Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information - for instance, the dynamics describing the effects of causal relations - which is lost when following this approach. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus makes use of the information that is shared. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under hidden confounding

Characterizing Van Kampen Squares via Descent Data

Categories in which cocones satisfy certain exactness conditions w.r.t. pullbacks are subject to current research activities in theoretical computer science. Usually, exactness is expressed in terms of properties of the pullback functor associated with the cocone. Even in the case of non-exactness, researchers in model semantics and rewriting theory inquire an elementary characterization of the image of this functor. In this paper we will investigate this question in the special case where the cocone is a cospan, i.e. part of a Van Kampen square. The use of Descent Data as the dominant categorical tool yields two main results: A simple condition which characterizes the reachable part of the above mentioned functor in terms of liftings of involved equivalence relations and (as a consequence) a necessary and sufficient condition for a pushout to be a Van Kampen square formulated in a purely algebraic manner.Comment: In Proceedings ACCAT 2012, arXiv:1208.430

Quantum Mechanical Aspects of Cell Microtubules: Science Fiction or Realistic Possibility?

Recent experimental research with marine algae points towards quantum entanglement at ambient temperature, with correlations between essential biological units separated by distances as long as 20 Angstr\"oms. The associated decoherence times, due to environmental influences, are found to be of order 400 fs. This prompted some authors to connect such findings with the possibility of some kind of quantum computation taking place in these biological entities: within the decoherence time scales, the cell "quantum calculates" the optimal "path" along which energy and signal would be transported more efficiently. Prompted by these experimental results, in this talk I remind the audience of a related topic proposed several years ago in connection with the possible r\^ole of quantum mechanics and/or field theory on dissipation-free energy transfer in microtubules (MT), which constitute fundamental cell substructures. Quantum entanglement between tubulin dimers was argued to be possible, provided there exists sufficient isolation from other environmental cell effects. The model was based on certain ferroelectric aspects of MT. In the talk I review the model and the associated experimental tests so far and discuss future directions, especially in view of the algae photo-experiments.Comment: 31 pages latex, 11 pdf figures, uses special macros, Invited Plenary Talk at DICE2010, Castello Pasquini, Castiglioncello (Italy), September 13-18 201

Optimal design of multi-channel microreactor for uniform residence time distribution

Multi-channel microreactors can be used for various applications that require chemical or electrochemical reactions in either liquid, gaseous or multi phase. For an optimal control of the chemical reactions, one key parameter for the design of such microreactors is the residence time distribution of the fluid, which should be as uniform as possible in the series of microchannels that make up the core of the reactor. Based on simplifying assumptions, an analytical model is proposed for optimizing the design of the collecting and distributing channels which supply the series of rectangular microchannels of the reactor, in the case of liquid flows. The accuracy of this analytical approach is discussed after comparison with CFD simulations and hybrid analytical-CFD calculations that allow an improved refinement of the meshing in the most complex zones of the flow. The analytical model is then extended to the case of microchannels with other cross-sections (trapezoidal or circular segment) and to gaseous flows, in the continuum and slip flow regimes. In the latter case, the model is based on second-order slip flow boundary conditions, and takes into account the compressibility as well as the rarefaction of the gas flow

Shear viscosity of a crosslinked polymer melt

We investigate the static shear viscosity on the sol side of the vulcanization transition within a minimal mesoscopic model for the Rouse-dynamics of a randomly crosslinked melt of phantom polymers. We derive an exact relation between the viscosity and the resistances measured in a corresponding random resistor network. This enables us to calculate the viscosity exactly for an ensemble of crosslinks without correlations. The viscosity diverges logarithmically as the critical point is approached. For a more realistic ensemble of crosslinks amenable to the scaling description of percolation, we prove the scaling relation $k=\phi-\beta$ between the critical exponent $k$ of the viscosity, the thermal exponent $\beta$ associated with the gel fraction and the crossover exponent $\phi$ of a random resistor network.Comment: 8 pages, uses Europhysics Letters style; Revisions: results extende