Physics-Transfer Learning for Material Strength Screening

The strength of materials, like many problems in the natural sciences, spans multiple length and time scales, and the solution has to balance accuracy and performance. Peierls stress is one of the central concepts in crystal plasticity that measures the strength through the resistance of a dislocation to plastic flow. The determination of Peierls stress involves a multiscale nature depending on both elastic lattice responses and the energy landscape of crystal slips. Material screening by strength via the Peierls stress from first-principles calculations is computationally intractable for the nonlocal characteristics of dislocations, and not included in the state-of-the-art computational material databases. In this work, we propose a physics-transfer framework to learn the physics of crystal plasticity from empirical atomistic simulations and then predict the Peierls stress from chemically accurate density functional theory-based calculations of material parameters. Notably, the strengths of single-crystalline metals can be predicted from a few single-point calculations for the deformed lattice and on the {\gamma} surface, allowing efficient, high-throughput screening for material discovery. Uncertainty quantification is carried out to assess the accuracy of models and sources of errors, showing reduced physical and system uncertainties in the predictions by elevating the fidelity of training models. This physics-transfer framework can be generalized to other problems facing the accuracy-performance dilemma, by harnessing the hierarchy of physics in the multiscale models of materials science

A Bearing-Angle Approach for Unknown Target Motion Analysis Based on Visual Measurements

Vision-based estimation of the motion of a moving target is usually formulated as a bearing-only estimation problem where the visual measurement is modeled as a bearing vector. Although the bearing-only approach has been studied for decades, a fundamental limitation of this approach is that it requires extra lateral motion of the observer to enhance the target's observability. Unfortunately, the extra lateral motion conflicts with the desired motion of the observer in many tasks. It is well-known that, once a target has been detected in an image, a bounding box that surrounds the target can be obtained. Surprisingly, this common visual measurement especially its size information has not been well explored up to now. In this paper, we propose a new bearing-angle approach to estimate the motion of a target by modeling its image bounding box as bearing-angle measurements. Both theoretical analysis and experimental results show that this approach can significantly enhance the observability without relying on additional lateral motion of the observer. The benefit of the bearing-angle approach comes with no additional cost because a bounding box is a standard output of object detection algorithms. The approach simply exploits the information that has not been fully exploited in the past. No additional sensing devices or special detection algorithms are required

When Dataflow Analysis Meets Large Language Models

Dataflow analysis is a powerful code analysis technique that reasons dependencies between program values, offering support for code optimization, program comprehension, and bug detection. Existing approaches require the successful compilation of the subject program and customizations for downstream applications. This paper introduces LLMDFA, an LLM-powered dataflow analysis framework that analyzes arbitrary code snippets without requiring a compilation infrastructure and automatically synthesizes downstream applications. Inspired by summary-based dataflow analysis, LLMDFA decomposes the problem into three sub-problems, which are effectively resolved by several essential strategies, including few-shot chain-of-thought prompting and tool synthesis. Our evaluation has shown that the design can mitigate the hallucination and improve the reasoning ability, obtaining high precision and recall in detecting dataflow-related bugs upon benchmark programs, outperforming state-of-the-art (classic) tools, including a very recent industrial analyzer.Comment: 15 pages, 16 figures, 5 table

model-based script synthesis for fuzzing

Kernel fuzzing is important for finding critical kernel vulnerabilities. Close-source (e.g., Windows) operating system kernel fuzzing is even more challenging due to the lack of source code. Existing approaches fuzz the kernel by modeling syscall sequences from traces or static analysis of system codes. However, a common limitation is that they do not learn and mutate the syscall sequences to reach different kernel states, which can potentially result in more bugs or crashes. In this paper, we propose WinkFuzz, an approach to learn and mutate traced syscall sequences in order to reach different kernel states. WinkFuzz learns syscall dependencies from the trace, identifies potential syscalls in the trace that can have dependent subsequent syscalls, and applies the dependencies to insert more syscalls while preserving the dependencies into the trace. Then WinkFuzz fuzzes the synthesized new syscall sequence to find system crashes. We applied WinkFuzz to four seed applications and found a total increase in syscall number of 70.8\%, with a success rate of 61\%, within three insert levels. The average time for tracing, dependency analysis, recovering model script, and synthesizing script was 600, 39, 34, and 129 seconds respectively. The instant fuzzing rate is 3742 syscall executions per second. However, the average fuzz efficiency dropped to 155 syscall executions per second when the initializing time, waiting time, and other factors were taken into account. We fuzzed each seed application for 24 seconds and, on average, obtained 12.25 crashes within that time frame.Comment: 12 pages, conference pape

