The term DNA sequencing refers to methods for determining the order of the nucleotides
bases adenine,guanine,cytosine and thymine in a molecule of DNA. The first DNA sequence
were obtained by academic researchers,using laboratories methods based on 2- dimensional
chromatography in the early 1970s. By the development of dye based sequencing method
with automated analysis,DNA sequencing has become easier and faster.
Mục tiêu của bài giảng này nhằm giúp người học biết được cách thiết kế Sequence Diagram, biết được các thành phần trong Sequence Diagram, biết được cách sử dụng Power Designer để tạo Sequence Diagram, biết cách xây dựng một số Sequence Diagram của một số ứng dụng.
Automated DNA sequencing is a core research tool used by almost every research biochemistry lab. It is used to determine the sequence of DNA, or the genetic code, that serves as the blueprint of life for every organism on Earth.
Ebook Sequences: Picture Stories for ESL is a reproducible book for beginner ESL students. It includes 60 units. Each unit contains a drawings page. On each drawings page there is a sequence of six drawings, mostly without words or captions. The drawings show the sequence of events that go with a particular activity, such as going grocery shopping or visiting a doctor.
[ Team LiB ] Recipe 4.4 Getting a Sequence Value from Oracle Problem When you add a row into an Oracle table that uses a sequence to generate the value for a primary key column, the value assigned to the column in the DataTable is replaced by a value generated by the database.
Analytic number theorists usually seek to show that sequences which appear naturally in arithmetic are “well-distributed” in some appropriate sense. In various discrepancy problems, combinatorics researchers have analyzed limitations to equidistribution, as have Fourier analysts when working with the “uncertainty principle”. In this article we ﬁnd that these ideas have a natural setting in the analysis of distributions of sequences in analytic number theory, formulating a general principle, and giving several examples. ...
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns.
In 1991, David Gale and Raphael Robinson, building on explorations carried out by Michael Somos in the 1980s, introduced a three-parameter family of rational recurrence relations, each of which (with suitable initial conditions) appeared to give rise to a sequence of integers, even though a priori the recurrence might produce non-integral rational numbers. Throughout the '90s, proofs of integrality were known only for individual special cases. In the early '00s, Sergey Fomin and Andrei Zelevinsky proved Gale and Robinson's integrality conjecture.
Here follows a collection of sequences, including sequences, which satisfy some simple difference equations.
The reader is also referred to Calculus 3b. Since my aim also has been to demonstrate some
solution strategy I have as far as possible structured the examples according to the following form
A Awareness, i.e. a short description of what is the problem.
D Decision, i.e. a reflection over what should be done with the problem.
I Implementation, i.e. where all the calculations are made.
C Control, i.e. a test of the result.
This is an ideal form of a general procedure of solution.
Classical Information Extraction (IE) systems ﬁll slots in domain-speciﬁc frames. This paper reports on S EQ, a novel open IE system that leverages a domainindependent frame to extract ordered sequences such as presidents of the United States or the most common causes of death in the U.S. S EQ leverages regularities about sequences to extract a coherent set of sequences from Web text. S EQ nearly doubles the area under the precision-recall curve compared to an extractor that does not exploit these regularities. ...
Supervised sequence-labeling systems in natural language processing often suffer from data sparsity because they use word types as features in their prediction tasks. Consequently, they have difﬁculty estimating parameters for types which appear in the test set, but seldom (or never) appear in the training set. We demonstrate that distributional representations of word types, trained on unannotated text, can be used to improve performance on rare words. We incorporate aspects of these representations into the feature space of our sequence-labeling systems. ...
The tree sequence based translation model allows the violation of syntactic boundaries in a rule to capture non-syntactic phrases, where a tree sequence is a contiguous sequence of subtrees. This paper goes further to present a translation model based on non-contiguous tree sequence alignment, where a non-contiguous tree sequence is a sequence of sub-trees and gaps. Compared with the contiguous tree sequencebased model, the proposed model can well handle non-contiguous phrases with any large gaps by means of non-contiguous tree sequence alignment. ...
Here follow some guidelines for solution of problems concerning sequences and power series. It should
be emphasized that my purpose has never been to write an alternative textbook on these matters. If
I would have done so, I would have arranged the subject differently. Nevertheless, it is my hope that
the present text can be a useful supplement to the ordinary textbooks, in which one can find all the
necessary proofs which are skipped here.
We present a novel machine translation model which models translation by a linear sequence of operations. In contrast to the “N-gram” model, this sequence includes not only translation but also reordering operations. Key ideas of our model are (i) a new reordering approach which better restricts the position to which a word or phrase can be moved, and is able to handle short and long distance reorderings in a uniﬁed way, and (ii) a joint sequence model for the translation and reordering probabilities which is more ﬂexible than standard phrase-based MT. ...
This paper proposes a forest-based tree sequence to string translation model for syntaxbased statistical machine translation, which automatically learns tree sequence to string translation rules from word-aligned sourceside-parsed bilingual texts. The proposed model leverages on the strengths of both tree sequence-based and forest-based translation models.
While Active Learning (AL) has already been shown to markedly reduce the annotation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned about the internal structure of the selected sequences (typically, sentences). We propose a semisupervised AL approach for sequence labeling where only highly uncertain subsequences are presented to human annotators, while all others in the selected sequences are automatically labeled.
Situation entities (SEs) are the events, states, generic statements, and embedded facts and propositions introduced to a discourse by clauses of text. We report on the ﬁrst datadriven models for labeling clauses according to the type of SE they introduce. SE classiﬁcation is important for discourse mode identiﬁcation and for tracking the temporal progression of a discourse.
We present new statistical models for jointly labeling multiple sequences and apply them to the combined task of partof-speech tagging and noun phrase chunking. The model is based on the Factorial Hidden Markov Model (FHMM) with distributed hidden states representing partof-speech and noun phrase sequences. We demonstrate that this joint labeling approach, by enabling information sharing between tagging/chunking subtasks, outperforms the traditional method of tagging and chunking in succession.