bluuebunny/crossref_metadata_2025_split huggingface dataset:
The involvement of Kv3.4 voltage‐gated potassium channel in the growth of an oral squamous cell carcinoma cell line
Authors: Lew Tien‐Shen , Chang Che‐Shoa , Fang Kung‐Ping , Chen Chian‐Yi , Chen Cheng‐Hsiung , Lin Shu‐Chun 
Published: September 2004

Background:  In our previous study, an A‐type voltage‐gated K+ channel, Kv3.4, was found more frequently expressed in oral squamous cell carcinoma (OSCC) when compared with non‐cancerous matched oral tissue. An OSCC cell line, OECM‐1, was found to have moderate level of Kv3.4 expression.Methods:  To further elucidate the roles of Kv3.4 for the involvement of neoplastic process, we amplified Kv3.4 coding sequence by reverse transcriptase polymerase chain reaction (RT‐PCR), constructed an expression vector carrying this sequence and then stably transfected into OECM‐1 OSCC cells.Results:  We demonstrated the integration and constitutive expression of Kv3.4 in the cell. A unique A‐type current elicited by such expression in OECM‐1 cells was defined by patch clamp analysis. This current pattern can be reversibly blocked by an A‐type K+ channel blocker 2 mM 4‐aminopyridine (4‐AP). The acquisition of Kv3.4 activity in OECM‐1 cells bestowed growth advantage. However, in 3T3 cell, transfected Kv3.4 caused only limited increase of growth without forming transformation foci.Conclusion:  The present study established a stable keratinocyte system carrying functional Kv3.4 and increase of growth, by which the anti‐Kv3.4 modalities for potential OSCC control can be further investigated.
Relative frequency of solitary melanocytic lesions of the oral mucosa
Authors: Buchner A. , Merrell P. W. , Carpenter W. M. 
Published: September 2004

Background:  Solitary pigmented lesions of melanocytic origin are uncommon in the oral mucosa. These lesions include the oral and labial melanotic macule, oral melanocytic nevus, oral melanoacanthoma, oral melanoma and atypical melanocytic proliferation. The purpose of the study was twofold: to report a large series of solitary melanocytic lesions from one source, and to determine the relative frequency of these lesions.Methods:  The study was based on a systematic search of the files of the Pacific Oral and Maxillofacial Pathology Laboratory, University of the Pacific, San Francisco for solitary pigmented melanocytic lesions (benign and malignant) accessed during the years 1984–2002.Results:  Of the 89 430 biopsies accessed during the 19‐year period, 773 (0.83%) cases of solitary pigmented melanocytic lesions in the oral mucosa were identified. Oral and labial melanotic macules were the most common melanocytic lesions comprising 86.1% of the entire group and 0.7% of the total number of accessed biopsies. The vermilion border and gingiva were the most common sites (31.1% and 31.0% respectively). Oral melanocytic nevi comprised 11.8% of the entire melanocytic group and 0.1% of the total number of biopsies. The most common site was the palate (44%). Intramucosal nevi were the most common (64%), followed by compound nevi (16.5%) and common blue nevi (16.5%). Junctional nevi were uncommon (3.0%). Oral melanoacanthoma comprised only 0.9% of the entire melanocytic group and 0.008% of the total number of biopsies. Oral melanoma and atypical melanocytic proliferation were the least common lesions each comprising 0.6% of the entire melanocytic group and 0.006% of the total number of biopsies. The most common site for oral melanoma was the palate (60%).Conclusion:  The palate was the most common location for both melanocytic nevi and oral melanoma. Thus, all melanocytic lesions in the palate should be viewed with caution and biopsy is recommended to rule out melanoma. Further studies are required to elucidate the entity of oral atypical melanocytic proliferation.
An exploration of point, annual, and lifetime prevalence in characterizing recurrent aphthous stomatitis in USA children and youths
Authors: Shulman Jay D. 
Published: September 2004

