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Articles de revues sur le sujet « Synergistic regularization »

Auteur : Grafiati

Publié le 29 mars 2025

Mis à jour le 31 juillet 2025

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1

Cueva, Evelyn, Alexander Meaney, Samuli Siltanen, and Matthias J. Ehrhardt. "Synergistic multi-spectral CT reconstruction with directional total variation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (2021): 20200198. http://dx.doi.org/10.1098/rsta.2020.0198.

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This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel. We propose to fuse these available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyse the use of directional total variation within variational

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Mehranian, Abolfazl, Martin A. Belzunce, Claudia Prieto, Alexander Hammers, and Andrew J. Reader. "Synergistic PET and SENSE MR Image Reconstruction Using Joint Sparsity Regularization." IEEE Transactions on Medical Imaging 37, no. 1 (2018): 20–34. http://dx.doi.org/10.1109/tmi.2017.2691044.

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Cui, Xinye, Houpu Li, Yanting Yu, Shaofeng Bian, and Guojun Zhai. "A Hybrid Dropout Method for High-Precision Seafloor Topography Reconstruction and Uncertainty Quantification." Applied Sciences 15, no. 11 (2025): 6113. https://doi.org/10.3390/app15116113.

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Seafloor topography super-resolution reconstruction is critical for marine resource exploration, geological monitoring, and navigation safety. However, sparse acoustic data frequently result in the loss of high-frequency details, and traditional deep learning models exhibit limitations in uncertainty quantification, impeding their practical application. To address these challenges, this study systematically investigates the combined effects of various regularization strategies and uncertainty quantification modules. It proposes a hybrid dropout model that jointly optimizes high-precision recon

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Perelli, Alessandro, and Martin S. Andersen. "Regularization by denoising sub-sampled Newton method for spectral CT multi-material decomposition." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2200 (2021): 20200191. http://dx.doi.org/10.1098/rsta.2020.0191.

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Spectral Computed Tomography (CT) is an emerging technology that enables us to estimate the concentration of basis materials within a scanned object by exploiting different photon energy spectra. In this work, we aim at efficiently solving a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function using a randomized second order method. By approximating the Newton step using a sketching of the Hessian of the likelihood function,

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Jørgensen, J. S., E. Ametova, G. Burca, et al. "Core Imaging Library - Part I: a versatile Python framework for tomographic imaging." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (2021): 20200192. http://dx.doi.org/10.1098/rsta.2020.0192.

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We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic da

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Bahadur, Rabya, Saeed ur Rehman, Ghulam Rasool, and Muhammad AU Khan. "Synergy Estimation Method for Simultaneous Activation of Multiple DOFs Using Surface EMG Signals." NUST Journal of Engineering Sciences 14, no. 2 (2022): 66–73. http://dx.doi.org/10.24949/njes.v14i2.661.

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Surface electromyography signals are routinely used for designing prosthetic control systems. The concept of synergy estimation for muscle control interpretation is being explored extensively. Synergies estimated for a single active degree of freedom (DoF) are found to be uncorrelated and provide better results when used for single movement classification; however, an increase of simultaneously active DoFs leads to complex limb movements and multiple DoF detection becomes a challenge. Synergy estimation is a non-convex optimization technique, to provide better estimation this paper proposes th

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Zhong, Lihua, Tong Ye, Yuyao Yang, et al. "Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks." Processes 12, no. 9 (2024): 1791. http://dx.doi.org/10.3390/pr12091791.

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As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon res

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Du, Lehui, Baolin Qu, Fang Liu, et al. "Precise prediction of the radiation pneumonitis with RPI: An explorative preliminary mathematical model using genotype information." Journal of Clinical Oncology 37, no. 15_suppl (2019): e14569-e14569. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e14569.

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e14569 Background: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for the radiotherapy of lung cancer. Reliable predictive factors or methods are strongly demanded by radiation oncologists. The purpose of this study is by determining the effectiveness of both genetic and non-genetic factors on their impact on the development of RP, to develop a clinically practicable approach for the risk assessment of RP. Methods: One hundred eighteen lung cancer patients who received radiotherapy were enrolled. RP events were prospectively scored using the

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Di Sciacca, G., L. Di Sieno, A. Farina, et al. "Enhanced diffuse optical tomographic reconstruction using concurrent ultrasound information." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (2021): 20200195. http://dx.doi.org/10.1098/rsta.2020.0195.

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Multimodal imaging is an active branch of research as it has the potential to improve common medical imaging techniques. Diffuse optical tomography (DOT) is an example of a low resolution, functional imaging modality that typically has very low resolution due to the ill-posedness of its underlying inverse problem. Combining the functional information of DOT with a high resolution structural imaging modality has been studied widely. In particular, the combination of DOT with ultrasound (US) could serve as a useful tool for clinicians for the formulation of accurate diagnosis of breast lesions.

