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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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Topology optimization of additively manufactured continuous fiber-reinforced structures with constraints on fiber path geometry

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Structural and Multidisciplinary Optimization 2025年第5期68卷 1-29页

作者： Wong, Janet Sanders, Emily D. Rosen, David W. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta USA Institute of High Performance Computing Agency for Science Technology and Research Singapore Republic of Singapore

Additive manufacturing has enlarged the design space of variable stiffness continuous fiber-reinforced polymers by enabling increased freedom in the fibers’ geometric layout. Topology optimization can be used to fully harness such freedom in design of continuous fiber-reinforced polymer structures, but constraints are still needed to avoid manufacturing-induced defects like fiber buckling, gaps between fiber tows, or overlapping fiber tows. This paper introduces a topology optimization formulation for maximally-stiff structures composed of continuous fiber-reinforced polymers with constraints on structural volume and fiber path geometry. Density design variables define the structure’s topology and geometry, and fiber orientation design variables define the local fiber orientation. The fiber orientation design variables are defined at support points of radial basis functions so that differentiable functions describing the curl and divergence of the fiber orientation field can be used to control the local fiber curvature and growth of gaps or overlaps between fiber tows, respectively. The augmented Lagrangian method is used to handle the large number of local curl and divergence constraints. At present, this work is limited to a single, two-dimensional laminate. Several design examples illustrate how the curl and divergence constraints can be controlled to meet local fiber tow curvature restrictions and limit the growth of gaps and overlaps between fiber tows based on process-specific additive manufacturing constraints, and how the constraints influence the optimized topology, geometry, and fiber layout.

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Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation

arXiv

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arXiv 2025年

作者： Lian, Junhong Ao, Xiang Liu, Xinyu Liu, Yang He, Qing Institute of Computing Technology Chinese Academy of Sciences Beijing China China Institute of Intelligent Computing Technology CAS Suzhou China High Performance Computer Research Center ICT CAS University of Chinese Academy of Sciences CAS China

Personalized news headline generation aims to provide users with attention-grabbing headlines that are tailored to their preferences. Prevailing methods focus on user-oriented content preferences, but most of them overlook the fact that diverse stylistic preferences are integral to users’ panoramic interests, leading to suboptimal personalization. In view of this, we propose a novel Stylistic-Content Aware Personalized Headline Generation (SCAPE) framework. SCAPE extracts both content and stylistic features from headlines with the aid of large language model (LLM) collaboration. It further adaptively integrates users’ long- and short-term interests through a contrastive learning-based hierarchical fusion network. By incorporating the panoramic interests into the headline generator, SCAPE reflects users’ stylistic-content preferences during the generation process. Extensive experiments on the real-world dataset PENS demonstrate the superiority of SCAPE over baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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AceWGS: An LLM-Aided Framework to Accelerate Catalyst Design for Water-Gas Shift Reactions

arXiv

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arXiv 2025年

作者： Chattoraj, Joyjit Hamadicharef, Brahim Chang, Teo Shi Zeng, Yingzhi Poh, Chee Kok Chen, Luwei Tan, Teck Leong Computing and Intelligence Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore Department of Catalysis and Green Process Engineering Institute of Sustainability for Chemicals Energy and Environment Agency for Science Technology and Research Singapore627833 Singapore Materials Science and Chemistry Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore

While the Water-Gas Shift (WGS) reaction plays a crucial role in hydrogen production for fuel cells, finding suitable catalysts to achieve high yields for low-temperature WGS reactions remains a persistent challenge. Artificial Intelligence (AI) has shown promise in accelerating catalyst design by exploring vast candidate spaces, however, two key gaps limit its effectiveness. First, AI models primarily train on numerical data, which fail to capture essential text-based information, such as catalyst synthesis methods. Second, the cross-disciplinary nature of catalyst design requires seamless collaboration between AI, theory, experiments, and numerical simulations, often leading to communication barriers. To address these gaps, we present AceWGS, a Large Language Models (LLMs)-aided framework to streamline WGS catalyst design. AceWGS interacts with researchers through natural language, answering queries based on four features: (i) answering general queries, (ii) extracting information about the database comprising WGS-related journal articles, (iii) comprehending the context described in these articles, and (iv) identifying catalyst candidates using our proposed AI inverse model. We presented a practical case study demonstrating how AceWGS can accelerate the catalyst design process. AceWGS, built with open-source tools, offers an adjustable framework that researchers can readily adapt for a range of AI-accelerated catalyst design applications, supporting seamless integration across cross-disciplinary studies. © 2025, CC BY-NC-ND.

