Lower and upper bounds on obtaining history independence

History independent data structures, presented by Micciancio, are data structures that possess a strong security property: even if an intruder manages to get a copy of the data structure, the memory layout of the structure yields no additional information on the history of operations applied on the structure beyond the information obtainable from the content itself. Naor and Teague proposed a stronger notion of history independence in which the intruder may break into the system several times without being noticed and still obtain no additional information from reading the memory layout of the data structure. An open question posed by Naor and Teague is whether these two notions are equally hard to obtain. In this paper we provide a separation between the two requirements for comparison-based algorithms. We show very strong lower bounds for obtaining the stronger notion of history independence for a large class of data structures, including, for example, the heap and the queue abstract data structures. We also provide complementary upper bounds showing that the heap abstract data structure may be made weakly history independent in the comparison based model without incurring any additional (asymptotic) cost on any of its operations. (A similar result is easy for the queue.) Thus, we obtain the first separation between the two notions of history independence. The gap we obtain is exponential: some operations may be executed in logarithmic time (or even in constant time) with the weaker definition, but require linear time with the stronger definition.     

Branching and circular features in high dimensional data

Large observations and simulations in scientific research give rise to high-dimensional data sets that present many challenges and opportunities in data analysis and visualization. Researchers in application domains such as engineering, computational biology, climate study, imaging and motion capture are faced with the problem of how to discover compact representations of high-dimensional data while preserving their intrinsic structure. In many applications, the original data is projected onto low-dimensional space via dimensionality reduction techniques prior to modeling. One problem with this approach is that the projection step in the process can fail to preserve structure in the data that is only apparent in high dimensions. Conversely, such techniques may create structural illusions in the projection, implying structure not present in the original high-dimensional data. Our solution is to utilize topological techniques to recover important structures in high-dimensional data that contains non-trivial topology. Specifically, we are interested in high-dimensional branching structures. We construct local circle-valued coordinate functions to represent such features. Subsequently, we perform dimensionality reduction on the data while ensuring such structures are visually preserved. Additionally, we study the effects of global circular structures on visualizations. Our results reveal never-before-seen structures on real-world data sets from a variety of applications.     

Concepts: Stored or Created?

Are concepts stable entities, unchanged from context to context? Or rather are they context-dependent structures, created on the fly? We argue that this does not constitute a genuine dilemma. Our main thesis is that the more a pattern of features is general and shared, the more it qualifies as a concept. Contextualists have not shown that conceptual structures lack a stable, general core, acting as an attractor on idiosyncratic information. What they have done instead is to give a contribution to the comprehension of how conceptual structure organized around such a stable core can produce contextually appropriate representations on demand.     

Solid modelling using a volume encoding data structure

A strong emphasis has lately been put in research to develop methods for Solid Modelling using surface or volume encoding data structures instead of conventional edge-face-vertex or polyhedral representations for 3-D objects.This article describes a Solid Modelling system using a volume encoding data structure called OCTREE which divides a solid into cubes.     

Augmenting text and voice conversations with dynamic, interactive abstractions using P2P networking

This paper discusses an extension of MUSLI, a visual language based on a movie metaphor, for augmenting text and voice messages. We begin by describing the MUSLI framework, object structure, and possible language structures. The language supports the animation of symbols and icons, together with interactive access to databases of multimedia objects that are alternative representations of the symbols. The structure of the elements and the language relations between them serve as a semantic foundation for an augmented voice communication system. We shall discuss the inherent advantages of this approach, such as suitability for mobile technology, and give several examples of possible MUSLI ‘conversations’.     

Holographic reduced representations

Associative memories are conventionally used to represent data with very simple structure: sets of pairs of vectors. This paper describes a method for representing more complex compositional structure in distributed representations. The method uses circular convolution to associate items, which are represented by vectors. Arbitrary variable bindings, short sequences of various lengths, simple frame-like structures, and reduced representations can be represented in a fixed width vector. These representations are items in their own right and can be used in constructing compositional structures. The noisy reconstructions extracted from convolution memories can be cleaned up by using a separate associative memory that has good reconstructive properties.     

学习信度网的结构

一、等价的信度网结构学习信度网的结构,就是通过分析实例数据库,建立能够表达实例数据所包含信息的信度网的结构。任何一个由所有的结点(变量)构成的有向无环图都可能作为信度网的结构。如对图1(d)所示的关于吸烟、性别及肺癌的实例数据库,其三种可能的信度网结构如图1     

Sentence entailment in compositional distributional semantics

Distributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these from words to phrases and sentences. In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein. In this setting, grammatical structures are formalised by morphisms of a compact closed category and meanings of words are formalised by objects of the same category. These can be instantiated in the form of vectors or density matrices. This paper concerns the applications of this model to phrase and sentence level entailment. We argue that entropy-based distances of vectors and density matrices provide a good candidate to measure word-level entailment, show the advantage of density matrices over vectors for word level entailments, and prove that these distances extend compositionally from words to phrases and sentences. We exemplify our theoretical constructions on real data and a toy entailment dataset and provide preliminary experimental evidence.     

