Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework

Materials-by-design has been historically challenging due to complex process-microstructure-property relations. Conventional analytical or simulation-based approaches suffer from low accuracy or long computational time and poor transferability, further limiting their applications in solving the inverse material design problem. Here, we establish a deep learning and genetic algorithm framework that integrates forward prediction and inverse exploration. This framework provides an end-to-end solution to achieve application-specific mechanical properties by microstructure optimization. In this study, we select the widely used Ti-6Al-4V to demonstrate the effectiveness of this framework by tailoring its microstructure and achieving various yield strength and elastic modulus across a large design space, while minimizing the stress concentration factor. Compared with conventional methods, our framework is efficient, versatile, and readily transferrable to other materials and properties. Paired with additive manufacturing's potential in controlling local microstructural features, our method has far-reaching potential for accelerating the development of application-specific, high-performing materials.Comment: 19 pages, 5 figure

Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion

Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly detection focus solely on real-time detection, meaning that anomaly alerts are issued as soon as anomalies occur. However, anomalies can propagate and escalate into failures, making faster-than-real-time anomaly detection highly desirable for expediting downstream analysis and intervention. This paper proposes Maat, the first work to address anomaly anticipation of performance metrics in cloud services. Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting of metric forecasting and anomaly detection on forecasts. The metric forecasting stage employs a conditional denoising diffusion model to enable multi-step forecasting in an auto-regressive manner. The detection stage extracts anomaly-indicating features based on domain knowledge and applies isolation forest with incremental learning to detect upcoming anomalies. Thus, our method can uncover anomalies that better conform to human expertise. Evaluation on three publicly available datasets demonstrates that Maat can anticipate anomalies faster than real-time comparatively or more effectively compared with state-of-the-art real-time anomaly detectors. We also present cases highlighting Maat's success in forecasting abnormal metrics and discovering anomalies.Comment: This paper has been accepted by the Research track of the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention

Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems. Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joint information among different types of data. Consequently, many false predictions occur. To better understand the manifestations of system anomalies, we conduct a systematical study on a large amount of heterogeneous data, i.e., logs and metrics. Our study demonstrates that logs and metrics can manifest system anomalies collaboratively and complementarily, and neither of them only is sufficient. Thus, integrating heterogeneous data can help recover the complete picture of a system's health status. In this context, we propose Hades, the first end-to-end semi-supervised approach to effectively identify system anomalies based on heterogeneous data. Our approach employs a hierarchical architecture to learn a global representation of the system status by fusing log semantics and metric patterns. It captures discriminative features and meaningful interactions from heterogeneous data via a cross-modal attention module, trained in a semi-supervised manner. We evaluate Hades extensively on large-scale simulated data and datasets from Huawei Cloud. The experimental results present the effectiveness of our model in detecting system anomalies. We also release the code and the annotated dataset for replication and future research.Comment: In Proceedings of the 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). arXiv admin note: substantial text overlap with arXiv:2207.0291

An Adaptive Resilience Testing Framework for Microservice Systems

Resilience testing, which measures the ability to minimize service degradation caused by unexpected failures, is crucial for microservice systems. The current practice for resilience testing relies on manually defining rules for different microservice systems. Due to the diverse business logic of microservices, there are no one-size-fits-all microservice resilience testing rules. As the quantity and dynamic of microservices and failures largely increase, manual configuration exhibits its scalability and adaptivity issues. To overcome the two issues, we empirically compare the impacts of common failures in the resilient and unresilient deployments of a benchmark microservice system. Our study demonstrates that the resilient deployment can block the propagation of degradation from system performance metrics (e.g., memory usage) to business metrics (e.g., response latency). In this paper, we propose AVERT, the first AdaptiVE Resilience Testing framework for microservice systems. AVERT first injects failures into microservices and collects available monitoring metrics. Then AVERT ranks all the monitoring metrics according to their contributions to the overall service degradation caused by the injected failures. Lastly, AVERT produces a resilience index by how much the degradation in system performance metrics propagates to the degradation in business metrics. The higher the degradation propagation, the lower the resilience of the microservice system. We evaluate AVERT on two open-source benchmark microservice systems. The experimental results show that AVERT can accurately and efficiently test the resilience of microservice systems

Privacy-preserving design of graph neural networks with applications to vertical federated learning