Sirtuins in osteoarthritis: current understanding

Osteoarthritis (OA) is a common disease characterized by severe chronic joint pain, that imposes a large burden on elderly people. OA is a highly heterogeneous disease, and multiple etiologies contribute to its progression. Sirtuins (SIRTs) are Class III histone deacetylases (HDACs) that regulate a comprehensive range of biological processes such as gene expression, cell differentiation, and organism development, and lifespan. Over the past three decades, increasing evidence has revealed that SIRTs are not only important energy sensors but also protectors against metabolic stresses and aging, and an increasing number of studies have focused on the functions of SIRTs in OA pathogenesis. In this review, we illustrate the biological functions of SIRTs in OA pathogenesis from the perspectives of energy metabolism, inflammation, autophagy and cellular senescence. Moreover, we offer insights into the role played by SIRTs in regulating circadian rhythm, which has recently been recognized to be crucial in OA development. Here, we provide the current understanding of SIRTs in OA to guide a new direction for OA treatment exploration

VulMatch: Binary-level Vulnerability Detection Through Signature

Similar vulnerability repeats in real-world software products because of code reuse, especially in wildly reused third-party code and libraries. Detecting repeating vulnerabilities like 1-day and N-day vulnerabilities is an important cyber security task. Unfortunately, the state-of-the-art methods suffer from poor performance because they detect patch existence instead of vulnerability existence and infer the vulnerability signature directly from binary code. In this paper, we propose VulMatch to extract precise vulnerability-related binary instructions to generate the vulnerability-related signature. VulMatch detects vulnerability existence based on binary signatures. Unlike previous approaches, VulMatch accurately locates vulnerability-related instructions by utilizing source and binary codes. Our experiments were conducted using over 1000 vulnerable instances across seven open-source projects. VulMatch significantly outperformed the baseline tools Asm2vec and Palmtree. Besides the performance advantages over the baseline tools, VulMatch offers a better feature by providing explainable reasons during vulnerability detection. Our empirical studies demonstrate that VulMatch detects fine-grained vulnerability that the state-of-the-art tools struggle with. Our experiment on commercial firmware demonstrates VulMatch is able to find vulnerabilities in real-world scenario.Comment: 15 pages IEEE journal templat

Ochronotic arthropathy effectively treated with total hip and total knee arthroplasty: a case report

Ochronosis is a rare autosomal recessive disorder of tyrosine metabolism characterized by multilevel spinal degeneration and arthritis of large weight-bearing joints, which is referred to as ochronotic arthropathy. In this case report, we describe diagnosis and treatment of ochronotic arthropathy in a patient who underwent total hip arthroplasty (THA) and total knee arthroplasty (TKA). The Harris hip score was 26 preoperatively and 45, 68, 76, 90, 92, and 94 at 1, 3, 6, 9, 11, and 14 months, respectively, postoperatively. The forgotten joint score (FJS) of the hip was 27.8, 52.8, 81.1, 89.0, 90.6, and 92.4 at 1, 3, 6, 9, 11, and 14 months, respectively, postoperatively. TKA was performed 8 months after THA. The Knee Society Score was 36 before TKA and 74, 82, and 90 at 1, 3, and 6 months, respectively, after TKA. The FJS of the knee was 36.6, 63.9, and 84.5 at 1, 3, and 6 months, respectively, after TKA. The patient’s knee range of motion returned to normal, with significant reduction in pain and improved satisfaction levels after TKA. THA and TKA can achieve good clinical outcomes in patients with ochronosis accompanied by severe joint pain

