Konnen Computer das Sprachproblem losen (Can Computers Solve the Language Problem)?

ERIC Educational Resources Information Center

Zeilinger, Michael

1972-01-01

Various computer applications in linguistics, primarily speech synthesis and machine translation, are reviewed. Although the computer proves useful for statistics, dictionary building and programmed instruction, the promulgation of a world auxiliary language is considered a more human and practical solution to the international communication…

Are we there yet?

PubMed

Cristianini, Nello

2010-05-01

Statistical approaches to Artificial Intelligence are behind most success stories of the field in the past decade. The idea of generating non-trivial behaviour by analysing vast amounts of data has enabled recommendation systems, search engines, spam filters, optical character recognition, machine translation and speech recognition, among other things. As we celebrate the spectacular achievements of this line of research, we need to assess its full potential and its limitations. What are the next steps to take towards machine intelligence? 2010 Elsevier Ltd. All rights reserved.

Machine Translation in Post-Contemporary Era

ERIC Educational Resources Information Center

Lin, Grace Hui Chin

2010-01-01

This article focusing on translating techniques via personal computer or laptop reports updated artificial intelligence progresses before 2010. Based on interpretations and information for field of MT [Machine Translation] by Yorick Wilks' book, "Machine Translation, Its scope and limits," this paper displays understandable theoretical frameworks…

Dependency Structures for Statistical Machine Translation

ERIC Educational Resources Information Center

Bach, Nguyen

2012-01-01

Dependency structures represent a sentence as a set of dependency relations. Normally the dependency structures from a tree connect all the words in a sentence. One of the most defining characters of dependency structures is the ability to bring long distance dependency between words to local dependency structures. Another the main attraction of…

Identifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort.

PubMed

Daems, Joke; Vandepitte, Sonia; Hartsuiker, Robert J; Macken, Lieve

2017-01-01

Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices' translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected.

Identifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort

PubMed Central

Daems, Joke; Vandepitte, Sonia; Hartsuiker, Robert J.; Macken, Lieve

2017-01-01

Translation Environment Tools make translators’ work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices’ translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected. PMID:28824482

Machine Translation Project

NASA Technical Reports Server (NTRS)

Bajis, Katie

1993-01-01

The characteristics and capabilities of existing machine translation systems were examined and procurement recommendations were developed. Four systems, SYSTRAN, GLOBALINK, PC TRANSLATOR, and STYLUS, were determined to meet the NASA requirements for a machine translation system. Initially, four language pairs were selected for implementation. These are Russian-English, French-English, German-English, and Japanese-English.

An Evaluation of Online Machine Translation of Arabic into English News Headlines: Implications on Students' Learning Purposes

ERIC Educational Resources Information Center

Kadhim, Kais A.; Habeeb, Luwaytha S.; Sapar, Ahmad Arifin; Hussin, Zaharah; Abdullah, Muhammad Ridhuan Tony Lim

2013-01-01

Nowadays, online Machine Translation (MT) is used widely with translation software, such as Google and Babylon, being easily available and downloadable. This study aims to test the translation quality of these two machine systems in translating Arabic news headlines into English. 40 Arabic news headlines were selected from three online sources,…

Quantum neural network based machine translator for Hindi to English.

PubMed

Narayan, Ravi; Singh, V P; Chakraverty, S

2014-01-01

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

Experiments with Cross-Language Information Retrieval on a Health Portal for Psychology and Psychotherapy.

PubMed

Andrenucci, Andrea

2016-01-01

Few studies have been performed within cross-language information retrieval (CLIR) in the field of psychology and psychotherapy. The aim of this paper is to to analyze and assess the quality of available query translation methods for CLIR on a health portal for psychology. A test base of 100 user queries, 50 Multi Word Units (WUs) and 50 Single WUs, was used. Swedish was the source language and English the target language. Query translation methods based on machine translation (MT) and dictionary look-up were utilized in order to submit query translations to two search engines: Google Site Search and Quick Ask. Standard IR evaluation measures and a qualitative analysis were utilized to assess the results. The lexicon extracted with word alignment of the portal's parallel corpus provided better statistical results among dictionary look-ups. Google Translate provided more linguistically correct translations overall and also delivered better retrieval results in MT.

Quantum Neural Network Based Machine Translator for Hindi to English

PubMed Central

Singh, V. P.; Chakraverty, S.

2014-01-01

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation. PMID:24977198

Using Linguistic Knowledge in Statistical Machine Translation

DTIC Science & Technology

2010-09-01

on newswire test data . . . . . . . . . . . . . . . . . . . . . 65 3.4 Arabic to English MT results for Arabic morphological segmentation, measured on...web test data. . . . . . . . . . . . . . . . . . . . . . . . 65 3.5 Recombination Results. Percentage of sentences with mis-combined words...scores for syntactic reordering of the Spoken Language Domain. 90 5.1 Normalized likelihood of the test set alignments without decision trees, and then

Second Evaluation of the SYSTRAN Automatic Translation System. Final Report.

ERIC Educational Resources Information Center

Van Slype, Georges

The machine translation system SYSTRAN was assessed for translation quality and system productivity. The test was carried out on translations from English to French dealing with food science and technology. Machine translations were compared to manual translations of the same texts. SYSTRAN was found to be a useful system of information…

Bean Soup Translation: Flexible, Linguistically-Motivated Syntax for Machine Translation

ERIC Educational Resources Information Center

Mehay, Dennis Nolan

2012-01-01

Machine translation (MT) systems attempt to translate texts from one language into another by translating words from a "source language" and rearranging them into fluent utterances in a "target language." When the two languages organize concepts in very different ways, knowledge of their general sentence structure, or…

Translation: Aids, Robots, and Automation.

ERIC Educational Resources Information Center

Andreyewsky, Alexander

1981-01-01

Examines electronic aids to translation both as ways to automate it and as an approach to solve problems resulting from shortage of qualified translators. Describes the limitations of robotic MT (Machine Translation) systems, viewing MAT (Machine-Aided Translation) as the only practical solution and the best vehicle for further automation. (MES)

Exploring the Further Integration of Machine Translation in English-Chinese Cross Language Information Access

ERIC Educational Resources Information Center

Wu, Dan; He, Daqing

2012-01-01

Purpose: This paper seeks to examine the further integration of machine translation technologies with cross language information access in providing web users the capabilities of accessing information beyond language barriers. Machine translation and cross language information access are related technologies, and yet they have their own unique…

Dictionary Based Machine Translation from Kannada to Telugu

NASA Astrophysics Data System (ADS)

Sindhu, D. V.; Sagar, B. M.

2017-08-01

Machine Translation is a task of translating from one language to another language. For the languages with less linguistic resources like Kannada and Telugu Dictionary based approach is the best approach. This paper mainly focuses on Dictionary based machine translation for Kannada to Telugu. The proposed methodology uses dictionary for translating word by word without much correlation of semantics between them. The dictionary based machine translation process has the following sub process: Morph analyzer, dictionary, transliteration, transfer grammar and the morph generator. As a part of this work bilingual dictionary with 8000 entries is developed and the suffix mapping table at the tag level is built. This system is tested for the children stories. In near future this system can be further improved by defining transfer grammar rules.

The Impact of Machine Translation and Computer-aided Translation on Translators

NASA Astrophysics Data System (ADS)

Peng, Hao

2018-03-01

Under the context of globalization, communications between countries and cultures are becoming increasingly frequent, which make it imperative to use some techniques to help translate. This paper is to explore the influence of computer-aided translation on translators, which is derived from the field of the computer-aided translation (CAT) and machine translation (MT). Followed by an introduction to the development of machine and computer-aided translation, it then depicts the technologies practicable to translators, which are trying to analyze the demand of designing the computer-aided translation so far in translation practice, and optimize the designation of computer-aided translation techniques, and analyze its operability in translation. The findings underline the advantages and disadvantages of MT and CAT tools, and the serviceability and future development of MT and CAT technologies. Finally, this thesis probes into the impact of these new technologies on translators in hope that more translators and translation researchers can learn to use such tools to improve their productivity.

Feasibility Study on Fully Automatic High Quality Translation: Volume II. Final Technical Report.

ERIC Educational Resources Information Center

Lehmann, Winifred P.; Stachowitz, Rolf

This second volume of a two-volume report on a fully automatic high quality translation (FAHQT) contains relevant papers contributed by specialists on the topic of machine translation. The papers presented here cover such topics as syntactical analysis in transformational grammar and in machine translation, lexical features in translation and…

An Evaluation of Output Quality of Machine Translation (Padideh Software vs. Google Translate)

ERIC Educational Resources Information Center

Azer, Haniyeh Sadeghi; Aghayi, Mohammad Bagher

2015-01-01

This study aims to evaluate the translation quality of two machine translation systems in translating six different text-types, from English to Persian. The evaluation was based on criteria proposed by Van Slype (1979). The proposed model for evaluation is a black-box type, comparative and adequacy-oriented evaluation. To conduct the evaluation, a…

Adaptation of machine translation for multilingual information retrieval in the medical domain.

PubMed

Pecina, Pavel; Dušek, Ondřej; Goeuriot, Lorraine; Hajič, Jan; Hlaváčová, Jaroslava; Jones, Gareth J F; Kelly, Liadh; Leveling, Johannes; Mareček, David; Novák, Michal; Popel, Martin; Rosa, Rudolf; Tamchyna, Aleš; Urešová, Zdeňka

2014-07-01

We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve effectiveness of cross-lingual IR. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech-English, German-English, and French-English. MT quality is evaluated on data sets created within the Khresmoi project and IR effectiveness is tested on the CLEF eHealth 2013 data sets. The search query translation results achieved in our experiments are outstanding - our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech-English, from 23.03 to 40.82 for German-English, and from 32.67 to 40.82 for French-English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French-English. For Czech-English and German-English, the increased MT quality does not lead to better IR results. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance - better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions. Copyright © 2014 Elsevier B.V. All rights reserved.

Machine Translation for Academic Purposes

ERIC Educational Resources Information Center

Lin, Grace Hui-chin; Chien, Paul Shih Chieh

2009-01-01

Due to the globalization trend and knowledge boost in the second millennium, multi-lingual translation has become a noteworthy issue. For the purposes of learning knowledge in academic fields, Machine Translation (MT) should be noticed not only academically but also practically. MT should be informed to the translating learners because it is a…

The Dostoevsky Machine in Georgetown: scientific translation in the Cold War.

PubMed

Gordin, Michael D

2016-04-01

Machine Translation (MT) is now ubiquitous in discussions of translation. The roots of this phenomenon - first publicly unveiled in the so-called 'Georgetown-IBM Experiment' on 9 January 1954 - displayed not only the technological utopianism still associated with dreams of a universal computer translator, but was deeply enmeshed in the political pressures of the Cold War and a dominating conception of scientific writing as both the goal of machine translation as well as its method. Machine translation was created, in part, as a solution to a perceived crisis sparked by the massive expansion of Soviet science. Scientific prose was also perceived as linguistically simpler, and so served as the model for how to turn a language into a series of algorithms. This paper follows the rise of the Georgetown program - the largest single program in the world - from 1954 to the (as it turns out, temporary) collapse of MT in 1964.

Machine Translation as a Model for Overcoming Some Common Errors in English-into-Arabic Translation among EFL University Freshmen

ERIC Educational Resources Information Center

El-Banna, Adel I.; Naeem, Marwa A.

2016-01-01

This research work aimed at making use of Machine Translation to help students avoid some syntactic, semantic and pragmatic common errors in translation from English into Arabic. Participants were a hundred and five freshmen who studied the "Translation Common Errors Remedial Program" prepared by the researchers. A testing kit that…

Evaluating SPLASH-2 Applications Using MapReduce

NASA Astrophysics Data System (ADS)

Zhu, Shengkai; Xiao, Zhiwei; Chen, Haibo; Chen, Rong; Zhang, Weihua; Zang, Binyu

MapReduce has been prevalent for running data-parallel applications. By hiding other non-functionality parts such as parallelism, fault tolerance and load balance from programmers, MapReduce significantly simplifies the programming of large clusters. Due to the mentioned features of MapReduce above, researchers have also explored the use of MapReduce on other application domains, such as machine learning, textual retrieval and statistical translation, among others.

Machine Translation in the German Classroom: Detection, Reaction, Prevention

ERIC Educational Resources Information Center

Steding, Soren

2009-01-01

There are many websites today that offer free machine translations and although beginning students of German are not always proficient enough to judge the quality of these translations or to fully understand certain translation results, they use these services nonetheless for their assignments. The problem for the educator is to distinguish…

NASA's online machine aided indexing system

NASA Technical Reports Server (NTRS)

Silvester, June P.; Genuardi, Michael T.; Klingbiel, Paul H.

1993-01-01

This report describes the NASA Lexical Dictionary, a machine aided indexing system used online at the National Aeronautics and Space Administration's Center for Aerospace Information (CASI). This system is comprised of a text processor that is based on the computational, non-syntactic analysis of input text, and an extensive 'knowledge base' that serves to recognize and translate text-extracted concepts. The structure and function of the various NLD system components are described in detail. Methods used for the development of the knowledge base are discussed. Particular attention is given to a statistically-based text analysis program that provides the knowledge base developer with a list of concept-specific phrases extracted from large textual corpora. Production and quality benefits resulting from the integration of machine aided indexing at CASI are discussed along with a number of secondary applications of NLD-derived systems including on-line spell checking and machine aided lexicography.

Quantifying the Efficiency of a Translator: The Effect of Syntactical and Literal Written Translations on Language Comprehension Using the Machine Translation System FALCon

ERIC Educational Resources Information Center

McCulloh, Ian A.; Morton, Jillian; Jantzi, Jennifer K.; Rodriguez, Amy M.; Graham, John

2008-01-01

This study introduces a new method of evaluating human comprehension in the context of machine translation using a language translation program known as the FALCon (Forward Area Language Converter). The participants include 48 freshmen from the United States Military Academy enrolled in the General Psychology course, PL100. Results of this study…

Soviet Patent Bulletin Processing: A Particular Application of Machine Translation.

ERIC Educational Resources Information Center

Bostad, Dale A.

1985-01-01

Describes some of the processes involved in the data structure manipulation and machine translation of a specific text form, namely, Soviet patent bulletins. The effort to modify this system in order to do specialized processing and translation is detailed. (Author/SED)

Automatic translation among spoken languages

NASA Technical Reports Server (NTRS)

Walter, Sharon M.; Costigan, Kelly

1994-01-01

The Machine Aided Voice Translation (MAVT) system was developed in response to the shortage of experienced military field interrogators with both foreign language proficiency and interrogation skills. Combining speech recognition, machine translation, and speech generation technologies, the MAVT accepts an interrogator's spoken English question and translates it into spoken Spanish. The spoken Spanish response of the potential informant can then be translated into spoken English. Potential military and civilian applications for automatic spoken language translation technology are discussed in this paper.

A translator and simulator for the Burroughs D machine

NASA Technical Reports Server (NTRS)

Roberts, J.

1972-01-01

The D Machine is described as a small user microprogrammable computer designed to be a versatile building block for such diverse functions as: disk file controllers, I/O controllers, and emulators. TRANSLANG is an ALGOL-like language, which allows D Machine users to write microprograms in an English-like format as opposed to creating binary bit pattern maps. The TRANSLANG translator parses TRANSLANG programs into D Machine microinstruction bit patterns which can be executed on the D Machine simulator. In addition to simulation and translation, the two programs also offer several debugging tools, such as: a full set of diagnostic error messages, register dumps, simulated memory dumps, traces on instructions and groups of instructions, and breakpoints.

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

PubMed

Dethier, Julie; Nuyujukian, Paul; Eliasmith, Chris; Stewart, Terry; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

2011-01-01

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

Quantifying the Efficiency of a Translator: The Effect of Syntactical and Literal Written Translations on Language Comprehension Using the Machine Translation System FALCon (Foreign Area Language Converter)

ERIC Educational Resources Information Center

McCulloh, Ian A.; Morton, Jillian; Jantzi, Jennifer K.; Rodriguez, Amy M.; Graham, John

2008-01-01

The purpose of this study is to introduce a new method of evaluating human comprehension in the context of machine translation using a language translation program known as the FALCon (Forward Area Language Converter). The FALCon works by converting documents into digital images via scanner, and then converting those images to electronic text by…

English to Sanskrit Machine Translation Using Transfer Based approach

NASA Astrophysics Data System (ADS)

Pathak, Ganesh R.; Godse, Sachin P.

2010-11-01

Translation is one of the needs of global society for communicating thoughts and ideas of one country with other country. Translation is the process of interpretation of text meaning and subsequent production of equivalent text, also called as communicating same meaning (message) in another language. In this paper we gave detail information on how to convert source language text in to target language text using Transfer Based Approach for machine translation. Here we implemented English to Sanskrit machine translator using transfer based approach. English is global language used for business and communication but large amount of population in India is not using and understand the English. Sanskrit is ancient language of India most of the languages in India are derived from Sanskrit. Sanskrit can be act as an intermediate language for multilingual translation.

The Integration of Project-Based Methodology into Teaching in Machine Translation

ERIC Educational Resources Information Center

Madkour, Magda

2016-01-01

This quantitative-qualitative analytical research aimed at investigating the effect of integrating project-based teaching methodology into teaching machine translation on students' performance. Data was collected from the graduate students in the College of Languages and Translation, at Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi…

Chunk Alignment for Corpus-Based Machine Translation

ERIC Educational Resources Information Center

Kim, Jae Dong

2011-01-01

Since sub-sentential alignment is critically important to the translation quality of an Example-Based Machine Translation (EBMT) system, which operates by finding and combining phrase-level matches against the training examples, we developed a new alignment algorithm for the purpose of improving the EBMT system's performance. This new…

Machine translation project alternatives analysis

NASA Technical Reports Server (NTRS)

Bajis, Catherine J.; Bedford, Denise A. D.

1993-01-01

The Machine Translation Project consists of several components, two of which, the Project Plan and the Requirements Analysis, have already been delivered. The Project Plan details the overall rationale, objectives and time-table for the project as a whole. The Requirements Analysis compares a number of available machine translation systems, their capabilities, possible configurations, and costs. The Alternatives Analysis has resulted in a number of conclusions and recommendations to the NASA STI program concerning the acquisition of specific MT systems and related hardware and software.

The evolution and practical application of machine translation system (1)

NASA Astrophysics Data System (ADS)

Tominaga, Isao; Sato, Masayuki

This paper describes a development, practical applicatioin, problem of a system, evaluation of practical system, and development trend of machine translation. Most recent system contains next four problems. 1) the vagueness of a text, 2) a difference of the definition of the terminology between different language, 3) the preparing of a large-scale translation dictionary, 4) the development of a software for the logical inference. Machine translation system is already used practically in many industry fields. However, many problems are not solved. The implementation of an ideal system will be after 15 years. Also, this paper described seven evaluation items detailedly. This English abstract was made by Mu system.

When Machines Think: Radiology's Next Frontier.

PubMed

Dreyer, Keith J; Geis, J Raymond

2017-12-01

Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans' abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017.

Interlingual Machine Translation: Prospects and Setbacks

ERIC Educational Resources Information Center

Acikgoz, Firat; Sert, Olcay

2006-01-01

This study, in an attempt to rise above the intricacy of "being informed on the verge of globalization," is founded on the premise that Machine Translation (MT) applications searching for an ideal key to find a universal foundation for all natural languages have a restricted say over the translation process at various discourse levels. Our paper…

Statistical Machine Translation of Japanese

DTIC Science & Technology

2007-03-01

hiragana and katakana) syllabaries…………………….. 20 3.2 Sample Japanese sentence showing kanji and kana……………………... 21 3.5 Japanese formality example...syllabary. 19 Figure 3.1. Japanese kana syllabaries, hiragana for native Japanese words, word endings, and particles, and katakana for foreign...Figure 3.2. Simple Japanese sentence showing the use of kanji, hiragana , and katakana. Kanji is used for nouns and verb, adjective, and

Improving Domain-specific Machine Translation by Constraining the Language Model

DTIC Science & Technology

2012-07-01

performance. To make up for the lack of parallel training data, one assumption is that more monolingual target language data should be used in building the...target language model. Prior work on domain-specific MT has focused on training target language models with monolingual 2 domain-specific data...showed that the using a large dictionary extracted from medical domain documents in a statistical MT system to generalize the training data significantly

U.S. Army Research Laboratory (ARL) Corporate Dari Document Transcription and Translation Guidelines

DTIC Science & Technology

2012-10-01

text file format. 15. SUBJECT TERMS Transcription, Translation, guidelines, ground truth, Optical character recognition , OCR, Machine Translation, MT...foreign language into a target language in order to train, test, and evaluate optical character recognition (OCR) and machine translation (MT) embedded...graphic element and should not be transcribed. Elements that are not part of the primary text such as handwritten annotations or stamps should not be

JTEC panel report on machine translation in Japan

NASA Technical Reports Server (NTRS)

Carbonell, Jaime; Rich, Elaine; Johnson, David; Tomita, Masaru; Vasconcellos, Muriel; Wilks, Yorick

1992-01-01

The goal of this report is to provide an overview of the state of the art of machine translation (MT) in Japan and to provide a comparison between Japanese and Western technology in this area. The term 'machine translation' as used here, includes both the science and technology required for automating the translation of text from one human language to another. Machine translation is viewed in Japan as an important strategic technology that is expected to play a key role in Japan's increasing participation in the world economy. MT is seen in Japan as important both for assimilating information into Japanese as well as for disseminating Japanese information throughout the world. Most of the MT systems now available in Japan are transfer-based systems. The majority of them exploit a case-frame representation of the source text as the basis of the transfer process. There is a gradual movement toward the use of deeper semantic representations, and some groups are beginning to look at interlingua-based systems.

Machine Translation of Public Health Materials From English to Chinese: A Feasibility Study

PubMed Central

Desai, Loma

2015-01-01

Background Chinese is the second most common language spoken by limited English proficiency individuals in the United States, yet there are few public health materials available in Chinese. Previous studies have indicated that use of machine translation plus postediting by bilingual translators generated quality translations in a lower time and at a lower cost than human translations. Objective The purpose of this study was to investigate the feasibility of using machine translation (MT) tools (eg, Google Translate) followed by human postediting (PE) to produce quality Chinese translations of public health materials. Methods From state and national public health websites, we collected 60 health promotion documents that had been translated from English to Chinese through human translation. The English version of the documents were then translated to Chinese using Google Translate. The MTs were analyzed for translation errors. A subset of the MT documents was postedited by native Chinese speakers with health backgrounds. Postediting time was measured. Postedited versions were then blindly compared against human translations by bilingual native Chinese quality raters. Results The most common machine translation errors were errors of word sense (40%) and word order (22%). Posteditors corrected the MTs at a rate of approximately 41 characters per minute. Raters, blinded to the source of translation, consistently selected the human translation over the MT+PE. Initial investigation to determine the reasons for the lower quality of MT+PE indicate that poor MT quality, lack of posteditor expertise, and insufficient posteditor instructions can be barriers to producing quality Chinese translations. Conclusions Our results revealed problems with using MT tools plus human postediting for translating public health materials from English to Chinese. Additional work is needed to improve MT and to carefully design postediting processes before the MT+PE approach can be used routinely in public health practice for a variety of language pairs. PMID:27227135

Machine Translation-Assisted Language Learning: Writing for Beginners

ERIC Educational Resources Information Center

Garcia, Ignacio; Pena, Maria Isabel

2011-01-01

The few studies that deal with machine translation (MT) as a language learning tool focus on its use by advanced learners, never by beginners. Yet, freely available MT engines (i.e. Google Translate) and MT-related web initiatives (i.e. Gabble-on.com) position themselves to cater precisely to the needs of learners with a limited command of a…

Advancement in Productivity of Arabic into English Machine Translation Systems from 2008 to 2013

ERIC Educational Resources Information Center

Abu-Al-Sha'r, Awatif M.; AbuSeileek, Ali F.

2013-01-01

This paper attempts to compare between the advancements in the productivity of Arabic into English Machine Translation Systems between two years, 2008 and 2013. It also aims to evaluate the progress achieved by various systems of Arabic into English electronic translation between the two years. For tracing such advancement, a comparative analysis…

Ausdruckskraft und Regelmaessigkeit: Was Esperanto fuer automatische Uebersetzung geeignet macht (Expressiveness and Formal Regularity: What Makes Esperanto Suitable for Machine Translation).

ERIC Educational Resources Information Center

Schubert, Klaus

1988-01-01

Describes DLT, the multilingual machine translation system that uses Esperanto as an intermediate language in which substantial portions of the translation subprocesses are carried out. The criteria for choosing an intermediate language and the reasons for preferring Esperanto over other languages are explained. (Author/DJD)

An Evaluative Study of Machine Translation in the EFL Scenario of Saudi Arabia

ERIC Educational Resources Information Center

Al-Tuwayrish, Raneem Khalid

2016-01-01

Artificial Intelligence or AI as it is popularly known and its corollary, Machine Translation (MT) have long engaged scientists, thinkers and linguists alike in the twenty first century. However, the wider question that lies in the relation between technology and translation is, What does technology do to language? This is an important question in…

Machine vs. human translation of SNOMED CT terms.

PubMed

Schulz, Stefan; Bernhardt-Melischnig, Johannes; Kreuzthaler, Markus; Daumke, Philipp; Boeker, Martin

2013-01-01

In the context of past and current SNOMED CT translation projects we compare three kinds of SNOMED CT translations from English to German by: (t1) professional medical translators; (t2) a free Web-based machine translation service; (t3) medical students. 500 SNOMED CT fully specified names from the (English) International release were randomly selected. Based on this, German translations t1, t2, and t3 were generated. A German and an Austrian physician rated the translations for linguistic correctness and content fidelity. Kappa for inter-rater reliability was 0.4 for linguistic correctness and 0.23 for content fidelity. Average ratings of linguistic correctness did not differ significantly between human translation scenarios. Content fidelity was rated slightly better for student translators compared to professional translators. Comparing machine to human translation, the linguistic correctness differed about 0.5 scale units in favour of the human translation and about 0.25 regarding content fidelity, equally in favour of the human translation. The results demonstrate that low-cost translation solutions of medical terms may produce surprisingly good results. Although we would not recommend low-cost translation for producing standardized preferred terms, this approach can be useful for creating additional language-specific entry terms. This may serve several important use cases. We also recommend testing this method to bootstrap a crowdsourcing process, by which term translations are gathered, improved, maintained, and rated by the user community.

Modeling and prediction of human word search behavior in interactive machine translation

NASA Astrophysics Data System (ADS)

Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

2017-12-01

As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

Translation Analysis on Civil Engineering Text Produced by Machine Translator

NASA Astrophysics Data System (ADS)

Sutopo, Anam

2018-02-01

Translation is extremely needed in communication since people have serious problem in the language used. Translation activity is done by the person in charge for translating the material. Translation activity is also able to be done by machine. It is called machine translation, reflected in the programs developed by programmer. One of them is Transtool. Many people used Transtool for helping them in solving the problem related with translation activities. This paper wants to deliver how important is the Transtool program, how effective is Transtool program and how is the function of Transtool for human business. This study applies qualitative research. The sources of data were document and informant. This study used documentation and in dept-interviewing as the techniques for collecting data. The collected data were analyzed by using interactive analysis. The results of the study show that, first; Transtool program is helpful for people in translating the civil engineering text and it functions as the aid or helper, second; the working of Transtool software program is effective enough and third; the result of translation produced by Transtool is good for short and simple sentences and not readable, not understandable and not accurate for long sentences (compound, complex and compound complex) thought the result is informative. The translated material must be edited by the professional translator.

Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques

DTIC Science & Technology

2007-08-01

In this domain, queries typically show a deeply nested structure, which makes the semantic parsing task rather challenging , e.g.: What states border...only 80% of the GEOQUERY queries are semantically tractable, which shows that GEOQUERY is indeed a more challenging domain than ATIS. Note that none...a particularly challenging task, because of the inherent ambiguity of natural languages on both sides. It has inspired a large body of research. In

Our Policies, Their Text: German Language Students' Strategies with and Beliefs about Web-Based Machine Translation

ERIC Educational Resources Information Center

White, Kelsey D.; Heidrich, Emily

2013-01-01

Most educators are aware that some students utilize web-based machine translators for foreign language assignments, however, little research has been done to determine how and why students utilize these programs, or what the implications are for language learning and teaching. In this mixed-methods study we utilized surveys, a translation task,…

Complex Networks Analysis of Manual and Machine Translations

NASA Astrophysics Data System (ADS)

Amancio, Diego R.; Antiqueira, Lucas; Pardo, Thiago A. S.; da F. Costa, Luciano; Oliveira, Osvaldo N.; Nunes, Maria G. V.

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by MT tools can be distinguished from their manual counterparts by means of metrics such as in- (ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better MT tools and automatic evaluation metrics.

Linguistic steganography on Twitter: hierarchical language modeling with manual interaction

NASA Astrophysics Data System (ADS)

Wilson, Alex; Blunsom, Phil; Ker, Andrew D.

2014-02-01

This work proposes a natural language stegosystem for Twitter, modifying tweets as they are written to hide 4 bits of payload per tweet, which is a greater payload than previous systems have achieved. The system, CoverTweet, includes novel components, as well as some already developed in the literature. We believe that the task of transforming covers during embedding is equivalent to unilingual machine translation (paraphrasing), and we use this equivalence to de ne a distortion measure based on statistical machine translation methods. The system incorporates this measure of distortion to rank possible tweet paraphrases, using a hierarchical language model; we use human interaction as a second distortion measure to pick the best. The hierarchical language model is designed to model the speci c language of the covers, which in this setting is the language of the Twitter user who is embedding. This is a change from previous work, where general-purpose language models have been used. We evaluate our system by testing the output against human judges, and show that humans are unable to distinguish stego tweets from cover tweets any better than random guessing.

Chapter 16: text mining for translational bioinformatics.

PubMed

Cohen, K Bretonnel; Hunter, Lawrence E

2013-04-01

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

Evaluating the Use of Machine Translation Post-Editing in the Foreign Language Class

ERIC Educational Resources Information Center

Nino, Ana

2008-01-01

Generalised access to the Internet and globalisation has led to increased demand for translation services and a resurgence in the use of machine translation (MT) systems. MT post-editing or the correction of MT output to an acceptable standard is known to be one of the ways to face the huge demand on multilingual communication. Given that the use…

Learning disordered topological phases by statistical recovery of symmetry

NASA Astrophysics Data System (ADS)

Yoshioka, Nobuyuki; Akagi, Yutaka; Katsura, Hosho

2018-05-01

We apply the artificial neural network in a supervised manner to map out the quantum phase diagram of disordered topological superconductors in class DIII. Given the disorder that keeps the discrete symmetries of the ensemble as a whole, translational symmetry which is broken in the quasiparticle distribution individually is recovered statistically by taking an ensemble average. By using this, we classify the phases by the artificial neural network that learned the quasiparticle distribution in the clean limit and show that the result is totally consistent with the calculation by the transfer matrix method or noncommutative geometry approach. If all three phases, namely the Z2, trivial, and thermal metal phases, appear in the clean limit, the machine can classify them with high confidence over the entire phase diagram. If only the former two phases are present, we find that the machine remains confused in a certain region, leading us to conclude the detection of the unknown phase which is eventually identified as the thermal metal phase.

Machine Aids to Translation.

ERIC Educational Resources Information Center

Brinkmann, Karl-Heinz

1981-01-01

Describes the TEAM Program System of the Siemens Language Services Department, particularly the main features of its terminology data bank. Discusses criteria to which stored terminology must conform and methods of data bank utilization. Concludes by summarizing the consequences that machine-aided translation development has had for the…

Machine Translation: The Alternative for the 21st Century?

ERIC Educational Resources Information Center

Cribb, V. Michael

2000-01-01

Outlines a scenario for the future of Teaching English as a Second or Other Languages that has seldom, if ever been considered in academic discussion: that advances in and availability of quality machine translation could mitigate the need for English language learning. (Author/VWL)

Translation of Japanese Noun Compounds at Super-Function Based MT System

NASA Astrophysics Data System (ADS)

Zhao, Xin; Ren, Fuji; Kuroiwa, Shingo

Noun compounds are frequently encountered construction in nature language processing (NLP), consisting of a sequence of two or more nouns which functions syntactically as one noun. The translation of noun compounds has become a major issue in Machine Translation (MT) due to their frequency of occurrence and high productivity. In our previous studies on Super-Function Based Machine Translation (SFBMT), we have found that noun compounds are very frequently used and difficult to be translated correctly, the overgeneration of noun compounds can be dangerous as it may introduce ambiguity in the translation. In this paper, we discuss the challenges in handling Japanese noun compounds in an SFBMT system, we present a shallow method for translating noun compounds by using a word level translation dictionary and target language monolingual corpus.

Experimental validation of a distribution theory based analysis of the effect of manufacturing tolerances on permanent magnet synchronous machines

NASA Astrophysics Data System (ADS)

Boscaino, V.; Cipriani, G.; Di Dio, V.; Corpora, M.; Curto, D.; Franzitta, V.; Trapanese, M.

2017-05-01

An experimental study on the effect of permanent magnet tolerances on the performances of a Tubular Linear Ferrite Motor is presented in this paper. The performances that have been investigated are: cogging force, end effect cogging force and generated thrust. It is demonstrated that: 1) the statistical variability of the magnets introduces harmonics in the spectrum of the cogging force; 2) the value of the end effect cogging force is directly linked to the values of then remanence field of the external magnets placed on the slider; 3) the generated thrust and its statistical distribution depend on the remanence field of the magnets placed on the translator.

Development of German-English Machine Translation System.

ERIC Educational Resources Information Center

Lehmann, Winifred P.; Stachowitz, Rolf

This report documents efforts over a five-month period toward completion of a pilot system for machine translation of German scientific and technical literature into English. The report is divided into three areas: grammar formalism, programming, and linguistics. Work on grammar formalism concentrated mainly on increasing the power of the…

MACHINE TRANSLATION RESEARCH DURING THE PAST TWO YEARS.

ERIC Educational Resources Information Center

LEHMANN, W.P.

THE AUTHOR RECOUNTS THE RISE IN IMPORTANCE OF MACHINE TRANSLATION, WHICH TOGETHER WITH LANGUAGE LEARNING AND TEACHING COMPRISE THE MAJOR FIELDS OF APPLIED LINGUISTICS. MUCH OF THE RECENT THEORETICAL WORK ON LANGUAGE DEALS WITH THE PROBLEM OF THE RELATIONSHIP BETWEEN THE SURFACE SYNTACTIC STRUCTURE OF LANGUAGE AND THE UNDERLYING STRUCTURE. THE…

A Double-Sided Linear Primary Permanent Magnet Vernier Machine

PubMed Central

2015-01-01

The purpose of this paper is to present a new double-sided linear primary permanent magnet (PM) vernier (DSLPPMV) machine, which can offer high thrust force, low detent force, and improved power factor. Both PMs and windings of the proposed machine are on the short translator, while the long stator is designed as a double-sided simple iron core with salient teeth so that it is very robust to transmit high thrust force. The key of this new machine is the introduction of double stator and the elimination of translator yoke, so that the inductance and the volume of the machine can be reduced. Hence, the proposed machine offers improved power factor and thrust force density. The electromagnetic performances of the proposed machine are analyzed including flux, no-load EMF, thrust force density, and inductance. Based on using the finite element analysis, the characteristics and performances of the proposed machine are assessed. PMID:25874250

A double-sided linear primary permanent magnet vernier machine.

PubMed

Du, Yi; Zou, Chunhua; Liu, Xianxing

2015-01-01

The purpose of this paper is to present a new double-sided linear primary permanent magnet (PM) vernier (DSLPPMV) machine, which can offer high thrust force, low detent force, and improved power factor. Both PMs and windings of the proposed machine are on the short translator, while the long stator is designed as a double-sided simple iron core with salient teeth so that it is very robust to transmit high thrust force. The key of this new machine is the introduction of double stator and the elimination of translator yoke, so that the inductance and the volume of the machine can be reduced. Hence, the proposed machine offers improved power factor and thrust force density. The electromagnetic performances of the proposed machine are analyzed including flux, no-load EMF, thrust force density, and inductance. Based on using the finite element analysis, the characteristics and performances of the proposed machine are assessed.

Machine Translation from Text

NASA Astrophysics Data System (ADS)

Habash, Nizar; Olive, Joseph; Christianson, Caitlin; McCary, John

Machine translation (MT) from text, the topic of this chapter, is perhaps the heart of the GALE project. Beyond being a well defined application that stands on its own, MT from text is the link between the automatic speech recognition component and the distillation component. The focus of MT in GALE is on translating from Arabic or Chinese to English. The three languages represent a wide range of linguistic diversity and make the GALE MT task rather challenging and exciting.

Machine-Aided Translation: From Terminology Banks to Interactive Translation Systems.

ERIC Educational Resources Information Center

Greenfield, Concetta C.; Serain, Daniel

The rapid growth of the need for technical translations in recent years has led specialists to utilize computer technology to improve the efficiency and quality of translation. The two approaches considered were automatic translation and terminology banks. Since the results of fully automatic translation were considered unsatisfactory by various…

Machine Translation in China--A Report.

ERIC Educational Resources Information Center

Yong-quan, Liu

1980-01-01

Reviews the history of machine translation (MT) research in China. Describes Chinese MT techniques as based on syntactic analysis, full use of fixed phrases, the key role of funtion words, and emphasis on formal analysis without neglecting meaning. (Available from ALLC, Dr. Rex Last, German Dept., Univ. of Hull, Hull HU57RX, England.) (Author/MES)

Chinese-English Machine Translation System.

ERIC Educational Resources Information Center

Wang, William S-Y; And Others

The report documents results of a two-year R&D effort directed at the completion of a prototype system for Chinese-English machine translation of S&T literature. The system, designated QUINCE, accepts Chinese input exactly as printed, with no pre-editing of any kind, and produces English output on experimental basis. Coding of Chinese text via…

Development of Chinese-English Machine Translation System. Fnal Technical Report.

ERIC Educational Resources Information Center

Wang, William S-Y; Chan, Stephen W.

The report documents progress and results of a 2-1/3 year effort to further the prototype Chinese-English Machine Translation System. Additional rules were incorporated into the existing grammar for Chinese analysis and interlingual transfer, with emphasis on the latter. CHIDIC was updated and revised. Approximately 16,000 new entries were added…

Modeling workflow to design machine translation applications for public health practice

PubMed Central

Turner, Anne M.; Brownstein, Megumu K.; Cole, Kate; Karasz, Hilary; Kirchhoff, Katrin

2014-01-01

Objective Provide a detailed understanding of the information workflow processes related to translating health promotion materials for limited English proficiency individuals in order to inform the design of context-driven machine translation (MT) tools for public health (PH). Materials and Methods We applied a cognitive work analysis framework to investigate the translation information workflow processes of two large health departments in Washington State. Researchers conducted interviews, performed a task analysis, and validated results with PH professionals to model translation workflow and identify functional requirements for a translation system for PH. Results The study resulted in a detailed description of work related to translation of PH materials, an information workflow diagram, and a description of attitudes towards MT technology. We identified a number of themes that hold design implications for incorporating MT in PH translation practice. A PH translation tool prototype was designed based on these findings. Discussion This study underscores the importance of understanding the work context and information workflow for which systems will be designed. Based on themes and translation information workflow processes, we identified key design guidelines for incorporating MT into PH translation work. Primary amongst these is that MT should be followed by human review for translations to be of high quality and for the technology to be adopted into practice. Counclusion The time and costs of creating multilingual health promotion materials are barriers to translation. PH personnel were interested in MT's potential to improve access to low-cost translated PH materials, but expressed concerns about ensuring quality. We outline design considerations and a potential machine translation tool to best fit MT systems into PH practice. PMID:25445922

Exploring Local Public Health Workflow in the Context of Automated Translation Technologies

PubMed Central

Mandel, Hannah; Turner, Anne M.

2013-01-01

Despite the growing limited English proficiency (LEP) population in the US, and federal regulations requiring multilingual health information be available for LEP individuals, there is a lack of available high quality multilingual health promotion materials. The costs and personnel time associated with creating high quality translations serve as barriers to their creation, especially in resource limited public health settings. To explore the potential adoption of novel machine translation and document dissemination technologies for improving the creation and sharing of translated public health materials, we interviewed key health department personnel in Washington State. We analyzed translation workflow, elucidated key themes regarding public health translation work, and assessed attitudes towards electronic document exchange and machine translation. Public health personnel expressed the need for human quality assurance and oversight, but appreciated the potential of novel information technologies to assist in the production and dissemination of translated materials for public health practice. PMID:24551385

Local health department translation processes: potential of machine translation technologies to help meet needs.

PubMed

Turner, Anne M; Mandel, Hannah; Capurro, Daniel

2013-01-01

Limited English proficiency (LEP), defined as a limited ability to read, speak, write, or understand English, is associated with health disparities. Despite federal and state requirements to translate health information, the vast majority of health materials are solely available in English. This project investigates barriers to translation of health information and explores new technologies to improve access to multilingual public health materials. We surveyed all 77 local health departments (LHDs) in the Northwest about translation needs, practices, barriers and attitudes towards machine translation (MT). We received 67 responses from 45 LHDs. Translation of health materials is the principle strategy used by LHDs to reach LEP populations. Cost and access to qualified translators are principle barriers to producing multilingual materials. Thirteen LHDs have used online MT tools. Many respondents expressed concerns about the accuracy of MT. Overall, respondents were positive about its potential use, if low costs and quality could be assured.

Local Health Department Translation Processes: Potential of Machine Translation Technologies to Help Meet Needs

PubMed Central

Turner, Anne M.; Mandel, Hannah; Capurro, Daniel

2013-01-01

Limited English proficiency (LEP), defined as a limited ability to read, speak, write, or understand English, is associated with health disparities. Despite federal and state requirements to translate health information, the vast majority of health materials are solely available in English. This project investigates barriers to translation of health information and explores new technologies to improve access to multilingual public health materials. We surveyed all 77 local health departments (LHDs) in the Northwest about translation needs, practices, barriers and attitudes towards machine translation (MT). We received 67 responses from 45 LHDs. Translation of health materials is the principle strategy used by LHDs to reach LEP populations. Cost and access to qualified translators are principle barriers to producing multilingual materials. Thirteen LHDs have used online MT tools. Many respondents expressed concerns about the accuracy of MT. Overall, respondents were positive about its potential use, if low costs and quality could be assured. PMID:24551414

Real English: A Translator to Enable Natural Language Man-Machine Conversation.

ERIC Educational Resources Information Center

Gautin, Harvey

This dissertation presents a pragmatic interpreter/translator called Real English to serve as a natural language man-machine communication interface in a multi-mode on-line information retrieval system. This multi-mode feature affords the user a library-like searching tool by giving him access to a dictionary, lexicon, thesaurus, synonym table,…

Web-Based Machine Translation as a Tool for Promoting Electronic Literacy and Language Awareness

ERIC Educational Resources Information Center

Williams, Lawrence

2006-01-01

This article addresses a pervasive problem of concern to teachers of many foreign languages: the use of Web-Based Machine Translation (WBMT) by students who do not understand the complexities of this relatively new tool. Although networked technologies have greatly increased access to many language and communication tools, WBMT is still…

Foreign Developments in Information Processing and Machine Translation, No. 1

DTIC Science & Technology

1960-09-29

technicians] (Sestier (A.) -- La Traduction automatfguT"" des textes ecrits scJQntifiqaes ej^J^chplc^es dxun langage~ dans__un’"*""* ’’^t^’T^^i...are more and more comprehensible to others than machine translation technicians will result. Sketch of a proaram. This outline of work xtfiich will

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge.

PubMed

Mouriño-García, Marcos A; Pérez-Rodríguez, Roberto; Anido-Rifón, Luis E

2017-01-01

The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space. The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic an- notator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts. The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001. Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers. Schattauer GmbH.

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge*. Spanish-English Cross-language Case Study.

PubMed

Mouriño-García, Marcos A; Pérez-Rodríguez, Roberto; Anido-Rifón, Luis E

2017-10-26

The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space. The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic annotator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts. The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001. Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers.

Calibrating random forests for probability estimation.

PubMed

Dankowski, Theresa; Ziegler, Andreas

2016-09-30

Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illustrated with data from the German Stroke Study Collaboration. In most simulation scenarios, both methods led to similar improvements. In the simulation scenario in which the stricter assumptions of Elkan's method were not met, the logistic regression-based re-calibration approach for random forests outperformed Elkan's method. It also performed better on the stroke data than Elkan's method. The strength of Elkan's method is its general applicability to any probability machine. However, if the strict assumptions underlying this approach are not met, the logistic regression-based approach is preferable for updating random forests for probability estimation. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Understanding and Writing G & M Code for CNC Machines

ERIC Educational Resources Information Center

Loveland, Thomas

2012-01-01

In modern CAD and CAM manufacturing companies, engineers design parts for machines and consumable goods. Many of these parts are cut on CNC machines. Whether using a CNC lathe, milling machine, or router, the ideas and designs of engineers must be translated into a machine-readable form called G & M Code that can be used to cut parts to precise…

Biosignature Discovery for Substance Use Disorders Using Statistical Learning.

PubMed

Baurley, James W; McMahan, Christopher S; Ervin, Carolyn M; Pardamean, Bens; Bergen, Andrew W

2018-02-01

There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Climbing the Tower of Babel: Perfecting Machine Translation

DTIC Science & Technology

2011-02-16

Center) used MT tools to translate extraordinary numbers of Russian technical documents. 10 For the Air Force, the manpower and time savings were...recognition.htm. Granted, this number is tempered by the rules of a specific language that would disallow specific word orderings, or mandate particular word...sequences, (e.g., in English, prepositions can only be followed by articles, etc) but the overall numbers convey the complexity of the machine

Evolution of Replication Machines

PubMed Central

Yao, Nina Y.; O'Donnell, Mike E.

2016-01-01

The machines that decode and regulate genetic information require the translation, transcription and replication pathways essential to all living cells. Thus, it might be expected that all cells share the same basic machinery for these pathways that were inherited from the primordial ancestor cell from which they evolved. A clear example of this is found in the translation machinery that converts RNA sequence to protein. The translation process requires numerous structural and catalytic RNAs and proteins, the central factors of which are homologous in all three domains of life, bacteria, archaea and eukarya. Likewise, the central actor in transcription, RNA polymerase, shows homology among the catalytic subunits in bacteria, archaea and eukarya. In contrast, while some “gears” of the genome replication machinery are homologous in all domains of life, most components of the replication machine appear to be unrelated between bacteria and those of archaea and eukarya. This review will compare and contrast the central proteins of the “replisome” machines that duplicate DNA in bacteria, archaea and eukarya, with an eye to understanding the issues surrounding the evolution of the DNA replication apparatus. PMID:27160337

Extracting Date/Time Expressions in Super-Function Based Japanese-English Machine Translation

NASA Astrophysics Data System (ADS)

Sasayama, Manabu; Kuroiwa, Shingo; Ren, Fuji

Super-Function Based Machine Translation(SFBMT) which is a type of Example-Based Machine Translation has a feature which makes it possible to expand the coverage of examples by changing nouns into variables, however, there were problems extracting entire date/time expressions containing parts-of-speech other than nouns, because only nouns/numbers were changed into variables. We describe a method for extracting date/time expressions for SFBMT. SFBMT uses noun determination rules to extract nouns and a bilingual dictionary to obtain correspondence of the extracted nouns between the source and the target languages. In this method, we add a rule to extract date/time expressions and then extract date/time expressions from a Japanese-English bilingual corpus. The evaluation results shows that the precision of this method for Japanese sentences is 96.7%, with a recall of 98.2% and the precision for English sentences is 94.7%, with a recall of 92.7%.

A comparison of human and machine translation of health promotion materials for public health practice: time, costs, and quality.

PubMed

Turner, Anne M; Bergman, Margo; Brownstein, Megumu; Cole, Kate; Kirchhoff, Katrin

2014-01-01

Most local public health departments serve limited English proficiency groups but lack sufficient resources to translate the health promotion materials that they produce into different languages. Machine translation (MT) with human postediting could fill this gap and work toward decreasing health disparities among non-English speakers. (1) To identify the time and costs associated with human translation (HT) of public health documents, (2) determine the time necessary for human postediting of MT, and (3) compare the quality of postedited MT and HT. A quality comparison of 25 MT and HT documents was performed with public health translators. The public health professionals involved were queried about the workflow, costs, and time for HT of 11 English public health documents over a 20-month period. Three recently translated documents of similar size and topic were then machine translated, the time for human postediting was recorded, and a blind quality analysis was performed. Seattle/King County, Washington. Public health professionals. (1) Estimated times for various HT tasks; (2) observed postediting times for MT documents; (3) actual costs for HT; and (4) comparison of quality ratings for HT and MT. Human translation via local health department methods took 17 hours to 6 days. While HT postediting words per minute ranged from 1.58 to 5.88, MT plus human postediting words per minute ranged from 10 to 30. The cost of HT ranged from $130 to $1220; MT required no additional costs. A quality comparison by bilingual public health professionals showed that MT and HT were equivalently preferred. MT with human postediting can reduce the time and costs of translating public health materials while maintaining quality similar to HT. In conjunction with postediting, MT could greatly improve the availability of multilingual public health materials.

Argonne Braille Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grunwald, A.

1977-07-01

Development of a braille machine is summarized. It is noted that the machine has reached the stage where development of the system appears both possible and desirable. Sections are included containing papers on computer translation and auxiliary equipment, and on letters and awards in recognition of the braille machine development. (JRD)

A Double-Edged Sword: The Merits and the Policy Implications of Google Translate in Higher Education

ERIC Educational Resources Information Center

Mundt, Klaus; Groves, Michael

2016-01-01

Machine translation, specifically Google Translate, is freely available, and is improving in its ability to provide grammatically accurate translations. This development has the potential to provoke a major transformation in the internationalization process at universities, since students may be, in the future, able to use technology to circumvent…

Statistical Machine Learning for Structured and High Dimensional Data

DTIC Science & Technology

2014-09-17

AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under

On-Demand Associative Cross-Language Information Retrieval

NASA Astrophysics Data System (ADS)

Geraldo, André Pinto; Moreira, Viviane P.; Gonçalves, Marcos A.

This paper proposes the use of algorithms for mining association rules as an approach for Cross-Language Information Retrieval. These algorithms have been widely used to analyse market basket data. The idea is to map the problem of finding associations between sales items to the problem of finding term translations over a parallel corpus. The proposal was validated by means of experiments using queries in two distinct languages: Portuguese and Finnish to retrieve documents in English. The results show that the performance of our proposed approach is comparable to the performance of the monolingual baseline and to query translation via machine translation, even though these systems employ more complex Natural Language Processing techniques. The combination between machine translation and our approach yielded the best results, even outperforming the monolingual baseline.

A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

PubMed Central

Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu

2014-01-01

The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features. PMID:24473281

Learning Machine, Vietnamese Based Human-Computer Interface.

ERIC Educational Resources Information Center

Northwest Regional Educational Lab., Portland, OR.

The sixth session of IT@EDU98 consisted of seven papers on the topic of the learning machine--Vietnamese based human-computer interface, and was chaired by Phan Viet Hoang (Informatics College, Singapore). "Knowledge Based Approach for English Vietnamese Machine Translation" (Hoang Kiem, Dinh Dien) presents the knowledge base approach,…

Traduccion automatica mediante el ordenador (Automatic Translation Using a Computer).

ERIC Educational Resources Information Center

Bueno, Julian L.

This report on machine translation contains a brief history of the field; a description of the processes involved; a discussion of systems currently in use, including three software packages on the market (Teaching Assistant, Translate, and Globalink); reflections on implications for teaching; observations of results obtained when elements of…

An Overall Perspective of Machine Translation with Its Shortcomings

ERIC Educational Resources Information Center

Akbari, Alireza

2014-01-01

The petition for language translation has strikingly augmented recently due to cross-cultural communication and exchange of information. In order to communicate well, text should be translated correctly and completely in each field such as legal documents, technical texts, scientific texts, publicity leaflets, and instructional materials. In this…

Discriminative feature-rich models for syntax-based machine translation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dixon, Kevin R.

This report describes the campus executive LDRD %E2%80%9CDiscriminative Feature-Rich Models for Syntax-Based Machine Translation,%E2%80%9D which was an effort to foster a better relationship between Sandia and Carnegie Mellon University (CMU). The primary purpose of the LDRD was to fund the research of a promising graduate student at CMU; in this case, Kevin Gimpel was selected from the pool of candidates. This report gives a brief overview of Kevin Gimpel's research.

Searching to Translate and Translating to Search: When Information Retrieval Meets Machine Translation

ERIC Educational Resources Information Center

Ture, Ferhan

2013-01-01

With the adoption of web services in daily life, people have access to tremendous amounts of information, beyond any human's reading and comprehension capabilities. As a result, search technologies have become a fundamental tool for accessing information. Furthermore, the web contains information in multiple languages, introducing another barrier…

Development of German-English Machine Translation System. Final Technical Report.

ERIC Educational Resources Information Center

Lehmann, Winfred P.; Stachowitz, Rolf A.

This report describes work on a pilot system for a fully automatic, high-quality translation of German scientific and technical text into English and gives the results of an experiment designed to show the system's capability to produce quality mechanical translation. The areas considered were: (1) grammar formalism, mainly involving the addition…

ON THE QUANTITATIVE EVALUATION OF THE TERMINOLOGY OF A VOCABULARY.

ERIC Educational Resources Information Center

KRAVETS, L.G.

THE CREATION OF AN INDUSTRIAL SYSTEM OF MACHINE TRANSLATION WITH AUTOMATIC INDEXING OF THE TRANSLATED MATERIALS PRESUMES THE DEVELOPMENT OF DICTIONARIES WHICH PROVIDE FOR THE IDENTIFICATION OF KEY WORDS AND WORD COMBINATIONS, FOLLOWED BY THEIR TRANSLATION INTO THE DESCRIPTORS OF THE SEARCH LANGUAGE. THREE SIGNS WHICH SHOW THAT A GIVEN WORD IS A…

CART (Communication Access Realtime Translation). PEPNet Tipsheet

ERIC Educational Resources Information Center

Larson, Judy, Comp.

1999-01-01

Communication Access Realtime Translation--(CART)--is the instant translation of the spoken word into English text performed by a CART reporter using a stenotype machine, notebook computer and realtime software. The text is then displayed on a computer monitor or other display device for the student who is deaf or hard of hearing to read. This…

The Fortran-P Translator: Towards Automatic Translation of Fortran 77 Programs for Massively Parallel Processors

DOE PAGES

O'keefe, Matthew; Parr, Terence; Edgar, B. Kevin; ...

1995-01-01

Massively parallel processors (MPPs) hold the promise of extremely high performance that, if realized, could be used to study problems of unprecedented size and complexity. One of the primary stumbling blocks to this promise has been the lack of tools to translate application codes to MPP form. In this article we show how applications codes written in a subset of Fortran 77, called Fortran-P, can be translated to achieve good performance on several massively parallel machines. This subset can express codes that are self-similar, where the algorithm applied to the global data domain is also applied to each subdomain. Wemore » have found many codes that match the Fortran-P programming style and have converted them using our tools. We believe a self-similar coding style will accomplish what a vectorizable style has accomplished for vector machines by allowing the construction of robust, user-friendly, automatic translation systems that increase programmer productivity and generate fast, efficient code for MPPs.« less

Learning molecular energies using localized graph kernels.

PubMed

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-21

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Learning molecular energies using localized graph kernels

NASA Astrophysics Data System (ADS)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Development of a HIPAA-compliant environment for translational research data and analytics.

PubMed

Bradford, Wayne; Hurdle, John F; LaSalle, Bernie; Facelli, Julio C

2014-01-01

High-performance computing centers (HPC) traditionally have far less restrictive privacy management policies than those encountered in healthcare. We show how an HPC can be re-engineered to accommodate clinical data while retaining its utility in computationally intensive tasks such as data mining, machine learning, and statistics. We also discuss deploying protected virtual machines. A critical planning step was to engage the university's information security operations and the information security and privacy office. Access to the environment requires a double authentication mechanism. The first level of authentication requires access to the university's virtual private network and the second requires that the users be listed in the HPC network information service directory. The physical hardware resides in a data center with controlled room access. All employees of the HPC and its users take the university's local Health Insurance Portability and Accountability Act training series. In the first 3 years, researcher count has increased from 6 to 58.

A Comparison of Human and Machine Translation of Health Promotion Materials for Public Health Practice: Time, Costs, and Quality

PubMed Central

Turner, Anne M.; Bergman, Margo; Brownstein, Megumu; Cole, Kate; Kirchhoff, Katrin

2017-01-01

Context Most local public health departments serve limited English proficiency groups but lack sufficient resources to translate the health promotion materials that they produce into different languages. Machine translation (MT) with human postediting could fill this gap and work toward decreasing health disparities among non–English speakers. Objectives (1) To identify the time and costs associated with human translation (HT) of public health documents, (2) determine the time necessary for human postediting of MT, and (3) compare the quality of postedited MT and HT. Design A quality comparison of 25 MT and HT documents was performed with public health translators. The public health professionals involved were queried about the workflow, costs, and time for HT of 11 English public health documents over a 20-month period. Three recently translated documents of similar size and topic were then machine translated, the time for human postediting was recorded, and a blind quality analysis was performed. Setting Seattle/King County, Washington. Participants Public health professionals. Main Outcome Measures (1) Estimated times for various HT tasks; (2) observed postediting times for MT documents; (3) actual costs for HT; and (4) comparison of quality ratings for HT and MT. Results Human translation via local health department methods took 17 hours to 6 days. While HT postediting words per minute ranged from 1.58 to 5.88, MT plus human postediting words per minute ranged from 10 to 30. The cost of HT ranged from $130 to $1220; MT required no additional costs. A quality comparison by bilingual public health professionals showed that MT and HT were equivalently preferred. Conclusions MT with human postediting can reduce the time and costs of translating public health materials while maintaining quality similar to HT. In conjunction with postediting, MT could greatly improve the availability of multilingual public health materials. PMID:24084391

Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

DTIC Science & Technology

2015-09-12

AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-11-1-0239 5c.  PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY

SignMT: An Alternative Language Learning Tool

ERIC Educational Resources Information Center

Ditcharoen, Nadh; Naruedomkul, Kanlaya; Cercone, Nick

2010-01-01

Learning a second language is very difficult, especially, for the disabled; the disability may be a barrier to learn and to utilize information written in text form. We present the SignMT, Thai sign to Thai machine translation system, which is able to translate from Thai sign language into Thai text. In the translation process, SignMT takes into…

MBASIC batch processor architectural overview

NASA Technical Reports Server (NTRS)

Reynolds, S. M.

1978-01-01

The MBASIC (TM) batch processor, a language translator designed to operate in the MBASIC (TM) environment is described. Features include: (1) a CONVERT TO BATCH command, usable from the ready mode; and (2) translation of the users program in stages through several levels of intermediate language and optimization. The processor is to be designed and implemented in both machine-independent and machine-dependent sections. The architecture is planned so that optimization processes are transparent to the rest of the system and need not be included in the first design implementation cycle.

CLIR Experiments at Maryland for TREC-2002: Evidence Combination for Arabic-English Retrieval

DTIC Science & Technology

2002-01-01

translation resources of three types (machine translation lexicons, a printed bilingual dictionary that had been manually rekeyed, and translation...on both the term list and the collection). • The Salmone Arabic-to-English dictionary , which was made available for use in the TREC-CLIR track by...Tufts University. No translation preference information is provided in this dictionary , but it does include rich markup describing morphology and part

A Conjoint Analysis Framework for Evaluating User Preferences in Machine Translation

PubMed Central

Kirchhoff, Katrin; Capurro, Daniel; Turner, Anne M.

2013-01-01

Despite much research on machine translation (MT) evaluation, there is surprisingly little work that directly measures users’ intuitive or emotional preferences regarding different types of MT errors. However, the elicitation and modeling of user preferences is an important prerequisite for research on user adaptation and customization of MT engines. In this paper we explore the use of conjoint analysis as a formal quantitative framework to assess users’ relative preferences for different types of translation errors. We apply our approach to the analysis of MT output from translating public health documents from English into Spanish. Our results indicate that word order errors are clearly the most dispreferred error type, followed by word sense, morphological, and function word errors. The conjoint analysis-based model is able to predict user preferences more accurately than a baseline model that chooses the translation with the fewest errors overall. Additionally we analyze the effect of using a crowd-sourced respondent population versus a sample of domain experts and observe that main preference effects are remarkably stable across the two samples. PMID:24683295

Translations on USSR Resources, Number 767.

DTIC Science & Technology

1978-01-19

photography and so on). The amount of data obtained as a result of additional surveys makes it possible to evaluate the intensity and configuration...machine tools , chemical products, refrigerators, as well as potatoes and products of livestock breeding. The Kazakh SSR made an enormous leap in its...of the fuel and water power resources of Georgia, Azerbaydzhan and Armenia. Petroleum, transport and electrical machine building, machine tool

Conquering Language Babel in the Classroom

ERIC Educational Resources Information Center

Minichino, Mario; Berson, Michael J.

2012-01-01

This article is an exploration of the available applications for speech to speech real-time translation software for use in the classroom. Three different types of machine language translation (MLT) software and devices are reviewed for their features and practical application in secondary education classrooms.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

PubMed

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V; Boahen, Kwabena

2013-06-01

Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Learning molecular energies using localized graph kernels

DOE PAGES

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

2017-03-21

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Learning molecular energies using localized graph kernels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

The Evaluation and Systems Analysis of the SYSTRAN Machine Translation System

DTIC Science & Technology

1977-01-01

DiFondi (IRDT) I. KIY *0*01 (Cu.wMu. .~ .‘~~.. lid. it a....Wp .11 id.iiSiIp Op e4.Sk s~~S.,) Machine Traca lation Evaluation scan Dictionary Update S...ntic Expression Dictionary Update *55? **Ct fCMi~ uw * lid. It -- p Sdsffl~~ Sr Sidsi ,~~~Siv) This report is the product of contractual effort to...translated end then corrected by a b ilingu.ai. exper t in each field. two types of corrections were considered iaplweatabi e, stan dictionary update and

Teaching Translation and Interpreting 2: Insights, Aims, Visions. [Selection of] Papers from the Second Language International Conference (Elsinore, Denmark, June 4-6, 1993).

ERIC Educational Resources Information Center

Dollerup, Cay, Ed.; Lindegaard, Annette, Ed.

This selection of papers starts with insights into multi- and plurilingual settings, then proceeds to discussions of aims for practical work with students, and ends with visions of future developments within translation for the mass media and the impact of machine translation. Papers are: "Interpreting at the European Commission";…

Actes des Journees de linguistique (Proceedings of the Linguistics Conference) (6th, 1992).

ERIC Educational Resources Information Center

Brousseau, Martin, Ed.; And Others

Papers, all in French, presented at a conference on linguistics include: "Machine Translation: Historic Aspects" (Ghada Attieh); "Translation and Conditioning" (Stephanie Bedard); "Semantic or Pre-Semantic Structures? From Perception to Causation" (Denise Belanger); "Presentation of Poetry Sung in Maroc"…

Optical-Fiber-Welding Machine

NASA Technical Reports Server (NTRS)

Goss, W. C.; Mann, W. A.; Goldstein, R.

1985-01-01

Technique yields joints with average transmissivity of 91.6 percent. Electric arc passed over butted fiber ends to melt them together. Maximum optical transmissivity of joint achieved with optimum choice of discharge current, translation speed, and axial compression of fibers. Practical welding machine enables delicate and tedious joining operation performed routinely.

Translations on North Korea No. 622

DTIC Science & Technology

1978-10-13

Pyongyang Power Station 5 July Electric Factory Hamhung Machine Tool Factory Kosan Plastic Pipe Factory Sog’wangea Plastic Pipe Factory 8...August Factory Double Chollima Hamhung Disabled Veterans’ Plastic Goods Factory Mangyongdae Machine Tool Factory Kangso Coal Mine Tongdaewon Garment...21 Jul 78 p 4) innovating in machine tool production (NC 21 Jul 78 p 2) in 40 days of the 蔴 days of combat" raised coal production 10 percent

Translations on Eastern Europe Political, Sociological, and Military Affairs No. 1482.

DTIC Science & Technology

1977-12-06

Soviet deliveries of complete industrial installations have become the basis of new sectors of Polish industry: machine - tool building, automotive...develop mutually advan- tageous cooperation. Poland has become an important supplier of industrial equipment and machines , and also of seagoing ships...to the USSR. Every fifth ship that sails under the Soviet flag has been built by Polish shipbuilders. We have supplied machines and equipment for

Creation of a Machine File and Subsequent Computer-Assisted Production of Publishing Outputs, Including a Translation Journal and an Index.

ERIC Educational Resources Information Center

Buckland, Lawrence F.; Weaver, Vance

Reported are the findings of the Uspekhi experiment in creating a labeled machine file, as well as sample products of this system - an article from a scientific journal and an index page. Production cost tables are presented for the machine file, primary journals, and journal indexes. Comparisons were made between the 1965 predicted costs and the…

A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation

PubMed Central

Chao, Lidia S.; Lu, Yi; Xing, Junwen

2014-01-01

Data selection has shown significant improvements in effective use of training data by extracting sentences from large general-domain corpora to adapt statistical machine translation (SMT) systems to in-domain data. This paper performs an in-depth analysis of three different sentence selection techniques. The first one is cosine tf-idf, which comes from the realm of information retrieval (IR). The second is perplexity-based approach, which can be found in the field of language modeling. These two data selection techniques applied to SMT have been already presented in the literature. However, edit distance for this task is proposed in this paper for the first time. After investigating the individual model, a combination of all three techniques is proposed at both corpus level and model level. Comparative experiments are conducted on Hong Kong law Chinese-English corpus and the results indicate the following: (i) the constraint degree of similarity measuring is not monotonically related to domain-specific translation quality; (ii) the individual selection models fail to perform effectively and robustly; but (iii) bilingual resources and combination methods are helpful to balance out-of-vocabulary (OOV) and irrelevant data; (iv) finally, our method achieves the goal to consistently boost the overall translation performance that can ensure optimal quality of a real-life SMT system. PMID:24683356

HiVy automated translation of stateflow designs for model checking verification

NASA Technical Reports Server (NTRS)

Pingree, Paula

2003-01-01

tool set enables model checking of finite state machines designs. This is acheived by translating state-chart specifications into the input language of the Spin model checker. An abstract syntax of hierarchical sequential automata (HSA) is provided as an intermediate format tool set.

Model Considerations for Memory-based Automatic Music Transcription

NASA Astrophysics Data System (ADS)

Albrecht, Štěpán; Šmídl, Václav

2009-12-01

The problem of automatic music description is considered. The recorded music is modeled as a superposition of known sounds from a library weighted by unknown weights. Similar observation models are commonly used in statistics and machine learning. Many methods for estimation of the weights are available. These methods differ in the assumptions imposed on the weights. In Bayesian paradigm, these assumptions are typically expressed in the form of prior probability density function (pdf) on the weights. In this paper, commonly used assumptions about music signal are summarized and complemented by a new assumption. These assumptions are translated into pdfs and combined into a single prior density using combination of pdfs. Validity of the model is tested in simulation using synthetic data.

Some Problems in German to English Machine Translation

DTIC Science & Technology

1974-12-01

fron Benanti^e is a slippery business, especially when I have just clalwsd to subscribe to the idea that the structure of an utterance is intinately...from the English translation on page 15, the example paragraph can be divided Into elm 134 sections. These diviaions can be characterized at

Adjusting Beliefs via Transformed Fuzzy Priors

NASA Astrophysics Data System (ADS)

Rattanadamrongaksorn, T.; Sirikanchanarak, D.; Sirisrisakulchai, J.; Sriboonchitta, S.

2018-02-01

Instead of leaving a decision to a pure data-driven system, intervention and collaboration by human would be preferred to fill the gap that machine cannot perform well. In financial applications, for instance, the inference and prediction during structural changes by critical factors; such as market conditions, administrative styles, political policies, etc.; have significant influences to investment strategies. With the conditions differing from the past, we believe that the decision should not be made by only the historical data but also with human estimation. In this study, the updating process by data fusion between expert opinions and statistical observations is thus proposed. The expert’s linguistic terms can be translated into mathematical expressions by the predefined fuzzy numbers and utilized as the initial knowledge for Bayesian statistical framework via the possibility-to-probability transformation. The artificial samples on five scenarios were tested in the univariate problem to demonstrate the methodology. The results showed the shifts and variations appeared on the parameters of the distributions and, as a consequence, adjust the degrees of belief accordingly.

Simple Machines Curriculum. [Teachers' Manual.

ERIC Educational Resources Information Center

Anoka-Hennepin Independent School District No. 11, Coon Rapids, MN.

This manual provides suggestions for investigating simple machines and the teaching of certain basic concepts which pertain to them. Many of the lessons are designed to be used with the commercially available LEGO kits, in an effort to teach concepts in a way in which students must translate pictures shown in two dimension into three-dimensional…

Visible Machine Learning for Biomedicine.

PubMed

Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; Ideker, Trey

2018-06-14

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology. Copyright © 2018. Published by Elsevier Inc.

National Machine Guarding Program: Part 1. Machine safeguarding practices in small metal fabrication businesses.

PubMed

Parker, David L; Yamin, Samuel C; Brosseau, Lisa M; Xi, Min; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2015-11-01

Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine-related hazards in 221 business. Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

2013-06-01

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach.

PubMed

Surkis, Alisa; Hogle, Janice A; DiazGranados, Deborah; Hunt, Joe D; Mazmanian, Paul E; Connors, Emily; Westaby, Kate; Whipple, Elizabeth C; Adamus, Trisha; Mueller, Meridith; Aphinyanaphongs, Yindalon

2016-08-05

Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4. The combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum.

PC-assisted translation of photogrammetric papers

NASA Astrophysics Data System (ADS)

Güthner, Karlheinz; Peipe, Jürgen

A PC-based system for machine translation of photogrammetric papers from the English into the German language and vice versa is described. The computer-assisted translating process is not intended to create a perfect interpretation of a text but to produce a rough rendering of the content of a paper. Starting with the original text, a continuous data flow is effected into the translated version by means of hardware (scanner, personal computer, printer) and software (OCR, translation, word processing, DTP). An essential component of the system is a photogrammetric microdictionary which is being established at present. It is based on several sources, including e.g. the ISPRS Multilingual Dictionary.

The Linguistic Core Approach to StructuredTranslation and Analysis of Low Resource Languages

DTIC Science & Technology

2017-09-02

grammars from no training data and partial training data (as given by GFL). We are now anno - tating GFL for English, Portuguese, Chinese and...translation. In Proc. EMNLP, 2014. [20] F. Drewes, H.- J . Kreowski, and A. Habel. Hyperedge replacement graph grammars. Handbook of Graph Grammars, 1:95–162...tory, Jena, 2015. [41] B. Jones, J . Andreas, D. Bauer, K-M. Hermann, and K. Knight. Semantics- based machine translation with hyperedge replacement

UNITRAN (UNIversal TRANslator): A Principle-Based Approach to Machine Translation.

DTIC Science & Technology

1987-12-01

C*TENDONOSLA*) C*YENDO NOS LA *))) 0 %4 APPENDIX D. TRANSLATION SYSTEM PARAMETERS 222 0 1 :MERGES I ((A EL LADO DE ) (AL-.LADO- DE )) ((ACERCA DE ...not only permits a language to be de - ’Slocuim’s system ( 1994a) relies on a separate set of context-free language-specific rules for each source and...requirements as small subject domain, narrow linguistic coverage, and enormous lexical entries (as found in exclusively semantic-based systems). Thus

Getting America Ready for Japanese Science and Technology Held at Washington, DC on 7-8 February 1985.

DTIC Science & Technology

1985-05-15

TRANSLATION: A LONG-TERM SOLUTION MACHINE TRANSLATIONS: DEVELOPMENTS AND PROSPECTS 96 Robert A. Russell, Assistant Professor, Department of Asian and...chairman of the board of WCC, Chicago. He is a member of the Japanese National Automatic Translation Telephone Development Committee. 1 ROBERT A. RUSSELL...redussol- Westerism through indexing enineiring student to trai- speaking M Ono"e langisage. 067 pubfiahang-some in services. said Robert W. Gib- law

Bombsights and Adding Machines: Translating Wartime Technology into Peacetime Sales

ERIC Educational Resources Information Center

Tremblay, Michael

2010-01-01

On 10 February 1947, A.C. Buehler, the president of the Victor Adding Machine Company presented Norden Bombsight #4120 to the Smithsonian Institute. This sight was in service on board the Enola Gay when it dropped the first atomic bomb on Hiroshima. Through this public presentation, Buehler forever linked his company to the Norden Bombsight, the…

Information integration and diagnosis analysis of equipment status and production quality for machining process

NASA Astrophysics Data System (ADS)

Zan, Tao; Wang, Min; Hu, Jianzhong

2010-12-01

Machining status monitoring technique by multi-sensors can acquire and analyze the machining process information to implement abnormity diagnosis and fault warning. Statistical quality control technique is normally used to distinguish abnormal fluctuations from normal fluctuations through statistical method. In this paper by comparing the advantages and disadvantages of the two methods, the necessity and feasibility of integration and fusion is introduced. Then an approach that integrates multi-sensors status monitoring and statistical process control based on artificial intelligent technique, internet technique and database technique is brought forward. Based on virtual instrument technique the author developed the machining quality assurance system - MoniSysOnline, which has been used to monitoring the grinding machining process. By analyzing the quality data and AE signal information of wheel dressing process the reason of machining quality fluctuation has been obtained. The experiment result indicates that the approach is suitable for the status monitoring and analyzing of machining process.

Actes des Journees de Linguistique (Proceedings of the Linguistics Conference) (11th, Quebec, Canada, March 20-21, 1997).

ERIC Educational Resources Information Center

Caouette, Claudine, Ed.; Larrivee, Pierre, Ed.

English translations of articles in French in this issue include these: "Discourse Reported in the Print Media"; "Comparison of Register in Quebec and French Speakers"; "Method of Description of Specialized Verbs in View of Machine Translation Applications"; "Dialectal Areas in the Brazilian State of Rio Grande…

The Circle of Meaning: From Translation to Paraphrasing and Back

ERIC Educational Resources Information Center

Madnani, Nitin

2010-01-01

The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is…

Development of a Machine-Vision System for Recording of Force Calibration Data

NASA Astrophysics Data System (ADS)

Heamawatanachai, Sumet; Chaemthet, Kittipong; Changpan, Tawat

This paper presents the development of a new system for recording of force calibration data using machine vision technology. Real time camera and computer system were used to capture images of the reading from the instruments during calibration. Then, the measurement images were transformed and translated to numerical data using optical character recognition (OCR) technique. These numerical data along with raw images were automatically saved to memories as the calibration database files. With this new system, the human error of recording would be eliminated. The verification experiments were done by using this system for recording the measurement results from an amplifier (DMP 40) with load cell (HBM-Z30-10kN). The NIMT's 100-kN deadweight force standard machine (DWM-100kN) was used to generate test forces. The experiments setup were done in 3 categories; 1) dynamics condition (record during load changing), 2) statics condition (record during fix load), and 3) full calibration experiments in accordance with ISO 376:2011. The captured images from dynamics condition experiment gave >94% without overlapping of number. The results from statics condition experiment were >98% images without overlapping. All measurement images without overlapping were translated to number by the developed program with 100% accuracy. The full calibration experiments also gave 100% accurate results. Moreover, in case of incorrect translation of any result, it is also possible to trace back to the raw calibration image to check and correct it. Therefore, this machine-vision-based system and program should be appropriate for recording of force calibration data.

