Amine-bis(phenolate) complexes of chromium for carbon dioxide/cyclohexene oxide copolymerization and group(I) and (II) complexes for the ring opening polymerization of rac-lactide

Finding novel methods for polymer synthesis has been of particular interest as of late from both a chemical and commercial standpoint. This focus stems from the recognized need for biodegradable and biocompatible polymers, originating from renewable resources, and is a legitimate endeavor by society to ease the burden imposed on the environment. Society’s conscience also recognizes the great need to utilize the carbon dioxide that is released in the atmosphere and it continues to make noble efforts in order not to contribute further to the greenhouse effect caused by greenhouse gases such as carbon dioxide. Therefore, the use of carbon dioxide as a starting material and the incorporation of it in the product of a chemical synthesis are currently developing research areas. Accordingly, combining the aim of environmentally friendly polycarbonate synthesis with incorporation of carbon dioxide in the polymer chain was the goal of the research conducted in the first part of this thesis. Additionally, biodegradable and biocompatible polylactide from lactide, originating from renewable feedstocks, was produced by biocompatible complexes in the second part. Polycarbonate synthesis was carried out in the presence of novel chromium(III) catalysts which were synthesized via salt metathesis reactions from tetradentate tripodal amine-bis(phenol) ligands. The complexes were characterized by MALDI-TOF mass spectrometry, UV-Vis and IR spectroscopy, magnetic susceptibility measurement and elemental analysis. The catalyst precursors selectively produced polycarbonates from epoxides and carbon dioxide with high yields and moderate molecular weights. Mechanistic studies revealed a first order dependence on catalyst concentration and that ring opening of the epoxide was initiated by a nucleophilic species originating either from the complex or from an externally added nucleophile. Biocompatible magnesium-, lithium-, sodium-, potassium- and calcium complexes of an amino-bis(phenolato) ligand were also prepared and characterized for the synthesis of polylactide, a biodegradable and biocompatible polymer. The magnesium and the sodium complexes in particular showed high catalytic activity and good control over the reaction. The complexes were active even under relatively low catalyst loading (0.1 – 0.2 mol%) and produced polymers of high purity as revealed by NMR, DSC and TGA

Forecasting electricity consumption by aggregating specialized experts

33 pagesInternational audienceWe consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (1997) and an adaptation of fixed-share rules of Herbster and Warmuth (1998) in this setting. We then apply these rules to the sequential short-term (one-day-ahead) forecasting of electricity consumption; to do so, we consider two data sets, a Slovakian one and a French one, respectively concerned with hourly and half-hourly predictions. We follow a general methodology to perform the stated empirical studies and detail in particular tuning issues of the learning parameters. The introduced aggregation rules demonstrate an improved accuracy on the data sets at hand; the improvements lie in a reduced mean squared error but also in a more robust behavior with respect to large occasional errors

Neural arbitration between social and individual learning systems

Decision making requires integrating self-gathered information with advice from others. However, the arbitration process by which one source of information is selected over the other has not been fully elucidated. In this study, we formalised arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modelling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our relative precision definition of arbitration. Functional neuroimaging demonstrated arbitration signals that were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favour of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favour of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioural and neural levels

Hierarchical prediction errors in midbrain and septum during social learning

Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling, and genetics to address this question in two separate samples (N=35, N=47). Participants played a game requiring inference on an advisor's intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser's trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support "theory of mind", but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs ("expected uncertainty") about the adviser's fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others. Copyright The Authors (2017). Published by Oxford University Press

The active inference approach to ecological perception: general information dynamics for natural and artificial embodied cognition

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents—who shape and are shaped by their environment—offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework (AIF) makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness

Coupling Reactions of Carbon Dioxide with Epoxides Catalyzed by Vanadium Aminophenolate Complexes

A series of vanadium compounds supported by tetradentate amino-bis(phenolate) ligands were screened for catalytic reactivity in the reaction of propylene oxide (PO) with carbon dioxide, [VO(OMe)(O2NOBuMeMeth)] (1), [VO(OMe)(ON2OBuMe)] (2), [VO(OMe)(O2NNBuBuPy)] (3), and [VO(OMe)(O2NOBuBuFurf)] (4) (where (O2NOBuMeMeth) = MeOCH2CH2N(CH2ArO-)2, Ar = 3,5-C6H2-Me, tBu]; (ON2OBuMe) = -OArCH2NMeCH2 CH2NMeCH2ArO-, Ar = 3,5-C6H2-Me, tBu; (O2NNBuBuPy) = C5H4NCH2N(CH2ArO-)2, Ar = 3,5-C6H2-tBu2; (O2NOBuBuFurf) = C4H3OCH2N(CH2ArO-)2, Ar = 3,5-C6H2-tBu2). They showed similar reactivities but reaction rates were greater for 2, which was studied in more detail. TOF for conversion of PO over 500 h-1 were observed. Activation energies were determined experimentally via in situ IR spectroscopy for propylene carbonate (48.2 kJ mol-1), styrene carbonate (45.6 kJ mol-1) and cyclohexene carbonate (54.7 kJ mol-1) formation

A causal account of the brain network computations underlying strategic social behavior

During competitive interactions, humans have to estimate the impact of their own actions on their opponent's strategy. Here we provide evidence that neural computations in the right temporoparietal junction (rTPJ) and interconnected structures are causally involved in this process. By combining inhibitory continuous theta-burst transcranial magnetic stimulation with model-based functional MRI, we show that disrupting neural excitability in the rTPJ reduces behavioral and neural indices of mentalizing-related computations, as well as functional connectivity of the rTPJ with ventral and dorsal parts of the medial prefrontal cortex. These results provide a causal demonstration that neural computations instantiated in the rTPJ are neurobiological prerequisites for the ability to integrate opponent beliefs into strategic choice, through system-level interaction within the valuation and mentalizing networks

Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment

<div><p>Peoples' subjective attitude towards costs such as, e.g., risk, delay or effort are key determinants of inter-individual differences in goal-directed behaviour. Thus, the ability to learn about others' prudent, impatient or lazy attitudes is likely to be critical for social interactions. Conversely, how adaptive such attitudes are in a given environment is highly uncertain. Thus, the brain may be tuned to garner information about how such costs ought to be arbitrated. In particular, observing others' attitude may change one's uncertain belief about how to best behave in related difficult decision contexts. In turn, learning <i>from</i> others' attitudes is determined by one's ability to learn <i>about</i> others' attitudes. We first derive, from basic optimality principles, the computational properties of such a learning mechanism. In particular, we predict two apparent cognitive biases that would arise when individuals are learning about others’ attitudes: (i) people should overestimate the degree to which they resemble others (false-consensus bias), and (ii) they should align their own attitudes with others’ (social influence bias). We show how these two biases non-trivially interact with each other. We then validate these predictions experimentally by profiling people's attitudes both before and after guessing a series of cost-benefit arbitrages performed by calibrated artificial agents (which are impersonating human individuals).</p></div