiou
Association between cystatin C and the interaction of pulmonary tuberculosis with chronic diseases
Purpose: To determine the association between Cystatin C (Cys C) levels and the interaction of pulmonary tuberculosis (PTB) with chronic diseases (CD).Methods: Participants (n = 356) were selected randomly from The First Affiliated Hospital of Wannan Medical College, China, and divided into 4 groups: normal control group (n = 80), PTB group (n = 98), chronic disease group (n = 146), and PTB combined with chronic disease group (PTB+CD, n = 31). The investigation included information on demographics and analysis of blood samples for Cys C, liver function, renal function, blood glucose and other biochemical indices.Results: The highest level of Cys C was obtained in PTB + CD group. Before and after adjusting eGFR, there was no association between Cys C and PTB or/and chronic disease. However abnormal levels of Cys C were significantly higher in PTB+CD group after adjusting eGFR (OR = 4.014, p = 0.0125).Conclusion: Higher levels of Cys C may be associated with chronic diseases co existing with PTB.Keywords: Cystatin C, Pulmonary tuberculosis, Chronic diseases, Inflammatio
MANAS: Multi-Agent Neural Architecture Search
The Neural Architecture Search (NAS) problem is typically formulated as a
graph search problem where the goal is to learn the optimal operations over
edges in order to maximise a graph-level global objective. Due to the large
architecture parameter space, efficiency is a key bottleneck preventing NAS
from its practical use. In this paper, we address the issue by framing NAS as a
multi-agent problem where agents control a subset of the network and coordinate
to reach optimal architectures. We provide two distinct lightweight
implementations, with reduced memory requirements (1/8th of state-of-the-art),
and performances above those of much more computationally expensive methods.
Theoretically, we demonstrate vanishing regrets of the form O(sqrt(T)), with T
being the total number of rounds. Finally, aware that random search is an,
often ignored, effective baseline we perform additional experiments on 3
alternative datasets and 2 network configurations, and achieve favourable
results in comparison
- …