178 research outputs found
Stabilisation of descriptor Markovian jump systems with partially unknown transition probabilities
This paper is concerned with the stability and stabilisation problems for continuous-time descriptor Markovian jump systems with partially unknown transition probabilities. In terms of a set of coupled linear matrix inequalities (LMIs), a necessary and sufficient condition is firstly proposed, which ensures the systems to be regular, impulse-free and stochastically stable. Moreover, the corresponding necessary and sufficient condition on the existence of a mode-dependent state-feedback controller, which guarantees the closed-loop systems stochastically admissible by employing the LMI technique, is derived; the stabilizing state-feedback gain can also be expressed via solutions of the LMIs. Finally, numerical examples are given to demonstrate the validity of the proposed methods
Efficient Real-time Path Planning with Self-evolving Particle Swarm Optimization in Dynamic Scenarios
Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing
static path planning problems. Nevertheless, such application on dynamic
scenarios has been severely precluded by PSO's low computational efficiency and
premature convergence downsides. To address these limitations, we proposed a
Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor
operations, thereby enhancing computational efficiency. Harnessing the
computational advantage of TOF, a variant of PSO, designated as Self-Evolving
Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by
a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous
optimization of its own hyper-parameters to evade premature convergence.
Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation
(AT) mechanism that substantially elevates the real-time performance of SEPSO
on dynamic path planning problems were introduced. Comprehensive experiments on
four widely used benchmark optimization functions have been initially conducted
to corroborate the validity of SEPSO. Following this, a dynamic simulation
environment that encompasses moving start/target points and dynamic/static
obstacles was employed to assess the effectiveness of SEPSO on the dynamic path
planning problem. Simulation results exhibit that the proposed SEPSO is capable
of generating superior paths with considerably better real-time performance (67
path planning computations per second in a regular desktop computer) in
contrast to alternative methods. The code of this paper can be accessed here.Comment: 10 pages, 7 figures, 10 table
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
To retrieve more relevant, appropriate and useful documents given a query,
finding clues about that query through the text is crucial. Recent deep
learning models regard the task as a term-level matching problem, which seeks
exact or similar query patterns in the document. However, we argue that they
are inherently based on local interactions and do not generalise to ubiquitous,
non-consecutive contextual relationships. In this work, we propose a novel
relevance matching model based on graph neural networks to leverage the
document-level word relationships for ad-hoc retrieval. In addition to the
local interactions, we explicitly incorporate all contexts of a term through
the graph-of-word text format. Matching patterns can be revealed accordingly to
provide a more accurate relevance score. Our approach significantly outperforms
strong baselines on two ad-hoc benchmarks. We also experimentally compare our
model with BERT and show our advantages on long documents.Comment: To appear at AAAI 202
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Learning to restore multiple image degradations within a single model is
quite beneficial for real-world applications. Nevertheless, existing works
typically concentrate on regarding each degradation independently, while their
relationship has been less exploited to ensure the synergistic learning. To
this end, we revisit the diverse degradations through the lens of singular
value decomposition, with the observation that the decomposed singular vectors
and singular values naturally undertake the different types of degradation
information, dividing various restoration tasks into two groups,\ie, singular
vector dominated and singular value dominated. The above analysis renders a
more unified perspective to ascribe the diverse degradations, compared to
previous task-level independent learning. The dedicated optimization of
degraded singular vectors and singular values inherently utilizes the potential
relationship among diverse restoration tasks, attributing to the Decomposition
Ascribed Synergistic Learning (DASL). Specifically, DASL comprises two
effective operators, namely, Singular VEctor Operator (SVEO) and Singular VAlue
Operator (SVAO), to favor the decomposed optimization, which can be lightly
integrated into existing convolutional image restoration backbone. Moreover,
the congruous decomposition loss has been devised for auxiliary. Extensive
experiments on blended five image restoration tasks demonstrate the
effectiveness of our method, including image deraining, image dehazing, image
denoising, image deblurring, and low-light image enhancement.Comment: 13 page
Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection
We present a generalized and scalable method, called Gen-LaneNet, to detect
3D lanes from a single image. The method, inspired by the latest
state-of-the-art 3D-LaneNet, is a unified framework solving image encoding,
spatial transform of features and 3D lane prediction in a single network.
