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The ³ MLCT-excited [Ru(bpz)₃]²⁺ and the spin-flip excited states of [Cr(dqp)₂]³⁺ underwent photoinduced electron-transfer reactions with 12 amine-based electron donors similarly well, but provided cage escape quantum yields differing by up to an order of magnitude. In three exemplary benchmark photoredox reactions performed with different electron donors, the differences in the reaction rates observed when using either [Ru(bpz)₃]²⁺ or [Cr(dqp)₂]³⁺ as photocatalyst correlated with the magnitude of the cage escape quantum yields. These correlations indicate that the cage escape quantum yields play a decisive role in the reaction rates and quantum efficiencies of the photoredox reactions, and also illustrate that luminescence quenching experiments are insufficient for obtaining quantitative insights into photoredox reactivity.

From a purely physical chemistry perspective, these findings are not a priori surprising as the rate of photoproduct formation in an overall reaction comprising several consecutive elementary steps can be expressed as the product of the quantum yields of the individual elementary steps^45,46. A recent report on solvent-dependent cage escape and photoredox studies suggested that the correlations between photoredox product formation rates and cage escape quantum yields might be observable¹¹, but we are unaware of previous reports that have been able to demonstrate that the rate of product formation in several batch-type photoreactions correlates with the cage escape quantum yields determined from laser experiments. Synthetic photochemistry and mechanistic investigations are often conducted under substantially different conditions, which can lead to controversial discrepancies^47,48,49, whereas here their mutual agreement seems remarkable, particularly given the complexity of the overall reactions.

The available data and the presented analysis suggest that the different cage escape behaviours of [Ru(bpz)₃]²⁺ and [Cr(dqp)₂]³⁺ originate in the fact that for any given electron donor, in-cage reverse electron transfer is ~0.3 eV more exergonic for the Ru^II complex than for the Cr^III complex. Thermal reverse electron transfer between caged radical pairs therefore occurs more deeply in the Marcus inverted region with [Ru(bpz)₃]²⁺ than with [Cr(dqp)₂]³⁺, decelerating in-cage charge recombination in the Ru^II complex and increasing the cage escape quantum yields compared with the Cr^III complex (Fig. 3D).

Yin et al. realize a FeFET based compute-in-memory annealer as an efficient combinatorial optimization solver through algorithm-hardware co-design with a FeFET chip, matrix lossless compression, and a multi-epoch simulated annealing algorithm.

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In this study, we use the latest advances in natural language processing to build tractable models of the ability to interpret instructions to guide actions in novel settings and the ability to produce a description of a task once it has been learned. RNNs can learn to perform a set of psychophysical tasks simultaneously using a pretrained language transformer to embed a natural language instruction for the current task. Our best-performing models can leverage these embeddings to perform a brand-new model with an average performance of 83% correct. Instructed models that generalize performance do so by leveraging the shared compositional structure of instruction embeddings and task representations, such that an inference about the relations between practiced and novel instructions leads to a good inference about what sensorimotor transformation is required for the unseen task. Finally, we show a network can invert this information and provide a linguistic description for a task based only on the sensorimotor contingency it observes.

Our models make several predictions for what neural representations to expect in brain areas that integrate linguistic information in order to exert control over sensorimotor areas. Firstly, the CCGP analysis of our model hierarchy suggests that when humans must generalize across (or switch between) a set of related tasks based on instructions, the neural geometry observed among sensorimotor mappings should also be present in semantic representations of instructions. This prediction is well grounded in the existing experimental literature where multiple studies have observed the type of abstract structure we find in our sensorimotor-RNNs also exists in sensorimotor areas of biological brains^3,36,37. Our models theorize that the emergence of an equivalent task-related structure in language areas is essential to instructed action in humans. One intriguing candidate for an area that may support such representations is the language selective subregion of the left inferior frontal gyrus. This area is sensitive to both lexico-semantic and syntactic aspects of sentence comprehension, is implicated in tasks that require semantic control and lies anatomically adjacent to another functional subregion of the left inferior frontal gyrus, which is implicated in flexible cognition^38,39,40,41. We also predict that individual units involved in implementing sensorimotor mappings should modulate their tuning properties on a trial-by-trial basis according to the semantics of the input instructions, and that failure to modulate tuning in the expected way should lead to poor generalization. This prediction may be especially useful to interpret multiunit recordings in humans. Finally, given that grounding linguistic knowledge in the sensorimotor demands of the task set improved performance across models (Fig. 2e), we predict that during learning the highest level of the language processing hierarchy should likewise be shaped by the embodied processes that accompany linguistic inputs, for example, motor planning or affordance evaluation⁴².

One notable negative result of our study is the relatively poor generalization performance of GPTNET (XL), which used at least an order of magnitude more parameters than other models. This is particularly striking given that activity in these models is predictive of many behavioral and neural signatures of human language processing^10,11. Given this, future imaging studies may be guided by the representations in both autoregressive models and our best-performing models to delineate a full gradient of brain areas involved in each stage of instruction following, from low-level next-word prediction to higher-level structured-sentence representations to the sensorimotor control that language informs.

Dive into the captivating story of Gz9p3, an ancient galaxy that’s challenging our understanding of the cosmos. Revealed by the James Webb Space Telescope, this galactic giant, observed just 510 million years after the Big Bang, is reshaping our views on early universe galactic formation. Join us as we explore the mysteries and wonders of Gz9p3, a window into the universe’s dawn.

Chapters:
00:00 Introduction.
00:54 Unveiling Gz9p3: A Glimpse into the Past.
03:16 Cosmic Collisions: Sculpting Galaxies.
05:03 Rethinking Early Universe Cosmology.
06:25 Outro.
07:13 Enjoy.

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