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Quantum spin systems: Toroidal classification and geometric duality

Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

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computer Modeling in Engineering & sciences 2024年第3期138卷 2471-2495页

作者： Wimonmas Bamrungsetthapong Pramote Charongrattanasakul Division of Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiPathum Thani12110Thailand Division of Mathematics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology KrungthepBangkok10120Thailand

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull *** control chart developed supports the examination of the mean lifespan variation for a particular product in the process of *** control limit levels are used:the warning control limit,inner control limit,and outer control ***,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control *** control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control ***,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

关键词： Modified DS np chart generalizedmultiple dependent state sampling time truncated life test Weibull distribution average run length average sample size

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Hierarchical GraphCut Phase Unwrapping based on Invariance of Diffeomorphisms Framework

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 546-554页

作者： Gao, Xiang Wang, Xinmu Zhao, Zhou Huang, Junqi Gu, Xianfeng David Department of Computer science Stony Brook University NY USA Deparment of Applied Mathematics and Statistics Stony Brook University NY USA

Recent years have witnessed rapid advancements in 3D scanning technologies, with diverse applications spanning VR/AR, digital human creation, and medical imaging. Structured-light scanning with phase-shifting techniques is preferred for its use of non-radiative, low-intensity visible light and high accuracy, making it well suited for human-centric applications such as capturing 4D facial dynamics. A key step in these systems is phase unwrapping, which recovers continuous phase values from measurements that are inherently wrapped modulo 2π. The goal is to estimate the unwrapped phase count k, an integer-valued variable in the equation Φ = ∅ + 2πk, where ∅ is the wrapped phase and Φ is the true phase. However, the presence of noise, occlusions, and piecewise continuous phase functions induced by complex 3D surface geometry makes the inverse reconstruction of the true phase extremely challenging. This is because phase unwrapping is an inherently ill-posed problem: measurements only provide modulo 2π values, and recovering the correct unwrapped phase count requires strong assumptions about the smoothness or continuity of the underlying 3D surface. Existing methods typically involve a trade-off between speed and accuracy: Fast approaches lack precision, while accurate algorithms are too slow for real-time use. To overcome these limitations, this work proposes a novel phase unwrapping framework that reformulates GraphCut-based unwrapping as a pixel-labeling problem. This framework helps significantly improve the estimation of the unwrapped phase count k through the invariance property of diffeomorphisms applied in image space via conformal and optimal transport (OT) maps. An odd number of diffeomorphisms are precomputed from the input phase data, and a hierarchical GraphCut algorithm is applied in each corresponding domain. The resulting label maps are fused via majority voting to efficiently and robustly estimate the unwrapped phase count k at each pixel, using an odd nu

关键词： Transillumination

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Generation of Cycle Permutation Graphs and Permutation Snarks 50th

Generation of Cycle Permutation Graphs and Permutation Sna...

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50th International Conference on Current Trends in Theory and Practice of computer science, SOFSEM 2025

作者： Goedgebeur, Jan Renders, Jarne Department of Computer Science KU Leuven Kulak Kortrijk8500 Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

ISBN: (纸本)9783031826696

We present an algorithm for the efficient generation of all pairwise non-isomorphic cycle permutation graphs, i.e. cubic graphs with a 2-factor consisting of two chordless cycles, and non-hamiltonian cycle permutation graphs, from which the permutation snarks can easily be computed. This allows us to generate all cycle permutation graphs up to order 34 and all permutation snarks up to order 46, improving upon previous computational results by Brinkmann et al. Moreover, we give several improved lower bounds for interesting permutation snarks, such as for a smallest permutation snark of order 6mod8 or a smallest permutation snark of girth at least 6. These computational results also allow us to complete a characterisation of the orders for which non-hamiltonian cycle permutation graphs exist, answering an open question by Klee from 1972, and yield many more counterexamples to a conjecture by Zhang. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hamiltonians

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An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks

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Multimedia Tools and Applications 2025年第8期84卷 4393-4434页

作者： Lawrence, Morolake Oladayo Jimoh, Rasheed Gbenga Yahya, Waheed Babatunde Department of Computer Science Faculty of Computing & Applied Sciences Baze University Abuja Nigeria Department of Computer Science Faculty of Communication & Information Sciences University of Ilorin Kwara State Ilorin Nigeria Department of Statistics Faculty of Physical Sciences University of Ilorin Kwara State Ilorin Nigeria

