Mechanistic investigations of bipyrimidine-promoted palladium-catalyzed allylic acetoxylation of olefins

Several pyridine-like ligands were found to improve Pd(OAc)2-catalyzed allylic oxidation of allylbenzene to cinnamyl acetate by p-benzoquinone in acetic acid. The best ligand examined, bipyrimidine, was used to identify the catalyst precursor for this system, (bipyrimidine)Pd(OAc)2, which was fully characterized. Mechanistic studies suggest the reaction takes place through disproportionation of (bipyrimidine)Pd(OAc)2 to form a bipyrimidine-bridged dimer, which reacts with olefin to form a Pd^II-olefin adduct, followed by allylic C–H activation to produce (η^3-allyl)Pd^II species. The (η^3-allyl)Pd^II intermediate undergoes a reversible acetate attack to generate a Pd^0-(allyl acetate) adduct, which subsequently reacts with p-benzoquinone to release allyl acetate and regenerate (bipyrimidine)Pd(OAc)2. No KIE is observed for the competition experiment between allylbenzene-d0 and allylbenzene-d5 (CD2=CDCD2C6H5), suggesting that allylic C–H activation is not rate-determining. Catalytic allylic acetoxylations of other terminal olefins as well as cyclohexene were also effected by (bipyrimidine)Pd(OAc)2

Asymptotic normality of extreme value estimators on C[0,1]C[0,1]

Consider $n$ i.i.d. random elements on $C[0,1]$. We show that, under an appropriate strengthening of the domain of attraction condition, natural estimators of the extreme-value index, which is now a continuous function, and the normalizing functions have a Gaussian process as limiting distribution. A key tool is the weak convergence of a weighted tail empirical process, which makes it possible to obtain the results uniformly on $[0,1]$. Detailed examples are also presented.Comment: Published at http://dx.doi.org/10.1214/009053605000000831 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Block-Structured Supermarket Models

Supermarket models are a class of parallel queueing networks with an adaptive control scheme that play a key role in the study of resource management of, such as, computer networks, manufacturing systems and transportation networks. When the arrival processes are non-Poisson and the service times are non-exponential, analysis of such a supermarket model is always limited, interesting, and challenging. This paper describes a supermarket model with non-Poisson inputs: Markovian Arrival Processes (MAPs) and with non-exponential service times: Phase-type (PH) distributions, and provides a generalized matrix-analytic method which is first combined with the operator semigroup and the mean-field limit. When discussing such a more general supermarket model, this paper makes some new results and advances as follows: (1) Providing a detailed probability analysis for setting up an infinite-dimensional system of differential vector equations satisfied by the expected fraction vector, where "the invariance of environment factors" is given as an important result. (2) Introducing the phase-type structure to the operator semigroup and to the mean-field limit, and a Lipschitz condition can be obtained by means of a unified matrix-differential algorithm. (3) The matrix-analytic method is used to compute the fixed point which leads to performance computation of this system. Finally, we use some numerical examples to illustrate how the performance measures of this supermarket model depend on the non-Poisson inputs and on the non-exponential service times. Thus the results of this paper give new highlight on understanding influence of non-Poisson inputs and of non-exponential service times on performance measures of more general supermarket models.Comment: 65 pages; 7 figure

Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc