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SSRN 2022年

作者： Qasymeh, Montasir Hunza, Muhammad Asjad, M. Abbas, T. Teklu, Berihu Eleuch, Hicheh Department of Physics COMSATS University Islamabad45550 Pakistan Department of Applied Mathematics and Sciences Khalifa University Abu Dhabi127788 United Arab Emirates Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi5991 United Arab Emirates Khalifa University Abu Dhabi127788 United Arab Emirates Department of Applied Physics and Astronomy University of Sharjah Sharjah United Arab Emirates Institute for Quantum Science and Engineering Texas AM University College StationTX77843 United States Abu Dhabi University United Arab Emirates

We consider a hybrid atom-optomechanical system incorporating N identical $\Lambda$-type atoms. The system is subjected to dual optical and phononic drives. We show that by exploiting the optomechanical linear and quadratic interactions, multiple electromagnetic transparency windows are attained. Furthermore, owing to the incorporated mechanical pump, the transparency windows are controlled and tuned. For instance, by adjusting the phase of the external mechanical pump, additional controlling parameters are enabled, and the absorption/emission profiles are enhanced. Our present study provides an efficient approach to modifying propagating signals inside the quantum devices incorporating cavity-optomechanical systems. © 2022, The Authors. All rights reserved.

关键词： Transparency

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CellMirror: Deciphering Cell Populations from Spatial Transcriptomics Data by Interpretable Contrastive Learning

CellMirror: Deciphering Cell Populations from Spatial Transc...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Junjie Xia Jiangnan Cui Zhenyu Huang Shuangquan Zhang Fang Yao Yijian Zhang Chunman Zuo Department of Applied Mathematics Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Institute Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Affiliated Shenzhen Maternity and Child Healthcare Hospital Institute of Maternal and Child Medicine Southern Medical University Shenzhen China Department of General Surgery Xinhua Hospital Affiliated to Shanghai Jiao Tong University of Medicine Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial transcriptomics (ST) has enabled us elucidating tumor microenvironments, however, the technological limitations without single-cell resolution severely hinder its application. Here, we propose CellMirror, an interpretable contrastive learning model to decipher heterogeneous cell populations in ST data by single-cell RNA-sequencing (scRNA-seq) data. Specifically, CellMirror learns the disentangled shared (representing biological variations in both data) and salient features (specific to ST data) by two contrastive variational encoders, while constructing the relations between genes and features by a shared linear decoder. In various cancer samples, CellMirror outperforms other tools in learning common features for label transfer, and interpretation of features. Particularly, in breast cancer studies, CellMirror detects finer domains in ST data missed by other methods, and is robust to dissect cell populations in ST data using independent scRNA-seq data. These results demonstrate applications of CellMirror in interpreting the complex tumor structure in ST data by integrating scRNA-seq data.

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Continuous and Discrete Data Assimilation with Noisy Observations for the Rayleigh-Benard Convection: A Computational Study

arXiv

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arXiv 2022年

作者： Hammoud, Mohamad Abed El Rahman Le Maître, Olivier Titi, Edriss S. Hoteit, Ibrahim Knio, Omar Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Centre de Mathématiques Appliquées CNRS and Inria Ecole Polytechnique Palaiseau91120 France Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Mathematics Texas A & M University College StationTX77843 United States Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Obtaining accurate high-resolution representations of model outputs is essential to describe the system dynamics. In general, however, only spatially- and temporally-coarse observations of the system states are available. These observations can also be corrupted by noise. Downscaling is a process/scheme in which one uses coarse scale observations to reconstruct the high-resolution solution of the system states. Continuous Data Assimilation (CDA) is a recently introduced downscaling algorithm that constructs an increasingly accurate representation of the system states by continuously nudging the large scales using the coarse observations. We introduce a Discrete Data Assimilation (DDA) algorithm as a downscaling algorithm based on CDA with discrete-in-time nudging. We then investigate the performance of the CDA and DDA algorithms for downscaling noisy observations of the Rayleigh-Bénard convection system in the chaotic regime. In this computational study, a set of noisy observations was generated by perturbing a reference solution with Gaussian noise before downscaling them. The downscaled fields are then assessed using various error- and ensemble-based skill scores. The CDA solution was shown to converge towards the reference solution faster than that of DDA but at the cost of a higher asymptotic error. The numerical results also suggest a quadratic relationship between the 2 error and the noise level for both CDA and DDA. Cubic and quadratic dependences of the DDA and CDA expected errors on the spatial resolution of the observations were obtained, respectively. © 2022, CC BY.

关键词： Errors

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A probability-based fuzzy algorithm for multi-attribute decision-analysis with application to aviation disaster decision-making

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Decision Analytics Journal 2023年 8卷

作者： Agrawal, Anurag Vijay Soni, Mukesh Keshta, Ismail Savithri, V. Abdinabievna, Pardaeva Shakhnoza Singh, Shweta Department of Electronics and Communication Engineering J.P. Institute of Engineering and Technology Uttar Pradesh Meerut 250001 India Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali 140413 India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of science and humanities/Mathematics Karpagam Academy of Higher education (Deemed to be University) Eachanari Tamil Nadu Coimbatore 641021 India The department of Bank Accounting and Audit Tashkent Institute of Finance Tashkent Uzbekistan Electronics and communication department IES college of technology Bhopal India

This research addresses the challenges of multi-attribute group decision-making in a hesitant fuzzy environment, where randomness and uncertainty introduce biases into practical making choices. A probability-based double uncertain fuzzy (PDHF) algorithm is proposed to overcome these challenges. The algorithm constructs probability-based double hesitant fuzzy information matrices for each decision expert. The maximum deviation and entropy methods determine objective weights for decision experts and attribute indicators. Combining an improved scoring function and deviation function generates a comprehensive decision evaluation information matrix for decision experts. Subsequently, a decision algorithm that integrates probability-based double hesitant fuzzy sets with the preference ranking organization method (PROM) is employed to obtain the final decision result. As part of a case study, the algorithm is used to evaluate emergency action plans for aviation disaster accidents. Comparing the results with those from the TOPSIS, VIKOR, and PDHFS decision-making algorithms shows that the suggested PDHF algorithm works well and can be trusted for the multi-attribute group decision-making problems in hesitant fuzzy environments. © 2023 The Author(s)

关键词： Aviation disaster accidents Entropy method Hesitant fuzzy environment Making choices Maximum deviation method Multi-attribute Probability-based

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Effects of the Cattaneo–Christov flux model on thermally radiative flow of tetra hybrid nanofluid with surface tension gradient

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Case Studies in Thermal engineering 2025年 72卷

作者： Mostafa Mohamed Okasha Munawar Abbas Faiza Benabdallah Talib K. Ibrahim Tatyana Orlova Ali Akgül Murad Khan Hassani Abdullah A. Faqihi Hakim AL. Garalleh Ahmed M. Galal Department of Mechanical Engineering College of Engineering Northern Border University Arar Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 602105 Tamil Nadu India Department of Mathematics The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan Department of Industrial and Systems Engineering College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Department of Physics and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Siirt University Art and Science Faculty Department of Mathematics 56100 Siirt Turkey Department of Computer Engineering Biruni University Topkapı 34010 Istanbul Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Mersin 10 Nicosia Turkey Applied Science Research Center Applied Science Private University Amman Jordan Ghazni University Department of Mathematics Afghanistan Department of Industrial Engineering College of Engineering and Computer Science Jazan University P. O. Box 706 Jazan 45142 Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia Department of Mechanical Engineering College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Saudi Arabia Production Engineering and Mechanical Design Department Faculty of Engineering Mansoura University P.O 35516 Mansoura Egypt

The importance of thermal radiation and Stefan blowing on the boundary layer flow of tetra hybrid nanofluid flow across a disk are examined in the present work employing the Cattaneo-Christov flux model. Additionally, the Marangoni convection effect and Stefan blowing are examined. Tetra-hybrid nanofluids are employed in sophisticated thermal systems like heat exchangers, solar energy systems, and cooling technologies because of their improved thermal conductivity and heat transmission properties. Under microgravity conditions, a surface tension differential in the plasma causes the intriguing scientific phenomenon known as Marangoni convection. Important uses of the Marangoni effect include welding, convection or Bernard cells, crystal growth, electron beam melting of metals, soap film stability, and more. In order to improve heat exchangers in chemical processes, optimize thermal management in power plants, and provide effective cooling systems for intricate machinery, the model is essential. Furthermore, it is a flexible tool for contemporary engineering solutions that advance sustainable practices by improving the energy efficiency of renewable technologies. To solve the problem, the Homotopy analysis approach is applied, and graphs are used to show the analytical outcomes. The temperature and velocity fields rise as the Stefan blowing parameter rises, while the solutal profile falls.

