Academic Articles & Research Papers

Quantum Computing for Finance: State-of-the-Art and Future Prospects

This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in fin...

A Survey of Quantum Computing for Finance

Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the fi...

Distributed systems meet economics: pricing in the cloud

Cloud computing allows users to perform computation in a public cloud with a pricing scheme typically based on incurred resource consumption. While cloud comput-ing is often considered as merely a new application for classic distributed sys...

A Survey of Task Allocation and Load Balancing in Distributed Systems

In past decades, significant attention has been devoted to the task allocation and load balancing in distributed systems. Although there have been some related surveys about this subject, each of which only made a very preliminary review on...

A Behavioral Approach to Control of Distributed Systems

This paper develops a theory of control for distributed systems (i.e., those defined by systems of constant coefficient partial differential operators) via the behavioral approach of Willems. The study here is algebraic in the sense that it...

Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, s...

Neural Network Methods for Natural Language Processing

Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...

Hyperbolic Neural Networks

Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...

Artificial neural networks for speech and vision

Neural Networks for speech processing. Neural Networks in the acquisition of speech by machine. The nervous system - fantasy and reality. Processing of complex stimuli in the mammalian cochlear nucleus. On the possible role of auditory peri...

Optimization using neural networks

The design of feedback (or recurrent) neural networks to produce good solutions to complex optimization problems is discussed. The theoretical basis for applying neural networks to optimization problems is reviewed, and a design rule that s...

A NEURAL NETWORK PRIMER

Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates indep...

Neural Networks in Applied Statistics

Artificial neural networks are computer algorithms or computer programs derived in part from attempts to model the activity of nerve cells. They have been applied to pattern recognition, classification, and optimization problems in the phys...

Neural networks for optimization and signal processing

From the Publisher: Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs pro...

Applications of Neural Networks in Electromagnetics

From the Book: Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...

Introduction to Artificial Neural Network

Applied Deep Learning

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks, in this compr...

PG-MACO Optimization Method for Ship Pipeline Layout

Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, ec...

EdgeAIGC: Model caching and resource allocation for edge artificial intelligence generated content

With the rapid development of generative artificial intelligence technology, the traditional cloud-based centralized model training and inference face significant limitations due to high transmission latency and costs, which restrict user-s...

Optimization of Process Parameters of Air Classifier Based on Particle Trajectory

Under the rapid development of the world’s powder technology, powder materials are used in a wide range of fields such as metallurgy, chemical industry and petroleum. Turbo air classifier is one of the most important mechanical equipment in...

Joint Optimization of Transform and Quantization for High Efficiency Video Coding

In high efficiency video coding (HEVC), transformation and quantization are separately performed to eliminate the perceptual redundancy of visual signals. However, a uniform quantizer can inevitably degrade the compression efficiency of fix...