Generation and context memory.

Generation enhances memory for occurrence but may not enhance other aspects of memory. The present study further delineates the negative generation effect in context memory reported in N. W. Mulligan (2004). First, the negative generation effect occurred for perceptual attributes of the target item (its color and font) but not for extratarget aspects of context (location and background color). Second, nonvisual generation tasks with either semantic or nonsemantic generation rules (antonym and rhyme generation, respectively) produced the same pattern of results. In contrast, a visual (or data-driven) generation task (letter transposition) did not disrupt context memory for color. Third, generating nonwords produced no effect on item memory but persisted in producing a negative effect on context memory for target attributes, implying that (a) the negative generation effect in context memory is not mediated by semantic encoding, and (b) the negative effect on context memory can be dissociated from the positive effect on item memory. The results are interpreted in terms of the processing account of generation. The original, perceptual-conceptual version of this account is too narrow, but a modified processing account, based on a more generic visual versus nonvisual processing distinction, accommodates the results. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A hierarchical model for structure learning based on the physiological characteristics of neurons

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability. The characteristics of typical ANN models are fixed connections, with evolved weights, globalized representations, and globalized optimizations, all based on a mathematical approach. This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc. In this paper, we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing. A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures. The basic characteristics of this model are localized and random connections, field limitations of neuron fan-in and fan-out, dynamic behavior of neurons, and samples represented through different sub-circuits of neurons specialized into different response patterns. At the end of this paper, some important aspects of error correction, capacity, learning efficiency, and soundness of structural representation are analyzed theoretically. This paper has demonstrated the feasibility and advantages of structure learning and representation. This model can serve as a fundamental element of cognitive systems such as perception and associative memory.     

论科技论文英语摘要的表现形式

论述科技论文中英语摘要的文体风格、时态、语态和基本要求，并结合例句介绍了常用的表现形式，使之简洁、准确和流畅。     

基于Curvelet变换的多次波去除技术

本文基于波动方程的自由表面多次波压制预测减去法,采用近期迅速发展的多尺度变换系统中的Curvelet变换替代减去法,收到了较好的效果。该变换具有最优稀疏约束条件,能使一次波在一组基函数上的投影能量尽可能小。其主要实现过程为:对地震记录数据进行Curvelet变换,以预测多次波的Curvelet域的系数为阈值,采用类似去噪的手段去除多次波。该方法能满足去除多次波过程的能量最小准则。文中在Curvelet算法数值实验中尝试了先把地震记录做Radon变换,并用Curvelet阈值法减去Radon域的多次波,再把去除了多次波的数据做反Radon变换即为多次波去除结果。数值实验结果表明,在Radon域进行Curvelet变换去除多次波的效果更好,并能更好地保留一次波的能量。     

Orientation-based unique representation for planar curves and shapes

This paper presents an orientation-based representation for planar curves and shapes. The new representation can uniquely represent all types, of planar shapes, be it convex, nonconvex, polygonal, smoothly curved, or piecewise smooth shapes. It is based on a new parameterization, theabsolute integral orientation. This representation is invariant under translation and rotation. The absolute integral orientation is a parameter invariant under scaling. As a result, matching of similar shapes (i.e., determination of the relative orientation and the scaling factor) using the absolute integral orientation as the parameter is easier than using the arclength as the parameter. In addition, the new representation has the feature of adaptive sampling, making it more compact and efficient than arc-length-based representations.     

Kripke Semantics for Modal Substructural Logics

We introduce Kripke semantics for modal substructural logics, and provethe completeness theorems with respect to the semantics. Thecompleteness theorems are proved using an extended Ishihara's method ofcanonical model construction (Ishihara, 2000). The framework presentedcan deal with a broad range of modal substructural logics, including afragment of modal intuitionistic linear logic, and modal versions ofCorsi's logics, Visser's logic, Méndez's logics and relevant logics.     

基于参考点的模糊知识表示

本文分析了模糊关系模型的知识表示途径，并由此提出基于参考点的模糊知识表示，分析了它与人类记忆经验性知识的相信性，并证明模糊关系模型能以任意精度逼近具有有限个极值点的有界连续函数。     

一个面向语义的解释器自动生成技术

本文给出从指称语义自动生成解释器后端的一种技术.解释器被表示成PASCAL_like形式.