Creation and Divine Providence in Plotinus

In this paper, we argue that Plotinus denies deliberative forethought about the physical cosmos to the demiurge on the basis of certain basic and widely shared Platonic and Aristotelian assumptions about the character of divine thought. We then discuss how Plotinus can nonetheless maintain that the cosmos is «providentially» ordered

Creation and Divine Providence in Plotinus

In this paper, we argue that Plotinus denies deliberative forethought about the physical cosmos to the demiurge on the basis of certain basic and widely shared Platonic and Aristotelian assumptions about the character of divine thought. We then discuss how Plotinus can nonetheless maintain that the cosmos is «providentially» ordered

Quantum Hamiltonian Learning Using Imperfect Quantum Resources

Identifying an accurate model for the dynamics of a quantum system is a vexing problem that underlies a range of problems in experimental physics and quantum information theory. Recently, a method called quantum Hamiltonian learning has been proposed by the present authors that uses quantum simulation as a resource for modeling an unknown quantum system. This approach can, under certain circumstances, allow such models to be efficiently identified. A major caveat of that work is the assumption of that all elements of the protocol are noise-free. Here, we show that quantum Hamiltonian learning can tolerate substantial amounts of depolarizing noise and show numerical evidence that it can tolerate noise drawn from other realistic models. We further provide evidence that the learning algorithm will find a model that is maximally close to the true model in cases where the hypothetical model lacks terms present in the true model. Finally, we also provide numerical evidence that the algorithm works for non-commuting models. This work illustrates that quantum Hamiltonian learning can be performed using realistic resources and suggests that even imperfect quantum resources may be valuable for characterizing quantum systems.Comment: 16 pages 11 Figure

Ongoing Emergence: A Core Concept in Epigenetic Robotics

We propose ongoing emergence as a core concept in epigenetic robotics. Ongoing emergence refers to the continuous development and integration of new skills and is exhibited when six criteria are satisfied: (1) continuous skill acquisition, (2) incorporation of new skills with existing skills, (3) autonomous development of values and goals, (4) bootstrapping of initial skills, (5) stability of skills, and (6) reproducibility. In this paper we: (a) provide a conceptual synthesis of ongoing emergence based on previous theorizing, (b) review current research in epigenetic robotics in light of ongoing emergence, (c) provide prototypical examples of ongoing emergence from infant development, and (d) outline computational issues relevant to creating robots exhibiting ongoing emergence

Cocoon viability and evidence for delayed hatching by the earthworm Lumbricus terrestris in a laboratory-based study.

Clitellate Lumbricus terrestris, obtained from 5 commercial suppliers (A-E) and also field collected (F) - grassland in Preston, Lancashire, UK, were kept under controlled environmental conditions (15 °C and 24 h darkness) in a sterilised loam soil and surface-fed with horse manure. Survival, biomass and cocoon production was monitored every 4 weeks over 1 y. Collected cocoons were maintained in water-filled Petri dishes on filter paper. Time to hatch and cocoon viability was recorded over a 2 y period. Cocoon production ranged from 15.1 – 32.2 ind.-1 y-1. Cocoon production was initially low followed by a period of high production (12-36 weeks) and then fell (36-52 weeks). Time for cocoon hatching ranged from 132-731 days. Hatching success after 2 years was 58–90% across treatments, with a total viability (including cocoons dissected after the 2 year period) of 88-94%. Evidence of 2 distinct hatching peaks was recorded, separated by a period of approximately 12 months in treatments of most of the commercially obtained earthworms. Cocoon incubation periods are in excess of those previously recorded under similar laboratory conditions (e.g. 90–280 days). Furthermore, viability is also higher than previously recorded (e.g. 67.9–83%). These differences are mainly attributable to the extended length of cocoon observation. Origin, age, unknown pre-treatment (in A-E) and experimental conditions (e.g. a constant temperature regime) may have influenced incubation times. However, it is suggested that asynchronous and delayed hatching within cohorts and ability for cocoons to remain viable for extended periods allows this K-selected species to maximise reproductive potential. This proposed “bet-hedging” strategy is worthy of further laboratory and field-based investigation