Translations from Kommunist, Number 13, September 1978

DTIC Science & Technology

1978-10-30

programmed machine tool here is merely a component of a more complex reprogrammable technological system. This includes the robot machine tools with...sufficient possibilities for changing technological operations and processes and automated technological lines. 52 The reprogrammable automated sets will...simulate the possibilities of such sets. A new technological level will be developed in industry related to reprogrammable automated sets, their design

National machine guarding program: Part 1. Machine safeguarding practices in small metal fabrication businesses

PubMed Central

Yamin, Samuel C.; Brosseau, Lisa M.; Xi, Min; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2015-01-01

Background Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. Methods The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine‐related hazards in 221 business. Results Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. Conclusions The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. Am. J. Ind. Med. 58:1174–1183, 2015. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc. PMID:26332060

Support vector machine and mel frequency Cepstral coefficient based algorithm for hand gestures and bidirectional speech to text device

NASA Astrophysics Data System (ADS)

Balbin, Jessie R.; Padilla, Dionis A.; Fausto, Janette C.; Vergara, Ernesto M.; Garcia, Ramon G.; Delos Angeles, Bethsedea Joy S.; Dizon, Neil John A.; Mardo, Mark Kevin N.

2017-02-01

This research is about translating series of hand gesture to form a word and produce its equivalent sound on how it is read and said in Filipino accent using Support Vector Machine and Mel Frequency Cepstral Coefficient analysis. The concept is to detect Filipino speech input and translate the spoken words to their text form in Filipino. This study is trying to help the Filipino deaf community to impart their thoughts through the use of hand gestures and be able to communicate to people who do not know how to read hand gestures. This also helps literate deaf to simply read the spoken words relayed to them using the Filipino speech to text system.

Coordinating Council. Second Meeting: International Acquisitions

NASA Technical Reports Server (NTRS)

1990-01-01

The theme of this NASA Scientific and Technical Information Program Coordinating Council was International Acquisitions. Included are both visuals for presentations and reports on discussions related to the topics. Presentations were made on the following topics: Coordination council organization international plan, STI global network, International aerospace climate, Foreign exchange program, Foreign activities RMS & AIAA, NASA translation program, A.F. machine translation system, and CIRC cooperation.

Reading Strategies in a L2: A Study on Machine Translation

ERIC Educational Resources Information Center

Karnal, Adriana Riess; Pereira, Vera Vanmacher

2015-01-01

This article aims at understanding cognitive strategies which are involved in reading academic texts in English as a L2/FL. Specifically, we focus on reading comprehension when a text is read either using Google translator or not. From this perspective we must consider the reading process in its complexity not only as a decoding process. We follow…

Resistance gene identification from Larimichthys crocea with machine learning techniques

NASA Astrophysics Data System (ADS)

Cai, Yinyin; Liao, Zhijun; Ju, Ying; Liu, Juan; Mao, Yong; Liu, Xiangrong

2016-12-01

The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. It is meaningful to identify and predict R-gene of Larimichthys crocea (L.Crocea). It is friendly for breeding and the marine environment as well. Large amounts of L.Crocea’s immune mechanisms have been explored by biological methods. However, much about them is still unclear. In order to break the limited understanding of the L.Crocea’s immune mechanisms and to detect new R-gene and R-gene-like genes, this paper came up with a more useful combination prediction method, which is to extract and classify the feature of available genomic data by machine learning. The effectiveness of feature extraction and classification methods to identify potential novel R-gene was evaluated, and different statistical analyzes were utilized to explore the reliability of prediction method, which can help us further understand the immune mechanisms of L.Crocea against pathogens. In this paper, a webserver called LCRG-Pred is available at http://server.malab.cn/rg_lc/.

Evaluation of machinability and flexural strength of a novel dental machinable glass-ceramic.

PubMed

Qin, Feng; Zheng, Shucan; Luo, Zufeng; Li, Yong; Guo, Ling; Zhao, Yunfeng; Fu, Qiang

2009-10-01

To evaluate the machinability and flexural strength of a novel dental machinable glass-ceramic (named PMC), and to compare the machinability property with that of Vita Mark II and human enamel. The raw batch materials were selected and mixed. Four groups of novel glass-ceramics were formed at different nucleation temperatures, and were assigned to Group 1, Group 2, Group 3 and Group 4. The machinability of the four groups of novel glass-ceramics, Vita Mark II ceramic and freshly extracted human premolars were compared by means of drilling depth measurement. A three-point bending test was used to measure the flexural strength of the novel glass-ceramics. The crystalline phases of the group with the best machinability were identified by X-ray diffraction. In terms of the drilling depth, Group 2 of the novel glass-ceramics proves to have the largest drilling depth. There was no statistical difference among Group 1, Group 4 and the natural teeth. The drilling depth of Vita MK II was statistically less than that of Group 1, Group 4 and the natural teeth. Group 3 had the least drilling depth. In respect of the flexural strength, Group 2 exhibited the maximum flexural strength; Group 1 was statistically weaker than Group 2; there was no statistical difference between Group 3 and Group 4, and they were the weakest materials. XRD of Group 2 ceramic showed that a new type of dental machinable glass-ceramic containing calcium-mica had been developed by the present study and was named PMC. PMC is promising for application as a dental machinable ceramic due to its good machinability and relatively high strength.

Financial Statistics. Higher Education General Information Survey (HEGIS) [machine-readable data file].

ERIC Educational Resources Information Center

Center for Education Statistics (ED/OERI), Washington, DC.

The Financial Statistics machine-readable data file (MRDF) is a subfile of the larger Higher Education General Information Survey (HEGIS). It contains basic financial statistics for over 3,000 institutions of higher education in the United States and its territories. The data are arranged sequentially by institution, with institutional…

Flexible Endian Adjustment for Cross Architecture Binary Translation

NASA Astrophysics Data System (ADS)

Zhu, Tong; Liu, Bo; Guan, Haibing; Liang, Alei

Different architectures and/or ISA (Instruction Set Architecture) representations hold different data arranging formats in the memory. Therefore, the adjustment of byte packing order (endianness) is indispensable in cross- architecture binary translation if the source and target machines are of heterogeneous endianness, which may otherwise cause system failure. The issue is inconspicuous but may lead to significant performance bottleneck. This paper investigates the key aspects of endianness and finds several solutions to endian adjustment for cross-architecture binary translation. In particular, it considers the two principal methods of this field - byte swapping and address swizzling, and gives a comparison of them in our DBT (Dynamic Binary Translator) - CrossBit.

Translation lexicon acquisition from bilingual dictionaries

NASA Astrophysics Data System (ADS)

Doermann, David S.; Ma, Huanfeng; Karagol-Ayan, Burcu; Oard, Douglas W.

2001-12-01

Bilingual dictionaries hold great potential as a source of lexical resources for training automated systems for optical character recognition, machine translation and cross-language information retrieval. In this work we describe a system for extracting term lexicons from printed copies of bilingual dictionaries. We describe our approach to page and definition segmentation and entry parsing. We have used the approach to parse a number of dictionaries and demonstrate the results for retrieval using a French-English Dictionary to generate a translation lexicon and a corpus of English queries applied to French documents to evaluation cross-language IR.

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

GeoSegmenter: A statistically learned Chinese word segmenter for the geoscience domain

NASA Astrophysics Data System (ADS)

Huang, Lan; Du, Youfu; Chen, Gongyang

2015-03-01

Unlike English, the Chinese language has no space between words. Segmenting texts into words, known as the Chinese word segmentation (CWS) problem, thus becomes a fundamental issue for processing Chinese documents and the first step in many text mining applications, including information retrieval, machine translation and knowledge acquisition. However, for the geoscience subject domain, the CWS problem remains unsolved. Although a generic segmenter can be applied to process geoscience documents, they lack the domain specific knowledge and consequently their segmentation accuracy drops dramatically. This motivated us to develop a segmenter specifically for the geoscience subject domain: the GeoSegmenter. We first proposed a generic two-step framework for domain specific CWS. Following this framework, we built GeoSegmenter using conditional random fields, a principled statistical framework for sequence learning. Specifically, GeoSegmenter first identifies general terms by using a generic baseline segmenter. Then it recognises geoscience terms by learning and applying a model that can transform the initial segmentation into the goal segmentation. Empirical experimental results on geoscience documents and benchmark datasets showed that GeoSegmenter could effectively recognise both geoscience terms and general terms.

Evaluating a Pivot-Based Approach for Bilingual Lexicon Extraction

PubMed Central

Kim, Jae-Hoon; Kwon, Hong-Seok; Seo, Hyeong-Won

2015-01-01

A pivot-based approach for bilingual lexicon extraction is based on the similarity of context vectors represented by words in a pivot language like English. In this paper, in order to show validity and usability of the pivot-based approach, we evaluate the approach in company with two different methods for estimating context vectors: one estimates them from two parallel corpora based on word association between source words (resp., target words) and pivot words and the other estimates them from two parallel corpora based on word alignment tools for statistical machine translation. Empirical results on two language pairs (e.g., Korean-Spanish and Korean-French) have shown that the pivot-based approach is very promising for resource-poor languages and this approach observes its validity and usability. Furthermore, for words with low frequency, our method is also well performed. PMID:25983745

An artificial molecular machine that builds an asymmetric catalyst

NASA Astrophysics Data System (ADS)

De Bo, Guillaume; Gall, Malcolm A. Y.; Kuschel, Sonja; De Winter, Julien; Gerbaux, Pascal; Leigh, David A.

2018-05-01

Biomolecular machines perform types of complex molecular-level tasks that artificial molecular machines can aspire to. The ribosome, for example, translates information from the polymer track it traverses (messenger RNA) to the new polymer it constructs (a polypeptide)1. The sequence and number of codons read determines the sequence and number of building blocks incorporated into the biomachine-synthesized polymer. However, neither control of sequence2,3 nor the transfer of length information from one polymer to another (which to date has only been accomplished in man-made systems through template synthesis)4 is easily achieved in the synthesis of artificial macromolecules. Rotaxane-based molecular machines5-7 have been developed that successively add amino acids8-10 (including β-amino acids10) to a growing peptide chain by the action of a macrocycle moving along a mono-dispersed oligomeric track derivatized with amino-acid phenol esters. The threaded macrocycle picks up groups that block its path and links them through successive native chemical ligation reactions11 to form a peptide sequence corresponding to the order of the building blocks on the track. Here, we show that as an alternative to translating sequence information, a rotaxane molecular machine can transfer the narrow polydispersity of a leucine-ester-derivatized polystyrene chain synthesized by atom transfer radical polymerization12 to a molecular-machine-made homo-leucine oligomer. The resulting narrow-molecular-weight oligomer folds to an α-helical secondary structure13 that acts as an asymmetric catalyst for the Juliá-Colonna epoxidation14,15 of chalcones.

Man-machine analysis of translation and work tasks of Skylab films

NASA Technical Reports Server (NTRS)

Morrow, J. R.; Boelter, J.

1978-01-01

Selected film segments were digitized. An efficiency of translation scale was developed, and each of 200 segments of film were rated with regard to the astronauts translation characteristics. Results indicated that in general the astronauts were able to acclimate themselves to the zero g environment quite well. Results also indicated that astronauts tended to translate in 1 g orientations when in the experimental compartment and the wardroom which were architecturally 1 g. However, when the astronauts were in the forward compartment, which was zero g oriented, they began to translate more frequently in a zero g manner. There appeared to be improvements in translation across time. These improvements appeared more so in the forward compartment than in the wardroom or the experimental compartment. Possible changes in the architecture of the wardroom and the experimental compartment were suggested in order to improve translation within these compartments.

EXACT2: the semantics of biomedical protocols

PubMed Central

2014-01-01

Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549

The complexity of translationally invariant low-dimensional spin lattices in 3D

NASA Astrophysics Data System (ADS)

Bausch, Johannes; Piddock, Stephen

2017-11-01

In this theoretical paper, we consider spin systems in three spatial dimensions and consider the computational complexity of estimating the ground state energy, known as the local Hamiltonian problem, for translationally invariant Hamiltonians. We prove that the local Hamiltonian problem for 3D lattices with face-centered cubic unit cells and 4-local translationally invariant interactions between spin-3/2 particles and open boundary conditions is QMAEXP-complete, where QMAEXP is the class of problems which can be verified in exponential time on a quantum computer. We go beyond a mere embedding of past hard 1D history state constructions, for which the local spin dimension is enormous: even state-of-the-art constructions have local dimension 42. We avoid such a large local dimension by combining some different techniques in a novel way. For the verifier circuit which we embed into the ground space of the local Hamiltonian, we utilize a recently developed computational model, called a quantum ring machine, which is especially well suited for translationally invariant history state constructions. This is encoded with a new and particularly simple universal gate set, which consists of a single 2-qubit gate applied only to nearest-neighbour qubits. The Hamiltonian construction involves a classical Wang tiling problem as a binary counter which translates one cube side length into a binary description for the encoded verifier input and a carefully engineered history state construction that implements the ring machine on the cubic lattice faces. These novel techniques allow us to significantly lower the local spin dimension, surpassing the best translationally invariant result to date by two orders of magnitude (in the number of degrees of freedom per coupling). This brings our models on par with the best non-translationally invariant construction.

Preliminary study of online machine translation use of nursing literature: quality evaluation and perceived usability

PubMed Central

2012-01-01

Background Japanese nurses are increasingly required to read published international research in clinical, educational, and research settings. Language barriers are a significant obstacle, and online machine translation (MT) is a tool that can be used to address this issue. We examined the quality of Google Translate® (English to Japanese and Korean to Japanese), which is a representative online MT, using a previously verified evaluation method. We also examined the perceived usability and current use of online MT among Japanese nurses. Findings Randomly selected nursing abstracts were translated and then evaluated for intelligibility and usability by 28 participants, including assistants and research associates from nursing universities throughout Japan. They answered a questionnaire about their online MT use. From simple comparison of mean scores between two language pairs, translation quality was significantly better, with respect to both intelligibility and usability, for Korean-Japanese than for English-Japanese. Most respondents perceived a language barrier. Online MT had been used by 61% of the respondents and was perceived as not useful enough. Conclusion Nursing articles translated from Korean into Japanese by an online MT system could be read at an acceptable level of comprehension, but the same could not be said for English-Japanese translations. Respondents with experience using online MT used it largely to grasp the overall meanings of the original text. Enrichment in technical terms appeared to be the key to better usability. Users will be better able to use MT outputs if they improve their foreign language proficiency as much as possible. Further research is being conducted with a larger sample size and detailed analysis. PMID:23151362

Discovery of numerous novel small genes in the intergenic regions of the Escherichia coli O157:H7 Sakai genome

PubMed Central

Hücker, Sarah M.; Ardern, Zachary; Goldberg, Tatyana; Schafferhans, Andrea; Bernhofer, Michael; Vestergaard, Gisle; Nelson, Chase W.; Schloter, Michael; Rost, Burkhard; Scherer, Siegfried

2017-01-01

In the past, short protein-coding genes were often disregarded by genome annotation pipelines. Transcriptome sequencing (RNAseq) signals outside of annotated genes have usually been interpreted to indicate either ncRNA or pervasive transcription. Therefore, in addition to the transcriptome, the translatome (RIBOseq) of the enteric pathogen Escherichia coli O157:H7 strain Sakai was determined at two optimal growth conditions and a severe stress condition combining low temperature and high osmotic pressure. All intergenic open reading frames potentially encoding a protein of ≥ 30 amino acids were investigated with regard to coverage by transcription and translation signals and their translatability expressed by the ribosomal coverage value. This led to discovery of 465 unique, putative novel genes not yet annotated in this E. coli strain, which are evenly distributed over both DNA strands of the genome. For 255 of the novel genes, annotated homologs in other bacteria were found, and a machine-learning algorithm, trained on small protein-coding E. coli genes, predicted that 89% of these translated open reading frames represent bona fide genes. The remaining 210 putative novel genes without annotated homologs were compared to the 255 novel genes with homologs and to 250 short annotated genes of this E. coli strain. All three groups turned out to be similar with respect to their translatability distribution, fractions of differentially regulated genes, secondary structure composition, and the distribution of evolutionary constraint, suggesting that both novel groups represent legitimate genes. However, the machine-learning algorithm only recognized a small fraction of the 210 genes without annotated homologs. It is possible that these genes represent a novel group of genes, which have unusual features dissimilar to the genes of the machine-learning algorithm training set. PMID:28902868

Analyzing Array Manipulating Programs by Program Transformation

NASA Technical Reports Server (NTRS)

Cornish, J. Robert M.; Gange, Graeme; Navas, Jorge A.; Schachte, Peter; Sondergaard, Harald; Stuckey, Peter J.

2014-01-01

We explore a transformational approach to the problem of verifying simple array-manipulating programs. Traditionally, verification of such programs requires intricate analysis machinery to reason with universally quantified statements about symbolic array segments, such as "every data item stored in the segment A[i] to A[j] is equal to the corresponding item stored in the segment B[i] to B[j]." We define a simple abstract machine which allows for set-valued variables and we show how to translate programs with array operations to array-free code for this machine. For the purpose of program analysis, the translated program remains faithful to the semantics of array manipulation. Based on our implementation in LLVM, we evaluate the approach with respect to its ability to extract useful invariants and the cost in terms of code size.

Machine-Assisted Translation in West Germany

DTIC Science & Technology

1977-03-04

the Ger- man, English , French or Russian languages are recorded, where it is known that they have been used in translation or teaching assignments...German literary language encompasses about 300,000 words, English about 600,000, and the vocabulary of a person of average education is about...Terminology Recording and Evaluation Method ) was developed in the language service of the Siemens company in Munich as an aid for in-house application

Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

NASA Astrophysics Data System (ADS)

Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

2017-09-01

This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

Machinability of Al 6061 Deposited with Cold Spray Additive Manufacturing

NASA Astrophysics Data System (ADS)

Aldwell, Barry; Kelly, Elaine; Wall, Ronan; Amaldi, Andrea; O'Donnell, Garret E.; Lupoi, Rocco

2017-10-01

Additive manufacturing techniques such as cold spray are translating from research laboratories into more mainstream high-end production systems. Similar to many additive processes, finishing still depends on removal processes. This research presents the results from investigations into aspects of the machinability of aluminum 6061 tubes manufactured with cold spray. Through the analysis of cutting forces and observations on chip formation and surface morphology, the effect of cutting speed, feed rate, and heat treatment was quantified, for both cold-sprayed and bulk aluminum 6061. High-speed video of chip formation shows changes in chip form for varying material and heat treatment, which is supported by the force data and quantitative imaging of the machined surface. The results shown in this paper demonstrate that parameters involved in cold spray directly impact on machinability and therefore have implications for machining parameters and strategy.

An introduction to quantum machine learning

NASA Astrophysics Data System (ADS)

Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

2015-04-01

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Reversibility in Quantum Models of Stochastic Processes

NASA Astrophysics Data System (ADS)

Gier, David; Crutchfield, James; Mahoney, John; James, Ryan

Natural phenomena such as time series of neural firing, orientation of layers in crystal stacking and successive measurements in spin-systems are inherently probabilistic. The provably minimal classical models of such stochastic processes are ɛ-machines, which consist of internal states, transition probabilities between states and output values. The topological properties of the ɛ-machine for a given process characterize the structure, memory and patterns of that process. However ɛ-machines are often not ideal because their statistical complexity (Cμ) is demonstrably greater than the excess entropy (E) of the processes they represent. Quantum models (q-machines) of the same processes can do better in that their statistical complexity (Cq) obeys the relation Cμ >= Cq >= E. q-machines can be constructed to consider longer lengths of strings, resulting in greater compression. With code-words of sufficiently long length, the statistical complexity becomes time-symmetric - a feature apparently novel to this quantum representation. This result has ramifications for compression of classical information in quantum computing and quantum communication technology.

Natural Language Processing.

ERIC Educational Resources Information Center

Chowdhury, Gobinda G.

2003-01-01

Discusses issues related to natural language processing, including theoretical developments; natural language understanding; tools and techniques; natural language text processing systems; abstracting; information extraction; information retrieval; interfaces; software; Internet, Web, and digital library applications; machine translation for…

UNIVERSAL TRANSLATOR,

DTIC Science & Technology

all languages with the aid of electron machines is being derived to show how easy it would be to decode even ’dead’ languages, and languages of the foginess of Andromeda , if such a language ever existed. (Author)

Statistical learning algorithms for identifying contrasting tillage practices with landsat thematic mapper data

USDA-ARS?s Scientific Manuscript database

Tillage management practices have direct impact on water holding capacity, evaporation, carbon sequestration, and water quality. This study examines the feasibility of two statistical learning algorithms, such as Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for cla...

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

BLS Machine-Readable Data and Tabulating Routines.

ERIC Educational Resources Information Center

DiFillipo, Tony

This report describes the machine-readable data and tabulating routines that the Bureau of Labor Statistics (BLS) is prepared to distribute. An introduction discusses the LABSTAT (Labor Statistics) database and the BLS policy on release of unpublished data. Descriptions summarizing data stored in 25 files follow this format: overview, data…

Breaking the language barrier: machine assisted diagnosis using the medical speech translator.

PubMed

Starlander, Marianne; Bouillon, Pierrette; Rayner, Manny; Chatzichrisafis, Nikos; Hockey, Beth Ann; Isahara, Hitoshi; Kanzaki, Kyoko; Nakao, Yukie; Santaholma, Marianne

2005-01-01

In this paper, we describe and evaluate an Open Source medical speech translation system (MedSLT) intended for safety-critical applications. The aim of this system is to eliminate the language barriers in emergency situation. It translates spoken questions from English into French, Japanese and Finnish in three medical subdomains (headache, chest pain and abdominal pain), using a vocabulary of about 250-400 words per sub-domain. The architecture is a compromise between fixed-phrase translation on one hand and complex linguistically-based systems on the other. Recognition is guided by a Context Free Grammar Language Model compiled from a general unification grammar, automatically specialised for the domain. We present an evaluation of this initial prototype that shows the advantages of this grammar-based approach for this particular translation task in term of both reliability and use.

On the Stability of Jump-Linear Systems Driven by Finite-State Machines with Markovian Inputs

NASA Technical Reports Server (NTRS)

Patilkulkarni, Sudarshan; Herencia-Zapana, Heber; Gray, W. Steven; Gonzalez, Oscar R.

2004-01-01

This paper presents two mean-square stability tests for a jump-linear system driven by a finite-state machine with a first-order Markovian input process. The first test is based on conventional Markov jump-linear theory and avoids the use of any higher-order statistics. The second test is developed directly using the higher-order statistics of the machine s output process. The two approaches are illustrated with a simple model for a recoverable computer control system.

Machine-aided indexing at NASA

NASA Technical Reports Server (NTRS)

Silvester, June P.; Genuardi, Michael T.; Klingbiel, Paul H.

1994-01-01

This report describes the NASA Lexical Dictionary (NLD), a machine-aided indexing system used online at the National Aeronautics and Space Administration's Center for AeroSpace Information (CASI). This system automatically suggests a set of candidate terms from NASA's controlled vocabulary for any designated natural language text input. The system is comprised of a text processor that is based on the computational, nonsyntactic analysis of input text and an extensive knowledge base that serves to recognize and translate text-extracted concepts. The functions of the various NLD system components are described in detail, and production and quality benefits resulting from the implementation of machine-aided indexing at CASI are discussed.

Modeling Stochastic Kinetics of Molecular Machines at Multiple Levels: From Molecules to Modules

PubMed Central

Chowdhury, Debashish

2013-01-01

A molecular machine is either a single macromolecule or a macromolecular complex. In spite of the striking superficial similarities between these natural nanomachines and their man-made macroscopic counterparts, there are crucial differences. Molecular machines in a living cell operate stochastically in an isothermal environment far from thermodynamic equilibrium. In this mini-review we present a catalog of the molecular machines and an inventory of the essential toolbox for theoretically modeling these machines. The tool kits include 1), nonequilibrium statistical-physics techniques for modeling machines and machine-driven processes; and 2), statistical-inference methods for reverse engineering a functional machine from the empirical data. The cell is often likened to a microfactory in which the machineries are organized in modular fashion; each module consists of strongly coupled multiple machines, but different modules interact weakly with each other. This microfactory has its own automated supply chain and delivery system. Buoyed by the success achieved in modeling individual molecular machines, we advocate integration of these models in the near future to develop models of functional modules. A system-level description of the cell from the perspective of molecular machinery (the mechanome) is likely to emerge from further integrations that we envisage here. PMID:23746505

Evaluation of molecular brain changes associated with environmental stress in rodent models compared to human major depressive disorder: A proteomic systems approach.

PubMed

Cox, David Alan; Gottschalk, Michael Gerd; Stelzhammer, Viktoria; Wesseling, Hendrik; Cooper, Jason David; Bahn, Sabine

2016-11-25

Rodent models of major depressive disorder (MDD) are indispensable when screening for novel treatments, but assessing their translational relevance with human brain pathology has proved difficult. Using a novel systems approach, proteomics data obtained from post-mortem MDD anterior prefrontal cortex tissue (n = 12) and matched controls (n = 23) were compared with equivalent data from three commonly used preclinical models exposed to environmental stressors (chronic mild stress, prenatal stress and social defeat). Functional pathophysiological features associated with depression-like behaviour were identified in these models through enrichment of protein-protein interaction networks. A cross-species comparison evaluated which model(s) represent human MDD pathology most closely. Seven functional domains associated with MDD and represented across at least two models such as "carbohydrate metabolism and cellular respiration" were identified. Through statistical evaluation using kernel-based machine learning techniques, the social defeat model was found to represent MDD brain changes most closely for four of the seven domains. This is the first study to apply a method for directly evaluating the relevance of the molecular pathology of multiple animal models to human MDD on the functional level. The methodology and findings outlined here could help to overcome translational obstacles of preclinical psychiatric research.

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules.

PubMed

Pronobis, Wiktor; Tkatchenko, Alexandre; Müller, Klaus-Robert

2018-06-12

Machine learning (ML) based prediction of molecular properties across chemical compound space is an important and alternative approach to efficiently estimate the solutions of highly complex many-electron problems in chemistry and physics. Statistical methods represent molecules as descriptors that should encode molecular symmetries and interactions between atoms. Many such descriptors have been proposed; all of them have advantages and limitations. Here, we propose a set of general two-body and three-body interaction descriptors which are invariant to translation, rotation, and atomic indexing. By adapting the successfully used kernel ridge regression methods of machine learning, we evaluate our descriptors on predicting several properties of small organic molecules calculated using density-functional theory. We use two data sets. The GDB-7 set contains 6868 molecules with up to 7 heavy atoms of type CNO. The GDB-9 set is composed of 131722 molecules with up to 9 heavy atoms containing CNO. When trained on 5000 random molecules, our best model achieves an accuracy of 0.8 kcal/mol (on the remaining 1868 molecules of GDB-7) and 1.5 kcal/mol (on the remaining 126722 molecules of GDB-9) respectively. Applying a linear regression model on our novel many-body descriptors performs almost equal to a nonlinear kernelized model. Linear models are readily interpretable: a feature importance ranking measure helps to obtain qualitative and quantitative insights on the importance of two- and three-body molecular interactions for predicting molecular properties computed with quantum-mechanical methods.

Experiments in balance with a 2D one-legged hopping machine

NASA Astrophysics Data System (ADS)

Raibert, M. H.; Brown, H. B., Jr.

1984-03-01

The ability to balance is important to the mobility obtained by legged creatures found in nature, and may someday lead to versatile legged vehicles. In order to study the role of balance in legged locomotion and to develop appropriate control strategies, a 2D hopping machine was constructed for experimentation. The machine has one leg on which it hops and runs, making balance a prime consideration. Control of the machine's locomotion was decomposed into three separate parts: a vertical height control part, a horizontal velocity part, and an angular attitude control part. Experiments showed that the three part control scheme, while very simple to implement, was powerful enough to permit the machine to hop in place, to run at a desired rate, to translate from place to place, and to leap over obstacles. Results from modeling and computer simulation of a similar one-legged device are described by Raibert (1983).

State but not District Nutrition Policies Are Associated with Less Junk Food in Vending Machines and School Stores in US Public Schools

PubMed Central

KUBIK, MARTHA Y.; WALL, MELANIE; SHEN, LIJUAN; NANNEY, MARILYN S.; NELSON, TOBEN F.; LASKA, MELISSA N.; STORY, MARY

2012-01-01

Background Policy that targets the school food environment has been advanced as one way to increase the availability of healthy food at schools and healthy food choice by students. Although both state- and district-level policy initiatives have focused on school nutrition standards, it remains to be seen whether these policies translate into healthy food practices at the school level, where student behavior will be impacted. Objective To examine whether state- and district-level nutrition policies addressing junk food in school vending machines and school stores were associated with less junk food in school vending machines and school stores. Junk food was defined as foods and beverages with low nutrient density that provide calories primarily through fats and added sugars. Design A cross-sectional study design was used to assess self-report data collected by computer-assisted telephone interviews or self-administered mail questionnaires from state-, district-, and school-level respondents participating in the School Health Policies and Programs Study 2006. The School Health Policies and Programs Study, administered every 6 years since 1994 by the Centers for Disease Control and Prevention, is considered the largest, most comprehensive assessment of school health policies and programs in the United States. Subjects/setting A nationally representative sample (n = 563) of public elementary, middle, and high schools was studied. Statistical analysis Logistic regression adjusted for school characteristics, sampling weights, and clustering was used to analyze data. Policies were assessed for strength (required, recommended, neither required nor recommended prohibiting junk food) and whether strength was similar for school vending machines and school stores. Results School vending machines and school stores were more prevalent in high schools (93%) than middle (84%) and elementary (30%) schools. For state policies, elementary schools that required prohibiting junk food in school vending machines and school stores offered less junk food than elementary schools that neither required nor recommended prohibiting junk food (13% vs 37%; P = 0.006). Middle schools that required prohibiting junk food in vending machines and school stores offered less junk food than middle schools that recommended prohibiting junk food (71% vs 87%; P = 0.07). Similar associations were not evident for district-level polices or high schools. Conclusions Policy may be an effective tool to decrease junk food in schools, particularly in elementary and middle schools. PMID:20630161

Full-band error control and crack-free surface fabrication techniques for ultra-precision fly cutting of large-aperture KDP crystals

NASA Astrophysics Data System (ADS)

Zhang, F. H.; Wang, S. F.; An, C. H.; Wang, J.; Xu, Q.

2017-06-01

Large-aperture potassium dihydrogen phosphate (KDP) crystals are widely used in the laser path of inertial confinement fusion (ICF) systems. The most common method of manufacturing half-meter KDP crystals is ultra-precision fly cutting. When processing KDP crystals by ultra-precision fly cutting, the dynamic characteristics of the fly cutting machine and fluctuations in the fly cutting environment are translated into surface errors at different spatial frequency bands. These machining errors should be suppressed effectively to guarantee that KDP crystals meet the full-band machining accuracy specified in the evaluation index. In this study, the anisotropic machinability of KDP crystals and the causes of typical surface errors in ultra-precision fly cutting of the material are investigated. The structures of the fly cutting machine and existing processing parameters are optimized to improve the machined surface quality. The findings are theoretically and practically important in the development of high-energy laser systems in China.

Polish Adaptation of Wrist Evaluation Questionnaires.

PubMed

Czarnecki, Piotr; Wawrzyniak-Bielęda, Anna; Romanowski, Leszek

2015-01-01

Questionnaires evaluating hand and wrist function are a very useful tool allowing for objective and systematic recording of symptoms reported by the patients. Most questionnaires generally accepted in clinical practice are available in English and need to be appropriately adapted in translation and undergo subsequent validation before they can be used in another culture and language. The process of translation of the questionnaires was based on the generally accepted guidelines of the International Quality of Life Assessment Project (IQOLA). First, the questionnaires were translated from English into Polish by two independent translators. Then, a joint version of the translation was prepared collectively and translated back into English. Each stage was followed by a written report. The translated questionnaires were then evaluated by a group of patients. We selected 31 patients with wrist problems and asked them to complete the PRWE, Mayo, Michigan and DASH questionnaires twice at intervals of 3-10 days. The results were submitted for statistical analysis. We found a statistically significant (p<0.05) correlation for the two completions of the questionnaires. A comparison of the PRWE and Mayo questionnaires with the DASH questionnaire also showed a statistically significant correlation (p<0.05). Our results indicate that the cultural adaptation of the translated questionnaires was successful and that the questionnaires may be used in clinical practice.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Angers, Crystal Plume; Bottema, Ryan; Buckley, Les

Purpose: Treatment unit uptime statistics are typically used to monitor radiation equipment performance. The Ottawa Hospital Cancer Centre has introduced the use of Quality Control (QC) test success as a quality indicator for equipment performance and overall health of the equipment QC program. Methods: Implemented in 2012, QATrack+ is used to record and monitor over 1100 routine machine QC tests each month for 20 treatment and imaging units ( http://qatrackplus.com/ ). Using an SQL (structured query language) script, automated queries of the QATrack+ database are used to generate program metrics such as the number of QC tests executed and themore » percentage of tests passing, at tolerance or at action. These metrics are compared against machine uptime statistics already reported within the program. Results: Program metrics for 2015 show good correlation between pass rate of QC tests and uptime for a given machine. For the nine conventional linacs, the QC test success rate was consistently greater than 97%. The corresponding uptimes for these units are better than 98%. Machines that consistently show higher failure or tolerance rates in the QC tests have lower uptimes. This points to either poor machine performance requiring corrective action or to problems with the QC program. Conclusions: QATrack+ significantly improves the organization of QC data but can also aid in overall equipment management. Complimenting machine uptime statistics with QC test metrics provides a more complete picture of overall machine performance and can be used to identify areas of improvement in the machine service and QC programs.« less

Air-Bearing-Piston Suspension System

NASA Technical Reports Server (NTRS)

Mullen, Donald; Bishop, Stephen J.

1992-01-01

Suspension system based on air-bearing piston holds up steel ball against gravitation while allowing ball to translate vertically and rotate freely. System designed to simulate effect of microgravity on ball. Applicable to suppression of vibrations and delicate machining processes.

Method and system for assembling miniaturized devices

DOEpatents

Montesanti, Richard C.; Klingmann, Jeffrey L.; Seugling, Richard M.

2013-03-12

An apparatus for assembling a miniaturized device includes a manipulator system including six manipulators operable to position and orient components of the miniaturized device with submicron precision and micron-level accuracy. The manipulator system includes a first plurality of motorized axes, a second plurality of manual axes, and force and torque and sensors. Each of the six manipulators includes at least one translation stage, at least one rotation stage, tooling attached to the at least one translation stage or the at least one rotation stage, and an attachment mechanism disposed at a distal end of the tooling and operable to attach at least a portion of the miniaturized device to the tooling. The apparatus also includes an optical coordinate-measuring machine (OCMM) including a machine-vision system, a laser-based distance-measuring probe, and a touch probe. The apparatus also includes an operator control system coupled to the manipulator system and the OCMM.

Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA

PubMed Central

Liu, Tieming; Shepherd, Scott; Paiva, William

2018-01-01

Objectives The objective of this study was to compare the performance of two popularly used early sepsis diagnostic criteria, systemic inflammatory response syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA), using statistical and machine learning approaches. Methods This retrospective study examined patient visits in Emergency Department (ED) with sepsis related diagnosis. The outcome was 28-day in-hospital mortality. Using odds ratio (OR) and modeling methods (decision tree [DT], multivariate logistic regression [LR], and naïve Bayes [NB]), the relationships between diagnostic criteria and mortality were examined. Results Of 132,704 eligible patient visits, 14% died within 28 days of ED admission. The association of qSOFA ≥2 with mortality (OR = 3.06; 95% confidence interval [CI], 2.96–3.17) greater than the association of SIRS ≥2 with mortality (OR = 1.22; 95% CI, 1.18–1.26). The area under the ROC curve for qSOFA (AUROC = 0.70) was significantly greater than for SIRS (AUROC = 0.63). For qSOFA, the sensitivity and specificity were DT = 0.39, LR = 0.64, NB = 0.62 and DT = 0.89, LR = 0.63, NB = 0.66, respectively. For SIRS, the sensitivity and specificity were DT = 0.46, LR = 0.62, NB = 0.62 and DT = 0.70, LR = 0.59, NB = 0.58, respectively. Conclusions The evidences suggest that qSOFA is a better diagnostic criteria than SIRS. The low sensitivity of qSOFA can be improved by carefully selecting the threshold to translate the predicted probabilities into labels. These findings can guide healthcare providers in selecting risk-stratification measures for patients presenting to an ED with sepsis. PMID:29770247

TU-FG-BRB-08: Challenges, Limitations and Future Outlook Towards Clinical Translation of Proton Acoustic Range Verification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yousefi, S; Ahmad, M; Xiang, L

Purpose: To report our investigations of proton acoustic imaging, including computer simulations and preliminary experimental studies at clinical facilities. The ultimate achievable accuracy, sensitivity and clinical translation challenges are discussed. Methods: The acoustic pulse due to pressure rise was estimated using finite element model. Since the ionoacoustic pulse is highly dependent on the proton pulse width and energy, multiple pulse widths were studied. Based on the received signal spectrum at piezoelectric ultrasound transducer with consideration of random thermal noise, maximum spatial resolution of the proton-acoustic imaging modality was calculated. The simulation studies defined the design specifications of the system tomore » detect proton acoustic signal from Hitachi and Mevion clinical machines. A 500 KHz hydrophone with 100 dB amplification was set up in a water tank placed in front of the proton nozzle A 40 MHz data acquisition was synchronized by a trigger signal provided by the machine. Results: Given 30–800 mGy dose per pulse at the Bragg peak, the minimum number of protons detectable by the proton acoustic technique was on the order of 10×10^6 per pulse. The broader pulse widths produce signal with lower acoustic frequencies, with 10 µs pulses producing signals with frequency less than 100 kHz. As the proton beam pulse width increases, a higher dose rate is required to measure the acoustic signal. Conclusion: We have established the minimal detection limit for protonacoustic range validation for a variety of pulse parameters. Our study indicated practical proton-acoustic range verification can be feasible with a pulse shorter than 10 µs, 5×10^6 protons/pulse, 50 nA beam current and a highly sensitive ultrasonic transducer. The translational challenges into current clinical machines include proper magnetic shielding of the measurement equipment, providing a clean trigger signal from the proton machine, providing a shorter proton beam pulse and higher dose per pulse.« less

Breaking the Language Barrier

NASA Technical Reports Server (NTRS)

1981-01-01

Preparation for the Apollo Soyuz mission entailed large-scale informational exchange that was accomplished by a computerized translation system. Based on this technology of commercial machine translation, a system known as SYSTRAN II was developed by LATSEC, Inc. and the World Translation Company of Canada. This system increases the output of a human translator by five to eight times, affording cost savings by allowing a large increase in document production without hiring additional people. Extra savings accrue from automatic production of camera-ready copy. Applications include translation of service manuals, proposals and tenders, planning studies, catalogs, list of parts and prices, textbooks, technical reports and education/training materials. System is operational for six language pairs. Systran users include Xerox Corporation, General Motors of Canada, Bell Northern Research of Canada, the U.S. Air Force and the European Commission. The company responsible for the production of SYSTRAN II has changed its name to SYSTRAN.

