pwc-archive/papers-with-abstracts huggingface dataset:
Deep Demixing: Reconstructing the Evolution of Network Epidemics
Authors: Boning Li, Gojko Čutura, Ananthram Swami, Santiago Segarra
Date: 2023-06-11 00:00:00

We propose the deep demixing (DDmix) model, a graph autoencoder that can reconstruct epidemics evolving over networks from partial or aggregated temporal information. Assuming knowledge of the network topology but not of the epidemic model, our goal is to estimate the complete propagation path of a disease spread. A data-driven approach is leveraged to overcome the lack of model awareness. To solve this inverse problem, DDmix is proposed as a graph conditional variational autoencoder that is trained from past epidemic spreads. DDmix seeks to capture key aspects of the underlying (unknown) spreading dynamics in its latent space. Using epidemic spreads simulated in synthetic and real-world networks, we demonstrate the accuracy of DDmix by comparing it with multiple (non-graph-aware) learning algorithms. The generalizability of DDmix is highlighted across different types of networks. Finally, we showcase that a simple post-processing extension of our proposed method can help identify super-spreaders in the reconstructed propagation path.
Optimal Inference in Contextual Stochastic Block Models
Authors: O. Duranthon, L. Zdeborová
Date: 2023-06-06 00:00:00
Tasks: Community Detection, Node Classification, Stochastic Block Model

The contextual stochastic block model (cSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the cSBM has been widely used as a synthetic dataset for evaluating the performance of graph-neural networks (GNNs) for semi-supervised node classification. We consider a probabilistic Bayes-optimal formulation of the inference problem and we derive a belief-propagation-based algorithm for the semi-supervised cSBM; we conjecture it is optimal in the considered setting and we provide its implementation. We show that there can be a considerable gap between the accuracy reached by this algorithm and the performance of the GNN architectures proposed in the literature. This suggests that the cSBM, along with the comparison to the performance of the optimal algorithm, readily accessible via our implementation, can be instrumental in the development of more performant GNN architectures.
A step towards digital operations -- A novel grey-box approach for modelling the heat dynamics of Ultra-low temperature freezing chambers
Authors: Tao Huang, Peder Bacher, Jan Kloppenborg Møller, Francesco D'Ettorre, Wiebke Brix Markussen
Date: 2023-06-13 00:00:00
Tasks: energy management

Ultra-low temperature (ULT) freezers store perishable bio-contents and have high energy consumption, which highlight a demand for reliable methods for intelligent surveillance and smart energy management. This study introduces a novel grey-box modelling approach based on stochastic differential equations to describe the heat dynamics of the ULT freezing chambers. The proposed modelling approach only requires temperature data measured by the embedded sensors and uses data from the regular operation periods for model identification. The model encompasses three states: chamber temperature, envelope temperature, and local evaporator temperature. Special attention is given to the local evaporator temperature state, which is modelled as a time-variant system, to characterize the time delay and dynamic variations in cooling intensity. Two ULT freezers with different operational patterns are modelled. The unknown model parameters are estimated using the maximum likelihood method. The results demonstrate that the models can accurately predict the chamber temperature measured by the control probe (RMSE < 0.19 {\deg}C) and are promising to be applied for forecasting future states. In addition, the model for local evaporator temperature can effectively adapt to different operational patterns and provide insight into the local cooling supply status. The proposed approach greatly promotes the practical feasibility of grey-box modelling of the heat dynamics for ULT freezers and can serve several potential digital applications. A major limitation of the modelling approach is the low identifiability, which can potentially be addressed by inferring model parameters based on relative parameter changes.
Non-Asymptotic State and Disturbance Estimation for a Class of Triangular Nonlinear Systems using Modulating Functions
Authors: Yasmine Marani, Ibrahima N'Doye, Taous-Meriem Laleg-Kirati
Date: 2023-06-13 00:00:00

Dynamical models are often corrupted by model uncertainties, external disturbances, and measurement noise. These factors affect the performance of model-based observers and as a result, affect the closed-loop performance. Therefore, it is critical to develop robust model-based estimators that reconstruct both the states and the model disturbances while mitigating the effect of measurement noise in order to ensure good system monitoring and closed-loop performance when designing controllers. In this article, a robust step by step non-asymptotic observer for triangular nonlinear systems for the joint estimation of the state and the disturbance is developed. The proposed approach provides a sequential estimation of the states and the disturbance in finite time using smooth modulating functions. The robustness of the proposed observer is both in the sense of model disturbances and measurement noise. In fact, the structure of triangular systems combined with the modulating function-based method allows the estimation of the states independently of model disturbances and the integral operator involved in the modulating function-based method mitigates the noise. Additionally, the modulating function method shifts the derivative from the noisy output to the smooth modulating function which strengthens its robustness properties. The applicability of the proposed modulating function-based estimator is illustrated in numerical simulations and compared to a second-order sliding mode super twisting observer under different measurement noise levels.
An Evaluation of Multi-Component Weft-Knitted Twill Structures for Sensing Tensile Force
Authors: Roland Aigner, Frank Hepper
Date: 2023-06-13 00:00:00

