226 research outputs found
A hetero Diels-Alder concerted vs aldol step-wise mechanism in the cyclization of silyloxyazadienes with aldehydes: a theoretical study.
No full textMean Field Games of Controls with Dirichlet Boundary Conditions
No full textIn this paper, we study a mean-field games system with Dirichlet boundary conditions in a closed domain and in a mean-field game of controls setting, that is in which the dynamics of each agent is affected not only by the average position of the rest of the agents but also by their average optimal choice. This setting allows the modeling of more realistic real-life scenarios in which agents not only will leave the domain at a certain point in time (like during the evacuation of pedestrians or in debt refinancing dynamics) but also act competitively to anticipate the strategies of the other agents. We shall establish the existence of Nash Equilibria for such class of mean-field game of controls systems under certain regularity assumptions on the dynamics and the Lagrangian cost. Much of the paper is devoted to establishing several a priori estimates which are needed to circumvent the fact that the mass is not conserved (as we are in a Dirichlet boundary condition setting). In the conclusive sections, we provide examples of systems falling into our framework as well as numerical implementations
Towards a novel probabilistic graphical model of sequential data: Fundamental notions and a solution to the problem of parameter learning
No full textProbabilistic graphical modeling via Hybrid Random Fields (HRFs) was introduced recently, and shown to improve over Bayesian Networks (BNs) and Markov Random Fields (MRFs) in terms of computational efficiency and modeling capabilities (namely, HRFs subsume BNs and MRFs). As in traditional graphical models, HRFs express a joint distribution over a fixed collection of random variables. This paper introduces the major definitions of a proper dynamic extension of regular HRFs (including latent variables), aimed at modeling arbitrary-length sequences of sets of (time-dependent) random variables under Markov assumptions. Suitable maximum pseudo-likelihood algorithms for learning the parameters of the model from data are then developed. The resulting learning machine is expected to fit scenarios whose nature involves discovering the stochastic (in)dependencies amongst the random variables, and the corresponding variations over time
Oligothiophene-S,S-dioxides: A new class of thiophene-based materials
No full textWe report here that bissilylated oligothiophenes may be selectively oxidized at the thienyl sulfurs by m-chloroperbenzoic acid (m-CPBA) to afford stable S,S-dioxides having alternate aromatic and nonaromatic moieties. These compounds are characterized by enhanced electron delocalization, smaller optical gap, and greater electron affinity than the “fully aromatic” precursors
Recursive neural networks for density estimation over generalized random graphs
No full textStructured data in the form of labeled graphs (with variable order and topology) may be thought of as the outcomes of a random graph (RG) generating process characterized by an underlying probabilistic law. This paper formalizes the notions of generalized RG (GRG) and probability density function (pdf) for GRGs. Thence, a “universal” learning machine (combining the encoding module of a recursive neural network and a radial basis functions' network) is introduced for estimating the unknown pdf from an unsupervised sample of GRGs. A maximum likelihood training algorithm is presented and constrained so as to ensure that the resulting model satisfies the axioms of probability. Techniques for preventing the model from degenerate solutions are proposed, as well as variants of the algorithm suitable to the tasks of graphs classification and graphs clustering. The major properties of the machine are discussed. The approach is validated empirically through experimental investigations in the estimation of pdfs for synthetic and real-life GRGs, in the classification of images from the Caltech Benchmark data set and molecules from the Mutagenesis data set, and in clustering of images from the LabelMe data set
The importance of being systemically important financial institutions
No full textWe investigate whether financial markets reacted to the regulatory changes implied by the publication of the list of systemically important financial institutions (SIFI) and the new rules designed to address the
too-big-to-fail problem of systemic banks. By applying event study methodology to a sample of 70 of the world’s largest banks, we assess whether the stock prices of SIFIs reacted significantly and differently
from those of other large banks not deemed to be systemically important following the release of information regarding the methodology used to identify SIFIs and their new capital requirements; the disclosure of the first list of 29 SIFIs; and the publication of the updated list of 28 SIFIs. Overall, we determine that financial markets did not univocally react to the new regulation regarding SIFIs. However markets discriminated between high and low capitalized banks and they correctly estimated the probable effects of the additional capital requirement
Thiophene S-oxides: Orbital energies and electrochemical properties
No full textAb initio calculations and experimental oxidation and reduction potentials show that the functionalization of thiophene to the corresponding S-oxide leads to only a minor change in ionization potential but to a dramatic increase in the electron affinity
Electrochemical and optical properties of poly (3 -methylthiophenes) electrosynthesized by 3,3'-, 3,4'- and 4,4'-dimethyl-2,2'-bithiophene
No full text3,3'-, 3,4'- and 4,4'-dimethyl-2,2'-bithiophene are used as starting molecules for electrochemical polymerization of poly(3-methylthiophenes). The resulting polymers show different optical properties, interpreted on the basis of the conformation of the starting dimers determined by force field MMP2 calculations. © 1993, Taylor & Francis Group, LLC. All rights reserved
Leader formation with mean-field birth and death models
No full textWe provide a mean-field description for a leader-follower dynamics with mass transfer among the two populations. This model allows the transition from followers to leaders and vice versa, with scalar-valued transition rates depending nonlinearly on the global state of the system at each time. We first prove the existence and uniqueness of solutions for the leader-follower dynamics, under suitable assumptions. We then establish, for an appropriate choice of the initial datum, the equivalence of the system with a PDE-ODE system, that consists of a continuity equation over the state space and an ODE for the transition from leader to follower or vice versa. We further introduce a stochastic process approximating the PDE, together with a jump process that models the switch between the two populations. Using a propagation of chaos argument, we show that the particle system generated by these two processes converges in probability to a solution of the PDE-ODE system. Finally, several numerical simulations of social interactions dynamics modeled by our system are discussed
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