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arXiv 2024年

作者： Srivastava, Akriti Rashid, Mubasher Department of Mathematics and Statistics Indian Institute of Technology Kanpur Kanpur208016 India Department of Mathematics Dayananda Sagar College of Engineering Bangalore560078 India

Cellular decision-making (CDM) is a dynamic phenomenon crucial for development and diseases. CDM is often, if not invariably, controlled by regulatory networks defining interactions between genes and transcription factor proteins. Traditional studies have focussed on molecular switches such as positive feedback circuits that upon multimerization exhibit at most bistability. However, higher-order dynamics such as tristability is also prominent in many biological processes. It is thus imperative to identify a"minimal" circuit that can alone explain mono, bi, and tristable dynamics. In this work, we consider a two-component positive feedback network with an autoloop and explore these regimes of stability for different degrees of multimerization and the choice of Boolean logic functions. We report that this network can exhibit numerous dynamical scenarios such as bi-and tristability, hysteresis, and biphasic kinetics, explaining the possibilities of abrupt cell state transitions and the smooth state swap without a step-like switch. Specifically, while with monomeric regulation and competitive OR logic, the circuit exhibits mono-and bistability and biphasic dynamics, with non-competitive AND and OR logics only monostability can be achieved. To obtain bistability in the latter cases, we show that the autoloop must have (at least) dimeric regulation. In pursuit of higher-order stability, we show that tristability occurs with higher degrees of multimerization and with non-competitive OR logic only. Our results, backed by rigorous analytical calculations and numerical examples, thus explain the association between multistability, multimerization, and logic in this"minimal" circuit. Since this circuit underlies various biological processes, including epithelial-mesenchymal transition which often drives carcinoma metastasis, these results can thus offer crucial inputs to control cell state transition by manipulating dimerization and the logic of regulation in cells. © 2024, CC

关键词： Dynamics

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Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第1期2卷 40-51页

作者： Ferrando, Robert Pagnier, Laurent Mieth, Robert Liang, Zhirui Dvorkin, Yury Bienstock, Daniel Chertkov, Michael University of Arizona Program in Applied Mathematics and Department of Mathematics TucsonAZ85721 United States Department of Industrial and Systems Engineering at Rutgers University PiscatawayNJ08854 United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218-2625 United States Elec. and Comp. Eng. and Ralph o'Connor Sustainable Energy Institute Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218-2625 United States Columbia University IEOR New YorkNY10027 United States

This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels. © 2023 IEEE.

关键词： Costs

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FaceEngine: A Tracking-Based Framework for Real-Time Face Recognition in Video Surveillance System

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SN Computer Science 2024年第5期5卷 609页

作者： Imran, Ahsan Ahmed, Riad Hasan, Md Mehedi Ahmed, M. Helal Uddin Azad, A.K.M. Alyami, Salem A. Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka 1000 Bangladesh Department of Computer Science and Engineering BRAC University Dhaka 1212 Bangladesh Department of Management Information Systems University of Dhaka Dhaka 1000 Bangladesh Department of Mathematics and Statistics College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

The escalating concern over worldwide security and criminal activities has led to the emergence and significance of closed-circuit television video surveillance systems as an essential tool for diverse security purposes. These systems are extensively implemented and serve a crucial function in the surveillance and upkeep of security. The predominant purpose of video surveillance systems is to gather data primarily for evidentiary purposes subsequent to the occurrence of a criminal incident. The demand for video surveillance systems capable of autonomously monitoring and promptly identifying criminals or intruders in real-time is steadily increasing. Nevertheless, the existing facial recognition methods pose difficulties in reliably identifying individuals who are in motion within a video frame. Moreover, conventional approaches necessitate a substantial quantity of photographs in order to achieve precise recognition following the acquisition of an individual’s facial pattern. In order to tackle these concerns, we developed the implementation of input optimisation algorithms alongside a novel framework for real-time face recognition in the context of video surveillance. The input optimization algorithms, integrated with adaptive thresholding techniques, effectively reduce the need for manual outlier removal by actively identifying outliers for each specific case. The application of this optimisation strategy has demonstrated a substantial enhancement in both the efficiency and precision of our system in comparison to alternative baseline methodologies. Through the use of a reduced set of input image, our system is capable of attaining a heightened degree of improvements. Specifically, employing tracking and temporal voting techniques enables our system to accomplish a real-time face recognition accuracy of 90.91%. The findings of this study suggest that our approach has the potential to be a valuable tool in various applications that necessitate rapid and precise fac

关键词： Computer vision Deep learning Face recognition Image processing Tracking Video surveillance

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Numerical Analysis of Novel Coronavirus (2019-nCov) Pandemic Model with Advection

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Computers, Materials & Continua 2021年第6期67卷 2933-2953页

作者： Shumaila Azam Nauman Ahmed Ali Raza Muhammad Sajid Iqbal Muhammad Rafiq Ilyas Khan Kottakkaran Sooppy Nisar Muhammad Ozair Ahmad Zafar Iqbal Department of Mathematics and Statistics The University of LahoreLahorePakistan Department of Mathematics National College of Business Administration and Economics LahorePakistan Faculty of Engineering University of Central Punjab Lahore Pakistan Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City72915Vietnam Department of Mathematics College of Arts and Science at Wadi AldawaserPrince Sattam Bin Abdulaziz University11991AlkharjKingdom of Saudi Arabia Department of Mathematics University of Management and TechnologyLahorePakistan

Recently,the world is facing the terror of the novel corona-virus,termed as *** health institutes and researchers are continuously striving to control this *** this article,the SEIAR(susceptible,exposed,infected,symptomatically infected,asymptomatically infected and recovered)infection model of COVID-19 with a constant rate of advection is studied for the disease propagation.A simple model of the disease is extended to an advection model by accommodating the advection process and some appropriate parameters in the *** continuous model is transposed into a discrete numerical model by discretizing the domains,*** analyze the disease dynamics,a structure preserving non-standard finite difference scheme is *** steady states of the continuous system are described i.e.,virus free steady state and virus existing steady *** results show that both the steady states of the numerical design coincide with the fixed points of the continuous SEIAR *** of the state variables is ensured by applying the M-matrix theory.A result for the positivity property is *** the proposed numerical design,two different types of the stability are *** stability and linear stability for the projected scheme is examined by applying some standard *** Neuman stability test is applied to ensure linear *** reproductive number is described and its pivotal role in stability analysis is also *** and convergence of the numerical model is also *** graphs are presented via computer simulations to prove the worth and efficiency of the quarantine factor is explored graphically,which is helpful in controlling the disease *** the end,the conclusion of the study is also rendered.

关键词： 2019-nCov infectious model with advection reproductive number stability analysis numerical graphs

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Non-Newtonian Fluid Flow in Micromachines: Magnetized Carreau Fluid Behavior with Slip and Viscous Dissipation at Inclined Stagnation Points

SSRN

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SSRN 2024年

作者： Liu, Hongjuan Khan, Shahzeb Dehraj, Sanaullah Haider, Ali Liu, Shengjun Ayub, Assad School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Mathematics & Statistics Hazara University Manshera21300 Pakistan Department of Mathematics Government College Mansehra21300 Pakistan School of Mathematics and Statistics Xi’an Key Laboratory of Scientific Computation and Applied Statistics Northwestern Polytechnical University Xi’an710072 China Department of Mathematics and Statistics Quaid-e-Awam University of Engineering Science and Technology 67405 Sakrand Road Nawabshah Pakistan

Non-Newtonian fluid flow in micromachines has unique rheological properties and can be leveraged to enhance the performance and functionality of microscale devices. Viscosity of non-Newtonian fluid under different shear rates, can improve the efficiency of fluid transport, mixing, and control within micromachines. This paper presents the analysis of stagnation point flow of inclined magnetized Carreau fluid with slip constraints. This study employs a spectral relaxation method to analyze the flow and investigate the impacts of numerous parameters, such as the inclination angle and magnetic field strength. The study provides numerous applications in field of Chemical engineering and aerospace engineering. © 2024, The Authors. All rights reserved.

关键词： Porous materials

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms 3

Bitcoin price prediction based on financial data, technical ...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Arjmand, Masoud Kazeminia, Saman Sajedi, Hedieh University of Tehran Kish International Campus Department of Computer Engineering Kish Iran NHL Stenden University of Applied Science Department of Computer Vision and Data Science Leeuwarden Netherlands University of Tehran Department of Mathematics Statistics and Computer Science Tehran Iran

ISBN: (纸本)9798350381580

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model's efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results. © 2024 IEEE.

关键词： Forecasting

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Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces 38

Taming "data-hungry" reinforcement learning? Stability in co...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Duan, Yaqi Wainwright, Martin J. Department of Technology Operations and Statistics Stern School of Business New York University New YorkNY10012 United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering and Computer Science Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States

We introduce a novel framework for analyzing reinforcement learning (RL) in continuous state-action spaces, and use it to prove fast rates of convergence in both off-line and on-line settings. Our analysis highlights two key stability properties, relating to how changes in value functions and/or policies affect the Bellman operator and occupation measures. We argue that these properties are satisfied in many continuous state-action Markov decision processes. Our analysis also offers fresh perspectives on the roles of pessimism and optimism in off-line and on-line RL. © 2024 Neural information processing systems foundation. All rights reserved.

