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The concept of remote epitaxy involves a two-dimensional van der Waals layer covering the substrate surface, which still enable adatoms to follow the atomic motif of the underlying substrate. The mode of growth must be carefully defined as defects, e.g., pinholes, in two-dimensional materials can allow direct epitaxy from the substrate, which, in combination with lateral epitaxial overgrowth, could also form an epilayer. Here, we show several unique cases that can only be observed for remote epitaxy, distinguishable from other two-dimensional material-based epitaxy mechanisms. We first grow BaTiO₃on patterned graphene to establish a condition for minimizing epitaxial lateral overgrowth. By observing entire nanometer-scale nuclei grown aligned to the substrate on pinhole-free graphene confirmed by high-resolution scanning transmission electron microscopy, we visually confirm that remote epitaxy is operative at the atomic scale. Macroscopically, we also show variations in the density of GaN microcrystal arrays that depend on the ionicity of substrates and the number of graphene layers.

 

Investigating attacks across multiple hosts is challenging. The true dependencies between security-sensitive files, network endpoints, or memory objects from different hosts can be easily concealed by dependency explosion or undefined program behavior (e.g., memory corruption). Dynamic information flow tracking (DIFT) is a potential solution to this problem, but, existing DIFT techniques only track information flow within a single host and lack an efficient mechanism to maintain and synchronize the data flow tags globally across multiple hosts. In this paper, we propose RTAG, an efficient data flow tagging and tracking mechanism that enables practical cross-host attack investigations. RTAG is based on three novel techniques. First, by using a record-and-replay technique, it decouples the dependencies between different data flow tags from the analysis, enabling lazy synchronization between independent and parallel DIFT instances of different hosts. Second, it takes advantage of systemcall-level provenance information to calculate and allocate the optimal tag map in terms of memory consumption. Third, it embeds tag information into network packets to track cross-host data flows with less than 0.05% network bandwidth overhead. Evaluation results show that RTAG is able to recover the true data flows of realistic cross-host attack scenarios. Performance wise, RTAG reduces the memory consumption of DIFT-based analysis by up to 90% and decreases the overall analysis time by 60%-90% compared with previous investigation systems. 

Directed self‐assembly of block copolymers is a key enabler for nanofabrication of devices with sub‐10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self‐assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self‐assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.