Background:  This report presents and contrasts the prevalence of recurrent aphthous stomatitis (RAS) in 51 471 children and adolescents from two major studies: the Third National Health and Nutrition Examination Survey, 1988–1994 (NHANES III) and the National Survey of Oral Health in USA Schoolchildren, 1986–1987 (OHSC), large USA studies based on multistage probability sampling.Methods:  Prevalence proportions, 95% confidence limits and multivariate logistic regression models were constructed for point, 12‐month, and lifetime RAS prevalence using SAS‐callable SUDAAN 8.0.2.Results:  Examinations were performed on 51 471 children and adolescents. Point prevalence was 1.51% (NHANES III) and 1.21% (OHSC); annual prevalence was 19.84% (NHANES III); and lifetime prevalence was 40.18% (OHSC). Multivariate logistic models showed that being white, having a history of herpes labialis, (NHANES III), and being white and an adolescent (OHSC) were predictors of RAS.Conclusions:  Caution should be used in interpreting 12‐month and lifetime RAS prevalence based a subject's recall.
EFFICIENT REBALANCING OF B-TREES WITH RELAXED BALANCE
Authors: LARSEN KIM S. , FAGERBERG ROLF 
Published: September 2004

B-trees with relaxed balance have been defined to facilitate fast updating on shared-memory asynchronous parallel architectures. To obtain this, rebalancing has been uncoupled from the updating so that extensive locking can be avoided in connection with updates. We analyze B-trees with relaxed balance, and prove that each update gives rise to at most [loga(N/2)]+1 rebalancing operations, where a is the degree of the B-tree, and N is the bound on its maximal size since it was last in balance. Assuming that the size of nodes is at least twice the degree, we prove that rebalancing can be performed in amortized constant time. So, in the long run, rebalancing is constant time on average, even if any particular update could give rise to a logarithmic number of rebalancing operations. We also prove that the amount of rebalancing done at any particular level decreases exponentially going from the leaves towards the root. This is important since the higher up in the tree a lock due to a rebalancing operation occurs, the larger a subtree which cannot be accessed by other processes for the duration of that lock. All of these results are in fact obtained for the more general (a, b)-trees, so we have results for both of the common B-tree versions as well as 2-3 trees and 2-3-4 trees.
INDUCTIVE INFERENCE OF LIMITING PROGRAMS WITH BOUNDED NUMBER OF MIND CHANGES
Authors: VĪKSNA JURIS 
Published: September 2004

We consider inductive inference of total recursive functions in the case, when produced hypotheses are allowed some finite number of times to change “their mind” about each value of identifiable function. Such type of identification, which we call inductive inference of limiting programs with bounded number of mind changes, by its power lies somewhere between the traditional criteria of inductive inference and recently introduced inference of limiting programs. We consider such model of inductive inference for EX and BC types of identification, and we study • tradeoffs between the number of allowed mind changes and the number of anomalies, and • relations between classes of functions identifiable with different probabilities. For the case of probabilistic identification we establish probabilistic hierarchies which are quite unusual for EX and BC types of inference.
FAMILIES OF NONCOUNTING LANGUAGES AND THEIR LEARNABILITY FROM POSITIVE DATA
Authors: KOBAYASHI SATOSHI , YOKOMORI TAKASHI 
Published: September 2004

This paper introduces some subclasses of noncounting languages and presents some results on the learnability of the classes from positive data. We first establish several relationships among the language classes introduced and the class of reversible languages. Especially, we introduce the notion of local parsability, and define a class (k, l)-CLTS, which is a subclass of the class of concatenations of strictly locally testable languages. We show its close relation to the class of reversible languages. We then study on the relationship between the closure of the Boolean operations and the learnability in the limit from positive data only. Further, we explore the learnability question of some subclasses of noncounting languages in the model of identification in the limit from positive data. In particular, we show that, for each k, l≥1, (k, l)-CLTS is identifiable in the limit from positive data using reversible automata with the conjectures updated in polynomial time. Some possible applications of the result are also briefly discussed.
ON THE ISOMORPHISM CONJECTURE FOR 2-DFA REDUCTIONS
Authors: AGRAWAL MANINDRA , VENKATESH S. 
Published: September 2004

