Welding joints are formed by welding two or more workpieces, made of metals or plastics, according to a particular geometry. The most common types are butt and lap joints; there are various lesser used welding joints including flange and corner joints. Invite you to reference the following presentations.
Designation: C 475 – 94
Standard Speciﬁcation for
Joint Compound and Joint Tape for Finishing Gypsum Board1
This standard is issued under the ﬁxed designation C 475; the number immediately following the designation indicates the year of original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A superscript epsilon (e) indicates an editorial change since the last revision or reapproval. This standard has been approved for use by agencies of the Department of Defense.
1. Scope 1.
The first edition of Couplings and Joints was published in 1986 and it took me
5 years to put it together. When I started work on this edition, I thought the task
would be a lot simpler, but its preparation has taken me almost as long. The new
edition is almost 100 pages longer. It has over 700 illustrations and tables (over
150 more than in the first edition) to help the reader understand coupling
design, selection, and application.
A thought-provoking book about the future of bone and joint disorders. This is the Decade of Bone and Joint, a time where rapid developments in our understanding of these disorders contend with massive increases in these chronic conditions throughout the world. By drawing on current knowledge and expertise, the book considers future scenarios such as the development of scientific theories, technology, prevention, diagnosis and treatment.
You have a choice in life. You can sputter and stumble and creak your way along in a process of painful, slow decline -- or you can take charge of your health and become a human dynamo. And there is no better way to insure a long, pain-free life than performing the right daily combination of joint mobility and strength-flexibility exercises.
Most previous work on multilingual sentiment analysis has focused on methods to adapt sentiment resources from resource-rich languages to resource-poor languages. We present a novel approach for joint bilingual sentiment classification at the sentence level that augments available labeled data in each language with unlabeled parallel data. We rely on the intuition that the sentiment labels for parallel sentences should be similar and present a model that jointly learns improved monolingual sentiment classifiers for each language. ...
We learn a joint model of sentence extraction and compression for multi-document summarization. Our model scores candidate summaries according to a combined linear model whose features factor over (1) the n-gram types in the summary and (2) the compressions used. We train the model using a marginbased objective whose loss captures end summary quality. Because of the exponentially large set of candidate summaries, we use a cutting-plane algorithm to incrementally detect and add active constraints efﬁciently. ...
The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efﬁcient decoding very hard. As a result, effective high order features representing rich contexts are inconvenient to use. In this work, we propose a novel stacked subword model for this task, concerning both efﬁciency and effectiveness.
Recent work on temporal relation identiﬁcation has focused on three types of relations between events: temporal relations between an event and a time expression, between a pair of events and between an event and the document creation time. These types of relations have mostly been identiﬁed in isolation by event pairwise comparison. However, this approach neglects logical constraints between temporal relations of different types that we believe to be helpful. We therefore propose a Markov Logic model that jointly identiﬁes relations of all three relation types simultaneously. ...
We present a global joint model for lemmatization and part-of-speech prediction. Using only morphological lexicons and unlabeled data, we learn a partiallysupervised part-of-speech tagger and a lemmatizer which are combined using features on a dynamically linked dependency structure of words. We evaluate our model on English, Bulgarian, Czech, and Slovene, and demonstrate substantial improvements over both a direct transduction approach to lemmatization and a pipelined approach, which predicts part-of-speech tags before lemmatization. ...
In this paper, we present a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging. Our word-character hybrid model offers high performance since it can handle both known and unknown words. We describe our strategies that yield good balance for learning the characteristics of known and unknown words and propose an errordriven policy that delivers such balance by acquiring examples of unknown words from particular errors in a training corpus.
Morphological processes in Semitic languages deliver space-delimited words which introduce multiple, distinct, syntactic units into the structure of the input sentence. These words are in turn highly ambiguous, breaking the assumption underlying most parsers that the yield of a tree for a given sentence is known in advance. Here we propose a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
We propose a cascaded linear model for joint Chinese word segmentation and partof-speech tagging. With a character-based perceptron as the core, combined with realvalued features such as language models, the cascaded model is able to efﬁciently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly. Experiments show that the cascaded model achieves improved accuracies on both segmentation only and joint segmentation and part-of-speech tagging. On the Penn Chinese Treebank 5.0, we obtain an error reduction of 18.
The central study question for the Unified Quest 2004 wargame (UQ 04), cosponsored by Joint Forces Command and the United States Army, focused on identifying the concepts and capabilities required to counteract an adversary who, having lost most of his conventional capability, seeks victory through a
The Ministry of Defence (MOD) of the United Kingdom has selected the Joint Strike Fighter (JSF) as a replacement for its Harrier aircraft and may buy up to 150 JSF aircraft. This report seeks to inform the MOD about the overlap of final assembly and repair, assess the suitability of four UK aerospace companies as potential
We describe a joint model for understanding user actions in natural language utterances. Our multi-layer generative approach uses both labeled and unlabeled utterances to jointly learn aspects regarding utterance’s target domain (e.g. movies), intention (e.g., ﬁnding a movie) along with other semantic units (e.g., movie name). We inject information extracted from unstructured web search query logs as prior information to enhance the generative process of the natural language utterance understanding model....
In hierarchical phrase-based SMT systems, statistical models are integrated to guide the hierarchical rule selection for better translation performance. Previous work mainly focused on the selection of either the source side of a hierarchical rule or the target side of a hierarchical rule rather than considering both of them simultaneously. This paper presents a joint model to predict the selection of hierarchical rules.
This paper presents a joint optimization method of a two-step conditional random ﬁeld (CRF) model for machine transliteration and a fast decoding algorithm for the proposed method. Our method lies in the category of direct orthographical mapping (DOM) between two languages without using any intermediate phonemic mapping. In the two-step CRF model, the ﬁrst CRF segments an input word into chunks and the second one converts each chunk into one unit in the target language. In this paper, we propose a method to jointly optimize the two-step CRFs and also a fast algorithm to realize it. ...
Marking up search queries with linguistic annotations such as part-of-speech tags, capitalization, and segmentation, is an important part of query processing and understanding in information retrieval systems. Due to their brevity and idiosyncratic structure, search queries pose a challenge to existing NLP tools. To address this challenge, we propose a probabilistic approach for performing joint query annotation. First, we derive a robust set of unsupervised independent annotations, using queries and pseudo-relevance feedback. ...
We present an unsupervised model for joint phrase alignment and extraction using nonparametric Bayesian methods and inversion transduction grammars (ITGs). The key contribution is that phrases of many granularities are included directly in the model through the use of a novel formulation that memorizes phrases generated not only by terminal, but also non-terminal symbols. This allows for a completely probabilistic model that is able to create a phrase table that achieves competitive accuracy on phrase-based machine translation tasks directly from unaligned sentence pairs. ...