Genetic analyses of the bidirectional associations between common mental disorders and asthma

ObjectiveAlthough extensive research has explored the link between mental disorders and asthma, the characteristics and patterns of this association are still unclear. Our study aims to examine the genetic causal links between common mental disorders (specifically, anxiety and depression) and asthma.MethodsWe conducted genetic analyses including linkage disequilibrium score regression (LDSC) and bidirectional two-sample Mendelian randomization (MR) analyses, and utilized summary statistics from recent large-scale Genome-Wide Association Studies (GWASs) in European populations, covering sensation of anxiety or depression, anxiety sensation, depression sensation, anxiety disorders, major depression disorder (MDD), and asthma.ResultsLDSC revealed significant genetic correlations among sensation of anxiety or depression, MDD and asthma (P < 0.017), highlighting potential genetic correlation between anxiety disorders and asthma (P < 0.05 yet > 0.017). In bidirectional two-sample MR, inverse-variance weighted (IVW) analyses suggested that genetic liability to asthma was significantly associated with an increased risk of sensation of anxiety or depression (OR = 4.760, 95%CI: 1.645–13.777), and MDD (OR = 1.658, 95%CI: 1.477–1.860). Conversely, IVW analyses indicated that genetic liability to anxiety disorders was not associated with an increased risk of asthma (P > 0.01), nor was genetic liability to asthma associated with an increased risk of anxiety disorders (P > 0.01). Furthermore, no significant genetic causal relationships were observed for other studied traits. Multivariate MR, after adjusting for body mass index and alcohol consumption, further corroborated the independent causal effect of genetic predisposition to MDD on the risk of asthma (OR = 1.460, 95% CI: 1.285–1.660).ConclusionOur study establishes MDD as a predisposing factor for asthma. Meanwhile, anxiety disorders are not causal risk factors for asthma, nor is the reverse true. It is recommended to closely monitor asthma symptoms in patients with MDD

Artificial intelligence planning and 3D printing augmented modules in the treatment of a complicated hip joint revision: a case report

Total hip revision with osseous defects can be very difficult. Artificial intelligence offers preoperative planning, real-time measurement, and intraoperative judgment, which can guide prothesis placement more accurately. Three-dimensional printed metel augment modules which are made according to the individualized osseous anatomy, can fit the osseous defects well and provide mechanical support. In this case, we used AI to plan the size and position of the acetabular cup and 3D-printed augmented modules in a complicated hip revision with an acetabular bone defects, which achieved stable fixation and relieved hip pain postoperatively

HE3^3DB: An Efficient and Elastic Encrypted Database Via Arithmetic-And-Logic Fully Homomorphic Encryption

As concerns are increasingly raised about data privacy, encrypted database management system (DBMS) based on fully homomorphic encryption (FHE) attracts increasing research attention, as FHE permits DBMS to be directly outsourced to cloud servers without revealing any plaintext data. However, the real-world deployment of FHE-based DBMS faces two main challenges: i) high computational latency, and ii) lack of elastic query processing capability, both of which stem from the inherent limitations of the underlying FHE operators. Here, we introduce HE$^3$DB, a fully homomorphically encrypted, efficient and elastic DBMS framework based on a new FHE infrastructure. By proposing and integrating new arithmetic and logic homomorphic operators, we devise fast and high-precision homomorphic comparison and aggregation algorithms that enable a variety of SQL queries to be applied over FHE ciphertexts, e.g., compound filter-aggregation, sorting, grouping, and joining. In addition, in contrast to existing encrypted DBMS that only support aggregated information retrieval, our framework permits further server-side analytical processing over the queried FHE ciphertexts, such as private decision tree evaluation. In the experiment, we rigorously study the efficiency and flexibility of HE$^3$DB. We show that, compared to the state-of-the-art techniques,HE$^3$DB can homomorphically evaluate end-to-end SQL queries as much as $41\times$ -$299\times$ faster than the state-of-the-art solution, completing a TPC-H query over a 16-bit 10K-row database within 241 seconds