Modeling stochastic kinetics of molecular machines at multiple levels: from molecules to modules.

PubMed

Chowdhury, Debashish

2013-06-04

A molecular machine is either a single macromolecule or a macromolecular complex. In spite of the striking superficial similarities between these natural nanomachines and their man-made macroscopic counterparts, there are crucial differences. Molecular machines in a living cell operate stochastically in an isothermal environment far from thermodynamic equilibrium. In this mini-review we present a catalog of the molecular machines and an inventory of the essential toolbox for theoretically modeling these machines. The tool kits include 1), nonequilibrium statistical-physics techniques for modeling machines and machine-driven processes; and 2), statistical-inference methods for reverse engineering a functional machine from the empirical data. The cell is often likened to a microfactory in which the machineries are organized in modular fashion; each module consists of strongly coupled multiple machines, but different modules interact weakly with each other. This microfactory has its own automated supply chain and delivery system. Buoyed by the success achieved in modeling individual molecular machines, we advocate integration of these models in the near future to develop models of functional modules. A system-level description of the cell from the perspective of molecular machinery (the mechanome) is likely to emerge from further integrations that we envisage here. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

E-TIF: An Electronic Terminology Interchange Format.

ERIC Educational Resources Information Center

Melby, Alan

1995-01-01

Emphasizes the importance of terminology in an age of machine-based translation systems. Discusses differences between lexicography and terminology. Concludes with an argument for a new system based on the Text Encoding Initiative-based notions of elements and attributes. (CFR)

The Statistical Basis of Chemical Equilibria.

ERIC Educational Resources Information Center

Hauptmann, Siegfried; Menger, Eva

1978-01-01

Describes a machine which demonstrates the statistical bases of chemical equilibrium, and in doing so conveys insight into the connections among statistical mechanics, quantum mechanics, Maxwell Boltzmann statistics, statistical thermodynamics, and transition state theory. (GA)

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

Multilevel Analysis in Analyzing Speech Data

ERIC Educational Resources Information Center

Guddattu, Vasudeva; Krishna, Y.

2011-01-01

The speech produced by human vocal tract is a complex acoustic signal, with diverse applications in phonetics, speech synthesis, automatic speech recognition, speaker identification, communication aids, speech pathology, speech perception, machine translation, hearing research, rehabilitation and assessment of communication disorders and many…

Strategies, Language Transfer and the Simulation of the Second Language Learner's Mental Operations.

ERIC Educational Resources Information Center

Smith, Mike Sharwood

1979-01-01

An attempt is made to describe second language behavior and language transfer in cybernetic terms. This should make it possible to translate language into machine language and to clarify psycholinguistic explanations of second language performance. (PMJ)

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Ex-vivo machine perfusion for kidney preservation.

PubMed

Hamar, Matyas; Selzner, Markus

2018-06-01

Machine perfusion is a novel strategy to decrease preservation injury, improve graft assessment, and increase organ acceptance for transplantation. This review summarizes the current advances in ex-vivo machine-based kidney preservation technologies over the last year. Ex-vivo perfusion technologies, such as hypothermic and normothermic machine perfusion and controlled oxygenated rewarming, have gained high interest in the field of organ preservation. Keeping kidney grafts functionally and metabolically active during the preservation period offers a unique chance for viability assessment, reconditioning, and organ repair. Normothermic ex-vivo kidney perfusion has been recently translated into clinical practice. Preclinical results suggest that prolonged warm perfusion appears superior than a brief end-ischemic reconditioning in terms of renal function and injury. An established standardized protocol for continuous warm perfusion is still not available for human grafts. Ex-vivo machine perfusion represents a superior organ preservation method over static cold storage. There is still an urgent need for the optimization of the perfusion fluid and machine technology and to identify the optimal indication in kidney transplantation. Recent research is focusing on graft assessment and therapeutic strategies.

Unsupervised quality estimation model for English to German translation and its application in extensive supervised evaluation.

PubMed

Han, Aaron L-F; Wong, Derek F; Chao, Lidia S; He, Liangye; Lu, Yi

2014-01-01

With the rapid development of machine translation (MT), the MT evaluation becomes very important to timely tell us whether the MT system makes any progress. The conventional MT evaluation methods tend to calculate the similarity between hypothesis translations offered by automatic translation systems and reference translations offered by professional translators. There are several weaknesses in existing evaluation metrics. Firstly, the designed incomprehensive factors result in language-bias problem, which means they perform well on some special language pairs but weak on other language pairs. Secondly, they tend to use no linguistic features or too many linguistic features, of which no usage of linguistic feature draws a lot of criticism from the linguists and too many linguistic features make the model weak in repeatability. Thirdly, the employed reference translations are very expensive and sometimes not available in the practice. In this paper, the authors propose an unsupervised MT evaluation metric using universal part-of-speech tagset without relying on reference translations. The authors also explore the performances of the designed metric on traditional supervised evaluation tasks. Both the supervised and unsupervised experiments show that the designed methods yield higher correlation scores with human judgments.

A Python Analytical Pipeline to Identify Prohormone Precursors and Predict Prohormone Cleavage Sites

PubMed Central

Southey, Bruce R.; Sweedler, Jonathan V.; Rodriguez-Zas, Sandra L.

2008-01-01

Neuropeptides and hormones are signaling molecules that support cell–cell communication in the central nervous system. Experimentally characterizing neuropeptides requires significant efforts because of the complex and variable processing of prohormone precursor proteins into neuropeptides and hormones. We demonstrate the power and flexibility of the Python language to develop components of an bioinformatic analytical pipeline to identify precursors from genomic data and to predict cleavage as these precursors are en route to the final bioactive peptides. We identified 75 precursors in the rhesus genome, predicted cleavage sites using support vector machines and compared the rhesus predictions to putative assignments based on homology to human sequences. The correct classification rate of cleavage using the support vector machines was over 97% for both human and rhesus data sets. The functionality of Python has been important to develop and maintain NeuroPred (http://neuroproteomics.scs.uiuc.edu/neuropred.html), a user-centered web application for the neuroscience community that provides cleavage site prediction from a wide range of models, precision and accuracy statistics, post-translational modifications, and the molecular mass of potential peptides. The combined results illustrate the suitability of the Python language to implement an all-inclusive bioinformatics approach to predict neuropeptides that encompasses a large number of interdependent steps, from scanning genomes for precursor genes to identification of potential bioactive neuropeptides. PMID:19169350

Preoperative Planning and Intraoperative Technique for Accurate Translation of a Distal First Metatarsal Osteotomy.

PubMed

Wynes, Jacob; Lamm, Bradley M; Andrade, Bijan J; Malay, D Scot

2016-01-01

We used preoperative radiographic and intraoperative anatomic measurements to predict and achieve, respectively, the precise amount of capital fragment lateral translation required to restore anatomic balance to the first metatarsophalangeal joint. Correlation was used to relate the amount of capital fragment translation and operative reduction of the first intermetatarsal angle (IMA), hallux abductus angle (HAA), tibial sesamoid position (TSP), metatarsus adductus angle, and first metatarsal length. The mean capital fragment lateral translation was 5.54 ± 1.64 mm, and the mean radiographic reductions included a first IMA of 5.04° ± 2.85°, an HAA of 9.39° ± 8.38°, and a TSP of 1.38 ± 0.9. These changes were statistically (p < .001) and clinically (≥32.55%) significant. The mean reduction of the metatarsus adductus angle was 0.66° ± 4.44° and that for the first metatarsal length was 0.33 ± 7.27 mm, and neither of these were statistically (p = .5876 and 0.1247, respectively) or clinically (≤3.5%) significant. Pairwise correlations between the amount of lateral translation of the capital fragment and the first IMA, HAA, and TSP values were moderately positive and statistically significant (r = 0.4412, p = .0166; r = 0.5391, p = .0025; and r = 0.3729, p = .0463; respectively). In contrast, the correlation with metatarsus adductus and the first metatarsal shortening were weak and not statistically significant (r = 0.2296, p = .2308 and r = -0.2394, p = .2109, respectively). The results of our study indicate that predicted preoperative and executed intraoperative lateral translation of the capital fragment correlates with statistically and clinically significant reductions in the first IMA, HAA, and TSP. Copyright © 2016 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Walking robot: A design project for undergraduate students

NASA Technical Reports Server (NTRS)

1990-01-01

The design and construction of the University of Maryland walking machine was completed during the 1989 to 1990 academic year. It was required that the machine be capable of completing a number of tasks including walking a straight line, turning to change direction, and manuevering over an obstacle such as a set of stairs. The machine consists of two sets of four telescoping legs that alternately support the entire structure. A gear box and crank arm assembly is connected to the leg sets to provide the power required for the translational motion of the machine. By retracting all eight legs, the robot comes to rest on a central Bigfoot support. Turning is accomplished by rotating this machine about this support. The machine can be controlled by using either a user-operated remote tether or the onboard computer for the execution of control commands. Absolute encoders are attached to all motors to provide the control computer with information regarding the status of the motors. Long and short range infrared sensors provide the computer with feedback information regarding the machine's position relative to a series of stripes and reflectors. These infrared sensors simulate how the robot might sense and gain information about the environment of Mars.

Evaluating the Security of Machine Learning Algorithms

DTIC Science & Technology

2008-05-20

Two far-reaching trends in computing have grown in significance in recent years. First, statistical machine learning has entered the mainstream as a...computing applications. The growing intersection of these trends compels us to investigate how well machine learning performs under adversarial conditions... machine learning has a structure that we can use to build secure learning systems. This thesis makes three high-level contributions. First, we develop a

MetaJC++: A flexible and automatic program transformation technique using meta framework

NASA Astrophysics Data System (ADS)

Beevi, Nadera S.; Reghu, M.; Chitraprasad, D.; Vinodchandra, S. S.

2014-09-01

Compiler is a tool to translate abstract code containing natural language terms to machine code. Meta compilers are available to compile more than one languages. We have developed a meta framework intends to combine two dissimilar programming languages, namely C++ and Java to provide a flexible object oriented programming platform for the user. Suitable constructs from both the languages have been combined, thereby forming a new and stronger Meta-Language. The framework is developed using the compiler writing tools, Flex and Yacc to design the front end of the compiler. The lexer and parser have been developed to accommodate the complete keyword set and syntax set of both the languages. Two intermediate representations have been used in between the translation of the source program to machine code. Abstract Syntax Tree has been used as a high level intermediate representation that preserves the hierarchical properties of the source program. A new machine-independent stack-based byte-code has also been devised to act as a low level intermediate representation. The byte-code is essentially organised into an output class file that can be used to produce an interpreted output. The results especially in the spheres of providing C++ concepts in Java have given an insight regarding the potential strong features of the resultant meta-language.

Mechanically Compliant Electronic Materials for Wearable Photovoltaics and Human-Machine Interfaces

NASA Astrophysics Data System (ADS)

O'Connor, Timothy Francis, III

Applications of stretchable electronic materials for human-machine interfaces are described herein. Intrinsically stretchable organic conjugated polymers and stretchable electronic composites were used to develop stretchable organic photovoltaics (OPVs), mechanically robust wearable OPVs, and human-machine interfaces for gesture recognition, American Sign Language Translation, haptic control of robots, and touch emulation for virtual reality, augmented reality, and the transmission of touch. The stretchable and wearable OPVs comprise active layers of poly-3-alkylthiophene:phenyl-C61-butyric acid methyl ester (P3AT:PCBM) and transparent conductive electrodes of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) and devices could only be fabricated through a deep understanding of the connection between molecular structure and the co-engineering of electronic performance with mechanical resilience. The talk concludes with the use of composite piezoresistive sensors two smart glove prototypes. The first integrates stretchable strain sensors comprising a carbon-elastomer composite, a wearable microcontroller, low energy Bluetooth, and a 6-axis accelerometer/gyroscope to construct a fully functional gesture recognition glove capable of wirelessly translating American Sign Language to text on a cell phone screen. The second creates a system for the haptic control of a 3D printed robot arm, as well as the transmission of touch and temperature information.

Lysine acetylation sites prediction using an ensemble of support vector machine classifiers.

PubMed

Xu, Yan; Wang, Xiao-Bo; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang

2010-05-07

Lysine acetylation is an essentially reversible and high regulated post-translational modification which regulates diverse protein properties. Experimental identification of acetylation sites is laborious and expensive. Hence, there is significant interest in the development of computational methods for reliable prediction of acetylation sites from amino acid sequences. In this paper we use an ensemble of support vector machine classifiers to perform this work. The experimentally determined acetylation lysine sites are extracted from Swiss-Prot database and scientific literatures. Experiment results show that an ensemble of support vector machine classifiers outperforms single support vector machine classifier and other computational methods such as PAIL and LysAcet on the problem of predicting acetylation lysine sites. The resulting method has been implemented in EnsemblePail, a web server for lysine acetylation sites prediction available at http://www.aporc.org/EnsemblePail/. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

Electrophoretic Deformation of Individual Transfer RNA Molecules Reveals Their Identity.

PubMed

Henley, Robert Y; Ashcroft, Brian Alan; Farrell, Ian; Cooperman, Barry S; Lindsay, Stuart M; Wanunu, Meni

2016-01-13

It has been hypothesized that the ribosome gains additional fidelity during protein translation by probing structural differences in tRNA species. We measure the translocation kinetics of different tRNA species through ∼3 nm diameter synthetic nanopores. Each tRNA species varies in the time scale with which it is deformed from equilibrium, as in the translocation step of protein translation. Using machine-learning algorithms, we can differentiate among five tRNA species, analyze the ratios of tRNA binary mixtures, and distinguish tRNA isoacceptors.

A Grammar Library for Information Structure

ERIC Educational Resources Information Center

Song, Sanghoun

2014-01-01

This dissertation makes substantial contributions to both the theoretical and computational treatment of information structure, with an eye toward creating natural language processing applications such as multilingual machine translation systems. The aim of the present dissertation is to create a grammar library of information structure for the…

CDISC SHARE, a Global, Cloud-based Resource of Machine-Readable CDISC Standards for Clinical and Translational Research

PubMed Central

Hume, Samuel; Chow, Anthony; Evans, Julie; Malfait, Frederik; Chason, Julie; Wold, J. Darcy; Kubick, Wayne; Becnel, Lauren B.

2018-01-01

The Clinical Data Interchange Standards Consortium (CDISC) is a global non-profit standards development organization that creates consensus-based standards for clinical and translational research. Several of these standards are now required by regulators for electronic submissions of regulated clinical trials’ data and by government funding agencies. These standards are free and open, available for download on the CDISC Website as PDFs. While these documents are human readable, they are not amenable to ready use by electronic systems. CDISC launched the CDISC Shared Health And Research Electronic library (SHARE) to provide the standards metadata in machine-readable formats to facilitate the automated management and implementation of the standards. This paper describes how CDISC SHARE’S standards can facilitate collecting, aggregating and analyzing standardized data from early design to end analysis; and its role as a central resource providing information systems with metadata that drives process automation including study setup and data pipelining. PMID:29888049

Selected aspects of microelectronics technology and applications: Numerically controlled machine tools. Technology trends series no. 2

NASA Astrophysics Data System (ADS)

Sigurdson, J.; Tagerud, J.

1986-05-01

A UNIDO publication about machine tools with automatic control discusses the following: (1) numerical control (NC) machine tool perspectives, definition of NC, flexible manufacturing systems, robots and their industrial application, research and development, and sensors; (2) experience in developing a capability in NC machine tools; (3) policy issues; (4) procedures for retrieval of relevant documentation from data bases. Diagrams, statistics, bibliography are included.

Industrial machine systems risk assessment: a critical review of concepts and methods.

PubMed

Etherton, John R

2007-02-01

Reducing the risk of work-related death and injury to machine operators and maintenance personnel poses a continuing occupational safety challenge. The risk of injury from machinery in U.S. workplaces is high. Between 1992 and 2001, there were, on average, 520 fatalities per year involving machines and, on average, 3.8 cases per 10,000 workers of nonfatal caught-in-running-machine injuries involving lost workdays. A U.S. task group recently developed a technical reference guideline, ANSI B11 TR3, "A Guide to Estimate, Evaluate, & Reduce Risks Associated with Machine Tools," that is intended to bring machine tool risk assessment practice in the United States up to or above the level now required by the international standard, ISO 14121. The ANSI guideline emphasizes identifying tasks and hazards not previously considered, particularly those associated with maintenance; and it further emphasizes teamwork among line workers, engineers, and safety professionals. The value of this critical review of concepts and methods resides in (1) its linking current risk theory to machine system risk assessment and (2) its exploration of how various risk estimation tools translate into risk-informed decisions on industrial machine system design and use. The review was undertaken to set the stage for a field evaluation study on machine risk assessment among users of the ANSI B11 TR3 method.

The Perseus computational platform for comprehensive analysis of (prote)omics data.

PubMed

Tyanova, Stefka; Temu, Tikira; Sinitcyn, Pavel; Carlson, Arthur; Hein, Marco Y; Geiger, Tamar; Mann, Matthias; Cox, Jürgen

2016-09-01

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

Translating an AI application from Lisp to Ada: A case study

NASA Technical Reports Server (NTRS)

Davis, Gloria J.

1991-01-01

A set of benchmarks was developed to test the performance of a newly designed computer executing both Lisp and Ada. Among these was AutoClassII -- a large Artificial Intelligence (AI) application written in Common Lisp. The extraction of a representative subset of this complex application was aided by a Lisp Code Analyzer (LCA). The LCA enabled rapid analysis of the code, putting it in a concise and functionally readable form. An equivalent benchmark was created in Ada through manual translation of the Lisp version. A comparison of the execution results of both programs across a variety of compiler-machine combinations indicate that line-by-line translation coupled with analysis of the initial code can produce relatively efficient and reusable target code.

Translations on Eastern Europe Scientific Affairs, Number 560

DTIC Science & Technology

1977-10-04

Miklos Szilagyi . TAPNEG; prepares digitalized printed wiring diagram control punch tape on an ADMAP-2 graphing machine with reflection on the x axis...FOKAL 16 KE; BME, Dr Zsolt Illyefalvi-Vitez; BME, Dr Miklos Szilagyi . TESTOP-10; the program provides measurement and diagnostics for logic cards

Natural Language Processing: Toward Large-Scale, Robust Systems.

ERIC Educational Resources Information Center

Haas, Stephanie W.

1996-01-01

Natural language processing (NLP) is concerned with getting computers to do useful things with natural language. Major applications include machine translation, text generation, information retrieval, and natural language interfaces. Reviews important developments since 1987 that have led to advances in NLP; current NLP applications; and problems…

A Novel Approach to Creating Disambiguated Multilingual Dictionaries

ERIC Educational Resources Information Center

Boguslavsky, Igor; Cardenosa, Jesus; Gallardo, Carolina

2009-01-01

Multilingual lexicons are needed in various applications, such as cross-lingual information retrieval, machine translation, and some others. Often, these applications suffer from the ambiguity of dictionary items, especially when an intermediate natural language is involved in the process of the dictionary construction, since this language adds…

Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics

PubMed Central

2018-01-01

As an intrinsic part of the Internet of Things (IoT) ecosystem, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave) communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC) device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs) with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy) of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation. PMID:29329248

Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics.

PubMed

Jung, Haejoon; Lee, In-Ho

2018-01-12

As an intrinsic part of the Internet of Things (IoT) ecosystem, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave) communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC) device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs) with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy) of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation.

University of Maryland walking robot: A design project for undergraduate students

NASA Technical Reports Server (NTRS)

Olsen, Bob; Bielec, Jim; Hartsig, Dave; Oliva, Mani; Grotheer, Phil; Hekmat, Morad; Russell, David; Tavakoli, Hossein; Young, Gary; Nave, Tom

1990-01-01

The design and construction required that the walking robot machine be capable of completing a number of tasks including walking in a straight line, turning to change direction, and maneuvering over an obstable such as a set of stairs. The machine consists of two sets of four telescoping legs that alternately support the entire structure. A gear-box and crank-arm assembly is connected to the leg sets to provide the power required for the translational motion of the machine. By retracting all eight legs, the robot comes to rest on a central Bigfoot support. Turning is accomplished by rotating the machine about this support. The machine can be controlled by using either a user operated remote tether or the on-board computer for the execution of control commands. Absolute encoders are attached to all motors (leg, main drive, and Bigfoot) to provide the control computer with information regarding the status of the motors (up-down motion, forward or reverse rotation). Long and short range infrared sensors provide the computer with feedback information regarding the machine's relative position to a series of stripes and reflectors. These infrared sensors simulate how the robot might sense and gain information about the environment of Mars.

GSHSite: Exploiting an Iteratively Statistical Method to Identify S-Glutathionylation Sites with Substrate Specificity

PubMed Central

Chen, Yi-Ju; Lu, Cheng-Tsung; Huang, Kai-Yao; Wu, Hsin-Yi; Chen, Yu-Ju; Lee, Tzong-Yi

2015-01-01

S-glutathionylation, the covalent attachment of a glutathione (GSH) to the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-glutathionylation remains unknown. Based on a total of 1783 experimentally identified S-glutathionylation sites from mouse macrophages, this work presents an informatics investigation on S-glutathionylation sites including structural factors such as the flanking amino acids composition and the accessible surface area (ASA). TwoSampleLogo presents that positively charged amino acids flanking the S-glutathionylated cysteine may influence the formation of S-glutathionylation in closed three-dimensional environment. A statistical method is further applied to iteratively detect the conserved substrate motifs with statistical significance. Support vector machine (SVM) is then applied to generate predictive model considering the substrate motifs. According to five-fold cross-validation, the SVMs trained with substrate motifs could achieve an enhanced sensitivity, specificity, and accuracy, and provides a promising performance in an independent test set. The effectiveness of the proposed method is demonstrated by the correct identification of previously reported S-glutathionylation sites of mouse thioredoxin (TXN) and human protein tyrosine phosphatase 1b (PTP1B). Finally, the constructed models are adopted to implement an effective web-based tool, named GSHSite (http://csb.cse.yzu.edu.tw/GSHSite/), for identifying uncharacterized GSH substrate sites on the protein sequences. PMID:25849935

Application of Social Network Analysis Techniques to Machine Translated Documents

DTIC Science & Technology

2010-04-01

Mandela, por la nueva Asamblea nacional sudafricana. La Comisión electoral independiente , una gigantesca maquinaria administrativa con unos 300.000...seat perpetration 2 surpassed 1 ultramoderno 1 recount 3 party 1 sellar 1 posting 1 suspended 1 urns 1 regions 1 president 2 south_african 1

Sensory Aids for the Blind.

ERIC Educational Resources Information Center

National Academy of Sciences - National Research Council, Washington, DC. Committee on Prosthetics Research and Development.

The problems of providing sensory aids for the blind are presented and a report on the present status of aids discusses direct translation and recognition reading machines as well as mobility aids. Aspects of required research considered are the following: assessment of needs; vision, audition, taction, and multimodal communication; reading aids,…

An Operational System for Subject Switching between Controlled Vocabularies: A Computational Linguistics Approach.

ERIC Educational Resources Information Center

Silvester, June P.; And Others

This report describes a new automated process that pioneers full-scale operational use of subject switching by the NASA (National Aeronautics and Space Administration) Scientific and Technical Information (STI) Facility. The subject switching process routinely translates machine-readable subject terms from one controlled vocabulary into the…

Temporal Sequences Quantify the Contributions of Individual Fixations in Complex Perceptual Matching Tasks

ERIC Educational Resources Information Center

Busey, Thomas; Yu, Chen; Wyatte, Dean; Vanderkolk, John

2013-01-01

Perceptual tasks such as object matching, mammogram interpretation, mental rotation, and satellite imagery change detection often require the assignment of correspondences to fuse information across views. We apply techniques developed for machine translation to the gaze data recorded from a complex perceptual matching task modeled after…

A visual programming environment for the Navier-Stokes computer

NASA Technical Reports Server (NTRS)

Tomboulian, Sherryl; Crockett, Thomas W.; Middleton, David

1988-01-01

The Navier-Stokes computer is a high-performance, reconfigurable, pipelined machine designed to solve large computational fluid dynamics problems. Due to the complexity of the architecture, development of effective, high-level language compilers for the system appears to be a very difficult task. Consequently, a visual programming methodology has been developed which allows users to program the system at an architectural level by constructing diagrams of the pipeline configuration. These schematic program representations can then be checked for validity and automatically translated into machine code. The visual environment is illustrated by using a prototype graphical editor to program an example problem.

An information-carrying and knowledge-producing molecular machine. A Monte-Carlo simulation.

PubMed

Kuhn, Christoph

2012-02-01

The concept called Knowledge is a measure of the quality of genetically transferred information. Its usefulness is demonstrated quantitatively in a Monte-Carlo simulation on critical steps in a origin of life model. The model describes the origin of a bio-like genetic apparatus by a long sequence of physical-chemical steps: it starts with the presence of a self-replicating oligomer and a specifically structured environment in time and space that allow for the formation of aggregates such as assembler-hairpins-devices and, at a later stage, an assembler-hairpins-enzyme device-a first translation machine.

Health Informatics via Machine Learning for the Clinical Management of Patients.

PubMed

Clifton, D A; Niehaus, K E; Charlton, P; Colopy, G W

2015-08-13

To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Fast femtosecond laser ablation for efficient cutting of sintered alumina substrates

NASA Astrophysics Data System (ADS)

Oosterbeek, Reece N.; Ward, Thomas; Ashforth, Simon; Bodley, Owen; Rodda, Andrew E.; Simpson, M. Cather

2016-09-01

Fast, accurate cutting of technical ceramics is a significant technological challenge because of these materials' typical high mechanical strength and thermal resistance. Femtosecond pulsed lasers offer significant promise for meeting this challenge. Femtosecond pulses can machine nearly any material with small kerf and little to no collateral damage to the surrounding material. The main drawback to femtosecond laser machining of ceramics is slow processing speed. In this work we report on the improvement of femtosecond laser cutting of sintered alumina substrates through optimisation of laser processing parameters. The femtosecond laser ablation thresholds for sintered alumina were measured using the diagonal scan method. Incubation effects were found to fit a defect accumulation model, with Fth,1=6.0 J/cm2 (±0.3) and Fth,∞=2.5 J/cm2 (±0.2). The focal length and depth, laser power, number of passes, and material translation speed were optimised for ablation speed and high quality. Optimal conditions of 500 mW power, 100 mm focal length, 2000 μm/s material translation speed, with 14 passes, produced complete cutting of the alumina substrate at an overall processing speed of 143 μm/s - more than 4 times faster than the maximum reported overall processing speed previously achieved by Wang et al. [1]. This process significantly increases processing speeds of alumina substrates, thereby reducing costs, making femtosecond laser machining a more viable option for industrial users.

Gesture-Controlled Interfaces for Self-Service Machines

NASA Technical Reports Server (NTRS)

Cohen, Charles J.; Beach, Glenn

2006-01-01

Gesture-controlled interfaces are software- driven systems that facilitate device control by translating visual hand and body signals into commands. Such interfaces could be especially attractive for controlling self-service machines (SSMs) for example, public information kiosks, ticket dispensers, gasoline pumps, and automated teller machines (see figure). A gesture-controlled interface would include a vision subsystem comprising one or more charge-coupled-device video cameras (at least two would be needed to acquire three-dimensional images of gestures). The output of the vision system would be processed by a pure software gesture-recognition subsystem. Then a translator subsystem would convert a sequence of recognized gestures into commands for the SSM to be controlled; these could include, for example, a command to display requested information, change control settings, or actuate a ticket- or cash-dispensing mechanism. Depending on the design and operational requirements of the SSM to be controlled, the gesture-controlled interface could be designed to respond to specific static gestures, dynamic gestures, or both. Static and dynamic gestures can include stationary or moving hand signals, arm poses or motions, and/or whole-body postures or motions. Static gestures would be recognized on the basis of their shapes; dynamic gestures would be recognized on the basis of both their shapes and their motions. Because dynamic gestures include temporal as well as spatial content, this gesture- controlled interface can extract more information from dynamic than it can from static gestures.

Measurement of aspheric mirror by nanoprofiler using normal vector tracing

NASA Astrophysics Data System (ADS)

Kitayama, Takao; Shiraji, Hiroki; Yamamura, Kazuya; Endo, Katsuyoshi

2016-09-01

Aspheric or free-form optics with high accuracy are necessary in many fields such as third-generation synchrotron radiation and extreme-ultraviolet lithography. Therefore the demand of measurement method for aspherical or free-form surface with nanometer accuracy increases. Purpose of our study is to develop a non-contact measurement technology for aspheric or free-form surfaces directly with high repeatability. To achieve this purpose we have developed threedimensional Nanoprofiler which detects normal vectors of sample surface. The measurement principle is based on the straightness of laser light and the accurate motion of rotational goniometers. This machine consists of four rotational stages, one translational stage and optical head which has the quadrant photodiode (QPD) and laser source. In this measurement method, we conform the incident light beam to reflect the beam by controlling five stages and determine the normal vectors and the coordinates of the surface from signal of goniometers, translational stage and QPD. We can obtain three-dimensional figure from the normal vectors and their coordinates by surface reconstruction algorithm. To evaluate performance of this machine we measure a concave aspheric mirror with diameter of 150 mm. As a result we achieve to measure large area of 150mm diameter. And we observe influence of systematic errors which the machine has. Then we simulated the influence and subtracted it from measurement result.

Translating statistical images to text summaries for partially sighted persons on mobile devices: iconic image maps approach

NASA Astrophysics Data System (ADS)

Williams, Godfried B.

2005-03-01

This paper attempts to demonstrate a novel based idea for transforming statistical image data to text using autoassociative and unsupervised artificial neural network and iconic image maps using the shape and texture genetic algorithm, underlying concepts translating the image data to text. Full details of experiments could be assessed at http://www.uel.ac.uk/seis/applications/.

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.

Machine Learning in the Presence of an Adversary: Attacking and Defending the SpamBayes Spam Filter

DTIC Science & Technology

2008-05-20

Machine learning techniques are often used for decision making in security critical applications such as intrusion detection and spam filtering...filter. The defenses shown in this thesis are able to work against the attacks developed against SpamBayes and are sufficiently generic to be easily extended into other statistical machine learning algorithms.

Testing meta tagger

DTIC Science & Technology

2017-12-21

rank , and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on...Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.[1] Arthur Samuel...an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning " in 1959 while at IBM[2]. Evolved

A simulator study on information requirements for precision hovering

NASA Technical Reports Server (NTRS)

Lemons, J. L.; Dukes, T. A.

1975-01-01

A fixed base simulator study of an advanced helicopter instrument display utilizing translational acceleration, velocity and position information is reported. The simulation involved piloting a heavy helicopter using the Integrated Trajectory Error Display (ITED) in a precision hover task. The test series explored two basic areas. The effect on hover accuracy of adding acceleration information was of primary concern. Also of interest was the operators' ability to use degraded information derived from less sophisticated sources. The addition of translational acceleration to a display containing velocity and position information did not appear to improve the hover performance significantly. However, displayed acceleration information seemed to increase the damping of the man machine system. Finally, the pilots could use translational information synthesized from attitude and angular acceleration as effectively as perfect acceleration.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Machine learning Z2 quantum spin liquids with quasiparticle statistics

NASA Astrophysics Data System (ADS)

Zhang, Yi; Melko, Roger G.; Kim, Eun-Ah

2017-12-01

After decades of progress and effort, obtaining a phase diagram for a strongly correlated topological system still remains a challenge. Although in principle one could turn to Wilson loops and long-range entanglement, evaluating these nonlocal observables at many points in phase space can be prohibitively costly. With growing excitement over topological quantum computation comes the need for an efficient approach for obtaining topological phase diagrams. Here we turn to machine learning using quantum loop topography (QLT), a notion we have recently introduced. Specifically, we propose a construction of QLT that is sensitive to quasiparticle statistics. We then use mutual statistics between the spinons and visons to detect a Z2 quantum spin liquid in a multiparameter phase space. We successfully obtain the quantum phase boundary between the topological and trivial phases using a simple feed-forward neural network. Furthermore, we demonstrate advantages of our approach for the evaluation of phase diagrams relating to speed and storage. Such statistics-based machine learning of topological phases opens new efficient routes to studying topological phase diagrams in strongly correlated systems.

Statistical complex fatigue data for SAE 4340 steel and its use in design by reliability

NASA Technical Reports Server (NTRS)

Kececioglu, D.; Smith, J. L.

1970-01-01

A brief description of the complex fatigue machines used in the test program is presented. The data generated from these machines are given and discussed. Two methods of obtaining strength distributions from the data are also discussed. Then follows a discussion of the construction of statistical fatigue diagrams and their use in designing by reliability. Finally, some of the problems encountered in the test equipment and a corrective modification are presented.

Intensity statistics in the presence of translational noncrystallographic symmetry.

PubMed

Read, Randy J; Adams, Paul D; McCoy, Airlie J

2013-02-01

In the case of translational noncrystallographic symmetry (tNCS), two or more copies of a component in the asymmetric unit of the crystal are present in a similar orientation. This causes systematic modulations of the reflection intensities in the diffraction pattern, leading to problems with structure determination and refinement methods that assume, either implicitly or explicitly, that the distribution of intensities is a function only of resolution. To characterize the statistical effects of tNCS accurately, it is necessary to determine the translation relating the copies, any small rotational differences in their orientations, and the size of random coordinate differences caused by conformational differences. An algorithm to estimate these parameters and refine their values against a likelihood function is presented, and it is shown that by accounting for the statistical effects of tNCS it is possible to unmask the competing statistical effects of twinning and tNCS and to more robustly assess the crystal for the presence of twinning.

The effect of the use of a TNF-alpha inhibitor in hypothermic machine perfusion on kidney function after transplantation.

PubMed

Diuwe, Piotr; Domagala, Piotr; Durlik, Magdalena; Trzebicki, Janusz; Chmura, Andrzej; Kwiatkowski, Artur

2017-08-01

One of the most important problems in transplantation medicine is the ischemia/reperfusion injury of the organs to be transplanted. The aim of the present study was to assess the effect of tumor necrosis factor-alpha (TNF-alpha) inhibitor etanercept on the machine perfusion hypothermia of renal allograft kidney function and organ perfusion. No statistically significant differences were found in the impact of the applied intervention on kidney machine perfusion during which the average flow and vascular resistance were evaluated. There were no statistically significant differences in the occurrence of delayed graft function (DGF). Fewer events in patients who received a kidney from the etanercept treated Group A compared to the patients who received a kidney from the control Group B were observed when comparing the functional DGF and occurrence of acute rejection episodes, however, there was no statistically significant difference. In summary, no effect of treatment with etanercept an inhibitor of TNF-alpha in a hypothermic machine perfusion on renal allograft renal survival and its perfusion were detected in this study. However, treatment of the isolated organ may be important for the future of transplantation medicine. Copyright © 2017 Elsevier Inc. All rights reserved.

A study on the performance comparison of metaheuristic algorithms on the learning of neural networks

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2017-08-01

The learning or training process of neural networks entails the task of finding the most optimal set of parameters, which includes translation vectors, dilation parameter, synaptic weights, and bias terms. Apart from the traditional gradient descent-based methods, metaheuristic methods can also be used for this learning purpose. Since the inception of genetic algorithm half a century ago, the last decade witnessed the explosion of a variety of novel metaheuristic algorithms, such as harmony search algorithm, bat algorithm, and whale optimization algorithm. Despite the proof of the no free lunch theorem in the discipline of optimization, a survey in the literature of machine learning gives contrasting results. Some researchers report that certain metaheuristic algorithms are superior to the others, whereas some others argue that different metaheuristic algorithms give comparable performance. As such, this paper aims to investigate if a certain metaheuristic algorithm will outperform the other algorithms. In this work, three metaheuristic algorithms, namely genetic algorithms, particle swarm optimization, and harmony search algorithm are considered. The algorithms are incorporated in the learning of neural networks and their classification results on the benchmark UCI machine learning data sets are compared. It is found that all three metaheuristic algorithms give similar and comparable performance, as captured in the average overall classification accuracy. The results corroborate the findings reported in the works done by previous researchers. Several recommendations are given, which include the need of statistical analysis to verify the results and further theoretical works to support the obtained empirical results.

Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase.

PubMed

Singh, Nidhi; Shah, Priyanka; Dwivedi, Hemlata; Mishra, Shikha; Tripathi, Renu; Sahasrabuddhe, Amogh A; Siddiqi, Mohammad Imran

2016-11-15

N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protein interactions. It is a studied and validated pre-clinical drug target for fungal and parasitic infections. In the present study, a machine learning approach, docking studies and CoMFA analysis have been integrated with the objective of translation of knowledge into a pipelined workflow towards the identification of putative hits through the screening of large compound libraries. In the proposed pipeline, the reported parasitic NMT inhibitors have been used to develop predictive machine learning classification models. Simultaneously, a TbNMT complex model was generated to establish the relationship between the binding mode of the inhibitors for LmNMT and TbNMT through molecular dynamics simulation studies. A 3D-QSAR model was developed and used to predict the activity of the proposed hits in the subsequent step. The hits classified as active based on the machine learning model were assessed as the potential anti-trypanosomal NMT inhibitors through molecular docking studies, predicted activity using a QSAR model and visual inspection. In the final step, the proposed pipeline was validated through in vitro experiments. A total of seven hits have been proposed and tested in vitro for evaluation of dual inhibitory activity against Leishmania donovani and Trypanosoma brucei. Out of these five compounds showed significant inhibition against both of the organisms. The common topmost active compound SEW04173 belongs to a pyrazole carboxylate scaffold and is anticipated to enrich the chemical space with enhanced potency through optimization.