Design of multi-invariant data structures for robust sharedaccesses in multiprocessor systems

Multiprocessor systems are widely used in many application programs to enhance system reliability and performance. However, reliability does not come naturally with multiple processors. We develop a multi-invariant data structure approach to ensure efficient and robust access to shared data structures in multiprocessor systems. Essentially, the data structure is designed to satisfy two invariants, a strong invariant, and a weak invariant. The system operates at its peak performance when the strong invariant is true. The system will operate correctly even when only the weak invariant is true, though perhaps at a lower performance level. The design ensures that the weak invariant will always be true in spite of fail-stop processor failures during the execution. By allowing the system to converge to a state satisfying only the weak invariant, the overhead for incorporating fault tolerance can be reduced. We present the basic idea of multi-invariant data structures. We also develop design rules that systematically convert fault-intolerant data abstractions into corresponding fault-tolerant versions. In this transformation, we augment the data structure and access algorithms to ensure that the system always converges to the weak invariant, even in the presence of fail-stop processor failures. We also design methods for the detection of integrity violations and for restoring the strong invariant. Two data structures, namely binary search tree and double-linked list, are used to illustrate the concept of multi-invariant data structures     

Graphs in Scientific Visualization: A Survey

Graphs represent general node‐link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history, yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph‐based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely partition‐wise, relationship‐wise, structure‐wise and provenance‐wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph‐based visualization pipeline. We also point out research trends and remaining challenges in graph‐based representations and techniques for scientific visualization.     

Variational mixture of Bayesian independent component analyzers

There has been growing interest in subspace data modeling over the past few years. Methods such as principal component analysis, factor analysis, and independent component analysis have gained in popularity and have found many applications in image modeling, signal processing, and data compression, to name just a few. As applications and computing power grow, more and more sophisticated analyses and meaningful representations are sought. Mixture modeling methods have been proposed for principal and factor analyzers that exploit local gaussian features in the subspace manifolds. Meaningful representations may be lost, however, if these local features are nongaussian or discontinuous. In this article, we propose extending the gaussian analyzers mixture model to an independent component analyzers mixture model. We employ recent developments in variational Bayesian inference and structure determination to construct a novel approach for modeling nongaussian, discontinuous manifolds. We automatically determine the local dimensionality of each manifold and use variational inference to calculate the optimum number of ICA components needed in our mixture model. We demonstrate our framework on complex synthetic data and illustrate its application to real data by decomposing functional magnetic resonance images into meaningful-and medically useful-features.     

基于Louvain算法的图数据三维树形可视化

提出一种图数据的三维树形可视化方法,基于Louvain算法对图数据中的复杂的网络关系进行层次聚类,利用三维树形映射表达聚类结果,直观展示隐含于图数据中的结构关系,通过在三维场景中旋转、缩放、移动、拾取高亮等交互操作多视角地展示数据。集成开源图数据库Neo4j研发原型系统,并开展案例数据实验。实验结果表明,该方法不仅能够简洁灵活地展示图数据的总体层次结构,还能够多样化地表达数据细节,为利用虚拟现实技术探索图数据的潜在信息提供有效的技术支持。     

A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated.     

Deciding the isomorphism problem in classes of unary automatic structures

We solve the isomorphism problem for certain classes of unary automatic structures: unary automatic equivalence relations, unary automatic linear orders, and unary automatic trees. That is, we provide algorithms which decide whether two given elements of these classes are isomorphic. In doing so, we define new finite representations for these structures which give normal forms.¹     

一个新的理性秘密共享方案*

对n个理性参与者的秘密共享问题进行了探讨与研究。这一问题首先是由Halpern和Teague提出的,他们考虑了当秘密共享的参与者是理性参与者时所带来的问题,并给出了当参与者人数n≥3时的解决方案,但是当n=2时他们认为是不可实现的。通过秘密份额的不确定性实现了只有两个理性参与者时的秘密共享方案,并将此方案推广到多个参与者的情况,且给出了其正确性证明。     

基于多粒度时序结构表示的异常检测算法在储层含油性检测中应用

传统储层含油性勘测方法利用地震波穿过地层时产生的相关地震属性和地质钻井资料结合传统地球物理方法进行综合研判,但该类勘测方法往往存在研判成本高且对专家先验知识依赖性强的问题。针对该问题,以江苏油田苏北盆地的地震资料为基础,并结合含油样本的稀疏性和随机性,提出了一种基于多粒度时序结构表示的异常检测算法,直接利用叠后地震道数据进行预测。该算法首先对于单个地震道数据提取多粒度时序结构并形成独立特征表示;其次,在提取多个粒度时序结构表示的基础上进行特征融合,以形成对地震道数据的融合表示;最后,通过对融合后的特征采用代价敏感方法进行联合训练和判别,从而得到对于该地震数据的含油性勘测结果。所提算法在江苏油田实际原始地震资料上进行了实验仿真,实验结果表明：所提算法相比长短期记忆（LSTM）和门控循环单元（GRU）算法在曲线下方的面积（AUC）指标上均提升了10%。