From Term Models to Domains

Let B be the closed term model of the λ-calculus in which terms with the same Böhm tree are identified. We investigate which partial equivalence relations (PERs) on B can be regarded as predomains or domains. Inside the realizability topos on B, such PERs can be regarded simply as sets in a particular model of constructive set theory. No well-behaved partial order has been identified for any class of PERs; but it is still possible to isolate those PERs which have "suprema of chains" in a certain sense, and all maps between such PERs in the model preserve such suprema of chains. One can also define what it means for such a PER to have a "bottom"; partial function spaces provide an example. For these PERs, fixed points of arbitrary endofunctions exist and are computed by the fixed point combinator y. The categories of predomains are closed under the formation of total and partial function spaces, polymorphic types, and convex powerdomains. They in fact form reflective subcategories of the realizability topos; and in this set-theoretic context, these constructions are very simple to describe. We illustrate the theory by discussing an interpretation of PCF, and proving a computational adequacy theorem. None of the usual counterexamples to full abstraction are applicable to our model.     

From UML Models to Graph Transformation Systems

In this paper we present an approach that allows to validate properties of UML models. The approach is based on an integrated semantics for central parts of the UML. We formally cover UML use case, class, object, statechart, collaboration, and sequence diagrams. Additionally full OCL is supported in the common UML fashion. Our semantics is based on the translation of a UML model into a graph transformation system consisting of graph transformation rules and a working graph that represents the system state. By applying the rules on the working graph, the evolution of the modeled system is simulated.     

From Images to Shape Models for Object Detection

We present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes).     

Learning to Parse Natural Language with Maximum Entropy Models

This paper presents a machine learning system for parsing natural language that learns from manually parsed example sentences, and parses unseen data at state-of-the-art accuracies. Its machine learning technology, based on the maximum entropy framework, is highly reusable and not specific to the parsing problem, while the linguistic hints that it uses to learn can be specified concisely. It therefore requires a minimal amount of human effort and linguistic knowledge for its construction. In practice, the running time of the parser on a test sentence is linear with respect to the sentence length. We also demonstrate that the parser can train from other domains without modification to the modeling framework or the linguistic hints it uses to learn. Furthermore, this paper shows that research into rescoring the top 20 parses returned by the parser might yield accuracies dramatically higher than the state-of-the-art.     

探析自然形态对室内设计的启示

设计艺术的历史和实践向我们表明,自然是设计永恒的表现主题之一,也是设计活动取之不竭的素材资源。设计源于自然而又高于自然,自然界是设计的源泉和动力。本文主要是分析自然、自然形态和室内设计的关系,从中得出自然形态对室内设计的几点启示。     

隐马尔可夫模型及其在自然语言处理中的应用

基于隐马尔可夫模型(HMM)对汉语文本进行了词性标注,首先介绍隐马尔可夫模型的基本概念,然后着重介绍了隐马尔可夫模型的三个基本问题以及解决问题的基本算法,最后演示了隐马尔可夫模型在词性标注中的简单应用.     

模拟自然形态的儿童包装设计

面对儿童包装市场呈现千篇一律使用卡通形象这一现象,本文提出运用模拟自然形态这一新的设计手法在儿童包装设计的外部造型以及内部结构封面。     

Natural Architecture and Constructed Forms: Structure and Surfaces from Idea to Drawing

This work grew out of didactic experience in architecture classes at the universities of Florence and Parma. The comprehension of geometric schemes in regular organic objects formed the basis of teaching drawing and scientific representation, such as formal architectural synthesis. This exercise may offer also a valid starting point to help students approach mathematics, and help them to imagine and plan the increasingly complex surfaces of late contemporary architecture.     

自然语言处理中的模型

本文简单介绍了自然语言处理发展的现状,讨论了自然语言处理模型,将其分为三大类：分析模型、统计模型及混合模型。具体介绍了分析模型原理及存在的问题,重点讨论了各种统计模型的特点及局限性,最后简单介绍了混合模型,并指出目前自然语言处理技术中存在的问题。     

Anisotropic Smoothness Classes: From Finite Element Approximation to Image Models

We propose and study quantitative measures of smoothness f ? A(f) which are adapted to anisotropic features such as edges in images or shocks in PDE’s. These quantities govern the rate of approximation by adaptive finite elements, when no constraint is imposed on the aspect ratio of the triangles, the simplest example being \(A_{p}(f)=\|\sqrt{|\mathrm{det}(d^{2}f)|}\|_{L^{\tau}}\) which appears when approximating in the L ^p norm by piecewise linear elements when \(\frac{1}{\tau}=\frac{1}{p}+1\). The quantities A(f) are not semi-norms, and therefore cannot be used to define linear function spaces. We show that these quantities can be well defined by mollification when f has jump discontinuities along piecewise smooth curves. This motivates for using them in image processing as an alternative to the frequently used total variation semi-norm which does not account for the smoothness of the edges.     

