Sorry, you need to enable JavaScript to visit this website.

Neural vocoders model the raw audio waveform and synthesize highquality audio, but even the highly efficient ones, like MB-MelGAN
and LPCNet, fail to run real-time on a low-end device like a smartglass. A pure digital signal processing (DSP) based vocoder can
be implemented via lightweight fast Fourier transforms (FFT), and
therefore, is a magnitude faster than any neural vocoder. A DSP
vocoder often gets a lower audio quality due to consuming oversmoothed acoustic model predictions of approximate representations

As deep neural networks and the datasets used to train them get larger, the default approach to integrating them into re-
search and commercial projects is to download a pre-trained model and fine tune it. But these models can have uncertain
provenance, opening up the possibility that they embed hidden malicious behavior such as trojans or backdoors, where
small changes to an input (triggers) can cause the model toproduce incorrect outputs (e.g., to misclassify). This paper

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.