A Practical Blended Analysis for Dynamic Features in JavaScript

The JavaScript Blended Analysis Framework is designed to perform a general-purpose, practical combined static/dynamic analysis of JavaScript programs, while handling dynamic features such as run-time generated code and variadic func- tions. The idea of blended analysis is to focus static anal- ysis on a dynamic calling structure collected at runtime in a lightweight manner, and to rene the static analysis us- ing additional dynamic information. We perform blended points-to analysis of JavaScript with our framework and compare results with those computed by a pure static points- to analysis. Using JavaScript codes from actual webpages as benchmarks, we show that optimized blended analysis for JavaScript obtains good coverage (86.6% on average per website) of the pure static analysis solution and nds ad- ditional points-to pairs (7.0% on average per website) con- tributed by dynamically generated/loaded code

Adaptive Context-sensitive Analysis for JavaScript

Context sensitivity is a technique to improve program analysis precision by distinguishing between function calls. A specific context-sensitive analysis is usually designed to accommodate the programming paradigm of a particular programming language. JavaScript features both the object-oriented and functional programming paradigms. Our empirical study suggests that there is no single context-sensitive analysis that always produces precise results for JavaScript applications. This observation motivated us to design an adaptive analysis, selecting a context-sensitive analysis from multiple choices for each function. Our two-staged adaptive context-sensitive analysis first extracts function characteristics from an inexpensive points-to analysis and then chooses a specialized context-sensitive analysis per function based on the heuristics. The experimental results show that our adaptive analysis achieved more precise results than any single context-sensitive analysis for several JavaScript programs in the benchmarks

DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization

DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed host programs. However, existing DL compilers rely on a tracing mechanism, which involves feeding a runtime input to a neural network program and tracing the program execution paths to generate the computational graph necessary for compilation. Unfortunately, this mechanism falls short when dealing with modern dynamic neural networks (DyNNs) that possess varying computational graphs depending on the inputs. Consequently, conventional DL compilers struggle to accurately compile DyNNs into executable code. To address this limitation, we propose \tool, a general approach that enables any existing DL compiler to successfully compile DyNNs. \tool tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process. Specifically, \tool develops program analysis and program transformation techniques to convert a dynamic neural network into multiple sub-neural networks. Each sub-neural network is devoid of conditional statements and is compiled independently. Furthermore, \tool synthesizes a host module that models the control flow of the DyNNs and facilitates the invocation of the sub-neural networks. Our evaluation demonstrates the effectiveness of \tool, achieving a 100\% success rate in compiling all dynamic neural networks. Moreover, the compiled executables generated by \tool exhibit significantly improved performance, running between $1.12\times$ and $20.21\times$ faster than the original DyNNs executed on general-purpose DL frameworks.Comment: This paper has been accepted to ISSTA 202

Cure the headache of Transformers via Collinear Constrained Attention

As the rapid progression of practical applications based on Large Language Models continues, the importance of extrapolating performance has grown exponentially in the research domain. In our study, we identified an anomalous behavior in Transformer models that had been previously overlooked, leading to a chaos around closest tokens which carried the most important information. We've coined this discovery the "headache of Transformers". To address this at its core, we introduced a novel self-attention structure named Collinear Constrained Attention (CoCA). This structure can be seamlessly integrated with existing extrapolation, interpolation methods, and other optimization strategies designed for traditional Transformer models. We have achieved excellent extrapolating performance even for 16 times to 24 times of sequence lengths during inference without any fine-tuning on our model. We have also enhanced CoCA's computational and spatial efficiency to ensure its practicality. We plan to open-source CoCA shortly. In the meantime, we've made our code available in the appendix for reappearing experiments.Comment: 16 pages, 6 figure

A Ginzburg–Landau model for linear global modes in open shear flows

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Continual Learning in Predictive Autoscaling

Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. However, in practice, its prediction task often suffers from performance degradation under abnormal traffics caused by external events (such as sales promotional activities and applications re-configurations), for which a common solution is to re-train the model with data of a long historical period, but at the expense of high computational and storage costs. To better address this problem, we propose a replay-based continual learning method, i.e., Density-based Memory Selection and Hint-based Network Learning Model (DMSHM), using only a small part of the historical log to achieve accurate predictions. First, we discover the phenomenon of sample overlap when applying replay-based continual learning in prediction tasks. In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set. Then we implement hint-based network learning based on hint representation to optimize the parameters. Finally, we conduct experiments on public and industrial datasets to demonstrate that our proposed method outperforms state-of-the-art continual learning methods in terms of memory capacity and prediction accuracy. Furthermore, we demonstrate remarkable practicability of DMSHM in real industrial applications

Evaluation and characterization of HSPA5 (GRP78) expression profiles in normal individuals and cancer patients with COVID-19

