Disentangled Causal Graph Learning forOnline Unsupervised Root Cause Analysis

The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure recovery and mitigate system damages or financial losses. However, previous research has mostly focused on developing offline RCA algorithms, which often require manually initiating the RCA process, a significant amount of time and data to train a robust model, and then being retrained from scratch for a new system fault. In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model. CORAL consists of Trigger Point Detection, Incremental Disentangled Causal Graph Learning, and Network Propagation-based Root Cause Localization. The Trigger Point Detection component aims to detect system state transitions automatically and in near-real-time. To achieve this, we develop an online trigger point detection approach based on multivariate singular spectrum analysis and cumulative sum statistics. To efficiently update the RCA model, we propose an incremental disentangled causal graph learning approach to decouple the state-invariant and state-dependent information. After that, CORAL applies a random walk with restarts to the updated causal graph to accurately identify root causes. The online RCA process terminates when the causal graph and the generated root cause list converge. Extensive experiments on three real-world datasets with case studies demonstrate the effectiveness and superiority of the proposed framework

Differentially Private Generative Adversarial Networks with Model Inversion

To protect sensitive data in training a Generative Adversarial Network (GAN), the standard approach is to use differentially private (DP) stochastic gradient descent method in which controlled noise is added to the gradients. The quality of the output synthetic samples can be adversely affected and the training of the network may not even converge in the presence of these noises. We propose Differentially Private Model Inversion (DPMI) method where the private data is first mapped to the latent space via a public generator, followed by a lower-dimensional DP-GAN with better convergent properties. Experimental results on standard datasets CIFAR10 and SVHN as well as on a facial landmark dataset for Autism screening show that our approach outperforms the standard DP-GAN method based on Inception Score, Fr\'echet Inception Distance, and classification accuracy under the same privacy guarantee.Comment: Best Student Paper Award of 13th IEEE International Workshop on Information Forensics and Security (WIFS 2021), Montpellier, Franc

Interfacial Interaction Enhanced Rheological Behavior in PAM/CTAC/Salt Aqueous Solution—A Coarse-Grained Molecular Dynamics Study

Interfacial interactions within a multi-phase polymer solution play critical roles in processing control and mass transportation in chemical engineering. However, the understandings of these roles remain unexplored due to the complexity of the system. In this study, we used an efficient analytical method—a nonequilibrium molecular dynamics (NEMD) simulation—to unveil the molecular interactions and rheology of a multiphase solution containing cetyltrimethyl ammonium chloride (CTAC), polyacrylamide (PAM), and sodium salicylate (NaSal). The associated macroscopic rheological characteristics and shear viscosity of the polymer/surfactant solution were investigated, where the computational results agreed well with the experimental data. The relation between the characteristic time and shear rate was consistent with the power law. By simulating the shear viscosity of the polymer/surfactant solution, we found that the phase transition of micelles within the mixture led to a non-monotonic increase in the viscosity of the mixed solution with the increase in concentration of CTAC or PAM. We expect this optimized molecular dynamic approach to advance the current understanding on chemical–physical interactions within polymer/surfactant mixtures at the molecular level and enable emerging engineering solutions

An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model

High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these modelsâ?? capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent

Propagation Characteristics of Explosive Waves in Layered Media Numerical Analysis

The layered media under one-dimensional strain with different wave-impedance materials have been studied. The three typical prototypes have been analysized, including steel plate, aluminum foam, and concrete as the middle layer, and the upper and lower layers are concrete material. The attenuation of the amplitude of stress at different positions, the peak stress and the duration at the dissimilar material interface, and the absorbing energy distribution in different layers for different models have been obtained by numerical simulation. The material of the middle layer with lower impedance can effectively reduce the amplitude of stress, increase the duration of explosive wave, and change the distribution of energy in different layers. But the influence of the middle layer with higher impedance material on layered media is contrary. The middle layer with soft material is the better matching of wave impedance to explosive wave propagation. The analytical conclusions are of great significance for the design of protective structures against the explosion-induced hazards and minesafety protection from outburst and explosion.Defence Science Journal, 2009, 59(5), pp.499-504, DOI:http://dx.doi.org/10.14429/dsj.59.155

