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arXiv 2025年

作者： Hagemann, Niklas Kneib, Thomas Möllenhoff, Kathrin Institute of Medical Statistics and Computational Biology Faculty of Medicine University of Cologne Germany Division of Mathematics Department of Mathematics and Computer Science University of Cologne Germany Chair of Statistics and Campus Institute Data Science Georg August University Göttingen Germany

A central focus in survival analysis is examining how covariates influence survival time. These covariate effects are often found to be either time-varying, heterogeneous - such as being specific to patients, treatments, or subgroups - or exhibit both characteristics simultaneously. While the standard model, the Cox proportional hazards model, allows neither time-varying nor heterogeneous effects, several extensions to the Cox model as well as alternative modeling frameworks have been introduced. However, no unified framework for incorporating heterogeneously time-varying effects of covariates has been proposed. Such effects occur when a covariate influences survival not only in a heterogeneous and time-varying manner, but when the time-variation is also heterogeneous. We propose to model such effects by introducing heterogeneously time-varying coefficients to piecewise exponential additive mixed models. We deploy functional random effects, also known as factor smooths, to model such coefficients as the interaction effect of heterogeneity and timevariation. Our approach allows for non-linear time-effects due to being based on penalized splines and uses an efficient random effects basis to model the heterogeneity. Using a penalized basis prevents overfitting in case of absence of such effects. In addition, the penalization mostly solves the problem of choosing the number of intervals which is usually present in unregularized piecewise exponential approaches. We demonstrate the superiority of our approach in comparison to competitors by means of a simulation study. Finally, the practical application and relevance are outlined by presenting a brain tumor case study. © 2025, CC BY.

关键词： Regression analysis

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Adapting Segment Anything Model for Ulcer Segmentation in Inflammatory Bowel Disease 46

Adapting Segment Anything Model for Ulcer Segmentation in In...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Cai, Lingrui Stidham, Ryan W. Wittrup, Emily Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Division of Gastroenterology Department of Internal Medicine Michigan Medicine Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI United States

ISBN: (纸本)9798350371499

Inflammatory Bowel Disease (IBD) is a widespread gastrointestinal disorder characterized by inflammation and ulceration, affecting millions of individuals. Mucosal injury assessment through colonoscopy plays a crucial role in determining disease severity and therapeutic efficacy in IBD. This study draws inspiration from the Segment Anything Model (SAM) framework and tailors it for ulcer segmentation using a Parameter-Efficient Transfer Learning (PETL) approach and incorporating binary cross-entropy loss with boundary loss. Comparative assessments against state-of-the-art UNet-based convolutional neural network models underscore SAM's superior performance, especially when faced with limited training samples. Our ulcer segmentation models demonstrate Dice, IoU, and 95%HD scores of 61.92±0.69, 75.18±0.38, and 10.07±0.05 in the colon dataset, and 55.60±0.82, 77.00±0.67, and 7.74±0.23 in the ileum dataset, respectively. This study highlights the potential of our approach to significantly enhance the efficiency and accuracy of IBD diagnosis and assessment through cutting-edge computer vision techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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A Decision Support System to Compile Environmental Mitigations from Hydropower Licensing Documents

A Decision Support System to Compile Environmental Mitigatio...

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IEEE International Conference on Big data

作者： Hong-Jun Yoon Tom Ruggles Huanhuan Zhao Debjani Singh Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee USA Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge Tennessee USA Data Science and Engineering University of Tennessee Knoxville Knoxville Tennessee

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge in the respective field, resulting in a labor-intensive process when executed by humans. Furthermore, the task of identifying multiple class labels in extensive texts presents a challenge due to intra- and inter-reader variability, making the process time-consuming and ***’re introducing a user-friendly graphical interface, fortified with a BERT model-powered decision support system. This advanced system aims to augment efficiency, curtail data collection time, and sustain high precision in data acquisition. It is instrumental in deciphering and synthesizing intricate texts teeming with a spectrum of expressions, even within similar mitigation categories. Such tasks traditionally demand substantial human effort and specialized knowledge in the *** system is specifically engineered for the task of extracting environmental mitigation information to promote sustainable hydropower development from licenses issued by the Federal Energy Regulatory Commission (FERC). These license documents are comprehensive, each containing over 15,000 words and requiring the identification of 135 different class labels. We anticipate that our system will boost reading speed, improve the consistency of classification outputs among readers, and contribute to the development of a robust scientific database of environmental mitigations associated with the 2,000+ non-federal hydropower facilities licensed by FERC in the United States.

