Computational Science - ICCS 2024

Name: Computational Science - ICCS 2024 | 24th International Conference, Malaga, Spain, July 2-4, 2024, Proceedings, Part VI
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24th International Conference, Malaga, Spain, July 2-4, 2024, Proceedings, Part VI

Leonardo Franco Clélia de Mulatier Maciej Paszynski Valeria V. Krzhizhanovskaya Jack J. Dongarra Peter M. A. Sloot(Herausgeber*in)

Springer (Verlag)

Erschienen am 29. Juni 2024

XXIII, 414 Seiten

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Inhalt

Intro
Preface
Organization
Contents - Part VI
Network Models and Analysis: From Foundations to Artificial Intelligence
Representation Learning in Multiplex Graphs: Where and How to Fuse Information?
1 Introduction
2 Related Work
3 Information Fusion in Multiplex Graphs
3.1 Experimental Setup and Evaluation
3.2 Baseline Approaches (no Fusion)
3.3 Graph-Level Fusion
3.4 Prediction-Level Fusion
3.5 GNN-Level Fusion
3.6 Embedding-Level Fusion
4 Conclusions, Research Gaps and Future Work
References
Data Augmentation to Improve Molecular Subtype Prognosis Prediction in Breast Cancer
1 Introduction
2 Related Works
3 Breast Cancer Cohort
4 Data Augmentation Methods
5 Experiments and Results
6 Conclusion and Future Work
References
Threshold Optimization in Constructing Comparative Network Models: A Case Study on Enhancing Laparoscopic Surgical Skill Assessment with Edge Betweenness
1 Introduction
2 Methodology
2.1 Needle Passing Task
2.2 EMG Data Collection and Pre-processing
2.3 NASA-TLX Scores
2.4 Nodes and Edges
2.5 Edge Betweenness and Modularity for Threshold Selection
3 Results
3.1 Enrichment Analysis
4 Discussion
5 Conclusion
References
Graph Vertex Embeddings: Distance, Regularization and Community Detection
1 Introduction
2 Related Work
3 Problem Statement
4 Method
4.1 Embeddings as Solution to Optimization Problem
4.2 Regularized Embeddings
4.3 Distance Functions
5 Experiments
5.1 Datasets
5.2 Graph Analysis
5.3 Graph Drawing
5.4 Community Detection in Graphs
6 Conclusions
References
A Robust Network Model for Studying Microbiomes in Precision Agriculture Applications
1 Introduction
2 Methods
2.1 Overview of Workflow
2.2 Data Description
2.3 Biological Feature Grouping
2.4 Co-expression Network Analysis
2.5 Characterizing Robust Biological Functions
2.6 Comparison of OTUs Pairs Underlying KEGG Modules
2.7 Bacterial Functional Enrichment Analysis
3 Results and Discussion
3.1 A Comparison of Networks in High and Low Biomass Groups
3.2 Co-expression Networks Analysis
3.3 Characterization of Distinctive KEGG Modules in High and Low Biomass Groups
3.4 Comparison of Dynamics of OTUs Pairs Underlying KEGG Modules
4 Conclusion
References
A Graph-Theory Based fMRI Analysis
1 Introduction
2 Background
2.1 fMRI
2.2 Machine Learning
2.3 Graph Theory
2.4 Network Alignment
3 Material and Methods
3.1 Dataset Description
3.2 Pipeline Description
3.3 Proposed Solution
4 Results
5 Conclusions
References
A Pipeline for the Analysis of Multilayer Brain Networks
1 Introduction
2 Background
2.1 Definition of Multilayer Networks
2.2 Brain Network Applications
3 Materials and Methods
3.1 Data Source
3.2 Construction and Analysis of Multilayer Networks
4 Result and Discussion
5 Conclusion
References
Numerical Algorithms and Computer Arithmetic for Computational Science
Modified CORDIC Algorithm for Givens Rotator
1 Introduction
2 Review of Algorithms and Solutions
3 CORDIC Algorithm and Its Modified Version
3.1 Realisationation
4 Measurements
5 Conclusion
References
Numerical Aspects of Hyperbolic Geometry
1 Introduction
2 Hyperbolic Geometry and Representations
3 Representation Variants
4 Tessellations
5 Tests
6 Experimental Results
7 Comparison Based on Non-numerical Advantages
8 Conclusions
References
A Numerical Feed-Forward Scheme for the Augmented Kalman Filter
1 Introduction
2 Problem Settings and Kalman Filter Estimation of the Forcing Term
3 A Feed-Forward Strategy for the Augmented Kalman Filter
3.1 Modeling the Feed-Forward Action
3.2 Feed-Forward Reference Extraction
3.3 Tuning the Feed-Forward Gain GFF
3.4 Regularization Issues
4 Numerical Experiments
4.1 Inadequacy of the KF Proportional Action with a Diffusive Gain
4.2 The Effect of Regularization and Block-Diagonal Reference Extraction