Investigation of temperature stress tolerance in Arabidopsis STTM165/166 using electrophysiology and RNA-Seq

Plant electrical signals have been shown to be generated in response to various environmental stresses, but the relationship between these signals and stress tolerance is not well understood. In this study, we used the Arabidopsis STTM165/166 mutant, which exhibits enhanced temperature tolerance, to examine this relationship. Surface recording techniques were utilized to compare the generation ratio and duration characteristics of electrical signals in the STTM165/166 mutant and wild type (WT). Patch-clamp recording was employed to assess ion channel currents, specifically those of calcium ions. The current intensity of the mutant was found to be lower than that of the WT. As calcium ions are involved in the generation of plant electrical signals, we hypothesized that the reduced calcium channel activity in the mutant increased its electrical signal threshold. RNA-Seq analysis revealed differential expression of AHA genes in the STTM165/166 mutant, which may contribute to the prolonged depolarization phenotype. Gene Ontology enrichment of differentially expressed genes (DEGs) identified associations between these DEGs and various stresses, including temperature, salt, and those related to the jasmonic acid and abscisic acid pathways. These findings provide experimental evidence for the use of plant electrical signals in characterizing stress tolerance and explore potential ion mechanisms through patch-clamp recording and DEG Gene Ontology analysis. They also emphasize the need for further research on the relationship between plant electrical signals and stress tolerance.Comment: 20 pages, 5 figure

Improved performance of the rechargeable hybrid aqueous battery at near full state-of-charge

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.electacta.2018.03.152 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/For the first time, a green lignin/silica nanocomposite (LSC) is introduced to the rechargeable hybrid aqueous Zn/LiMn2O4 battery (ReHAB) as additive in the cathode formulation. Lignin acts as a key role to regulate and control the structure of LSC, intending to enhance the stability of the ReHAB by improving the float charge performance while maintaining other electrochemical performances of the battery. The lignin/silica nanocomposites (LSCs) are characterized by X-ray diffraction, scanning electron microscopy, surface area and porosimetry analyzer, and transmission electron microscopy. The results show that amorphous, uniform and mesoporous LSC-1 is prepared at the mass ratio of 1:2 of lignin to silica. LSC-1 used as the cathode additive improves the float charge performance of ReHAB via decreasing the float charge capacity by 57%. To compensate the loss of conductivity caused by LSC-1 and increase the capacity of the battery, graphene (G) is added. Compared to the reference battery, battery using the cathode containing 3 wt% combined additive of LSC-1 and G at mass ratio of 1:1, has 50% lower float charge capacity, higher rate performance and better cyclability. Up to a discharge capacity of 95 mAh g−1 is still obtained after 300 cycles of 100% depth-of-discharge.National Natural Science Foundation of China [21436004]Natural Science Foundation of Guangdong Province [2017A030308012]Positec Canada Ltd.Chinese Scholarship Council (CSC

Liquid chromatography at critical conditions (LCCC): Capabilities and limitations for polymer analysis

This paper investigates liquid chromatography at critical condition (LCCC) for polymer analysis. Based on controversial claims on the separation of cyclic polymers from linear analogues in the literature, the efficiency of LCCC for separation and purity analysis is questioned. Polyisobutylene (PIB) and poly(3,6-dioxa-1,8-octanedithiols) (polyDODT) were used for the study. The structure of low molecular weight cyclic and linear polyDODT was demonstrated by MALDI-ToF. NMR did not show the presence of thiol end groups in higher molecular weight PIB-disulfide and polyDODT samples, so they were considered cyclic polymers. When a low molecular weight polyDODT oligomer with only traces of cycles, as demonstrated by MALDI-ToF, was mixed with an M_n = 27 K g/mol cyclic sample, LCCC did not detect the presence of linear oligomers at 6 wt%. Based on the data presented here, it can be concluded that the LCCC method is not capable of measuring <6 wt% linear contamination so earlier claims for cyclic polystyrene (PS) samples purified by LCCC having <3% linear contaminants are questioned