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IEEE International Conference on Robotics and Biomimetics

作者： De-Xing Huang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou Ning Ma Long Yan The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The School of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau The Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing China

ISBN: (纸本)9781665481106

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (6~12 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians.

关键词： Image registration Estimation error Three-dimensional displays Navigation Biomimetics Medical services Real-time systems

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Generalized Planning with Deep Reinforcement Learning

arXiv

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arXiv 2020年

作者： Rivlin, Or Hazan, Tamir Karpas, Erez Technion Autonomous Systems Program Technion - Israel Institute of Technology Haifa3200003 Faculty of Industrial Engineering and Management Technion - Israel Institute of Technology Haifa3200003 Israel

A hallmark of intelligence is the ability to deduce general principles from examples, which are correct beyond the range of those observed. Generalized Planning deals with finding such principles for a class of planning problems, so that principles discovered using small instances of a domain can be used to solve much larger instances of the same domain. In this work we study the use of Deep Reinforcement Learning and Graph Neural Networks to learn such generalized policies and demonstrate that they can generalize to instances that are orders of magnitude larger than those they were trained on. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Curriculum-based Deep Reinforcement Learning for Quantum Control

arXiv

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arXiv 2020年

作者： Ma, Hailan Dong, Daoyi Ding, Steven X. Chen, Chunlin The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia University of Duisburg-Essen Duisburg47057 Germany Department of Control and Systems Engineering School of Management and Engineering Nanjing University Nanjing210093 China

Deep reinforcement learning has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel deep reinforcement learning approach by constructing a curriculum consisting of a set of intermediate tasks defined by a fidelity threshold. Tasks among a curriculum can be statically determined using empirical knowledge or adaptively generated with the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based deep reinforcement learning (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical simulations on closed quantum systems and open quantum systems demonstrate that the proposed method exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with fewer control pulses. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Correction to: Trinomial tree based option pricing model in supply chain financing

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Annals of Operations Research 2022年第1期332卷 1285-1285页

作者： Yunzhang, Huo Lee, Carman K. M. Shuzhu, Zhang Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong China Department of Information Management Zhejiang University of Finance & Economics Hangzhou China

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Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53 ⁎ ⁎

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IFAC-PapersOnLine 2021年第9期54卷 488-495页

作者： Caroline Moosmüller Christopher J. Tralie Mahdi Kooshkbaghi Zehor Belkhatir Maryam Pouryahya José Reyes Joseph O. Deasy Allen R. Tannenbaum Ioannis G. Kevrekidis C. Moosmüller and C. Tralie contributed equally to this work Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Medical Physics Memorial Sloan-Kettering Cancer Center NY USA Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore MD 21218 USA Department of Mathematics and Computer Science Ursinus College Collegeville PA USA Program in Applied and Computational Mathematics Princeton University NJ USA School of Engineering and Sustainable Development De Montfort University Leicester UK Cancer Biology and Genetics Program and Computational and Systems Biology Program Memorial Sloan-Kettering Cancer Center New York NY 10065 USA and Department of Systems Biology Harvard Medical School Boston MA 02115 USA Departments of Computer Science and Applied Mathematics & Statistics Stony Brook University NY USA

In this paper we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress response, which prevents a cell from dividing if severe DNA damage is present. When this response system is malfunctioning, e.g. due to mutations in p53, uncontrolled cell proliferation may lead to the development of cancer. Understanding the behavior of p53 is thus crucial to prevent its failing. It has been shown in various experiments that periodicity of the p53 signal is one of the main descriptors of its dynamics, and that its pulsing behavior (regular vs. spontaneous) indicates the level and type of cellular stress. In the present work, we introduce an algorithm to score the local periodicity of a given time series (such as the p53 signal), which we call Detrended Autocorrelation Periodicity Scoring (DAPS). It applies pitch detection (via autocorrelation) on sliding windows of the entire time series to describe the overall periodicity by a distribution of localized pitch scores. We apply DAPS to the p53 time series obtained from single cell experiments and establish a correlation between the periodicity scoring of a cell’s p53 signal and the number of cell division events. In particular, we show that high periodicity scoring of p53 is correlated to a low number of cell divisions and vice versa. We show similar results with a more computationally intensive state-of-the-art periodicity scoring algorithm based on topology known as Sw1PerS. This correlation has two major implications: It demonstrates that periodicity scoring of the p53 signal is a good descriptor for cellular stress, and it connects the high variability of p53 periodicity observed in cell populations to the variability in the number of cell division events.

