Использование слабо формализуемых зависимостей в модели функциональных отношений

Рассматриваются вопросы формирования слабо формализуемых, так называемых, оценочных зависимостей в составе модели функциональных отношений и их использования для оценки состояния объекта и определения влияния отдельных атрибутов на обобщенную оценку объекта. Приводятся результаты использования предлагаемого подхода для анализа уровня энергосбережения региона и определения приоритетных мероприятий по развитию энергосберегающего бизнеса

Generating titles for millions of browse pages on an e-Commerce site

We present two approaches to generate titles for browse pages in five different languages, namely English, German, French, Italian and Spanish. These browse pages are structured search pages in an e-commerce domain. We first present a rule-based approach to generate these browse page titles. In addition, we also present a hybrid approach which uses a phrase-based statistical machine translation engine on top of the rule-based system to assemble the best title. For the two languages English and German we have access to a large amount of already available rule-based generated and curated titles. For these languages we present an automatic post-editing approach which learns how to post-edit the rule-based titles into curated titles

Quality Estimation for Automatically Generated Titles of eCommerce Browse Pages.

At eBay, we are automatically generating a large amount of natural language titles for eCommerce browse pages using machine translation (MT) technology. While automatic approaches can generate millions of titles very fast, they are prone to errors. We therefore develop quality estimation (QE) methods which can automatically detect titles with low quality in order to prevent them from going live. In this paper, we present different approaches: The first one is a Random Forest (RF) model that explores hand-crafted, robust features, which are a mix of established features commonly used in Machine Translation Quality Estimation (MTQE) and new features developed specifically for our task. The second model is based on Siamese Networks (SNs) which embed the metadata input sequence and the generated title in the same space and do not require hand-crafted features at all. We thoroughly evaluate and compare those approaches on in-house data. While the RF models are competitive for scenarios with smaller amounts of training data and somewhat more robust, they are clearly outperformed by the SN models when the amount of training data is larger

Autophosphorylation on S614 inhibits the activity and the transforming potential of BRAF

International audienceThe BRAF proto-oncogene serine/threonine-protein kinase, known as BRAF, belongs to the RAF kinase family. It regulates the MAPK/ERK signalling pathway affecting several cellular processes such as growth, survival, differentiation, and cellular transformation. BRAF is mutated in ~8% of all human cancers with the V600E mutation constituting ~90% of mutations. Here, we have used quantitative mass spectrometry to map and compare phosphorylation site patterns between BRAF and BRAF V600E. We identified sites that are shared as well as several quantitative differences in phosphorylation abundance. The highest difference is phosphorylation of S614 in the activation loop which is ~5fold enhanced in BRAF V600E. Mutation of S614 increases the kinase activity of both BRAF and BRAF V600E and the transforming ability of BRAF V600E. The phosphorylation of S614 is mitogen inducible and the result of autophosphorylation. These data suggest that phosphorylation at this site is inhibitory, and part of the physiological shut-down mechanism of BRAF signalling

An organelle-specific protein landscape identifies novel diseases and molecular mechanisms

Contains fulltext : 158967.pdf (publisher's version ) (Open Access)Cellular organelles provide opportunities to relate biological mechanisms to disease. Here we use affinity proteomics, genetics and cell biology to interrogate cilia: poorly understood organelles, where defects cause genetic diseases. Two hundred and seventeen tagged human ciliary proteins create a final landscape of 1,319 proteins, 4,905 interactions and 52 complexes. Reverse tagging, repetition of purifications and statistical analyses, produce a high-resolution network that reveals organelle-specific interactions and complexes not apparent in larger studies, and links vesicle transport, the cytoskeleton, signalling and ubiquitination to ciliary signalling and proteostasis. We observe sub-complexes in exocyst and intraflagellar transport complexes, which we validate biochemically, and by probing structurally predicted, disruptive, genetic variants from ciliary disease patients. The landscape suggests other genetic diseases could be ciliary including 3M syndrome. We show that 3M genes are involved in ciliogenesis, and that patient fibroblasts lack cilia. Overall, this organelle-specific targeting strategy shows considerable promise for Systems Medicine

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

Word confidence measures for machine translation

Due to continuous research which led to improved concepts and algorithms, the quality of automatically generated translation has significantly improved in recent years. However, the performance of machine translation systems is still not perfect. For human users dealing with these systems, it is desirable to obtain a reliable indication of possible errors in the system output. The same holds for applications based on machine translation technologies. They could explore the knowledge about possible mistakes.The aim of this work is to provide knowledge about when a translation generated by the system is incorrect by calculating measures of confidence for each word in this translation. This topic has hardly been investigated in machine translation before. Different ways of determining confidence measures are proposed and experimentally evaluated in this thesis. The basic concept behind all these approaches are word posterior probabilities.The main problem which has to be solved for the computation of word posterior probabilities is to define the underlying concept. There exists no intuitive definition of this concept. Possible approaches include the word posterior probability of a word based on its position in the sentence and the occurrence in any position. Several solutions to this problem are presented in this thesis. Furthermore, different approaches to the calculation of word posterior probabilities are introduced and compared. They can be divided into two categories: system-based methods which explore knowledge provided by the translation system that has generated the translations, and direct methods which are independent of the translation system. The system-based techniques make use of system output, such as word graphs or N-best lists. The direct confidence measures take other knowledge sources, such as word or phrase lexica, into account. The word posterior probabilities can directly be applied as confidence measuresas follows: For a given translation generated by a machine translation system, the posterior probabilities of all words are determined and compared to a threshold. All words whose posterior probability is above this threshold are tagged as correct and all others are tagged as incorrect. To evaluate the proposed confidence measures, the information on which words are correct is needed. In machine translation, it is not intuitively clear how to determine thecorrectness of single words. As a solution to this problem, several different ways of deriving word error measures from existing machine translation evaluation metrics are investigated. From the formulation of the posterior risk for different error measures, a theoretical foundation of the word posterior probabilities is derived.The different confidence measures explore information from various knowledge sources, such as sentence probabilities provided by the machine translationsystem and statistical word and phrase lexica. To explore the knowledge fromall these sources, a combination of several confidence measures is investigated. The suggested methods are evaluated on different translation tasks and language pairs. In order to assess the general discriminative power of the confidence measures, they are tested on output from four different machine translation systems. Three of those are state-of-the-art phrase-based systems, and the fourth is an established rule-based system. A significant improvement in terms of confidence error rate is achieved in all settings.In this work, applications of confidence measures that improve translation quality of state-of-the-art systems are investigated. These include rescoringwith confidence measures and their use in an interactive machine translation system