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arXiv 2024年

作者： Adams, Betony Hassasfar, Abbas Sinayskiy, Ilya Nunn, Alistair Guy, Geoffrey Petruccione, Francesco School of Data Science and Computational Thinking Stellenbosch University Stellenbosch7602 South Africa School of Physics Stellenbosch University Stellenbosch7602 South Africa South Africa School of Chemistry and Physics University of KwaZulu-Natal Durban South Africa The Guy Foundation Dorset United Kingdom Research Centre for Optimal Health University of Westminster London United Kingdom

For the first time in history, humankind might conceivably begin to imagine itself as a multi-planetary species. This goal will entail technical innovation in a number of contexts, including that of healthcare. All life on Earth shares an evolution that is coupled to specific environmental conditions, including gravitational and magnetic fields. While the human body may be able to adjust to short term disruption of these fields during space flights, any long term settlement would have to take into consideration the effects that different fields will have on biological systems, within the space of one lifetime, but also across generations. Magnetic fields, for example, influence the growth of stem cells in regenerative processes. Circadian rhythms are profoundly influenced by magnetic fields, a fact that will likely have an effect on mental as well as physical health. Even the brain responds to small perturbations of this field. One possible mechanism for the effects of weak magnetic fields on biological systems has been suggested to be the radical pair mechanism. The radical pair mechanism originated in the context of spin chemistry to describe how magnetic fields influence the yields of chemical reactions. This mechanism was subsequently incorporated into the field of quantum biology. Quantum biology, most generally, is the study of whether non-trivial quantum effects play any meaningful role in biological systems. The radical pair mechanism has been used most consistently in this context to describe the avian compass. Recently, however, a number of studies have investigated other biological contexts in which the radical pair might play a role, from the action of anaesthetics and antidepressants, to microtubule development and the proper function of the circadian clock. With this in mind, we investigated how magnetic fields ranging in strengths comparable to those of the planets in our own solar system would influence the dynamics of the radical pair, with possible

关键词： Stem cells

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Compact quantum kernel-based binary classifier

arXiv

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arXiv 2022年

作者： Blank, Carsten da Silva, Adenilton J. de Albuquerque, Lucas P. Petruccione, Francesco Kyungdeock Park, Daniel Data Cybernetics ssc GmbH Landbergs am Lech Germany Centro de Informática Universidade Federal de Pernambuco Pernambuco Recife Brazil School of Chemistry and Physics University of KwaZulu-Natal Durban South Africa South Africa School of Data Science and Computational Thinking Department of Physics Stellenbosch University Stellenbosch7602 South Africa Department of Applied Statistics Yonsei University Seoul03722 Korea Republic of Department of Statistics and Data Science Yonsei University Seoul03722 Korea Republic of

Quantum computing opens exciting opportunities for kernel-based machine learning methods, which have broad applications in data analysis. Recent works show that quantum computers can efficiently construct a model of a classifier by engineering the quantum interference effect to carry out the kernel evaluation in parallel. For practical applications of these quantum machine learning methods, an important issue is to minimize the size of quantum circuits. We present the simplest quantum circuit for constructing a kernel-based binary classifier. This is achieved by generalizing the interference circuit to encode data labels in the relative phases of the quantum state and by introducing compact amplitude encoding, which encodes two training data vectors into one quantum register. When compared to the simplest known quantum binary classifier, the number of qubits is reduced by two and the number of steps is reduced linearly with respect to the number of training data. The two-qubit measurement with post-selection required in the previous method is simplified to single-qubit measurement. Furthermore, the final quantum state has a smaller amount of entanglement than that of the previous method, which advocates the cost-effectiveness of our method. Our design also provides a straightforward way to handle an imbalanced data set, which is often encountered in many machine learning problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Cost effectiveness

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Citrus black spot caused by Phyllosticta citricarpa found on Valencia oranges in Botswana

New Disease Reports

引用

New Disease Reports 2024年第1期49卷

作者： Mawere, P. Coetzee, B. Ntlogelang, K. Mmapetla, Z. Sebua, K. van Niekerk, J. Carstens, E. Department of Crop Production Plant Protection Division Gaborone Botswana Department of Plant Pathology Stellenbosch University Stellenbosch South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Citrus Research International Nelspruit South Africa