The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM). The surging developments of graph representation learning (GRL) have opened up new opportunities for FRM applications under FL via efficiently utilizing the graph-structured data generated from underlying transaction networks. Meanwhile, transaction information is often considered highly sensitive. To prevent data leakage during training, it is critical to develop FL protocols with formal privacy guarantees. In this paper, we present an end-to-end GRL framework in the VFL setting called VESPER, which is built upon a general privatization scheme termed perturbed message passing (PMP) that allows the privatization of many popular graph neural architectures.Based on PMP, we discuss the strengths and weaknesses of specific design choices of concrete graph neural architectures and provide solutions and improvements for both dense and sparse graphs. Extensive empirical evaluations over both public datasets and an industry dataset demonstrate that VESPER is capable of training high-performance GNN models over both sparse and dense graphs under reasonable privacy budgets

CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

Pathological image analysis is a crucial field in computer-aided diagnosis, where deep learning is widely applied. Transfer learning using pre-trained models initialized on natural images has effectively improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, which has great potential to overcome the challenge of expensive annotations. Thus, studies focusing on pathological SSL pre-training call for a comprehensive and standardized dataset, similar to the ImageNet in computer vision. This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization. The CPIA dataset contains 21,427,877 standardized images, covering over 48 organs/tissues and about 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). A four-scale WSI standardization process is proposed based on the uniform resolution in microns per pixel (MPP), while the ROIs are divided into three scales artificially. This multi-scale dataset is built with the diagnosis habits under the supervision of experienced senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. The CPIA dataset along with baselines is available at https://github.com/zhanglab2021/CPIA_Dataset

Effects of the Chinese herbal formula San-Huang Gu-Ben Zhi-Ke treatment on stable chronic obstructive pulmonary disease: a randomized, double-blind, placebo-controlled trial

Objective: The aim of this study was to evaluate the efficacy and safety of the Chinese herbal formula San-Huang Gu-Ben Zhi-Ke (SHGBZK) as a treatment for patients with stable chronic obstructive pulmonary disease (COPD) diagnosed with lung-spleen Qi deficiency.Method: A randomized, double-blind, placebo-controlled trial was designed. 98 adults aged between 40 and 80 years with stable COPD diagnosed with lung-spleen Qi deficiency were included. All participants received basic treatment for COPD. Patients in the experimental group took SHGBZK, while the control group took placebo. The primary outcome was the frequency of acute exacerbation. The secondary outcomes were lung function, symptom score, exercise capacity and quality of life.Results: Of 98 patients who underwent randomization, 50 patients in the SHGBZK group and 48 in the placebo group were included in the full analysis set. After 24-week therapy and 28-week follow-up, patients in treatment group had significant improvements in symptom, exercise capacity and quality of life. After Subgroup analysis, the frequency of acute exacerbation in patients with a COPD Assessment Test (CAT) score of at least 10 or a modified Medical Research Council (mMRC) score of at least 2 was significantly lower in the SHGBZK group than in the placebo group. Lung function in patients with frequent exacerbation was significantly higher in the SHGBZK group than in the placebo group. The incidence of adverse events was generally similar in the two groups.Conclusion: SHGBZK had beneficial effects on symptom, exercise capacity and quality of life in stable COPD patients. SHGBZK also had the potential to reduce the frequency of exacerbation and improve lung function in specific groups of COPD patients.Clinical Trial Registration:https://www.chictr.org.cn/showproj.html?proj=26933, identifier ChiCTR180001634

Nodal and Nematic Superconducting Phases in NbSe2_2 Monolayers from Competing Superconducting Channels

Transition metal dichalcogenides like 2H-NbSe$_2$ in their two-dimensional (2D) form exhibit Ising superconductivity with the quasiparticle spins are firmly pinned in the direction perpendicular to the basal plane. This enables them to withstand exceptionally high magnetic fields beyond the Pauli limit for superconductivity. Using field-angle-resolved magnetoresistance experiments for fields rotated in the basal plane we investigate the field-angle dependence of the upper critical field (Hc$_2$), which directly reflects the symmetry of the superconducting order parameter. We observe a six-fold nodal symmetry superposed on a two-fold symmetry. This agrees with theoretical predictions of a nodal topological superconducting phase near Hc$_2$, together with a nematic superconducting state. We demonstrate that in NbSe$_2$ such unconventional superconducting states can arise from the presence of several competing superconducting channels