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learning Robust Observable to Address Noise in Quantum machine learning 20th

Learning Robust Observable to Address Noise in Quantum Mac...

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20th international conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in Computer science, Computer Engineering and applied Computing, CSCE 2024

作者： Khanal, Bikram Rivas, Pablo Department of Computer Science Baylor University WacoTX United States

ISBN: (纸本)9783031858833

Quantum machine learning (QML) has emerged as a promising field that combines the power of quantum computing with the principles of machine learning. One of the significant challenges in QML is dealing with noise in quantum systems, especially in the Noisy Intermediate-Scale Quantum (NISQ) era. Noise in quantum systems can introduce errors in quantum computations and degrade the performance of quantum algorithms. In this paper, we propose a framework for learning observables that are robust against noisy channels in quantum systems. We demonstrate that it is possible to learn observables that remain invariant under the effects of noise and show that this can be achieved through a machine-learning approach. We present a toy example using a Bell state under a depolarization channel to illustrate the concept of robust observables. We then describe a machine-learning framework for learning such observables across six two-qubit quantum circuits and five noisy channels. Our results show that it is possible to learn observables that are more robust to noise than conventional observables. We discuss the implications of this finding for quantum machine learning, including potential applications in enhancing the stability of QML models in noisy environments. By developing techniques for learning robust observables, we can improve the performance and reliability of quantum machine learning models in the presence of noise, contributing to the advancement of practical QML applications in the NISQ era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum noise

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Fusion of machine learning and Deep Neural Networks for Pulmonary Arteries and Veins Segmentation in Lung Cancer Surgery Planning 27th

Fusion of Machine Learning and Deep Neural Networks for Pulm...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Cheng, Hongyu Zheng, Limin Yan, Zeyu Zhang, Haoran Meng, Bo Xu, Xiaowei Shenzhen University Shenzhen China Easylinkin Technology Co. Ltd. Wuhan China The Hong Kong Polytechnic University Hongkong China Guangdong Provincial People’s Hospital Guangzhou China

ISBN: (纸本)9783031781971

Lung cancer, the second most common type of cancer worldwide, is primarily treated through surgery. During the operations, preserving pulmonary arteries and veins is a crucial problem. In recent years, 3D visualization techniques like virtual reality and 3D printing have been increasingly used in clinical practice for lung cancer surgery planning. Under the success of these techniques, automatic segmentation of pulmonary arteries and veins plays a key role. Particularly, the state-of-art approaches rely on two techniques, i.e. the deep neural networks (DNNs) or the traditional machine learning (ML) method, and both techniques have respective shortages. Basically, the ML-based methods generally demonstrate a limited performance, while the DNN-based methods lack sufficient annotation for accurate segmentation. In response to such a dilemma, this paper proposes a fusion method to combine the DNN-based and ML-based methods to segment pulmonary arteries and veins for lung cancer surgery planning. Particularly, the anatomy prior mask corresponding to pulmonary arteries and veins are identified using the marching cubes algorithm and Attention U-Net. Subsequently, an enhanced attention U-Net, is used to integrate the original CT scans with the anatomy prior mask to generate the refined segmentation results. Following this, an anatomy structure enhancement module is used to refine the segmentation further by refining disconnected vessel segments and correcting misclassified vessels based on anatomy prior masks. We experimented the proposed approach on a private dataset of 95 CT scans collected from patients after surgery, and then annotated by lung cancer experts. The results demonstrate that our approach outperforms the existing methods with an improvement of 5.1% to 16.2% in Dice score. The dataset and code have also been published [1] to facilitate further research in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease

Individualized Multi-horizon MRI Trajectory Prediction for ...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： He, Rosemary Ang, Gabriella Tward, Daniel Departments of Computer Science University of California Los Angeles United States Departments of Computational Medicine University of California Los Angeles United States Departments of Neurology University of California Los Angeles United States

ISBN: (纸本)9783031845246

Neurodegeneration as measured through magnetic resonance imaging (MRI) is recognized as a potential biomarker for diagnosing Alzheimer’s disease (AD), but is generally considered less specific than amyloid or tau based biomarkers. Due to a large amount of variability in brain anatomy between different individuals, we hypothesize that leveraging MRI time series can help improve specificity, by treating each patient as their own baseline. Here we turn to conditional variational autoencoders to generate individualized MRI predictions given the subject’s age, disease status and one previous scan. Using serial imaging data from the Alzheimer’s Disease Neuroimaging Initiative*, we train a novel architecture to build a latent space distribution which can be sampled from to generate future predictions of changing anatomy. This enables us to extrapolate beyond the dataset and predict MRIs up to 10 years. We evaluated the model on a held-out set from ADNI and an independent dataset (from Open Access Series of Imaging Studies). By comparing to several alternatives, we show that our model produces more individualized images with higher resolution. Further, if an individual already has a follow-up MRI, we demonstrate a usage of our model to compute a likelihood ratio classifier for disease status. In practice, the model may be able to assist in early diagnosis of AD and provide a counterfactual baseline trajectory for treatment effect estimation. Furthermore, it generates a synthetic dataset that can potentially be used for downstream tasks such as anomaly detection and classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Prototype-Guided Contrastive Knowledge Graph Representation learning for Diagnosis Prediction 4th

Prototype-Guided Contrastive Knowledge Graph Representation ...