Cancer is one of the most devastating health conditions in the world. In the diagnosis and treatment of the various forms of cancer illness, studies have shown that early detection of the cancer by clinical methods usually takes a considerable lengthy time. This informs searching for an alternative non-clinical diagnosis of cancer cases using microarray technology. Therefore, this study develops an efficient feature selection and classification method for high-dimensional microarray cancer data by combining Genetic Algorithms (GA) and Deep Belief Networks (DBN). The study employed a GA for selecting the most informative gene biomarker and DBN for the classification of biological samples. In a Monte Carlo experiment, the simulated and real-life microarray datasets were partitioned into 95% training and 5% test samples at 100 to 1000 epochs. The classifier was constructed using the training datasets while its efficiency was assessed on the test sample using the Misclassification Error Rate, Sensitivity, Specificity, and Receiver Operating Characteristic Analysis. The GA and DBN were implemented using Caret and Deepnet (R statistical packages). The proposed GADBN method was implemented using simulated and real-life datasets, which yielded out-of-bag average classification accuracy of 98.8% and 93.1% respectively. The proposed GADBN outperformed the other existing classifiers;and the GADBN method was more efficient with a smaller Misclassification Error Rate (MER) for Leukaemia 1 (0.18), Prostate 1 (0.35) and Prostate 3 (0.09) datasets than some of the existing methods under various performance indices considered. The proposed model is a powerful and effective instrument for identifying useful features in microarray cancer data and classifying the cancer types accordingly. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Availability assessment of repairable linear consecutive k-out-of-n: F systems incorporating uncertainty and multiple failure modes

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Life Cycle Reliability and Safety Engineering 2025年 1-10页

作者： Khati, Amisha Singh, S.B. Department of Mathematics Statistics and Computer Science G. B. Pant University of Agriculture and Technology Pantnagar India Department of Applied Science and Humanities RIT Roorkee India

This study evaluates the interval-valued availability of a linear consecutive k-out-of-n: F system while considering uncertainty, where the probability that the system will perform as intended is not known with precision. The proposed system is susceptible to µ number of different failure modes and undergoes periodic inspections. Each failure mode has a randomly assigned failure time, and upon failure due to the mth failure mode, an immediate corrective repair is carried out, taking an arbitrary length of time ψm (m = 1, 2,…, µ). By integrating these factors, this research establishes theorems describing point and steady-state availability bounds. Subsequently, it conducts a sensitivity assessment to evaluate how variations in the inspection period affect the system’s availability. The findings are demonstrated with an instance of an oil pipeline system. This study offers valuable insights into the reliability and availability modelling of complex systems under uncertainty, with applications across diverse engineering domains. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2025.

关键词： Interval valued point availability Interval valued steady state availability Linear consecutive k-out-of n: F system Multiple failure modes Sensitivity analysis Uncertainty

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Explain the Explainer: Interpreting Model-Agnostic Counterfactual Explanations of a Deep Reinforcement Learning Agent

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1443-1457页

作者： Chen, Ziheng Silvestri, Fabrizio Tolomei, Gabriele Wang, Jia Zhu, He Ahn, Hongshik Stony Brook University Department of Applied Mathematics and Statistics Stony BrookNY11794 United States Sapienza University of Rome Department of Computer Engineering The Department of Computer Science Rome00185 Italy Xi'An Jiaotong-Liverpool University Department of Intelligent Science Suzhou215000 China Rutgers University-New Brunswick Department of Computer Science PiscatawayNJ08854 United States

Counterfactual examples (CFs) are one of the most popular methods for attaching post hoc explanations to machine learning models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood;thus, they are hard to generalize for complex models and inefficient for large datasets. This article aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task. We then find the optimal CFs via deep reinforcement learning (DRL) with discrete-continuous hybrid action space. In addition, we develop a distillation algorithm to extract decision rules from the DRL agent's policy in the form of a decision tree to make the process of generating CFs itself interpretable. Extensive experiments conducted on six tabular datasets have shown that ReLAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both the classification and regression tasks. Finally, we show the ability of our method to provide actionable recommendations and distill interpretable policy explanations in two practical real-world use cases. © 2020 IEEE.

关键词： Reinforcement learning

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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

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Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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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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Improved Bounds on the Size of Permutation codes under Kendall τ-metric

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IEEE Transactions on Information Theory 2025年第6期71卷 4156-4166页

作者： Parvaresh, Farzad Sobhani, Reza Abdollahi, Alireza Bagherian, Javad Jafari, Fatemeh Khatami, Maryam University of Isfahan Faculty of Engineering Department of Electrical Engineering Isfahan Iran School of Mathematics Tehran19395-5746 Iran University of Isfahan Faculty of Mathematics and Statistics Department of Applied Mathematics and Computer Science Isfahan81746-73441 Iran University of Isfahan Faculty of Mathematics and Statistics Department of Pure Mathematics Isfahan81746-73441 Iran

In order to overcome the challenges caused by flash memories and also to protect against errors related to reading information stored in DNA molecules in the shotgun sequencing method, the rank modulation method has been proposed. In the rank modulation framework, codewords are permutations. In this paper, we study the largest size P(n, d) of permutation codes of length n, i.e., subsets of the set Sn of all permutations on {1, ..., n} with the minimum distance at least (Formula presented) under the Kendall τ-metric. By presenting an algorithm and two theorems, we improve the known lower and upper bounds for P(n, d). In particular, we show that P(n, d) = 4 for all n ≥ 6 and (Formula presented). Additionally, we prove that for any prime number n and integer (Formula presented). This result greatly improves the upper bound of P(n, 3) for all primes n ≥ 37. © 1963-2012 IEEE.

关键词： Flash memory

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