DLLens: Testing Deep Learning Libraries via LLM-aided Synthesis

Testing is a major approach to ensuring the quality of deep learning (DL) libraries. Existing testing techniques commonly adopt differential testing to relieve the need for test oracle construction. However, these techniques are limited in finding implementations that offer the same functionality and generating diverse test inputs for differential testing. This paper introduces DLLens, a novel differential testing technique for DL library testing. Our insight is that APIs in different DL libraries are commonly designed to accomplish various computations for the same set of published DL algorithms. Although the mapping of these APIs is not often one-to-one, we observe that their computations can be mutually simulated after proper composition and adaptation. The use of these simulation counterparts facilitates differential testing for the detection of functional DL library bugs. Leveraging the insight, we propose DLLens as a novel mechanism that utilizes a large language model (LLM) to synthesize valid counterparts of DL library APIs. To generate diverse test inputs, DLLens incorporates a static analysis method aided by LLM to extract path constraints from all execution paths in each API and its counterpart's implementations. These path constraints are then used to guide the generation of diverse test inputs. We evaluate DLLens on two popular DL libraries, TensorFlow and PyTorch. Our evaluation shows that DLLens can synthesize counterparts for more than twice as many APIs found by state-of-the-art techniques on these libraries. Moreover, DLLens can extract 26.7% more constraints and detect 2.5 times as many bugs as state-of-the-art techniques. DLLens has successfully found 56 bugs in recent TensorFlow and PyTorch libraries. Among them, 41 are previously unknown, 39 of which have been confirmed by developers after reporting, and 19 of those confirmed bugs have been fixed by developers

Temperature Dependence of Meso Constitutive Parameters for Advanced High-Strength Steel

Temperature significantly influences the fracture toughness of dual-phase steel, with internal void damage closely linked to sheet metal fracture. Microstructural damage models are vital for studying material failure, and precise calibration of their parameters is crucial for numerical analysis reliability. This paper focuses on accurately calibrating these parameters using a combination of experimental and numerical simulations based on the Hill'48-Gurson-Tvergaard-Needleman (GTN) model. Employing a central composite experimental design, response surface methodology, and genetic algorithms, the study determines relevant damage parameters for dual-phase steel at different temperatures. The research carefully analyses the evolution patterns of damage parameters concerning sheet metal and corresponding temperatures. The study contributes to understanding damage parameter evolution patterns, offering a predictive method for dual-phase steel sheet damage parameters across varying temperature conditions

GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph

Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of vision-language models (VLMs) under the low-data regime, where only a few additional parameters are introduced to excavate the task-specific knowledge based on the general and powerful representation of VLMs. However, most adapter-style works face two limitations: (i) modeling task-specific knowledge with a single modality only; and (ii) overlooking the exploitation of the inter-class relationships in downstream tasks, thereby leading to sub-optimal solutions. To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge (i.e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph. In particular, the dual knowledge graph is established with two sub-graphs, i.e., a textual knowledge sub-graph, and a visual knowledge sub-graph, where the nodes and edges represent the semantics/classes and their correlations in two modalities, respectively. This enables the textual feature of each prompt to leverage the task-specific structure knowledge from both textual and visual modalities, yielding a more effective classifier for downstream tasks. Extensive experimental results on 11 benchmark datasets reveal that our GraphAdapter significantly outperforms previous adapter-based methods. The code will be released at https://github.com/lixinustc/GraphAdapterComment: Accepted by NeurIPS 2023. The manuscript will be further revised based on the review

From malaria fighter to diabetes guardian: the emerging role of artesunate in treating diabetes and diabetic complications

