Cooperative Online Learning: Keeping your Neighbors Updated

We study an asynchronous online learning setting with a network of agents. At each time step, some of the agents are activated, requested to make a prediction, and pay the corresponding loss. The loss function is then revealed to these agents and also to their neighbors in the network. Our results characterize how much knowing the network structure affects the regret as a function of the model of agent activations. When activations are stochastic, the optimal regret (up to constant factors) is shown to be of order $\sqrt{\alpha T}$, where $T$ is the horizon and $\alpha$ is the independence number of the network. We prove that the upper bound is achieved even when agents have no information about the network structure. When activations are adversarial the situation changes dramatically: if agents ignore the network structure, a $\Omega(T)$ lower bound on the regret can be proven, showing that learning is impossible. However, when agents can choose to ignore some of their neighbors based on the knowledge of the network structure, we prove a $O(\sqrt{\overline{\chi} T})$ sublinear regret bound, where $\overline{\chi} \ge \alpha$ is the clique-covering number of the network

EPR and pulsed ENDOR study of intermediates from reactions of aromatic azides with group 13 metal trichlorides

The reactions of group 13 metal trichlorides with aromatic azides were examined by CW EPR and pulsed ENDOR spectroscopies. Complex EPR spectra were obtained from reactions of aluminium, gallium and indium trichlorides with phenyl azides containing a variety of substituents. Analysis of the spectra showed that 4-methoxy-, 3-methoxy- and 2-methoxyphenyl azides all gave ‘dimer’ radical cations [ArNHC6H4NH2]+• and trimers [ArNHC6H4NHC6H4NH2]+• followed by polymers. 4-Azidobenzonitrile, with its electron-withdrawing substituent, did not react. In general the aromatic azides appeared to react most rapidly with AlCl3 but this reagent tended to generate much polymer. InCl3 was the least reactive group 13 halide. DFT computations of the radical cations provided corroborating evidence and suggested that the unpaired electrons were accommodated in extensive π-delocalised orbitals. A mechanism to account for the reductive conversion of aromatic azides to the corresponding anilines and thence to the dimers and trimers is proposedPublisher PDFPeer reviewe

On some properties of Lagrangian dispersion models with non-Gaussian noise

The properties of a stochastic model with non-Gaussian random noise describing turbulent dispersion have been investigated, with reference to its Mathematical structure and to its behaviour simulating the inertial subrange. The process is Markovian, mean-square continuous and with correlated increments. The model is influenced by the turbulence inhomogeneities also at the smallest scales, that is, it does not correctly simulate the existence of a well-developed inertial subrange. Some numerical computations have been performed confirming the theoretical results

Stochastic maximum principle for optimal liquidation with control-dependent terminal time

In this paper we study a general optimal liquidation problem with a control-dependent stopping time which is the first time the stock holding becomes zero or a fixed terminal time, whichever comes first. We prove a stochastic maximum principle (SMP) which is markedly different in its Hamiltonian condition from that of the standard SMP with fixed terminal time. We present a simple example in which the optimal solution satisfies the SMP in this paper but fails the standard SMP in the literature

Cooperative Online Learning

In this preliminary (and unpolished) version of the paper, we study an asynchronous online learning setting with a network of agents. At each time step, some of the agents are activated, requested to make a prediction, and pay the corresponding loss. Some feedback is then revealed to these agents and is later propagated through the network. We consider the case of full, bandit, and semi-bandit feedback. In particular, we construct a reduction to delayed single-agent learning that applies to both the full and the bandit feedback case and allows to obtain regret guarantees for both settings. We complement these results with a near-matching lower bound

Shaping the auditory peripersonal space with motor planning in immersive virtual reality

Immersive audio technologies require personalized binaural synthesis through headphones to provide perceptually plausible virtual and augmented reality (VR/AR) simulations. We introduce and apply for the first time in VR contexts the quantitative measure called premotor reaction time (pmRT) for characterizing sonic interactions between humans and the technology through motor planning. In the proposed basic virtual acoustic scenario, listeners are asked to react to a virtual sound approaching from different directions and stopping at different distances within their peripersonal space (PPS). PPS is highly sensitive to embodied and environmentally situated interactions, anticipating the motor system activation for a prompt preparation for action. Since immersive VR applications benefit from spatial interactions, modeling the PPS around the listeners is crucial to reveal individual behaviors and performances. Our methodology centered around the pmRT is able to provide a compact description and approximation of the spatiotemporal PPS processing and boundaries around the head by replicating several well-known neurophysiological phenomena related to PPS, such as auditory asymmetry, front/back calibration and confusion, and ellipsoidal action fields

Qualitative study of a class of nonlinear boundary value problems at resonance

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25816/1/0000379.pd