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Minimal nonlocality in multipartite quantum systems

Physical Review A 2025年第2期111卷 022416-022416页

作者： Guang-Bao Xu Yong-Qi Zhang Dong-Huan Jiang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao 266590 China

Quantum nonlocality, rooted in the quantum state discrimination task, contributes to the development of the theory of local distinguishability of quantum states. Recently, Zhu et al. [Phys. A (Amsterdam, Neth.) 624, 128956 (2023)] found that a nonlocal set of orthogonal product states (OPSs) can be perfectly identified by local operations and classical communication after removing a specific quantum state in a bipartite quantum system. They called this phenomenon minimal nonlocality. It is obvious that minimal nonlocality effectively reflects the lower boundary of the elements contained in a nonlocal set of OPSs. In this paper, we generalize the methods of constructing nonlocal sets with minimal nonlocality from bipartite systems to multipartite systems and from orthogonal product states to entangled states. First, we give a method to construct a set of OPSs with minimal nonlocality in the Cd⊗Cd⊗Cd quantum system, and then we extend it to the ⊗i=1nCd quantum system for d≥4 and n≥3. In addition, we give a method to construct a nonlocal set of orthogonal states with entanglement relations in the Cd⊗Cd quantum system, and we prove that this set has minimal nonlocality, which can also be extended to the ⊗i=1nCd quantum system.

关键词： Quantum entanglement

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Alzheimer's Disease Prediction Using Convolutional Neural Networks Pre Trained Architecture

Alzheimer's Disease Prediction Using Convolutional Neural Ne...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Sahil, P Sam Sinha, Bhawesh K Akash, Thirumala K Ahmed, Mohammed Riyaz HKBK College of Engineering Computer Science and Engineering Bangalore India Swami Vivekananda University Data Science and Engineering West Bengal Kolkata India Analyst 1 Infrastructure Service DXC Technology Bangalore India HKBK College of Engineering Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350379525

Alzheimer's Disease (AD) is a progressive neu-rodegenerative disorder that significantly impacts memory and cognitive abilities. Accurate early-stage diagnosis is critical for implementing preventive measures before irreversible brain damage occurs. This paper presents a novel approach for AD prediction using convolutional neural networks (CNN) with pre-trained architectures, leveraging transfer learning to enhance classification accuracy. MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were used, categorized into three classes: AD (Alzheimer's Disease), CI (Mild Cognitive Impaired) and CN (Common Normal). The proposed method em-ploys pre-trained models such as VGG16, DenseNet121, Xception, ResNet152V2, NasNetLarge and InceptionResNetV2, fine-tuned for AD detection. Experimental results demonstrate that our approach achieved classification exceeding 90 % for precision and recall, highlighting the effectiveness of CNNs in handling large-scale medical data. These findings underscore the potential of deep learning models for supporting clinical diagnosis, reducing time and cost, and aiding in the early detection of Alzheimer's Disease. © 2024 IEEE.

关键词： Convolutional neural networks

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Modeling a Hybrid Stemmer for Kokborok

Modeling a Hybrid Stemmer for Kokborok

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Debbarma, Pankaj Hrangkhawl, Pusparwng Debbarma, Swapan Tripura Institute of Technology Department of Computer Science and Engineering Narsingarh India Ambedkar College Department of Information Technology Fatikroy India National Institute of Technology Department of Computer Science and Engineering Agartala India

ISBN: (纸本)9798350399387

Kokborok is a resource scarce and vulnerable language and is spoken only by around a million people in the north-east Indian state of Tripura. Lots of unstructured textual data in Kokborok is now available however efficient data mining for such a resource is not applicable. Reasons are the absence of an efficient pre-processing step that can help in solving the information retrieval or natural language processing. In this work, we present a model of hybrid stemming algorithm for the vulnerable and resource-scarce Kokborok language and assess its performance using various methods. Evaluation of the hybrid stemmer was performed on a corpus of 52,482 words that resulted in achieving an accuracy of 89.28%. Recall and Precision of 89.28% and 89.61 %, respectively, with F1-measure of 89.44 has been found for the stemmer. © 2023 IEEE.

关键词： Data mining

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Research on crop classification algorithm for time-series remote sensing images with improved U-net++ incorporating dual attention 4

Research on crop classification algorithm for time-series re...