However, we propose unique designs for Gen-LaneNet in two folds. First, we
introduce a new geometry-guided lane anchor representation in a new coordinate
frame and apply a specific geometric transformation to directly calculate real
3D lane points from the network output. We demonstrate that aligning the lane
points with the underlying top-view features in the new coordinate frame is
critical towards a generalized method in handling unfamiliar scenes. Second, we
present a scalable two-stage framework that decouples the learning of image
segmentation subnetwork and geometry encoding subnetwork. Compared to
3D-LaneNet, the proposed Gen-LaneNet drastically reduces the amount of 3D lane
labels required to achieve a robust solution in real-world application.
Moreover, we release a new synthetic dataset and its construction strategy to
encourage the development and evaluation of 3D lane detection methods. In
experiments, we conduct extensive ablation study to substantiate the proposed
Gen-LaneNet significantly outperforms 3D-LaneNet in average precision(AP) and
F-score
Comprehensive analysis of hypoxia-related genes for prognosis value, immune status, and therapy in osteosarcoma patients
Osteosarcoma is a common malignant bone tumor in children and adolescents. The overall survival of osteosarcoma patients is remarkably poor. Herein, we sought to establish a reliable risk prognostic model to predict the prognosis of osteosarcoma patients. Patients ’ RNA expression and corresponding clinical data were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus databases. A consensus clustering was conducted to uncover novel molecular subgroups based on 200 hypoxia-linked genes. A hypoxia-risk models were established by Cox regression analysis coupled with LASSO regression. Functional enrichment analysis, including Gene Ontology annotation and KEGG pathway analysis, were conducted to determine the associated mechanisms. Moreover, we explored relationships between the risk scores and age, gender, tumor microenvironment, and drug sensitivity by correlation analysis. We identified two molecular subgroups with significantly different survival rates and developed a risk model based on 12 genes. Survival analysis indicated that the high-risk osteosarcoma patients likely have a poor prognosis. The area under the curve (AUC) value showed the validity of our risk scoring model, and the nomogram indicates the model’s reliability. High-risk patients had lower Tfh cell infiltration and a lower stromal score. We determined the abnormal expression of three prognostic genes in osteosarcoma cells. Sunitinib can promote osteosarcoma cell apoptosis with down-regulation of KCNJ3 expression. In summary, the constructed hypoxia-related risk score model can assist clinicians during clinical practice for osteosarcoma prognosis management. Immune and drug sensitivity analysis can provide essential insights into subsequent mechanisms. KCNJ3 may be a valuable prognostic marker for osteosarcoma development
Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention
Computed Tomography (CT) plays an important role in monitoring
radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the
PF lesions is highly desired for diagnosis and treatment follow-up. However,
the task is challenged by ambiguous boundary, irregular shape, various position
and size of the lesions, as well as the difficulty in acquiring a large set of
annotated volumetric images for training. To overcome these problems, we
propose a novel convolutional neural network called PF-Net and incorporate it
into a semi-supervised learning framework based on Iterative Confidence-based
Refinement And Weighting of pseudo Labels (I-CRAWL). Our PF-Net combines 2D and
3D convolutions to deal with CT volumes with large inter-slice spacing, and
uses multi-scale guided dense attention to segment complex PF lesions. For
semi-supervised learning, our I-CRAWL employs pixel-level uncertainty-based
confidence-aware refinement to improve the accuracy of pseudo labels of
unannotated images, and uses image-level uncertainty for confidence-based image
weighting to suppress low-quality pseudo labels in an iterative training
process. Extensive experiments with CT scans of Rhesus Macaques with
radiation-induced PF showed that: 1) PF-Net achieved higher segmentation
accuracy than existing 2D, 3D and 2.5D neural networks, and 2) I-CRAWL
outperformed state-of-the-art semi-supervised learning methods for the PF
lesion segmentation task. Our method has a potential to improve the diagnosis
of PF and clinical assessment of side effects of radiotherapy for lung cancers.Comment: 12 pages, 9 figures. Submitted to IEEE TM
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