关键词： Water gas shift

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Generation of Mobility Patterns for Private Vehicles using Multi-headed Sequence Generative Adversarial Networks

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Transportation Research Procedia 2025年 82卷 908-922页

作者： Pritee Agrawal Vasundhara Jayaraman Jeremy Oon Feng Ling Muhamad Azfar Bin Ramli Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Republic of Singapore

Effective transport planning strategies seek to improve the urban transportation systems by addressing traffic congestion problems, identifying emission hotspots, incentivizing public transit to discourage private vehicle usage and adoption of electric vehicles. Strategy planners and policy makers are increasingly relying on new emerging methods such as full population scaled agent-based macroscopic transport simulation models to study the interaction between mobility patterns of the commuters and their impact on the transportation systems. This is because the behavioural changes in daily travelling patterns of commuters play a major role in predicting the future travel demand for the development of a robust and reliable urban transportation system. Traditionally, household travel surveys have been conducted periodically to understand the current travel behaviour and plan for the future development. However, it is impossible for traditional surveys to sample a large majority of the daily commuter trips, particularly for private vehicles and therefore in its raw form, it cannot be used in a full sample agent-based simulation. In this paper, we use the household travel survey data from Singapore to generate the synthetic mobility patterns for the complete population of private vehicle users that is capable of preserving the data privacy whilst retaining the statistical features of the original data. This is accomplished by employing a multi-headed gated recurrent unit (GRU) based generative model that enables the generation of synthetic mobility patterns that provide a suitable representation of the real mobility patterns of the larger set of commuters. We define and utilize two accuracy evaluation metrics that quantify the quality of the generated synthetic mobility trips. Our experimental results have shown that we are able to capture 90% of the correctness of the original mobility dataset of private vehicle users.

关键词： Mobility patterns surveys simulation models Generative adversarial networks Gated recurrent unit

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Serious Games as Emerging E-Health Interventions: Design of an Exergame for the SMILER Project 13th

Serious Games as Emerging E-Health Interventions: Design of...

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13th International Conference on Games and Learning Alliance, GALA 2024

作者： Augello, Agnese Caggianese, Giuseppe Gaglio, Giuseppe Fulvio Gallo, Luigi Malizia, Velia La Grutta, Stefania Tancredi, Giancarlo Turchetta, Attilio Vacca, Rosa Anna Scala, Iris Institute for High Performance Computing and Networking CNR Palermo Italy Institute for High Performance Computing and Networking CNR Naples Italy University of Palermo Palermo Italy Department of Information Science and Technology Pegaso University Naples Italy Institute of Translational Pharmacology CNR Palermo Italy Pediatric Department Sapienza University of Rome Rome Italy Sport Medicine Unit Bambino Gesù Children’s Hospital Roma Italy Institute of Biomembranes Bioenergetics and Molecular Biotechnologies CNR Bari Italy Department of Maternal and Child Health Federico II University Napoli Italy

ISBN: (纸本)9783031782688

This paper outlines the design and implementation of an exergame developed under the strategic Italian CNR project SMILER (Serious gaMes in chILdren with nEurological or Respiratory disorders). The project aims at investigating how Serious Games (SG) can enhance the health conditions of young people with chronic diseases, specifically targeting two categories: individuals with Down Syndrome and those with Asthma. The SG employs a narrative strategy within an immersive, interactive setup. The child’s movements are captured and translated into actions performed by an in-game avatar, who is central to the story. Additionally, a wristband continuously monitors the child’s physiological data, ensuring they remain within the aerobic zone. In this document, the game’s architectural modules and design decisions are described, along with introducing the experimental plan to explore the game’s impact on transcriptome profiling (CTRA) gene patterns and neuropsychological outcomes through an upcoming pilot study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Game design