The degree structure of complete sets under 2DFA reductions is investigated. It is shown that, for any class [Formula: see text] that is closed under log-lin reductions: • All complete sets for the class [Formula: see text] under 2DFA reductions are also complete under one-one, length-increasing 2DFA reductions and are first-order isomorphic. • The 2DFA-isomorphism conjecture is false, i.e., the complete sets under 2DFA reductions are not isomorphic to each other via 2DFA reductions.
ON THE PROPOSITIONAL SLDNF-RESOLUTION
Authors: PLAZA JAN A. 
Published: September 2004

We consider propositional logic programs with negations. We define notions of constructive transformation and constructive completion of a program. We use these notions to characterize SLDNF-resolution in classical, intuitionistic and intermediate logics, and also to derive a characterization in modal logics of knowledge. We show that the three-valued and four-valued fix-point or declarative semantics for program P are equivalent to the two-valued semantics for the constructive version of P. We argue that it would be beneficial to replace Negation as Failure by constructive transformation, and it would be beneficial to use the semantics for the constructive version of the program instead of multivalued semantics for the original program.
MACHINE VISION 1992–1996, TECHNOLOGY PROGRAM IN FINLAND
Authors: SOINI ANTTI J. 
Published: September 2004

Machine vision technology has attracted a strong interest among Finnish research organizations, which has resulted in many innovative products for industry. Despite this goal users were very skeptical towards machine vision and its robustness in harsh industrial environments. Therefore the Technology Development Centre, TEKES, which funds technology related research and development projects in universities and individual companies in Finland, decided to start a national technology program, "Machine Vision 1992–1996". Led by industry, the program boosts research in machine vision technology and seeks to put the research results to work in practical industrial applications. The emphasis is on nationally important, demanding applications. The program will create new business for machine vision producers and encourage the process and manufacturing industry to take advantage of this new technology. So far 60 companies and all major universities and research centers in Finland are working on our forty different projects. The key themes are Process Control, Robot Vision and Quality Control.
AN INTELLIGENT CAMERA FOR ACTIVE VISION
Authors: PAU L.-F. 
Published: September 2004

Much research is currently going on about the processing of one or two-camera imagery, possibly combined with other sensors and actuators, in view of achieving attentive vision, i.e. processing selectively some parts of a scene possibly with another resolution. Attentive vision in turn is an element of active vision where the outcome of the image processing triggers changes in the image acquisition geometry and/or of the environment. Almost all this research is assuming classical imaging, scanning and conversion geometries, such as raster based scanning and processing of several digitized outputs on separate image processing units. A consortium of industrial companies comprising Digital Equipment Europe, Thomson CSF, and a few others, have taken a more radical view of this. To meet active vision requirements in industry, an intelligent camera is being designed and built, comprised of three basic elements: – a unique Thomson CSF CCD sensor architecture with random addressing – the DEC Alpha 21064 275MHz processor chip, sharing the same internal data bus as the digital sensor output – a generic library of basic image manipulation, control and image processing functions, executed right in the sensor-internal bus-processor unit, so that only higher level results or commands get exchanged with the processing environment. Extensions to color imaging (with lower spatial resolution), and to stereo imaging, are relatively straightforward. The basic sensor is 1024*1024 pixels with 2*10 bits addresses, and a 2.5 ms (400 frames/second) image data rate compatible with the Alpha bus and 64 bits addressing. For attentive vision, several connex fields of max 40 000 pixels, min 5*3 pixels, can be read and addressed within each 2.5 ms image frame. There is nondestructive readout, and the image processing addressing over 64 bits shall allow for 8 full pixel readouts in one single word. The main difficulties have been identified as the access and reading delays, the signal levels, and dimensioning of some buffer arrays in the processor. The commercial applications targeted initially will be in industrial inspection, traffic control and document imaging. In all of these fields, selective position dependent processing shall take place, followed by feature dependent processing. Very large savings are expected both in terms of solutions costs to the end users, development time, as well as major performance gains for the ultimate processes. The reader will appreciate that at this stage no further implementation details can be given.
RECENT DEVELOPMENTS IN WOOD INSPECTION
Authors: SILVÉN OLLI , KAUPPINEN HANNU 
Published: September 2004