关键词： Thermal radiation Cattaneo-Christov model Tetra hybrid nanofluid Stefan blowing Heat generation

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Mathematical modelling and projection of Buruli ulcer transmission dynamics using classical and fractional derivatives: A case study of Cameroon

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Partial Differential Equations in applied mathematics 2023年 8卷

作者： Fandio, Rubin Abboubakar, Hamadjam Fouda, Henri Paul Ekobena Kumar, Anoop Nisar, Kottakkaran Sooppy The University of Yaoundé I Laboratory of Biophysics P.O. Box 812 Cameroon The University of Ngaoundéré University Institute of Technology Department of Computer Engineering P.O. Box 455 Ngaoundéré Cameroon The University of Yaoundé I Faculty of Science Department of Physics P.O. Box 812 Cameroon Central University of Punjab School of Basic and Applied Sciences Department of Mathematics and Statistics Bathinda Punjab 151001 India Department of Mathematics College of Science and Humanities in Alkharj Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia

The goal of this work is to derive and study a new model which traduces the transmission dynamics of the Buruli ulcer (BU), in which we replace mass action incidences with standard incidences, consider latent period, and by using both integer and fractional derivatives. We first present reported data of Buruli in Cameroon between 01/01/2001 and 31/12/2014, and the statistical model that approximates real data as closely as possible, and thus make a forecasting in the next sixteen years. After that, we formulate a Susceptible humans-Exposed humans-Infectious humans-Recovered humans-Susceptible humans;Susceptible vectors-Infectious vectors (SEIRS-SI) type compartmental model of Buruli Ulcer using integer derivative. We compute the basic reproduction number denoted by R₀ and prove the asymptotic stability of the Buruli-free equilibrium whenever R₀<1. Then, for R₀>1, we prove the existence of an unique endemic equilibrium point and its global stability using the general theory of Lyapunov. We perform parameter estimation to calibrate our model using real data from Cameroon, while sensitivity analysis is conducted to determine important parameters in the BU dynamics. From this model calibration, we obtain R₀=2.0843 which is greater than one and implies that BU ulcer is endemic in the country. To determine whether if or not the BU admits one or multiple waves, we compute the strength number denoted by A₀. We find that A₀≈17>0 which means that Bu admits multiple epidemic waves. We then replace integer derivative with the Caputo fractional derivative. Asymptotic stability of equilibrium points are also performed for the fractional model. This follows by the proof of existence and the uniqueness of solutions of the fractional model. We then construct a numerical scheme based on the Adams–Bashforth–Moulton (ABM) Method. Theoretical results are validated from numerical simulations. These last also permits to eva

关键词： Asymptotic stability Buruli ulcer Caputo derivative Parameter estimation Strength number

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Financial Fraud Detection using Quantum Graph Neural Networks

arXiv

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arXiv 2023年

作者： Innan, Nouhaila Sawaika, Abhishek Dhor, Ashim Dutta, Siddhant Thota, Sairupa Gokal, Husayn Patel, Nandan Khan, Muhammad Al-Zafar Theodonis, Ioannis Bennai, Mohamed Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M’sick Hassan II University of Casablanca Morocco Quantum Formalism Fellow Zaiku Group Ltd Liverpool United Kingdom Indian Institute of Technology Bombay India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India Dwarkadas J. Sanghvi College of Engineering India Guru Nanak Institutions Technical Campus Hyderabad India ASTI Academy Dubai United Arab Emirates Vellore Institute of Technology Chennai India School of Computer Science and Applied Mathematics University of the Witwatersrand 1 Jan Smuts Ave Braamfontein Johannesburg Gauteng2000 South Africa Department of Physics School of Applied Mathematical and Physical Sciences National Technical University of Athens Greece

Financial fraud detection is essential for preventing significant financial losses and maintaining the reputation of financial institutions. However, conventional methods of detecting financial fraud have limited effectiveness, necessitating the need for new approaches to improve detection rates. In this paper, we propose a novel approach for detecting financial fraud using Quantum Graph Neural Networks (QGNNs). QGNNs are a type of neural network that can process graph-structured data and leverage the power of Quantum Computing (QC) to perform computations more efficiently than classical neural networks. Our approach uses Variational Quantum Circuits (VQC) to enhance the performance of the QGNN. In order to evaluate the efficiency of our proposed method, we compared the performance of QGNNs to Classical Graph Neural Networks using a real-world financial fraud detection dataset. The results of our experiments showed that QGNNs achieved an AUC of 0.85, which outperformed classical GNNs. Our research highlights the potential of QGNNs and suggests that QGNNs are a promising new approach for improving financial fraud detection. © 2023, CC BY.