关键词： systems biology cellular systems time series modeling bioinformatics computational biology

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Vaccine distribution strategies against polio: An analysis of turkey scenario

Vaccine distribution strategies against polio: An analysis o...

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2019 Institute of Industrial and systems Engineers Annual Conference and Expo, IISE 2019

作者： Günay, Elif Elçin Park, Kijung Aydogan, Sena Okudan Kremer, Gül E. Department of Industrial Engineering Sakarya University Sakarya 54050 Turkey Department of Industrial and Manufacturing Systems Engineering Iowa State University AmesIA50011 United States Department of Industrial and Management Engineering Incheon National University Incheon22012 Korea Republic of Department of Industrial Engineering Gazi University Maltepe Ankara06570 Turkey

ISBN: (纸本)9781713814092

As a result of the ongoing Syrian civil war, almost 3 million refugees moved to Turkey since 2011 because of security reasons. However, the government operated refugee camps have been largely inadequate to accommodate this huge number of refugees. Therefore, almost 91% of the Syrian refugees live out of government-operated camps. According to a Turkish Disaster and Emergency management Agency (AFAD) report, 45.4% of the children under 5 years old who live out of camps are not vaccinated against polio. This presents a serious health threat to the local population and the whole region. In order to deal with this potential risk, local vaccine distribution strategies that encourage vaccination should be developed for the cities that are close to Syrian border. We develop a mathematical model with which we determine the optimal number of polio vaccines that needs to be administered for each age group and the region considering the vaccination history of individuals. Different vaccination strategies (i) by age, (ii) by region, and (iii) by age-population are compared for cost effectiveness. © 2019 IISE Annual Conference and Expo 2019. All rights reserved.

关键词： Cost effectiveness

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Ubiquitous Power Law Scaling in Nonlinear Self-Excited Hawkes Processes

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Physical Review Letters 2021年第18期127卷 188301-188301页

作者： Kiyoshi Kanazawa Didier Sornette Faculty of Engineering Information and Systems University of Tsukuba Tennodai Tsukuba Ibaraki 305-8573 Japan and JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Management Technology and Economics ETH Zurich Zurich 8092 Switzerland and Institute of Risk Analysis Prediction and Management (Risks-X) Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology (SUSTech) Shenzhen 518055 China

The origin(s) of the ubiquity of probability distribution functions with power law tails is still a matter of fascination and investigation in many scientific fields from linguistic, social, economic, computer sciences to essentially all natural sciences. In parallel, self-excited dynamics is a prevalent characteristic of many systems, from the physics of shot noise and intermittent processes, to seismicity, financial and social systems. Motivated by activation processes of the Arrhenius form, we bring the two threads together by introducing a general class of nonlinear self-excited point processes with fast-accelerating intensities as a function of “tension.” Solving the corresponding master equations, we find that a wide class of such nonlinear Hawkes processes have the probability distribution functions of their intensities described by a power law on the condition that (i) the intensity is a fast-accelerating function of tension, (ii) the distribution of marks is two sided with nonpositive mean, and (iii) it has fast-decaying tails. In particular, Zipf’s scaling is obtained in the limit where the average mark is vanishing. This unearths a novel mechanism for power laws including Zipf’s law, providing a new understanding of their ubiquity.

关键词： Nonequilibrium statistical mechanics Random walks Scaling laws of complex systems Stochastic dynamical systems Chaos & nonlinear dynamics Functional analytical methods Master equation Monte Carlo methods Non-Markovian processes Perturbation theory Stochastic differential equations

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Transplantation of human kidney organoids elicited a robust allogeneic response in a humanized mouse model

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Kidney International 2025年第6期107卷 1011-1016页