We present multi-component knitted resistive sensors for tracking tensile force. The knits were fabricated using a Twill structure, which is a simple pattern featuring anisotropic elastic behavior, providing high stability along course-direction. Our sensors are made of two commercially available conductive yarn types, with highly different linear resistance. We present a variety of integration methods using the proposed Twill structure, all of which can be easily replicated on a two-bed weft-knitting machine. We evaluate the performance of the resulting sensor variations, with respect to consistency, hysteresis, short-term and long-term relaxation and drift, among other metrics. We found that particulars of the knit's loop composition have a crucial effect on the consistency of the sensor readings. Furthermore, we show that knitting resistive yarn more tightly than the substrate material gives superior results and that improving elastic recoil by adding Lycra to the supporting substrate can considerably improve performance.
Learning with Delayed Payoffs in Population Games using Kullback-Leibler Divergence Regularization
Authors: Shinkyu Park, Naomi Ehrich Leonard
Date: 2023-06-13 00:00:00
Tasks: Decision Making

We study a multi-agent decision problem in large population games. Agents from multiple populations select strategies for repeated interactions with one another. At each stage of these interactions, agents use their decision-making model to revise their strategy selections based on payoffs determined by an underlying game. Their goal is to learn the strategies that correspond to the Nash equilibrium of the game. However, when games are subject to time delays, conventional decision-making models from the population game literature may result in oscillations in the strategy revision process or convergence to an equilibrium other than the Nash. To address this problem, we propose the Kullback-Leibler Divergence Regularized Learning (KLD-RL) model, along with an algorithm that iteratively updates the model's regularization parameter across a network of communicating agents. Using passivity-based convergence analysis techniques, we show that the KLD-RL model achieves convergence to the Nash equilibrium without oscillations, even for a class of population games that are subject to time delays. We demonstrate our main results numerically on a two-population congestion game and a two-population zero-sum game.
Require Process Control? LSTMc is all you need!
Authors: Niranjan Sitapure, Joseph S Kwon
Date: 2023-06-13 00:00:00
Tasks: All, Point Tracking

Over the past three decades, numerous controllers have been developed to regulate complex chemical processes, but they have certain limitations. Traditional PI/PID controllers often require customized tuning for various set-point scenarios. On the other hand, MPC frameworks involve resource-intensive steps, and the utilization of black-box machine learning (ML) models can lead to issues such as local minima and infeasibility. Thus, there is a need for an alternative controller paradigm that combines the simplicity of a PI controller with the grade-to-grade (G2G) transferability of an MPC approach. To this end, we developed a novel LSTM controller (LSTMc) as a model-free data-driven controller framework. The LSTMc considers an augmented input tensor that incorporates information on state evolution and error dynamics for the current and previous $W$ time steps, to predict the manipulated input at the next step ($u_{t+1}$). To demonstrate LSTMc, batch crystallization of dextrose was taken as a representative case study. The desired output for set-point tracking was the mean crystal size ($\bar{L}$), with the manipulated input being the jacket temperature ($T_j$). Extensive training data, encompassing 7000+ different operating conditions, was compiled to ensure comprehensive training of LSTMc across a wide state space region. For comparison, we also designed a PI controller and an LSTM-MPC for different set-point tracking cases. The results consistently showed that LSTMc achieved the lowest set-point deviation ($<$2\%), three times lower than the MPC. Remarkably, LSTMc maintained this superior performance across all set points, even when sensor measurements contained noise levels of 10\% to 15\%. In summary, by effectively leveraging process data and utilizing sequential ML models, LSTMc offers a superior controller design approach.
Set-based state estimation and fault diagnosis using constrained zonotopes and applications
Authors: Brenner S. Rego
Date: 2023-06-12 00:00:00
Tasks: Fault Diagnosis, parameter estimation, State Estimation