关键词： Timing circuits

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The complexity of nonconvex-strongly-concave minimax optimization

arXiv

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arXiv 2021年

作者： Zhang, Siqi Yang, Junchi Guzmán, Cristóbal Kiyavash, Negar He, Niao UrbanaIL United States Department of Applied Mathematics University of Twente Netherlands. And Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile College of Management of Technology EPFL Switzerland Department of Computer Science ETH Zürich Switzerland

This paper studies the complexity for finding approximate stationary points of nonconvex-strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth finite-sum settings. We establish nontrivial lower complexity bounds of Ω(√κ∆Lǫ−2) and Ω(n + √nκ∆Lǫ−2) for the two settings, respectively, where κ is the condition number, L is the smoothness constant, and ∆ is the initial gap. Our result reveals substantial gaps between these limits and best-known upper bounds in the literature. To close these gaps, we introduce a generic acceleration scheme that deploys existing gradient-based methods to solve a sequence of crafted strongly-convex-strongly-concave subproblems. In the general setting, the complexity of our proposed algorithm nearly matches the lower bound;in particular, it removes an additional poly-logarithmic dependence on accuracy present in previous works. In the averaged smooth finite-sum setting, our proposed algorithm improves over previous algorithms by providing a nearly-tight dependence on the condition number. Copyright © 2021, The Authors. All rights reserved.

关键词： Number theory

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A Recursive Bayesian Solution for the Excess Over Threshold Distribution with Stochastic Parameters

A Recursive Bayesian Solution for the Excess Over Threshold ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Douglas E. Johnston Petar M. Djuric Department of Applied Mathematics Farmingdale State College U.S.A. Department of Electrical and Computer Engineering Stony Brook University U.S.A.

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

In this paper, we propose a new approach for analyzing extreme values that are witnessed in financial markets. Our goal is to compute the predictive distribution of extreme events that are clustered in time and, as opposed to modeling just the maximum of a block of observations, we model the conditional tail for the underlying random process. We apply a stochastic parameterization of the generalized Pareto distribution to model the asymptotic behavior of this conditional tail, or excess distribution. We utilize a Rao-Blackwellized particle filter, which reduces the parameter space, and we derive a concise, recursive solution for the parameters of the distribution. Using the filter, the predictive distribution of the parameters, conditioned on the past data, is computed at each sample-time. We test our model on simulated data which show an improvement over the block-maximum and the maximum likelihood approaches both in parameter estimation and predictive performance.

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Decoding the Functional Interactome of Non-model Organisms with PHILHARMONIC 29th

Decoding the Functional Interactome of Non-model Organisms...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Sledzieski, Sam Versavel, Charlotte Singh, Rohit Ocitti, Faith Devkota, Kapil Kumar, Lokender Shpilker, Polina Roger, Liza Yang, Jinkyu Lewinski, Nastassja Putnam, Hollie Berger, Bonnie Klein-Seetharaman, Judith Cowen, Lenore Center for Computational Biology Flatiron Institute New YorkNY United States Department of Computer Science Tufts University MedfordMA United States Departments of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Biotechnology Faculty of Applied Sciences and Biotechnology Shoolini University Himachal Pradesh Solan173229 India School of Molecular Sciences and School of Ocean Futures Arizona State University TempeAZ United States Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Chemical and Life Science Engineering Virginia Commonwealth University RichmondVA United States Department of Biological Sciences University of Rhode Island KingstonRI United States Computer Science and Artificial Intelligence Laboratory and Department of Mathematics MIT CambridgeMA United States School of Molecular Sciences and College of Health Solutions Arizona State University PhoenixAZ United States

ISBN: (纸本)9783031902512

We introduce PHILHARMONIC, a computational framework that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our novel clustering approach produces highly informative functional modules by de-noising the predicted network. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We initially perform remote homology-based functional annotation by leveraging hmmscan and GODomainMiner to assign initial functions to proteins at large evolutionary distances;our clusters then enable us to newly assign functions to uncharacterized proteins through "function by association." We validate the ability of PHILHARMONIC to recover gold-standard functional enrichments in the well-annotated fruit fly D. melanogaster, and apply it to investigate stress response in the reef-building coral P. damicornis and its algal symbiont C. goreaui. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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