5 Discussion and Conclusions
References
Calculation of the Sigmoid Activation Function in FPGA Using Rational Fractions
1 Introduction
2 Artificial Neural Nets and Data Representations
3 Rational Fractions and Calculation of the Sigmoid Activation Function
3.1 Rational Fractions
3.2 Rational Fractions in Processors
3.3 Calculation of the Sigmoid Function
4 Experimental Results
5 Conclusion
References
Parallel Vectorized Algorithms for Computing Trigonometric Sums Using AVX-512 Extensions
1 Introduction
2 Goertzel and Reinsch Algorithms
3 Divide-and-conquer Approach
3.1 Goertzel Algorithm
3.2 Reinsch Algorithm
4 Implementation of the Algorithms
5 Results of Experiments
6 Conclusions and Future Works
References
File I/O Cache Performance of Supercomputer Fugaku Using an Out-of-Core Direct Numerical Simulation Code of Turbulence
1 Introduction
2 Out-of-Core Direct Numerical Simulation Code
2.1 Direct Numerical Simulation Code Implementation
2.2 Out-of-Core Implementation Concept
3 Fugaku Architecture
4 Evaluation of Compute Node (CN)-Cache Performance
4.1 Performance Evaluation of the I/O Cache with IOR
4.2 Performance Evaluation of the I/O Cache with a Simple Program Similar to the Ooc-DNS Code
4.3 Performance Evaluation of I/O Cache with the Ooc-DNS Code
4.4 Execution of Ooc-DNS Code with 8,1923 Grid Points
5 Conclusions
References
A Novel Computational Approach for Wind-Driven Flows over Deformable Topography
1 Introduction
2 Governing Equations for Wind-Driven Flows over Deformable Topography
3 Hybrid Finite Element/Finite Volume Solver
3.1 Coupling Conditions at the Interface
4 Computational Results
4.1 Accuracy Results
4.2 Wind-Driven Circulation Flow by Pipe Failure in the Topography
5 Conclusions
References
Unleashing the Potential of Mixed Precision in AI-Accelerated CFD Simulation on Intel CPU/GPU Architectures
1 Introduction
2 Related Work
3 Floating-Point Data Formats and Mixed Precision in AI-Accelerated CFD Simulation
4 Intel CPU and GPU Architectures in Deep Learning Mixed-Precision Computation
5 AI-Accelerated CFD Simulation
5.1 CFD Simulation of Steady Flow Around a Motorcycle and Rider
5.2 Integration of AI Acceleration with CFD Solver
5.3 Training Dataset
6 Experimental Results
6.1 Testing Platforms
6.2 Accuracy and Performance Results
7 Conclusion
References
Quantum Computing
The Significance of the Quantum Volume for Other Algorithms: A Case Study for Quantum Amplitude Estimation
1 Introduction
1.1 Quantum Volume
1.2 QAE as a Numerical Integration Technique
1.3 Related Work
1.4 Our Contributions
2 Preliminaries
2.1 The Noise Model
2.2 QAE Without Noise
2.3 QAE on Noisy Processors
2.4 Dummy QAE Circuits
2.5 Fisher Information
2.6 A Monte Carlo Example
3 Results
3.1 Success in a Quantum Volume Experiment
3.2 Results for the Error Model
3.3 Speed of the QPU
3.4 Estimations for the Size of Circuits
4 Application of the Noise Model to QAE
4.1 General Functions
4.2 Shallow Functions
5 Conclusions
References
KetGPT - Dataset Augmentation of Quantum Circuits Using Transformers
1 Introduction
2 Evolution and Structure of Transformers
2.1 Tokenizer
2.2 Feed-Forward Neural Network
2.3 Self-attention
3 KetGPT - Transformers for Quantum Circuit Generation
3.1 Input Dataset and Data Preprocessing
3.2 Generator: Architecture and Tokenizer
3.3 Verification Method: KetGPT Classifier
3.4 Implementation Details
4 Results and Discussion
4.1 Manual Inspection
4.2 Classifier-Based Evaluation
4.3 Analysis Based on Circuit Structure
5 Conclusion and Outlook
6 Software Availability
References
Design Considerations for Denoising Quantum Time Series Autoencoder
1 Introduction
2 QuTSAE Design
2.1 Design Choices for QuTSAE Architecture
2.2 Design Choices for QuTSAE Input Encoding
2.3 Design Choices for QuTSAE Output Decoding/Cost Function
2.4 Design Choices for QuTSAE Encoder/Decoder Ansatze
3 Experiments
3.1 Determining the Optimum Ansatz Size
3.2 Impact of Latent Space on Performance
3.3 Time Series Denoising
4 Conclusions
References
Optimizing Quantum Circuits Using Algebraic Expressions
1 Introduction
2 Proposed Approach
3 Representing Quantum Circuits as Algebraic Expressions
4 Optimizing the Algebraic Expressions
5 Results
6 Conclusion
References
Implementing 3-SAT Gadgets for Quantum Annealers with Random Instances
