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We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Classical BP routing provides a simple yet powerful distributed solution for resource allocation in wireless multi-hop networks but has poor delay performance. A low-cost approach to improve this delay performance is to favor shorter paths by incorporating pre-defined biases in the BP computation, such as a bias based on the shortest path (hop) distance to the destination.

This paper investigates negative sampling for contrastive learning in the context of audio-text retrieval. The strategy for negative sampling refers to selecting negatives (either audio clips or textual descriptions) from a pool of candidates for a positive audio-text pair. We explore sampling strategies via model-estimated within-modality and cross-modality relevance scores for audio and text samples. With a constant training setting on the retrieval system from [1], we study eight sampling strategies, including hard and semi-hard negative sampling.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease most often associated with memory deficits and cognitive decline. With the aging population, there has been much interest in automated methods for cognitive impairment detection. One approach that has attracted attention in recent years is AD detection through spontaneous speech. While the results are promising, it is not certain whether the learned speech features can be generalized across languages. To fill this gap, the ADReSS-M challenge was organized.

Time-of-Flight imaging aims to retrieve the 3D geometry of a scene from the delay that a modulated light waveform experiences when interacting with the former.
Multi-path interference, arising from translucent objects or concave geometries, poses a challenge when the problem is to be solved from few measurements.

Time-of-Flight imaging aims to retrieve the 3D geometry of a scene from the delay that a modulated light waveform experiences when interacting with the former.
Multi-path interference, arising from translucent objects or concave geometries, poses a challenge when the problem is to be solved from few measurements.

Time-of-Flight imaging aims to retrieve the 3D geometry of a scene from the delay that a modulated light waveform experiences when interacting with the former.
Multi-path interference, arising from translucent objects or concave geometries, poses a challenge when the problem is to be solved from few measurements.

Traditional phase retrieval is generally concerned with recovering a signal from its Fourier magnitude measurements whose inherent ambiguities make this problem especially difficult. In this work, we present an efficient phase retrieval technique from the single fractional Fourier transform (FrFT) magnitude measurement. Specifically, the FrFT measurement can be well-combined with signal priors via a generalized alternating projection framework, which can effectively alleviate the ambiguities of phase retrieval and the stagnation problem of numerical iterative processes.

Entropy metrics (for example, permutation entropy) are nonlinear measures of irregularity in time series (one-dimensional data). Some of these entropy metrics can be generalised to data on periodic structures such as a grid or lattice pattern (two-dimensional data) using its symmetry, thus enabling their application to images. However, these metrics have not been developed for signals sampled on irregular domains, defined by a graph.