Symbolic reinterpretation of HLA gene products--impact on interpretation of HLA data at the molecular level

In the HLA system genes are defined by antibody/antigen reactions and are denoted by single symbolic identifiers. This symbolization assumes a one-to-one correspondence between antibodies, antigens and genes. It is important, however, to label each reagent with symbols corresponding to all genes coding for antigens with which the reagent will react. The problems of cross-reactive groups and unexplained linkage relations may be elucidated by the redefinition and clarification of certain HLA antigens. A computer program can suggest such labelling schemes using input given by phenotype reaction patterns with a panel of reagents. When this program was applied to data on the class I HLA antigens a genetic model was suggested that differs somewhat from the currently accepted view. The new model is consistent with applicable and available family data on recombinants and has implications for the interpretation of data at the DNA level.     

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management.     

Goal/plan analysis via distributed semantic representations in a connectionist system

In this paper we describe DYNASTY, a multi-module distributed connectionist system designed to perform a very high-level symbolic task, namely, comprehension of goal/plan-based stories. DYNASTY has two phases of operation: learning and performance. During learning, each DYNASTY module acquires both the knowledge and skill to perform its specified subtask, through backpropagation learning on a data set of propositions. In addition to modifying their connection weights, DYNASTY modules automatically form distributed semantic representations (DSRs) of the lexical and conceptual symbols used in training the modules. Each DSR encodes, as an activation vector, both structural and sequential information inherent in the training data. During performance, DRSs are passed among various connectionist modules, thus supporting communication and modularity. In addition, DSRs of words with similar meanings end up having similar DSRs. This feature gives DYNASTY the ability to generalize, e.g., generate appropriate inferences when given novel yet similar inputs.     

Symbolic object code analysis

Software model checkers quickly reach their limits when being applied to verifying pointer safety properties in source code that includes function pointers and inlined assembly. This article introduces a novel technique for checking pointer safety violations, called symbolic object code analysis (SOCA), which is based on bounded symbolic execution, incorporates path-sensitive slicing, and employs the SMT solver Yices as its execution and verification engine. Extensive experimental results of a prototypic SOCA Verifier, using the Verisec suite and almost 10,000 Linux device driver functions as benchmarks, show that SOCA performs competitively to modern source-code model checkers, scales well when applied to real operating systems code and pointer safety issues, and effectively explores niches of pointer-complex software that current software verifiers do not reach.     

Symbolic modular deadlock analysis

Methods in object-oriented concurrent libraries often encapsulate internal synchronization details. As a result of information hiding, clients calling the library methods may cause thread safety violations by invoking methods in an unsafe manner. This is frequently a cause of deadlocks. Given a concurrent library, we present a technique for inferring interface contracts that specify permissible concurrent method calls and patterns of aliasing among method arguments. In this work, we focus on deriving contracts that guarantee deadlock-free execution for the methods in the library. The contracts also help client developers by documenting required assumptions about the library methods. Alternatively, the contracts can be statically enforced in the client code to detect potential deadlocks in the client. Our technique combines static analysis with a symbolic encoding scheme for tracking lock dependencies, allowing us to synthesize contracts using a SMT solver. Additionally, we investigate extensions of our technique to reason about deadlocks in libraries that employ signaling primitives such as wait-notify for cooperative synchronization. Our prototype tool analyzes over a million lines of code for some widely-used Java libraries within an hour, thus demonstrating its scalability and efficiency. Furthermore, the contracts inferred by our approach have been able to pinpoint real deadlocks in clients, i.e. deadlocks that have been a part of bug-reports filed by users and developers of client code.     

Expert document retrieval via semantic measurement

A new technology for intelligent full text document retrieval is presented. The retrieval of a document is treated as an expert system problem, recognizing that human document retrieval is expert behavior. The technology is semantic measurement. A working prototype system, LIBRARY, has been built based on the technology. Input is a request for information, in unrestricted technical English; output is all documents with measured content similar to that of the request, ranked in order of relevance. Retrieval is unaffected by similarity or dissimilarity of terms between request and document. LIBRARY's performance is comparable to that of an expert human librarian, representing a significant improvement over traditional document retrieval systems.     

