An On-Line Algorithm for the Location of Cross Point Faults in Programmable Logic Arrays

An on-line algorithm is developed for the location of single cross point faults in a PLA (FPLA). The main feature of the valgorithm is the determination of a fault set corresponding to the response obtained for a failed test. For the apparently small number of faults in this set, all other tests are generated and a fault table is formed. Subsequently, an adaptive procedure is used to diagnose the fault. Functional equivalence test is carried out to determine the actual fault class if the adaptive testing results in a set of faults with identical tests. The large amount of computation time and storage required in the determination, a priori, of all the fault equivalence classes or in the construction of a fault dictionary are not needed here. A brief study of functional equivalence among the cross point faults is also made.

Philip Slomovitz papers, undated, 1933-1966.

Contains correspondence as editor of the Detroit Jewish chronicle and of The Jewish news. Of special interest is the correspondence with members of the U.S. Congress relating to aid to Israel, material relating to the boycott of Viceroy cigarettes, the question of the Jewish descent of Franklin Delano Roosevelt, and the dictionary definition of "Jew."

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.

Liberty

In terms of critical discourse, Liberty contributes to the ongoing aesthetic debate on ‘the sublime.’ Philosopher Immanuel Kant (1724–1804) defined the sublime as a failure of rationality in response to sensory overload: a state where the imagination is suspended, without definitive reference points—a state beyond unequivocal ‘knowing.’ I believe the events of September 11, 2001 eluded our understanding in much the same way, leaving us in a moment of suspension between awe and horror. It was an event that couldn’t be understood in terms of scope or scale. It was a moment of overload, which is so difficult to capture in art. With my work I attempt to rekindle that moment of suspension. Like the events of 9/11, Liberty defies definition. Its form is constantly changing; it is always presenting us with new layers of meaning. Nobody quite had a handle on the events that followed 9/11, because the implications were constantly shifting. In the same way, Liberty cannot be contained or defined at any moment in time. Like the events of 9/11, the full story cannot be told in a snapshot. One of the dictionary definitions for the word ‘sublime’ is the conversion of ‘a solid substance directly into a gas, without there being an intermediate liquid phase’. With this in mind, I would like to present Liberty as a work that is literally ‘sublime.’ But what’s really interesting to me about Liberty is that it presents the sublime on all levels: in its medium, in its subject matter (that moment of suspension), and in its formal (formless) presentation. On every level Liberty is sublime—subverting all tangible reference points and eluding capture entirely. Liberty is based on the Statue of Liberty in New York. However, unlike that statue which has stood in New York since 1886 and can be reasonably expected to stand for millennia, this work takes on diminishing proportions, carved as it is in carbon dioxide, a mysterious, previously unexplored medium—one which smokes, snows and dramatically vanishes into a harmless gas. Like the material this work is carved from, the civil liberties of the free world are diminishing fast, since 9/11 and before. This was my thought when I first conceived this work. Now it’s become evident that Liberty expresses a lot more than just this: it demonstrates the erosion of civil liberties, yes. However, it also presents the intangible, indefinable moments in the days and months that followed 9/11. The sculptural work will last for only a short time, and thereafter will exist only in documentation. During this time, the form is continually changing and self-refining, until it disappears entirely, to be inhaled, metabolised and literally taken to heart by viewers.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Diccionario Enciclopédico de Música en Mexico

The work is the most extensive encyclopedic compilation on Mexican music published to the date, in the form of a dictionary. It includes composer's bios, list of works, musical institutions, instruments, performers, theatres, and many other categories about the music of Mexico. Most of entries include bibliography.

Lexicon forestale.

592 s.

Dokumentation och beskrivning som språkplanering : perspektiv från arbete med tre tecknade minoritetsspråk