Diabetes mellitus (DM) is a metabolic disease influenced by both genetic and environmental factors. The global incidence of DM is rising, and its multiple complications seriously affect patients’ quality of life and create a huge economic burden. At present, the prevention and treatment of DM mainly rely on oral or subcutaneous drugs, although oral drugs are more acceptable, they may produce more side effects and have limited effect on the treatment of diabetic complications. Artesunate (ART) is a first-line antimalarial drug widely used worldwide. Whether orally or intravenously, ART has high bioavailability and excellent pharmacokinetic properties in humans, and has shown good tolerance and safety in patients of multiple ages. Recent pharmacological studies have shown that, except for its antimalarial properties, ART also has a wide range of therapeutic potential for DM and its complications. This review aims to synthesize the latest research results, summarize and discuss the current role and mechanism of ART in improving diabetes and its complications, and provide a theoretical basis for the subsequent exploration of the anti-diabetes mechanism and the development of new antidiabetic agents based on ART, which has great clinical significance for strengthening the prevention and treatment effects of DM and its complications

Evaluation of a novel scoring system based on thrombosis and inflammation for predicting stroke-associated pneumonia: A retrospective cohort study

BackgroundInflammation and thrombosis are involved in the development of stroke-associated pneumonia (SAP). Our aim was to evaluate the predictive value of a novel, simplified, thrombo-inflammatory prognostic score (TIPS) that combines both inflammatory and thrombus biomarkers in the early phase of ischemic stroke (IS).MethodsThe study population consisted of 897 patients with a first diagnosis of IS admitted to the emergency department of five tertiary hospitals in China. Of these, the data from 70% of patients was randomly selected to derive the model and the other 30% for model validation. A TIPS of “2” was indicative of high inflammation and thrombosis biomarkers and “1” of one biomarker, with “0” indicative of absence of biomarkers. Multivariate logistic regression analyses were used to identify the association between TIPS and SAP.ResultsThe TIPS was an independent predictor of SAP and 90-day mortality, with the incidence of SAP being significantly higher for patients with a high TIPS. The TIPS provided superior predictive value for SAP than clinical scores (A2DS2) and biomarkers currently used in practice, for both the derivation and validation sets. Mediation analysis revealed that TIPS provided a predictive value than either thrombotic (NLR) and inflammatory (D-dimer) biomarkers alone.ConclusionThe TIPS score may be a useful tool for early identification of patients at high-risk for SAP after IS

Research progress and prospect of advanced geological exploration in shaft and roadway driving

Mechanical rock breaking represented by boring machines is the direction of future development of vertical shaft and roadway excavation technology. In order to ensure the safety of rapid mechanical and intelligent excavation of shaft and roadway, advanced geological exploration is an essential link. The development status and characteristics of conventional advanced geological exploration technology and advanced geological exploration technology during excavation were classified and summarized from the aspects of detection range, applicable conditions, and advantages and disadvantages. Conventional advanced detection techniques each have a certain scope of application and have been well applied in the construction environment of blasting excavation method. When facing the complex construction environment of boring machines, conventional advanced detection techniques are difficult to apply. And advanced geological exploration technology during excavation can synchronously achieve excavation and advanced geological exploration, real-time prediction of unfavorable geology in front of the working face, which is the focus of research on advanced geological exploration technology for mechanized and intelligent excavation of shaft and roadway. The full face excavation machine for vertical shafts is the development direction and trend of comprehensive mechanized shaft sinking. However, its construction environment is very complex, and advanced geological detection based on seismic wave of boring machines rock breaking source is an effective prediction method. The difficulty of advanced detection method for rock breaking source of full face shaft boring machines lies in the dual complexity of construction environment and seismic wave field of rock breaking source. Solutions are proposed from multiple perspectives. For the source pilot signal, adopting multiple methods for joint denoising to suppress interference waves from rock breaking sources. For seismic record signals, a seismic record reconstruction method with cross correlation as the core is adopted to restore the effective wave field. Conduct research on full space three-dimensional detection and high-precision imaging of vertical shafts, etc. In addition, conducting joint inversion with multiple excavation geophysical methods can improve the reliability and interpretation accuracy of geological identification. The development of integrated equipment for excavation and exploration of vertical shaft tunneling machines is the direction of future in-depth research

