Yes, today I am going to teach you a very simple way to make a vector portrait (scroll
down to see how it looks like). And this really is vector (you can resize it without losing
the quality). And to do this, we are going to use Photoshop together with the pretty good
and absolutely free, open-source vector program: Inkscape. It works good for portrait, but
also on other things.
A three-stage method for compressing bi-level line-drawing images is proposed. In the first stage, the raster image is vectorized using a combination of skeletonizing and line tracing algorithm. A feature image is then reconstructed from the extracted vector elements. In the second stage, the original image is processed by a feature-based filter for removing noise near the borders of the extracted line elements. This improves the image quality and results in more compressible raster image. In the final stage, the filtered raster image is compressed using the baseline JBIG algorithm....
Photoshop Elementsis one of the most powerful image editing
programs on the planet, bar none. The key to harnessing the power of the program
and getting the best results from your images is understanding how the program works,
understanding images, and using the inherent capabilities of the program optimally to
make the best image adjustments. Getting the most out of your images using Photoshop
Elements is what this book is about.
VECTOR DISSIPATIVITY THEORY FOR DISCRETE-TIME LARGE-SCALE NONLINEAR DYNAMICAL SYSTEMS
WASSIM M. HADDAD, QING HUI, VIJAYSEKHAR CHELLABOINA, AND SERGEY NERSESOV Received 15 October 2003
In analyzing large-scale systems, it is often desirable to treat the overall system as a collection of interconnected subsystems. Solution properties of the large-scale system are then deduced from the solution properties of the individual subsystems and the nature of the system interconnections.
This paper describes methods for relating (threading) multiple newspaper articles, and for visualizing various characteristics of them by using a directed graph. A set of articles is represented by a set of word vectors, and the similarity between the vectors is then calculated. The graph is constructed from the similarity matrix. By applying some constraints on the chronological ordering of articles, an efficient threading algorithm that runs in O(n) time (where n is the number of articles) is obtained. ...
An Adenovirus Vector-Mediated Reverse Genetics System for Inﬂuenza A Virus Generation established an alternative reverse genetics system for inﬂuenza virus generation by using an adenovirus vector (AdV) which achieves highly efﬁcient gene transfer independent of cell transfection efﬁciency.
High-Level Recombinant Protein Production in CHO Cells Using Lentiviral Vectors and the Cumate Gene-Switch developed an efﬁcient system to generate in less than 2 months, starting from the cDNA, pools of CHO cells stably expressing high-level of recombinant proteins. It is based on lentiviral vectors (LVs) for stable transduction coupled with the cumate gene-switch for inducible and efﬁcient gene expression.
The gene 4CL1 was isolated from Chinese red pine (Pinus massoniana Lamb) and ligated into vector pPTN289 to perform transformation vector pPTN289-4CL1. This construction was transformed into Agrobacterium tumefaciens strain C58, and then transformed into Chinaberrytree (Melia azedarach L.). The transgenic Chinaberrytree was screened on selection medium (MS + 0.5mg/l 6-BA + 1mg/l vitamine B5 + 30g/l sucrose + 8g/l agar + 500mg/l Cefotaxime + 1mg/l PPT) and then extracted total DNA and tested the existence of interested gene using PCR method. ...
We present a syntactically enriched vector model that supports the computation of contextualized semantic representations in a quasi compositional fashion. It employs a systematic combination of ﬁrst- and second-order context vectors. We apply our model to two different tasks and show that (i) it substantially outperforms previous work on a paraphrase ranking task, and (ii) achieves promising results on a wordsense similarity task; to our knowledge, it is the ﬁrst time that an unsupervised method has been applied to this task. ...
Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term–document information as well as rich sentiment content.
Graph-based dependency parsing can be sped up signiﬁcantly if implausible arcs are eliminated from the search-space before parsing begins. State-of-the-art methods for arc ﬁltering use separate classiﬁers to make pointwise decisions about the tree; they label tokens with roles such as root, leaf, or attaches-tothe-left, and then ﬁlter arcs accordingly. Because these classiﬁers overlap substantially in their ﬁltering consequences, we propose to train them jointly, so that each classiﬁer can focus on the gaps of the others. ...
This paper introduces an unsupervised vector approach to disambiguate words in biomedical text that can be applied to all-word disambiguation. We explore using contextual information from the Uniﬁed Medical Language System (UMLS) to describe the possible senses of a word. We experiment with automatically creating individualized stoplists to help reduce the noise in our dataset. We compare our results to SenseClusters and Humphrey et al. (2006) using the NLM-WSD dataset and with SenseClusters using conﬂated data from the 2005 Medline Baseline. ...
Reading proﬁciency is a fundamental component of language competency. However, ﬁnding topical texts at an appropriate reading level for foreign and second language learners is a challenge for teachers. This task can be addressed with natural language processing technology to assess reading level. Existing measures of reading level are not well suited to this task, but previous work and our own pilot experiments have shown the beneﬁt of using statistical language models.
A term-list is a list of content words that characterize a consistent text or a concept. This paper presents a new method for translating a term-list by using a corpus in the target language. The method first retrieves alternative translations for each input word from a bilingual dictionary. It then determines the most 'coherent' combination of alternative translations, where the coherence of a set of words is defined as the proximity among multi-dimensional vectors produced from the words on the basis of co-occurrence statistics. ...
Collocational word similarity is considered a source of text cohesion that is hard to measure and quantify. The work presented here explores the use of information from a training corpus in measuring word similarity and evaluates the method in the text segmentation task. An implementation, the V e c T i l e system, produces similarity curves over texts using pre-compiled vector representations of the contextual behavior of words. The performance of this system is shown to improve over that of the purely string-based TextTiling algorithm (Hearst, 1997). 1 Background ...
We explore how active learning with Support Vector Machines works well for a non-trivial task in natural language processing. We use Japanese word segmentation as a test case. In particular, we discuss how the size of a pool affects the learning curve. It is found that in the early stage of training with a larger pool, more labeled examples are required to achieve a given level of accuracy than those with a smaller pool. In addition, we propose a novel technique to use a large number of unlabeled examples effectively by adding them gradually to a pool. ...
A novel plasmid vector pSELECT-1 is described which
can be used for highly efficient site-directed in vitro
mutagenesis. The mutagenesis method is based on the
use of single-stranded DNA and two primers, one
mutagenic primer and a second correction primer
which corrects a defect in the ampicillin resistance
gene on the vector and reverts the vector to ampicillin
resistance. Using T4 DNA polymerase and T4 DNA
ligase the two primers are physically linked on the
template. The non-mutant DNA strand is selected
against by growth in the presence of ampicillin.
Generalized Vector Space Models (GVSM) extend the standard Vector Space Model (VSM) by embedding additional types of information, besides terms, in the representation of documents. An interesting type of information that can be used in such models is semantic information from word thesauri like WordNet. Previous attempts to construct GVSM reported contradicting results. The most challenging problem is to incorporate the semantic information in a theoretically sound and rigorous manner and to modify the standard interpretation of the VSM.
The following will be discussed in this chapter: Identify the characteristics of distance vector routing protocols, describe the network discovery process of distance vector routing protocols using Routing Information Protocol (RIP), describe the processes to maintain accurate routing tables used by distance vector routing protocols,...
Objectives: Identify the characteristics of distance vector routing protocols. Describe the network discovery process of distance vector routing protocols using Routing Information Protocol (RIP). Describe the processes to maintain accurate routing tables used by distance vector routing protocols.