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We describe our contribution to the 2nd e-Prevention challenge, which focuses on the unsupervised non-psychotic (Track 1) and psychotic (Track 2) relapse detection using wearable-based digital phenotyping. We exploit the measurements gathered from the gyroscope, the accelerometer, and the heart rate-related sensors embedded in a smartwatch. We also include the available sleep information in our experiments. Four dedicated autoencoders are trained to learn embedded representations from each one of the considered modalities.

In recent years, deep learning systems have shown a concerning trend toward increased complexity and higher energy consumption. As researchers in this domain and organizers of one of the Detection and Classification of Acoustic Scenes and Events challenges task, we recognize the importance of addressing the environmental impact of data-driven SED systems. In this paper, we propose an analysis focused on SED systems based on the challenge submissions. This includes a comparison across the past two years and a detailed analysis of this year’s SED systems.

The recent successful detection of gravitational waves (GWs) at nanohertz based on pulsar timing arrays has underscored the growing signiffcance of searching for new pulsars, which serve as valuable probes for GWs. However, one of the challenges in this endeavor is the lack of labeled data, which can lead to overfftting and poor generalization in supervised deep neural networks. In this paper, we propose a self-supervised pretext task based on signal con-texts to obtain discriminative radio signal representation.

Graph contrastive learning aims to learn a representative model by maximizing the agreement between different views of the same graph. Existing studies usually allow multifarious noise in data augmentation, and suffer from trivial and inconsistent generation of graph views. Moreover, they mostly impose contrastive constraints on pairwise representations, limiting the structural correlations among multiple nodes. Both problems may hinder graph contrastive learning, leading to suboptimal node representations.

The generalized minimax concave (GMC) penalty is a nonconvex sparse regularizer which can preserve the overall-convexity of the sparse least squares problem. In this paper, we study the solution path of a special but important instance of the GMC model termed the scaled GMC (sGMC) model. We show that despite the nonconvexity of the regularizer, there exists a solution path of the sGMC model which is piecewise linear as a function of the regularization parameter, and we propose an efficient algorithm for computing a solution path of this type.

The X-ray security inspection aims to identify any restricted items to protect public safety. Due to the lack of focus on unsupervised learning in this field, using pre-trained models on natural images leads to suboptimal results in downstream tasks. Previous works would lose the relative positional relationships during the pre-training process, which is detrimental for X-ray images that lack texture and rely on shape. In this paper, we propose the jigsaw style MAE (J-MAE) to preserve the relative position information by shuffling the position encoding of visible patches.

The X-ray security inspection aims to identify any restricted items to protect public safety. Due to the lack of focus on unsupervised learning in this field, using pre-trained models on natural images leads to suboptimal results in downstream tasks. Previous works would lose the relative positional relationships during the pre-training process, which is detrimental for X-ray images that lack texture and rely on shape. In this paper, we propose the jigsaw style MAE (J-MAE) to preserve the relative position information by shuffling the position encoding of visible patches.

Predicting fluid cognition via neuroimaging data is essential for understanding the neural mechanisms underlying various complex cognitions in the human brain. Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for fluid cognition. In addition, interactions between SC and FC within distributed association regions are related to improvements in fluid cognition. However, existing learning-based methods that leverage both modality-specific embeddings and high-order interactions between the two modalities for prediction are scarce.

Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few consider the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local data. This bound assumes unique prior knowledge for each client and variable aggregation weights. We also introduce an objective function and an innovative Gibbs-based algorithm for the optimization of the derived bound.

Unsupervised continual learning (UCL) of image representation has garnered attention due to practical need. However, recent UCL methods focus on mitigating the catastrophic forgetting with a replay buffer (i.e., rehearsal-based strategy), which needs much extra storage. To overcome this drawback, we propose a novel rememory-based SimSiam (RM-SimSiam) method to reduce the dependency on replay buffer. The core idea of RM-SimSiam is to store and remember the old knowledge with a data-free historical module instead of replay buffer.