Effect of Dimension and Shape of Magnet on the Performance AC Generator with Translation Motion

NASA Astrophysics Data System (ADS)

Indriani, A.; Dimas, S.; Hendra

2018-02-01

The development of power plants using the renewable energy sources is very rapid. Renewable energy sources used solar energy, wind energy, ocean wave energy and other energy. All of these renewable energy sources require a processing device or a change of motion system to become electrical energy. One processing device is a generator which have work principle of converting motion (mechanical) energy into electrical energy with rotary shaft, blade and other motion components. Generator consists of several types of rotation motion and linear motion (translational). The generator have components such as rotor, stator and anchor. In the rotor and stator having magnet and winding coil as an electric generating part of the electric motion force. Working principle of AC generator with linear motion (translation) also apply the principle of Faraday that is using magnetic induction which change iron magnet to produce magnetic flux. Magnetic flux is captured by the stator to be converted into electrical energy. Linear motion generators consist of linear induction machine, wound synchronous machine field, and permanent magnet synchronous [1]. Performance of synchronous generator of translation motion is influenced by magnet type, magnetic shape, coil winding, magnetic and coil spacing and others. In this paper focus on the neodymium magnet with varying shapes, number of coil windings and gap of magnetic distances. This generator work by using pneumatic mechanism (PLTGL) for power plants system. Result testing of performance AC generator translation motion obtained that maximum voltage, current and power are 63 Volt for diameter winding coil 0.15 mm, number of winding coil 13000 and distance of magnet 20 mm. For effect shape of magnet, maximum voltage happen on rectangle magnet 30x20x5 mm with 4.64 Volt. Voltage and power on effect of diameter winding coil is 14.63 V and 17.82 W at the diameter winding coil 0.7 and number of winding coil is 1260 with the distance of magnet 25 mm.

Statistical Capability Study of a Helical Grinding Machine Producing Screw Rotors

NASA Astrophysics Data System (ADS)

Holmes, C. S.; Headley, M.; Hart, P. W.

2017-08-01

Screw compressors depend for their efficiency and reliability on the accuracy of the rotors, and therefore on the machinery used in their production. The machinery has evolved over more than half a century in response to customer demands for production accuracy, efficiency, and flexibility, and is now at a high level on all three criteria. Production equipment and processes must be capable of maintaining accuracy over a production run, and this must be assessed statistically under strictly controlled conditions. This paper gives numerical data from such a study of an innovative machine tool and shows that it is possible to meet the demanding statistical capability requirements.

Using statistical and machine learning to help institutions detect suspicious access to electronic health records.

PubMed

Boxwala, Aziz A; Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila

2011-01-01

To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs.

Using statistical and machine learning to help institutions detect suspicious access to electronic health records

PubMed Central

Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila

2011-01-01

Objective To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. Methods From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. Results The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. Limitations The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. Conclusion The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs. PMID:21672912

Actes des Journees de linguistique (Proceedings of the Linguistics Conference) (9th, 1995).

ERIC Educational Resources Information Center

Audette, Julie, Ed.; And Others

Papers (entirely in French) presented at the conference on linguistics include these topics: language used in the legislature of New Brunswick; cohesion in the text of Arabic-speaking language learners; automatic adverb recognition; logic of machine translation in teaching revision; expansion in physics texts; discourse analysis and the syntax of…

ELNET--The Electronic Library Database System.

ERIC Educational Resources Information Center

King, Shirley V.

1991-01-01

ELNET (Electronic Library Network), a Japanese language database, allows searching of index terms and free text terms from articles and stores the full text of the articles on an optical disc system. Users can order fax copies of the text from the optical disc. This article also explains online searching and discusses machine translation. (LRW)

A Morphological Analyzer for Vocalized or Not Vocalized Arabic Language

NASA Astrophysics Data System (ADS)

El Amine Abderrahim, Med; Breksi Reguig, Fethi

This research has been to show the realization of a morphological analyzer of the Arabic language (vocalized or not vocalized). This analyzer is based upon our object model for the Arabic Natural Language Processing (NLP) and can be exploited by NLP applications such as translation machine, orthographical correction and the search for information.

Translingual Fine-Grained Morphosyntactic Analysis and Its Application to Machine Translation

ERIC Educational Resources Information Center

Drabek, Elliott Franco

2009-01-01

English and a small set of other languages have a wealth of available linguistic knowledge resources and annotated language data, but the great majority of the world's languages have little or none. This dissertation describes work which leverages the detailed and accurate morphosyntactic analyses available for English to improve analytical…

Automated implementation of rule-based expert systems with neural networks for time-critical applications

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.; Huang, Song; Govind, Girish

1991-01-01

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Computational biology for cardiovascular biomarker discovery.

PubMed

Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

2009-07-01

Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using 'omic' information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of 'omic' data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of 'omic' approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Risk estimation using probability machines.

PubMed

Dasgupta, Abhijit; Szymczak, Silke; Moore, Jason H; Bailey-Wilson, Joan E; Malley, James D

2014-03-01

Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a "risk machine", will share properties from the statistical machine that it is derived from.

Fluctuations in protein synthesis from a single RNA template: stochastic kinetics of ribosomes.

PubMed

Garai, Ashok; Chowdhury, Debashish; Ramakrishnan, T V

2009-01-01

Proteins are polymerized by cyclic machines called ribosomes, which use their messenger RNA (mRNA) track also as the corresponding template, and the process is called translation. We explore, in depth and detail, the stochastic nature of the translation. We compute various distributions associated with the translation process; one of them--namely, the dwell time distribution--has been measured in recent single-ribosome experiments. The form of the distribution, which fits best with our simulation data, is consistent with that extracted from the experimental data. For our computations, we use a model that captures both the mechanochemistry of each individual ribosome and their steric interactions. We also demonstrate the effects of the sequence inhomogeneities of real genes on the fluctuations and noise in translation. Finally, inspired by recent advances in the experimental techniques of manipulating single ribosomes, we make theoretical predictions on the force-velocity relation for individual ribosomes. In principle, all our predictions can be tested by carrying out in vitro experiments.

Programming and Tuning a Quantum Annealing Device to Solve Real World Problems

NASA Astrophysics Data System (ADS)

Perdomo-Ortiz, Alejandro; O'Gorman, Bryan; Fluegemann, Joseph; Smelyanskiy, Vadim

2015-03-01

Solving real-world applications with quantum algorithms requires overcoming several challenges, ranging from translating the computational problem at hand to the quantum-machine language to tuning parameters of the quantum algorithm that have a significant impact on the performance of the device. In this talk, we discuss these challenges, strategies developed to enhance performance, and also a more efficient implementation of several applications. Although we will focus on applications of interest to NASA's Quantum Artificial Intelligence Laboratory, the methods and concepts presented here apply to a broader family of hard discrete optimization problems, including those that occur in many machine-learning algorithms.

Machine Learning Methods for Attack Detection in the Smart Grid.

PubMed

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

Student's Conceptions in Statistical Graph's Interpretation

ERIC Educational Resources Information Center

Kukliansky, Ida

2016-01-01

Histograms, box plots and cumulative distribution graphs are popular graphic representations for statistical distributions. The main research question that this study focuses on is how college students deal with interpretation of these statistical graphs when translating graphical representations into analytical concepts in descriptive statistics.…

Implementing Machine Learning in Radiology Practice and Research.

PubMed

Kohli, Marc; Prevedello, Luciano M; Filice, Ross W; Geis, J Raymond

2017-04-01

The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk. Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.

Advances in natural language processing.

PubMed

Hirschberg, Julia; Manning, Christopher D

2015-07-17

Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today's researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area. Copyright © 2015, American Association for the Advancement of Science.

Statistical downscaling of GCM simulations to streamflow using relevance vector machine

NASA Astrophysics Data System (ADS)

Ghosh, Subimal; Mujumdar, P. P.

2008-01-01

General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.

Breaking Free of Sample Size Dogma to Perform Innovative Translational Research

PubMed Central

Bacchetti, Peter; Deeks, Steven G.; McCune, Joseph M.

2011-01-01

Innovative clinical and translational research is often delayed or prevented by reviewers’ expectations that any study performed in humans must be shown in advance to have high statistical power. This supposed requirement is not justifiable and is contradicted by the reality that increasing sample size produces diminishing marginal returns. Studies of new ideas often must start small (sometimes even with an N of 1) because of cost and feasibility concerns, and recent statistical work shows that small sample sizes for such research can produce more projected scientific value per dollar spent than larger sample sizes. Renouncing false dogma about sample size would remove a serious barrier to innovation and translation. PMID:21677197

Solution of a tridiagonal system of equations on the finite element machine

NASA Technical Reports Server (NTRS)

Bostic, S. W.

1984-01-01

Two parallel algorithms for the solution of tridiagonal systems of equations were implemented on the Finite Element Machine. The Accelerated Parallel Gauss method, an iterative method, and the Buneman algorithm, a direct method, are discussed and execution statistics are presented.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

NASA Astrophysics Data System (ADS)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

The measurement of an aspherical mirror by three-dimensional nanoprofiler

NASA Astrophysics Data System (ADS)

Tokuta, Yusuke; Okita, Kenya; Okuda, Kohei; Kitayama, Takao; Nakano, Motohiro; Nakatani, Shun; Kudo, Ryota; Yamamura, Kazuya; Endo, Katsuyoshi

2015-09-01

Aspherical optical elements with high accuracy are important in several fields such as third-generation synchrotron radiation and extreme-ultraviolet lithography. Then the demand of measurement method for aspherical or free-form surface with nanometer resolution is rising. Our purpose is to develop a non-contact profiler to measure free-form surfaces directly with repeatability of figure error of less than 1 nm PV. To achieve this purpose we have developed three-dimensional Nanoprofiler which traces normal vectors of sample surface. The measurement principle is based on the straightness of LASER light and the accuracy of a rotational goniometer. This machine consists of four rotational stages, one translational stage and optical head which has the quadrant photodiode (QPD) and LASER head at optically equal position. In this measurement method, we conform the incident light beam to reflect the beam by controlling five stages and determine the normal vectors and the coordinates of the surface from signal of goniometers, translational stage and QPD. We can obtain three-dimensional figure from the normal vectors and the coordinates by a reconstruction algorithm. To evaluate performance of this machine we measure a concave aspherical mirror ten times. From ten results we calculate measurement repeatability, and we evaluate measurement uncertainty to compare the result with that measured by an interferometer. In consequence, the repeatability of measurement was 2.90 nm (σ) and the difference between the two profiles was +/-20 nm. We conclude that the two profiles was correspondent considering systematic errors of each machine.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Software Independent Verification and Validation (SIV&V) Simplified

DTIC Science & Technology

2006-12-01

Configuration Item I/O Input/Output I2V2 Independent Integrated Verification and Validation IBM International Business Machines ICD Interface...IPT Integrated Product Team IRS Interface Requirements Specification ISD Integrated System Diagram ITD Integrated Test Description ITP ...programming languages such as COBOL (Common Business Oriented Language) (Codasyl committee 1960), and FORTRAN (FORmula TRANslator) ( IBM 1952) (Robat 11

The Visual Uncertainty Paradigm for Controlling Screen-Space Information in Visualization

ERIC Educational Resources Information Center

Dasgupta, Aritra

2012-01-01

The information visualization pipeline serves as a lossy communication channel for presentation of data on a screen-space of limited resolution. The lossy communication is not just a machine-only phenomenon due to information loss caused by translation of data, but also a reflection of the degree to which the human user can comprehend visual…

State-based verification of RTCP-nets with nuXmv

NASA Astrophysics Data System (ADS)

Biernacka, Agnieszka; Biernacki, Jerzy; Szpyrka, Marcin

2015-12-01

The paper deals with an algorithm of translation of RTCP-nets' (real-time coloured Petri nets) coverability graphs into nuXmv state machines. The approach enables users to verify RTCP-nets with model checking techniques provided by the nuXmv tool. Full details of the algorithm are presented and an illustrative example of the approach usefulness is provided.

Finding Relevant Data in a Sea of Languages

DTIC Science & Technology

2016-04-26

full machine-translated text , unbiased word clouds , query-biased word clouds , and query-biased sentence...and information retrieval to automate language processing tasks so that the limited number of linguists available for analyzing text and spoken...the crime (stock market). The Cross-LAnguage Search Engine (CLASE) has already preprocessed the documents, extracting text to identify the language

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

Statistical Literacy as a Function of Online versus Hybrid Course Delivery Format for an Introductory Graduate Statistics Course

ERIC Educational Resources Information Center

Hahs-Vaughn, Debbie L.; Acquaye, Hannah; Griffith, Matthew D.; Jo, Hang; Matthews, Ken; Acharya, Parul

2017-01-01

Statistical literacy refers to understanding fundamental statistical concepts. Assessment of statistical literacy can take the forms of tasks that require students to identify, translate, compute, read, and interpret data. In addition, statistical instruction can take many forms encompassing course delivery format such as face-to-face, hybrid,…

On the suitability of Elekta’s Agility 160 MLC for tracked radiation delivery: closed-loop machine performance

NASA Astrophysics Data System (ADS)

Glitzner, M.; Crijns, S. P. M.; de Senneville, B. Denis; Lagendijk, J. J. W.; Raaymakers, B. W.

2015-03-01

For motion adaptive radiotherapy, dynamic multileaf collimator tracking can be employed to reduce treatment margins by steering the beam according to the organ motion. The Elekta Agility 160 MLC has hitherto not been evaluated for its tracking suitability. Both dosimetric performance and latency are key figures and need to be assessed generically, independent of the used motion sensor. In this paper, we propose the use of harmonic functions directly fed to the MLC to determine its latency during continuous motion. Furthermore, a control variable is extracted from a camera system and fed to the MLC. Using this setup, film dosimetry and subsequent γ statistics are performed, evaluating the response when tracking (MRI)-based physiologic motion in a closed-loop. The delay attributed to the MLC itself was shown to be a minor contributor to the overall feedback chain as compared to the impact of imaging components such as MRI sequences. Delay showed a linear phase behaviour of the MLC employed in continuously dynamic applications, which enables a general MLC-characterization. Using the exemplary feedback chain, dosimetry showed a vast increase in pass rate employing γ statistics. In this early stage, the tracking performance of the Agility using the test bench yielded promising results, making the technique eligible for translation to tracking using clinical imaging modalities.

Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

PubMed

Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

2014-01-01

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

Nanomedicine: Tiny Particles and Machines Give Huge Gains

PubMed Central

Tong, Sheng; Fine, Eli J.; Lin, Yanni; Cradick, Thomas J.; Bao, Gang

2014-01-01

Nanomedicine is an emerging field that integrates nanotechnology, biomolecular engineering, life sciences and medicine; it is expected to produce major breakthroughs in medical diagnostics and therapeutics. Nano-scale structures and devices are compatible in size with proteins and nucleic acids in living cells. Therefore, the design, characterization and application of nano-scale probes, carriers and machines may provide unprecedented opportunities for achieving a better control of biological processes, and drastic improvements in disease detection, therapy, and prevention. Recent advances in nanomedicine include the development of nanoparticle-based probes for molecular imaging, nano-carriers for drug/gene delivery, multi-functional nanoparticles for theranostics, and molecular machines for biological and medical studies. This article provides an overview of the nanomedicine field, with an emphasis on nanoparticles for imaging and therapy, as well as engineered nucleases for genome editing. The challenges in translating nanomedicine approaches to clinical applications are discussed. PMID:24297494

Using complex networks for text classification: Discriminating informative and imaginative documents

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-01-01

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts.

Effect of Vocalization of the Holy Quran With and Without Translation on Pregnancy Outcomes: A Randomized Clinical Trial.

PubMed

Mirghafourvand, Mojgan; Sehhati Shafaie, Fahimeh; Mohammad-Alizadeh-Charandabi, Sakineh; Jabbari, Batoul

2016-09-01

During recent decades, research in Iran in the area of the Quran and medical science has been seriously engaged in. With respect to the tendency toward spirituality and alternative medicine, we tried to find other aspects of the influence of the Quran. This study aimed to determine the effect of vocalizations of the Holy Quran with and without translation on the consequences of pregnancy (the prevalence of preterm delivery, caesarean delivery, and neonatal anthropometric indices) in women admitted to health care centers in Urmia, Iran. This was a three-armed parallel-group randomized clinical trial in which 168 pregnant women (25-28 weeks) in their first and second pregnancies were divided into three groups of 56 (Holy Quran with translation, Holy Quran without translation, and control group) by randomized blocking. The intervention was implemented once a week for three weeks in the health center, and on other days of the week, the participants listened at home to a CD they were given. The intervention and the control groups all received routine pregnancy care once a week. These mothers were tracked during their labor. Outcomes including gestational age at delivery, delivery type, and neonatal anthropometric indices were recorded based on the mother's records. There was no statistically significant difference between the groups in terms of demographic and obstetric characteristics before the intervention. In comparison with the control group, the probability of preterm delivery was lower in the Holy Quran with translation group (odds ratio: 0.3, CI 95%: 0.1-1.2) and in the Holy Quran without translation group (0.6, 0.2-1.9) as compared to the control group. However, this difference was not statistically significant. Similarly, the probability of caesarean delivery was lower in the Holy Quran with translation group (0.6, 0.3-1.4) and the Holy Quran without translation group (0.5, 0.2-1.2) as compared to the control group. Based on one-way ANOVA, there was no statistically significant difference between the study groups regarding the infants' anthropometric indices. Based on the results of this study, despite the lower prevalence of preterm labor and caesarean section in the intervention groups as compared to the control group, no statistically significant effect was seen. This was apparently due to the small sample size.

Automated document analysis system

NASA Astrophysics Data System (ADS)

Black, Jeffrey D.; Dietzel, Robert; Hartnett, David

2002-08-01

A software application has been developed to aid law enforcement and government intelligence gathering organizations in the translation and analysis of foreign language documents with potential intelligence content. The Automated Document Analysis System (ADAS) provides the capability to search (data or text mine) documents in English and the most commonly encountered foreign languages, including Arabic. Hardcopy documents are scanned by a high-speed scanner and are optical character recognized (OCR). Documents obtained in an electronic format bypass the OCR and are copied directly to a working directory. For translation and analysis, the script and the language of the documents are first determined. If the document is not in English, the document is machine translated to English. The documents are searched for keywords and key features in either the native language or translated English. The user can quickly review the document to determine if it has any intelligence content and whether detailed, verbatim human translation is required. The documents and document content are cataloged for potential future analysis. The system allows non-linguists to evaluate foreign language documents and allows for the quick analysis of a large quantity of documents. All document processing can be performed manually or automatically on a single document or a batch of documents.

Performance evaluation of various classifiers for color prediction of rice paddy plant leaf

NASA Astrophysics Data System (ADS)

Singh, Amandeep; Singh, Maninder Lal

2016-11-01

The food industry is one of the industries that uses machine vision for a nondestructive quality evaluation of the produce. These quality measuring systems and softwares are precalculated on the basis of various image-processing algorithms which generally use a particular type of classifier. These classifiers play a vital role in making the algorithms so intelligent that it can contribute its best while performing the said quality evaluations by translating the human perception into machine vision and hence machine learning. The crop of interest is rice, and the color of this crop indicates the health status of the plant. An enormous number of classifiers are available to solve the purpose of color prediction, but choosing the best among them is the focus of this paper. Performance of a total of 60 classifiers has been analyzed from the application point of view, and the results have been discussed. The motivation comes from the idea of providing a set of classifiers with excellent performance and implementing them on a single algorithm for the improvement of machine vision learning and, hence, associated applications.

Trends of Occupational Fatalities Involving Machines, United States, 1992–2010

PubMed Central

Marsh, Suzanne M.; Fosbroke, David E.

2016-01-01

Background This paper describes trends of occupational machine-related fatalities from 1992–2010. We examine temporal patterns by worker demographics, machine types (e.g., stationary, mobile), and industries. Methods We analyzed fatalities from Census of Fatal Occupational Injuries data provided by the Bureau of Labor Statistics to the National Institute for Occupational Safety and Health. We used injury source to identify machine-related incidents and Poisson regression to assess trends over the 19-year period. Results There was an average annual decrease of 2.8% in overall machine-related fatality rates from 1992 through 2010. Mobile machine-related fatality rates decreased an average of 2.6% annually and stationary machine-related rates decreased an average of 3.5% annually. Groups that continued to be at high risk included older workers; self-employed; and workers in agriculture/forestry/fishing, construction, and mining. Conclusion Addressing dangers posed by tractors, excavators, and other mobile machines needs to continue. High-risk worker groups should receive targeted information on machine safety. PMID:26358658

Making texts in electronic health records comprehensible to consumers: a prototype translator.

PubMed

Zeng-Treitler, Qing; Goryachev, Sergey; Kim, Hyeoneui; Keselman, Alla; Rosendale, Douglas

2007-10-11

Narrative reports from electronic health records are a major source of content for personal health records. We designed and implemented a prototype text translator to make these reports more comprehensible to consumers. The translator identifies difficult terms, replaces them with easier synonyms, and generates and inserts explanatory texts for them. In feasibility testing, the application was used to translate 9 clinical reports. Majority (68.8%) of text replacements and insertions were deemed correct and helpful by expert review. User evaluation demonstrated a non-statistically significant trend toward better comprehension when translation is provided (p=0.15).

Making Texts in Electronic Health Records Comprehensible to Consumers: A Prototype Translator

PubMed Central

Zeng-Treitler, Qing; Goryachev, Sergey; Kim, Hyeoneui; Keselman, Alla; Rosendale, Douglas

2007-01-01

Narrative reports from electronic health records are a major source of content for personal health records. We designed and implemented a prototype text translator to make these reports more comprehensible to consumers. The translator identifies difficult terms, replaces them with easier synonyms, and generates and inserts explanatory texts for them. In feasibility testing, the application was used to translate 9 clinical reports. Majority (68.8%) of text replacements and insertions were deemed correct and helpful by expert review. User evaluation demonstrated a non-statistically significant trend toward better comprehension when translation is provided (p=0.15). PMID:18693956

Impact and Estimation of Balance Coordinate System Rotations and Translations in Wind-Tunnel Testing

NASA Technical Reports Server (NTRS)

Toro, Kenneth G.; Parker, Peter A.

2017-01-01

Discrepancies between the model and balance coordinate systems lead to biases in the aerodynamic measurements during wind-tunnel testing. The reference coordinate system relative to the calibration coordinate system at which the forces and moments are resolved is crucial to the overall accuracy of force measurements. This paper discusses sources of discrepancies and estimates of coordinate system rotation and translation due to machining and assembly differences. A methodology for numerically estimating the coordinate system biases will be discussed and developed. Two case studies are presented using this methodology to estimate the model alignment. Examples span from angle measurement system shifts on the calibration system to discrepancies in actual wind-tunnel data. The results from these case-studies will help aerodynamic researchers and force balance engineers to better the understand and identify potential differences in calibration systems due to coordinate system rotation and translation.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems.

PubMed

Yu, Michael Ku; Kramer, Michael; Dutkowski, Janusz; Srivas, Rohith; Licon, Katherine; Kreisberg, Jason; Ng, Cherie T; Krogan, Nevan; Sharan, Roded; Ideker, Trey

2016-02-24

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

STATISTICAL EVALUATION OF CONFOCAL MICROSCOPY IMAGES

EPA Science Inventory

Abstract

In this study the CV is defined as the Mean/SD of the population of beads or pixels. Flow cytometry uses the CV of beads to determine if the machine is aligned correctly and performing properly. This CV concept to determine machine performance has been adapted to...

Signal detection using support vector machines in the presence of ultrasonic speckle

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine L.; Pitas, Ioannis

2002-04-01

Support Vector Machines are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.

PubMed

Taylor, R Andrew; Pare, Joseph R; Venkatesh, Arjun K; Mowafi, Hani; Melnick, Edward R; Fleischman, William; Hall, M Kennedy

2016-03-01

Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). In this proof-of-concept study, a local big data-driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions. © 2015 by the Society for Academic Emergency Medicine.

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

PubMed Central

Taylor, R. Andrew; Pare, Joseph R.; Venkatesh, Arjun K.; Mowafi, Hani; Melnick, Edward R.; Fleischman, William; Hall, M. Kennedy

2018-01-01

Objectives Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data–driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. Methods This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. Results There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). Conclusions In this proof-of-concept study, a local big data–driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions. PMID:26679719

Risk estimation using probability machines

PubMed Central

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

AstroML: "better, faster, cheaper" towards state-of-the-art data mining and machine learning

NASA Astrophysics Data System (ADS)

Ivezic, Zeljko; Connolly, Andrew J.; Vanderplas, Jacob

2015-01-01

We present AstroML, a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy, and distributed under an open license. AstroML contains a growing library of statistical and machine learning routines for analyzing astronomical data in Python, loaders for several open astronomical datasets (such as SDSS and other recent major surveys), and a large suite of examples of analyzing and visualizing astronomical datasets. AstroML is especially suitable for introducing undergraduate students to numerical research projects and for graduate students to rapidly undertake cutting-edge research. The long-term goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data analysis in astronomy and astrophysics (see http://www.astroml.org).

Claims-Based Authentication for a Web-Based Enterprise

DTIC Science & Technology

2013-07-01

authority must use known and registered (or in specific cases defined ) certificate revocation and currency-checking software . B. Translation of...Machines and services are issued software certificates that contain the public key with the private key generated and remaining in hardware...publicly available) information. A hardware token that contains the certificate is preferred to software -only certificates. For enterprise users

Information Sciences Assessment for Asia and Australasia

DTIC Science & Technology

2009-10-16

entertainment and home services - Machine Translation for international cooperation - NLU + Affective Computing for education - Intelligent Optimization for...into an emotion. ETTS, embedded Mandarin, music retrieval. Also, research in areas of computer graphics, digital media processing  Intelligent...many from outside China, 40% in phase 2 Sales volume in 2007 130 * 100 million RMB SAP (1st), CITI, AIG, EDS, Capgemini, ILOG, Infosys, HCL, Sony

Computer-Aided Transcription in the Courts. Executive Summary. National Center Publication No. R-0058.

ERIC Educational Resources Information Center

National Center for State Courts, Williamsburg, VA.

This report summarizes the findings of the Computer-Aided Transcription (CAT) Project, which conducted a 14-month study of the technology and use of computer systems for translating into English the shorthand notes taken by court reporters on stenotype machines. Included are the state of the art of CAT at the end of 1980 and anticipated future…

Speech Processing and Recognition (SPaRe)

DTIC Science & Technology

2011-01-01

results in the areas of automatic speech recognition (ASR), speech processing, machine translation (MT), natural language processing ( NLP ), and...Processing ( NLP ), Information Retrieval (IR) 16. SECURITY CLASSIFICATION OF: UNCLASSIFED 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME...Figure 9, the IOC was only expected to provide document submission and search; automatic speech recognition (ASR) for English, Spanish, Arabic , and

Enacting the Semantic Web: Ontological Orderings, Negotiated Standards, and Human-Machine Translations

ERIC Educational Resources Information Center

McCarthy, Matthew T.

2017-01-01

Artificial intelligence (AI) that is based upon semantic search has become one of the dominant means for accessing information in recent years. This is particularly the case in mobile contexts, as search-based AI are embedded in each of the major mobile operating systems. The implications are such that information is becoming less a matter of…

Optimal Achievable Encoding for Brain Machine Interface

DTIC Science & Technology

2017-12-22

dictionary-based encoding approach to translate a visual image into sequential patterns of electrical stimulation in real time , in a manner that...including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and...networks, and by applying linear decoding to complete recorded populations of retinal ganglion cells for the first time . Third, we developed a greedy

Impact of Machine-Translated Text on Entity and Relationship Extraction

DTIC Science & Technology

2014-12-01

20 1 1. Introduction Using social network analysis tools is an important asset in...semantic modeling software to automatically build detailed network models from unstructured text. Contour imports unstructured text and then maps the text...onto an existing ontology of frames at the sentence level, using FrameNet, a structured language model, and through Semantic Role Labeling ( SRL

Articles on Practical Cybernetics. Computer-Developed Computers; Heuristics and Modern Sciences; Linguistics and Practice; Cybernetics and Moral-Ethical Considerations; and Men and Machines at the Chessboard.

ERIC Educational Resources Information Center

Berg, A. I.; And Others

Five articles which were selected from a Russian language book on cybernetics and then translated are presented here. They deal with the topics of: computer-developed computers, heuristics and modern sciences, linguistics and practice, cybernetics and moral-ethical considerations, and computer chess programs. (Author/JY)

Software Testbed for Developing and Evaluating Integrated Autonomous Systems

DTIC Science & Technology

2015-03-01

EUROPA planning system for plan generation. The adaptive controller executes the new plan, using augmented, hierarchical finite state machines to...using the Internet Communications Engine ( ICE ), an object-oriented toolkit for building distributed applications. TABLE OF CONTENTS 1...ANML model is translated into the New Domain Definition Language (NDDL) and sent to NASA???s EUROPA planning system for plan generation. The adaptive

The Writing of Chinese Characters by CFL Learners: Can Writing on Facebook and Using Machine Translation Help?

ERIC Educational Resources Information Center

Zhang, Qi; Lu, Zhouxiang

2014-01-01

The current study investigates the applications of the pinyin input system, a Chinese word processing method, for writing on Facebook in order to help CFL (Chinese as a foreign language) learners from two Irish universities to improve their handwriting in Chinese characters on paper. The data were collected from writing activities conducted over…

A Semisupervised Support Vector Machines Algorithm for BCI Systems

PubMed Central

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

A coordination theory for intelligent machines

NASA Technical Reports Server (NTRS)

Wang, Fei-Yue; Saridis, George N.

1990-01-01

A formal model for the coordination level of intelligent machines is established. The framework of the coordination level investigated consists of one dispatcher and a number of coordinators. The model called coordination structure has been used to describe analytically the information structure and information flow for the coordination activities in the coordination level. Specifically, the coordination structure offers a formalism to (1) describe the task translation of the dispatcher and coordinators; (2) represent the individual process within the dispatcher and coordinators; (3) specify the cooperation and connection among the dispatcher and coordinators; (4) perform the process analysis and evaluation; and (5) provide a control and communication mechanism for the real-time monitor or simulation of the coordination process. A simple procedure for the task scheduling in the coordination structure is presented. The task translation is achieved by a stochastic learning algorithm. The learning process is measured with entropy and its convergence is guaranteed. Finally, a case study of the coordination structure with three coordinators and one dispatcher for a simple intelligent manipulator system illustrates the proposed model and the simulation of the task processes performed on the model verifies the soundness of the theory.

TOOTHPASTEV6.11.3

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sankel, David J.; Clair, Aaron B. St.; Langsfield, Joshua D.

2006-11-01

Toothpaste is a graphical user interface and Computer Aided Drafting/Manufacturing (CAD/CAM) software package used to plan tool paths for Galil Motion Control hardware. The software is a tool for computer controlled dispensing of materials. The software may be used for solid freeform fabrication of components or the precision printing of inks. Mathematical calculations are used to produce a set of segments and arcs that when coupled together will fill space. The paths of the segments and arcs are then translated into a machine language that controls the motion of motors and translational stages to produce tool paths in three dimensions.more » As motion begins material(s) are dispensed or printed along the three-dimensional pathway.« less

Man-machine analysis of translation and work tasks of Skylab films

NASA Technical Reports Server (NTRS)

Hosler, W. W.; Boelter, J. G.; Morrow, J. R., Jr.; Jackson, J. T.

1979-01-01

An objective approach to determine the concurrent validity of computer-graphic models is real time film analysis. This technique was illustrated through the procedures and results obtained in an evaluation of translation of Skylab mission astronauts. The quantitative analysis was facilitated by the use of an electronic film analyzer, minicomputer, and specifically supportive software. The uses of this technique for human factors research are: (1) validation of theoretical operator models; (2) biokinetic analysis; (3) objective data evaluation; (4) dynamic anthropometry; (5) empirical time-line analysis; and (6) consideration of human variability. Computer assisted techniques for interface design and evaluation have the potential for improving the capability for human factors engineering.

Investigation of roughing machining simulation by using visual basic programming in NX CAM system

NASA Astrophysics Data System (ADS)

Hafiz Mohamad, Mohamad; Nafis Osman Zahid, Muhammed

2018-03-01

This paper outlines a simulation study to investigate the characteristic of roughing machining simulation in 4th axis milling processes by utilizing visual basic programming in NX CAM systems. The selection and optimization of cutting orientation in rough milling operation is critical in 4th axis machining. The main purpose of roughing operation is to approximately shape the machined parts into finished form by removing the bulk of material from workpieces. In this paper, the simulations are executed by manipulating a set of different cutting orientation to generate estimated volume removed from the machine parts. The cutting orientation with high volume removal is denoted as an optimum value and chosen to execute a roughing operation. In order to run the simulation, customized software is developed to assist the routines. Operations build-up instructions in NX CAM interface are translated into programming codes via advanced tool available in the Visual Basic Studio. The codes is customized and equipped with decision making tools to run and control the simulations. It permits the integration with any independent program files to execute specific operations. This paper aims to discuss about the simulation program and identifies optimum cutting orientations for roughing processes. The output of this study will broaden up the simulation routines performed in NX CAM systems.

Advanced Telecommunications Technologies in Rural Communities: Factors Affecting Use.

ERIC Educational Resources Information Center

Leistritz, F. Larry; Allen, John C.; Johnson, Bruce B.; Olsen, Duane; Sell, Randy

1997-01-01

A survey of 2,000 rural residents in 6 states (36% response) found that 56% used answering machines, 48% fax machines, 46% personal computers, 27% cell phones, and 25% modems. Higher use was associated with higher income and education. Distance from the nearest metropolitan statistical area increased use. A large majority believed…

OFFICE MACHINES USED IN BUSINESS TODAY.

ERIC Educational Resources Information Center

COOK, FRED S.; MALICHE, ELEANOR

INTERVIEWS OF 239 BUSINESSES OF THE BAY CITY STANDARD METROPOLITAN STATISTICAL AREA OF MICHIGAN PROVIDED INFORMATION ON (1) THE TYPE AND NUMBER OF MACHINES USED IN BUSINESS, (2) THE TRAINING DEMANDED BY EMPLOYERS FOR PERSONNEL USING THIS OFFICE EQUIPMENT, (3) THE EXTENT OF ON-THE-JOB TRAINING GIVEN BY EMPLOYERS, (4) THE IMPLICATIONS FOR VOCATIONAL…

Anomaly detection for machine learning redshifts applied to SDSS galaxies

NASA Astrophysics Data System (ADS)

Hoyle, Ben; Rau, Markus Michael; Paech, Kerstin; Bonnett, Christopher; Seitz, Stella; Weller, Jochen

2015-10-01

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million `clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 `anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed `anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

Technology for Large-Scale Translation of Clinical Practice Guidelines: A Pilot Study of the Performance of a Hybrid Human and Computer-Assisted Approach.

PubMed

Van de Velde, Stijn; Macken, Lieve; Vanneste, Koen; Goossens, Martine; Vanschoenbeek, Jan; Aertgeerts, Bert; Vanopstal, Klaar; Vander Stichele, Robert; Buysschaert, Joost

2015-10-09

The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, began in 2011 to optimize quality of care by promoting evidence-based decision making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL MultiTerm terminology databases) from medical terminology databases, and support from online machine translation. This resulted in a validated translation memory, which is now in use for the translation of new and updated guidelines. The objective of this experiment was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator. We conducted a pilot study in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (1) by certificated junior translators without medical specialization using the hybrid method, (2) by an experienced medical translator without this support, and (3) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The human translation edit rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of translation acceptability and adequacy. The average number of words per guideline was 1195 and the mean total translation time was 100.2 minutes/1000 words. No meaningful differences were found in the translation speed for new guidelines. The translation of updated guidelines was 59 minutes/1000 words faster (95% CI 2-115; P=.044) in the computer-aided group. Revisions due to terminology accounted for one third of the overall revisions by the medical proofreader. Use of the hybrid human and computer-aided translation by a non-expert translator makes the translation of updates of clinical practice guidelines faster and cheaper because of the benefits of translation memory. For the translation of new guidelines, there was no apparent benefit in comparison with the efficiency of translation unsupported by translation memory (whether by an expert or non-expert translator).

Technology for Large-Scale Translation of Clinical Practice Guidelines: A Pilot Study of the Performance of a Hybrid Human and Computer-Assisted Approach

PubMed Central

2015-01-01

Background The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, began in 2011 to optimize quality of care by promoting evidence-based decision making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL MultiTerm terminology databases) from medical terminology databases, and support from online machine translation. This resulted in a validated translation memory, which is now in use for the translation of new and updated guidelines. Objective The objective of this experiment was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator. Methods We conducted a pilot study in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (1) by certificated junior translators without medical specialization using the hybrid method, (2) by an experienced medical translator without this support, and (3) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The human translation edit rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of translation acceptability and adequacy. Results The average number of words per guideline was 1195 and the mean total translation time was 100.2 minutes/1000 words. No meaningful differences were found in the translation speed for new guidelines. The translation of updated guidelines was 59 minutes/1000 words faster (95% CI 2-115; P=.044) in the computer-aided group. Revisions due to terminology accounted for one third of the overall revisions by the medical proofreader. Conclusions Use of the hybrid human and computer-aided translation by a non-expert translator makes the translation of updates of clinical practice guidelines faster and cheaper because of the benefits of translation memory. For the translation of new guidelines, there was no apparent benefit in comparison with the efficiency of translation unsupported by translation memory (whether by an expert or non-expert translator). PMID:26453372

Technical Report: Reference photon dosimetry data for Varian accelerators based on IROC-Houston site visit data.