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Lu, Yifan, Ziqi Zhang, Chunfeng Yuan, et al. "Set Prediction Guided by Semantic Concepts for Diverse Video Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3909–17. http://dx.doi.org/10.1609/aaai.v38i4.28183.

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Diverse video captioning aims to generate a set of sentences to describe the given video in various aspects. Mainstream methods are trained with independent pairs of a video and a caption from its ground-truth set without exploiting the intra-set relationship, resulting in low diversity of generated captions. Different from them, we formulate diverse captioning into a semantic-concept-guided set prediction (SCG-SP) problem by fitting the predicted caption set to the ground-truth set, where the set-level relationship is fully captured. Specifically, our set prediction consists of two synergisti

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Mauludiah, Siska Farizah, Cahyo Crysdian, and Yunifa Miftachul Arif. "Enhancing Repeat Buyer Classification with Multi Feature Engineering in Logistic Regression." Applied Information System and Management (AISM) 8, no. 1 (2025): 103–10. https://doi.org/10.15408/aism.v8i1.45025.

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This study presents a novel approach to improving repeat buyer classification on e-commerce platforms by integrating Kullback-Leibler (KL) divergence with logistic regression and focused feature engineering techniques. Repeat buyers are a critical segment for driving long-term revenue and customer retention, yet identifying them accurately poses challenges due to class imbalance and the complexity of consumer behavior. This research uses KL divergence in a new way to help choose important features and evaluate the model, making it easier to understand and more effective at classifying repeat b

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Perez-Rathke, Alan, Justin Balko, and Jens Meiler. "Abstract 6268: JANUS: A unified deep learning framework for predicting peptide binding to MHC I and II alleles." Cancer Research 85, no. 8_Supplement_1 (2025): 6268. https://doi.org/10.1158/1538-7445.am2025-6268.

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The accurate prediction of peptide binding to major histocompatibility complex (MHC) molecules is crucial for advancing cancer immunotherapy, particularly in the design of personalized cancer vaccines and in identifying antigens which may be predictive of immunotherapy-related adverse events. Existing computational models for prediction of peptide binding are generally restricted to a single MHC class. We hypothesize that a unified model capable of predicting peptide binding to both MHC I or MHC II alleles can lead to a synergistic improvement in model performance and thereby facilitate advanc

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Lin, Haojia, Guangbin Wang, Ying Lv, and Changsheng Shao. "Insulated bearing fault diagnosis method based on shape-aware attention and dynamic physical information guidance." Measurement Science and Technology 36, no. 7 (2025): 076125. https://doi.org/10.1088/1361-6501/adee36.

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Abstract Most of the existing physical models of insulating bearings ignore the coupling dynamic effects between the insulating coating and the substrate, and the commonly used static physical guidance methods are difficult to adapt to the dynamic changes between data during the training process, which aggravates the domain offset between simulation data and actual data. To this end, an insulating bearing diagnosis method (SAKA-DPG) that constructs shape-aware attention (SAKA) and dynamic physical information guidance (DPG) is proposed in this paper. Firstly, the SAKA attention mechanism is co

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Chen, Yu, Haotian Liu, Jianwei Zhang, and Jiang Wu. "A Data-Driven Methodology for Industrial Design Optimization and Consumer Preference Modeling: An Application of Computer-Aided Design in Sustainable Refrigerator Design Research." Symmetry 17, no. 4 (2025): 621. https://doi.org/10.3390/sym17040621.

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Addressing the insufficient identification of key consumer requirements in refrigerator design and the current limitations in understanding the impacts and underlying mechanisms of product design on sustainability, this study develops an interdisciplinary methodological framework that synergizes industrial design principles with advanced computer-aided design techniques and deep neural network approaches. Initially, consumer decision preferences concerning essential product attributes and sustainability indicators are systematically elucidated through semi-structured interviews and multi-sourc

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Anacleto, Adilson, Karina Beatriz dos Santos Ferreira da Rocha, Raíssa Leal Calliari, Maike dos Santos, and Sandro Deretti. "Production Arrangement of Cachaça: Comparative Study Between Morretes in the Paraná Coast and Luiz Alves in Itajaí Valley - Santa Catarina." Revista de Gestão Social e Ambiental 18, no. 2 (2024): e07510. http://dx.doi.org/10.24857/rgsa.v18n2-158.

