The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-
terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As …

In this work, we study the 1-bit convolutional neural networks (CNNs), of which both the
weights and activations are binary. While being efficient, the classification accuracy of the …

Recent vision and learning studies show that learning compact hash codes can facilitate
massive data processing with significantly reduced storage and computation. Particularly …

This paper proposes a novel Peer-to-Peer (P2P) joint electricity and carbon (E&C) trading
model to co-optimize the energy and carbon emissions permit transactions considering the …

Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …

This work studies the sparse adversarial attack, which aims to generate adversarial
perturbations onto partial positions of one benign image, such that the perturbed image is …

This article presents a two-timescale duplex neurodynamic approach to mixed-integer
optimization, based on a biconvex optimization problem reformulation with additional …

This work studies the model compression for deep convolutional neural networks (CNNs)
via filter pruning. The workflow of a traditional pruning consists of three sequential stages …

With the rapid development of science and technology, high-dimensional data have been
widely used in various fields. Due to the complex characteristics of high-dimensional data, it …

Deep hashing is an appealing approach for large-scale image retrieval. Most existing
supervised deep hashing methods learn hash functions using pairwise or triple image …