scholarly journals An Error Analysis Framework for Shallow Surface Realization

2021 ◽  
Vol 9 ◽  
pp. 429-446
Author(s):  
Anastasia Shimorina ◽  
Yannick Parmentier ◽  
Claire Gardent
Keyword(s):  
Error Analysis ◽  
Performance Metrics ◽  
Analysis Framework ◽  
Language Generation ◽  
Impact Performance ◽  
Dependency Tree ◽  
Wide Range ◽  
Low Performance ◽  
Main Components ◽  
Surface Realization

Abstract The metrics standardly used to evaluate Natural Language Generation (NLG) models, such as BLEU or METEOR, fail to provide information on which linguistic factors impact performance. Focusing on Surface Realization (SR), the task of converting an unordered dependency tree into a well-formed sentence, we propose a framework for error analysis which permits identifying which features of the input affect the models’ results. This framework consists of two main components: (i) correlation analyses between a wide range of syntactic metrics and standard performance metrics and (ii) a set of techniques to automatically identify syntactic constructs that often co-occur with low performance scores. We demonstrate the advantages of our framework by performing error analysis on the results of 174 system runs submitted to the Multilingual SR shared tasks; we show that dependency edge accuracy correlate with automatic metrics thereby providing a more interpretable basis for evaluation; and we suggest ways in which our framework could be used to improve models and data. The framework is available in the form of a toolkit which can be used both by campaign organizers to provide detailed, linguistically interpretable feedback on the state of the art in multilingual SR, and by individual researchers to improve models and datasets.1

scholarly journals Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity

2021 ◽  
Vol 11 (13) ◽  
pp. 5859
Author(s):  
Fernando N. Santos-Navarro ◽  
Yadira Boada ◽  
Alejandro Vignoni ◽  
Jesús Picó
Keyword(s):  
Gene Expression ◽  
Gene Transcription ◽  
Performance Metrics ◽  
Cell Mass ◽  
Gene Copy ◽  
Substrate Uptake ◽  
Promoter Strength ◽  
Expression Levels ◽  
Trade Offs ◽  
Wide Range

Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.

scholarly journals Validation and standardization of DNA extraction and library construction methods for metagenomics-based human fecal microbiome measurements

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dieter M. Tourlousse ◽  
Koji Narita ◽  
Takamasa Miura ◽  
Mitsuo Sakamoto ◽  
Akiko Ohashi ◽  
...  
Keyword(s):  
Dna Extraction ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Collaborative Study ◽  
Human Microbiome ◽  
Second Phase ◽  
Intermediate Precision ◽  
Fecal Microbiome ◽  
Library Construction ◽  
Wide Range

Abstract Background Validation and standardization of methodologies for microbial community measurements by high-throughput sequencing are needed to support human microbiome research and its industrialization. This study set out to establish standards-based solutions to improve the accuracy and reproducibility of metagenomics-based microbiome profiling of human fecal samples. Results In the first phase, we performed a head-to-head comparison of a wide range of protocols for DNA extraction and sequencing library construction using defined mock communities, to identify performant protocols and pinpoint sources of inaccuracy in quantification. In the second phase, we validated performant protocols with respect to their variability of measurement results within a single laboratory (that is, intermediate precision) as well as interlaboratory transferability and reproducibility through an industry-based collaborative study. We further ascertained the performance of our recommended protocols in the context of a community-wide interlaboratory study (that is, the MOSAIC Standards Challenge). Finally, we defined performance metrics to provide best practice guidance for improving measurement consistency across methods and laboratories. Conclusions The validated protocols and methodological guidance for DNA extraction and library construction provided in this study expand current best practices for metagenomic analyses of human fecal microbiota. Uptake of our protocols and guidelines will improve the accuracy and comparability of metagenomics-based studies of the human microbiome, thereby facilitating development and commercialization of human microbiome-based products.

scholarly journals Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture

2021 ◽  
Vol 11 (8) ◽  
pp. 3623
Author(s):  
Omar Said ◽  
Amr Tolba
Keyword(s):  
Packet Loss ◽  
Large Scale ◽  
Performance Metrics ◽  
Medical System ◽  
Healthcare Data ◽  
High Altitude Platforms ◽  
Wide Range ◽  
Simulation Results ◽  
Healthcare Architecture ◽  
The Internet Of Things

