Environmental, social and governance pressures should feature in future scenario planning about the transition to a low carbon future. As low-carbon energy technologies advance, markets are driving demand for energy transition metals. Increased extraction rates will augment the stress placed on people and the environment in extractive locations. To quantify this stress, we develop a set of global composite environmental, social and governance indicators, and examine mining projects across 20 metal commodities to identify the co-occurrence of environmental, social and governance risk factors. Our findings show that 84% of platinum resources and 70% of cobalt resources are located in high-risk contexts. Reflecting heightened demand, major metals like iron and copper are set to disturb more land. Jurisdictions extracting energy transition metals in low-risk contexts are positioned to develop and maintain safeguards against mining-related social and environmental risk factors.

The cerebral cortex utilizes spatiotemporal continuity in the world to help build invariant representations. In vision, these might be representations of objects. The temporal continuity typical of objects has been used in an associative learning rule with a short-term memory trace to help build invariant object representations. In this paper, we show that spatial continuity can also provide a basis for helping a system to self-organize invariant representations. We introduce a new learning paradigm "continuous transformation learning" which operates by mapping spatially similar input patterns to the same postsynaptic neurons in a competitive learning system. As the inputs move through the space of possible continuous transforms (e.g. translation, rotation, etc.), the active synapses are modified onto the set of postsynaptic neurons. Because other transforms of the same stimulus overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facilitated. We demonstrate that a hierarchical model of cortical processing in the ventral visual system can be trained with continuous transform learning, and highlight differences in the learning of invariant representations to those achieved by trace learning.

Massive high-redshift quiescent compact galaxies (nicknamed red nuggets) have been traditionally connected to present-day elliptical galaxies, often overlooking the relationships that they may have with other galaxy types. We use large bulge-disk decomposition catalogues based on the Sloan Digital Sky Survey (SDSS) to check the hypothesis that red nuggets have survived as compact cores embedded inside the haloes or disks of present-day massive galaxies. In this study, we designate a compact core as the bulge component that satisfies a prescribed compactness criterion. Photometric and dynamic mass-size and mass-density relations are used to show that, in the inner regions of galaxies at z ∼ 0.1, there are abundant compact cores matching the peculiar properties of the red nuggets, an abundance comparable to that of red nuggets at z ∼ 1.5. Furthermore, the morphology distribution of the present-day galaxies hosting compact cores is used to demonstrate that, in addition to the standard channel connecting red nuggets with elliptical galaxies, a comparable fraction of red nuggets might have ended up embedded in disks. This result generalises the inside-out formation scenario; present-day massive galaxies can begin as dense spheroidal cores (red nuggets), around which either a spheroidal halo or a disk are formed later.