Association between cognitive impairment and risk of atrial fibrillation: The Atherosclerosis Risk in Communities study

Background: Atrial fibrillation (AF) is reportedly a risk factor for cognitive impairment. Interestingly, recent studies have emphasized that impaired cognition is probably an initiating factor of cardiovascular disease. Thus, we aimed to explore the association between impaired cognition and the risk of AF, and clarify the potential mechanisms. Methods: Participants of visit 2 (1991–1993) in the Atherosclerosis Risk in Communities study were included. Global cognition z-scores and factor scores were calculated using the word fluency, delayed word recall, and digit symbol substitution tests. AF incidents were diagnosed by electrocardiography and inpatient records. The association of cognitive decline with AF risk and left atrial volume index (LAVI) was explored using Cox proportional hazards and linear regression models, respectively. Results: During the median follow-up of 18.2 ± 6.2 years, 2056/11,675 (17.6%) participants developed AF. Participants in the lowest quartile of global cognition z- and factor scores had a higher risk of AF (hazard ratio [HR]: 1.271, 95% confidence interval [CI]: 1.094–1.477, p = 0.002; HR: 1.305, 95% CI: 1.110–1.535, p = 0.001, respectively) than those in the highest quartile. Global cognition z- and factor scores were negatively correlated with the LAVI (B: –0.411, 95% CI: –0.749 to –0.074, p = 0.017; B: –0.425, 95% CI: –0.833 to –0.017, p = 0.041, respectively). Conclusions: Cognitive decline is significantly associated with a higher risk of AF, with atrial remodeling being a potential mechanism. Our results extend previous findings of the brain-heart axis and indicate the effects of cognitive injury on cardiac function and structure. Registration: URL: https://www.clinicaltrials.gov; unique identifier: NCT0000513

Accelerated biological aging: unveiling the path to cardiometabolic multimorbidity, dementia, and mortality

BackgroundCardiometabolic multimorbidity (CMM) and aging are increasing public health concerns. This prospective study used UK Biobank cohort to investigate the relationship between biological aging and the trajectory of CMM to dementia and mortality.MethodsCMM is the coexistence of at least two cardiometabolic diseases (CMD), including stroke, ischemic heart disease, and diabetes. Biological age was calculated using the KDM-BA and PhenoAge algorithms. Accelerated aging indicated biological age advances more rapidly than chronological age.ResultsThe study included 415,147 individuals with an average age of 56.5 years. During the average 11-year follow-up period, CMD-free individuals with accelerated aging had a significantly greater risk of CMD (KDM-BA, HR 1.456; PhenoAge, HR 1.404), CMM (KDM-BA, HR 1.952; PhenoAge, HR 1.738), dementia (KDM-BA, HR 1.243; PhenoAge, HR 1.212), and mortality (KDM-BA, HR 1.821; PhenoAge, HR 2.047) in fully-adjusted Cox regression models (p < 0.05 for all). Accelerated aging had adjusted HRs of 1.489 (KDM-BA) and 1.488 (PhenoAge) for CMM, 1.434 (KDM-BA) and 1.514 (PhenoAge) for dementia, and 1.943 (KDM-BA) and 2.239 (PhenoAge) for mortality in participants with CMD at baseline (p < 0.05 for all). CMM significantly mediated accelerated aging’s indirect effects on dementia by 13.7% (KDM-BA, HR) and 21.6% (PhenoAge); those on mortality were 4.7% (KDM-BA) and 5.2% (PhenoAge). The population attributable-risk of Life’s Essential 8 score (≥80 vs. <80) were 0.79 and 0.43 for KDM-BA and PhenoAge accelerated aging, respectively.ConclusionBiological aging involves the entire trajectory of CMM from a CMD-free state to CMD, to CMM, and ultimately to dementia and death. Life’s Essential 8 may be a potential target to counter age acceleration