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Imaging nanomaterials in hybrid systems is critical to understanding the structure and functionality of these systems. However, current technologies such as scanning electron microscopy (SEM) may obtain high resolution/contrast images at the cost of damaging or contaminating the sample. For example, to prevent the charging of organic substrate/matrix, a very thin layer of metal is coated on the surface, which will permanently contaminate the sample and eliminate the possibility of reusing it for following processes. Conversely, examining the sample without any modifications, in pursuit of high-fidelity digital images of its unperturbed state, can come at the cost of low-quality images that are challenging to process. Here, a solution is proposed for the case where no rightness threshold is available to reliably judge whether a region is covered with nanomaterials. The method examines local brightness variability to detect nanomaterial deposits. Very good agreement with manually obtained values of the coverage is observed, and a strong case is made for the method’s automatability. Although the developed methodology is showcased in the context of SEM images of Polydimethylsiloxane (PDMS) substrates on which silicone dioxide (SiO2) nanoparticles are assembled, the underlying concepts may be extended to situations where straightforward brightness thresholding is not viable. 

Abstract Bacterial colonies growing on solid surfaces can exhibit robust expansion kinetics, with constant radial growth and saturating vertical expansion, suggesting a common developmental program. Here, we study this process forEscherichia colicells using a combination of modeling and experiments. We show that linear radial colony expansion is set by the verticalization of interior cells due to mechanical constraints rather than radial nutrient gradients as commonly assumed. In contrast, vertical expansion slows down from an initial linear regime even while radial expansion continues linearly. This vertical slowdown is due to limitation of cell growth caused by vertical nutrient gradients, exacerbated by concurrent oxygen depletion. Starvation in the colony interior results in a distinct death zone which sets in as vertical expansion slows down, with the death zone increasing in size along with the expanding colony. Thus, our study reveals complex heterogeneity within simple monoclonal bacterial colonies, especially along the vertical dimension. The intricate dynamics of such emergent behavior can be understood quantitatively from an interplay of mechanical constraints and nutrient gradients arising from obligatory metabolic processes. 

Graph-based anomaly detection is pivotal in diverse security applications, such as fraud detection in transaction networks and intrusion detection for network traffic. Standard approaches, including Graph Neural Networks (GNNs), often struggle to generalize across shifting data distributions. For instance, we observe that a real-world eBay transaction dataset revealed an over 50% decline in fraud detection accuracy when adding data from only a single new day to the graph due to data distribution shifts. This highlights a critical vulnerability in purely data-driven approaches. Meanwhile, real-world domain knowledge, such as "simultaneous transactions in two locations are suspicious," is more stable and a common existing component of real-world detection strategies. To explicitly integrate such knowledge into data-driven models such as GCNs, we propose KnowGraph, which integrates domain knowledge with data-driven learning for enhanced graph-based anomaly detection. KnowGraph comprises two principal components: (1) a statistical learning component that utilizes a main model for the overarching detection task, augmented by multiple specialized knowledge models that predict domain-specific semantic entities; (2) a reasoning component that employs probabilistic graphical models to execute logical inferences based on model outputs, encoding domain knowledge through weighted first-order logic formulas. In addition, KnowGraph has leveraged the Predictability-Computability-Stability (PCS) framework for veridical data science to estimate and mitigate prediction uncertainties. Empirically, KnowGraph has been rigorously evaluated on two significant real-world scenarios: collusion detection in the online marketplace eBay and intrusion detection within enterprise networks. Extensive experiments on these large-scale real-world datasets show that KnowGraph consistently outperforms state-of-the-art baselines in both transductive and inductive settings, achieving substantial gains in average precision when generalizing to completely unseen test graphs. Further ablation studies demonstrate the effectiveness of the proposed reasoning component in improving detection performance, especially under extreme class imbalance. These results highlight the potential of integrating domain knowledge into data-driven models for high-stakes, graph-based security applications. 

Abstract While the positive relationship between plant diversity and ecosystem functioning is frequently observed and often attributed to direct plant–plant interactions, it remains unclear whether and how the effects of plant diversity endure through soil legacy effects, particularly at the level of genotypic diversity. We manipulated the genotypic diversity ofScirpus mariqueterand tested its soil legacy effects on a conspecific phytometer under low‐ and high‐water availability conditions. We found that genotypic diversity enhanced phytometer productivity through soil legacies, with stronger effects under low‐water availability conditions, improving its resistance to water stress. Moreover, this effect was attributed to the association between asexual and sexual reproductive strategies by increasing ramet number to ensure plant survival under low‐water availability and promoting sexual reproduction to escape stress. The observed diversity effects were primarily associated with increased levels of microbial biomass in soils trained by populations with diverse genotypes. Our findings highlight the importance of plant genotypic diversity in modulating ecosystem functioning through soil legacies and call for management measures that promote genetic diversity to make ecosystems sustainable in the face of climate change. 

With the rapidly increasing capabilities and adoption of code agents for AI-assisted coding and software development, safety and security concerns, such as generating or executing malicious code, have become significant barriers to the real-world deployment of these agents. To provide comprehensive and practical evaluations on the safety of code agents, we propose RedCode, an evaluation platform with benchmarks grounded in four key principles: real interaction with systems, holistic evaluation of unsafe code generation and execution, diverse input formats, and high-quality safety scenarios and tests. RedCode consists of two parts to evaluate agents’ safety in unsafe code execution and generation: (1) RedCode-Exec provides challenging code prompts in Python as inputs, aiming to evaluate code agents’ ability to recognize and handle unsafe code. We then map the Python code to other programming languages (e.g., Bash) and natural text summaries or descriptions for evaluation, leading to a total of over 4,000 testing instances. We provide 25 types of critical vulnerabilities spanning various domains, such as websites, file systems, and operating systems. We provide a Docker sandbox environment to evaluate the execution capabilities of code agents and design corresponding evaluation metrics to assess their execution results. (2) RedCode-Gen provides 160 prompts with function signatures and docstrings as input to assess whether code agents will follow instructions to generate harmful code or software. Our empirical findings, derived from evaluating three agent frameworks based on 19 LLMs, provide insights into code agents’ vulnerabilities. For instance, evaluations on RedCode-Exec show that agents are more likely to reject executing unsafe operations on the operating system, but are less likely to reject executing technically buggy code, indicating high risks. Unsafe operations described in natural text lead to a lower rejection rate than those in code format. Additionally, evaluations on RedCode-Gen reveal that more capable base models and agents with stronger overall coding abilities, such as GPT4, tend to produce more sophisticated and effective harmful software. Our findings highlight the need for stringent safety evaluations for diverse code agents. Our dataset and code are publicly available at https://github.com/AI-secure/RedCode.