关键词： Decision support systems Analytical models Annotations databases Prevention and mitigation Natural languages Hydroelectric power generation Licenses data models data mining

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Grid-connected microgrid:design and feasibility analysis for a local community in Bangladesh

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Clean Energy 2022年第3期6卷 447-459页

作者： Dewan Mahnaaz Mahmud S.M.Masum Ahmed Sayeed Hasan Mohammad Zeyad Energy and Technology Research Division Advanced BioinformaticsComputational Biology and Data Science LaboratoryBangladesh Faculty of Engineering Erasmus Mundus Joint MSc in Smart Cities and Communities(SMACCs)University of Mons(UMONS)Belgium School of Engineering and Physical Sciences(EPS) Erasmus Mundus Joint MSc in Smart Cities and Communities(SMACCs)Heriot-Watt University(HWU)UK

Global demand for electricity is growing significantly in developing *** energy accounts for barely 3%of total energy consumption in *** of renewable energy,***,are increasingly being acknowledged as viable supply-side choices for *** article presents a grid-connected microgrid design based on meteorological data for a local community situated in Mohammadpur,*** study presents a feasible design of a system that gives the lowest cost of energy production and emissions that is evaluated using software named Hybrid Optimization Multiple Energy Resources(HOMER Pro).Comparison and assessment of the net present cost,cost of energy,operating cost and environmental emission for five different feasible microgrids are analysed concerning real-time ***,a suitable case is sorted out and proposed for the local community for *** proposed case offers a$0.0442/kWh cost of energy,which is~32%cheaper than the current rate with a 57.5%renewable fraction and a payback period of 16.86 *** of this local community will have access to considerably more clean energy at a lower price by this study;also this design could sell the excess energy to the grid to avoid frequent electricity outages.

关键词： renewable energy communities microgrid community energy photovoltaic batteries emission cost of energy

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Short-term Weather Forecasting for Wind Energy Generation using A Deep Learning Technique 5

Short-term Weather Forecasting for Wind Energy Generation us...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Zim, Abid Hasan Zeyad, Mohammad Ahmed, S.M. Masum Hossain, Eftakhar Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh ABCD Laboratory Bangladesh Chattogram Bangladesh Heriot-Watt University Edinburgh United Kingdom University of Antwerp Faculty of Applied Engineering - Construction Antwerp Belgium

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

Inadequate or erroneous weather predictions have a great impact on wind turbine energy output. Certain weather fluctuations and interruptions affect the efficient operation of wind turbines. Short-term weather predictions that are calibrated and verified make it simple to reduce the threat presented by meteorological uncertainty. Conventional physics-based techniques for forecasting are relatively weaker than data-driven approaches since wind turbine components usually have a multipart nonlinear relationship. Compared to physical methods, non-parametric alternatives like deep learning are thought to be more stable and lead to fewer mistakes in analyst predictions. This research presents the state-of-the-art Temporal Convolutional Network (TCN), one of the deep learning methods, which is introduced as a means of enhancing the precision of short-term forecasting of weather variables. TCN is an architecture that makes use of casual convolutions and dilations in order to be adaptable for sequential data with its temporality and huge receptive fields. Seven significant meteorological parameters from the NASA POWER dataset of Chouchala Sea Beach in Chittagong, Bangladesh, over a period of four months were used in this research. The accuracy was measured using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results obtained from the suggested TCN model demonstrate that the predicted outcome is pretty accurate and consistent. © 2022 IEEE.

