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Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements

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arXiv 2024年

作者： Tucker, Kris Rege, Amit Kiran Smith, Conor Monteleoni, Claire Albash, Tameem Department of Applied Math University of Colorado Boulder United States Department of Computer Science University of Colorado Boulder United States Center for Quantum Information and Control University of New Mexico University of New Mexico AlbuquerqueNM United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States INRIA Centre de Recherche de Paris France Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States

We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning where the weak measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised and unsupervised contexts. We demonstrate that the model benefits from larger training sets establishing that it is in fact "learning," and we show robustness to errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single particle relaxation. © 2024, CC BY.

关键词： Qubits

Real time analysis of Intelligent placing system for vehicles using IOT with Deep learning

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Materials Today: Proceedings 2022年 51卷 339-343页

作者： Sushma Jaiswal Dilip Kumar Sharma Tarun Jaiswal Bhimraj Basumatary Mohit Tiwari Tripti Tiwari Computer Science & Information Technology (CSIT) Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur Chhattisgarh India Department of Mathematics Jaypee University of Engineering and Technology Guna MP India Department of Computer Application National Institute of Technology (NIT) Raipur Chhattisgarh 492010 India Department of Math. Sciences Bodoland University Kokrajhar Assam India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering A-4 Rohtak Road Paschim Vihar Delhi India Department of Management Studies BVIMR A-4 Rohtak Road Paschim Vihar Delhi India

There is an enormous in number of vehicles in last few years. So, it becomes important to make effective use of technology to enable inconvenience free parking at public and/or private places. In traditional placing systems, drivers face difficulty in finding available placing slots. These systems ignore the fact of placing the vehicles on roads, time management in peak hours, wrong parking of a vehicle in a placing slot. Moreover, the traditional systems require more human intervention in a placing zone. To deal with above said issues, there is an urgent requirement of developing Real Time Intelligent placing system. In this proposed system a Real Time Intelligent placing system based on IOT and Deep learning techniques to answer the real time management of placing and uncertainties. The proposed solution utilizes smart sensors, cloud computing and cyber physical system. Development of graphical user interface for administrator and end-user is a major challenge as it requires ensuring smooth monitoring, control and security of placing system. Moreover, it needs to establish effortless coordination with an end-user. The proposed system is successful in smartly addressing the challenges such as indicating status of placing slot well in advance to end-user, use of reserved and unreserved placing slots, wrong placing, unauthorized placing, real time analysis of free and occupied slots, detecting multiple objects in a placing slot such as bike in car slot, fault detection in one or more components and traffic management during peak hours. The system reduces the human work, saves time, money and energy.

关键词： Real Tisme Intelligent Placing Image processing Internet of Things Cloud computing

A well-posedness result for an extended KdV equation

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Partial Differential Equations in applied math.matics 2024年 10卷

作者： Berjawi, M. El Arwadi, T. Israwi, S. Department of mathematics and Computer science Faculty of science Beirut Arab University Beirut Lebanon Laboratoire de mathématiques-EDST Faculté des sciences et EDST Université Libanaise Hadat Lebanon Arts Sciences and Technology University in Lebanon CRAMS: Center for Research in Applied Mathematics and Statistics Faculty of sciences Beirut Lebanon

Among the most interesting things Russell discovered was there is a math.matical relation between the height of the wave, the depth of the wave when water at rest and the speed at which the wave travels. KdV equation was the first math.matical unidimensional model describing the phenomenon observed by Scott-Russell. Despite its nonlinearity and dispersivity terms, KdV equation has being interesting because of its integrability, and specially being a soliton equation. In this paper, we aim to analyze an extended KdV equation. The construction of approximate solution of this equation will be done in H^s(R), for s>[Formula presented], using the modified energy method, its uniqueness and continuity with respect to the initial data is presented referring to a Bona–Smith technique. © 2024

关键词： KdV Model Well posedness

Linear fractional Galton-Watson processes in random environment and perpetuities

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arXiv 2021年

作者： Alsmeyer, Gerold Inst. Math. Stochastics Department of Mathematics and Computer Science University of Münster Orléans-Ring 10 MünsterD-48149 Germany

Linear fractional Galton-Watson branching processes in i.i.d. random environment are, on the quenched level, intimately connected to random difference equations by the evolution of the random parameters of their linear fractional marginals. On the other hand, any random difference equation defines an autoregressive Markov chain (a random affine recursion) which can be positive recurrent, null recurrent and transient and which, as the forward iterations of an iterated function system, has an a.s. convergent counterpart in the positive recurrent case given by the corresponding backward iterations. The present expository article aims to provide an explicit view at how these aspects of random difference equations and their stationary limits, called perpetuities, enter into the results and the analysis, especially in quenched regime. Although most of the results presented here are known, we hope that the offered perspective will be welcomed by some readers. Copyright © 2021, The Authors. All rights reserved.