1 Introduction
2 Fundamental Concepts
2.1 The 3-SAT and Max-3-SAT Problems
2.2 The QUBO Problem and Its Equivalence in Ising Models
2.3 Quantum Annealing
3 Related Work
3.1 Nuesslein2n+m
3.2 Nuessleinn+m
4 New Approaches
4.1 CJ1n+m
4.2 CJ2n+m
5 Experimental Results
6 Conclusion and Future Work
A Number of Physical Qubits Required per Gadget
References
Quantum Annealers Chain Strengths: A Simple Heuristic to Set Them All
1 Introduction
2 Quantum Annealing and Minor Embedding Methods
3 Related Work
4 Method
5 Logical Qubit Structure
6 Embeddings and Chain Break Analysis
7 Chain Strength Setting Heuristic
8 Conclusion
References
Quantum Variational Algorithms for the Aircraft Deconfliction Problem
1 Introduction
2 Problem Representation and Assumptions
2.1 Classical Formulation
2.2 Quantum Formulation and Encoding
3 Application
3.1 Quantum Approximate Optimization Algorithm
3.2 Quantum Alternating Operator Ansatz
4 Experimental Results
5 Conclusions and Future Work
References
Adaptive Sampling Noise Mitigation Technique for Feedback-Based Quantum Algorithms
1 Introduction
2 Preliminaries
2.1 Quantum Lyapunov Control
2.2 Feedback-Based Quantum Optimization Algorithm
3 Proposed Design of the Controller
4 Application to the Maximum Clique Problem
5 Conclusion and Future Work
References
Towards Federated Learning on the Quantum Internet
1 Introduction
2 Background
2.1 Quantum Networks and the Quantum Internet
2.2 Variational Quantum Circuits
2.3 Quantum Federated Learning
3 Related Work
4 Approach
4.1 Quantum Clients
4.2 Architecture
4.3 Quantum Circuits
5 Experimental Setup
5.1 Datasets
5.2 Training
5.3 Simulation
6 Results
6.1 Multiple Clients, Single Dataset
6.2 Multiple Clients, Multiple Datasets
7 Conclusion
References
Statistical Model Checking for Entanglement Swapping in Quantum Networks
1 Introduction
2 Relevant Preliminaries and Previous Work
2.1 (Statistical) Model Checking
2.2 SeQUeNCe
3 Integrating MultiVeStA with SeQUeNCe
4 Queries and Results
4.1 Experimental Setup
4.2 Impact of Time-Limit () on the Success Probability
4.3 Impact of Quantum Memory Lifetime () on the Success Probability
4.4 Impact of retrial_limit and link_quality on success probability
4.5 Impact of schedule on success probability
4.6 Impact of on Probability of BSM Being Attempted
4.7 Running Time
5 Conclusions and Future Work
References
Noise Robustness of a Multiparty Quantum Summation Protocol
1 Introduction
2 Preliminaries
2.1 Distributed Quantum Computing
2.2 Noise Models
3 Simulations with Noise
3.1 Dephasing Noise
3.2 Depolarising Noise
4 Analytical Study
4.1 Proof of Probability Distribution
4.2 Understanding the Noisy Distribution
5 Protocol Without Trusted Server
6 Conclusions and Outlook
A Proofs of Results of Section4
References
Hybrid Approach to Public-Key Algorithms in the Near-Quantum Era
1 Introduction
2 Known Attacks Against Post-quantum Instances
2.1 Attack on Rainbow
2.2 Attack on SIKE
2.3 Recent Breakthrough Against LWE
3 Quantum Computing
4 Analyses
4.1 Methodology
4.2 Classical Schemes Based on Factoring
4.3 Elliptic Curve Cryptography
5 Forecasting Evolution of Quantum Computers: When Practical Attacks Will Be Possible
5.1 Forecasting Based on a Statistical Model
6 Recommendations and Closing Remarks
References
Unsafe Mechanisms of Bluetooth, E0 Stream Cipher Cryptanalysis with Quantum Annealing
1 Introduction
2 Bluetooth Encryption Overview
2.1 Encryption Procedure Concept
2.2 Stream Generation Algorithm E0 Description
3 Attack Idea
3.1 Current Cryptanalysis
3.2 Proposed Attack Scheme
3.3 Probability of Recovering Used Encryption Key
4 Results
4.1 Graph of the Obtained Optimization Problem
4.2 Embedding the Problem Graph in the Hardware Graph of the D-Wave Quantum Annealer
4.3 Dedicated Architecture of Quantum Annealer
5 Conclusion
References
Towards an In-Depth Detection of Malware Using Multi-QCNN
1 Dataset and Preprocessing
1.1 Data
1.2 Splitting the Image into Sub-images
2 Framework
2.1 Description of the QCNN and Its Training on a Section Image
2.2 Training a Scoring Function
3 Experiments and Results
4 Conclusion and Future Work
References
Correction to: A Numerical Feed-Forward Scheme for the Augmented Kalman Filter
Author Index

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