自然图像中混叠的概率模型

我们为建立自然图像中混叠的概率模型提供了一个数学框架。假设：(1)图像是由三维物体的二维投影轮廓组成．(2)在简化条件下．我们推导出自然图像的一种解析概率密度函数。已经证明．这种解析表达式可以很好地比配观测到的密度函数(直方图)。我们首先推导图像像素的边缘概率密度函数,然后推广到多维边缘概率密度函数,最后提出了几种改进措施。     

Significance of Models of Computation,from Turing Model to Natural Computation

The increased interactivity and connectivity of computational devices along with the spreading of computational tools and computational thinking across the fields, has changed our understanding of the nature of computing. In the course of this development computing models have been extended from the initial abstract symbol manipulating mechanisms of stand-alone, discrete sequential machines, to the models of natural computing in the physical world, generally concurrent asynchronous processes capable of modelling living systems, their informational structures and dynamics on both symbolic and sub-symbolic information processing levels. Present account of models of computation highlights several topics of importance for the development of new understanding of computing and its role: natural computation and the relationship between the model and physical implementation, interactivity as fundamental for computational modelling of concurrent information processing systems such as living organisms and their networks, and the new developments in logic needed to support this generalized framework. Computing understood as information processing is closely related to natural sciences; it helps us recognize connections between sciences, and provides a unified approach for modeling and simulating of both living and non-living systems.     

Management Information Models Integration: From Existing Approaches to New Unifying Guidelines

The evolution towards a higher-level managementrequires management information obtained from physicalto application elements as well as distinct resources(e.g., PC, UNIX workstations). This issue is difficult because the modeling of managementinformation is realized in many different ways and theinformation model integration represents an acutecurrent concern. In this paper we discuss thisintegration issue by specifying the sources of ManagementInformation Base (MIB) heterogeneity and identifyingtheoretical means characterizing approaches dealing withinformation models integration. Based on these results, the analysis of some existingintegration approaches led to dissatisfactions thusjustifying our contribution: integration guidelines andtheir implementation.     

Mining Appearance Models Directly From Compressed Video

In this paper, we propose an approach for learning appearance models of moving objects directly from compressed video. The appearance of a moving object changes dynamically in video due to varying object poses, lighting conditions, and partial occlusions. Efficiently mining the appearance models of objects is a crucial and challenging technology to support content-based video coding, clustering, indexing, and retrieval at the object level. The proposed approach learns the appearance models of moving objects in the spatial-temporal dimension of video data by taking advantage of the MPEG video compression format. It detects a moving object and recovers the trajectory of each macroblock covered by the object using the motion vector present in the compressed stream. The appearances are then reconstructed in the DCT domain along the object's trajectory, and modeled as a mixture of Gaussians (MoG) using DCT coefficients. We prove that, under certain assumptions, the MoG model learned from the DCT domain can achieve pixel-level accuracy when transformed back to the spatial domain, and has a better band-selectivity compared to the MoG model learned in the spatial domain. We finally cluster the MoG models to merge the appearance models of the same object together for object-level content analysis.     

Dynamic Spatial Equilibrium Models: an Application to the Natural Gas Spot Markets

Networks and Spatial Economics - European gas markets have gone through profound restructuring processes in the last 20 years. Directive 98/30/EC represented the first attempt to liberalize this...     

自然语言处理中主题模型的发展

主题模型在自然语言处理领域受到了越来越多的关注.在该领域中,主题可以看成是词项的概率分布.主题模型通过词项在文档级的共现信息抽取出语义相关的主题集合,并能够将词项空间中的文档变换到主题空间,得到文档在低维空间中的表达.作者从主题模型的起源隐性语义索引出发,对概率隐性语义索引以及LDA等在主题模型发展中的重要阶段性工作进...     

Learning Probabilistic Models for Contour Completion in Natural Images

Using a large set of human segmented natural images, we study the statistics of region boundaries. We observe several power law distributions which likely arise from both multi-scale structure within individual objects and from arbitrary viewing distance. Accordingly, we develop a scale-invariant representation of images from the bottom up, using a piecewise linear approximation of contours and constrained Delaunay triangulation to complete gaps. We model curvilinear grouping on top of this graphical/geometric structure using a conditional random field to capture the statistics of continuity and different junction types. Quantitative evaluations on several large datasets show that our contour grouping algorithm consistently dominates and significantly improves on local edge detection.     

Identification of Continuous-Time ARX Models From Irregularly Sampled Data

The problem of estimating the parameters in a continuous-time ARX process from unevenly sampled data is studied. A solution where the differentiation operator is replaced by a difference operator is suggested. In the paper, results are given for how the difference operator should be chosen in order to obtain consistent parameter estimates. The proposed method is considerably faster than conventional methods, such as the maximum likelihood method. The Crameacuter-Rao bound for estimation of the parameters is computed. In the derivation, the Slepian-Bangs formula is used together with a state-space framework, resulting in a closed form expression for the Crameacuter-Rao bound. Numerical studies indicate that the Crameacuter-Rao bound is reached by the proposed method