Article
Multidisciplinary Sciences
Dominique Joubert, J. D. Stigter, Jaap Molenaar
Summary: Structural identifiability is crucial in model development, and problematic initial values may lead to unidentifiability, which can be resolved by changing these values.
SCIENTIFIC REPORTS
(2021)
Article
Chemistry, Physical
Matthieu Dubarry, David Howey, Billy Wu
Summary: Digital twins are cyber-physical systems that integrate real-time sensor data and models for accurate predictions and optimal decisions in specific assets. In the case of batteries, this concept has been applied at different scales. However, a comprehensive approach is needed for battery digital twins to achieve their full potential in industrial settings. Standardized and transparent data sharing, as well as principled methods to quantify and propagate uncertainty, are essential. Physical modeling and sensing approaches for battery manufacturing and thermal runaway also need improvement.
Article
Biology
Sean R. Bittner, Agostina Palmigiano, Alex T. Piet, Chunyu A. Duan, Carlos D. Brody, Kenneth D. Miller, John Cunningham
Summary: Theoretical neuroscience relies on circuit models to explain neural mechanisms and computations, emphasizing the importance of selecting appropriate model parameters and solving inverse problems. A new technique called Emergent Property Inference (EPI) introduces modern probabilistic modeling tools for inferring parameter distributions based on computational properties in circuit models.
Review
Computer Science, Interdisciplinary Applications
Nicholas N. Lam, Paul D. Docherty, Rua Murray
Summary: This systematic review discusses the impact of practical identifiability (PI) on parameter identification of models, explores the role of different methods in aiding experimental design, and emphasizes the importance of considering the modeling context and research objectives when choosing a PI approach.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Engineering, Electrical & Electronic
Milad Ghanbarpour, Ali Naderi, Behzad Ghanbari, Saeed Haghiri, Arash Ahmadi
Summary: A neuron is the main cell of a nervous system that transmits messages through electrical signals. This study proposes a multiplier-less mathematical equation to implement important neuron models in a more efficient and cost-effective manner. The suggested model accurately recreates the behavioral characteristics of the original neuron models and can be synthesized and implemented on a reconfigurable board. The results show increased frequency, reduced power consumption, and the ability to implement a larger number of neurons.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Mathematics, Applied
Wei Yao, Yingchen Li, Zhihao Ou, Mingzhu Sun, Qiongxu Ma, Guanghong Ding
Summary: Acupuncture, as an important part of traditional Chinese medicine, has widely accepted efficacy, but its scientific basis is still lacking exploration. Some experimental studies have shown that acupuncture can alter cell membrane potential, thereby affecting nerve signal transmission. Based on theoretical models and numerical calculations, this paper proposes a hypothesis that mechanical stimulation can induce a current that leads to repeated discharge of cell membrane potential, and thus achieve analgesic effects.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Biochemical Research Methods
Matthew J. Simpson, Oliver J. Maclaren
Summary: Interpreting data using mechanistic mathematical models is crucial in science and engineering. A unified workflow, called Profile-Wise Analysis (PWA), combines mathematical models and experimental data to address parameter identifiability, parameter estimation, and model prediction. This workflow efficiently approximates the full likelihood-based prediction confidence set. Three case studies demonstrate the practical aspects of applying this workflow to different types of mathematical models.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Multidisciplinary Sciences
Sahil Bhola, Karthik Duraisamy
Summary: This paper proposes a novel information-theoretic approach to assess the practical identifiability of Bayesian statistical models globally. The method does not rely on any assumptions about the model structure or prior distribution, and can take into account different forms of uncertainties. By analyzing the dependencies between parameters, a subset of parameters that can be estimated with high certainty is found.
SCIENTIFIC REPORTS
(2023)
Article
Automation & Control Systems
Lei Wang, Romeo Ortega, Alexey Bobtsov, Jose Guadalupe Romero, Bowen Yi
Summary: In this paper, a new parameter estimator is proposed for linear regression models, ensuring global exponential convergence with only the necessary assumption of identifiability of the regression equation, which is shown to be equivalent to interval excitation of the regressor vector. An extension for - separable and monotonic - nonlinear parameterizations is also presented. The estimators are robust to additive measurement noise and non-slow parameter variations. Additionally, a version of the estimator that is robust against sinusoidal disturbances with unknown internal model is provided. Simulation results comparing the performance of the estimator with other algorithms are presented.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
Review
Chemistry, Physical
Malin Andersson, Moritz Streb, Jing Ying Ko, Verena Lofqvist Klass, Matilda Klett, Henrik Ekstrom, Mikael Johansson, Goran Lindbergh
Summary: Physics-based battery models play a crucial role in battery research, development, and control. Accurate parametrization is essential for obtaining useful information from the models. Parametrization of physics-based battery models from input-output data is a growing research area. Successful parametrization requires knowledge of the underlying physical system and understanding of parameter estimation theory. This paper reviews the key aspects of parametrization in this field.
JOURNAL OF POWER SOURCES
(2022)
Article
Biochemical Research Methods
Alexander Browning, Matthew Simpson
Summary: An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have been developed to harmonise this juxtaposition, yet cannot always resolve the mismatch between available data and the granularity required in mathematical models to answer important biological questions. Our study focuses on the interrelationship between the non-identifiable parameters in a complex model and the identifiable parameters in a simple surrogate model, aiming to provide additional biological insights from complex, non-identifiable models.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Engineering, Biomedical
Xiaolei Xu, Hua Deng, Yi Zhang, Jingwei Chen
Summary: This article presents a method for continuous grasping force estimation based on EMG signals. The method achieves high accuracy and low time delay through model simplification, signal processing, and parameter identification. The proposed method shows improved real-time performance and accuracy compared to traditional methods, and has potential for practical applications in the field of grasping force estimation.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Neurosciences
Minh-Son To, Suraj Honnuraiah, Greg J. Stuart
Summary: This study investigates the impact of voltage clamp errors on synaptic conductance estimates during concurrent excitation and inhibition onto dendrites. The results show that dendritically located conductances are consistently underestimated, especially inhibitory conductances. The study also reveals correlations and distortions in the estimated conductance time course.
Article
Automation & Control Systems
Krishnan Srinivasarengan, Jose Ragot, Christophe Aubrun, Didier Maquin
Summary: LPV models serve as a bridge between linear and nonlinear models, allowing for analysis of nonlinear models and introduction of varying model parameters. The identifiability of model parameters is a key issue, and this paper proposes an approach to verify the identifiability of unknown parameters.
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE
(2022)
Article
Mathematics
Hu Wang, Sha Wang, Yajuan Gu, Yongguang Yu
Summary: This paper introduces a simplified two-dimensional Hodgkin-Huxley model that is exposed to electric fields. The study investigates the Hopf bifurcations of the simplified model through qualitative analysis and numerical simulations. The paper derives a necessary and sufficient condition for the existence of Hopf bifurcations and obtains the conditions for supercritical and subcritical Hopf bifurcations. Bifurcation diagrams are provided for two parameters, and numerical examples are used to demonstrate the efficacy of the theoretical results.
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.