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Coupled-mode phase matching on a silicon chip

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Physical Review A 2025年第3期111卷 033504-033504页

作者： Chao Wu Pingyu Zhu Miaomiao Yu Weihong Luo Qilin Zheng Kaikai Zhang Yingwen Liu Ping Xu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China Hefei National Laboratory Hefei 230088 China

Phase matching lies at the heart of the classical nonlinear process for frequency conversion and all-optical processing, as well as the spontaneous parametric process for the quantum light sources generation. In this work, we experimentally demonstrate the coupled-mode phase-matching (CMPM) engineered by the two-paralleled-coupled waveguides on a silicon-on-insulator (SOI) chip, extending the converted beam and entangled photons to the S band and L band. We measure the spectrum and efficiency of wavelength conversion, which successfully validates the designed CMPM scheme. We further measure the pairs generation rate of the biphoton based on the spontaneous four-wave mixing, which features a narrow bandwidth consistent well with the theoretical simulations. Multiple CMPM conditions can be achieved by exploiting the combinations of even and odd supermodes of the coupled system and tuned by designing a proper coupling gap between the two waveguides. The CMPM can extend the FWM nonlinear process ranging from the O band to the U band and even the midinfrared, enriching the working wavelength to a large extent. In addition, the CMPM features a discrete-band narrow bandwidth, which will greatly enhance the encoding density of frequency and the spectral purity of generated photon. In addition, our methods can be easily extended to a wide range of materials such as silicon nitride, dispensing with the dispersion engineering for phase matching.

关键词： Silicon nitride

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Neuro Symbolic Knowledge Reasoning for Procedural Video Question Answering

arXiv

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arXiv 2025年

作者： Nguyen, Thanh-Son Yang, Hong Neoh, Tzeh Yuan Zhang, Hao Keat, Ee Yeo Fernando, Basura Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore

This paper introduces a new video question-answering (VQA) dataset that challenges models to leverage procedural knowledge for complex reasoning. It requires recognizing visual entities, generating hypotheses, and performing contextual, causal, and counterfactual reasoning. To address this, we propose neuro symbolic reasoning module that integrates neural networks and LLM-driven constrained reasoning over variables for interpretable answer generation. Results show that combining LLMs with structured knowledge reasoning with logic enhances procedural reasoning on the STAR benchmark and our dataset. Code and dataset at https://***/LUNAProject22/KML soon. 1 © 2025, CC BY-NC-ND.

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Situational Scene Graph for Structured Human-Centric Situation Understanding

Situational Scene Graph for Structured Human-Centric Situati...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Chinthani Sugandhika Chen Li Deepu Rajan Basura Fernando College of Computing and Data Science Nanyang Technological University Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Graph based representation has been widely used in modelling spatio-temporal relationships in video understanding. Although effective, existing graph-based approaches focus on capturing the human-object relationships while ignoring fine-grained semantic properties of the action components. These semantic properties are crucial for understanding the current situation, such as where does the action takes place, what tools are used and functional properties of the objects. In this work, we propose a graph-based representation called Situational Scene Graph (SSG) to encode both human-object relationships and the corresponding semantic properties. The semantic details are represented as predefined roles and values inspired by situation frame, which is originally designed to represent a single action. Based on our proposed representation, we introduce the task of situational scene graph generation and propose a multi-stage pipeline Interactive and Complementary Network (InComNet) to address the task. Given that the existing datasets are not applicable to the task, we further introduce a SSG dataset whose annotations consist of semantic role-value frames for human, objects and verb predicates of human-object relations. Finally, we demonstrate the effectiveness of our proposed SSG representation by testing on different downstream tasks. Experimental results show that the unified representation can not only benefit predicate classification and semantic role-value classification, but also benefit reasoning tasks on human-centric situation understanding. Code and data are available at https://***/LUNAProject22/SSG.

关键词： Computer vision Codes Annotations Semantics Pipelines Semisupervised learning Cognition Testing

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