HSPA5 (BiP, GRP78) has been reported as a potential host-cell receptor for SARS-Cov-2, but its expression profiles on different tissues including tumors, its susceptibility to SARS-Cov-2 virus and severity of its adverse effects on malignant patients are unclear. In the current study, HSPA5 has been found to be expressed ubiquitously in normal tissues and significantly increased in 14 of 31 types of cancer tissues. In lung cancer, mRNA levels of HSPAS were 253-fold increase than that of ACE2. Meanwhile, in both malignant tumors and matched normal samples across almost all cancer types, mRNA levels of HSPAS were much higher than those of ACE2. Higher expression of HSPAS significantly decreased patient overall survival (OS) in 7 types of cancers. Moreover, systematic analyses found that 7.15% of 5,068 COVID-19 cases have malignant cancer coincidental situations, and the rate of severe events of COVID-19 patients with cancers present a higher trend than that for all COVID-19 patients, showing a significant difference (33.33% vs 16.09%, p<0.01). Collectively, these data imply that the tissues with high HSPA5 expression, not low ACE2 expression, are susceptible to be invaded by SARS-CoV-2. Taken together, this study not only indicates the clinical significance of HSPA5 in COVID-19 disease and cancers, but also provides potential clues for further medical treatments and managements of COVID-19 patients

Carotid Atherosclerosis Detected by Ultrasonography: A National Cross‐Sectional Study

Background: Carotid atherosclerosis (CA) is a reflector of generalized atherosclerosis that is associated with systemic vascular disease. Data are limited on the epidemiology of carotid lesions in a large, nationally representative population sample. We aimed to evaluate the prevalence of CA detected by carotid ultrasonography and related risk factors based on a national survey in China. Methods and Results: A total of 107 095 residents aged ≥40 years from the China National Stroke Prevention Project underwent carotid ultrasound examination. Participants with carotid endarterectomy or carotid stenting and those with stroke or coronary heart disease were excluded. Data from 84 880 participants were included in the analysis. CA was defined as increased intima–media thickness (IMT) ≥1 mm or presence of plaques. Of the 84 880 participants, 46.4% were men, and the mean age was 60.7±10.3 years. The standardized prevalence of CA was 36.2% overall, increased with age, and was higher in men than in women. Prevalence of CA was higher among participants living in rural areas than in urban areas. Approximately 26.5% of participants had increased IMT, and 13.9% presented plaques. There was an age‐related increase in participants with increased IMT, plaque presence, and stenosis. In multiple logistic regression analysis, older age, male sex, residence in rural areas, smoking, alcohol consumption, physical inactivity, obesity, hypertension, diabetes mellitus, and dyslipidemia were associated with CA. Conclusions: CA was highly prevalent in a middle‐aged and older Chinese population. This result shows the potential clinical importance of focusing on primary prevention of atherosclerosis progression

Prevalence of metabolic syndrome among middle-aged and elderly adults in China: current status and temporal trends

Background: Metabolic syndrome (MetS) is a cluster of major risk factors for cardiovascular diseases. We aimed to estimate prevalence and distribution of MetS among middle-aged and elderly adults in China. Methods: The present analysis used data from a national study in 2014–2015. We defined MetS by different definitions, and compared results of the present study and previous nationally representative studies to illustrate possible temporal changes in MetS prevalence. Results: The estimated prevalence of MetS was 18.4% by the ATP III criteria, 34.0% by the revised ATP III criteria, and 26.9% by IDF criteria. The prevalence was higher in women, older adults, those with lower education level, and in economically developed regions. Contrasting with previous national studies, adults in urban areas had a lower rate of MetS than those in rural areas (odds ratio 0.94; 95% CI 0.92−0.97). Rural adults had worse deterioration or less improvement in abdominal obesity, overweight, hypertension, and high fasting plasma glucose, than urban adults, which was particularly striking for women. Conclusion: While measures to prevent and control cardiovascular diseases need to be strengthened in China, rapid increasing risk factors among rural residents and women should be prioritized in making public health policy decisions

Social Determinants of Community Health Services Utilization among the Users in China: A 4-Year Cross-Sectional Study

Background To identify social factors determining the frequency of community health service (CHS) utilization among CHS users in China. Methods Nationwide cross-sectional surveys were conducted in 2008, 2009, 2010, and 2011. A total of 86,116 CHS visitors selected from 35 cities were interviewed. Descriptive analysis and multinomial logistic regression analysis were employed to analyze characteristics of CHS users, frequency of CHS utilization, and the socio-demographic and socio-economic factors influencing frequency of CHS utilization. Results Female and senior CHS clients were more likely to make 3–5 and ≥6 CHS visits (as opposed to 1–2 visits) than male and young clients, respectively. CHS clients with higher education were less frequent users than individuals with primary education or less in 2008 and 2009; in later surveys, CHS clients with higher education were the more frequent users. The association between frequent CHS visits and family income has changed significantly between 2008 and 2011. In 2011, income status did not have a discernible effect on the likelihood of making ≥6 CHS visits, and it only had a slight effect on making 3–5 CHS visits. Conclusion CHS may play an important role in providing primary health care to meet the demands of vulnerable populations in China. Over time, individuals with higher education are increasingly likely to make frequent CHS visits than individuals with primary school education or below. The gap in frequency of CHS utilization among different economic income groups decreased from 2008 to 2011