PubMed

Kerns, James R; Followill, David S; Lowenstein, Jessica; Molineu, Andrea; Alvarez, Paola; Taylor, Paige A; Stingo, Francesco C; Kry, Stephen F

2016-05-01

Accurate data regarding linear accelerator (Linac) radiation characteristics are important for treatment planning system modeling as well as regular quality assurance of the machine. The Imaging and Radiation Oncology Core-Houston (IROC-H) has measured the dosimetric characteristics of numerous machines through their on-site dosimetry review protocols. Photon data are presented and can be used as a secondary check of acquired values, as a means to verify commissioning a new machine, or in preparation for an IROC-H site visit. Photon data from IROC-H on-site reviews from 2000 to 2014 were compiled and analyzed. Specifically, data from approximately 500 Varian machines were analyzed. Each dataset consisted of point measurements of several dosimetric parameters at various locations in a water phantom to assess the percentage depth dose, jaw output factors, multileaf collimator small field output factors, off-axis factors, and wedge factors. The data were analyzed by energy and parameter, with similarly performing machine models being assimilated into classes. Common statistical metrics are presented for each machine class. Measurement data were compared against other reference data where applicable. Distributions of the parameter data were shown to be robust and derive from a student's t distribution. Based on statistical and clinical criteria, all machine models were able to be classified into two or three classes for each energy, except for 6 MV for which there were eight classes. Quantitative analysis of the measurements for 6, 10, 15, and 18 MV photon beams is presented for each parameter; supplementary material has also been made available which contains further statistical information. IROC-H has collected numerous data on Varian Linacs and the results of photon measurements from the past 15 years are presented. The data can be used as a comparison check of a physicist's acquired values. Acquired values that are well outside the expected distribution should be verified by the physicist to identify whether the measurements are valid. Comparison of values to this reference data provides a redundant check to help prevent gross dosimetric treatment errors.

Specification of a new de-stoner machine: evaluation of machining effects on olive paste's rheology and olive oil yield and quality.

PubMed

Romaniello, Roberto; Leone, Alessandro; Tamborrino, Antonia

2017-01-01

An industrial prototype of a partial de-stoner machine was specified, built and implemented in an industrial olive oil extraction plant. The partial de-stoner machine was compared to the traditional mechanical crusher to assess its quantitative and qualitative performance. The extraction efficiency of the olive oil extraction plant, olive oil quality, sensory evaluation and rheological aspects were investigated. The results indicate that by using the partial de-stoner machine the extraction plant did not show statistical differences with respect to the traditional mechanical crushing. Moreover, the partial de-stoner machine allowed recovery of 60% of olive pits and the oils obtained were characterised by more marked green fruitiness, flavour and aroma than the oils produced using the traditional processing systems. The partial de-stoner machine removes the limitations of the traditional total de-stoner machine, opening new frontiers for the recovery of pits to be used as biomass. Moreover, the partial de-stoner machine permitted a significant reduction in the viscosity of the olive paste. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Crunching Numbers: What Cancer Screening Statistics Really Tell Us

Cancer.gov

Cancer screening studies have shown that more screening does not necessarily translate into fewer cancer deaths. This article explains how to interpret the statistics used to describe the results of screening studies.

Compiler writing system detail design specification. Volume 2: Component specification

NASA Technical Reports Server (NTRS)

Arthur, W. J.

1974-01-01

The logic modules and data structures composing the Meta-translator module are desribed. This module is responsible for the actual generation of the executable language compiler as a function of the input Meta-language. Machine definitions are also processed and are placed as encoded data on the compiler library data file. The transformation of intermediate language in target language object text is described.

Deep Gate Recurrent Neural Network

DTIC Science & Technology

2016-11-22

Schmidhuber. A system for robotic heart surgery that learns to tie knots using recurrent neural networks. In IEEE International Conference on...tasks, such as Machine Translation (Bahdanau et al. (2015)) or Robot Reinforcement Learning (Bakker (2001)). The main idea behind these networks is to...and J. Peters. Reinforcement learning in robotics : A survey. The International Journal of Robotics Research, 32:1238–1274, 2013. ISSN 0278-3649. doi

Toward Determining the Comprehensibility of Machine Translations

DTIC Science & Technology

2012-01-01

responses to a stimulus (Macmillan and Creelman , 1991). It has been applied in areas such as lie detection (truth/lie), inspection (ac- ceptable...1-1/(2N) (Macmil- lan and Creelman , 1991). Negative values, which usually indicate response confusion, were eliminated. The results of...Macmillan, Neil and C. Douglas Creelman . (1991). Detection theory: A User’s guide. Cambridge Univer- sity Press, pp. 10 &125. Marchant

Effect of Vocalization of the Holy Quran With and Without Translation on Pregnancy Outcomes: A Randomized Clinical Trial

PubMed Central

Mirghafourvand, Mojgan; Sehhati Shafaie, Fahimeh; Mohammad-Alizadeh-Charandabi, Sakineh; Jabbari, Batoul

2016-01-01

Background During recent decades, research in Iran in the area of the Quran and medical science has been seriously engaged in. With respect to the tendency toward spirituality and alternative medicine, we tried to find other aspects of the influence of the Quran. Objectives This study aimed to determine the effect of vocalizations of the Holy Quran with and without translation on the consequences of pregnancy (the prevalence of preterm delivery, caesarean delivery, and neonatal anthropometric indices) in women admitted to health care centers in Urmia, Iran. Materials and Methods This was a three-armed parallel-group randomized clinical trial in which 168 pregnant women (25-28 weeks) in their first and second pregnancies were divided into three groups of 56 (Holy Quran with translation, Holy Quran without translation, and control group) by randomized blocking. The intervention was implemented once a week for three weeks in the health center, and on other days of the week, the participants listened at home to a CD they were given. The intervention and the control groups all received routine pregnancy care once a week. These mothers were tracked during their labor. Outcomes including gestational age at delivery, delivery type, and neonatal anthropometric indices were recorded based on the mother’s records. Results There was no statistically significant difference between the groups in terms of demographic and obstetric characteristics before the intervention. In comparison with the control group, the probability of preterm delivery was lower in the Holy Quran with translation group (odds ratio: 0.3, CI 95%: 0.1-1.2) and in the Holy Quran without translation group (0.6, 0.2-1.9) as compared to the control group. However, this difference was not statistically significant. Similarly, the probability of caesarean delivery was lower in the Holy Quran with translation group (0.6, 0.3-1.4) and the Holy Quran without translation group (0.5, 0.2-1.2) as compared to the control group. Based on one-way ANOVA, there was no statistically significant difference between the study groups regarding the infants’ anthropometric indices. Conclusions Based on the results of this study, despite the lower prevalence of preterm labor and caesarean section in the intervention groups as compared to the control group, no statistically significant effect was seen. This was apparently due to the small sample size. PMID:28144462

Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning

PubMed Central

Wang, Shijun; Yao, Jianhua; Petrick, Nicholas; Summers, Ronald M.

2010-01-01

Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per scan rate of 5, the sensitivity of the SVM using the combined features improved from 0.77 (Group A) and 0.73 (Group B) to 0.83 (p ≤ 0.01). PMID:20953299

Development of a French Version of the Edmonton Symptom Assessment System-Revised: A Pilot Study of Palliative Care Patients' Perspectives.

PubMed

Nekolaichuk, Cheryl; Huot, Ann; Gratton, Valérie; Bush, Shirley H; Tarumi, Yoko; Watanabe, Sharon M

2017-09-01

The Edmonton Symptom Assessment System-revised (ESAS-r) is a nine-item self-report symptom intensity tool developed for palliative care patients, with the option of adding a 10th patient-specific symptom. Due to growing international uptake, the ESAS-r has been translated into different languages. There has not been agreement, however, regarding a standard process for translation into multiple languages, which also includes patients' perspectives. The purpose of this study was to develop a French version of the ESAS-r, using a standardized translation protocol, and to obtain palliative care patients' perspectives regarding this translated tool. We developed a French version of the ESAS-r, using a standard translation method, involving both professional translators (n = 2) and bilingual palliative care experts (n = 3). Fifteen Francophone participants recruited from palliative care sites in two urban centers in Canada completed the ESAS-r and provided feedback on the translation, in the presence of a trained interviewer. Descriptive statistics and thematic analysis were used to analyze the quantitative and qualitative data, respectively. Fifteen Francophone participants were recruited from palliative care sites in two urban centers in Canada. Participants completed the ESAS-r and provided feedback on the translation in the presence of a trained interviewer. Descriptive statistics and thematic analysis were used to analyze the quantitative and qualitative data, respectively. Based on participants' concerns, translations for four of the nine symptoms were revised: drowsiness, nausea, lack of appetite, and shortness of breath. Concerns expressed for three additional symptoms (depression, anxiety, and well-being) were related to overall difficulty rating these symptoms, not specific to the translation. The French version of the ESAS-r is a credible tool for symptom assessment in Francophone patients. The study findings provide a vital step in the development of a standardized translation protocol, including patients' perspectives, which can be applied to other languages.

Radioactive hot cell access hole decontamination machine

DOEpatents

Simpson, William E.

1982-01-01

Radioactive hot cell access hole decontamination machine. A mobile housing has an opening large enough to encircle the access hole and has a shielding door, with a door opening and closing mechanism, for uncovering and covering the opening. The housing contains a shaft which has an apparatus for rotating the shaft and a device for independently translating the shaft from the housing through the opening and access hole into the hot cell chamber. A properly sized cylindrical pig containing wire brushes and cloth or other disks, with an arrangement for releasably attaching it to the end of the shaft, circumferentially cleans the access hole wall of radioactive contamination and thereafter detaches from the shaft to fall into the hot cell chamber.

Using Ontologies to Formalize Services Specifications in Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Breitman, Karin Koogan; Filho, Aluizio Haendchen; Haeusler, Edward Hermann

2004-01-01

One key issue in multi-agent systems (MAS) is their ability to interact and exchange information autonomously across applications. To secure agent interoperability, designers must rely on a communication protocol that allows software agents to exchange meaningful information. In this paper we propose using ontologies as such communication protocol. Ontologies capture the semantics of the operations and services provided by agents, allowing interoperability and information exchange in a MAS. Ontologies are a formal, machine processable, representation that allows to capture the semantics of a domain and, to derive meaningful information by way of logical inference. In our proposal we use a formal knowledge representation language (OWL) that translates into Description Logics (a subset of first order logic), thus eliminating ambiguities and providing a solid base for machine based inference. The main contribution of this approach is to make the requirements explicit, centralize the specification in a single document (the ontology itself), at the same that it provides a formal, unambiguous representation that can be processed by automated inference machines.

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

PubMed Central

Cerasa, Antonio; Castiglioni, Isabella; Salvatore, Christian; Funaro, Angela; Martino, Iolanda; Alfano, Stefania; Donzuso, Giulia; Perrotta, Paolo; Gioia, Maria Cecilia; Gilardi, Maria Carla; Quattrone, Aldo

2015-01-01

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice. PMID:26648660

Mothers Consistently Alter Their Unique Vocal Fingerprints When Communicating with Infants.

PubMed

Piazza, Elise A; Iordan, Marius Cătălin; Lew-Williams, Casey

2017-10-23

The voice is the most direct link we have to others' minds, allowing us to communicate using a rich variety of speech cues [1, 2]. This link is particularly critical early in life as parents draw infants into the structure of their environment using infant-directed speech (IDS), a communicative code with unique pitch and rhythmic characteristics relative to adult-directed speech (ADS) [3, 4]. To begin breaking into language, infants must discern subtle statistical differences about people and voices in order to direct their attention toward the most relevant signals. Here, we uncover a new defining feature of IDS: mothers significantly alter statistical properties of vocal timbre when speaking to their infants. Timbre, the tone color or unique quality of a sound, is a spectral fingerprint that helps us instantly identify and classify sound sources, such as individual people and musical instruments [5-7]. We recorded 24 mothers' naturalistic speech while they interacted with their infants and with adult experimenters in their native language. Half of the participants were English speakers, and half were not. Using a support vector machine classifier, we found that mothers consistently shifted their timbre between ADS and IDS. Importantly, this shift was similar across languages, suggesting that such alterations of timbre may be universal. These findings have theoretical implications for understanding how infants tune in to their local communicative environments. Moreover, our classification algorithm for identifying infant-directed timbre has direct translational implications for speech recognition technology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

NASA Technical Reports Server (NTRS)

Shewhart, Mark

1991-01-01

Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Machine learning patterns for neuroimaging-genetic studies in the cloud.

PubMed

Da Mota, Benoit; Tudoran, Radu; Costan, Alexandru; Varoquaux, Gaël; Brasche, Goetz; Conrod, Patricia; Lemaitre, Herve; Paus, Tomas; Rietschel, Marcella; Frouin, Vincent; Poline, Jean-Baptiste; Antoniu, Gabriel; Thirion, Bertrand

2014-01-01

Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach.

PubMed

Borghetti, Brett J; Giametta, Joseph J; Rusnock, Christina F

2017-02-01

We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation. We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates. Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available. We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments. These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.

MSUSTAT.

ERIC Educational Resources Information Center

Mauriello, David

1984-01-01

Reviews an interactive statistical analysis package (designed to run on 8- and 16-bit machines that utilize CP/M 80 and MS-DOS operating systems), considering its features and uses, documentation, operation, and performance. The package consists of 40 general purpose statistical procedures derived from the classic textbook "Statistical…

Extracting laboratory test information from biomedical text

PubMed Central

Kang, Yanna Shen; Kayaalp, Mehmet

2013-01-01

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. Methods: The authors developed a symbolic information extraction (SIE) system to extract device and test specific information about four types of laboratory test entities: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. Results: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. Conclusions: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure. PMID:24083058

3D Visualization of Machine Learning Algorithms with Astronomical Data

NASA Astrophysics Data System (ADS)

Kent, Brian R.

2016-01-01

We present innovative machine learning (ML) methods using unsupervised clustering with minimum spanning trees (MSTs) to study 3D astronomical catalogs. Utilizing Python code to build trees based on galaxy catalogs, we can render the results with the visualization suite Blender to produce interactive 360 degree panoramic videos. The catalogs and their ML results can be explored in a 3D space using mobile devices, tablets or desktop browsers. We compare the statistics of the MST results to a number of machine learning methods relating to optimization and efficiency.

Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

PubMed

Fernandez, Michael; Abreu, Jose I; Shi, Hongqing; Barnard, Amanda S

2016-11-14

The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation vectors using machine learning techniques. Machine learning modeling revealed that the most relevant correlations appear at topological distances in the range of 1 to 42 with prediction accuracy higher than 80%. The data-driven model can statistically discriminate between graphene nanoflakes with different energy gaps on the basis of their molecular topology.

What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

PubMed

Binder, Harald

2014-07-01

This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Changes in compensatory eye movements associated with simulated stimulus conditions of spaceflight

NASA Technical Reports Server (NTRS)

Harm, Deborah L.; Zografos, Linda M.; Skinner, Noel C.; Parker, Donald E.

1993-01-01

Compensatory vertical eye movement gain (CVEMG) was recorded during pitch oscillation in darkness before, during, and immediately after exposures to the stimulus rearrangement produced by the Preflight Adaptation Trainer (PAT) Tilt-Translation Device (TTD). The TTD is designed to elicit adaptive responses that are similar to those observed in microgravity-adapted astronauts. The data from Experiment 1 yielded a statistically significant CVEMG decrease following 15 min of exposure to a stimulus rearrangement condition where the phase angle between subject pitch tilt and visual scene translation was 270 deg; statistically significant gain decreases were not observed following exposures either to a condition where the phase angle between subject pitch and scene translation was 90 deg or to a no-stimulus-rearrangement condition. Experiment 2 replicated the 270-deg-phase condition from Experiment 1 and extended the exposure duration from 30 to 45 min. Statistically significant additional changes in CVEMG associated with the increased exposure duration were not observed. The adaptation time constant estimated fram the combined data from Experiments 1 and 2 was 29 min.

Changes in Compensatory Eye Movements Associated with Simulated Stimulus Conditions of Spaceflight

NASA Technical Reports Server (NTRS)

Harm, Deborah L.; Zografos, Linda M.; Skinner, Noel C.; Parker, Donald E.

1993-01-01

Compensatory vertical eye movement gain (CVEMG) was recorded during pitch oscillation in darkness before, during and immediately after exposures to the stimulus rearrangement produced by the Preflight Adaptation Trainer (PAT) Tilt-Translation Device (TTD). The TTD is designed to elicit adaptive responses that are similar to those observed in microgravity-adapted astronauts. The data from Experiment 1 yielded a statistically significant CVEMG decrease following 15 minutes of exposure to a stimulus rearrangement condition where the phase angle between subject pitch tilt and visual scene translation was 270 degrees; statistically significant gain decreases were not observed following exposures either to a condition where the phase angle between subject pitch and scene translation was 90 degrees or to a no-stimulus-rearrangement condition. Experiment 2 replicated the 270 degree phase condition from Experiment 1 and extended the exposure duration from 30 to 45 minutes. Statistically significant additional changes in CVEMG associated with the increased exposure duration were not observed. The adaptation time constant estimated from the combined data from Experiments 1 and 2 was 29 minutes.

Translational Research for Occupational Therapy: Using SPRE in Hippotherapy for Children with Developmental Disabilities.

PubMed

Weissman-Miller, Deborah; Miller, Rosalie J; Shotwell, Mary P

2017-01-01

Translational research is redefined in this paper using a combination of methods in statistics and data science to enhance the understanding of outcomes and practice in occupational therapy. These new methods are applied, using larger data and smaller single-subject data, to a study in hippotherapy for children with developmental disabilities (DD). The Centers for Disease Control and Prevention estimates DD affects nearly 10 million children, aged 2-19, where diagnoses may be comorbid. Hippotherapy is defined here as a treatment strategy in occupational therapy using equine movement to achieve functional outcomes. Semiparametric ratio estimator (SPRE), a single-subject statistical and small data science model, is used to derive a "change point" indicating where the participant adapts to treatment, from which predictions are made. Data analyzed here is from an institutional review board approved pilot study using the Hippotherapy Evaluation and Assessment Tool measure, where outcomes are given separately for each of four measured domains and the total scores of each participant. Analysis with SPRE, using statistical methods to predict a "change point" and data science graphical interpretations of data, shows the translational comparisons between results from larger mean values and the very different results from smaller values for each HEAT domain in terms of relationships and statistical probabilities.

Translational Research for Occupational Therapy: Using SPRE in Hippotherapy for Children with Developmental Disabilities

PubMed Central

Miller, Rosalie J.; Shotwell, Mary P.

2017-01-01

Translational research is redefined in this paper using a combination of methods in statistics and data science to enhance the understanding of outcomes and practice in occupational therapy. These new methods are applied, using larger data and smaller single-subject data, to a study in hippotherapy for children with developmental disabilities (DD). The Centers for Disease Control and Prevention estimates DD affects nearly 10 million children, aged 2–19, where diagnoses may be comorbid. Hippotherapy is defined here as a treatment strategy in occupational therapy using equine movement to achieve functional outcomes. Semiparametric ratio estimator (SPRE), a single-subject statistical and small data science model, is used to derive a “change point” indicating where the participant adapts to treatment, from which predictions are made. Data analyzed here is from an institutional review board approved pilot study using the Hippotherapy Evaluation and Assessment Tool measure, where outcomes are given separately for each of four measured domains and the total scores of each participant. Analysis with SPRE, using statistical methods to predict a “change point” and data science graphical interpretations of data, shows the translational comparisons between results from larger mean values and the very different results from smaller values for each HEAT domain in terms of relationships and statistical probabilities. PMID:29097962

Statistical and Machine Learning forecasting methods: Concerns and ways forward

PubMed Central

Makridakis, Spyros; Assimakopoulos, Vassilios

2018-01-01

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784

Study of the Effect of Lubricant Emulsion Percentage and Tool Material on Surface Roughness in Machining of EN-AC 48000 Alloy

NASA Astrophysics Data System (ADS)

Soltani, E.; Shahali, H.; Zarepour, H.

2011-01-01

In this paper, the effect of machining parameters, namely, lubricant emulsion percentage and tool material on surface roughness has been studied in machining process of EN-AC 48000 aluminum alloy. EN-AC 48000 aluminum alloy is an important alloy in industries. Machining of this alloy is of vital importance due to built-up edge and tool wear. A L9 Taguchi standard orthogonal array has been applied as experimental design to investigate the effect of the factors and their interaction. Nine machining tests have been carried out with three random replications resulting in 27 experiments. Three type of cutting tools including coated carbide (CD1810), uncoated carbide (H10), and polycrystalline diamond (CD10) have been used in this research. Emulsion percentage of lubricant is selected at three levels including 3%, 5% and 10%. Statistical analysis has been employed to study the effect of factors and their interactions using ANOVA method. Moreover, the optimal factors level has been achieved through signal to noise ratio (S/N) analysis. Also, a regression model has been provided to predict the surface roughness. Finally, the results of the confirmation tests have been presented to verify the adequacy of the predictive model. In this research, surface quality was improved by 9% using lubricant and statistical optimization method.

Machine Translation Based Data Augmentation for Cantonese Keyword Spotting (Author’s Manuscript)

DTIC Science & Technology

2016-05-19

for the key- word k at the specific threshold t is defined as ATWV (k, t) = 1− PFR(k, t)− C · PFA (k, t) (1) where C = 999.9 is a constant, PFR and... PFA are probabil- ities of miss and false accept, respectively. MTWV is com- puted as a maximal ATWV over all possible values of t. 4. RESULTS In this

A Formal Model of Ambiguity and its Applications in Machine Translation

DTIC Science & Technology

2010-01-01

structure indicates linguisti- cally implausible segmentation that might be generated using dictionary - driven approaches...derivation. As was done in the monolingual case, the functions LHS, RHSi, RHSo and υ can be extended to a derivation δ. D(q) where q ∈V denotes the... monolingual parses. My algorithm runs more efficiently than O(n6) with many grammars (including those that required using heuristic search with other parsers

Parsing and Tagging of Bilingual Dictionary

DTIC Science & Technology

2003-09-01

LAMP-TR-106 CAR-TR-991 CS-TR-4529 UMIACS-TR-2003-97 PARSING ANS TAGGING OF BILINGUAL DICTIONARY Huanfeng Ma1,2, Burcu Karagol-Ayan1,2, David... dictionaries hold great potential as a source of lexical resources for training and testing automated systems for optical character recognition, machine...translation, and cross-language information retrieval. In this paper, we describe a system for extracting term lexicons from printed bilingual dictionaries

Translations on USSR Science and Technology Physical Sciences and Technology No. 7

DTIC Science & Technology

1977-02-28

cybernetics. [Answer] Immediately after the war , when the restoration of the national economy, which had been wrecked by the enemy, was started, Soviet...cyberneticization of economics and science will be developed at accelerated rates. 8545 CSO: 1870 CYBERNETICS, COMPUTERS AND AUTOMATION TECHNOLOGY...working storage of the machine exceeds 64 thousand alpha-numeric characters. Communication with the external world is effected by means of a main

Computer Generation of Natural Language from a Deep Conceptual Base

DTIC Science & Technology

1974-01-01

It would be useful to have machines which could read scientific documents, newspaper articles , novels, etc., and translate them into other...preparing abstracts :or articles and in headline writing (at least in those cases in which headlines are used as an indication of article content...above), a definite or indefinite article is attached to the noun phrase. The selection of color and size adjectives is made in .. fashion

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

PubMed Central

Perryman, Alexander L.; Stratton, Thomas P.; Ekins, Sean; Freundlich, Joel S.

2015-01-01

Purpose Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Methods Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). Results “Pruning” out the moderately unstable/moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 hour. Conclusions Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources. PMID:26415647

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PubMed

Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean; Freundlich, Joel S

2016-02-01

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

NASA Astrophysics Data System (ADS)

Rajangam, Sankaranarayani; Tseng, Po-He; Yin, Allen; Lehew, Gary; Schwarz, David; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

2016-03-01

Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

PubMed Central

Bak, N; Ebdrup, B H; Oranje, B; Fagerlund, B; Jensen, M H; Düring, S W; Nielsen, M Ø; Glenthøj, B Y; Hansen, L K

2017-01-01

Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D2 receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens. PMID:28398342

Spectrophores as one-dimensional descriptors calculated from three-dimensional atomic properties: applications ranging from scaffold hopping to multi-target virtual screening.

PubMed

Gladysz, Rafaela; Dos Santos, Fabio Mendes; Langenaeker, Wilfried; Thijs, Gert; Augustyns, Koen; De Winter, Hans

2018-03-07

Spectrophores are novel descriptors that are calculated from the three-dimensional atomic properties of molecules. In our current implementation, the atomic properties that were used to calculate spectrophores include atomic partial charges, atomic lipophilicity indices, atomic shape deviations and atomic softness properties. This approach can easily be widened to also include additional atomic properties. Our novel methodology finds its roots in the experimental affinity fingerprinting technology developed in the 1990's by Terrapin Technologies. Here we have translated it into a purely virtual approach using artificial affinity cages and a simplified metric to calculate the interaction between these cages and the atomic properties. A typical spectrophore consists of a vector of 48 real numbers. This makes it highly suitable for the calculation of a wide range of similarity measures for use in virtual screening and for the investigation of quantitative structure-activity relationships in combination with advanced statistical approaches such as self-organizing maps, support vector machines and neural networks. In our present report we demonstrate the applicability of our novel methodology for scaffold hopping as well as virtual screening.

Oak Ridge Bio-surveillance Toolkit (ORBiT): Integrating Big-Data Analytics with Visual Analysis for Public Health Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramanathan, Arvind; Pullum, Laura L; Steed, Chad A

In this position paper, we describe the design and implementation of the Oak Ridge Bio-surveillance Toolkit (ORBiT): a collection of novel statistical and machine learning tools implemented for (1) integrating heterogeneous traditional (e.g. emergency room visits, prescription sales data, etc.) and non-traditional (social media such as Twitter and Instagram) data sources, (2) analyzing large-scale datasets and (3) presenting the results from the analytics as a visual interface for the end-user to interact and provide feedback. We present examples of how ORBiT can be used to summarize ex- tremely large-scale datasets effectively and how user interactions can translate into the datamore » analytics process for bio-surveillance. We also present a strategy to estimate parameters relevant to dis- ease spread models from near real time data feeds and show how these estimates can be integrated with disease spread models for large-scale populations. We conclude with a perspective on how integrating data and visual analytics could lead to better forecasting and prediction of disease spread as well as improved awareness of disease susceptible regions.« less

Compact Microscope Imaging System with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

The figure presents selected views of a compact microscope imaging system (CMIS) that includes a miniature video microscope, a Cartesian robot (a computer- controlled three-dimensional translation stage), and machine-vision and control subsystems. The CMIS was built from commercial off-the-shelf instrumentation, computer hardware and software, and custom machine-vision software. The machine-vision and control subsystems include adaptive neural networks that afford a measure of artificial intelligence. The CMIS can perform several automated tasks with accuracy and repeatability . tasks that, heretofore, have required the full attention of human technicians using relatively bulky conventional microscopes. In addition, the automation and control capabilities of the system inherently include a capability for remote control. Unlike human technicians, the CMIS is not at risk of becoming fatigued or distracted: theoretically, it can perform continuously at the level of the best human technicians. In its capabilities for remote control and for relieving human technicians of tedious routine tasks, the CMIS is expected to be especially useful in biomedical research, materials science, inspection of parts on industrial production lines, and space science. The CMIS can automatically focus on and scan a microscope sample, find areas of interest, record the resulting images, and analyze images from multiple samples simultaneously. Automatic focusing is an iterative process: The translation stage is used to move the microscope along its optical axis in a succession of coarse, medium, and fine steps. A fast Fourier transform (FFT) of the image is computed at each step, and the FFT is analyzed for its spatial-frequency content. The microscope position that results in the greatest dispersal of FFT content toward high spatial frequencies (indicating that the image shows the greatest amount of detail) is deemed to be the focal position.

Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations

NASA Astrophysics Data System (ADS)

Wodlinger, B.; Downey, J. E.; Tyler-Kabara, E. C.; Schwartz, A. B.; Boninger, M. L.; Collinger, J. L.

2015-02-01

Objective. In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject’s left motor cortex. Approach. Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously. Main results. Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping. Significance. Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.

Inter-species pathway perturbation prediction via data-driven detection of functional homology.

PubMed

Hafemeister, Christoph; Romero, Roberto; Bilal, Erhan; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Bonneau, Richard; Tarca, Adi L

2015-02-15

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli. We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli. Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h. christoph.hafemeister@nyu.edu or atarca@med.wayne.edu. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Organic bioelectronics for electronic-to-chemical translation in modulation of neuronal signaling and machine-to-brain interfacing.

PubMed

Larsson, Karin C; Kjäll, Peter; Richter-Dahlfors, Agneta

2013-09-01

A major challenge when creating interfaces for the nervous system is to translate between the signal carriers of the nervous system (ions and neurotransmitters) and those of conventional electronics (electrons). Organic conjugated polymers represent a unique class of materials that utilizes both electrons and ions as charge carriers. Based on these materials, we have established a series of novel communication interfaces between electronic components and biological systems. The organic electronic ion pump (OEIP) presented in this review is made of the polymer-polyelectrolyte system poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). The OEIP translates electronic signals into electrophoretic migration of ions and neurotransmitters. We demonstrate how spatio-temporally controlled delivery of ions and neurotransmitters can be used to modulate intracellular Ca(2+) signaling in neuronal cells in the absence of convective disturbances. The electronic control of delivery enables strict control of dynamic parameters, such as amplitude and frequency of Ca(2+) responses, and can be used to generate temporal patterns mimicking naturally occurring Ca(2+) oscillations. To enable further control of the ionic signals we developed the electrophoretic chemical transistor, an analog of the traditional transistor used to amplify and/or switch electronic signals. Finally, we demonstrate the use of the OEIP in a new "machine-to-brain" interface by modulating brainstem responses in vivo. This review highlights the potential of communication interfaces based on conjugated polymers in generating complex, high-resolution, signal patterns to control cell physiology. We foresee widespread applications for these devices in biomedical research and in future medical devices within multiple therapeutic areas. This article is part of a Special Issue entitled Organic Bioelectronics-Novel Applications in Biomedicine. Copyright © 2012 Elsevier B.V. All rights reserved.

NeuroRex: A Clinical Neural Interface Roadmap for EEG-based Brain Machine Interfaces to a Lower Body Robotic Exoskeleton*

PubMed Central

Contreras-Vidal, Jose L.; Grossman, Robert G.

2013-01-01

In this communication, a translational clinical brain-machine interface (BMI) roadmap for an EEG-based BMI to a robotic exoskeleton (NeuroRex) is presented. This multi-faceted project addresses important engineering and clinical challenges: It addresses the validation of an intelligent, self-balancing, robotic lower-body and trunk exoskeleton (Rex) augmented with EEG-based BMI capabilities to interpret user intent to assist a mobility-impaired person to walk independently. The goal is to improve the quality of life and health status of wheelchair-bounded persons by enabling standing and sitting, walking and backing, turning, ascending and descending stairs/curbs, and navigating sloping surfaces in a variety of conditions without the need for additional support or crutches. PMID:24110003

Making extreme computations possible with virtual machines

NASA Astrophysics Data System (ADS)

Reuter, J.; Chokoufe Nejad, B.; Ohl, T.

2016-10-01

State-of-the-art algorithms generate scattering amplitudes for high-energy physics at leading order for high-multiplicity processes as compiled code (in Fortran, C or C++). For complicated processes the size of these libraries can become tremendous (many GiB). We show that amplitudes can be translated to byte-code instructions, which even reduce the size by one order of magnitude. The byte-code is interpreted by a Virtual Machine with runtimes comparable to compiled code and a better scaling with additional legs. We study the properties of this algorithm, as an extension of the Optimizing Matrix Element Generator (O'Mega). The bytecode matrix elements are available as alternative input for the event generator WHIZARD. The bytecode interpreter can be implemented very compactly, which will help with a future implementation on massively parallel GPUs.

A proposal of an architecture for the coordination level of intelligent machines

NASA Technical Reports Server (NTRS)

Beard, Randall; Farah, Jeff; Lima, Pedro

1993-01-01

The issue of obtaining a practical, structured, and detailed description of an architecture for the Coordination Level of Center for Intelligent Robotic Systems for Sapce Exploration (CIRSSE) Testbed Intelligent Controller is addressed. Previous theoretical and implementation works were the departure point for the discussion. The document is organized as follows: after this introductory section, section 2 summarizes the overall view of the Intelligent Machine (IM) as a control system, proposing a performance measure on which to base its design. Section 3 addresses with some detail implementation issues. An hierarchic petri-net with feedback-based learning capabilities is proposed. Finally, section 4 is an attempt to address the feedback problem. Feedback is used for two functions: error recovery and reinforcement learning of the correct translations for the petri-net transitions.

A multi-frame soft x-ray pinhole imaging diagnostic for single-shot applicationsa)

NASA Astrophysics Data System (ADS)

Wurden, G. A.; Coffey, S. K.

2012-10-01

For high energy density magnetized target fusion experiments at the Air Force Research Laboratory FRCHX machine, obtaining multi-frame soft x-ray images of the field reversed configuration (FRC) plasma as it is being compressed will provide useful dynamics and symmetry information. However, vacuum hardware will be destroyed during the implosion. We have designed a simple in-vacuum pinhole nosecone attachment, fitting onto a Conflat window, coated with 3.2 mg/cm2 of P-47 phosphor, and covered with a thin 50-nm aluminum reflective overcoat, lens-coupled to a multi-frame Hadland Ultra intensified digital camera. We compare visible and soft x-ray axial images of translating (˜200 eV) plasmas in the FRX-L and FRCHX machines in Los Alamos and Albuquerque.

A Developmental Approach to Machine Learning?

PubMed Central

Smith, Linda B.; Slone, Lauren K.

2017-01-01

Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order – with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines. PMID:29259573

Feature recognition and detection for ancient architecture based on machine vision

NASA Astrophysics Data System (ADS)

Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

2018-03-01

Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

Principle of maximum entropy for reliability analysis in the design of machine components

NASA Astrophysics Data System (ADS)

Zhang, Yimin

2018-03-01

We studied the reliability of machine components with parameters that follow an arbitrary statistical distribution using the principle of maximum entropy (PME). We used PME to select the statistical distribution that best fits the available information. We also established a probability density function (PDF) and a failure probability model for the parameters of mechanical components using the concept of entropy and the PME. We obtained the first four moments of the state function for reliability analysis and design. Furthermore, we attained an estimate of the PDF with the fewest human bias factors using the PME. This function was used to calculate the reliability of the machine components, including a connecting rod, a vehicle half-shaft, a front axle, a rear axle housing, and a leaf spring, which have parameters that typically follow a non-normal distribution. Simulations were conducted for comparison. This study provides a design methodology for the reliability of mechanical components for practical engineering projects.

Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology.

PubMed

Smith, Benjamin R; Ashton, Katherine M; Brodbelt, Andrew; Dawson, Timothy; Jenkinson, Michael D; Hunt, Neil T; Palmer, David S; Baker, Matthew J

2016-06-07

Fourier transform infrared (FTIR) spectroscopy has long been established as an analytical technique for the measurement of vibrational modes of molecular systems. More recently, FTIR has been used for the analysis of biofluids with the aim of becoming a tool to aid diagnosis. For the clinician, this represents a convenient, fast, non-subjective option for the study of biofluids and the diagnosis of disease states. The patient also benefits from this method, as the procedure for the collection of serum is much less invasive and stressful than traditional biopsy. This is especially true of patients in whom brain cancer is suspected. A brain biopsy is very unpleasant for the patient, potentially dangerous and can occasionally be inconclusive. We therefore present a method for the diagnosis of brain cancer from serum samples using FTIR and machine learning techniques. The scope of the study involved 433 patients from whom were collected 9 spectra each in the range 600-4000 cm(-1). To begin the development of the novel method, various pre-processing steps were investigated and ranked in terms of final accuracy of the diagnosis. Random forest machine learning was utilised as a classifier to separate patients into cancer or non-cancer categories based upon the intensities of wavenumbers present in their spectra. Generalised 2D correlational analysis was then employed to further augment the machine learning, and also to establish spectral features important for the distinction between cancer and non-cancer serum samples. Using these methods, sensitivities of up to 92.8% and specificities of up to 91.5% were possible. Furthermore, ratiometrics were also investigated in order to establish any correlations present in the dataset. We show a rapid, computationally light, accurate, statistically robust methodology for the identification of spectral features present in differing disease states. With current advances in IR technology, such as the development of rapid discrete frequency collection, this approach is of importance to enable future clinical translation and enables IR to achieve its potential.

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Treesearch

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

A New Mathematical Framework for Design Under Uncertainty

DTIC Science & Technology

2016-05-05

blending multiple information sources via auto-regressive stochastic modeling. A computationally efficient machine learning framework is developed based on...sion and machine learning approaches; see Fig. 1. This will lead to a comprehensive description of system performance with less uncertainty than in the...Bayesian optimization of super-cavitating hy- drofoils The goal of this study is to demonstrate the capabilities of statistical learning and

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

Machine Learning Predictions of a Multiresolution Climate Model Ensemble

NASA Astrophysics Data System (ADS)

Anderson, Gemma J.; Lucas, Donald D.

2018-05-01

Statistical models of high-resolution climate models are useful for many purposes, including sensitivity and uncertainty analyses, but building them can be computationally prohibitive. We generated a unique multiresolution perturbed parameter ensemble of a global climate model. We use a novel application of a machine learning technique known as random forests to train a statistical model on the ensemble to make high-resolution model predictions of two important quantities: global mean top-of-atmosphere energy flux and precipitation. The random forests leverage cheaper low-resolution simulations, greatly reducing the number of high-resolution simulations required to train the statistical model. We demonstrate that high-resolution predictions of these quantities can be obtained by training on an ensemble that includes only a small number of high-resolution simulations. We also find that global annually averaged precipitation is more sensitive to resolution changes than to any of the model parameters considered.