关键词： Wind turbines

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Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning

arXiv

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arXiv 2024年

作者： Shah, Ashka DePavia, Adela Hudson, Nathaniel Foster, Ian Stevens, Rick Department of Computer Science University of Chicago ChicagoIL United States Committee on Computational and Applied Mathematics University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

The aim in many sciences is to understand the mechanisms that underlie the observed distribution of variables, starting from a set of initial hypotheses. Causal discovery allows us to infer mechanisms as sets of cause and effect relationships in a generalized way-without necessarily tailoring to a specific domain. Causal discovery algorithms search over a structured hypothesis space, defined by the set of directed acyclic graphs, to find the graph that best explains the data. For high-dimensional problems, however, this search becomes intractable and scalable algorithms for causal discovery are needed to bridge the gap. In this paper, we define a novel causal graph partition that allows for divide-and-conquer causal discovery with theoretical guarantees. We leverage the idea of a superstructure-a set of learned or existing candidate hypotheses-to partition the search space. We prove under certain assumptions that learning with a causal graph partition always yields the Markov Equivalence Class of the true causal graph. We show our algorithm achieves comparable accuracy and a faster time to solution for biologically-tuned synthetic networks and networks up to 104 variables. This makes our method applicable to gene regulatory network inference and other domains with high-dimensional structured hypothesis spaces. © 2024, CC BY.

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Artificial Intelligence in IBD: How Will It Change Patient Management?

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Current Treatment Options in Gastroenterology 2023年第4期21卷 365-377页

作者： Stone, Molly L. Stidham, Ryan W. Division of Gastroenterology Department of Internal Medicine Michigan Medicine Ann Arbor USA Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor USA Michigan Institute for Data Science (MIDAS) University of Michigan Ann Arbor USA

Artificial intelligence (AI) is quickly evolving and will be integral to the management of IBD patients. In this review, we provide an overview of technologies powering applications for Crohn’s disease and ulcerative colitis, with particular attention to information extraction from imaging and text records. Recent data highlight machine learning capability to replicate expert interpretation and judgment at population scale with accuracy in endoscopic and histology disease grading. Computer vision techniques are also detecting important findings difficult for even expert clinicians to reliably appreciate, including dysplasia on colonoscopy and fibrosis on CT and MRI imaging. Further, segmentation and radiomics can extract more information than manually possible, enabling new possibilities for disease measurement. Finally, natural language processing (NLP) is showing promise automatically extracting IBD-related medical concepts from text and generating conversational responses for both clinicians and patients. Quickly mounting evidence supports AI reliability grading disease severity in endoscopy, histology, and imaging. In particular use cases, AI can surpass the ability of humans for disease feature detecting and the generation of new measures of IBD activity. Ultimately, AI-powered information extraction will provide significant incremental improvement in the personalization predictions of outcomes and treatment course in IBD.

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Modeling of Measurement Error in Financial Returns data

arXiv

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arXiv 2024年

作者： Jasra, Ajay Maama, Mohamed Mijatović, Aleksandar School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistics University of Warwick United Kingdom

In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period, we develop a stochastic model which models the true log-returns and maximum via a Lévy process and the data as a measurement error there-of. The main technical difficulty of trying to infer this model, for instance Bayesian parameter estimation, is that the joint transition density of the return and maximum is seldom known, nor can it be simulated exactly. Based upon the novel stick breaking representation of [12] we provide an approximation of the model. We develop a Markov chain Monte Carlo (MCMC) algorithm to sample from the Bayesian posterior of the approximated posterior and then extend this to a multilevel MCMC method which can reduce the computational cost to approximate posterior expectations, relative to ordinary MCMC. We implement our methodology on several applications including for real data. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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INTERPOLATORY NECESSARY OPTIMALITY CONDITIONS FOR REDUCED-ORDER MODELING OF PARAMETRIC LINEAR TIME-INVARIANT SYSTEMS

arXiv

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arXiv 2024年

作者： Mlinarić, Petar Benner, Peter Gugercin, Serkan Department of Mathematics Virginia Tech BlacksburgVA24061 United States Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg39106 Germany Department of Mathematics Division of Computational Modeling and Data Analytics Academy of Data Science Virginia Tech BlacksburgVA24061 United States

Interpolatory necessary optimality conditions for H2-optimal reduced-order modeling of non-parametric linear time-invariant (LTI) systems are known and well-investigated. In this work, using the general framework of L2-optimal reduced-order modeling of parametric stationary problems, we derive interpolatory H2 ⊗ L2-optimality conditions for parametric LTI systems with a general pole-residue form. We then specialize this result to recover known conditions for systems with parameter-independent poles and develop new conditions for a certain class of systems with parameter-dependent poles. Copyright © 2024, The Authors. All rights reserved.

关键词： Poles

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