Automated grading of lumber is attractive for the sawmill industry. Achieving better accuracy of quality grading and control of quality variation in volume production easily improves the profit obtained from it. We describe a color vision based framework to grading softwood lumber. The proposed inspection principle is to recognize the sound wood regions early, as this reduces the computational requirements at later defect recognition stages. The recognition stages are ordered based on their estimated costs and implementational complexity. This is important because color increases the data volumes significantly over grey scale images. Finally, the description of the board and its defects is passed to the quality grader which assigns the class giving the best price for each board. In comparative tests, the computational solutions have turned out to be simpler than with grey level images, compensating for the higher cost of the color imaging system. The effects of changes in the spectrum of illumination have also been evaluated to identify robust color features and to produce the requirements for color calibration.
SELF-ORGANIZING FEATURE EXTRACTION IN RECOGNITION OF WOOD SURFACE DEFECTS AND COLOR IMAGES
Authors: LAMPINEN JOUKO , SMOLANDER SEPPO 
Published: September 2004

A method for constructing classification features with unsupervised learning is presented. The method is based on clustering of the high dimensional measurements into a small number of features with self-organizing maps. The histograms of the self-organized features are classified with a multilayer perceptron network, that can pick up the relevant features and feature combinations from the histograms. The method is applied in two industrial problems, color image recognition for selection of optimal reproduction parameters in the printing press, and defect classification in wood surfaces. In both applications, the results were evaluated by the domain experts to be sufficient with respect to the application requirements. In the color image recognition the results were compared to the manually selected parameter settings, and in 12% of the test images there were distinquishable differences, with very few clear failures. Performance of the wood defect classification system was evaluated with a set of 400 knot images with 7 classes from spruce boards. The recognition rate was about 85% with only gray level images, giving about 90% accuracy for the final board grading, to be compared to 75–80% accuracy that can be maintained by a human inspector. As the methods are based on rather generic basic features and learning of the final features and classes from the samples, they are easily adaptable to different tasks, such as defect inspection in lumber or veneer, with different tree species or different cutting processes.
GROWTH TRANSFORMATIONS FOR PROBABILISTIC FUNCTIONS OF STOCHASTIC GRAMMARS
Authors: CASACUBERTA FRANCISCO 
Published: September 2004

Stochastic Grammars are the most usual models in Syntactic Pattern Recognition. Both components of a Stochastic Grammar, the characteristic grammar and the probabilities attached to the rules, can be learnt automatically from training samples. In this paper, first a review of some algorithms are presented to infer the probabilistic component of Stochastic Regular and Context-Free Grammars under the framework of the Growth Transformations. On the other hand, with Stochastic Grammars, the patterns must be represented as strings over a finite set of symbols. However, the most natural representation in many Syntactic Pattern Recognition applications (i.e. speech) is as sequences of vectors from a feature vector space, that is, a continuous representation. Therefore, to obtain a discrete representation of the patterns, some quantization errors are introduced in the representation process. To avoid this drawback, a formal presentation of a semi-continuous extension of the Stochastic Regular and Context-Free Grammars is studied and probabilistic estimation algorithms are developed in this paper. In this extension, sequences of vectors, instead of strings of symbols, can be processed with Stochastic Grammars.
3-D FACE MODELING AND ITS APPLICATIONS
Authors: HUANG THOMAS S. , TANG LI-AN 
Published: September 2004