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4th International Conference on Artificial Intelligence and computer engineering, ICAICE 2023

作者： Yan, Xingwang Yang, Yong Qu, Fuheng Changchun University of Science and Technology School of Computer Science and Technology Jilin Changchun130022 China Jilin Technology College of Electronic Information School of Software Jilin Jilin132021 China

ISBN: (纸本)9798400708831

In multispectral remote sensing image crop classification, in order to make full use of the complete spectral information of multispectral remote sensing image, time series data were used to replace the remote sensing image of a single time scene, which improved the accuracy of crop classification. Meanwhile, the original U-net++ algorithm based on the U-net++ algorithm was utilised to classify and extract crops in the study area, and the original U-net++ algorithm was not accurate enough for crop classification, with poor extraction ability for maize and soybean during the growing period. Therefore, based on these problems, the U-net++ model is improved in the paper, and the improved U-net++ algorithm incorporating the dual attention mechanism is proposed. Firstly, the CA attention module is added in the down-sampling stage of the model to enhance the extraction ability of shallow information;secondly, the ULSAM module is added in the up-sampling stage of the model to realise multi-scale feature extraction and to enhance the fine extraction of different crops in the growing period. The experimental results show that the accuracy of the proposed method in this paper is improved by 3% and 0.7% compared with UNet and UNet++, which can meet the needs of multi-spectral remote sensing crop image classification. © 2023 ACM.

关键词： Image classification

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Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs 31

Idea23D: Collaborative LMM Agents Enable 3D Model Generation...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Junhao Li, Xiang Ye, Xiaojun Li, Chao Fan, Zhaoxin Zhao, Hao Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China School of Software and Microelectronics Peking University China College of Computer Science Zhejiang University China College of Computer Science and Technology Harbin Engineering University China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence Beihang University China

ISBN: (纸本)9798891761964

With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still don't fully unleash human creativity. We often imagine 3D content made from multimodal inputs, such as what it would look like if my pet bunny were eating a doughnut on the table. In this paper, we explore a novel 3D AIGC approach: generating 3D content from IDEAs. An IDEA is a multimodal input composed of text, image, and 3D models. To our knowledge, this challenging and exciting 3D AIGC setting has not been studied before. We propose the new framework Idea23D, which combines three agents based on large multimodal models (LMMs) and existing algorithmic tools. These three LMM-based agents are tasked with prompt generation, model selection, and feedback reflection. They collaborate and critique each other in a fully automated loop, without human intervention. The framework then generates a text prompt to create 3D models that align closely with the input IDEAs. We demonstrate impressive 3D AIGC results that surpass previous methods. To comprehensively assess the 3D AIGC capabilities of Idea23D, we introduce the Eval3DAIGC-198 dataset, containing 198 multimodal inputs for 3D generation tasks. This dataset evaluates the alignment between generated 3D content and input IDEAs. Our user study and quantitative results show that Idea23D significantly improves the success rate and accuracy of 3D generation, with excellent compatibility across various LMM, Text-to-Image, and Image-to-3D models. Code and dataset are available at https://***/. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Improvements in Recommendation Systems Using Graph Neural Networks

Improvements in Recommendation Systems Using Graph Neural Ne...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Srivastava, Akhilesh Kumar Verma, Ishanki Garg, Puneet ABES Engineering College Department of Computer Science Engineering Ghaziabad India St. Andrews Institute of Technology and Management Department OfCSE Gurugram India

ISBN: (纸本)9798350376470

The concept of graph neural networks has shown improvements in many applications due to its ability to handle data involving complex relationships. As the population demonstrates a consistent upward trend, concomitant with this demographic expansion is the accrual of an augmented dataset. This project involves the use of graph neural networks in order to handle expanding data. Large dataset requires effective handling and so the concept of multiprocessing is used in order to handle data with maximum utilisation of CPU. Random walk is used for generating node embeddings for the graph. Node embeddings are then utilised for link prediction which is used for assessing the likelihood of relationships between the nodes. There is also an option of leveraging metapath schemes in order to guide the random walk. The key concepts also include variation of N ode2vec algorithm, use of neighbour sampling, use of negative sampling during training and noise contrastive estimation(NCE) as a loss function. © 2024 IEEE.

