When less is more in neural quality estimation of machine translation. An industry case study

Quality estimation (QE) of machine translation (MT), the task of predicting the quality of an MT output without human references, is particularly suitable in dynamic translation workflows, where translations need to be assessed continuously with no specific reference provided. In this paper, we investigate sentence-level neural QE and its applicability in an industry use case. We assess six QE approaches, which we divide into two-phase and one-phase approaches, based on quality and cost. Our evaluation shows that while two-phase systems perform best in terms of the predicted QE scores, their computational costs suggest that alternatives should be considered for large-scale translation production

Coffee resistance to the main diseases : leaf rust and coffee berry disease

Sucesso considerável tem sido obtido no uso do melhoramento clássico para o controle de doenças de plantas economicamente importantes, tais como a ferrugem alaranjada das folhas e a antracnose dos frutos do cafeeiro (CBD). Há um grande consenso de que o uso de plantas geneticamente resistentes é o meio mais apropriado e eficaz em termos de custos do controle das doenças das plantas, sendo também um dos elementos chave do melhoramento da produção agrícola. Tem sido também reconhecido que um melhor conhecimento do agente patogênico e dos mecanismos de defesa das plantas permitirá o desenvolvimento de novas abordagens no sentido de aumentar a durabilidade da resistência. Após uma breve descrição de conceitos na área da resistência das plantas às doenças, nesta revisão tentou-se dar uma idéia do progresso na investigação da ferrugem alaranjada do cafeeiro e do CBD relativamente ao processo de infecção e variabilidade dos agentes patogênicos, melhoramento do cafeeiro para a resistência e mecanismos de resistência do cafeeiro

Percutaneous Transhepatic Cholangioscopic Intervention in the Management of Complete Membranous Occlusion of Bilioenteric Anastomosis: Report of Two Cases

Postoperative biliary stricture is a relatively rare but serious complication of biliary surgery. Although Rouxen-Y hepaticojejunostomy or choledochojejunostomy are well-established and fundamental therapeutic approaches, their postoperative morbidity and mortality rates have been reported to be up to 33% and 13%, respectively. Recent studies suggest that percutaneous transhepatic intervention is an effective and less invasive therapeutic modality compared with traditional surgical treatment. Compared with fluoroscopic intervention, percutaneous with cholangioscopy may be more useful in biliary strictures, as it can provide visual information regarding the stricture site. We recently experienced two cases complete membranous occlusion of the bilioenteric anastomosis and successfully treated both patients using percutaneous transhepatic cholangioscopy

A Call to Action for Optimizing the Electronic Health Record in the Parenteral Nutrition Workflow

Parenteral nutrition (PN) is a complex therapeutic modality provided to neonates, children, and adults for various indications. Surveys have shown that current electronic health record (EHR) systems are in need of functionality enhancement for safe and optimal delivery of PN. This is a consensus statement from the American Society for Parenteral and Enteral Nutrition, the Academy of Nutrition and Dietetics, and the American Society of Health‐System Pharmacists outlining some of the key challenges to prescribing, order review/verification, compounding, and administration of PN using EHRs today and is a call to action for clinicians and vendors to optimize their EHRs regarding the PN build and workflow.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146340/1/ncp10095.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146340/2/ncp10095_am.pd

A Call to Action for Optimizing the Electronic Health Record in the Parenteral Nutrition Workflow: Executive Summary

Parenteral nutrition (PN) is a complex therapeutic modality provided to neonates, children, and adults for various indications. Surveys have shown that current electronic health record (EHR) systems are in need of functionality enhancement for safe and optimal delivery of PN. This is a consensus statement from the American Society for Parenteral and Enteral Nutrition, the Academy of Nutrition and Dietetics, and the American Society of Health‐System Pharmacists outlining some of the key challenges to prescribing, order review/verification, compounding, and administration of PN using EHRs today and is a call to action for clinicians and vendors to optimize their EHRs regarding the PN build and workflow.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146349/1/ncp10202.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146349/2/ncp10202_am.pd

A roadmap to neural automatic post-editing: an empirical approach

In a translation workflow, machine translation (MT) is almost always followed by a human post-editing step, where the raw MT output is corrected to meet required quality standards. To reduce the number of errors human translators need to correct, automatic post-editing (APE) methods have been developed and deployed in such workflows. With the advances in deep learning, neural APE (NPE) systems have outranked more traditional, statistical, ones. However, the plethora of options, variables and settings, as well as the relation between NPE performance and train/test data makes it difficult to select the most suitable approach for a given use case. In this article, we systematically analyse these different parameters with respect to NPE performance. We build an NPE “roadmap” to trace the different decision points and train a set of systems selecting different options through the roadmap. We also propose a novel approach for APE with data augmentation. We then analyse the performance of 15 of these systems and identify the best ones. In fact, the best systems are the ones that follow the newly-proposed method. The work presented in this article follows from a collaborative project between Microsoft and the ADAPT centre. The data provided by Microsoft originates from phrase-based statistical MT (PBSMT) systems employed in production. All tested NPE systems significantly increase the translation quality, proving the effectiveness of neural post-editing in the context of a commercial translation workflow that leverages PBSMT

Reliability analysis of systems with discrete event data using association rules

With the popularization of big data, an increasing number of discrete event data have been collected and recorded during system operations. These events are usually stored in the form of event logs, which contain rich information of system operations and have potential applications in fault diagnosis and failure prediction. In manufacturing processes, various levels of correlations exist among the events, which can be used to predict the occurrence of failure events. However, two challenges remain to be solved for effective reliability analysis and failure prediction: (1) how to leverage various information from the event log to predict the occurrence of failure events and (2) how to model the effects of multiple correlations on the prediction. To address these issues, this paper proposes a novel reliability model, which integrates Cox proportional hazards (PHs) regression into survival analysis and association rule mining methodology. The model is used to evaluate the probability of failure event, which occurs within a certain period of time conditional on the occurrence history of correlated events. To estimate parameters and predict occurrence of failure events in the model, an effective algorithm is proposed based on piecewise-constant time axis division, Cox PHs model, and maximum likelihood estimation. Unlike the existing literature, our model focuses on the interactions among events. The applicability of the proposed model is illustrated through a case study of a manufacturing company. Sensitivity analysis is conducted to illustrate the effectiveness of the proposed model

When less is more in neural quality estimation of machine translation. An industry case study

Quality estimation (QE) of machine translation (MT), the task of predicting the quality of an MT output without human references, is particularly suitable in dynamic translation workflows, where translations need to be assessed continuously with no specific reference provided. In this paper, we investigate sentence-level neural QE and its applicability in an industry use case. We assess six QE approaches, which we divide into two-phase and one-phase approaches, based on quality and cost. Our evaluation shows that while two-phase systems perform best in terms of the predicted QE scores, their computational costs suggest that alternatives should be considered for large-scale translation production