Tensors (also commonly seen as multi-linear operators or as multi-dimensional arrays) are ubiquitous in scientific computing and in data science, and so are the software efforts for tensor operations. Particularly in recent years, we have observed an explosion in libraries, compilers, packages, and toolboxes; unfortunately these efforts are very much scattered among the different scientific domains, and inevitably suffer from replication, suboptimal implementations, and in many cases, limited visibility. As a first step towards countering these inefficiencies, here we survey and loosely classify software packages related to tensor computations. Our aim is to assemble a comprehensive and up-to-date snapshot of the tensor software landscape, with the intention of helping both users and developers. Aware of the difficulties inherent in any multi-discipline survey, we very much welcome the reader's help in amending and expanding our software list, which currently features 72 projects.

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The Canonical Polyadic (CP) tensor decomposition is frequently used as a model in applications in a variety of different fields. Using jackknife resampling to estimate parameter uncertainties is often desirable but results in an increase of the already high computational cost. Upon observation that the resampled tensors, though different, are nearly identical, we show that it is possible to extend the recently proposed Concurrent ALS (CALS) technique to a jackknife resampling scenario. This extension gives access to the computational efficiency advantage of CALS for the price of a modest increase (typically a few percent) in the number of floating point operations. Numerical experiments on both synthetic and real-world datasets demonstrate that the new workflow based on a CALS extension can be several times faster than a straightforward workflow where the jackknife submodels are processed individually.

Autonomous subsurface mapping is a key characteristic of future robots to be realized in the construction domain, since it can be utilized in diverse applications of strategic importance. During the last years, the interest has been steered mainly towards the development of ground-penetrating radar (GPR) devices, rather than on the establishment of holistic subsurface reconstruction methods. To this end, the paper at hand introduces a simulation tool that comprises a) a surface operating rover and b) a sonar-based simulated GPR array capable, seamlessly integrated to build adjunct surface and subsurface maps. Specifically, by exploiting the onboard stereo camera of the robot and the GPR, mounted on a robotic-trailer topology, joint surface and subsurface mapping is performed. Further processing of the simulated GPR data is applied to detect and semantically annotate georeferenced buried utilities, while the localization of surface rover is also employed for the topographic correction of the accumulated Bscans during the robot's exploration. The proposed framework has been developed in the ROS framework and has been evaluated on the realistic simulation environment of Gazebo.