String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Finite-state models in the alignment of macromolecules.

Abstract: Minimum message length encoding is a technique of inductive inference with theoretical and practical advantages. It allows the posterior odds-ratio of two theories or hypotheses to be calculated. Here it is applied to problems of aligning or relating two strings, in particular two biological macromolecules. We compare the r-theory, that the strings are related, with the null-theory, that they are not related. If they are related, the probabilities of the various alignments can be calculated. This is done for one-, three-, and five-state models of relation or mutation. These correspond to linear and piecewise linear cost functions on runs of insertions and deletions. We describe how to estimate parameters of a model. The validity of a model is itself an hypothesis and can be objectively tested. This is done on real DNA strings and on artificial data. The tests on artificial data indicate limits on what can be inferred in various situations. The tests on real DNA support either the three- or five-state models over the one-state model. Finally, a fast, approximate minimum message length string comparison algorithm is described.

Integrating Joint n-gram Features into a Discriminative Training Framework

Abstract: Phonetic string transduction problems, such as letter-to-phoneme conversion and name transliteration, have recently received much attention in the NLP community In the past few years, two methods have come to dominate as solutions to supervised string transduction: generative joint n-gram models, and discriminative sequence models Both approaches benefit from their ability to consider large, flexible spans of source context when making transduction decisions However, they encode this context in different ways, providing their respective models with different information To combine the strengths of these two systems, we include joint n-gram features inside a state-of-the-art discriminative sequence model We evaluate our approach on several letter-to-phoneme and transliteration data sets Our results indicate an improvement in overall performance with respect to both the joint n-gram approach and traditional feature sets for discriminative models

Driver circuit for LED vehicle lamp

Abstract: A vehicle lamp driver circuit (10 in Fig. 1) for a LED array (11) includes a first LED string (12) in series with a second LED string (14). A LED driver (16) drives the first LED string and a second LED driver (18) drives the second LED string. In a STOP mode of operation, the current to both LED strings is controlled by the LED driver in series with the LED string. In a TAIL mode of operation, the current is provided to only one LED string. When there is reduced input voltage, operation of the LED strings is provided by switching circuits (20, 22) that short-out one LED in each LED string. Another vehicle lamp driver circuit (50 in Fig. 2) includes a first LED string (51) and a second LED string (52) in series with a control switch (56) having a feedback circuit (58) for maintaining constant current regulation to control the sum of the current in each LED string and reduce switching noise.

Teacher and Student Behavior in Suzuki String Lessons: Results from the International Research Symposium on Talent Education.

Abstract: The investigation described in this report documents the types of behavior that take place in the studios of nationally and/or regionally acclaimed string teachers whose instruction is based on the...

Self-evolution in a constructive binary string system

Abstract: We examine the qualitative dynamics of a catalytic self-organizing system of binary strings that is inspired by the chemical information processing metaphor. A string is interpreted in two different ways: either (a) as raw data or (b) as a machine that is able to process another string as data in order to produce a third one. This article focuses on the phenomena of evolution whose appearance is notable because no explicit mutation, recombination, or artificial selection operators are introduced. We call the system self-evolving because every variation is performed by the objects themselves in their machine form.