This paper describes some issues in building a 3-D human face modeling system which mainly consists of three parts: • Modeling human faces; • Analyzing facial motions; • Synthesizing facial expressions. A variety of techniques developed for this system are described in detail in this paper. Some preliminary results of applying this system to computer animation, video sequence compression and human face recognition are also shown.
CHARACTERISTIC-VIEW MODELING OF CURVED-SURFACE SOLIDS
Authors: CHEN SHUANG , FREEMAN HERBERT 
Published: September 2004

Given a 3-D object (solid), it is possible to divide the exterior viewing space of the solid into maximally connected subspaces of vantage points such that the perspective views of the solid from all vantage points within a subspace have identical labeled line-junction graphs. We refer to such subspaces as the characteristic-view domains (CVDs) of the solid, and to a representative perspective view from within a domain, as a characteristic view (CV). The concept suggests a scheme for modeling 3-D objects in which the infinite number of possible views of a given solid are represented by a finite set of characteristic views (together with their respective projection-defining parameters). The concept was introduced more than 15 years ago as a modeling scheme to facilitate real-time recognition of 3-D objects. However, the difficulty of determining the CVs hindered its application. This article describes a technique for computing the characteristic views of general solids bounded by quadric surfaces (including polyhedra as a special case), and suggests how the technique can be used for 3-D object recognition.
PROPAGATING COVARIANCE IN COMPUTER VISION
Authors: HARALICK ROBERT M. 
Published: September 2004

This paper describes how to propagate approximately additive random perturbations through any kind of vision algorithm step in which the appropriate random perturbation model for the estimated quantity produced by the vision step is also an additive random perturbation. We assume that the vision algorithm step can be modeled as a calculation (linear or non-linear) that produces an estimate that minimizes an implicit scaler function of the input quantity and the calculated estimate. The only assumption is that the scaler function has finite second partial derivatives and that the random perturbations are small enough so that the relationship between the scaler function evaluated at the ideal but unknown input and output quantities and the observed input quantity and perturbed output quantity can be approximated sufficiently well by a first order Taylor series expansion. The paper finally discusses the issues of verifying that the derived statistical behavior agrees with the experimentally observed statistical behavior.
LEARNING TO RECOGNIZE HAND-PRINTED CHINESE CHARACTERS USING INDUCTIVE LOGIC PROGRAMMING
Authors: AMIN ADNAN , SAMMUT CLAUDE , SUM K. C. 
Published: September 2004

Recognition of Chinese characters has been a major interest of researchers for many years, and a large number of research papers and reports have already been published in this area. There are several major problems: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to find. This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variations in writing styles. The advantages of machine learning are twofold: it can generalize over the large degree of variations between writing styles and recognition rules can be constructed by example. The paper also describes three methods of feature extraction for Chinese character recognition: regular expression, dominant point and modified Hough transform. These methods are then compared in terms of accuracy and efficiency.
COMPARISON OF VARIOUS ROUTINES FOR UNKNOWN ATTRIBUTE VALUE PROCESSING: THE COVERING PARADIGM
Authors: BRUHA IVAN , FRANEK FRANTISEK 
Published: September 2004

Simple inductive learning algorithms assume that all attribute values are available. The well-known Quinlan's paper1 discusses quite a few routines for the processing of unknown attribute values in the TDIDT family and analyzes seven of them. This paper introduces five routines for the processing of unknown attribute values that have been designed for the CN4 learning algorithm, a large extension of the well-known CN2. Both algorithms CN2 and CN4 induce lists of decision rules from examples applying the covering paradigm. CN2 offers two ways for the processing of unknown attribute values. The CN4's five routines differ in style of matching complexes with examples (objects) that involve unknown attribute values. The definition of matching is discussed in detail in the paper. The strategy of unknown value processing is described both for learning and classification phases in individual routines. The results of experiments with various percentages of unknown attribute values on real-world (mostly medical) data are presented and performances of all five routines are compared.