Nearly cloaking the elastic wave fields

In this work, we develop a general mathematical framework on regularized approximate cloaking of elastic waves governed by the Lam\'e system via the approach of transformation elastodynamics. Our study is rather comprehensive. We first provide a rigorous justification of the transformation elastodynamics. Based on the blow-up-a-point construction, elastic material tensors for a perfect cloak are derived and shown to possess singularities. In order to avoid the singular structure, we propose to regularize the blow-up-a-point construction to be the blow-up-a-small-region construction. However, it is shown that without incorporating a suitable lossy layer, the regularized construction would fail due to resonant inclusions. In order to defeat the failure of the lossless construction, a properly designed lossy layer is introduced into the regularized cloaking construction . We derive sharp asymptotic estimates in assessing the cloaking performance. The proposed cloaking scheme is capable of nearly cloaking an arbitrary content with a high accuracy

Anyon exclusions statistics on surfaces with gapped boundaries

An anyon exclusion statistics, which generalizes the Bose-Einstein and Fermi-Dirac statistics of bosons and fermions, was proposed by Haldane[1]. The relevant past studies had considered only anyon systems without any physical boundary but boundaries often appear in real-life materials. When fusion of anyons is involved, certain `pseudo-species' anyons appear in the exotic statistical weights of non-Abelian anyon systems; however, the meaning and significance of pseudo-species remains an open problem. In this paper, we propose an extended anyon exclusion statistics on surfaces with gapped boundaries, introducing mutual exclusion statistics between anyons as well as the boundary components. Motivated by Refs. [2, 3], we present a formula for the statistical weight of many-anyon states obeying the proposed statistics. We develop a systematic basis construction for non-Abelian anyons on any Riemann surfaces with gapped boundaries. From the basis construction, we have a standard way to read off a canonical set of statistics parameters and hence write down the extended statistical weight of the anyon system being studied. The basis construction reveals the meaning of pseudo-species. A pseudo-species has different `excitation' modes, each corresponding to an anyon species. The `excitation' modes of pseudo-species corresponds to good quantum numbers of subsystems of a non-Abelian anyon system. This is important because often (e.g., in topological quantum computing) we may be concerned about only the entanglement between such subsystems.Comment: 36 pages, 14 figure

Macro action selection with deep reinforcement learning in StarCraft

StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also widely accepted as a challenging testbed for AI research because of its enormous state space, partially observed information, multi-agent collaboration, and so on. With the help of annual AIIDE and CIG competitions, a growing number of SC bots are proposed and continuously improved. However, a large gap remains between the top-level bot and the professional human player. One vital reason is that current SC bots mainly rely on predefined rules to select macro actions during their games. These rules are not scalable and efficient enough to cope with the enormous yet partially observed state space in the game. In this paper, we propose a deep reinforcement learning (DRL) framework to improve the selection of macro actions. Our framework is based on the combination of the Ape-X DQN and the Long-Short-Term-Memory (LSTM). We use this framework to build our bot, named as LastOrder. Our evaluation, based on training against all bots from the AIIDE 2017 StarCraft AI competition set, shows that LastOrder achieves an 83% winning rate, outperforming 26 bots in total 28 entrants

Recovering complex elastic scatterers by a single far-field pattern

We consider the inverse scattering problem of reconstructing multiple impenetrable bodies embedded in an unbounded, homogeneous and isotropic elastic medium. The inverse problem is nonlinear and ill-posed. Our study is conducted in an extremely general and practical setting: the number of scatterers is unknown in advance; and each scatterer could be either a rigid body or a cavity which is not required to be known in advance; and moreover there might be components of multiscale sizes presented simultaneously. We develop several locating schemes by making use of only a single far-field pattern, which is widely known to be challenging in the literature. The inverse scattering schemes are of a totally direct"nature without any inversion involved. For the recovery of multiple small scatterers, the nonlinear inverse problem is linearized and to that end, we derive sharp asymptotic expansion of the elastic far-field pattern in terms of the relative size of the cavities. The asymptotic expansion is based on the boundary-layer-potential technique and the result obtained is of significant mathematical interest for its own sake. The recovery of regular-size/extended scatterers is based on projecting the measured far-field pattern into an admissible solution space. With a local tuning technique, we can further recover multiple multiscale elastic scatterers

Effective Bug Triage based on Historical Bug-Fix Information

International audienceFor complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this pa-per, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called BugFixer, which recommends developers for a new bug report based on historical bug-fix in-formation. BugFixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship be-tween the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, BugFixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate BugFixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects