TY - GEN A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei T1 - Colouring Fuzziness for Systems Biology T2 - Theoretical Computer Science N2 - Snoopy is a powerful modelling and simulation tool for various types of Petri nets, which have been applied to a wide range of biochemical reaction networks. We present an enhanced version of Snoopy, now supporting coloured and uncoloured stochastic, continuous and hybrid Petri Nets with fuzzy kinetic parameters. Colour helps to cope with modelling challenges imposed by larger and more complex networks. Fuzzy parameters are specifically useful when kinetic parameter values can not be precisely measured or estimated. By running fuzzy simulation we obtain output bands of the variables of interest induced by the effect of the fuzzy kinetic parameters. Simulation is always done on the uncoloured level. For this purpose, coloured fuzzy Petri nets are automatically unfolded to their corresponding uncoloured counterparts. Combining the power of fuzzy kinetic parameters with the modelling convenience of coloured Petri nets provides a new quality in user support with sophisticated modelling and analysis features. KW - fuzzy logic KW - Fuzzy kinetic parameters KW - Coloured fuzzy continuous, stochastic and hybrid Petri nets KW - Modelling and simulation KW - Modelling uncertainty Y1 - 2021 UR - https://www.sciencedirect.com/science/article/abs/pii/S0304397521002152?via%3Dihub U6 - https://doi.org/10.1016/j.tcs.2021.04.011 SN - 1879-2294 SN - 0304-3975 VL - 875 SP - 52 EP - 64 ER - TY - GEN A1 - Liu, Fei A1 - Assaf, George A1 - Chen, Ming A1 - Heiner, Monika T1 - A Petri nets-based framework for whole-cell modeling T2 - Biosystems N2 - Whole-cell modeling aims to incorporate all main genes and processes, and their interactions of a cell in one model. Whole-cell modeling has been regarded as the central aim of systems biology but also as a grand challenge, which plays essential roles in current and future systems biology. In this paper, we analyze whole-cell modeling challenges and requirements and classify them into three aspects (or dimensions): heterogeneous biochemical networks, uncertainties in components, and representation of cell structure. We then explore how to use different Petri net classes to address different aspects of whole-cell modeling requirements. Based on these analyses, we present a Petri nets-based framework for whole-cell modeling, which not only addresses many whole-cell modeling requirements, but also offers a graphical, modular, and hierarchical modeling tool. We think this framework can offer a feasible modeling approach for whole-cell model construction. KW - Whole-cell modeling KW - Systems biology KW - Petri nets KW - Modeling framework Y1 - 2021 UR - https://www.sciencedirect.com/science/article/abs/pii/S0303264721001738?via%3Dihub U6 - https://doi.org/10.1016/j.biosystems.2021.104533 SN - 0303-2647 SN - 1872-8324 VL - 210 ER - TY - GEN A1 - Fei, Tai A1 - Mukhopadhyay, Subhas A1 - Da Costa, João Paulo Javidi A1 - Gardill, Markus A1 - Liu, Shengheng A1 - Roychaudhuri, Chirasree A1 - Lan, Lan A1 - Demitri, Nevine T1 - Guest editorial special issue on smartness and robustness of spatial environment perception in automated systems T2 - IEEE Sensors Journal N2 - As the curtains close on this special issue dedicated to advanced sensor research, we reflect on the critical role sensor technology plays in the future of automation. Throughout this issue, we have explored significant advancements and ongoing challenges in developing intelligent, resilient automated systems. A key theme is the vital need for smarter, more robust sensing systems. Modern sensors, now more adaptable, adjust their operations based on external changes, crucial for maximizing automation in varied real-world situations. Efforts to enhance sensor robustness have produced improvements in performance in extreme conditions, cybersecurity, cost-effective solutions, and flexible system requirements while preserving performance. The addition of self-monitoring and calibration features allows for continuous refinement of sensor accuracy and rapid problem detection, boosting system reliability. The capacity for seamlessly integrating alternative technologies when specific sensors fail further strengthens system resilience. This issue includes 33 manuscripts selected from 65 submissions, exploring these diverse aspects. Y1 - 2024 U6 - https://doi.org/10.1109/JSEN.2024.3409137 SN - 1558-1748 VL - 24 IS - 14 SP - 21800 EP - 21800 ER - TY - GEN A1 - Berto, Federico A1 - Hua, Chuanbo A1 - Park, Junyoung A1 - Luttmann, Laurin A1 - Ma, Yining A1 - Bu, Fanchen A1 - Wang, Jiarui A1 - Ye, Haoran A1 - Kim, Minsu A1 - Choi, Sanghyeok A1 - Zepeda, Nayeli Gast A1 - Hottung, André A1 - Zhou, Jianan A1 - Bi, Jieyi A1 - Hu, Yu A1 - Liu, Fei A1 - Kim, Hyeonah A1 - Son, Jiwoo A1 - Kim, Haeyeon A1 - Angioni, Davide A1 - Kool, Wouter A1 - Cao, Zhiguang A1 - Zhang, Qingfu A1 - Kim, Joungho A1 - Zhang, Jie A1 - Shin, Kijung A1 - Wu, Cathy A1 - Ahn, Sungsoo A1 - Song, Guojie A1 - Kwon, Changhyun A1 - Tierney, Kevin A1 - Xie, Lin A1 - Park, Jinkyoo T1 - RL4CO : an extensive reinforcement learning for combinatorial optimization benchmark T2 - KDD '25 : proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 N2 - Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configurations of diverse environments, policy architectures, RL algorithms, and utilities with extensive documentation. RL4CO helps researchers build on existing successes while exploring and developing their own designs, facilitating the entire research process by decoupling science from heavy engineering. We finally provide extensive benchmark studies to inspire new insights and future work. RL4CO has already attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co. Y1 - 2025 SN - 979-8-4007-1454-2 U6 - https://doi.org/10.1145/3711896.3737433 SP - 5278 EP - 5289 PB - ACM CY - New York, NY, USA ER -