作者： Mon-Wei Yu, Samuel Choi, John Yongjoon Kady, Jamie De Cos, Marina Wong, Jenny S. King, Emily Younis, Nour Al Rahy, Nadim Al Chaar, Soltan Djebli, Houda Eskandari, Siawosh Samuel, Stephen Merhej, Tamara Sukumar, Nivedha Lin, Jia-Ren Chen, Jia-Yun Xu, Yilin Santagata, Sandro Weins, Astrid Yeung, Melissa Cravedi, Paolo Campbell, Kirk N. Lemos, Dario Bonventre, Joseph V. Brehm, Michael A. Azzi, Jamil Department of Medicine Nephrology Division Mount Sinai Hospital New York NY United States Department of Medicine Renal Division Brigham and Women's Hospital Harvard Medical School Boston MA United States Program in Molecular Medicine Diabetes Center of Excellence University of Massachusetts Chan Medical School Worcester MA United States Doctoral School of Science and Technology Lebanese University Beirut Lebanon Institute of Biological Science Faculty of Medical Sciences Mohammed VI Polytechnic University Benguerir Morocco Department of Biotechnology Northeastern University Boston MA United States Department of Systems Biology Harvard Medical School Boston MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Harvard Stem Cell Institute Cambridge MA United States Department of Medicine Division of Engineering in Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States

Human kidney organoids derived from embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs) have become novel tools for studying various kidney pathologies. Here, we transplanted ESC-derived kidney organoids into humanized mice with a mature human adaptive immune system developed through thymic education. As judged by histology and immunophenotyping, the transplanted HLA-mismatched kidney organoids trigged a robust alloimmune response, characterized by a dense immune cell infiltrate and enhanced memory T cell phenotype in the allograft 30 days post-transplantation. Multiplexed immunofluorescence revealed expression of functional markers of various immune cell infiltrates in response to organoid allografts, mimicking the T cell-mediated rejection process in humans. This validated our model as a novel platform to study various therapeutic strategies to control alloimmunity. Splenocytes isolated from organoid-transplanted hosts showed an alloantigen-specific memory response against 2D kidney organoids ex vivo. Overall, our study indicates that transplanting kidney organoids in humanized mice may be a valuable tool for studying human allogeneic immunity. © 2025 International Society of Nephrology

关键词： human kidney organoids humanized mice model kidney transplantation

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A STOCHASTIC programMING APPROACH FOR CHEMOTHERAPY APPOINTMENT SCHEDULING

arXiv

引用

arXiv 2020年

作者： Demir, Nur Banu Gul, Serhat Çelik, Melih Department of Industrial and Systems Engineering Wayne State University DetroitMI United States Department of Industrial Engineering TED University Ankara Turkey School of Management University of Bath Bath United Kingdom Department of Industrial Engineering TED University Çankaya Ankara06420 Turkey

Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in pre-medication and infusion durations. In this paper, we formulate a two-stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability and number of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real-life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling

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Machine learning applied to forecasting the manufacturing time of new products prototypes and ETO products: An exploratory study

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International Journal of Production Economics 2025年 287卷

作者： Roberto Canedo Rosa Marcelo Carneiro Gonçalves Sanderson César Macêdo Barbalho Federal Institute of Amazonas Campus Manaus Centro Manaus AM CEP: 69025-010 Brazil University of Brasilia Technology College. Department of Industrial Engineering Post-graduation Program of Mechatronic Systems Campus Darcy Ribeiro Brasilia DF CEP 70910-900 Brazil

Industry 4.0 is a transformative initiative that integrates various technologies and reshapes industrial processes, production methods, and business models. However, forecasting future events within this paradigm shift presents significant challenges. Predicting the cycle time for new product development (NPD) in a dynamic and competitive environment, especially in a highly globalized market driven by innovation, is crucial. Previous research has shown that prototype manufacturing lead times are key parameters for predicting NPD times-to-market. This study investigates the predictive capabilities of artificial intelligence algorithms in estimating manufacturing lead times under varying part geometries and materials at an aerospace and medical equipment company. By leveraging predictive analysis and machine learning techniques, specifically Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest (RF) algorithms, the proposed methodology demonstrates its efficacy and variations. The results show that top-performing models achieve an accuracy rate exceeding 87 % and an average absolute error of less than one day, which have significant practical benefits for project production planners. They can utilize the most popular AI frameworks on easier-to-use programming platforms to estimate the time required to manufacture their prototypes, predict their new product development (NPD) cycle times, and negotiate lead times for in-house and third-party manufacturing more effectively, thereby improving project planning and delivery.

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