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Advances in neural architecture search

National science Review 2024年第8期11卷 24-38页

作者： Xin Wang Wenwu Zhu Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University

Automated machine learning(AutoML) has achieved remarkable success in automating the non-trivial process of designing machine learning *** the focal areas of AutoML,neural architecture search(NAS) stands out,aiming to systematically explore the complex architecture space to discover the optimal neural architecture configurations without intensive manual *** has demonstrated its capability of dramatic performance improvement across a large number of real-world *** core components in NAS methodologies normally include(ⅰ) defining the appropriate search space,(ⅱ)designing the right search strategy and(ⅲ) developing the effective evaluation *** early NAS endeavors are characterized via groundbreaking architecture designs,the imposed exorbitant computational demands prompt a shift towards more efficient paradigms such as weight sharing and evaluation estimation,***,the introduction of specialized benchmarks has paved the way for standardized comparisons of NAS ***,the adaptability of NAS is evidenced by its capability of extending to diverse datasets,including graphs,tabular data and videos,etc.,each of which requires a tailored *** paper delves into the multifaceted aspects of NAS,elaborating on its recent advances,applications,tools,benchmarks and prospective research directions.

关键词： machine learning artificial intelligence neural architecture search

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Probing quantum causality with geometric asymmetry in spatial-temporal correlations

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science China(information sciences) 2024年第9期67卷 294-305页

作者： Yu MENG Zheng-Hao LIU Zhikuan ZHAO Peng YIN Yi-Tao WANG Wei LIU Zhi-Peng LI Yuan-Ze YANG Zhao-An WANG Jin-Shi XU Shang YU Jian-Shun TANG Chuan-Feng LI Guang-Can GUO CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Department of Computer Science Eidgen?ssische Technische Hochschule Zürich Hefei National Laboratory Department of Physics Imperial College London Research Center for Quantum Sensing Zhejiang Lab

Causation promotes the understanding of correlation to an advanced stage by elucidating its underlying mechanism. Although statisticians have specified the possible causal relations among correlations,inferring causal structures is impossible from only the observed correlations in the classical world. quantum correlations encapsulating the most defining aspects of quantum physics have taken a new turn for the causal inference problem — the two-point spatial and temporal quantum correlations with observationally discernible characteristics correspond exactly to the two most basic causal structures. However, a direct causal interpretation for quantum correlations has only been established in very limited cases. Here, we explore to what extent quantum correlations promote causal inference. Theoretically, we have found that the distinguishable causal regime of two-point Pauli correlations can be expanded from a single value to an asymmetric interval, and the causal structures determining the quantum correlations can be interpreted by a simple distance criterion. Experimentally, we have devised and implemented a versatile non-unital quantum channel in an optical architecture to directly observe such an asymmetric interval. The setup enabled quantum causal inference without any requirement of active intervention, which is impossible in the classical realm. Our work facilitates the identification of causal links among quantum variables and provides insight into characterizing causation and spatial-temporal correlation in quantum mechanics.

关键词： causality quantum information quantum channel channel-state duality non-unital channel

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Projection-algebras and quantum Logic

Journal of Applied Logics

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Journal of Applied Logics 2024年第4期11卷 463-498页

作者： Lehmann, Daniel Selim and Rachel Benin School of Computer Science and Engineering and Quantum Information Science Center Hebrew University Jerusalem91904 Israel

P-algebras are a non-commutative, non-associative generalization of Boolean algebras that are for quantum logic what Boolean algebras are for classical logic. P-algebras have type ⟨X, 0,′, ·⟩ where 0 is a constant,′ is unary and · is binary. Elements of X are called features. A partial order is defined on the set X of features by x ≤ y iff x · y = x. Features commute, i.e., x · y = y · x iff x · y ≤ x. Features x and y are said to be orthogonal iff x · y = 0 and orthogonality is a symmetric relation. The operation + is defined as the dual of · and it is commutative on orthogonal features. The closed subspaces of a separable Hilbert space form a P-algebra under orthogonal complementation and projection of a subspace onto another one. P-algebras are complemented orthomodular posets but they are not lattices. Existence of least upper bounds for ascending sequences is equivalent to the existence of least upper bounds for countable sets of pairwise orthogonal elements. Atomic algebras are defined and their main properties are studied. The logic of P-algebras is then completely characterized. The language contains a unary connective corresponding to the operation′ and a binary connective corresponding to the operation "·". It is a substructural logic of sequents where the Exchange rule is extremely limited. It is proved to be sound and complete for P-algebras. © 2024, College Publications. All rights reserved.

关键词： Hilbert spaces

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Distributed quantum Sensing Network with Geographically Constrained Measurement Strategies 48

Distributed Quantum Sensing Network with Geographically Cons...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cao, Yingkang Wu, Xiaodi Joint Center for Quantum Information and Computer Science University of Maryland Department of Computer Science College Park United States

ISBN: (纸本)9781728163277

Distributed quantum sensing network has the potential of enhancing the precision in estimating a global function of local parameters by utilizing an entangled probe, compared with that achieved with separable probes. This advantage is often characterized as a quadratic improvement of the quantum Cramér-Rao bound (QCRB). This argument is incomplete in that QCRB assumes a team of all-powerful sensors that can perform arbitrary joint measurements allowed by quantum mechanics. An immediate question arises as to whether such an advantage persists for isolated sensors with physically motivated constraints in their measurement strategies. In this paper, we first consider local operations and classical communication (LOCC) strategies and prove that the QCRB is indeed asymptotically attainable for arbitrary pure probe states, by extending previous work on single-parameter estimation [1]. We further numerically analyze a more restricted scenario where the sensors can only make independent local measurements, and provide evidence that the QCRB is not informative enough for comparing different probe states. © 2023 IEEE.

关键词： Probes

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ScenePalette:Contextually Exploring Object Collections Through Multiplex Relations in 3D Scenes

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Journal of computer science & Technology 2024年第5期39卷 1180-1192页

作者： Shao-Kui Zhang Wei-Yu Xie Chen Wang Song-Hai Zhang Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Beijing 100084China

This paper presents ScenePalette,a modeling tool that allows users to“draw”3D scenes interactively by placing objects on a canvas based on their contextual *** is inspired by an important intuition which was often ignored in previous work:a real-world 3D scene consists of the contextually reasonable organization of objects,*** typically place one double bed with several subordinate objects into a bedroom instead of different shapes of ***,abstracts 3D repositories as multiplex networks and accordingly encodes implicit relations between or among ***,basic statistics such as co-occurrence,in combination with advanced relations,are used to tackle object relationships of different *** experiments demonstrate that the latent space of ScenePalette has rich contexts that are essential for contextual representation and exploration.

关键词： computer graphics 3D scene context 3D repository exploration multiplex network embedding

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Scalable quantum Photonic Neural Network Processor via Cavity-Assisted Interactions

Scalable Quantum Photonic Neural Network Processor via Cavit...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Basani, Jasvith Raj Niu, Murphy Yuezhen Waks, Edo Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States Department of Computer Science Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States

We present an integrated photonic architecture that uses a single atom trapped in a cavity for deterministic high-fidelity quantum operations. Our design is unique in providing a photon-number-selective nonlinearity, ... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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