A Machine Learning Framework to Model Extreme Events for Nonlinear Marine Dynamics

Extreme events such as large motions and excess loadings of marine systems can result in damage to the device or loss of life. Since the system is exposed to a random ocean environment, these extreme events need to be understood from a statistical perspective to design a safe system. However, analysis of extreme events is challenging because most marine systems operate in the nonlinear region, especially when extreme events occur, and observation of the extreme events is relatively rare for a proper design. Conducting high-fidelity simulations or experimental tests to observe such events is cost-prohibitive. In the current research, a novel framework is proposed to randomly generate test environments that lead to a large response of the system. With the generated environment, large responses that would take a very long time to achieve can be observed within a much shorter time window. The time-domain context around the extreme event provides the user with rich insights towards the improvement of the design. The proposed framework consists of two modules, which are named as Threshold Exceedance Generator (TEG) and Design Response Estimator (DRE). The framework is data-driven, and its application requires minimal knowledge about the system from the user. The DRE module can identify a nonlinear marine system based on collected data. The TEG module can generate ocean environments that lead to large system response based on the system identification by the DRE module. Machine learning methods, especially neural networks, are heavily used in the proposed framework. In the thesis, the extreme generation problem in the marine field is described and addressed from a machine-learning perspective. To validate the framework, marine examples including linear wave propagation, nonlinear wave propagation, nonlinear ship roll, tank sloshing, and a floating object in waves are explored. Examples from such a wide range show that the framework can be used for linear or nonlinear systems and Gaussian or non-Gaussian environments. The cost and the amount of data to apply the method are estimated and measured. The comparison between the results from the framework and Monte Carlo Simulation fully demonstrates the accuracy and feasibility of using the data-driven approach.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163268/1/wenzhe_1.pd

Feature calibration for computer models

Computer model calibration involves using partial and imperfect observations of the real world to learn which values of a model's input parameters lead to outputs that are consistent with real-world observations. When calibrating models with high-dimensional output (e.g. a spatial field), it is common to represent the output as a linear combination of a small set of basis vectors. Often, when trying to calibrate to such output, what is important to the credibility of the model is that key emergent physical phenomena are represented, even if not faithfully or in the right place. In these cases, comparison of model output and data in a linear subspace is inappropriate and will usually lead to poor model calibration. To overcome this, we present kernel-based history matching (KHM), generalising the meaning of the technique sufficiently to be able to project model outputs and observations into a higher-dimensional feature space, where patterns can be compared without their location necessarily being fixed. We develop the technical methodology, present an expert-driven kernel selection algorithm, and then apply the techniques to the calibration of boundary layer clouds for the French climate model IPSL-CM.Comment: 50 page

DGMem: Learning Visual Navigation Policy without Any Labels by Dynamic Graph Memory

In recent years, learning-based approaches have demonstrated significant promise in addressing intricate navigation tasks. Traditional methods for training deep neural network navigation policies rely on meticulously designed reward functions or extensive teleoperation datasets as navigation demonstrations. However, the former is often confined to simulated environments, and the latter demands substantial human labor, making it a time-consuming process. Our vision is for robots to autonomously learn navigation skills and adapt their behaviors to environmental changes without any human intervention. In this work, we discuss the self-supervised navigation problem and present Dynamic Graph Memory (DGMem), which facilitates training only with on-board observations. With the help of DGMem, agents can actively explore their surroundings, autonomously acquiring a comprehensive navigation policy in a data-efficient manner without external feedback. Our method is evaluated in photorealistic 3D indoor scenes, and empirical studies demonstrate the effectiveness of DGMem.Comment: 8 pages, 6 figure

CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning

Face recognition technology is widely used in the financial field, and various types of liveness attack behaviors need to be addressed. Existing liveness detection algorithms are trained on specific training datasets and tested on testing datasets, but their performance and robustness in transferring to unseen datasets are relatively poor. To tackle this issue, we propose a face liveness detection method based on image-text pairs and contrastive learning, dividing liveness attack problems in the financial field into eight categories and using text information to describe the images of these eight types of attacks. The text encoder and image encoder are used to extract feature vector representations for the classification description text and face images, respectively. By maximizing the similarity of positive samples and minimizing the similarity of negative samples, the model learns shared representations between images and texts. The proposed method is capable of effectively detecting specific liveness attack behaviors in certain scenarios, such as those occurring in dark environments or involving the tampering of ID card photos. Additionally, it is also effective in detecting traditional liveness attack methods, such as printing photo attacks and screen remake attacks. The zero-shot capabilities of face liveness detection on five public datasets, including NUAA, CASIA-FASD, Replay-Attack, OULU-NPU and MSU-MFSD also reaches the level of commercial algorithms. The detection capability of proposed algorithm was verified on 5 types of testing datasets, and the results show that the method outperformed commercial algorithms, and the detection rates reached 100% on multiple datasets. Demonstrating the effectiveness and robustness of introducing image-text pairs and contrastive learning into liveness detection tasks as proposed in this paper

URANS Predictions of Resistance and Motions of the KCS in Head Waves

This paper presents a computational-fluid-dynamics framework that is focused on the prediction of resistance and motions in waves. In particular, the framework is developed to predict the performance during operation in an extreme irregular seaway. A wave-focusing technique is presented that allows for the investigation of extreme dynamical events such as heave, pitch, or water-on-deck. The body motion is solved with an unique algorithm for the six rigid-body degrees-of-freedom. Waves are generated with the waves2Foam toolbox. The solver is validated for the case of a freely heaving cylinder, the propagation of regular waves, and the added resistance and motions of the KRISO container ship (KCS) in regular and irregular waves. Results are presented for an encounter of the KCS in an extreme heave event.Office of Naval Research grants N00014-13-1-0558 and N00014-14-1-0577http://deepblue.lib.umich.edu/bitstream/2027.42/136158/1/Filip-NAME-Report355.pdfDescription of Filip-NAME-Report355.pdf : main articl

Morphine Suppresses IFN Signaling Pathway and Enhances AIDS Virus Infection

Background: Opioids exert a profound influence on immunomodulation and enhance HIV infection and replication. However, the mechanism(s) of their action remains to be determined. We thus investigated the impact of morphine on the intracellular innate antiviral immunity. Methodology/Principal Findings: Seven-day-cultured macrophages were infected with equal amounts of cell-free HIV Bal or SIV DeltaB670 for 2 h at 37uC after 24 h of treatment with or without morphine. Effect of morphine on HIV/SIV infection and replication was evaluated by HIV/SIV RT activity assay and indirect immunofluorescence for HIV p24 or SIV p28 antigen. The mRNA expression of cellular factors suppressed or induced by morphine treatment was analyzed by the real-time RT-PCR. We demonstrated that morphine treatment of human blood monocyte-derived macrophages significantly inhibited the expression of interferons (IFN-a, IFN-b and IFN-l) and IFN-inducible genes (APOBEC3C/3F/3G and 3H). The further experiments showed that morphine suppressed the expression of several key elements (RIG-I and IRF-7) in IFN signaling pathway. In addition, morphine treatment induced the expression of suppressor of cytokine signaling protein-1, 2, 3 (SOCS-1, 2, 3) and protein inhibitors of activated STAT-1, 3, X, Y (PIAS-1, 3, X, Y), the key negative regulators of IFN signaling pathway. Conclusions: These findings indicate that morphine impairs intracellular innate antiviral mechanism(s) in macrophages

Educational degree differences in the association between work stress and depression among Chinese healthcare workers: Job satisfaction and sleep quality as the mediators