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4th international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Park, Wooyeol Mulyadi, Ahmad Wisnu Kang, Eunsong Suk, Heung-Il Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9789819787012

The medical knowledge graph (KG) constructed from electronic health records (EHR) offers a comprehensive understanding of patients as it facilitates interconnections among medical codes. While KG has been proven to improve diagnostic accuracy, it inevitably contains noisy and incomplete attributes that could compromise the verdicts. Hence, direct utilization of medical KG for downstream clinical tasks may entail inadequacy, even by resorting to proper machine learning methods. Several prior studies have initiated noise filtering methods upon medical KG to relieve such issues by masking its task-irrelevant nodes and relations. However, they focused solely on a patient-wise perspective that feasibly overlooked essential shared traits from a (sub-)population viewpoint. This study proposes a novel prototype-guided predictive model called ProtoCare to predict diagnoses in EHR by unifying prototype and contrastive learning. ProtoCare leverages a set of prototypes to discover latent shared characteristics among groups of patients, while contrastive learning plays the role of noise filtering and KG refinement accounting for both learned patient and selected prototype features. Experimental results on the real-world EHR cohort exhibited that our proposed ProtoCare outperformed several baselines on the diagnosis prediction task. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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data Privacy Vocabulary (DPV) – Version 2.0 23rd

Data Privacy Vocabulary (DPV) – Version 2.0

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23rd international Semantic Web conference, ISWC 2024

作者： J. Pandit, Harshvardhan Esteves, Beatriz P. Krog, Georg Ryan, Paul Golpayegani, Delaram Flake, Julian ADAPT Centre Dublin City University Dublin Ireland IDLab Ghent University – imec Ghent Belgium Signatu AS Oslo Norway ADAPT Centre Dublin City University and Uniphar PLC Dublin Ireland ADAPT Centre Trinity College Dublin Dublin Ireland University of Koblenz Koblenz Germany

ISBN: (纸本)9783031778469

The data Privacy Vocabulary (DPV), developed by the W3C data Privacy Vocabularies and Controls Community Group (DPVCG), enables the creation of machine-readable, interoperable, and standards-based representations for describing the processing of personal data. The group has also published extensions to the DPV to describe specific applications to support legislative requirements such as the EU’s GDPR. The DPV fills a crucial niche in the state of the art by providing a vocabulary that can be embedded and used alongside other existing standards such as W3C ODRL, and which can be customised and extended for adapting to specifics of use-cases or domains. This article describes the version 2 iteration of the DPV in terms of its contents, methodology, current adoptions and uses, and future potential. It also describes the relevance and role of DPV in acting as a common vocabulary to support various regulatory (e.g., EU’s DGA and AI Act) and community initiatives (e.g., Solid) emerging across the globe. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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data Reconstruction: When You See It and When You Don’t 16

Data Reconstruction: When You See It and When You Don’t

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16th Innovations in Theoretical Computer science conference, ITCS 2025

作者： Cohen, Edith Kaplan, Haim Mansour, Yishay Moran, Shay Nissim, Kobbi Stemmer, Uri Tsfadia, Eliad Google Research Mountain ViewCA United States Tel Aviv University Israel Google Research Tel Aviv Israel Technion Haifa Israel Georgetown University WashingtonDC United States

ISBN: (纸本)9783959773614

We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise definition is much more nuanced than it appears, to the extent that a single all-encompassing definition may not exist. Thus, we employ a different strategy and aim to "sandwich" the concept of reconstruction attacks by addressing two complementing questions: (i) What conditions guarantee that a given system is protected against such attacks? (ii) Under what circumstances does a given attack clearly indicate that a system is not protected? More specifically, We introduce a new definitional paradigm – Narcissus Resiliency – to formulate a security definition for protection against reconstruction attacks. This paradigm has a self-referential nature that enables it to circumvent shortcomings of previously studied notions of security. Furthermore, as a side-effect, we demonstrate that Narcissus resiliency captures as special cases multiple well-studied concepts including differential privacy and other security notions of one-way functions and encryption schemes. We formulate a link between reconstruction attacks and Kolmogorov complexity. This allows us to put forward a criterion for evaluating when such attacks are convincingly successful. © Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer, and Eliad Tsfadia.