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Geometric Linearization for Constraint Hamiltonian Systems

arXiv

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arXiv 2024年

作者： Paliathanasis, Andronikos Institute of Systems Science Durban University of Technology PO Box 1334 Durban4000 South Africa School for Data Science and Computational Thinking Stellenbosch University 44 Banghoek Rd Stellenbosch7600 South Africa Departamento de Matemáticas Universidad Católica del Norte Avda. Angamos 0610 Casilla Antofagasta1280 Chile

This study investigates the geometric linearization of constraint Hamiltonian systems using the Jacobi metric and the Eisenhart lift. We establish a connection between linearization and maximally symmetric spacetimes, focusing on the Noether symmetries admitted by the constraint Hamiltonian systems. Specifically, for systems derived from the singular Lagrangian L (N, qk, q·k) = 21N gijq·iq·j − NV (qk), where N and qi are dependent variables and dim gij = n, the existence of n(n2+1) Noether symmetries is shown to be equivalent to the linearization of the equations of motion. The application of these results is demonstrated through various examples of special interest. This approach opens new directions in the study of differential equation linearization. © 2024, CC BY.

关键词： Equations of motion

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Development of Phylum Chordata Sound Recognition System using Machine Learning and Deep Learning Techniques: A Case Study of Catfish

Development of Phylum Chordata Sound Recognition System usin...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Oluwafikunmi Adegbenga Eshilokun Sunday Oladayo Oladejo Stephen Obono Ekwe Departments of Computer Science and Electrical Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Department of Electrical Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa

Sound recognition refers to the technology or process of identifying and classifying different sounds or audio signals. This study aims to develop a sound recognition system using machine learning (ML) and deep learning (DL) techniques to classify and identify the gender of catfish based on their speech patterns. The study explores a range of supervised ML and DL algorithms, such as the Artificial Neural Network (ANN), Naïve Bayes (NB), and others, to predict the gender of catfish. The extracted features from the audio signals of the catfish serve as inputs to these algorithms, which produce binary predictions classifying the catfish samples as either male or female. This study further explores data analysis of the audio signals from the male and female catfish. The automated sound recognition system proves to be a more efficient and accurate approach compared to traditional methods, which rely on time-consuming visual observations. Evaluation metrics such as Accuracy, Sensitivity, and more, were employed to gauge the effectiveness of the algorithms in predicting the gender of catfish based on speech patterns. Among the employed algorithms, ANN and NB demonstrate the highest accuracy, achieving a score of 89.47%.

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Enhancing Chronic Kidney Disease Prediction Through data Preprocessing Optimization and Machine Learning Techniques

Enhancing Chronic Kidney Disease Prediction Through Data Pre...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Stephen Obono Ekwe Sunday Oladayo Oladejo Department of Compt. Science & Electrical Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria Department of Elect. Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa

Health care is the improvement of health through the avoidance and cure of diseases. Chronic kidney disease (CKD) poses a substantial health issue, ranking as the eighth leading cause of death and the tenth leading cause of disability-adjusted life years for both genders. This research aims to utilize machine learning (ML) algorithms to accurately predict the presence of CKD, which affects people worldwide. Timely identification of CKD is crucial to prevent it from progressing to its final and most critical phase. Machine learning algorithms, such as Decision Tree (DT), Naïve Bayes (NB), and others, are used in this study for the prediction of CKD. Various health parameters such as age, albumin level, and more are used as input into each ML algorithm to predict the presence of CKD. data preprocessing is carried out to improve the quality of the data for more accurate prediction. The data is analyzed to uncover the connections between CKD health parameters and their impact on the occurrence of CKD. This research further examines the influence of different train and test data ratios on the accuracy of the employed algorithms. Evaluation metrics such as accuracy, sensitivity, and more are employed to gauge the effectiveness of each algorithm in making predictions. Random Forest stood out as the most accurate algorithm, with a score of 96.25%. Naive Bayes and XGBoost demonstrated the highest sensitivity, achieving a perfect score of 100%.

