211 research outputs found
Representing Alzheimer's Disease Progression via Deep Prototype Tree
For decades, a variety of predictive approaches have been proposed and
evaluated in terms of their predicting capability for Alzheimer's Disease (AD)
and its precursor - mild cognitive impairment (MCI). Most of them focused on
prediction or identification of statistical differences among different
clinical groups or phases (e.g., longitudinal studies). The continuous nature
of AD development and transition states between successive AD related stages
have been overlooked, especially in binary or multi-class classification.
Though a few progression models of AD have been studied recently, they mainly
designed to determine and compare the order of specific biomarkers. How to
effectively predict the individual patient's status within a wide spectrum of
AD progression has been understudied. In this work, we developed a novel
structure learning method to computationally model the continuum of AD
progression as a tree structure. By conducting a novel prototype learning with
a deep manner, we are able to capture intrinsic relations among different
clinical groups as prototypes and represent them in a continuous process for AD
development. We named this method as Deep Prototype Learning and the learned
tree structure as Deep Prototype Tree - DPTree. DPTree represents different
clinical stages as a trajectory reflecting AD progression and predict clinical
status by projecting individuals onto this continuous trajectory. Through this
way, DPTree can not only perform efficient prediction for patients at any
stages of AD development (77.8% accuracy for five groups), but also provide
more information by examining the projecting locations within the entire AD
progression process.Comment: Submitted to Information Processing in Medical Imaging (IPMI) 202
Comparison of the Weibull and the Crow-AMSAA Model in Prediction of Early Cable Joint Failures
Exploring the Influence of Information Entropy Change in Learning Systems
In this work, we explore the influence of entropy change in deep learning
systems by adding noise to the inputs/latent features. The applications in this
paper focus on deep learning tasks within computer vision, but the proposed
theory can be further applied to other fields. Noise is conventionally viewed
as a harmful perturbation in various deep learning architectures, such as
convolutional neural networks (CNNs) and vision transformers (ViTs), as well as
different learning tasks like image classification and transfer learning.
However, this paper aims to rethink whether the conventional proposition always
holds. We demonstrate that specific noise can boost the performance of various
deep architectures under certain conditions. We theoretically prove the
enhancement gained from positive noise by reducing the task complexity defined
by information entropy and experimentally show the significant performance gain
in large image datasets, such as the ImageNet. Herein, we use the information
entropy to define the complexity of the task. We categorize the noise into two
types, positive noise (PN) and harmful noise (HN), based on whether the noise
can help reduce the complexity of the task. Extensive experiments of CNNs and
ViTs have shown performance improvements by proactively injecting positive
noise, where we achieved an unprecedented top 1 accuracy of over 95% on
ImageNet. Both theoretical analysis and empirical evidence have confirmed that
the presence of positive noise can benefit the learning process, while the
traditionally perceived harmful noise indeed impairs deep learning models. The
different roles of noise offer new explanations for deep models on specific
tasks and provide a new paradigm for improving model performance. Moreover, it
reminds us that we can influence the performance of learning systems via
information entropy change.Comment: Information Entropy, CNN, Transforme
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The Roles of H19 in Regulating Inflammation and Aging.
Accumulating evidence suggests that long non-coding RNA H19 correlates with several aging processes. However, the role of H19 in aging remains unclear. Many studies have elucidated a close connection between H19 and inflammatory genes. Chronic systemic inflammation is an established factor associated with various diseases during aging. Thus, H19 might participate in the development of age-related diseases by interplay with inflammation and therefore provide a protective function against age-related diseases. We investigated the inflammatory gene network of H19 to understand its regulatory mechanisms. H19 usually controls gene expression by acting as a microRNA sponge, or through mir-675, or by leading various protein complexes to genes at the chromosome level. The regulatory gene network has been intensively studied, whereas the biogenesis of H19 remains largely unknown. This literature review found that the epithelial-mesenchymal transition (EMT) and an imprinting gene network (IGN) might link H19 with inflammation. Evidence indicates that EMT and IGN are also tightly controlled by environmental stress. We propose that H19 is a stress-induced long non-coding RNA. Because environmental stress is a recognized age-related factor, inflammation and H19 might serve as a therapeutic axis to fight against age-related diseases
Research on Fault Diagnosis Method Based on Rule Base Neural Network
The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method
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