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In Nature, liquids often circulate in channels textured with leaflets, cilia or porous walls that deform with the flow. These soft structures are optimized to passively control flows and inspire the design of novel microfluidic and soft robotic devices. Yet so far the relationship between the geometry of the soft structures and the properties of the flow remains poorly understood. Here, taking inspiration from the lymphatic system, we devise millimetric scale fluidic channels with asymmetric soft leaflets that passively increase (reduce) the channel resistance for forward (backward) flows. Combining experiments, numerics and analytical theory, we show that tuning the geometry of the leaflets controls the flow properties of the channel through an interplay between asymmetry and nonlinearity. In particular, we find the conditions for which flow asymmetry is maximal. Our results open the way to a better characterization of biological leaflet malformations and to more accurate control of flow orientation and pumping mechanisms for microfluidics and soft robotic systems.

Advancing our understanding of software vulnerabilities, automating their identification, the analysis of their impact, and ultimately their mitigation is necessary to enable the development of software that is more secure. While operating a vulnerability assessment tool that we developed and that is currently used by hundreds of development units at SAP, we manually collected and curated a dataset of vulnerabilities of open-source software and the commits fixing them. The data was obtained both from the National Vulnerability Database (NVD) and from project-specific Web resources that we monitor on a continuous basis. From that data, we extracted a dataset that maps 624 publicly disclosed vulnerabilities affecting 205 distinct open-source Java projects, used in SAP products or internal tools, onto the 1282 commits that fix them. Out of 624 vulnerabilities, 29 do not have a CVE identifier at all and 46, which do have a CVE identifier assigned by a numbering authority, are not available in the NVD yet. The dataset is released under an open-source license, together with supporting scripts that allow researchers to automatically retrieve the actual content of the commits from the corresponding repositories and to augment the attributes available for each instance. Also, these scripts allow to complement the dataset with additional instances that are not security fixes (which is useful, for example, in machine learning applications). Our dataset has been successfully used to train classifiers that could automatically identify security-relevant commits in code repositories. The release of this dataset and the supporting code as open-source will allow future research to be based on data of industrial relevance; also, it represents a concrete step towards making the maintenance of this dataset a shared effort involving open-source communities, academia, and the industry., Comment: This is a pre-print version of the paper that appears in the proceedings of The 16th International Conference on Mining Software Repositories (MSR), Data Showcase track