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arXiv 2024年

作者： Gonzales, Alvin Dilley, Daniel Li, Bikun Jiang, Liang Saleem, Zain H. Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States

We apply the quantum error detection scheme Pauli check sandwiching (PCS) to quantum networks by turning it into a distributed multiparty protocol. PCS is a distance 1 code and requires less resource overhead than standard quantum error correction and detection methods. We provide analytical equations for the final fidelity and postselection rate. We also introduce a recursive version of PCS for entanglement purification that only scales polynomially in the resources required as a function of the number of recursions. The recursive PCS scheme generates a family of distance 2 quantum codes. Our analytical results are benchmarked against BBPSSW in comparable scenarios. We also perform simulations with noisy gates for entanglement swapping and attain substantial fidelity improvements. Lastly, we discuss various setups and graph state properties of PCS. Copyright © 2024, The Authors. All rights reserved.

关键词： Error detection

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Genai4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models

arXiv

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arXiv 2024年

作者： Fan, Ming Zhang, Zezhong Lu, Dan Zhang, Guannan Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

We introduce Genai4UQ, a software package for inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting in scientific applications. Genai4UQ leverages a generative artificial intelligence (ai)-based conditional modeling framework to address the limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo methods. By replacing computationally intensive iterative processes with a direct, learned mapping, Genai4UQ enables efficient calibration of model input parameters and generation of output predictions directly from observations. The software’s design allows for rapid ensemble forecasting with robust uncertainty quantification, while maintaining high computational and storage efficiency. Genai4UQ simplifies the model training process through built-in auto-tuning of hyperparameters, making it accessible to users with varying levels of expertise. Its conditional generative framework ensures versatility, enabling applicability across a wide range of scientific domains. At its core, Genai4UQ transforms the paradigm of inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling. (The code and data are available at https://***/patrickfan/Genai4UQ). © 2024, CC BY.

关键词： Markov chains

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Quantum nonlocal modulation cancelation with distributed clocks

Optica Quantum

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Optica Quantum 2025年第1期3卷 45-54页

作者： Stephen D Chapman Suparna Seshadri Joseph M Lukens Nicholas A Peters Jason D McKinney Andrew M Weiner Hsuan-Hao Lu Elmore Family School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

We demonstrate nonlocal modulation of entangled photons with truly distributed radio frequency (RF) clocks. Leveraging a custom radio-over-fiber (RFoF) system characterized via classical spectral interference, we validate its effectiveness for quantum networking by multiplexing the RFoF clock with one photon from a frequency-bin-entangled pair and distributing the coexisting quantum-classical signals over fiber. Phase modulation of the two photons reveals nonlocal correlations in excellent agreement with theory: in-phase modulation produces additional sidebands in the joint spectral intensity, while out-of-phase modulation is nonlocally canceled. Our simple, feedback-free design attains subpicosecond synchronization—namely, drift less than ∼0.5 ps in a 5.5 km fiber over 30 min (fractionally only ∼2×10 −8 of the total fiber delay)—and should facilitate frequency-encoded quantum networking protocols such as high-dimensional quantum key distribution and entanglement swapping, unlocking frequency-bin qubits for practical quantum communications in deployed metropolitan-scale networks.

关键词： Entanglement distribution Laser communications Phase modulation Quantum communications Quantum key distribution Quantum teleportation

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Out-of-distribution detection learning with unreliable out-of-distribution sources 23

Out-of-distribution detection learning with unreliable out-o...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Haotian Zheng Qizhou Wang Zhen Fang Xiaobo Xia Feng Liu Tongliang Liu Bo Han Department of Computer Science Hong Kong Baptist University and School of Electronic Engineering Xidian University Department of Computer Science Hong Kong Baptist University Australian Artificial Intelligence Institute University of Technology Sydney Sydney AI Centre The University of Sydney School of Computing and Information Systems The University of Melbourne

Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open-world classification. However, it is typically hard to collect real out-of-distribution (OOD) data for training a predictor capable of discerning ID and OOD patterns. This obstacle gives rise to data generation-based learning methods, synthesizing OOD data via data generators for predictor training without requiring any real OOD data. Related methods typically pre-train a generator on ID data and adopt various selection procedures to find those data likely to be the OOD cases. However, generated data may still coincide with ID semantics, i.e., mistaken OOD generation remains, confusing the predictor between ID and OOD data. To this end, we suggest that generated data (with mistaken OOD generation) can be used to devise an auxiliary OOD detection task to facilitate real OOD detection. Specifically, we can ensure that learning from such an auxiliary task is beneficial if the ID and the OOD parts have disjoint supports, with the help of a well-designed training procedure for the predictor. Accordingly, we propose a powerful data generation-based learning method named Auxiliary Task-based OOD Learning (ATOL) that can relieve the mistaken OOD generation. We conduct extensive experiments under various OOD detection setups, demonstrating the effectiveness of our method against its advanced counterparts. The code is publicly available at: https://***/tmlr-group/ATOL.

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Utilizing Machine Learning in Medical Diagnosis: Systematic Review and Empirical Analysis

Utilizing Machine Learning in Medical Diagnosis: Systematic ...

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Information Technology (ACIT)

作者： Raed Alazaidah Mohammad Hassan Lara Al-Rbabah Ghassan Samara Marina Yusof Ala'a Saeb Al-Sherideh Department of Data Science AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan College of Computer Informatics and Media Universiti Teknologi MARA (UiTM) Selanger Malaysia Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection.

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Single and shift-multiplexing recording properties in self-referential holographic data storage with designed additional pattern

Single and shift-multiplexing recording properties in self-r...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Chijiwa, Kazuki Takabayashi, Masanori Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology 2-4 Hibikino Wakamatsu-ku Fukuoka Kitakyushu808-0196 Japan

The reconstruction quality and shift multiplexing properties of self-referential holographic data storage (SR-HDS) with additional patterns which are designed with a target intensity of nonuniform distributions such a...

ISBN: (纸本)9798350350012

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Acute lymphoblastic leukemia Classification Based on Convolutional Neural Network

Acute lymphoblastic leukemia Classification Based on Convolu...

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International Conference on Artificial Intelligence and Big Data (ICaiBD)

作者： Qile Fan Boan Qu Jingjing Teng Anqi Hui Teoh Teik Toe School of Communication and Information Engineering Nanjing University of Post and Telecommunications Nanjing China School of Electrical and Mechanical Engineering Beijing University of Chemical Technology Beijing China Electrical engineering and the automatization specialty Suzhou University of Science and Technology Suzhou China Computer Science andTechnology Xidian University of Computer Science and Technology Xi’an China NTU Business AI Lab Nanyang Technological University Singapore

Acute lymphoblastic leukemia (ALL) is a type of blood cancer that affects white blood cells, primarily affecting children. Early diagnosis is crucial for successful treatment and recovery. In recent years, convolutional neural networks (CNNs) have been increasingly used in medical image analysis, including the detection and classification of ALL from medical images. In this paper, we propose a model called QCResNet for the classification of ALL from peripheral blood smear images. Our proposed model achieved a high accuracy of 98.9% on a dataset of 15,135 images, outperforming several state-of-the-art methods. Our results demonstrate the potential of QCResNet for accurate and rapid effective diagnosis of acute lymphocytes.

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Parallel DGTD Algorithm with LTS Scheme for Electrical Large Problems Based on Supercomputer

Parallel DGTD Algorithm with LTS Scheme for Electrical Large...

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Electronics Design Automation (ISEDA), International Symposium of

作者： Wenhao Liu Liang Chen Ping Li State Key Laboratory of Radio Frequency Heterogeneous Integration Shanghai Jiao Tong University Shanghai China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

In this paper, we present a nodal discontinuous Galerkin time-domain (NDGTD) algorithm with a parallel scheme for solving transient Maxwell's equations. With the objective of analyzing the electromagnetic characteristics of electrical-large problems, the NDGTD algorithm is implemented on the supercomputer named “Tian- He” utilizing the message passing interface (MPI) for inter-thread communication. Additionally, to enhance time-marching efficiency in dealing with multi-scale problems, the local time stepping (L TS) technique is implemented based on the leapfrog integration scheme. To assess the efficiency and accuracy of the proposed parallel NDGTD algorithm, an investigation into the scattering properties of a stealthy aircraft model and the far-field characteristics of a patch antenna array is conducted.