Nowcasting Cloud Fields for U.S. Air Force Special Operations

DTIC Science & Technology

2017-03-01

application of Bayes’ Rule offers many advantages over Kernel Density Estimation (KDE) and other commonly used statistical post-processing methods...reflectance and probability of cloud. A statistical post-processing technique is applied using Bayesian estimation to train the system from a set of past...nowcasting, low cloud forecasting, cloud reflectance, ISR, Bayesian estimation, statistical post-processing, machine learning 15. NUMBER OF PAGES

LOOP IIId of the HCV IRES is essential for the structural rearrangement of the 40S-HCV IRES complex

PubMed Central

Angulo, Jenniffer; Ulryck, Nathalie; Deforges, Jules; Chamond, Nathalie; Lopez-Lastra, Marcelo; Masquida, Benoît; Sargueil, Bruno

2016-01-01

As obligatory intracellular parasites, viruses rely on cellular machines to complete their life cycle, and most importantly they recruit the host ribosomes to translate their mRNA. The Hepatitis C viral mRNA initiates translation by directly binding the 40S ribosomal subunit in such a way that the initiation codon is correctly positioned in the P site of the ribosome. Such a property is likely to be central for many viruses, therefore the description of host-pathogen interaction at the molecular level is instrumental to provide new therapeutic targets. In this study, we monitored the 40S ribosomal subunit and the viral RNA structural rearrangement induced upon the formation of the binary complex. We further took advantage of an IRES viral mutant mRNA deficient for translation to identify the interactions necessary to promote translation. Using a combination of structure probing in solution and molecular modeling we establish a whole atom model which appears to be very similar to the one obtained recently by cryoEM. Our model brings new information on the complex, and most importantly reveals some structural rearrangement within the ribosome. This study suggests that the formation of a ‘kissing complex’ between the viral RNA and the 18S ribosomal RNA locks the 40S ribosomal subunit in a conformation proficient for translation. PMID:26626152

Assessment of the information content of patterns: an algorithm

NASA Astrophysics Data System (ADS)

Daemi, M. Farhang; Beurle, R. L.

1991-12-01

A preliminary investigation confirmed the possibility of assessing the translational and rotational information content of simple artificial images. The calculation is tedious, and for more realistic patterns it is essential to implement the method on a computer. This paper describes an algorithm developed for this purpose which confirms the results of the preliminary investigation. Use of the algorithm facilitates much more comprehensive analysis of the combined effect of continuous rotation and fine translation, and paves the way for analysis of more realistic patterns. Owing to the volume of calculation involved in these algorithms, extensive computing facilities were necessary. The major part of the work was carried out using an ICL 3900 series mainframe computer as well as other powerful workstations such as a RISC architecture MIPS machine.

Compiling global name-space programs for distributed execution

NASA Technical Reports Server (NTRS)

Koelbel, Charles; Mehrotra, Piyush

1990-01-01

Distributed memory machines do not provide hardware support for a global address space. Thus programmers are forced to partition the data across the memories of the architecture and use explicit message passing to communicate data between processors. The compiler support required to allow programmers to express their algorithms using a global name-space is examined. A general method is presented for analysis of a high level source program and along with its translation to a set of independently executing tasks communicating via messages. If the compiler has enough information, this translation can be carried out at compile-time. Otherwise run-time code is generated to implement the required data movement. The analysis required in both situations is described and the performance of the generated code on the Intel iPSC/2 is presented.

Molecular mimicry between protein and tRNA.

PubMed

Nakamura, Y

2001-01-01

Mimicry is a sophisticated development in animals, fish, and plants that allows them to fool others by imitating a shape or color for diverse purposes, such as to prey, evade, lure, pollinate, or threaten. This is not restricted to the macro-world, but extends to the micro-world as molecular mimicry. Recent advances in structural and molecular biology uncovered a set of translation factors that resembles a tRNA shape and, in one case, even mimics a tRNA function for deciphering the genetic code. Nature must have evolved this art of molecular mimicry between protein and ribonucleic acid by using different protein structures until the translation factors sat in the cockpit of a ribosome machine, on behalf of tRNA, and achieved diverse actions. Structural, functional, and evolutionary aspects of molecular mimicry will be discussed.

Precision Timed Infrastructure: Design Challenges

DTIC Science & Technology

2013-09-19

timing constructs Clock synchronization and communication PRET Machines Other Platforms Fig. 1. Conceptual overview of translation steps between...2002. [3] A. Benveniste and G. Berry. The Synchronous Approach to Reactive and Real- Time Systems. Proceedings of the IEEE, 79(9):1270–1282, 1991. [4] D...and E. Lee. A programming model for time - synchronized distributed real- time systems. In Real Time and Embedded Technology and Applications Symposium, 2007. RTAS’07. 13th IEEE, pages

Translations on USSR Science and Technology, Physical Sciences and Technology, Number 16

DTIC Science & Technology

1977-08-05

34INVESTIGATION OF SPLITTING OF LIGHT NUCLEI WITH HIGH-ENERGY y -RAYS WITH THE METHOD OF WILSON’S CHAMBER OPERATING IN POWERFUL BEAMS OF ELECTRONIC...boast high reliability, high speed, and extremely modest power requirements. Information oh the Screen Visual display devices greatly facilitate...area of application of these units Includes navigation, control of power systems, machine tools, and manufac- turing processes. Th» ^»abilities of

AGARD Flight Test Instrumentation Series. Volume 18. Microprocessor Applications in Airborne Flight Test Instrumentation

DTIC Science & Technology

1987-02-01

flowcharting . 3. ProEram Codin in HLL. This stage consists of transcribing the previously designed program into R an t at can be translated into the machine...specified conditios 7. Documentation. Program documentation is necessary for user information, for maintenance, and for future applications. Flowcharts ...particular CP U. Asynchronous. Operating without reference to an overall timing source. BASIC. Beginners ’ All-purpose Symbolic Instruction Code; a widely

Divergence Measures Tool:An Introduction with Brief Tutorial

DTIC Science & Technology

2014-03-01

in detecting differences across a wide range of Arabic -language text files (they varied by genre, domain, spelling variation, size, etc.), our...other. 2 These measures have been put to many uses in natural language processing ( NLP ). In the evaluation of machine translation (MT...files uploaded into the tool must be .txt files in ASCII or UTF-8 format. • This tool has been tested on English and Arabic script**, but should

Translations on USSR Science and Technology, Biomedical and Behavioral Sciences, Number 15

DTIC Science & Technology

1977-11-16

processed. By applying systems theory to synthesis of complex man-machine systems we form ergatic organisms which not only have external and internal...without exception (and this is extremely important to emphasize) as a complex , integral formation, which through various traditions has acquired a...and outputs of the whole, which has a complex internal organization and structure, which we can no longer ignore in our analysis. Thus analysis and

Translations on Environmental Quality, Number 129.

DTIC Science & Technology

1977-01-27

agricultural procedures have not led to acidity in rivers in other parts of the country with similar soil conditions. Finally, the leaders of the project...Rosenqvist’s report gives interesting points of view concerning the conditions in the soil and the acidic precipitation, but the report builds on a series...bones, eggshells and poultry skin, used tires, waste gasoline, used cardboard, machine and engine noise, gas stench and toxic sprays, were right away

JPRS report: Science and technology. Central Eurasia

NASA Astrophysics Data System (ADS)

1994-05-01

Translated articles cover the following topics: optimal systems to detect and classify moving objects; multiple identification of optical readings in multisensor information and measurement system; method of first integrals in synthesis of optimal control; study of the development of turbulence in the region of a break above a triangular wing; electroerosion machining in aviation engine construction; and cumulation of a flat shock wave in a tube by a thin parietal gas layer of lower density.

Good Applications for Crummy Machine Translation

DTIC Science & Technology

1991-07-01

number of rewriting rules for transfer and generation processes is around 800, and it will be increased in the coming few months. The dictionary ...this time to do other useful tasks), as well as the time for the second dictionary update (on the grounds that these new or modified entrie., are not...little more than provide word-processing functionality, dictionary access and so on, but as time goes on, one might imagine functionality that begins to

UMass at TREC 2002: Cross Language and Novelty Tracks

DTIC Science & Technology

2002-01-01

resources – stemmers, dictionaries , machine translation, and an acronym database. We found that proper names were extremely important in this year’s queries...data by manually annotating 48 additional topics. 1. Cross Language Track We submitted one monolingual run and four cross-language runs. For the... monolingual run, the technology was essentially the same as the system we used for TREC 2001. For the cross-language run, we integrated some new

Good Applications for Crummy Machine Translation

DTIC Science & Technology

1993-01-01

for transfer and generation processes is around 800, and it will be increased in the coming few months. The dictionary contains about 16,000 items at...time to do other useful tasks), as well as the time for the second dictionary update (on the grounds that these new or modified entries are not intended...might do little more than provide word-processing functionality, dictionary access and so on, but as time goes on, one might imagine functionality

Assessing Statistical Competencies in Clinical and Translational Science Education: One Size Does Not Fit All

PubMed Central

Lindsell, Christopher J.; Welty, Leah J.; Mazumdar, Madhu; Thurston, Sally W.; Rahbar, Mohammad H.; Carter, Rickey E.; Pollock, Bradley H.; Cucchiara, Andrew J.; Kopras, Elizabeth J.; Jovanovic, Borko D.; Enders, Felicity T.

2014-01-01

Abstract Introduction Statistics is an essential training component for a career in clinical and translational science (CTS). Given the increasing complexity of statistics, learners may have difficulty selecting appropriate courses. Our question was: what depth of statistical knowledge do different CTS learners require? Methods For three types of CTS learners (principal investigator, co‐investigator, informed reader of the literature), each with different backgrounds in research (no previous research experience, reader of the research literature, previous research experience), 18 experts in biostatistics, epidemiology, and research design proposed levels for 21 statistical competencies. Results Statistical competencies were categorized as fundamental, intermediate, or specialized. CTS learners who intend to become independent principal investigators require more specialized training, while those intending to become informed consumers of the medical literature require more fundamental education. For most competencies, less training was proposed for those with more research background. Discussion When selecting statistical coursework, the learner's research background and career goal should guide the decision. Some statistical competencies are considered to be more important than others. Baseline knowledge assessments may help learners identify appropriate coursework. Conclusion Rather than one size fits all, tailoring education to baseline knowledge, learner background, and future goals increases learning potential while minimizing classroom time. PMID:25212569

Machine learning approach for automated screening of malaria parasite using light microscopic images.

PubMed

Das, Dev Kumar; Ghosh, Madhumala; Pal, Mallika; Maiti, Asok K; Chakraborty, Chandan

2013-02-01

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification. Copyright © 2012 Elsevier Ltd. All rights reserved.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

PubMed Central

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-01-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. PMID:27892471

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.

PubMed

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-28

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

NASA Astrophysics Data System (ADS)

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Effects of promotional materials on vending sales of low-fat items in teachers' lounges.

PubMed

Fiske, Amy; Cullen, Karen Weber

2004-01-01

This study examined the impact of an environmental intervention in the form of promotional materials and increased availability of low-fat items on vending machine sales. Ten vending machines were selected and randomly assigned to one of three conditions: control, or one of two experimental conditions. Vending machines in the two intervention conditions received three additional low-fat selections. Low-fat items were promoted at two levels: labels (intervention I), and labels plus signs (intervention II). The number of individual items sold and the total revenue generated was recorded weekly for each machine for 4 weeks. Use of promotional materials resulted in a small, but not significant, increase in the number of low-fat items sold, although machine sales were not significantly impacted by the change in product selection. Results of this study, although not statistically significant, suggest that environmental change may be a realistic means of positively influencing consumer behavior.

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

PubMed

Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

2017-04-01

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Do clinical and translational science graduate students understand linear regression? Development and early validation of the REGRESS quiz.

PubMed

Enders, Felicity

2013-12-01

Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P < 0.001). Postcourse students had similar results to practicing statisticians (mean (SD) of 20.1(3.5); P = 0.21). Students also showed significant improvement pre/postcourse in each of six domain areas (P < 0.001). The REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

NASA Astrophysics Data System (ADS)

Imbalzano, Giulio; Anelli, Andrea; Giofré, Daniele; Klees, Sinja; Behler, Jörg; Ceriotti, Michele

2018-06-01

Machine learning of atomic-scale properties is revolutionizing molecular modeling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the costs. The accuracy, speed, and reliability of machine learning potentials, however, depend strongly on the way atomic configurations are represented, i.e., the choice of descriptors used as input for the machine learning method. The raw Cartesian coordinates are typically transformed in "fingerprints," or "symmetry functions," that are designed to encode, in addition to the structure, important properties of the potential energy surface like its invariances with respect to rotation, translation, and permutation of like atoms. Here we discuss automatic protocols to select a number of fingerprints out of a large pool of candidates, based on the correlations that are intrinsic to the training data. This procedure can greatly simplify the construction of neural network potentials that strike the best balance between accuracy and computational efficiency and has the potential to accelerate by orders of magnitude the evaluation of Gaussian approximation potentials based on the smooth overlap of atomic positions kernel. We present applications to the construction of neural network potentials for water and for an Al-Mg-Si alloy and to the prediction of the formation energies of small organic molecules using Gaussian process regression.

Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data.

PubMed

Clark, Alex M; Williams, Antony J; Ekins, Sean

2015-01-01

The current rise in the use of open lab notebook techniques means that there are an increasing number of scientists who make chemical information freely and openly available to the entire community as a series of micropublications that are released shortly after the conclusion of each experiment. We propose that this trend be accompanied by a thorough examination of data sharing priorities. We argue that the most significant immediate benefactor of open data is in fact chemical algorithms, which are capable of absorbing vast quantities of data, and using it to present concise insights to working chemists, on a scale that could not be achieved by traditional publication methods. Making this goal practically achievable will require a paradigm shift in the way individual scientists translate their data into digital form, since most contemporary methods of data entry are designed for presentation to humans rather than consumption by machine learning algorithms. We discuss some of the complex issues involved in fixing current methods, as well as some of the immediate benefits that can be gained when open data is published correctly using unambiguous machine readable formats. Graphical AbstractLab notebook entries must target both visualisation by scientists and use by machine learning algorithms.

Development and Evaluation of Heartbeat: A Machine Perfusion Heart Preservation System.

PubMed

Li, Yongnan; Zeng, Qingdong; Liu, Gang; Du, Junzhe; Gao, Bingren; Wang, Wei; Zheng, Zhe; Hu, Shengshou; Ji, Bingyang

2017-11-01

Static cold storage is accompanied with a partial safe ischemic interval for donor hearts. In this current study, a machine perfusion system was built to provide a better preservation for the donor heart and assessment for myocardial function. Chinese mini-swine (weight 30-35 kg, n = 16) were randomly divided into HTK, Celsior, and Heartbeat groups. All donor hearts were respectively preserved for 8 hours under static cold storage or machine perfusion. The perfusion solution is aimed to maintain its homeostasis based on monitoring the Heartbeat group. The ultrastructure of myocardium suggests better myocardial protection in the Heartbeat group compared with HTK or Celsior-preserved hearts. The myocardial and coronary artery structural and functional integrity was evaluated by immunofluorescence and Western blots in the Heartbeat. In the Heartbeat group, donor hearts maintained a high adenosine triphosphate level. Bcl-2 and Beclin-1 protein demonstrates high expression in the Celsior group. The Heartbeat system can be used to preserve donor hearts, and it could guarantee the myocardial and endothelial function of hearts during machine perfusion. Translating Heartbeat into clinical practice, it is such as to impact on donor heart preservation for cardiac transplantation. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Envelope statistics of self-motion signals experienced by human subjects during everyday activities: Implications for vestibular processing.

PubMed

Carriot, Jérome; Jamali, Mohsen; Cullen, Kathleen E; Chacron, Maurice J

2017-01-01

There is accumulating evidence that the brain's neural coding strategies are constrained by natural stimulus statistics. Here we investigated the statistics of the time varying envelope (i.e. a second-order stimulus attribute that is related to variance) of rotational and translational self-motion signals experienced by human subjects during everyday activities. We found that envelopes can reach large values across all six motion dimensions (~450 deg/s for rotations and ~4 G for translations). Unlike results obtained in other sensory modalities, the spectral power of envelope signals decreased slowly for low (< 2 Hz) and more sharply for high (>2 Hz) temporal frequencies and thus was not well-fit by a power law. We next compared the spectral properties of envelope signals resulting from active and passive self-motion, as well as those resulting from signals obtained when the subject is absent (i.e. external stimuli). Our data suggest that different mechanisms underlie deviation from scale invariance in rotational and translational self-motion envelopes. Specifically, active self-motion and filtering by the human body cause deviation from scale invariance primarily for translational and rotational envelope signals, respectively. Finally, we used well-established models in order to predict the responses of peripheral vestibular afferents to natural envelope stimuli. We found that irregular afferents responded more strongly to envelopes than their regular counterparts. Our findings have important consequences for understanding the coding strategies used by the vestibular system to process natural second-order self-motion signals.

Machine vision system for measuring conifer seedling morphology

NASA Astrophysics Data System (ADS)

Rigney, Michael P.; Kranzler, Glenn A.

1995-01-01

A PC-based machine vision system providing rapid measurement of bare-root tree seedling morphological features has been designed. The system uses backlighting and a 2048-pixel line- scan camera to acquire images with transverse resolutions as high as 0.05 mm for precise measurement of stem diameter. Individual seedlings are manually loaded on a conveyor belt and inspected by the vision system in less than 0.25 seconds. Designed for quality control and morphological data acquisition by nursery personnel, the system provides a user-friendly, menu-driven graphical interface. The system automatically locates the seedling root collar and measures stem diameter, shoot height, sturdiness ratio, root mass length, projected shoot and root area, shoot-root area ratio, and percent fine roots. Sample statistics are computed for each measured feature. Measurements for each seedling may be stored for later analysis. Feature measurements may be compared with multi-class quality criteria to determine sample quality or to perform multi-class sorting. Statistical summary and classification reports may be printed to facilitate the communication of quality concerns with grading personnel. Tests were conducted at a commercial forest nursery to evaluate measurement precision. Four quality control personnel measured root collar diameter, stem height, and root mass length on each of 200 conifer seedlings. The same seedlings were inspected four times by the machine vision system. Machine stem diameter measurement precision was four times greater than that of manual measurements. Machine and manual measurements had comparable precision for shoot height and root mass length.

Effects of the sliding rehabilitation machine on balance and gait in chronic stroke patients - a controlled clinical trial.

PubMed

Byun, Seung-Deuk; Jung, Tae-Du; Kim, Chul-Hyun; Lee, Yang-Soo

2011-05-01

To investigate the effects of a sliding rehabilitation machine on balance and gait in chronic stroke patients. A non-randomized crossover design. Inpatient rehabilitation in a general hospital. Thirty patients with chronic stroke who had medium or high falling risk as determined by the Berg Balance Scale. Participants were divided into two groups and underwent four weeks of training. Group A (n = 15) underwent training with the sliding rehabilitation machine for two weeks with concurrent conventional training, followed by conventional training only for another two weeks. Group B (n = 15) underwent the same training in reverse order. The effect of the experimental period was defined as the sum of changes during training with sliding rehabilitation machine in each group, and the effect of the control period was defined as those during the conventional training only in each group. Functional Ambulation Category, Berg Balance Scale, Six-Minute Walk Test, Timed Up and Go Test, Korean Modified Barthel Index, Modified Ashworth Scale and Manual Muscle Test. Statistically significant improvements were observed in all parameters except Modified Ashworth Scale in the experimental period, but only in Six-Minute Walk Test (P < 0.01) in the control period. There were also statistically significant differences in the degree of change in all parameters in the experimental period as compared to the control period. The sliding rehabilitation machine may be a useful tool for the improvement of balance and gait abilities in chronic stroke patients.

Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review.

PubMed

Orrù, Graziella; Pettersson-Yeo, William; Marquand, Andre F; Sartori, Giuseppe; Mechelli, Andrea

2012-04-01

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Development of using the digital projection method to measure the contact angle of ball screw

NASA Astrophysics Data System (ADS)

Chen, Chun-Jen; Jywe, Wenyuh; Liu, Yu-Chun; Jwo, Hsin-Hong

The ball screw frequently used to drive or translate the parts on the precision machine, such as machine tool and motorized stage. Therefore they were most frequently used on the precision machine, semiconductor equipment, medical instrument and aero industry. The main parts of ball screw are screw, ball and nut. The contact angle between the screw, ball and nut will affect the performance (include loading and noise) and lifecycle of a ball screw. If the actual contact angle and the designed contact angle are not the same, the friction between the ball, screw and nut will increase and it will result in the thermal increase and lifecycle decrease. This paper combines the traditional profile projector and commercial digital camera to build an imaging based and noncontact measurements system. It can implement the contact angle measurement quickly and accurately. Three different pitch angles of ball screws were completed tests in this paper. The angle resolution of this measurement system is about 0.001 degree and its accuracy is about 0.05 degree.

Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier

NASA Astrophysics Data System (ADS)

Rama Krishna, K.; Ramachandran, K. I.

2018-02-01

Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.

Occupational Accidents with Agricultural Machinery in Austria.

PubMed

Kogler, Robert; Quendler, Elisabeth; Boxberger, Josef

2016-01-01

The number of recognized accidents with fatalities during agricultural and forestry work, despite better technology and coordinated prevention and trainings, is still very high in Austria. The accident scenarios in which people are injured are very different on farms. The common causes of accidents in agriculture and forestry are the loss of control of machine, means of transport or handling equipment, hand-held tool, and object or animal, followed by slipping, stumbling and falling, breakage, bursting, splitting, slipping, fall, and collapse of material agent. In the literature, a number of studies of general (machine- and animal-related accidents) and specific (machine-related accidents) agricultural and forestry accident situations can be found that refer to different databases. From the database Data of the Austrian Workers Compensation Board (AUVA) about occupational accidents with different agricultural machinery over the period 2008-2010 in Austria, main characteristics of the accident, the victim, and the employer as well as variables on causes and circumstances by frequency and contexts of parameters were statistically analyzed by employing the chi-square test and odds ratio. The aim of the study was to determine the information content and quality of the European Statistics on Accidents at Work (ESAW) variables to evaluate safety gaps and risks as well as the accidental man-machine interaction.

Travelogue--a newcomer encounters statistics and the computer.

PubMed

Bruce, Peter

2011-11-01

Computer-intensive methods have revolutionized statistics, giving rise to new areas of analysis and expertise in predictive analytics, image processing, pattern recognition, machine learning, genomic analysis, and more. Interest naturally centers on the new capabilities the computer allows the analyst to bring to the table. This article, instead, focuses on the account of how computer-based resampling methods, with their relative simplicity and transparency, enticed one individual, untutored in statistics or mathematics, on a long journey into learning statistics, then teaching it, then starting an education institution.

SU-F-T-457: A Filmless Method for Measurement of Couch Translation Per Gantry Rotation and Couch Speed for Tomotherapy Using ArcCheck

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, B; Wong, R; Geng, H

Purpose: To develop a filmless methodology based on an ArcCheck for QA measurement of the couch translation per gantry rotation and couch speed of a Tomotherapy unit. Methods: Two test plans recommended by TG148 were chosen for this study. A helical plan with 1 cm field size opened the leaves for 180 degrees at the 2nd, 7th and 12th of total 13 rotations and was used to verify if the couch travelled the expected distance per gantry rotation. The other test plan was a static plan with the gantry at 0°, 1cm field width and constant couch movement speed ofmore » 0.5mm/s. It was used for couch speed measurement. The ArcCheck was isocentrically set up and recorded movie files which took a snapshot exposure every 50ms. Due to the spiral pattern of diodes distribution, when one of the diodes of the ArcCheck located at the beam center, the dose profile as measured by the row of diodes which surrounded the center diode should have a symmetrical pattern. We divided the profile into left half A and right half B. Then a shape parameter was defined as S=Σ|(A−B)|/Σ(A+B). By searching the local minimum of S parameter, the beam center at different time could be located. The machine trajectory log data were also collected and analyzed for comparison. Results: The mean value of measured couch translation and couch speed by ArcCheck had less than 0.05% deviation from the planned values. For couch speed measurement, our result showed a mean value of 0.5002 with an uncertainty ±0.0031, which agreed very well with both the plan setting of 0.5 mm/s and the machine log data of 0.5005 mm/s. Conclusion: Couch translation measured using ArcCheck is accurate and comparable to the film-based measurement. This filmless method also provides a convenient and independent way for measuring couch speed.« less

Pre-use anesthesia machine check; certified anesthesia technician based quality improvement audit.

PubMed

Al Suhaibani, Mazen; Al Malki, Assaf; Al Dosary, Saad; Al Barmawi, Hanan; Pogoku, Mahdhav

2014-01-01

Quality assurance of providing a work ready machine in multiple theatre operating rooms in a tertiary modern medical center in Riyadh. The aim of the following study is to keep high quality environment for workers and patients in surgical operating rooms. Technicians based audit by using key performance indicators to assure inspection, passing test of machine worthiness for use daily and in between cases and in case of unexpected failure to provide quick replacement by ready to use another anesthetic machine. The anesthetic machines in all operating rooms are daily and continuously inspected and passed as ready by technicians and verified by anesthesiologist consultant or assistant consultant. The daily records of each machines were collected then inspected for data analysis by quality improvement committee department for descriptive analysis and report the degree of staff compliance to daily inspection as "met" items. Replaced machine during use and overall compliance. Distractive statistic using Microsoft Excel 2003 tables and graphs of sums and percentages of item studied in this audit. Audit obtained highest compliance percentage and low rate of replacement of machine which indicate unexpected machine state of use and quick machine switch. The authors are able to conclude that following regular inspection and running self-check recommended by the manufacturers can contribute to abort any possibility of hazard of anesthesia machine failure during operation. Furthermore in case of unexpected reason to replace the anesthesia machine in quick maneuver contributes to high assured operative utilization of man machine inter-phase in modern surgical operating rooms.

Improved analyses using function datasets and statistical modeling

Treesearch

John S. Hogland; Nathaniel M. Anderson

2014-01-01

Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space and have limited statistical functionality and machine learning algorithms. To address this issue, we developed a new modeling framework using C# and ArcObjects and integrated that framework...

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

PubMed

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

A multi-frame soft x-ray pinhole imaging diagnostic for single-shot applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wurden, G. A.; Coffey, S. K.

2012-10-15

For high energy density magnetized target fusion experiments at the Air Force Research Laboratory FRCHX machine, obtaining multi-frame soft x-ray images of the field reversed configuration (FRC) plasma as it is being compressed will provide useful dynamics and symmetry information. However, vacuum hardware will be destroyed during the implosion. We have designed a simple in-vacuum pinhole nosecone attachment, fitting onto a Conflat window, coated with 3.2 mg/cm{sup 2} of P-47 phosphor, and covered with a thin 50-nm aluminum reflective overcoat, lens-coupled to a multi-frame Hadland Ultra intensified digital camera. We compare visible and soft x-ray axial images of translating ({approx}200more » eV) plasmas in the FRX-L and FRCHX machines in Los Alamos and Albuquerque.« less

HGML: a hypertext guideline markup language.

PubMed Central

Hagerty, C. G.; Pickens, D.; Kulikowski, C.; Sonnenberg, F.

2000-01-01

Existing text-based clinical practice guidelines can be difficult to put into practice. While a growing number of such documents have gained acceptance in the medical community and contain a wealth of valuable information, the time required to digest them is substantial. Yet the expressive power, subtlety and flexibility of natural language pose challenges when designing computer tools that will help in their application. At the same time, formal computer languages typically lack such expressiveness and the effort required to translate existing documents into these languages may be costly. We propose a method based on the mark-up concept for converting text-based clinical guidelines into a machine-operable form. This allows existing guidelines to be manipulated by machine, and viewed in different formats at various levels of detail according to the needs of the practitioner, while preserving their originally published form. PMID:11079898

The NATO thesaurus project

NASA Technical Reports Server (NTRS)

Krueger, Jonathan

1990-01-01

This document describes functionality to be developed to support the NATO technical thesaurus. Described are the specificity of the thesaurus structure and function; the distinction between the thesaurus information and its representation in a given online, machine readable, or printed form; the enhancement of the thesaurus with the assignment of COSATI codes (fields and groups) to posting terms, the integration of DTIC DRIT and NASA thesauri related terminology, translation of posting terms into French; and the provision of a basis for system design.

(M-CAT) Minor Caliber Weapons Trainer MK-19, 40mm Machine Gun

DTIC Science & Technology

1989-07-24

microprocessor chip with an Intel 387 math coprocessor. The Nova 620 is a digital time base corrector. It is used to time base correct the video data...the circuit. After filtering, the horizontal and vertical position signals are converted to digital values by the Data Translation (DTX-311) analog...from the computer. Each frame of the video disk is individually digitized as to target size, location, and range. The guns azimuth and elevation are

IFLA General Conference, 1991. Division of Special Libraries Services: Section of Social Science Libraries; Section of Geography and Map Libraries; Section of Biological and Medical Sciences Libraries; Section of Art Libraries. Booklet 2.

ERIC Educational Resources Information Center

International Federation of Library Associations and Institutions, The Hague (Netherlands).

The 10 papers in this booklet were presented at meetings of 4 sections within the Division of Special Libraries: (1) "Information Ensurance of a Scientist" (V. Matveyev, USSR); (2) "Linguistic Barriers and Machine Translation" (Stanley Kalkus, USA); (3) "Maps for Planning" (V. I. Zhukov and L. G. Rudenko, USSR); (4)…

Translations on Eastern Europe Political, Sociological, and Military Affairs No. 1467

DTIC Science & Technology

1977-11-01

and still does. *A group of a few teenagers, not a large group, has been working there for many years. The group was engaged in photography ...inclucating the Pupils’ Code? It may be that errors existed back at the very assumption of this didactic affair, which, it is true, is not producing...textile industry; with Yugoslavia--in the production of locomotive types, of lorries, of machine- tools , of medium tonnage cargoes. The Romanian-West

Research in speech communication.

PubMed

Flanagan, J

1995-10-24

Advances in digital speech processing are now supporting application and deployment of a variety of speech technologies for human/machine communication. In fact, new businesses are rapidly forming about these technologies. But these capabilities are of little use unless society can afford them. Happily, explosive advances in microelectronics over the past two decades have assured affordable access to this sophistication as well as to the underlying computing technology. The research challenges in speech processing remain in the traditionally identified areas of recognition, synthesis, and coding. These three areas have typically been addressed individually, often with significant isolation among the efforts. But they are all facets of the same fundamental issue--how to represent and quantify the information in the speech signal. This implies deeper understanding of the physics of speech production, the constraints that the conventions of language impose, and the mechanism for information processing in the auditory system. In ongoing research, therefore, we seek more accurate models of speech generation, better computational formulations of language, and realistic perceptual guides for speech processing--along with ways to coalesce the fundamental issues of recognition, synthesis, and coding. Successful solution will yield the long-sought dictation machine, high-quality synthesis from text, and the ultimate in low bit-rate transmission of speech. It will also open the door to language-translating telephony, where the synthetic foreign translation can be in the voice of the originating talker.

Affine Transform to Reform Pixel Coordinates of EOG Signals for Controlling Robot Manipulators Using Gaze Motions

PubMed Central

Rusydi, Muhammad Ilhamdi; Sasaki, Minoru; Ito, Satoshi

2014-01-01

Biosignals will play an important role in building communication between machines and humans. One of the types of biosignals that is widely used in neuroscience are electrooculography (EOG) signals. An EOG has a linear relationship with eye movement displacement. Experiments were performed to construct a gaze motion tracking method indicated by robot manipulator movements. Three operators looked at 24 target points displayed on a monitor that was 40 cm in front of them. Two channels (Ch1 and Ch2) produced EOG signals for every single eye movement. These signals were converted to pixel units by using the linear relationship between EOG signals and gaze motion distances. The conversion outcomes were actual pixel locations. An affine transform method is proposed to determine the shift of actual pixels to target pixels. This method consisted of sequences of five geometry processes, which are translation-1, rotation, translation-2, shear and dilatation. The accuracy was approximately 0.86° ± 0.67° in the horizontal direction and 0.54° ± 0.34° in the vertical. This system successfully tracked the gaze motions not only in direction, but also in distance. Using this system, three operators could operate a robot manipulator to point at some targets. This result shows that the method is reliable in building communication between humans and machines using EOGs. PMID:24919013

FRX-L Research Status and Plans

NASA Astrophysics Data System (ADS)

Wurden, G. A.; Intrator, T. P.; Taccetti, J. M.; Furno, I. G.; Hsu, S. C.; Zhang, S. Y.; Degnan, J. H.; Grabowski, C.; Ruden, E. L.

2003-10-01

Our research plans for FRX-L, the field reversed configuration plasma injector at LANL for magnetized target fusion (MTF), have been planned for the next 4-year period. FRX-L has been successfully operating now for the last two years, although construction for both the machine and diagnostic sets is ongoing. Efforts in FY04 begin with continued improvements in the basic high density FRC parameters, through operation at increased magnetic fields and with the addition of a more effective main bank crowbar to reduce parasitic ringing in the high current main coil circuit. Translation experiments into a "fake" metal liner, perforated with diagnostic access ports, will start after designing and constructing the translation section. Another bank of capacitors will be added to power the additional guide and mirror coils. After demonstrating trapping of the plasma in the aluminum liner, and diagnosing sufficient plasma parameters (density, temperature, lifetime, purity), we will begin preparations for the integrated plasma/liner compression experiment at the Air Force Research Laboratory Shiva-Star machine in FY05. Construction of the new hardware will continue during FY06, and the first fusion-relevant demonstration of compression of plasma by an imploding metal liner is planned for FY07. Our MTF plans also include new initiatives with U of Washington, U of Wisconsin, and the University of New Mexico, in addition to ongoing theory ties to LLNL and GA.

47 CFR 54.5 - Terms and definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., address translation, protocol conversion, billing management, introductory information content, and... 1990s and identifiable from the most recent Metropolitan Statistical Area (MSA) list released by OMB, or... support mechanism, a “rural area” is an area that is entirely outside of a Core Based Statistical Area; is...

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Design features and results from fatigue reliability research machines.

NASA Technical Reports Server (NTRS)

Lalli, V. R.; Kececioglu, D.; Mcconnell, J. B.

1971-01-01

The design, fabrication, development, operation, calibration and results from reversed bending combined with steady torque fatigue research machines are presented. Fifteen-centimeter long, notched, SAE 4340 steel specimens are subjected to various combinations of these stresses and cycled to failure. Failure occurs when the crack in the notch passes through the specimen automatically shutting down the test machine. These cycles-to-failure data are statistically analyzed to develop a probabilistic S-N diagram. These diagrams have many uses; a rotating component design example given in the literature shows that minimum size and weight for a specified number of cycles and reliability can be calculated using these diagrams.

Bridging from Replication to Translation with a Thermal, Autonomous Replicator Made from Transfer RNA

NASA Astrophysics Data System (ADS)

Braun, Dieter; Möller, Friederike M.; Krammer, Hubert

2013-03-01

Central to the understanding of living systems is the interplay between DNA/RNA and proteins. Known as Eigen paradox, proteins require genetic information while proteins are needed for the replication of genes. RNA world scenarios focus on a base by base replication disconnected from translation. Here we used strategies from DNA machines to demonstrate a tight connection between a basic replication mechanism and translation. A pool of hairpin molecules replicate a two-letter code. The replication is thermally driven: the energy and negative entropy to drive replication is initially stored in metastable hairpins by kinetic cooling. Both are released by a highly specific and exponential replication reaction that is solely implemented by base hybridization. The duplication time is 30s. The reaction is monitored by fluorescence and described by a detailed kinetic model. The RNA hairpins usetransfer RNA sequences and the replication is driven by the simple disequilibrium setting of a thermal gradient The experiments propose a physical rather than a chemical scenario for the autonomous replication of protein encoding information. Supported by the NanoSystems Initiative Munich and ERC.

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

PubMed

Kim, Yong-Ku; Na, Kyoung-Sae

2018-01-03

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluation of Cepstrum Algorithm with Impact Seeded Fault Data of Helicopter Oil Cooler Fan Bearings and Machine Fault Simulator Data

DTIC Science & Technology

2013-02-01

of a bearing must be put into practice. There are many potential methods, the most traditional being the use of statistical time-domain features...accelerate degradation to test multiples bearings to gain statistical relevance and extrapolate results to scale for field conditions. Temperature...as time statistics , frequency estimation to improve the fault frequency detection. For future investigations, one can further explore the

The influence of maintenance quality of hemodialysis machines on hemodialysis efficiency.

PubMed

Azar, Ahmad Taher

2009-01-01

Several studies suggest that there is a correlation between dose of dialysis and machine maintenance. However, in spite of the current practice, there are conflicting reports regarding the relationship between dose of dialysis or patient outcome, and machine maintenance. In order to evaluate the impact of hemodialysis machine maintenance on dialysis adequacy Kt/V and session performance, data were processed on 134 patients on 3-times-per-week dialysis regimens by dividing the patients into four groups and also dividing the hemodialysis machines into four groups according to their year of installation. The equilibrated dialysis dose eq Kt/V, urea reduction ratio (URR) and the overall equipment effectiveness (OEE) were calculated in each group to show the effect hemodialysis machine efficiency on the overall session performance. The average working time per machine per month was 270 hours. The cumulative number of hours according to the year of installation was: 26,122 hours for machines installed in 1998; 21,596 hours for machines installed in 1999, 8362 hours for those installed in 2003 and 2486 hours for those installed in 2005. The mean time between failures (MTBF) was 1.8, 2.1, 4.2 and 6 months between failures for machines installed in 1999, 1998, 2003 and 2005, respectively. Statistical analysis demonstrated that the dialysis dose eq Kt/V and URR were increased as the overall equipment effectiveness (OEE) increases with regular maintenance procedures. Maintenance has become one of the most expedient approaches to guarantee high machine dependability. The efficiency of dialysis machine is relevant in assuring a proper dialysis adequacy.

Assessing a Novel Method to Reduce Anesthesia Machine Contamination: A Prospective, Observational Trial.