BackgroundDepressive status of medical personnel worldwide and especially in China is an important public health and social problem. There is a strong relationship between education and depression, but no studies have studied grouping healthcare workers (HCWs) with different educational degree to discuss whether there are differences in the factors that affect depression. This study aims to examine the role of job satisfaction and sleep quality in the relationship between work stress and depression among Chinese HCWs, and teste whether the mediation models are differed by the differences of educational degree.MethodsPatient Health Questionnaire-9 (PHQ-9) scale was used to test depression. Work stress was assessed using the Challenge-blocking stress scale (CBSS). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). HCWs’ satisfaction with their current work was assessed using the Job Satisfaction Index (JSI). The representative sample of HCWs was chosen using a multi-stage stratified cluster random sampling procedure and 844 HCWs were utilized to the statistical analysis of the study.ResultsIn the overall sample, sleep quality could mediate the relationship between work stress and depression in healthcare workers (p < 0.001, CMIN/DF = 3.816, GFI = 0.911, AGFI = 0.886, IFI = 0.943, TLI = 0.933, CFI = 0.942, RMSEA = 0.058, SRMR = 0.055, AIC = 1039.144), and the mediating effect accounted for 36.5%. After grouping educational qualifications, the model with sleep quality and job satisfaction as mediating variables reported a better fit in the group with low educational qualifications. The intermediary effect accounted for 50.6 and 4.43%, respectively. The highly educated group only has sleep quality as an intermediary variable in the structural model, and the mediating effect accounted for 75.4% (p < 0.001, CMIN/DF = 2.596, GFI = 0.887, AGFI = 0.857, IFI = 0.937, TLI = 0.926, CFI = 0.937, RMSEA = 0.044, SRMR = 0.056, AIC = 1481.322).ConclusionIn the overall sample, sleep quality could mediate the relationship between work stress and depression in HCWs. Among HCWs with technical secondary school education and below, job satisfaction can mediate the positive relationship between work stress and depression, while this mediating effect is not significant among HCWs with college degree and above

Upregulation of SOCS-3 and PIAS-3 Impairs IL-12-Mediated Interferon-Gamma Response in CD56+ T Cells in HCV-Infected Heroin Users

CD56(+) T cells are abundant in liver and play an important role in host innate immunity against viral infections, including hepatitis C virus (HCV) infection, a common infection among heroin abusers. We thus investigated the in vivo impact of heroin use or heroin use plus HCV infection on the CD56(+) T cell frequency and function.A total of 37 heroin users with (17) or without (20) HCV infection and 17 healthy subjects were included in the study. Although there was no significant difference in CD56(+) T cell frequency in PBMCs among three study groups, CD56(+) T cells isolated from the heroin users had significantly lower levels of constitutive interferon-gamma (IFN-gamma) expression than those from the normal subjects. In addition, when stimulated by interleukin (IL)-12, CD56(+) natural T cells from HCV-infected heroin users produced significantly lower levels of IFN-gamma than those from the normal subjects. This diminished ability to produce IFN-gamma by CD56(+) T cells was associated with the increased plasma HCV viral loads in the HCV-infected heroin users. Investigation of the mechanisms showed that although heroin use or heroin use plus HCV infection had little impact on the expression of the key positive regulators (IL-12 receptors, STAT-1, 3, 4, 5, JAK-2, and TYK-2) in IL-12 pathway, heroin use or heroin use plus HCV infection induced the expression of suppressor of cytokine signaling protein-3 (SOCS-3) and protein inhibitors of activated STAT-3 (PIAS-3), two key inhibitors of IL-12 pathway.These findings provide compelling in vivo evidence that heroin use or heroin use plus HCV infection impairs CD56(+) T cell-mediated innate immune function, which may account for HCV infection and persistence in liver

Write-Combined Logging: An Optimized Logging for Consistency in NVRAM

Nonvolatile memory (e.g., Phase Change Memory) blurs the boundary between memory and storage and it could greatly facilitate the construction of in-memory durable data structures. Data structures can be processed and stored directly in NVRAM. To maintain the consistency of persistent data, logging is a widely adopted mechanism. However, logging introduces write-twice overhead. This paper introduces an optimized write-combined logging to reduce the writes to NVRAM log. By leveraging the fastread and byte-addressable features of NVRAM, we can perform a read-and-compare operation before writes and thus issue writes in a finer-grained way. We tested our system on the benchmark suit STAMP which contains real-world applications. Experiment results show that our system can reduce the writes to NVRAM by 33%-34%, which can help extend the lifetime of NVRAM and improve performance. Averagely our system can improve performance by 7%-11%