This doctoral thesis develops new methods for set-based state estimation and active fault diagnosis (AFD) of (i) nonlinear discrete-time systems, (ii) discrete-time nonlinear systems whose trajectories satisfy nonlinear equality constraints (called invariants), (iii) linear descriptor systems, and (iv) joint state and parameter estimation of nonlinear descriptor systems. Set-based estimation aims to compute tight enclosures of the possible system states in each time step subject to unknown-but-bounded uncertainties. To address this issue, the present doctoral thesis proposes new methods for efficiently propagating constrained zonotopes (CZs) through nonlinear mappings. Besides, this thesis improves the standard prediction-update framework for systems with invariants using new algorithms for refining CZs based on nonlinear constraints. In addition, this thesis introduces a new approach for set-based AFD of a class of nonlinear discrete-time systems. An affine parametrization of the reachable sets is obtained for the design of an optimal input for set-based AFD. In addition, this thesis presents new methods based on CZs for set-valued state estimation and AFD of linear descriptor systems. Linear static constraints on the state variables can be directly incorporated into CZs. Moreover, this thesis proposes a new representation for unbounded sets based on zonotopes, which allows to develop methods for state estimation and AFD also of unstable linear descriptor systems, without the knowledge of an enclosure of all the trajectories of the system. This thesis also develops a new method for set-based joint state and parameter estimation of nonlinear descriptor systems using CZs in a unified framework. Lastly, this manuscript applies the proposed set-based state estimation and AFD methods using CZs to unmanned aerial vehicles, water distribution networks, and a lithium-ion cell.
Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods
Authors: Ali Al Kadhim, Harrison B. Prosper, Olivia F. Prosper
Date: 2023-06-13 00:00:00
Tasks: Astronomy, Epidemiology, valid

High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. When coupled with machine learning, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations without explicit use of the likelihood function. This is of particular interest when the latter is intractable. In this work, we introduce a simple extension of the recently proposed likelihood-free frequentist inference (LF2I) approach that has some computational advantages. Like LF2I, this extension yields provably valid confidence sets in parameter inference problems in which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology.
An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data
Authors: Gabriele Accarino, Davide Donno, Francesco Immorlano, Donatello Elia, Giovanni Aloisio
Date: 2023-06-07 00:00:00

Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger, larger and more destructive. The accurate detection and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate datasets through the use of deterministic tracking schemes that rely on subjective thresholds. Machine Learning (ML) models can complement deterministic approaches due to their ability to capture the mapping between the input climatic drivers and the geographical position of the TC center from the available data. This study presents a ML ensemble approach for locating TC center coordinates, embedding both TC classification and localization in a single end-to-end learning task. The ensemble combines TC center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis were used for model training and testing jointly with the International Best Track Archive for Climate Stewardship records. Results showed that the ML approach is well-suited for TC detection providing good generalization capabilities on out of sample data. In particular, it was able to accurately detect lower TC categories than those used for training the models. On top of this, the ensemble approach was able to further improve TC localization performance with respect to single model TC center estimates, demonstrating the good capabilities of the proposed approach.
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
Authors: Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin, Alexander Mitsos
Date: 2023-05-31 00:00:00
Tasks: Matrix Completion, Prediction

We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightforward in standard machine learning (ML) frameworks enabling automatic differentiation. In contrast to recent hybrid ML approaches, our approach does not rely on embedding a specific thermodynamic model inside the neural network and corresponding prediction limitations. Rather, Gibbs-Duhem consistency serves as regularization, with the flexibility of ML models being preserved. Our results show increased thermodynamic consistency and generalization capabilities for activity coefficient predictions by Gibbs-Duhem-informed graph neural networks and matrix completion methods. We also find that the model architecture, particularly the activation function, can have a strong influence on the prediction quality. The approach can be easily extended to account for other thermodynamic consistency conditions.
Kernel Choice Matters for Boundary Inference Using Local Polynomial Density: With Application to Manipulation Testing
Authors: Shunsuke Imai, Yuta Okamoto
Date: 2023-06-13 00:00:00
Tasks: Common Sense Reasoning

