The Large High Altitude Air Shower Observatory (LHAASO) Science White Paper

The Large High Altitude Air Shower Observatory (LHAASO) project is a new generation multi-component instrument, to be built at 4410 meters of altitude in the Sichuan province of China, with the aim to study with unprecedented sensitivity the spec trum, the composition and the anisotropy of cosmic rays in the energy range between 10$^{12}$ and 10$^{18}$ eV, as well as to act simultaneously as a wide aperture (one stereoradiant), continuously-operated gamma ray telescope in the energy range between 10$^{11}$ and $10^{15}$ eV. The experiment will be able of continuously surveying the TeV sky for steady and transient sources from 100 GeV to 1 PeV, t hus opening for the first time the 100-1000 TeV range to the direct observations of the high energy cosmic ray sources. In addition, the different observables (electronic, muonic and Cherenkov/fluorescence components) that will be measured in LHAASO will allow to investigate origin, acceleration and propagation of the radiation through a measurement of energy spec trum, elemental composition and anisotropy with unprecedented resolution. The remarkable sensitivity of LHAASO in cosmic rays physics and gamma astronomy would play a key-role in the comprehensive general program to explore the High Energy Universe. LHAASO will allow important studies of fundamental physics (such as indirect dark matter search, Lorentz invariance violation, quantum gravity) and solar and heliospheric physics. In this document we introduce the concept of LHAASO and the main science goals, providing an overview of the project.Comment: This document is a collaborative effort, 185 pages, 110 figure

Temporal-Difference Reinforcement Learning with Distributed Representations

Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the believed state of the world to distribute across sets of equivalent states. Distributed exponential discounting factors produce hyperbolic discounting in the behavior of the agent itself. We examine these issues in the context of a TD RL model in which state-belief is distributed over a set of exponentially-discounting “micro-Agents”, each of which has a separate discounting factor (γ). Each µAgent maintains an independent hypothesis about the state of the world, and a separate value-estimate of taking actions within that hypothesized state. The overall agent thus instantiates a flexible representation of an evolving world-state. As with other TD models, the value-error (δ) signal within the model matches dopamine signals recorded from animals in standard conditioning reward-paradigms. The distributed representation of belief provides an explanation for the decrease in dopamine at the conditioned stimulus seen in overtrained animals, for the differences between trace and delay conditioning, and for transient bursts of dopamine seen at movement initiation. Because each µAgent also includes its own exponential discounting factor, the overall agent shows hyperbolic discounting, consistent with behavioral experiments

Nonlocal approach to problems on longitudinal vibration in a short bar

In this article, we consider a problem with dynamic nonlocal conditions for a forth-order PDE with dominating mixed derivative. This problem is closely related to vibration problems, in particular, to longitudinal vibration in a short bar. The existence and uniqueness of a generalized solution are proved