关键词： Graph neural networks

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Leveraging Machine Learning for Real-time Anomaly Detection and Self-Repair in IoT Devices 2

Leveraging Machine Learning for Real-time Anomaly Detection ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Ali, S. Ahamed Sujatha, D. Michael, Raj.T.F. Ramesh, G. Agoramoorthy, Moorthy Computer Science and Engineering Easwari Engineering College Ramapuram India St. Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai India Simats Department of Computer Applications Sclas TN Chennai India K.L.N. College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369960

The Internet of Things (IoT) gadgets have completely transformed a number of different businesses by producing enormous amounts of data. A machine learning framework for real-time Internet of Things anomaly detection and self-repair is presented in this research for the first time. Incorporating data preparation, anomaly detection, and selfrepair are all components of the framework. Through the use of data preparation module normalization and dimensionality reduction, Internet of Things data analysis can be improved. Through the utilization of deep neural networks, support vector machines, and several other machine learning models that have been trained on previous data, abnormal behavior can be identified. The reliability of ensemble approaches is increased. The self-repair module is responsible for automatically repairing abnormalities by resetting the settings of the device, upgrading the firmware, and applying patches. Device disruptions are reduced to a minimum by validation. Within the context of smart home and industrial automation tests, events were identified with a high degree of precision and a low percentage of false positives. Because it automatically fixes problems, the self-repair module makes the Internet of Things ecosystem better. © 2023 IEEE.

关键词： Anomaly Detection Internet of Things (IoT) Machine Learning Self-Repair

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Towards Precision Oncology: A Study of Machine Learning Models for Breast Cancer Prediction 10

Towards Precision Oncology: A Study of Machine Learning Mode...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Gracious, L.A. Anto Pajila, P.J. Beslin Johncy, G. Anish, T.P. Prasanth, K. Siva Subramanian, R. R.M.K. College of Engineering and Technology Dept of Cse Tiruvallur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India St. Xaviers Catholic College of Engineering Dept of Cse Nagercoil India Computer Science and Engineering S.A.Engineering College Chennai India R.M.K. College of Engineering and Technology Dept of Cse Puduvoyal India

ISBN: (纸本)9798350384369

The rapid growth in Breast Cancer (BC) related death is frightening, making it the most prominent cancer type and a cause of death of women worldwide. It is most important to have advancements in predicting and diagnosing cancer for promoting a healthier life. To increase the advancement in treatment and to increase the survivability of patients it is important to achieve a high level of accuracy in cancer prediction. The ML techniques have given a huge contribution in the prediction process and diagnosing BC, gathering noticeable attention in research, and demonstrating the desired result. The ML algorithms like SVM, RF, LR, C4.5, KNN are applied to the BC Wisconsin Diagnostic dataset. After getting the results, performance evaluation and comparisons were conducted among the various classifiers. The aim of the research paper is to find out and diagnose the BC with the help of the applications of ML algorithms, and with the most effective factors like recall, F-measure accuracy, and precision. SVM gave a good performance comparing to the other classifies by achieving the accuracy of 97.2%. The research describes the role of ML in improving the BC prediction and diagnosis, and providing a clear understanding about the effectiveness of different algorithms to improve the health conditions. © 2024 IEEE.

关键词： Lung cancer

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Classification and Discovery of Brain Tumor Using Ensemble Deep Learning 2

Classification and Discovery of Brain Tumor Using Ensemble D...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Srilatha, Kasanagottu Sravani, Chekka Vasudevarao, Mikkilineni Kiran, Ajmeera Chitra, D. Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Department of Data Science Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India Dayananda Sagar Business Academy Department Of Computer Applications Karnataka India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350372892

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor. © 2024 IEEE.

关键词： Convolutional neural networks

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EIROB : An Emotionally Intelligent ROBot for Empathetic Support

EIROB : An Emotionally Intelligent ROBot for Empathetic Supp...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Ismail, Safad Vasudev, Harsha Saintgits College of Engineering Department of Computer Science and Engineering Kerala India University of Padua Human Inspired Technology Research Centre Department of Mathematics Italy

ISBN: (纸本)9798350376135

Social assistive robots enhance social interaction and well-being by providing companionship and support. Emotional Intelligence in these robots refers to their ability to identify, understand, and respond to human emotions. This study investigates how well a social robot named 'EIROB' can engage elderly persons by using Artificial Emotional Intelligence. EIROB employs a Multimodal Emotion Recognition Algorithm and a Multimodal Emotion Expression System to detect the user's emotional state and generate appropriate responses through an effective dialogue manager. Interaction with EIROB is facilitated by an Android app, which uses the mobile phone's inbuilt camera and microphone for facial expression and speech sentiment detection. The captured data is processed in the cloud that enables the model to be deployed remotely. This study aims to create an empathetic interaction model for senior citizens particularly during pandemics like COVID-19 to prevent feelings of isolation. The performance of the emotion recognition models is evaluated via accuracy, precision, recall, and F1-score to ensure the effectiveness of the system in recognizing and responding to user's emotional states. © 2024 IEEE.

关键词： Microrobots

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