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Objective: The research sought to promote a characterization of the cachaça LPA in Morretes, Paraná in comparison to an LPA in another Brazilian region, whose production is similar and is classified as one of the most developed in Brazil. Theoretical Framework: Studies about Local Productive Arrangements have been relevant to proposals and regional development, so the basis of the research was inserted in the introductory phase. Method: Between May 2022 and February 2023, an exploratory descriptive study was carried out with leaders of two cachaça production arrangements located in the south o

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Chakravarti, Sayak, Suman Mazumder, Harish Kumar, et al. "Establishing a Novel Pipeline That Combines in-Silico Prediction with in-Vitro and Ex-Vivo Validation to Discover Secondary Drug Combinations Against Relapsed and/or Refractory Multiple Myeloma." Blood 138, Supplement 1 (2021): 1615. http://dx.doi.org/10.1182/blood-2021-154521.

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Abstract Multiple myeloma (MM) is the second-most common hematological malignancy in the US. MM is an incurable, age-dependent plasma cell neoplasm with a 5-year survival rate of less than 50%. Extensive inter-individual variation in response to standard-of-care drugs like proteasome inhibitors (PIs) and immunomodulatory drugs (IMiDs), drug resistance, and dose-limiting toxicities are critical problems for the treatment of MM. Clinical success in anti-myeloma treatment, therefore, warrants continuous development of novel combination therapy strategies with the explicit goal to improve the ther

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Chakravarti, Sayak, Ujjal Kumar Mukherjee, Suman Mazumder, Timothy Moore, and Amit Kumar Mitra. "In silico Prediction Followed By I n Vitro validation Identifies a Survivin Inhibitor and an MCL-1 Inhibitor As a Potent Secondary Drug Against Refractory or Relapsed Mantle Cell Lymphoma." Blood 138, Supplement 1 (2021): 1191. http://dx.doi.org/10.1182/blood-2021-154479.

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Abstract Mantle cell lymphoma (MCL) is an aggressive lymphoid neoplasm that develops from malignant B-lymphocytes in the outer edge or mantle zone of a lymph node. This is a sub-type of B-cell non-Hodgkin lymphoma characterized by rapid clinical progression and poor response rate to conventional chemotherapeutic drugs with recurrent relapse resulting in a short estimated 5-year overall survival (OS) of 2-5 years depending on the clinical risk. Combination therapies such as R-CHOP, R-DHAP, Hyper-CVAD, VcR-CAP constitute the front-line chemotherapeutic treatment landscape for MCL. Despite good i

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Pliego, Alicia, Chantal Pauli, Lara Planas-Paz, et al. "Abstract A008: AI-Predict: Artificial intelligence-mediated drug synergy prediction and validation in cancer models." Clinical Cancer Research 31, no. 13_Supplement (2025): A008. https://doi.org/10.1158/1557-3265.aimachine-a008.

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Abstract Introduction: In precision oncology, monotherapies frequently result in resistance and disease relapse, emphasizing the need for rational drug combinations. Identifying effective combinations remains challenging due to the immense combinatorial space, tumor-specific molecular heterogeneity, and the limited scalability of experimental screening. Drug repurposing offers a promising and cost-effective alternative by leveraging compounds with known safety profiles. In recent years, machine learning approaches have been proposed to predict drug synergy, yet many remain limited by reliance

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Tsai, Yen-Hsi Richard, and Stanley Osher. "Total variation and level set methods in image science." Acta Numerica 14 (April 19, 2005): 509–73. http://dx.doi.org/10.1017/s0962492904000273.

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We review level set methods and the related techniques that are common in many PDE-based image models. Many of these techniques involve minimizing the total variation of the solution and admit regularizations on the curvature of its level sets. We examine the scope of these techniques in image science, in particular in image segmentation, interpolation, and decomposition, and introduce some relevant level set techniques that are useful for this class of applications. Many of the standard problems are formulated as variational models. We observe increasing synergistic progression of new tools a

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Sun Yan-Xu, Huang Juan, Gao Wei, et al. "Tomography of fast ion distribution function under neutral beam injection and ion cyclotron resonance heating on EAST." Acta Physica Sinica, 2023, 0. http://dx.doi.org/10.7498/aps.72.20230846.

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In magnetic confinement fusion devices, velocity-space tomography of fast-ion velocity distribution functions is crucial for investigating fast-ion distribution and transport. In the neutral beam injection (NBI) and ion cyclotron resonance heating (ICRF) synergistic heating experiments in Experimental Advanced Superconducting Tokamak (EAST), high-energy particles with energy exceeding NBI was observed. Simulations of synergistic effect on fast-ion velocity distribution functions given by TRANSP also showed the existence of particle with energy higher than NBI. To investigate the behavior of fa

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Lv, Ji, Guixia Liu, Yuan Ju, Ying Sun, and Weiying Guo. "Prediction of Synergistic Antibiotic Combinations by Graph Learning." Frontiers in Pharmacology 13 (March 8, 2022). http://dx.doi.org/10.3389/fphar.2022.849006.