The local polynomial density (LPD) estimator has been a useful tool for inference concerning boundary points of density functions. While it is commonly believed that kernel selection is not crucial for the performance of kernel-based estimators, this paper argues that this does not hold true for LPD estimators at boundary points. We find that the commonly used kernels with compact support lead to larger asymptotic and finite-sample variances. Furthermore, we present theoretical and numerical evidence showing that such unfavorable variance properties negatively affect the performance of manipulation testing in regression discontinuity designs, which typically suffer from low power. Notably, we demonstrate that these issues of increased variance and reduced power can be significantly improved just by using a kernel function with unbounded support. We recommend the use of the spline-type kernel (the Laplace density) and illustrate its superior performance.
JCCS-PFGM: A Novel Circle-Supervision based Poisson Flow Generative Model for Multiphase CECT Progressive Low-Dose Reconstruction with Joint Condition
Authors: Rongjun Ge, Yuting He, Cong Xia, Yang Chen, Daoqiang Zhang, Ge Wang
Date: 2023-06-13 00:00:00
Tasks: Anatomy, Diagnostic, SSIM

Multiphase contrast-enhanced computed tomography (CECT) scan is clinically significant to demonstrate the anatomy at different phases. In practice, such a multiphase CECT scan inherently takes longer time and deposits much more radiation dose into a patient body than a regular CT scan, and reduction of the radiation dose typically compromise the CECT image quality and its diagnostic value. With Joint Condition and Circle-Supervision, here we propose a novel Poisson Flow Generative Model (JCCS-PFGM) to promote the progressive low-dose reconstruction for multiphase CECT. JCCS-PFGM is characterized by the following three aspects: a progressive low-dose reconstruction scheme, a circle-supervision strategy, and a joint condition mechanism. Our extensive experiments are performed on a clinical dataset consisting of 11436 images. The results show that our JCCS-PFGM achieves promising PSNR up to 46.3dB, SSIM up to 98.5%, and MAE down to 9.67 HU averagely on phases I, II and III, in quantitative evaluations, as well as gains high-quality readable visualizations in qualitative assessments. All of these findings reveal our method a great potential to be adapted for clinical CECT scans at a much-reduced radiation dose.
Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease
Authors: Lan Wang, Ruiling He, Lili Zhao, Jia Wang, Zhengzi Geng, Tao Ren, Guo Zhang, Peng Zhang, Kaiqiang Tang, Chaofei Gao, Fei Chen, Liting Zhang, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Muhan Lv, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Yunfang Liu, Kun Wang, Meiqin Su, Li Bian, Ping An, Xin Zhang, Linxue Qian, Shao Li, Xiaolong Qi
Date: 2023-06-13 00:00:00

Objective: Bleeding from gastroesophageal varices (GEV) is a medical emergency associated with high mortality. We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of the liver and spleen to precisely assess the risk of GEV and high-risk gastroesophageal varices (HRV). Design: A prospective multicenter study was conducted in patients with compensated advanced chronic liver disease. 305 patients were enrolled from 12 hospitals, and finally 265 patients were included, with 1136 liver stiffness measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images generated by 2D-SWE. We leveraged deep learning methods to uncover associations between image features and patient risk, and thus conducted models to predict GEV and HRV. Results: A multi-modality Deep Learning Risk Prediction model (DLRP) was constructed to assess GEV and HRV, based on LSM and SSM images, and clinical information. Validation analysis revealed that the AUCs of DLRP were 0.91 for GEV (95% CI 0.90 to 0.93, p < 0.05) and 0.88 for HRV (95% CI 0.86 to 0.89, p < 0.01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM. Moreover, DLPR was better than the model using individual parameters, including LSM and SSM images. In HRV prediction, the 2D-SWE images of SSM outperform LSM (p < 0.01). Conclusion: DLRP shows excellent performance in predicting GEV and HRV over canonical risk indicators LSM and SSM. Additionally, the 2D-SWE images of SSM provided more information for better accuracy in predicting HRV than the LSM.
Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity
Authors: Bechir Hamdaoui, Nora Basha, Kathiravetpillai Sivanesan
Date: 2023-06-13 00:00:00
Tasks: Diversity

Deep learning-enabled device fingerprinting has proven efficient in enabling automated identification and authentication of transmitting devices. It does so by leveraging the transmitters' unique features that are inherent to hardware impairments caused during manufacturing to extract device-specific signatures that can be exploited to uniquely distinguish and separate between (identical) devices. Though shown to achieve promising performances, hardware fingerprinting approaches are known to suffer greatly when the training data and the testing data are generated under different channels conditions that often change when time and/or location changes. To the best of our knowledge, this work is the first to use MIMO diversity to mitigate the impact of channel variability and provide a channel-resilient device identification over flat fading channels. Specifically, we show that MIMO can increase the device classification accuracy by up to about $50\%$ when model training and testing are done over the same channel and by up to about $70\%$ when training and testing are done over different fading channels.