关键词： Differential privacy

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Reinforcement learning and Discrete Event Simulation applied to Production Scheduling: A Scoping Review 30th

Reinforcement Learning and Discrete Event Simulation Applied...

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30th international Joint conference on Industrial Engineering and Operations Management, IJCIEOM 2024

作者： Pereira, Pedro Amaral de Carvalho, Andréa Regina Nunes Saisse, Manoel Carlos Pego da Silva, Arthur Santâna de Simas Mauger Canova, Eduardo Félix National Institute of Technology Rio de Janeiro Brazil Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9783031807848

Models based on Reinforcement learning (RL) have been combined with Discrete Event Simulation (DES) to mimic the human learning process in decision-making in production scheduling (PS) problems. By conducting a survey on the current state of the art in research involving the RL-DES-PS trinomial, this scoping review seeks to summarize the findings of scientific literature, explain similarities and dissimilarities between the articles searched, and identify gaps and potential applications. The articles retrieved for the study were analyzed and a taxonomy of 12 relevant attributes was proposed. This taxonomy groups the attributes into four categories (scheduling context, RL modelling, DES attributes and DES-RL integration), and may lay a foundation for further explorations. This paper also provides insights of the existing gaps in the literature and poses questions for future research. The majority of the articles reviewed considered simplified versions of production systems, or a more limited modeling for the RL algorithm, which reinforces the need for further development and maturation in this field to address the challenges posed by production scheduling problems. Moreover, practitioners can utilize the insights from this paper by applying the proposed taxonomy as a supporting tool for developing improved DES-RL-based models, to tackle production scheduling problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Multi-objective Support Vector machine Classification Algorithm for Estimation of the Potential Atmospheric Water Harvesting 4th

Multi-objective Support Vector Machine Classification Algori...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Javadi Nejad, Hana Palermo, Stefania Anna Turco, Michele De Stefano, Angelafrancesca Pirouz, Behzad Dipartimento Di Ingegneria Civile University of Calabria CS Rende Italy SIGMAWATER S.r.l Rende87036 Italy Department of Mechanical Energy and Management Engineering University of Calabria CS Rende Italy

ISBN: (纸本)9783031812439

Different studies show the advantages of atmospheric water harvesting (AWH) in different climates for irrigation purposes, especially in dry periods. Moreover, we used the advantages of multi-objective optimization to reformulation of support vector machines (SVMs) classification. Therefore, the main purpose of this study is to develop a multi-objective support vector machine classification algorithm for the estimation of the potential AWH using major geographic and topographic indicators. In this regard, in this study, the dataset of AWH in different climates and regions has been used for training the classification learning algorithm. The test of the model has been done by using data from 10 case studies and three features, including climate type, elevation, and distance to the coastline. The results of the developed model could estimate the potential and suitability of AWH, and the verification of the results confirmed the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Natural Language Integration for Multimodal Few-Shot Class-Incremental learning: Image Classification Problem 20th

Natural Language Integration for Multimodal Few-Shot Class-...

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20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Temniranrat, Pitchayagan Kaothanthong, Natsuda Marukatat, Sanparith Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Pathum Thani Thailand

ISBN: (纸本)9789819600250

Few-Shot Class-Incremental learning (FSCIL) suffers two main problems, namely 1. the model optimization difficulty on few-shot samples of new classes, and 2. the catastrophic forgetting of formerly learned old classes caused by a limitation to reuse the samples of former training. To solve these problems in image classification task, we proposed improving the input feature of the classification layer by integrating a visual-semantic network for projecting an image feature onto a sentence-embeddings feature. The network applied a pre-trained language model and image-text descriptions to distill the multi-modal prior knowledge. The projected feature is called distilled-Word-Embeddings (dWE). We conducted experiments on a benchmark open dataset CUB-200-2011 to compare the effects of the three features: 1) image feature as a baseline, 2) sentence-embeddings feature taken from image descriptions, and 3) our proposed feature dWE. We found that utilizing multiple features outperformed a single feature of the same type. Compared with the baseline, using a combination of an image feature and dWE improved the average accuracy of all sessions from 48.73% to 49.94%. An average rate-of-change (ROC) of the classification accuracy per session was employed to evaluate the catastrophic forgetting. The ROC improved from baseline −6.15% to −3.16% with dWE and to −2.99% with the combination of an image feature and dWE. These can be considered as more than 40% of improvement from the baseline. Moreover, using the combination of an image feature and dWE gave higher ROC than most of the previous FSCIL techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification 3rd

Normative Modeling with Focal Loss and Adversarial Autoenc...

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3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Zhao, Songlin Zhou, Rong Zhang, Yu Chen, Yong He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Bioengineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783031820069

In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer’s Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at https://***/soz223/FAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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