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

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.

Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience. A widely accepted paradigm is to create an error detection mechanism that identifies the cases when audio is leading or lagging. We propose ModEFormer, which independently extracts audio and video embeddings using modality-specific transformers.

Maximum subarray is a classical problem in computer science that given an array of numbers aims to find a contiguous subarray with the largest sum. We focus on its use for a noisy statistical problem of localizing an interval with a mean different from background. While a naive application of maximum subarray fails at this task, both a penalized and a constrained version can succeed.

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.

Recent technological advances in design and processing speed have successfully demonstrated a new snapshot mosaic imaging sensor architecture (SSI), allowing miniaturized platforms to efficiently acquire the spatio-spectral content of the dynamic scenes from a single exposure. However, SSI systems have a fundamental trade-off between spatial and spectral resolution because they associate each pixel with a specific spectral band.

Presentation for ICASSP 2023: Modeling the Wave Equation Using Physics-Informed Neural Networks Enhanced with Attention to Loss Weights.

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection.

We propose a wireless localization algorithm based on complex-domain super multidimensional scaling (CD-SMDS) augmented with a self-localization (SL) component, whereby each target tracks its own motion by incorporating bearing in- formation, obtained e.g., from integrated inertial sensors.