Riemannian Natural Gradient Methods

This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in various machine learning and signal processing applications. By introducing the notion of Fisher information matrix in the manifold setting, we propose a novel Riemannian natural gradient method, which can be viewed as a natural extension of the natural gradient method from the Euclidean setting to the manifold setting. We establish the almost-sure global convergence of our proposed method under standard assumptions. Moreover, we show that if the loss function satisfies certain convexity and smoothness conditions and the input-output map satisfies a Riemannian Jacobian stability condition, then our proposed method enjoys a local linear -- or, under the Lipschitz continuity of the Riemannian Jacobian of the input-output map, even quadratic -- rate of convergence. We then prove that the Riemannian Jacobian stability condition will be satisfied by a two-layer fully connected neural network with batch normalization with high probability, provided that the width of the network is sufficiently large. This demonstrates the practical relevance of our convergence rate result. Numerical experiments on applications arising from machine learning demonstrate the advantages of the proposed method over state-of-the-art ones

Applications and technological challenges for heat recovery, storage and utilisation with latent thermal energy storage

Thermal energy storage (TES) technology is considered to have the greatest potential to balance the demand and supply overcoming the intermittency and fluctuation nature of real-world heat sources, making a more flexible, highly efficient and reliable thermal energy system. This article provides a comprehensive state-of-the-art review of latent thermal energy storage (LTES) technology with a particular focus on medium-high temperature phase change materials for heat recovery, storage and utilisation. This review aims to identify potential methods to design and optimise LTES heat exchangers for heat recovery and storage, bridging the knowledge gap between the present studies and future technological developments. The key focuses of current work can be described as follows: (1) Insight into moderate-high temperature phase change materials and thermal conductivity enhancement methods. (2) Various configurations of latent thermal energy storage heat exchangers and relevant heat transfer enhancement techniques (3) Applications of latent thermal energy storage heat exchangers with different thermal sources, including solar energy, industrial waste heat and engine waste heat, are discussed in detail

MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full resolution, is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. In addition, for each scene, RGBW of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using an RGBW sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.Comment: ECCV 2022 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Re-mosaic Challenge Report. MIPI workshop website: http://mipi-challenge.org/. arXiv admin note: substantial text overlap with arXiv:2209.07060, arXiv:2209.07530, arXiv:2209.0705

Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China

By combining the similarity matching (SM) method with the utilities additives discriminates (UTADIS) method, we propose a hybrid SM-UTADIS approach to detect falsified financial statements (FFS) of listed companies. To evaluate the performance of this hybrid approach, we conduct experiments using the annual financial ratios of listed traditional Chinese medicine (TCM) companies in China. There are three stages in the detection procedure. First, we use the cosine similarity matching method to select matched companies for each considered company, derive the deviation data of each considered company as a sample dataset to capture the intrinsic law of the financial data, and further divide these into training and testing datasets for the next two stages. Second, we put the training dataset into the UTADIS to train the SM-UTADIS model. Finally, we use the trained SM-UTADIS model to classify the testing dataset and evaluate the performance of the proposed method. Furthermore, we use other approaches, such as single UTADIS and logistic and SM-logistic regression models, to detect FFS. By comparing these results to those of the hybrid SM-UTADIS approach, we find that the proposed hybrid approach greatly improves the accuracy of FFS detection

An Effective Kalman Filter-Based Method for Groundwater Pollution Source Identification and Plume Morphology Characterization

The identification of unknown groundwater pollution sources and the characterization of pollution plume remains a challenging problem. In this study, we addressed this problem by a linked simulation-optimization approach. This approach couples a contaminant transport simulation model with a Kalman filter-based method to identify groundwater pollution source and characterize plume morphology. In the proposed methodology, the concentration field library, the covariance reduction with a Kalman filter, an alpha-cut technique of fuzzy set, and a linear programming model are integrated for solving this inverse problem. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem. The evaluation considered the random hydraulic conductivity filed, erroneous monitoring data, a prior information shortage of potential pollution sources, and an unexpected and unknown pumping well. The identified results indicate that, under these conditions, the proposed Kalman filter-based optimization model can give satisfactory estimations to pollution sources and plume morphology for domains with small and moderate heterogeneity but cannot validate the transport in the relatively high heterogeneous field

Alkali Effect on Alkali-Surfactant-Polymer (ASP) Flooding Enhanced Oil Recovery Performance: Two Large-Scale Field Tests’ Evidence

