BackgroundDepression and anxiety are known to be associated with stress-induced changes in the immune system. Bothersome tinnitus can be related to stress and often co-occurs with depression and anxiety. This study investigates associations of psychologica ...
Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault ...
Deep neural networks have completely revolutionized the field of machine
learning by achieving state-of-the-art results on various tasks ranging from
computer vision to protein folding. However, their application is hindered by
their large computational an ...
Parkinson's disease (PD), the second most common progressive neurodegenerative disease, develops and progresses for 10-15 years before the clinical diagnostic symptoms of the disease are manifested. Furthermore, several aspects of PD pathology overlap with ...
State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system's database and are used for recognition of ...
Neural functions operate in tightly controlled conditions that are mediated by multiple electrical and chemical phenomena. Brain disorders such as Parkinson's Disease and Alzheimer's Disease perturb these conditions and cause a loss of neurons, which impai ...
This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...
Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...
We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statisti ...
Network representations of complex systems are limited to pairwise interactions, but real-world systems often involve higher-order interactions. This Perspective looks at the new physics emerging from attempts to characterize these interactions. ...
The vulnerability of deep neural networks to adversarial attacks has posed significant threats to real-world applications, especially security-critical ones. Given a well-trained model, slight modifications to the input samples can cause drastic changes in ...
