The kinetics of homogenization of binary As x Se 100−x melts in the As concentration range 0% < x < 50% are followed in FT-Raman profiling experiments, and show that 2 gram sized melts in the middle concentration range 20% < x < 30% take nearly two weeks to homogenize when starting materials are reacted at 700 o C. In glasses of proven homogeneity, we find molar volumes to vary non-monotonically with composition, and the fragility index M displays a broad global minimum in the 20% < x < 30% range of x wherein M < 20.We show that properly homogenized samples have a lower measured fragility when compared to larger underreacted melts. The enthalpy of relaxation at T g , ∆H nr (x) shows a minimum in the 27% < x < 37% range. The super-strong nature of melt compositions in the 20% < x < 30% range suppresses melt diffusion at high temperatures leading to the slow kinetics of melt homogenization.

The rapid proliferation of intelligent systems (e.g., fully autonomous vehicles) in today's society relies on sensors with low latency and computational effort. Yet current sensing systems ignore most available a priori knowledge, notably in the design of the hardware level, such that they fail to extract as much task‐relevant information per measurement as possible. Here, a “learned integrated sensing pipeline” (LISP), including in an end‐to‐end fashion both physical and processing layers, is shown to enable joint learning of optimal measurement strategies and a matching processing algorithm, making use of a priori knowledge on task, scene, and measurement constraints. Numerical results demonstrate accuracy improvements around 15% for object recognition tasks with limited numbers of measurements, using dynamic metasurface apertures capable of transceiving programmable microwave patterns. Moreover, it is concluded that the optimal learned microwave patterns are nonintuitive, underlining the importance of the LISP paradigm in current sensorization trends.

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