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Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem. However, it is difficult for us to find effective antibiotic combinations in the vast chemical space. Herein, we propose a graph learning framework to predict synergistic antibiotic combinations. In this model, a network proximity method combined with network propagation was used to quantify the relationships of drug pairs, and we found that synergistic antibiotic combinations tend to have smaller network proximity. Therefore, networ

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Liu, Huan, Xinglin Zhang, Congyu Liao, Haobin Dong, Zheng Liu, and Xiangyun Hu. "Synergistic Hankel structured low-rank approximation with total variation regularization for complex magnetic anomaly detection." IEEE Transactions on Instrumentation and Measurement, 2023, 1. http://dx.doi.org/10.1109/tim.2023.3264036.

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Qin, Limu, Gang Yang, and Wen He. "Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains." Structural Health Monitoring, May 9, 2024. http://dx.doi.org/10.1177/14759217241245320.

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An effective structural health monitoring method of traction motor bearings is a powerful guarantee for the safety operation of high-speed trains. However, it is exceptionally difficult to detect bearing fault characteristics from the vibration signals of traction motor bearings operating at high rotational speeds. In this scenario, a generalized Shannon entropy sparse wavelet packet transform (GSWPT) for fault detection of motor bearings is proposed in this paper. Firstly, a generalized Shannon entropy sparse regularization method is proposed to obtain sparse wavelet reconstruction coefficien

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Yang, Huili, KyungPyo Hong, Justin J. Baraboo, et al. "GRASP reconstruction amplified with view‐sharing and KWIC filtering reduces underestimation of peak velocity in highly‐accelerated real‐time phase‐contrast MRI: A preliminary evaluation in pediatric patients with congenital heart disease." Magnetic Resonance in Medicine, December 12, 2023. http://dx.doi.org/10.1002/mrm.29974.

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AbstractPurposeTo develop a highly‐accelerated, real‐time phase contrast (rtPC) MRI pulse sequence with 40 fps frame rate (25 ms effective temporal resolution).MethodsHighly‐accelerated golden‐angle radial sparse parallel (GRASP) with over regularization may result in temporal blurring, which in turn causes underestimation of peak velocity. Thus, we amplified GRASP performance by synergistically combining view‐sharing (VS) and k‐space weighted image contrast (KWIC) filtering. In 17 pediatric patients with congenital heart disease (CHD), the conventional GRASP and the proposed GRASP amplified b

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Patra, Aswini Kumar, and Lingaraj Sahoo. "Explainable light-weight deep learning pipeline for improved drought stress identification." Frontiers in Plant Science 15 (November 28, 2024). http://dx.doi.org/10.3389/fpls.2024.1476130.

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IntroductionEarly identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor-based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis that aims to identify drought stress. While these approaches yield favorable results, real-time field applications require algorithms specifically designed for the complexities of natur

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Sivakumar, Nikita, Cameron Mura, and Shayn M. Peirce. "Innovations in integrating machine learning and agent-based modeling of biomedical systems." Frontiers in Systems Biology 2 (November 10, 2022). http://dx.doi.org/10.3389/fsysb.2022.959665.

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Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs

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Lei, M. K., X. P. Zhu, Bao Zhu, et al. "High-Performance Manufacturing of Canned Main Coolant Pump - Part I: Synergistical Design and Processing Optimization." Journal of Nuclear Engineering and Radiation Science, May 15, 2025, 1–34. https://doi.org/10.1115/1.4068691.

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Abstract Current work is divided into two parts. In Part I, the canned MCP with high dynamic performance is manufactured by the synergistical design and processing optimization. A coupled design and processing modeling of canned MCP puts into the system-components crosslinking from a full finite element dynamics model of system connected with the manufacturing thermodynamics models of components. The surface integrity of all components in process chains and subsequent service is pivoted in the system-components crosslinking on the material-product-process linking based on the data regularizati

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Lei, M. K., H. L. Che, Yupeng Li, et al. "High-Performance Manufacturing of Canned Main Coolant Pump — Part II: Reliability Evaluation." Journal of Nuclear Engineering and Radiation Science, May 3, 2025, 1–26. https://doi.org/10.1115/1.4068596.

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Abstract Hereby, Part I presents synergistical design and processing optimization of the canned MCP prototype manufactured by additive manufacturing, near net shape forging, joining/cladding, and surface modification/coating. In Part II, the reliability of canned MCP prototype manufactured by the optimal advanced processes is evaluated in the coupled design and processing modeling according to a quality control method in performance inequality due to accumulative surface integrity change of the key components during 60-year lifespan. The varied service conditions of key components, that depend

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