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Abstract Frogs are an ecologically diverse and phylogenetically ancient group of anuran amphibians that include important vertebrate cell and developmental model systems, notably the genusXenopus. Here we report a high-quality reference genome sequence for the western clawed frog,Xenopus tropicalis, along with draft chromosome-scale sequences of three distantly related emerging model frog species,Eleutherodactylus coqui,Engystomops pustulosus, andHymenochirus boettgeri. Frog chromosomes have remained remarkably stable since the Mesozoic Era, with limited Robertsonian (i.e., arm-preserving) translocations and end-to-end fusions found among the smaller chromosomes. Conservation of synteny includes conservation of centromere locations, marked by centromeric tandem repeats associated with Cenp-a binding surrounded by pericentromeric LINE/L1 elements. This work explores the structure of chromosomes across frogs, using a dense meiotic linkage map forX. tropicalisand chromatin conformation capture (Hi-C) data for all species. Abundant satellite repeats occupy the unusually long (~20 megabase) terminal regions of each chromosome that coincide with high rates of recombination. Both embryonic and differentiated cells show reproducible associations of centromeric chromatin and of telomeres, reflecting a Rabl-like configuration. Our comparative analyses reveal 13 conserved ancestral anuran chromosomes from which contemporary frog genomes were constructed. 

Fueled by massive amounts of data, models produced bymachine-learning (ML) algorithms, especially deep neural networks, arebeing used in diverse domains where trustworthiness is a concern, includ-ing automotive systems, finance, health care, natural language process-ing, and malware detection. Of particular concern is the use of ML algo-rithms in cyber-physical systems (CPS), such as self-driving cars andaviation, where an adversary can cause serious consequences.However, existing approaches to generating adversarial examples anddevising robust ML algorithms mostly ignore thesemanticsandcon-textof the overall system containing the ML component. For example,in an autonomous vehicle using deep learning for perception, not everyadversarial example for the neural network might lead to a harmful con-sequence. Moreover, one may want to prioritize the search for adversarialexamples towards those that significantly modify the desired semanticsof the overall system. Along the same lines, existing algorithms for con-structing robust ML algorithms ignore the specification of the overallsystem. In this paper, we argue that the semantics and specification ofthe overall system has a crucial role to play in this line of research. Wepresent preliminary research results that support this claim.