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QAOA Performance in Noisy Devices: The Effect of Classical Optimizers and Ansatz Depth

arXiv

引用

arXiv 2023年

作者： Pellow-Jarman, Aidan McFarthing, Shane Sinayskiy, Ilya Park, Daniel K. Pillay, Anban Petruccione, Francesco University of KwaZulu-Natal Durban3629 South Africa Qunova Computing Inc Daejeon34051 Korea Republic of Stellenbosch University Department of Physics Stellenbosch7600 South Africa South Africa Department of Applied Statistics Yonsei University Seoul03722 Korea Republic of Department of Statistics and Data Science Yonsei University Seoul03722 Korea Republic of South Africa Stellenbosch University School of Data Science and Computational Thinking Stellenbosch7600 South Africa

The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions for combinatorial optimization problems. The QAOA utilizes a quantum-classical loop, consisting of a quantum ansatz and a classical optimizer, to minimize some cost function, computed on the quantum device. This paper presents an investigation into the impact of realistic noise on the classical optimizer and the determination of optimal circuit depth for the Quantum Approximate Optimization Algorithm (QAOA) in the presence of noise. We find that, while there is no significant difference in the performance of classical optimizers in a state vector simulation, the Adam and AMSGrad optimizers perform best in the presence of shot noise. Under the conditions of real noise, the SPSA optimizer, along with ADAM and AMSGrad, emerge as the top performers. The study also reveals that the quality of solutions to some 5 qubit minimum vertex cover problems increases for up to around six layers in the QAOA circuit, after which it begins to decline. This analysis shows that increasing the number of layers in the QAOA in an attempt to increase accuracy may not work well in a noisy device. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Measure of invertible dynamical maps under convex combinations of noninvertible dynamical maps

arXiv

引用

arXiv 2022年

作者： Jagadish, Vinayak Srikanth, R. Petruccione, Francesco Institute of Theoretical Physics Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Bidalur post Devanahalli Bengaluru562164 India School for Data Science and Computational Thinking Stellenbosch University Stellenbosch7600 South Africa Stellenbosch7600 South Africa Quantum Research Group School of Chemistry and Physics University of KwaZulu-Natal Durban4001 South Africa

We study the convex combinations of the (d + 1) generalized Pauli dynamical maps in a Hilbert space of dimension d. For certain choices of the decoherence function, the maps are noninvertible and they remain under convex combinations as well. For the case of dynamical maps characterized by the decoherence function (1−e−ct)/n with the decoherence parameter n and decay factor c, we evaluate the fraction of invertible maps obtained upon mixing, which is found to increase superexponentially with dimension d. © 2022, CC BY.

关键词： Quantum theory

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Exploratory data Fusion of Untargeted Multimodal Lc-Hrms with Annotation by Lcms-Tof-Ion Mobility: White Wine Case Study

SSRN

引用

SSRN 2022年

作者： Mafata, Mpho Stander, Maria Aletta Masike, Keabetswe Buica, Astrid School for Data Science and Computational Thinking Stellenbosch University Private Bag X1 Matieland7602 South Africa South African Grape and Wine Research Institute Department of Viticulture and Oenology Stellenbosch University Private Bag X1 Matieland7602 South Africa Central Analytical Facility Stellenbosch University Private Bag X1 Matieland7602 South Africa

Applied sciences have increased focus on omics studies which merge data science with analytical tools. These studies often result in large amounts of data produced and the objective is to generate meaningful interpretations from them. This can sometimes mean combining and integrating different data sets through data fusion techniques. The most strategic course of action when dealing with products of unknown profile is to use exploratory approaches. For omics, this means using untargeted analytical methods and exploratory data analysis techniques. In the current study aimed to perform data fusion on untargeted multimodal (negative and positive mode) liquid chromatography-high resolution mass spectrometry (LC-HRMS) data using multiple factor analysis (MFA). The data fusion results were interpreted using agglomerative hierarchical clustering (AHC) on biplot projections. The study reduced the thousands of spectral signals processed to less than a hundred features (a primary parameter combination of retention time and mass-to-charge ratios, RT_m/z). The correlations between cluster members (samples and features from) were calculated and the top 10% highly correlated features were identified for each cluster. These features where then tentatively identified using secondary parameters (drift time, ion mobility constant, and collision cross section (CCS) values) from the ion mobility spectra. These ion mobility (secondary) parameters can be used for future studies in wine chemical analysis and added to the growing list of annotated chemical signals in applied sciences. © 2022, The Authors. All rights reserved.

关键词： Cluster analysis

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

引用

science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Sunday Oladayo Oladejo Stephen Obono Ekwe Mbuyu Sumbwanyanbe Ernest Mnkandla Oluwagbemiga Omotayo Shoewu Departments of Computer Science and Elect. Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Department of Elect. Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Department of Electrical Engineering CSET University of South Africa Johannesburg South Africa Department for Computer Science CSET University of South Africa Johannesburg South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages.

关键词： Support vector machines Machine learning algorithms Accuracy data analysis Smart cities Network slicing Prediction algorithms

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