Symbolic analysis of linear polyhedra

This paper concerns several analytical problems related to linear polyhedra in euclidean three-dimensional-space. Symbolic formulas for line, surface, and volume integration are given, and it is shown that domain integrals are computable in polynomial time. In particular, it is shown that mass, first and second moments, and products of inertia are computable inO(E) time, whereE is the number of edges of the boundary. Simple symbolic expressions for the normal derivatives of domain integrals are also derived. In particular, it is shown that they are closely linked to the topology of the integration domain, as well as that they are expressible as combinations of domain integrals over lower-order domains (faces, edges, and vertices). The symbolic results presented in this paper may lead to an easy incorporation of integral constraints, for example, concerning mass and inertia, in the engineering designing process of solid objects.     

Implementing QVT-R via semantic interpretation in UML-RSDS

Software and Systems Modeling - The QVT-Relations (QVT-R) model transformation language is an OMG standard notation for model transformation specification. It is highly declarative and supports (in...     

基于多目标人工鱼群算法的符号回归

针对现有符号回归方法仅关注拟合误差而忽略模型简化的问题,提出了一种基于多目标的人工鱼群算法,将拟合误差与模型复杂度同时作为目标函数进行优化.以二叉堆对语法树编码,优良分支得以稳定地遗传和继承,也更易解码.在引入蒙版、邻域、小生境、拥挤度等概念的基础上,设计和定义了适用于二叉堆编码的随机游动、觅食、追尾、逃脱等人工鱼行为算子.详尽的实验表明,提出算法在符号回归过程中能获取高质量的Pareto解.此外,对从Pareto前沿上选取折衷解及降低算法内存开销的方法也进行了讨论.     

Symbolic predictive analysis for concurrent programs

Predictive analysis aims at detecting concurrency errors during runtime by monitoring a concrete execution trace of a concurrent program. In recent years, various models based on the happens-before causality relations have been proposed for predictive analysis. However, these models often rely on only the observed runtime events and typically do not utilize the program source code. Furthermore, the enumerative algorithms they use for verifying safety properties in the predicted traces often suffer from the interleaving explosion problem. In this paper, we introduce a precise predictive model based on both the program source code and the observed execution events, and propose a symbolic algorithm to check whether a safety property holds in all feasible permutations of events of the given trace. Rather than explicitly enumerating and checking the interleavings, our method conducts the search using a novel encoding and symbolic reasoning with a satisfiability modulo theory solver. We also propose a technique to bound the number of context switches allowed in the interleavings during the symbolic search, to further improve the scalability of the algorithm.     

Enhancing pencil drawing patterns via using semantic information

Multimedia Tools and Applications - Pencil drawing is recognized as a typical non-photorealistic visual art form. It is attractive to automatically generate high-quality pencil drawings from...     

Mining consumer product data via latent semantic indexing

One important focus of data mining research is in the development of algorithms for extracting valuable information from large databases in order to facilitate business decisions. This study explores a new technique for data mining – latent semantic indexing (LSI). LSI is an efficient information retrieval method for textual documents. By determining the singular value decomposition (SVD) of a large sparse term-by-document matrix, LSI constructs an approximate vector space model which represents important associative relationships between terms and documents that are not evident in individual documents. This paper explores the applicability of the LSI model to numerical databases, namely consumer product data. By properly choosing attributes of data records as terms or documents, a term-by-document frequency matrix is built from which a distribution-based indexing scheme is employed to construct a correlated distribution matrix (CDM). An LSI-like vector space model is then used to detect useful or hidden patterns in the numerical data. The extracted information can then be validated using statistical hypotheses testing or resampling. LSI is an automatic yet intelligent indexing method. Its application to numerical data introduces a promising way to discover knowledge in important commercial application areas such as retail and consumer banking.     

