The cuneiform writing system served as the medium for transmitting knowledge in the ancient Near East for a period of over three thousand years. Cuneiform signs have a complex internal structure which is the subject of expert paleographic analysis, as variations in sign shapes bear witness to historical developments and transmission of writing and culture over time. However, prior automated techniques mostly treat sign types as categorical and do not explicitly model their highly varied internal configurations. In this work, we present an unsupervised approach for recovering the fine-grained internal configuration of cuneiform signs by leveraging powerful generative models and the appearance and structure of prototype font images as priors. Our approach, ProtoSnap, enforces structural consistency on matches found with deep image features to estimate the diverse configurations of cuneiform characters, snapping a skeleton-based template to photographed cuneiform signs. We provide a new benchmark of expert annotations and evaluate our method on this task. Our evaluation shows that our approach succeeds in aligning prototype skeletons to a wide variety of cuneiform signs. Moreover, we show that conditioning on structures produced by our method allows for generating synthetic data with correct structural configurations, significantly boosting the performance of cuneiform sign recognition beyond existing techniques, in particular over rare signs. Our code, data, and trained models are available at the project page: https://tau-vailab.github.io/ProtoSnap/

An integrated modeling platform was developed for assessing the potential release rates of ENMs and their environmental distribution. ENM release rates are estimated via life cycle assessment based approach by tracking the target ENM throughout its life cycle from production to release to the environment. Potential ENM exposure concentrations and mass distribution in the various environmental media (i.e., air, water soil, vegetation) are then evaluated via multimedia modeling that considers the environment as a collection of compartments linked by mechanistic intermedia transport processes. A web-based software implementation of the modeling platform enables rapid “what-if?” scenario analysis, which can be used to assess the response of environmental system to various scenarios of ENM releases, investigate the impact of geographical and meteorological parameters on ENM distribution in the environment, compare the impact of ENM production and potential releases on different regions, we well as estimate source release rates based on monitored ENM concentrations. Moreover, the present modeling platform is suitable for research and teaching regarding environmental multimedia impact assessment at both the undergraduate and graduate levels. It is envisioned that the present multimedia analysis platform can assist regulators, industry, and researchers to rapidly and critically assess the potential environmental implications of ENMs and thus ensure that nanotechnology is developed in a productive and environmentally responsible manner.

The impact of ZnO and TiO2nanoparticles (NPs) on soil bacterial communities for different exposure times and NP doses was explored via data visualization techniques. Interrelationships between NP and responses of bacterial taxa were illustrated by bipartite graphs, allowing fast identification of important soil bacterial taxa that are susceptible to NPs. Hierarchical clustering and nonmetric multi-dimensional scaling (NMDS) of the dataset demonstrated that, high dose of ZnO and TiO2NPs caused significant compositional changes in soil bacterial communities. The suitability of family level for NP impact assessment was demonstrated by the simplified NMDSs and the distance correlation between NP impacts summarized at different taxonomic levels. The present study demonstrates that visual exploration could potentially assist in knowledge discovery and interpretation of data on soil bacterial communities exposed to NPs and thus evaluate potential environmental impacts.