关键词： Patch antennas Message passing Scattering Aerospace electronics Supercomputers Time-domain analysis Transient analysis

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Hough Soft-Argmax Based Tillage Boundary Detection for Autonomous Tractor

SSRN

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SSRN 2023年

作者： Oh, Kanghan Seo, Dasom Oh, IL-Seok Kim, Gookwhan Wonkwang University Department of Computer Engineering Iksan Jeollabuk-do Korea Republic of Jeonbuk National University Division of Computer Science and Engineering Jeonju Jeollabuk-do Korea Republic of National Institute of Agricultural Sciences Agricultural Engineering 310 Nongsaengmyeong-ro Jeonju Korea Republic of

Over the past years, autonomous tractor has been an active research field for agricultural automation. A tillage boundary detection is an essential task for self-driving path planning. Recent studies have proposed machine vision-based methods to detect the tillage boundary, and significant progress is still being made. However, most existing methods heavily rely on local features, leading to sub-optimal performance. Moreover, since the previous works inevitably suffer from the local noises, a heuristic post-processing phase is required to obtain the final tillage lines. This study proposes a deep learning-based tillage boundary detection approach that can learn contextual line features. Our proposed approach is based on Hough soft-argmax, which incorporates the classical soft-argmax and Hough transform methods. Using the Hough soft-argmax, we transform deep CNN features into contexture line features and then directly regress Hough parameters by exploring the transformed vector space. Since the straight line is parameterized by Hough parameters, the proposed method is a one-shot end-to-end learning method and does not require any post-processing phase. We conducted an experiment by constructing a large-scale custom dataset for tillage boundary detection. The experimental result demonstrates the effectiveness of the proposed method in detecting the tillage boundary over the existing methods. © 2023, The Authors. All rights reserved.

关键词： Tillage

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Improvement of Security in Multi-Biometric Cryptosystem by Modulus Fuzzy Vault Algorithm 10

Improvement of Security in Multi-Biometric Cryptosystem by M...

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10th International Conference on Advances in Computing and Communications, ICACC 2021

作者： Sreemol, R. Santosh Kumar, M.B. Sreekumar, A. Cochin University of Science and Technology Department of Computer Applications Kerala India Cochin University of Science and Technology Division of Information Technology School of Engineering Kerala India

ISBN: (纸本)9781665439190

Numerous prevalent techniques build a Multi-Modal Biometric (MMB) system that struggles in offering security and also revocability onto the templates. This work proffered a MMB system centred on the Modulus Fuzzy Vault (MFV) aimed at resolving these issues. The methodology proposed includes Fingerprint (FP), Palmprint (PP), Ear and also Retina images. Utilizing the Boosted Double Plateau Histogram Equalization (BDPHE) technique, all images are improved. aimed at removing the unnecessary things as of the ear and the blood vessels are segmented as of the retina images utilizing the Modified Balanced Iterative Reducing and Clustering using Hierarchy (MBIRCH) technique. Next, the input traits features are extracted;then the essential features are chosen as of the features extracted utilizing the Bidirectional Deer Hunting optimization Algorithm (BDHOA). The features chosen are merged utilizing the Normalized Feature Level and Score Level (NFLSL) fusion. The features fused are saved securely utilizing Modulus Fuzzy Vault. Upto fusion, the procedure is repeated aimed at the query image template. Next, the de-Fuzzy Vault procedure is executed aimed at the query template, and then the key is detached by matching the query template's and input biometric template features. The key separated is analogized with the threshold that categorizes the user as genuine or else imposter. The proposed BDPHE and also MFV techniques function efficiently than the existent techniques. © 2021 IEEE.

关键词： Image enhancement

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