Grid multi-wing butterfly chaotic attractors generated from a new 3-D quadratic autonomous system

Due to the dynamic characteristics of the Lorenz system, multi-wing chaotic systems are still confined in the positive half-space and fail to break the threshold limit. In this paper, a new approach for generating complex grid multi-wing attractors that can break the threshold limit via a novel nonlinear modulating function is proposed from the firstly proposed double-wing chaotic system. The proposed method is different from that of classical multi-scroll chaotic attractors generated by odd-symmetric multi-segment linear functions from Chua system. The new system is autonomous and can generate various grid multi-wing butterfly chaotic attractors without requiring any external forcing, it also can produce grid multi-wing both on the xz-plane and yz-plane. Basic properties of the new system such as dissipation property, equilibrium, stability, the Lyapunov exponent spectrum and bifurcation diagram are introduced by numerical simulation, theoretical analysis and circuit experiment, which confirm that the multi-wing attractors chaotic system has more rich and complicated chaotic dynamics. Finally, a novel module-based unified circuit is designed which provides some principles and guidelines for future circuitry design and engineering application. The circuit experimental results are consistent with the numerical simulation results.&nbsp

Towards Ontology-Based Program Analysis

Program analysis is fundamental for program optimizations, debugging, and many other tasks. But developing program analyses has been a challenging and error-prone process for general users. Declarative program analysis has shown the promise to dramatically improve the productivity in the development of program analyses. Current declarative program analysis is however subject to some major limitations in supporting cooperations among analysis tools, guiding program optimizations, and often requires much effort for repeated program preprocessing. In this work, we advocate the integration of ontology into declarative program analysis. As a way to standardize the definitions of concepts in a domain and the representation of the knowledge in the domain, ontology offers a promising way to address the limitations of current declarative program analysis. We develop a prototype framework named PATO for conducting program analysis upon ontology-based program representation. Experiments on six program analyses confirm the potential of ontology for complementing existing declarative program analysis. It supports multiple analyses without separate program preprocessing, promotes cooperative Liveness analysis between two compilers, and effectively guides a data placement optimization for Graphic Processing Units (GPU)

From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generation

We introduce a new task named Story Ending Generation (SEG), whic-h aims at generating a coherent story ending from a sequence of story plot. Wepropose a framework consisting of a Generator and a Reward Manager for thistask. The Generator follows the pointer-generator network with coverage mech-anism to deal with out-of-vocabulary (OOV) and repetitive words. Moreover, amixed loss method is introduced to enable the Generator to produce story endingsof high semantic relevance with story plots. In the Reward Manager, the rewardis computed to fine-tune the Generator with policy-gradient reinforcement learn-ing (PGRL). We conduct experiments on the recently-introduced ROCStoriesCorpus. We evaluate our model in both automatic evaluation and human evalua-tion. Experimental results show that our model exceeds the sequence-to-sequencebaseline model by 15.75% and 13.57% in terms of CIDEr and consistency scorerespectively.Comment: 12 pages, 1 figure, NLPCC 201