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Mathematical modeling and analysis of schistosomiasis transmission dynamics

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第4期12卷 58-76页

作者： Umar T.Mustapha Salihu S.Musa Muhammad A.Lawan Aliyu Abba Evren Hincal Musa D.Mohammed Bashir D.Garba Rabiu B.Yunus Shehu A.Adamu Daihai He Department of Mathematics Near East University TRNC Mersin 10 Turkey Department of Mathematics Federal University Dutse Nigeria Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Department of Mathematics Kano University of Science and Technology Wudil Nigeria Department of Statistics Jigawa State Polytechnic DutseNigeria Department of Computer Engineering Institute of Continuing Education Kano University of Science and Technology Wudil Nigeria Department of Mathematics and Statistics Kano State Polytechnic Nigeria

Schistosomiasis is a parasitic disease from the family of Schistosomatidae and genus Schistosoma,which is caused by blood *** disease is endemic in many countries and still a serious threat to global public health and *** this paper,a new deterministic model is designed and analyzed qualitatively to explore the dynamics of schistosomiasis transmission in human,cattle and snail *** from our mathematical analysis show that the model has a disease-free equilibrium(DFE)which is locally asymptotically stable(LAS)whenever a particular epidemiological threshold quantity,also known as basic reproduction number(R0)is less than *** analysis shows that the model has a unique endemic equilibrium(EE)which is globally asymptotically stable whenever R0>1 and unstable when R0<***,we adopt partial rank correlation coefficient for sensitivity analysis to reveal the most important parameters for effective control and mitigation of schistosomiasis disease in a ***,we obtain some numerical results by simulating the entire dynamics of the model,which show that the infections in the compartments of each population decrease with respect to *** further indicates that avoiding contact with infected human,cattle or infested water is vital to prevent the spread of schistosomiasis disease infection.

关键词： Schistosomiasis disease basic reproduction number stability analysis sensitivity analysis

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Insight into the Hamilton and Crosser model for ternary hybrid nanofluid flow over a Riga wedge with heterogeneous catalytic reaction

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The European Physical Journal Special Topics 2024年 1-22页

作者： Obalalu, A. M. Shah, S. H. A. M. Darvesh, Adil Khan, Umair Ishak, Anuar Adegbite, Peter Ojewola, O. B. Muhammad, Taseer Galal, Ahmed M. Department of Mathematics and Statistics Kwara State University Malete Nigeria Department of Mathematics and Social Sciences Sukkur IBA University Sukkur Pakistan Department of Mathematics and Statistics Hazara University Mansehra Pakistan Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Mathematics Faculty of Science Sakarya University Serdivan/Sakarya Turkey Department of Computer Science and Mathematics Lebanese American University Byblos Lebanon Department of Pure and Applied Mathematics LAUTECH Ogbomosho Nigeria Department of Mathematics College of Science King Khalid University Abha Saudi Arabia Department of Mechanical Engineering College of Engineering in Wadi Alddawasir Prince Sattam bin Abdulaziz University Wadi Alddawasir Saudi Arabia Production Engineering and Mechanical Design Department Faculty of Engineering Mansoura University Mansoura Egypt

The present article is designed to study the Hamilton and Crosser model applied to the flow of ternary hybrid nanofluids over a Riga wedge, incorporating the effects of heterogeneous catalytic reactions. The complex interactions within the ternary hybrid nanofluids, comprising three distinct nanoparticles suspended in a base fluid, present significant challenges in accurately predicting flow and thermal characteristics. The Hamilton and Crosser model, known for its efficacy in determining the thermal conductivity of composite materials, is employed to analyze this intricate system. The analysis reveals the model's potential in offering a comprehensive understanding of the thermal and fluid dynamics involved, highlighting its suitability for predicting the behavior of ternary hybrid nanofluids in the presence of catalytic reactions. The governing model equations and boundary conditions are non-dimensionalized by introducing suitable similarity transformations. Thereafter, the computational Chebyshev collocation spectral technique implemented in the MATHEMATICA 11.3 software is used to calculate the numerical solution. The study reveals that the Casson parameter has a negative influence on the velocity distribution, causing it to reduce as the Casson parameter rises. This research contributes to the advancement of modeling techniques for complex fluid systems, with implications for enhanced design and optimization in various industrial and engineering applications.

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Shedding Light on Problems with Hyperbolic Graph Learning

arXiv

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arXiv 2024年

作者： Katsman, Isay Gilbert, Anna Department of Applied Mathematics Yale University United States Department of Applied Mathematics Department of Statistics Data Science Department of Electrical Engineering Yale University United States

Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance on a variety of graph tasks, node classification and link prediction included. Claims have also been made about the geometric suitability of particular hierarchical graph datasets to representation in hyperbolic space. Despite these claims, our work makes a surprising discovery: when simple Euclidean models with comparable numbers of parameters are properly trained in the same environment, in most cases, they perform as well, if not better, than all introduced hyperbolic graph representation learning models, even on graph datasets previously claimed to be the most hyperbolic (Chami et al., 2019) as measured by Gromov δ-hyperbolicity (i.e., perfect trees). This observation gives rise to a simple question: how can this be? We answer this question by taking a careful look at the field of hyperbolic graph representation learning as it stands today, and find that a number of papers fail to diligently present baselines, make faulty modelling assumptions when constructing algorithms, and use misleading metrics to quantify geometry of graph datasets. We take a closer look at each of these three problems, elucidate the issues, perform an analysis of methods, and introduce a parametric family of benchmark datasets to ascertain the applicability of (hyperbolic) graph neural networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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