Language Documentation and Description as Language Planning Working with Three Signed Minority Languages Sign languages are minority languages that typically have a low status in society. Language planning has traditionally been controlled from outside the sign-language community. Even though signed languages lack a written form, dictionaries have played an important role in language description and as tools in foreign language learning. The background to the present study on sign language documentation and description as language planning is empirical research in three dictionary projects in Finland-Swedish Sign Language, Albanian Sign Language, and Kosovar Sign Language. The study consists of an introductory article and five detailed studies which address language planning from different perspectives. The theoretical basis of the study is sociocultural linguistics. The research methods used were participant observation, interviews, focus group discussions, and document analysis. The primary research questions are the following: (1) What is the role of dictionary and lexicographic work in language planning, in research on undocumented signed language, and in relation to the language community as such? (2) What factors are particular challenges in the documentation of a sign language and should therefore be given special attention during lexicographic work? (3) Is a conventional dictionary a valid tool for describing an undocumented sign language? The results indicate that lexicographic work has a central part to play in language documentation, both as part of basic research on undocumented sign languages and for status planning. Existing dictionary work has contributed new knowledge about the languages and the language communities. The lexicographic work adds to the linguistic advocacy work done by the community itself with the aim of vitalizing the language, empowering the community, receiving governmental recognition for the language, and improving the linguistic (human) rights of the language users. The history of signed languages as low status languages has consequences for language planning and lexicography. One challenge that the study discusses is the relationship between the sign-language community and the hearing sign linguist. In order to make it possible for the community itself to take the lead in a language planning process, raising linguistic awareness within the community is crucial. The results give rise to questions of whether lexicographic work is of more importance for status planning than for corpus planning. A conventional dictionary as a tool for describing an undocumented sign language is criticised. The study discusses differences between signed and spoken/written languages that are challenging for lexicographic presentations. Alternative electronic lexicographic approaches including both lexicon and grammar are also discussed. Keywords: sign language, Finland-Swedish Sign Language, Albanian Sign Language, Kosovar Sign Language, language documentation and description, language planning, lexicography

Do wordnets also improve human performance on NLP tasks?

FinnWordNet is a wordnet for Finnish that complies with the format of the Princeton WordNet (PWN) (Fellbaum, 1998). It was built by translating the PrincetonWordNet 3.0 synsets into Finnish by human translators. It is open source and contains 117000 synsets. The Finnish translations were inserted into the PWN structure resulting in a bilingual lexical database. In natural language processing (NLP), wordnets have been used for infusing computers with semantic knowledge assuming that humans already have a sufficient amount of this knowledge. In this paper we present a case study of using wordnets as an electronic dictionary. We tested whether native Finnish speakers benefit from using a wordnet while completing English sentence completion tasks. We found that using either an English wordnet or a bilingual English Finnish wordnet significantly improves performance in the task. This should be taken into account when setting standards and comparing human and computer performance on these tasks.

Optimal sparsifying bases for frequency-domain optical-coherence tomography

We address the reconstruction problem in frequency-domain optical-coherence tomography (FDOCT) from under-sampled measurements within the framework of compressed sensing (CS). Specifically, we propose optimal sparsifying bases for accurate reconstruction by analyzing the backscattered signal model. Although one might expect Fourier bases to be optimal for the FDOCT reconstruction problem, it turns out that the optimal sparsifying bases are windowed cosine functions where the window is the magnitude spectrum of the laser source. Further, the windowed cosine bases can be phase locked, which allows one to obtain higher accuracy in reconstruction. We present experimental validations on real data. The findings reported in this Letter are useful for optimal dictionary design within the framework of CS-FDOCT. (C) 2012 Optical Society of America

HUMAN DETECTION USING SPARSE REPRESENTATION

The problem of human detection is challenging, more so, when faced with adverse conditions such as occlusion and background clutter. This paper addresses the problem of human detection by representing an extracted feature of an image using a sparse linear combination of chosen dictionary atoms. The detection along with the scale finding, is done by using the coefficients obtained from sparse representation. This is of particular interest as we address the problem of scale using a scale-embedded dictionary where the conventional methods detect the object by running the detection window at all scales.

Compressive sensing for music signals: comparison of transforms with coherent dictionaries

Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrinsic information rate which could be much lower than Nyquist rate, while guaranteeing good quality reconstruction for signals sparse in a linear transform domain. We explore the application of CS formulation to music signals. Since music signals comprise of both tonal and transient nature, we examine several transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), Fourier basis and also non-orthogonal warped transforms to explore the effectiveness of CS theory and the reconstruction algorithms. We show that for a given sparsity level, DCT, overcomplete, and warped Fourier dictionaries result in better reconstruction, and warped Fourier dictionary gives perceptually better reconstruction. “MUSHRA” test results show that a moderate quality reconstruction is possible with about half the Nyquist sampling.

Fragment-based real-time object tracking: a sparse representation approach

Real-time object tracking is a critical task in many computer vision applications. Achieving rapid and robust tracking while handling changes in object pose and size, varying illumination and partial occlusion, is a challenging task given the limited amount of computational resources. In this paper we propose a real-time object tracker in l(1) framework addressing these issues. In the proposed approach, dictionaries containing templates of overlapping object fragments are created. The candidate fragments are sparsely represented in the dictionary fragment space by solving the l(1) regularized least squares problem. The non zero coefficients indicate the relative motion between the target and candidate fragments along with a fidelity measure. The final object motion is obtained by fusing the reliable motion information. The dictionary is updated based on the object likelihood map. The proposed tracking algorithm is tested on various challenging videos and found to outperform earlier approach.