关键词： Difference equations

Analysing the use of graphs to represent the results of Systematic Reviews in Software Engineering

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arXiv 2021年

作者： Felizardo, Katia Romero Riaz, Mehwish Sulayman, Muhammad Mendes, Emília MacDonell, Stephen G. Maldonado, José Carlos Department of Computer Science University of São Paulo - USP São Carlos Brazil Department of Computer Science University of Auckland Auckland New Zealand SERL Comp. & Math. Sciences AUT University Auckland New Zealand

The presentation of results from Systematic Literature Reviews (SLRs) is generally done using tables. Prior research suggests that results summarized in tables are often difficult for readers to understand. One alternative to improve results' comprehensibility is to use graphical representations. The aim of this work is twofold: first, to investigate whether graph representations result is better comprehensibility than tables when presenting SLR results;second, to investigate whether interpretation using graphs impacts on performance, as measured by the time consumed to analyse and understand the data. We selected an SLR published in the literature and used two different formats to represent its results - tables and graphs, in three different combinations: (i) table format only;(ii) graph format only;and (iii) a mixture of tables and graphs. We conducted an experiment that compared the performance and capability of experts in SLR, as well as doctoral and masters students, in analysing and understanding the results of the SLR, as presented in one of the three different forms. We were interested in examining whether there is difference between the performance of participants using tables and graphs. The graphical representation of SLR data led to a reduction in the time taken for its analysis, without any loss in data comprehensibility. For our sample the analysis of graphical data proved to be faster than the analysis of tabular data. However, we found no evidence of a difference in comprehensibility whether using tables, graphical format or a combination. Overall we argue that graphs are a suitable alternative to tables when it comes to representing the results of an SLR. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

Neural FIM for learning Fisher information metrics from point cloud data

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arXiv 2023年

作者： Fasina, Oluwadamilola Huguet, Guillaume Tong, Alexander Zhang, Yanlei Wolf, Guy Nickel, Maximilian Adelstein, Ian Krishnaswamy, Smita Applied Math Program Yale University New HavenCT United States Mila - Quebec AI Institute MontrealQC Canada Department of Mathematics and Statistics Université de Montréal MontrealQC Canada Department of Computer Science and Operations Research Université de Montréal MontrealQC Canada FAIR Meta AI United States Department of Math Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells). Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

Supervised multiple kernel learning approaches for multi-omics data integration

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arXiv 2024年

作者： Briscik, Mitja Tazza, Gabriele Vidács, László Dillies, Marie-Agnès Déjean, Sébastien Institut de Mathématiques de Toulouse UMR5219 CNRS UPS Université de Toulouse Cedex 9 Toulouse31062 France Department of Computer Science Applied Artificial Intelligence Group University of Szeged Szeged6720 Hungary Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub ParisF‑75015 France

Background: Advances in high‑throughput technologies have originated an ever‑ increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learn‑ ing (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi‑omics inputs, despite being an underused tool in genomic data mining. Results: We provide novel MKL approaches based on different kernel fusion strategies To learn from the meta‑kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL‑ based models can outperform more complex, state‑of‑the‑art, supervised multi‑omics integrative approaches. Conclusion: Multiple kernel learning offers a natural framework for predictive models in multi‑omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio‑ data mining research, biomarker discovery and further development of methods for heterogeneous data integration. © 2024, CC BY.

关键词： Self-supervised learning

Radial kinks in a Schwarzschild-like geometry

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arXiv 2024年

作者： Caputo, Jean-Guy Dobrowolski, Tomasz Gatlik, Jacek Kevrekidis, Panayotis G. Laboratoire de Mathématiques INSA de Rouen Avenue de l’Université Saint-Etienne du Rouvray76801 France Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchoŗ azych 2 Cracow30-084 Poland AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow30-059 Poland Department of Mathematics and Statistics University of Massachusetts AmherstMA01003-4515 United States

We study the propagation of a domain wall (kink) of the 4 model in a radially symmetric environment defined by a gravity source. This source introduces a Schwarzschild-like geometry. We introduce an effective model that accurately describes the dynamics of the kink center. This description works well even outside the perturbation region, i.e., even for large masses of the gravitating object. We observed that such a spherical domain wall surrounding a star-type object inevitably "collapses", i.e., shrinks in radius towards the origin and offer an understanding of the latter phenomenology. The relevant analysis is presented for a circular domain wall and a spherical one. © 2024, CC BY.

关键词： Spheres

Towards Service Ontology for Web Services Storage and Discovery 4

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Towards Service Ontology for Web Services Storage and Discov...

4th International Symposium on Informatics and its Applications, ISIA 2020

作者： Kaouan, Moussa Bouchiha, Djelloul Benslimane, Sidi Mohamed Boukli-Hacene, Sofiane Ctr Univ Naama EEDIS Lab SBA Dept. Math. and Comp. Science Algeria Ecole Superieure en Informatique Sidi Bel-Abbes Algeria EEDIS Lab SBA Department of Computer Science Algeria

ISBN: (纸本)9781728196527

The ontology has marked its presence in several research fields in order to address issues in knowledge modeling' Artificial Intelligence, classification, clustering and more specifically in knowledge engineering. In the Web Service field, ontology is used principally to describe the Web Service interface for discovering, storing and composing Web services. This is well known as semantic Web services, such as OWL-S, WSMO and WSDL-S. Unfortunately, work in this field remain insufficient till now;so, supporting this description by adding a shared ontology (knowledge base) can improve the performance of the Web Service technology. Manual construction of ontology is a tedious and expensive task which requires domain expert intervention;an automatic ontological construction process will be interesting and less costly solution in terms of time and money. In this paper, we propose to use clustering algorithms to create an ontological network (Service Ontology (SO)) from a semantic Web Services corpus. The created SO can be used for storage and discovery of Web Services in a distributed and intelligent environment. © 2020 IEEE.

关键词： Web services