Alkali-surfactant-polymer (ASP) flooding is very promising chemical enhanced oil recovery (EOR) technology which can make an incremental oil recovery factor (IORF) of 30% original oil in place (OOIP). How to choose alkali in ASP flooding remains a question for a long time. As the world’s only and largest ASP flooding application place, Daqing Oilfield has always adhered to the strategy of parallel development of strong alkali ASP flooding (SASP) and weak alkali ASP flooding (WASP), but SASP is in a dominant position, indicated by more investments and more project numbers. This leaves an impression that SASP is better than WASP. However, WASP is drawing more interest than SASP recently. Moreover, as the ASP flooding in Daqing went from field tests to commercial applications since 2014, how to comprehensively consider the benefit and cost of ASP flooding has become a new focus at low oil prices. This paper compares two typical large-scale field tests (B-1-D SASP and B-2-X WASP) completed in Daqing Oilfield and analyzes and discusses the causes of this difference. The injection viscosity and interfacial tension (IFT) for the two field test areas are substantially equivalent under the conditions of Daqing Oilfield, and WASP is better than SASP when reservoir geological conditions are considered. WASP exhibits the same IORF of 30% as SASP while having a much better economic performance. For the SASP field test, the injected strong alkali NaOH makes the test behave unlike a typical strong ASP flooding due to the presence of CO2 in the formation fluid, which well explains why IORF is much higher than all the other SASPs but scaling is less severe than others. This paper confirms that under Daqing Oilfield reservoir conditions, it is the alkali difference that caused the performance difference of these two tests, although some minor uncertainties exist. WASP is better than the SASP providing the same conditions . In addition, the detailed information of the two ASP field tests provided can give reference for the implementation of ASP flooding in other oilfields. After all, the study of ASP flooding enhanced oil recovery technology under low oil prices requires great foresight and determination

Joint Estimation of Hydraulic and Biochemical Parameters for Reactive Transport Modelling with a Modified ILUES Algorithm

Multicomponent reactive transport modeling is a powerful tool for the comprehensive analysis of coupled hydraulic and biochemical processes. The performance of the simulation model depends on the accuracy of related model parameters whose values are usually difficult to determine from direct measurements. In this situation, estimates of these uncertain parameters can be obtained by solving inverse problems. In this study, an efficient data assimilation method, the iterative local updating ensemble smoother (ILUES), is employed for the joint estimation of hydraulic parameters, biochemical parameters and contaminant source characteristics in the sequential biodegradation process of tetrachloroethene (PCE). In the framework of the ILUES algorithm, parameter estimation is realized by updating local ensemble with the iterative ensemble smoother (IES). To better explore the parameter space, the original ILUES algorithm is modified by determining the local ensemble partly with a linear ranking selection scheme. Numerical case studies based on the sequential biodegradation of PCE are then used to evaluate the performance of the ILUES algorithm. The results show that the ILUES algorithm is able to achieve an accurate joint estimation of related model parameters in the reactive transport model

Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation

Joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for contaminant transport models in heterogeneous subsurface media. As for accurate characterization of hydraulic conductivities, both geostatistical modeling and groundwater inverse modeling are alternative approaches. In this study, an iterative ensemble smoother and sequential gaussian simulation (SGSIM) in geostatistics modeling were combined to realize the simultaneous inversion of contaminant sources and hydraulic conductivities, by using directly measured hydraulic conductivities and indirect hydraulic head and concentration data. To alleviate the high computational cost caused by repetitive evaluations of complex, high-dimensional groundwater models, SGSIM with the pilot points method was used. Considering the characteristics of the proposed method, four scenarios with ten cases were set up in terms of ensemble number and iteration number that affect the performance of the iterative ensemble smoother, the number of pilot points, and the observation data, respectively. The results for the synthetic example indicate that the ensemble size of 2000 and the pilot point number of 80 is an ideal combination of parameters, and the proposed method can successfully recover contaminant source information simultaneously with hydraulic conductivity

Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation

Joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for contaminant transport models in heterogeneous subsurface media. As for accurate characterization of hydraulic conductivities, both geostatistical modeling and groundwater inverse modeling are alternative approaches. In this study, an iterative ensemble smoother and sequential gaussian simulation (SGSIM) in geostatistics modeling were combined to realize the simultaneous inversion of contaminant sources and hydraulic conductivities, by using directly measured hydraulic conductivities and indirect hydraulic head and concentration data. To alleviate the high computational cost caused by repetitive evaluations of complex, high-dimensional groundwater models, SGSIM with the pilot points method was used. Considering the characteristics of the proposed method, four scenarios with ten cases were set up in terms of ensemble number and iteration number that affect the performance of the iterative ensemble smoother, the number of pilot points, and the observation data, respectively. The results for the synthetic example indicate that the ensemble size of 2000 and the pilot point number of 80 is an ideal combination of parameters, and the proposed method can successfully recover contaminant source information simultaneously with hydraulic conductivity

Assessing and Enhancing Adversarial Robustness for Review-Based Recommender System: A Design Science Approach

Whereas recommender system (RS) is ubiquitous in e-commerce platforms, recent years have seen grievous adversarial attacks on RS. However, no prior studies have evaluated RS’s adversarial vulnerability of utilizing online reviews. In this work, we follow the guidelines of adversarial robustness theory and adopt computational design science paradigm to design a novel “Min-Max” problem-based framework for assessing and enhancing adversarial robustness of review-based RS (R-RS). The framework includes an assessment component called Anchor Imitator (AIM) for crafting adversarial samples, and three enhancement components for copying with adversarial vulnerability, involving stochastic recommending process (SRP) that increases the difficulty of obtaining model information, weighted input dropout (WID) that reduces sensitivity on sensitive words, and weighted adversarial contrastive learning (WACL) that learns robust feature. We evaluate the devised framework on ground truth datasets, results demonstrate that R-RS is vulnerable to adversarial attack and the enhancement components significantly improve the adversarial robustness of R-RS