PubMed

Biddle, Chuck J; George-Gay, Beverly; Prasanna, Praveen; Hill, Emily M; Davis, Thomas C; Verhulst, Brad

2018-01-01

Anesthesia machines are known reservoirs of bacterial species, potentially contributing to healthcare associated infections (HAIs). An inexpensive, disposable, nonpermeable, transparent anesthesia machine wrap (AMW) may reduce microbial contamination of the anesthesia machine. This study quantified the density and diversity of bacterial species found on anesthesia machines after terminal cleaning and between cases during actual anesthesia care to assess the impact of the AMW. We hypothesized reduced bioburden with the use of the AMW. In a prospective, experimental research design, the AMW was used in 11 surgical cases (intervention group) and not used in 11 control surgical cases. Cases were consecutively assigned to general surgical operating rooms. Seven frequently touched and difficult to disinfect "hot spots" were cultured on each machine preceding and following each case. The density and diversity of cultured colony forming units (CFUs) between the covered and uncovered machines were compared using Wilcoxon signed-rank test and Student's t -tests. There was a statistically significant reduction in CFU density and diversity when the AMW was employed. The protective effect of the AMW during regular anesthetic care provides a reliable and low-cost method to minimize the transmission of pathogens across patients and potentially reduces HAIs.

Data Distribution System (DDS) and Solar Dynamic Observatory Ground Station (SDOGS) Integration Manager

NASA Technical Reports Server (NTRS)

Pham, Kim; Bialas, Thomas

2012-01-01

The DDS SDOGS Integration Manager (DSIM) provides translation between native control and status formats for systems within DDS and SDOGS, and the ASIST (Advanced Spacecraft Integration and System Test) control environment in the SDO MOC (Solar Dynamics Observatory Mission Operations Center). This system was created in response for a need to centralize remote monitor and control of SDO Ground Station equipments using ASIST control environment in SDO MOC, and to have configurable table definition for equipment. It provides translation of status and monitoring information from the native systems into ASIST-readable format to display on pages in the MOC. The manager is lightweight, user friendly, and efficient. It allows data trending, correlation, and storing. It allows using ASIST as common interface for remote monitor and control of heterogeneous equipments. It also provides failover capability to back up machines.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keller, J; Hardin, M; Giaddui, T

Purpose: To test whether unified vendor specified beam conformance for matched machines implies volumetric modulated arc radiotherapy(VMAT) delivery consistency. Methods: Twenty-two identical patient QA plans, eleven 6MV and eleven 15MV, were delivered to the Delta{sup 4}(Scandidos, Uppsala, Sweden) on two Varian TrueBEAM matched machines. Sixteen patient QA plans, nine 6 MV and seven 10 MV, were delivered to Delta{sup 4} on two Elekta Agility matched machines. The percent dose deviation(%DDev), distance-to-agreement(DTA), and the gamma analysis(γ) were collected for all plans and the differences in measurements were tabulated between matched machines. A paired t-test analysis of the data with an alphamore » of 0.05 determines statistical significance. Power(P) was calculated to detect a difference of 5%; all data except Elekta %DDev sets were strong with above a 0.85 power. Results: The average differences for Varian machines (%DDev, DTA, and γ) are 6.4%, 1.6% and 2.7% for 6MV, respectively, and 8.0%, 0.6%, and 2.5% for 15MV. The average differences for matched Elekta machines (%DDev, DTA, and γ) are 10.2%, 0.6% and 0.9% for 6 MV, respectively, and 7.0%, 1.9%, and 2.8% for 10MV.A paired t-test shows for Varian the %DDev difference is significant for 6MV and 15MV(p-value6MV=0.019, P6MV=0.96; p-value15MV=0.0003, P15MV=0.86). Differences in DTA are insignificant for both 6MV and 15MV(p-value6MV=0.063, P6MV=1; p-value15MV=0.907, P15MV=1). Varian differences in gamma are significant for both energies(p-value6MV=0.025, P6MV=0.99; p-value15MV=0.013, P15MV=1). A paired t-test shows for Elekta the difference in %DDev is significant for 6MV but not 10MV(p-value6MV=0.00065, P6MV=0.68; p-value10MV=0.262, P10MV=0.39). Differences in DTA are statistically insignificant(p-value6MV=0.803, P6MV = 1; p-value10MV=0.269, P10MV=1). Elekta differences in gamma are significant for 10MV only(p-value6MV=0.094, P6MV=1; p-value10MV=0.011, P10MV=1). Conclusion: These results show vendor specified beam conformance across machines does not ensure equivalent patient specific QA pass rates. Gamma differences are statistically significant in three of the four comparisons for two pairs of vendor matched machines.« less

Polymer models of interphase chromosomes

PubMed Central

Vasquez, Paula A; Bloom, Kerry

2014-01-01

Clear organizational patterns on the genome have emerged from the statistics of population studies of fixed cells. However, how these results translate into the dynamics of individual living cells remains unexplored. We use statistical mechanics models derived from polymer physics to inquire into the effects that chromosome properties and dynamics have in the temporal and spatial behavior of the genome. Overall, changes in the properties of individual chains affect the behavior of all other chains in the domain. We explore two modifications of chain behavior: single chain motion and chain-chain interactions. We show that there is not a direct relation between these effects, as increase in motion, doesn’t necessarily translate into an increase on chain interaction. PMID:25482191

Effect of overglazed and polished surface finishes on the compressive fracture strength of machinable ceramic materials.

PubMed

Asai, Tetsuya; Kazama, Ryunosuke; Fukushima, Masayoshi; Okiji, Takashi

2010-11-01

Controversy prevails over the effect of overglazing on the fracture strength of ceramic materials. Therefore, the effects of different surface finishes on the compressive fracture strength of machinable ceramic materials were investigated in this study. Plates prepared from four commercial brands of ceramic materials were either surface-polished or overglazed (n=10 per ceramic material for each surface finish), and bonded to flat surfaces of human dentin using a resin cement. Loads at failure were determined and statistically analyzed using two-way ANOVA and Bonferroni test. Although no statistical differences in load value were detected between polished and overglazed groups (p>0.05), the fracture load of Vita Mark II was significantly lower than those of ProCAD and IPS Empress CAD, whereas that of IPS e.max CAD was significantly higher than the latter two ceramic materials (p<0.05). It was concluded that overglazed and polished surfaces produced similar compressive fracture strengths irrespective of the machinable ceramic material tested, and that fracture strength was material-dependent.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Reducing lumber thickness variation using real-time statistical process control

Treesearch

Thomas M. Young; Brian H. Bond; Jan Wiedenbeck

2002-01-01

A technology feasibility study for reducing lumber thickness variation was conducted from April 2001 until March 2002 at two sawmills located in the southern U.S. A real-time statistical process control (SPC) system was developed that featured Wonderware human machine interface technology (HMI) with distributed real-time control charts for all sawing centers and...

Envelope statistics of self-motion signals experienced by human subjects during everyday activities: Implications for vestibular processing

PubMed Central

Carriot, Jérome; Jamali, Mohsen; Cullen, Kathleen E.

2017-01-01

There is accumulating evidence that the brain’s neural coding strategies are constrained by natural stimulus statistics. Here we investigated the statistics of the time varying envelope (i.e. a second-order stimulus attribute that is related to variance) of rotational and translational self-motion signals experienced by human subjects during everyday activities. We found that envelopes can reach large values across all six motion dimensions (~450 deg/s for rotations and ~4 G for translations). Unlike results obtained in other sensory modalities, the spectral power of envelope signals decreased slowly for low (< 2 Hz) and more sharply for high (>2 Hz) temporal frequencies and thus was not well-fit by a power law. We next compared the spectral properties of envelope signals resulting from active and passive self-motion, as well as those resulting from signals obtained when the subject is absent (i.e. external stimuli). Our data suggest that different mechanisms underlie deviation from scale invariance in rotational and translational self-motion envelopes. Specifically, active self-motion and filtering by the human body cause deviation from scale invariance primarily for translational and rotational envelope signals, respectively. Finally, we used well-established models in order to predict the responses of peripheral vestibular afferents to natural envelope stimuli. We found that irregular afferents responded more strongly to envelopes than their regular counterparts. Our findings have important consequences for understanding the coding strategies used by the vestibular system to process natural second-order self-motion signals. PMID:28575032

Statistical Analysis of NAS Parallel Benchmarks and LINPACK Results

NASA Technical Reports Server (NTRS)

Meuer, Hans-Werner; Simon, Horst D.; Strohmeier, Erich; Lasinski, T. A. (Technical Monitor)

1994-01-01

In the last three years extensive performance data have been reported for parallel machines both based on the NAS Parallel Benchmarks, and on LINPACK. In this study we have used the reported benchmark results and performed a number of statistical experiments using factor, cluster, and regression analyses. In addition to the performance results of LINPACK and the eight NAS parallel benchmarks, we have also included peak performance of the machine, and the LINPACK n and n(sub 1/2) values. Some of the results and observations can be summarized as follows: 1) All benchmarks are strongly correlated with peak performance. 2) LINPACK and EP have each a unique signature. 3) The remaining NPB can grouped into three groups as follows: (CG and IS), (LU and SP), and (MG, FT, and BT). Hence three (or four with EP) benchmarks are sufficient to characterize the overall NPB performance. Our poster presentation will follow a standard poster format, and will present the data of our statistical analysis in detail.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239

Translation of shuttle operations simulation from GPSS 2 to GPSS 1100

NASA Technical Reports Server (NTRS)

Marshall, A. J.

1972-01-01

A method has been developed which enables a programmer to convert the General Purpose Systems Simulator (GPSS) 2 simulation language into the GPSS 1100 language. To accomplish the conversion, a translator deck is used in addition to hand changes made by the analyst after translation. The conversion of a particular GPSS 2 program used at the Marshall Space Flight Center (MSFC) is reported and major changes required for compatibility of the two languages are summerized. Validation of the GPSS 1100 model was completed by comparing the results of the GPSS 2 statistics to the converted 1100 model.

Study on the Optimization and Process Modeling of the Rotary Ultrasonic Machining of Zerodur Glass-Ceramic

NASA Astrophysics Data System (ADS)

Pitts, James Daniel

Rotary ultrasonic machining (RUM), a hybrid process combining ultrasonic machining and diamond grinding, was created to increase material removal rates for the fabrication of hard and brittle workpieces. The objective of this research was to experimentally derive empirical equations for the prediction of multiple machined surface roughness parameters for helically pocketed rotary ultrasonic machined Zerodur glass-ceramic workpieces by means of a systematic statistical experimental approach. A Taguchi parametric screening design of experiments was employed to systematically determine the RUM process parameters with the largest effect on mean surface roughness. Next empirically determined equations for the seven common surface quality metrics were developed via Box-Behnken surface response experimental trials. Validation trials were conducted resulting in predicted and experimental surface roughness in varying levels of agreement. The reductions in cutting force and tool wear associated with RUM, reported by previous researchers, was experimentally verified to also extended to helical pocketing of Zerodur glass-ceramic.

AstroML: Python-powered Machine Learning for Astronomy

NASA Astrophysics Data System (ADS)

Vander Plas, Jake; Connolly, A. J.; Ivezic, Z.

2014-01-01

As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.org) provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in Python. The astroML module contains a host of general-purpose data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines being used for analysis of real astronomical datasets, while the associated textbook provides a curriculum resource for graduate-level courses focusing on practical statistics, machine learning, and data mining approaches within Astronomical research. This poster will highlight several of the more powerful and unique examples of analysis performed with astroML, all of which can be reproduced in their entirety on any computer with the proper packages installed.

Human anatomy nomenclature rules for the computer age.

PubMed

Neumann, Paul E; Baud, Robert; Sprumont, Pierre

2017-04-01

Information systems are increasing in importance in biomedical sciences and medical practice. The nomenclature rules of human anatomy were reviewed for adequacy with respect to modern needs. New rules are proposed here to ensure that each Latin term is uniquely associated with an anatomical entity, as short and simple as possible, and machine-interpretable. Observance of these recommendations will also benefit students and translators of the Latin terms into other languages. Clin. Anat. 30:300-302, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Integrated Systems of Automatic Radio Equipment,

DTIC Science & Technology

1982-05-20

Publishing House "Sovetskoye Radio", Moscow, 1968, pp. 1 -232 Country of origin: USSR This document is a machine translation. Requester: USAMICOM...5 Chapter 1 , Fundamental Characteristics of the Meters of Integrated Systems .. .................. ........... 12 Chapter 2...ts 3 3 3 7 Z, z 91 4 Ch, ch Ku I, i LU w s11 Sh, sh a 0 Y, y W . NJ Shch, shch X x K, k b It f A7 , L, 1 --- - - Y, y . MM M, m = I & . H H N, n 3 3

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

Computational Linguistics in Military Operations

DTIC Science & Technology

2010-01-01

information dominance at the operational and tactical level of war in future warfare. Discussion: Mastering culture and language in a foreign country is decisive to understand the operational environment. In addition, the ability to understand and speak a foreign language is a prerequisite to achieve truly comprehension of an unfamiliar culture. Lasting operations in Afghanistan and Iraq and the necessity to breach the language gap lead to progress in the field of Machine Translation and the development of technical solutions to close the gap in the past decade. This paper

Translations from Red Flag No. 9, 3 September 1978

DTIC Science & Technology

1978-11-06

of the Death of Great Leader and Teacher Chairman Mao (pp 18-23) (Hsu Shih -yu) • 23 Deeply Cherish the Memory of Comrade Lo Jui-ching, an...well. At the First National Conference on Learning From Tachai in Agriculture, Comrade Hua Kuo -feng called on all departments of the party Central^Com...in acting like "Chin Shih Huang." As to how to reorganize the farm machinery industry, however, the First Ministry of Machine Building should make

Genome-Wide Analysis of Translational Control in Tuberous Sclerosis Complex

DTIC Science & Technology

2012-07-01

particular non-AUG codons in the 5’UTR. However, these data was “noisy” and required a machine-learning algorithm to identify TIS codons. We develop...To investigate how nutrient signaling affects the folding of nascent chains, we used firefly luciferase (Luc) as a reporter because of its high...folding as the structural basis for the rapid de novo folding of firefly luciferase. Nat Struct Biol 6(7):697-705. 12. Gupta R, Kasturi P, Bracher A

NASA STI Program Coordinating Council Eleventh Meeting: NASA STI Modernization Plan

NASA Technical Reports Server (NTRS)

1993-01-01

The theme of this NASA Scientific and Technical Information Program Coordinating Council Meeting was the modernization of the STI Program. Topics covered included the activities of the Engineering Review Board in the creation of the Infrastructure Upgrade Plan, the progress of the RECON Replacement Project, the use and status of Electronic SCAN (Selected Current Aerospace Notices), the Machine Translation Project, multimedia, electronic document interchange, the NASA Access Mechanism, computer network upgrades, and standards in the architectural effort.

Balanced-Rotating-Spray Tank-And-Pipe-Cleaning System

NASA Technical Reports Server (NTRS)

Thaxton, Eric A.; Caimi, Raoul E. B.

1995-01-01

Spray head translates and rotates to clean entire inner surface of tank or pipe. Cleansing effected by three laterally balanced gas/liquid jets from spray head that rotates about longitudinal axis. Uses much less liquid. Cleaning process in system relies on mechanical action of jets instead of contaminant dissolution. Eliminates very difficult machining needed to make multiple converging/diverging nozzles within one spray head. Makes nozzle much smaller. Basic two-phase-flow, supersonic-nozzle design applied to other spray systems for interior or exterior cleaning.

Application of the Teager-Kaiser energy operator in bearing fault diagnosis.

PubMed

Henríquez Rodríguez, Patricia; Alonso, Jesús B; Ferrer, Miguel A; Travieso, Carlos M

2013-03-01

Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Stroke dynamics and frequency of 3 phacoemulsification machines.

PubMed

Tognetto, Daniele; Cecchini, Paolo; Leon, Pia; Di Nicola, Marta; Ravalico, Giuseppe

2012-02-01

To measure the working frequency and the stroke dynamics of the phaco tip of 3 phacoemulsification machines. University Eye Clinic of Trieste, Italy. Experimental study. A video wet fixture was assembled to measure the working frequency using a micro camera and a micropulsed strobe-light system. A different video wet fixture was created to measure tip displacement as vectorial movement at different phaco powers using a microscopic video apparatus. The working frequency of the Infiniti Ozil machine was 43.0 kHz in longitudinal mode and 31.6 kHz in torsional mode. The frequency of the Whitestar Signature machine was 29.0 kHz in longitudinal mode and 38.0 kHz with the Ellips FX handpiece. The Stellaris machine had a frequency of 28.8 kHz. The longitudinal stroke of the 3 machines at different phaco powers was statistically significantly different. The Stellaris machine had the highest stroke extent (139 μm). The lateral movement of the Infiniti Ozil and Whitestar Signature machines differed significantly. No movement on the y-axis was observed for the Infiniti Ozil machine in torsional mode. The elliptical path of the Ellips FX handpiece had different x and y components at different phaco powers. The 3 phaco machines performed differently in terms of working frequency and stroke dynamics. The knowledge of the peculiar lateral and elliptical path strokes of Infiniti and Whitestar Signature machines may allow the surgeon to fully use these features for lens removal. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Optical Neasurements Of Diamond-Turned Surfaces

NASA Astrophysics Data System (ADS)

Politch, Jacob

1989-07-01

We describe here a system for measuring very accurately diamond-turned surfaces. This system is based on heterodyne interfercmetry and measures surface height variations with an accuracy of 4A, and the spatial resolution is 1 micrometer. Fran the measured data we have calculated the statistical properties of the surface - enabling us to identify the spatial frequencies caused by the vibrations of the diamond - turning machine and the measuring machine as well as the frequency of the grid.

Biostatistical and medical statistics graduate education

PubMed Central

2014-01-01

The development of graduate education in biostatistics and medical statistics is discussed in the context of training within a medical center setting. The need for medical researchers to employ a wide variety of statistical designs in clinical, genetic, basic science and translational settings justifies the ongoing integration of biostatistical training into medical center educational settings and informs its content. The integration of large data issues are a challenge. PMID:24472088

Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

NASA Astrophysics Data System (ADS)

Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Li, Yuanyao; Catani, Filippo; Pourghasemi, Hamid Reza

2018-03-01

Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

Visualization of anthropometric measures of workers in computer 3D modeling of work place.

PubMed

Mijović, B; Ujević, D; Baksa, S

2001-12-01

In this work, 3D visualization of a work place by means of a computer-made 3D-machine model and computer animation of a worker have been performed. By visualization of 3D characters in inverse kinematic and dynamic relation with the operating part of a machine, the biomechanic characteristics of worker's body have been determined. The dimensions of a machine have been determined by an inspection of technical documentation as well as by direct measurements and recordings of the machine by camera. On the basis of measured body height of workers all relevant anthropometric measures have been determined by a computer program developed by the authors. By knowing the anthropometric measures, the vision fields and the scope zones while forming work places, exact postures of workers while performing technological procedures were determined. The minimal and maximal rotation angles and the translation of upper and lower arm which are basis for the analysis of worker burdening were analyzed. The dimensions of the seized space of a body are obtained by computer anthropometric analysis of movement, e.g. range of arms, position of legs, head, back. The influence of forming of a work place on correct postures of workers during work has been reconsidered and thus the consumption of energy and fatigue can be reduced to a minimum.

Harnessing Big Data for Systems Pharmacology

PubMed Central

Xie, Lei; Draizen, Eli J.; Bourne, Philip E.

2017-01-01

Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable. PMID:27814027

Harnessing Big Data for Systems Pharmacology.

PubMed

Xie, Lei; Draizen, Eli J; Bourne, Philip E

2017-01-06

Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

DREAMTools: a Python package for scoring collaborative challenges

PubMed Central

Cokelaer, Thomas; Bansal, Mukesh; Bare, Christopher; Bilal, Erhan; Bot, Brian M.; Chaibub Neto, Elias; Eduati, Federica; de la Fuente, Alberto; Gönen, Mehmet; Hill, Steven M.; Hoff, Bruce; Karr, Jonathan R.; Küffner, Robert; Menden, Michael P.; Meyer, Pablo; Norel, Raquel; Pratap, Abhishek; Prill, Robert J.; Weirauch, Matthew T.; Costello, James C.; Stolovitzky, Gustavo; Saez-Rodriguez, Julio

2016-01-01

DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org. Availability:  DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools. PMID:27134723

Data-driven advice for applying machine learning to bioinformatics problems

PubMed Central

Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

2017-01-01

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

Impact of Machine Virtualization on Timing Precision for Performance-critical Tasks

NASA Astrophysics Data System (ADS)

Karpov, Kirill; Fedotova, Irina; Siemens, Eduard

2017-07-01

In this paper we present a measurement study to characterize the impact of hardware virtualization on basic software timing, as well as on precise sleep operations of an operating system. We investigated how timer hardware is shared among heavily CPU-, I/O- and Network-bound tasks on a virtual machine as well as on the host machine. VMware ESXi and QEMU/KVM have been chosen as commonly used examples of hypervisor- and host-based models. Based on statistical parameters of retrieved distributions, our results provide a very good estimation of timing behavior. It is essential for real-time and performance-critical applications such as image processing or real-time control.

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

NASA Astrophysics Data System (ADS)

Govorov, Michael; Gienko, Gennady; Putrenko, Viktor

2018-05-01

In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

The prior statistics of object colors.

PubMed

Koenderink, Jan J

2010-02-01

The prior statistics of object colors is of much interest because extensive statistical investigations of reflectance spectra reveal highly non-uniform structure in color space common to several very different databases. This common structure is due to the visual system rather than to the statistics of environmental structure. Analysis involves an investigation of the proper sample space of spectral reflectance factors and of the statistical consequences of the projection of spectral reflectances on the color solid. Even in the case of reflectance statistics that are translationally invariant with respect to the wavelength dimension, the statistics of object colors is highly non-uniform. The qualitative nature of this non-uniformity is due to trichromacy.

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

A wearable computing platform for developing cloud-based machine learning models for health monitoring applications.

PubMed

Patel, Shyamal; McGinnis, Ryan S; Silva, Ikaro; DiCristofaro, Steve; Mahadevan, Nikhil; Jortberg, Elise; Franco, Jaime; Martin, Albert; Lust, Joseph; Raj, Milan; McGrane, Bryan; DePetrillo, Paolo; Aranyosi, A J; Ceruolo, Melissa; Pindado, Jesus; Ghaffari, Roozbeh

2016-08-01

Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved in the controlled settings such as the lab and clinic to unconstrained environments such as the home remains a challenge. In this paper, we present a novel wearable computing platform for unobtrusive collection of labeled datasets and a new paradigm for continuous development, deployment and evaluation of machine learning models to ensure robust model performance as we transition from the lab to home. Using this system, we train activity classification models across two studies and track changes in model performance as we go from constrained to unconstrained settings.

The Oregon State University wind studies. [economic feasibility of windpowered generators

NASA Technical Reports Server (NTRS)

Wilson, R. E.

1973-01-01

The economic feasibility of commercial use of wind generated power in selected areas of Oregon is assessed. A number of machines for generating power have been examined. These include the Savonius rotor, translators, conventional wind turbines, the circulation controlled rotor and the vertical axis winged turbine. Of these machines, the conventional wind turbine and the vertical axis winged turbine show the greatest promise on the basis of the power developed per unit of rotor blade area. Attention has been focused on the structural and fatigue analysis of rotors since the economics of rotary winged, wind generated power depends upon low cost, long lifetime rotors. Analysis of energy storage systems and tower design has also been undertaken. An economic means of energy storage has not been found to date. Tower design studies have produced cost estimates that are in general agreement with the cost of the updated Putnam 110-foot tower.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, J.P.; Bangs, A.L.; Butler, P.L.

Hetero Helix is a programming environment which simulates shared memory on a heterogeneous network of distributed-memory computers. The machines in the network may vary with respect to their native operating systems and internal representation of numbers. Hetero Helix presents a simple programming model to developers, and also considers the needs of designers, system integrators, and maintainers. The key software technology underlying Hetero Helix is the use of a compiler'' which analyzes the data structures in shared memory and automatically generates code which translates data representations from the format native to each machine into a common format, and vice versa. Themore » design of Hetero Helix was motivated in particular by the requirements of robotics applications. Hetero Helix has been used successfully in an integration effort involving 27 CPUs in a heterogeneous network and a body of software totaling roughly 100,00 lines of code. 25 refs., 6 figs.« less

Stability of a dragged viscous thread: Onset of ``stitching'' in a fluid-mechanical ``sewing machine''

NASA Astrophysics Data System (ADS)

Ribe, Neil M.; Lister, John R.; Chiu-Webster, Sunny

2006-12-01

A thin thread of viscous fluid that falls on a moving belt acts like a fluid-mechanical "sewing machine," exhibiting a rich variety of "stitch" patterns including meanders, translated coiling, slanted loops, braiding, figures-of-eight, W-patterns, side kicks, and period-doubled patterns. Using a numerical linear stability analysis, we determine the critical belt speed and oscillation frequency of the first bifurcation, at which a steady dragged viscous thread becomes unstable to transverse oscillations or "meandering." The predictions of the stability analysis agree closely with the experimental measurements of Chiu-Webster and Lister [J. Fluid Mech. 569, 89 (2006)]. Moreover, the critical belt speed and onset frequency for meandering are nearly identical to the contact-point migration speed and angular frequency, respectively, of steady coiling of a viscous thread on a stationary surface, implying a remarkable degree of dynamical similarity between the two phenomena.

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.

Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models

DTIC Science & Technology

2009-01-01

88 4 Monolingually -Derived Phrasal Paraphrase Generation for Statistical Ma- chine Translation 90 4.1...123 4.4 Spanish-English (S2E) results . . . . . . . . . . . . . . . . . . . . . . 125 4.5 Gains from using larger monolingual corpora for...96 4.2 Visual example of a phrasal distributional profile . . . . . . . . . . . . 103 4.3 Monolingual corpus-based distributional

Perception of socket alignment perturbations in amputees with transtibial prostheses.

PubMed

Boone, David A; Kobayashi, Toshiki; Chou, Teri G; Arabian, Adam K; Coleman, Kim L; Orendurff, Michael S; Zhang, Ming

2012-01-01

A person with amputation's subjective perception is the only tool available to describe fit and comfort to a prosthetist. However, few studies have investigated the effect of alignment on this perception. The aim of this article is to determine whether people with amputation could perceive the alignment perturbations of their prostheses and effectively communicate them. A randomized controlled perturbation of angular (3 and 6 degrees) and translational (5 and 10 mm) alignments in the sagittal (flexion, extension, and anterior and posterior translations) and coronal (abduction, adduction, and medial and lateral translations) planes were induced from an aligned condition in 11 subjects with transtibial prostheses. The perception was evaluated when standing (static) and immediately after walking (dynamic) using software that used a visual analog scale under each alignment condition. In the coronal plane, Friedman test demonstrated general statistical differences in static (p < 0.001) and dynamic (p < 0.001) measures of perceptions with angular perturbations. In the sagittal plane, it also demonstrated general statistical differences in late-stance dynamic measures of perceptions (p < 0.001) with angular perturbations, as well as in early-stance dynamic measures of perceptions (p < 0.05) with translational perturbations. Fisher exact test suggested that people with amputation's perceptions were good indicators for coronal angle malalignments but less reliable when defining other alignment conditions.

Effect of a Clinical and Translational Science Award institute on grant funding in a major research university.

PubMed

Kabo, Felichism W; Mashour, George A

2017-04-01

Previous studies have examined the impact of Clinical and Translational Science Awards programs on other outcomes, but not on grant seeking. The authors examined the effects on grant seeking of the Michigan Institute for Clinical & Health Research (MICHR), a Clinical and Translational Science Awards institute at the University of Michigan. We assessed over 63,000 grant proposals submitted at the University of Michigan in the years 2002-2012 using data from the university and MICHR's Tracking Metrics and Reporting System. We used a retrospective, observational study of the dynamics of grant-seeking success and award funding. Heckman selection models were run to assess MICHR's relationship with a proposal's success (selection), and subsequently the award's size (outcome). Models were run for all proposals and for clinical and translational research (CTR) proposals alone. Other covariates included proposal classification, type of grant award, academic unit, and year. MICHR had a positive and statistically significant relationship with success for both proposal types. For all grants, MICHR was associated with a 29.6% increase in award size. For CTR grants, MICHR had a statistically nonsignificant relationship with award size. MICHR's infrastructure, created to enable and enhance CTR, has also created positive spillovers for a broader spectrum of research and grant seeking.

Management of combined knee medial compartmental and patellofemoral osteoarthritis with lateral closing wedge osteotomy with anterior translation of the distal tibial fragment: Does the degree of anteriorization affect the functional outcome and posterior tibial slope?

PubMed

Sadek, Ahmed F; Osman, Mohammed K; Laklok, Mohamed A

2016-10-01

The aim of this study was to assess the effect of degree of anterior translation of the distal tibial fragment after lateral closing wedge high tibial osteotomy in patients having combined knee medial compartmental and patellofemoral osteoarthritis. A retrospective study was conducted on 64 patients who were operated on for combined knee medial compartmental and patellofemoral osteoarthritis, by lateral closing wedge high tibial osteotomy with anterior translation of the distal tibial fragment. They were divided into two groups; Group I comprising 32 patients (34 knees, mean age of 51.4±7years) whose degree of anterior translation was <1cm and Group II comprising 32 patients (33 knees, mean age of 52.2±8.3years) whose degree of anterior translation was >1.5cm. The final assessment was performed via: visual analog scale, postoperative Knee Society clinical rating system function score, active range of motion, time to union, degree of correction of mechanical axis, posterior tibial slope, and Insall-Salvati ratio. Group II patients exhibited statistically superior mean postoperative score and better return to their work than Group I (P=0.013, 0.076, respectively). Both groups showed statistically significant differences between the preoperative and postoperative evaluation parameters (P<0.001). The posterior tibial slope was decreased in both groups but with no significant difference (P=0.527). Lateral closing wedge high tibial osteotomy combined with anterior translation of the distal tibial fragment more than 1.5cm achieved significantly better postoperative functional knee score. Both groups exhibited comparatively decreased posterior tibial slope. Copyright © 2016 Elsevier B.V. All rights reserved.

Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure.

PubMed

Orlenko, Alena; Moore, Jason H; Orzechowski, Patryk; Olson, Randal S; Cairns, Junmei; Caraballo, Pedro J; Weinshilboum, Richard M; Wang, Liewei; Breitenstein, Matthew K

2018-01-01

With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated. In previous research, AutoML using high-dimensional data of varying types has been demonstrably robust, outperforming traditional approaches. However, considerations for application in clinical metabolic profiling remain to be evaluated. Particularly, regarding the robustness of AutoML to identify and adjust for common clinical confounders. In this study, we present a focused case study regarding AutoML considerations for using the Tree-Based Optimization Tool (TPOT) in metabolic profiling of exposure to metformin in a biobank cohort. First, we propose a tandem rank-accuracy measure to guide agnostic feature selection and corresponding threshold determination in clinical metabolic profiling endeavors. Second, while AutoML, using default parameters, demonstrated potential to lack sensitivity to low-effect confounding clinical covariates, we demonstrated residual training and adjustment of metabolite features as an easily applicable approach to ensure AutoML adjustment for potential confounding characteristics. Finally, we present increased homocysteine with long-term exposure to metformin as a potentially novel, non-replicated metabolite association suggested by TPOT; an association not identified in parallel clinical metabolic profiling endeavors. While warranting independent replication, our tandem rank-accuracy measure suggests homocysteine to be the metabolite feature with largest effect, and corresponding priority for further translational clinical research. Residual training and adjustment for a potential confounding effect by BMI only slightly modified the suggested association. Increased homocysteine is thought to be associated with vitamin B12 deficiency - evaluation for potential clinical relevance is suggested. While considerations for clinical metabolic profiling are recommended, including adjustment approaches for clinical confounders, AutoML presents an exciting tool to enhance clinical metabolic profiling and advance translational research endeavors.

On the suitability of the connection machine for direct particle simulation

NASA Technical Reports Server (NTRS)

Dagum, Leonard

1990-01-01

The algorithmic structure was examined of the vectorizable Stanford particle simulation (SPS) method and the structure is reformulated in data parallel form. Some of the SPS algorithms can be directly translated to data parallel, but several of the vectorizable algorithms have no direct data parallel equivalent. This requires the development of new, strictly data parallel algorithms. In particular, a new sorting algorithm is developed to identify collision candidates in the simulation and a master/slave algorithm is developed to minimize communication cost in large table look up. Validation of the method is undertaken through test calculations for thermal relaxation of a gas, shock wave profiles, and shock reflection from a stationary wall. A qualitative measure is provided of the performance of the Connection Machine for direct particle simulation. The massively parallel architecture of the Connection Machine is found quite suitable for this type of calculation. However, there are difficulties in taking full advantage of this architecture because of lack of a broad based tradition of data parallel programming. An important outcome of this work has been new data parallel algorithms specifically of use for direct particle simulation but which also expand the data parallel diction.

Force reflecting hand controller

NASA Technical Reports Server (NTRS)

Mcaffee, Douglas A. (Inventor); Snow, Edward R. (Inventor); Townsend, William T. (Inventor)

1993-01-01

A universal input device for interfacing a human operator with a slave machine such as a robot or the like includes a plurality of serially connected mechanical links extending from a base. A handgrip is connected to the mechanical links distal from the base such that a human operator may grasp the handgrip and control the position thereof relative to the base through the mechanical links. A plurality of rotary joints is arranged to connect the mechanical links together to provide at least three translational degrees of freedom and at least three rotational degrees of freedom of motion of the handgrip relative to the base. A cable and pulley assembly for each joint is connected to a corresponding motor for transmitting forces from the slave machine to the handgrip to provide kinesthetic feedback to the operator and for producing control signals that may be transmitted from the handgrip to the slave machine. The device gives excellent kinesthetic feedback, high-fidelity force/torque feedback, a kinematically simple structure, mechanically decoupled motion in all six degrees of freedom, and zero backlash. The device also has a much larger work envelope, greater stiffness and responsiveness, smaller stowage volume, and better overlap of the human operator's range of motion than previous designs.

Discomfort analysis in computerized numeric control machine operations.

PubMed

Muthukumar, Krishnamoorthy; Sankaranarayanasamy, Krishnasamy; Ganguli, Anindya Kumar

2012-06-01

The introduction of computerized numeric control (CNC) technology in manufacturing industries has revolutionized the production process, but there are some health and safety problems associated with these machines. The present study aimed to investigate the extent of postural discomfort in CNC machine operators, and the relationship of this discomfort to the display and control panel height, with a view to validate the anthropometric recommendation for the location of the display and control panel in CNC machines. The postural discomforts associated with CNC machines were studied in 122 male operators using Corlett and Bishop's body part discomfort mapping, subject information, and discomfort level at various time intervals from starting to end of a shift. This information was collected using a questionnaire. Statistical analysis was carried out using ANOVA. Neck discomfort due to the positioning of the machine displays, and shoulder and arm discomfort due to the positioning of controls were identified as common health issues in the operators of these machines. The study revealed that 45.9% of machine operators reported discomfort in the lower back, 41.8% in the neck, 22.1% in the upper-back, 53.3% in the shoulder and arm, and 21.3% of the operators reported discomfort in the leg. Discomfort increased with the progress of the day and was highest at the end of a shift; subject age had no effect on patient tendency to experience discomfort levels.

Discomfort Analysis in Computerized Numeric Control Machine Operations

PubMed Central

Sankaranarayanasamy, Krishnasamy; Ganguli, Anindya Kumar

2012-01-01

Objectives The introduction of computerized numeric control (CNC) technology in manufacturing industries has revolutionized the production process, but there are some health and safety problems associated with these machines. The present study aimed to investigate the extent of postural discomfort in CNC machine operators, and the relationship of this discomfort to the display and control panel height, with a view to validate the anthropometric recommendation for the location of the display and control panel in CNC machines. Methods The postural discomforts associated with CNC machines were studied in 122 male operators using Corlett and Bishop's body part discomfort mapping, subject information, and discomfort level at various time intervals from starting to end of a shift. This information was collected using a questionnaire. Statistical analysis was carried out using ANOVA. Results Neck discomfort due to the positioning of the machine displays, and shoulder and arm discomfort due to the positioning of controls were identified as common health issues in the operators of these machines. The study revealed that 45.9% of machine operators reported discomfort in the lower back, 41.8% in the neck, 22.1% in the upper-back, 53.3% in the shoulder and arm, and 21.3% of the operators reported discomfort in the leg. Conclusion Discomfort increased with the progress of the day and was highest at the end of a shift; subject age had no effect on patient tendency to experience discomfort levels. PMID:22993720

Health-promoting vending machines: evaluation of a pediatric hospital intervention.

PubMed

Van Hulst, Andraea; Barnett, Tracie A; Déry, Véronique; Côté, Geneviève; Colin, Christine

2013-01-01

Taking advantage of a natural experiment made possible by the placement of health-promoting vending machines (HPVMs), we evaluated the impact of the intervention on consumers' attitudes toward and practices with vending machines in a pediatric hospital. Vending machines offering healthy snacks, meals, and beverages were developed to replace four vending machines offering the usual high-energy, low-nutrition fare. A pre- and post-intervention evaluation design was used; data were collected through exit surveys and six-week follow-up telephone surveys among potential vending machine users before (n=293) and after (n=226) placement of HPVMs. Chi-2 statistics were used to compare pre- and post-intervention participants' responses. More than 90% of pre- and post-intervention participants were satisfied with their purchase. Post-intervention participants were more likely to state that nutritional content and appropriateness of portion size were elements that influenced their purchase. Overall, post-intervention participants were more likely than pre-intervention participants to perceive as healthy the options offered by the hospital vending machines. Thirty-three percent of post-intervention participants recalled two or more sources of information integrated in the HPVM concept. No differences were found between pre- and post-intervention participants' readiness to adopt healthy diets. While the HPVM project had challenges as well as strengths, vending machines offering healthy snacks are feasible in hospital settings.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.