Peaks in the Hartle-Hawking Wave Function from Sums over Topologies

Recent developments in ``Einstein Dehn filling'' allow the construction of infinitely many Einstein manifolds that have different topologies but are geometrically close to each other. Using these results, we show that for many spatial topologies, the Hartle-Hawking wave function for a spacetime with a negative cosmological constant develops sharp peaks at certain calculable geometries. The peaks we find are all centered on spatial metrics of constant negative curvature, suggesting a new mechanism for obtaining local homogeneity in quantum cosmology.Comment: 16 pages,LaTeX, no figures; v2: some changes coming from revision of a math reference: wave function peaks sharp but not infinite; v3: added paragraph in intro on interpretation of wave functio

Designing Ecosystems of Intelligence from First Principles

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants -- what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world -- also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing -- leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first -- and key -- step towards such an ecology.Comment: 23+18 pages, one figure, one six page appendi

Identification of a Novel Class of Farnesylation Targets by Structure-Based Modeling of Binding Specificity

Farnesylation is an important post-translational modification catalyzed by farnesyltransferase (FTase). Until recently it was believed that a C-terminal CaaX motif is required for farnesylation, but recent experiments have revealed larger substrate diversity. In this study, we propose a general structural modeling scheme to account for peptide binding specificity and recapitulate the experimentally derived selectivity profile of FTase in vitro. In addition to highly accurate recovery of known FTase targets, we also identify a range of novel potential targets in the human genome, including a new substrate class with an acidic C-terminal residue (CxxD/E). In vitro experiments verified farnesylation of 26/29 tested peptides, including both novel human targets, as well as peptides predicted to tightly bind FTase. This study extends the putative range of biological farnesylation substrates. Moreover, it suggests that the ability of a peptide to bind FTase is a main determinant for the farnesylation reaction. Finally, simple adaptation of our approach can contribute to more accurate and complete elucidation of peptide-mediated interactions and modifications in the cell

A case of pediatric Henoch-Schonlein purpura and thrombosis of spermatic veins

The authors report a case of thrombosis of the spermatic veins associated with Henoch-Schonlein purpura mimicking an acute scrotum, which responded to a low-molecular-weight heparin treatment

Sophisticated Affective Inference: Simulating Anticipatory Affective Dynamics of Imagining Future Events

In this paper, we combine sophisticated and deep-parametric active inference to create an agent whose affective states change as a consequence of its Bayesian beliefs about how possible future outcomes will affect future beliefs. To achieve this, we augment Markov Decision Processes with a Bayes-adaptive deep-temporal tree search that is guided by a free energy functional which recursively scores counterfactual futures. Our model reproduces the common phenomenon of rumination over a situation until unlikely, yet aversive and arousing situations emerge in one’s imagination. As a proof of concept, we show how certain hyperparameters give rise to neurocognitive dynamics that characterise imagination-induced anxiety