A semantic analysis of ORGPLAN

ORGPLAN is a problem-oriented language for organization problems (published in Vol. 10, No. 1 of Computers in Industry). In the present article, a proposition has been formulated that an examination of the semantic correctness of a text in the ORGPLAN language may be transformed into an examination of the consistency of the constraints systems generated by this text. An analysis of the constraint systems is realized on the grounds of the concept introduced for a type of constraint, and of the consistency rules formulated for the types of constraints.     

面向对象语义分析

针对语义分析进行了面向对象分析与设计，实现了类型检查，变量函数处理及将控制语句化为特殊函数进行处理，并就作用域分析设计了一种新颖的作用域树。整个分析过程简便灵活，具有良好的可操作性。     

Feature grammars for semantic analysis

In this paper, we present a formalism called feature grammar and its application to several problems of semantic analysis. Our extension concerns the structure of the feature value sets, which can be complex, and the definition of unification, which is dependent on this structure. Moreover, we introduce generation rules for feature symbols in order to determine well-formed symbols, which form the alphabet of a formal language for natural language analysis.     

密码协议的符号模型检测及分析

对密码协议模型检测的方法作了理论上的研究,并用SMV检测工具给出了一个实际分析的例子。结果表明,利用符号模型检测方法分析并发现密码协议重放攻击的漏洞是一种行之有效的方法。     

Symbolic data analysis tools for recommendation systems

Recommender systems have become an important tool to cope with the information overload problem by acquiring data about user behavior. After tracing the user’s behavior, through actions or rates, computational recommender systems use information- filtering techniques to recommend items. In order to recommend new items, one of the three major approaches is generally adopted: content-based filtering, collaborative filtering, or hybrid filtering. This paper presents three information-filtering methods, each of them based on one of these approaches. In our methods, the user profile is built up through symbolic data structures and the user and item correlations are computed through dissimilarity functions adapted from the symbolic data analysis (SDA) domain. The use of SDA tools has improved the performance of recommender systems, particularly concerning the find good items task measured by the half-life utility metric, when there is not much information about the user.     

Learning semantic features for action recognition via diffusion maps

Efficient modeling of actions is critical for recognizing human actions. Recently, bag of video words (BoVW) representation, in which features computed around spatiotemporal interest points are quantized into video words based on their appearance similarity, has been widely and successfully explored. The performance of this representation however, is highly sensitive to two main factors: the granularity, and therefore, the size of vocabulary, and the space in which features and words are clustered, i.e., the distance measure between data points at different levels of the hierarchy. The goal of this paper is to propose a representation and learning framework that addresses both these limitations.We present a principled approach to learning a semantic vocabulary from a large amount of video words using Diffusion Maps embedding. As opposed to flat vocabularies used in traditional methods, we propose to exploit the hierarchical nature of feature vocabularies representative of human actions. Spatiotemporal features computed around interest points in videos form the lowest level of representation. Video words are then obtained by clustering those spatiotemporal features. Each video word is then represented by a vector of Pointwise Mutual Information (PMI) between that video word and training video clips, and is treated as a mid-level feature. At the highest level of the hierarchy, our goal is to further cluster the mid-level features, while exploiting semantically meaningful distance measures between them. We conjecture that the mid-level features produced by similar video sources (action classes) must lie on a certain manifold. To capture the relationship between these features, and retain it during clustering, we propose to use diffusion distance as a measure of similarity between them. The underlying idea is to embed the mid-level features into a lower-dimensional space, so as to construct a compact yet discriminative, high level vocabulary. Unlike some of the supervised vocabulary construction approaches and the unsupervised methods such as pLSA and LDA, Diffusion Maps can capture local relationship between the mid-level features on the manifold. We have tested our approach on diverse datasets and have obtained very promising results.