Employment of the Internet of Things (IoT) technology in the healthcare field can contribute to recruiting heterogeneous medical devices and creating smart cooperation between them. This cooperation leads to an increase in the efficiency of the entire medical system, thus accelerating the diagnosis and curing of patients, in general, and rescuing critical cases in particular. In this paper, a large-scale IoT-enabled healthcare architecture is proposed. To achieve a wide range of communication between healthcare devices, not only are Internet coverage tools utilized but also satellites and high-altitude platforms (HAPs). In addition, the clustering idea is applied in the proposed architecture to facilitate its management. Moreover, healthcare data are prioritized into several levels of importance. Finally, NS3 is used to measure the performance of the proposed IoT-enabled healthcare architecture. The performance metrics are delay, energy consumption, packet loss, coverage tool usage, throughput, percentage of served users, and percentage of each exchanged data type. The simulation results demonstrate that the proposed IoT-enabled healthcare architecture outperforms the traditional healthcare architecture.

scholarly journals Detecting cybersecurity attacks across different network features and learners

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Joffrey L. Leevy ◽  
John Hancock ◽  
Richard Zuech ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Impact Performance ◽  
Detection Model ◽  
Wide Range ◽  
Research Questions

AbstractMachine learning algorithms efficiently trained on intrusion detection datasets can detect network traffic capable of jeopardizing an information system. In this study, we use the CSE-CIC-IDS2018 dataset to investigate ensemble feature selection on the performance of seven classifiers. CSE-CIC-IDS2018 is big data (about 16,000,000 instances), publicly available, modern, and covers a wide range of realistic attack types. Our contribution is centered around answers to three research questions. The first question is, “Does feature selection impact performance of classifiers in terms of Area Under the Receiver Operating Characteristic Curve (AUC) and F1-score?” The second question is, “Does including the Destination_Port categorical feature significantly impact performance of LightGBM and Catboost in terms of AUC and F1-score?” The third question is, “Does the choice of classifier: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Catboost, LightGBM, or XGBoost, significantly impact performance in terms of AUC and F1-score?” These research questions are all answered in the affirmative and provide valuable, practical information for the development of an efficient intrusion detection model. To the best of our knowledge, we are the first to use an ensemble feature selection technique with the CSE-CIC-IDS2018 dataset.

Abstract TP132: Selection and Activation of Sites in a Large Multi-Center Randomized Clinical Trial

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Bart M Demaerschalk ◽  
Robert D Brown ◽  
Virginia J Howard ◽  
MeeLee Tom ◽  
Mary E Longbottom ◽  
...  
Keyword(s):  
Site Selection ◽  
Performance Metrics ◽  
Interventional Neuroradiology ◽  
Interquartile Range ◽  
Carotid Revascularization ◽  
Training Requirements ◽  
Medical Centers ◽  
Wide Range ◽  
The Times ◽  
And Training

Introduction: Careful selection and timely activation of clinical sites in multicenter clinical trials is critical for successful enrollment, subject safety, and generalizability of results. Methods: In the Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis Trial (CREST-2), a multidisciplinary Site Selection Committee evaluated applicants referred via participation in CREST, CREST principal investigators (PIs) and other investigators, StrokeNet and industry partners. Data for consideration included performance metrics in CREST and other carotid trials and a site selection questionnaire containing information on the investigators as well as quantitative data on carotid procedures performed. Any FDA warning letters were reviewed. Results: The Committee met bi-weekly for 36 months (n=64 meetings). Applications from 176 sites between March 2014 and July 2016 were evaluated: 153 were approved, 7 are under Committee review, 5 were approved but withdrew, 5 were placed on a waiting list, and 6 were rejected. One-hundred-four sites have completed the regulatory and training requirements to randomize: 51 (49%) academic medical centers, 31 (30%) private hospital-based centers, 16 (15%) private office-based practices, and 6 (6%) Veterans Administration medical centers. The mean times from application-to- approval was 5.2 weeks (interquartile range, 1.9, 6.2), and from approval-to-randomization status was 46.7 weeks (interquartile range, 35.4, 51.7). Specialties of the 104 site PIs are vascular surgery for 35 (33.7%), cardiology for 30 (28.8%), neurology for 25 (24%), neurosurgery for 8 (7.7%), interventional radiology for 4 (3.8%), and interventional neuroradiology for 2 (1.9%). Conclusions: Careful site selection is time-consuming for prospective sites and for trial leadership. Times from application-to-site-approval were modest (mean = 5.2 weeks), in contrast to the times for completing regulatory and training requirements (mean = 46.7 weeks). However, subject enrollment by teams from a wide range of medical centers led by a multi-disciplinary cohort of PIs will promote the generalizability of trial results.

Creativity from perspective of axiology

2021 ◽  
Vol 92 (2) ◽  
pp. 10-20
Author(s):  
A. V Kiriakova ◽  
◽  
V.V. Moroz ◽  
Keyword(s):  
Evolutionary Process ◽  
Value Orientations ◽  
Point Of View ◽  
Modern World ◽  
Collective Creativity ◽  
The Social ◽  
Wide Range ◽  
Interdisciplinary Knowledge ◽  
Main Components ◽  
The Individual

Interest in creativity as a subject of research has been growing exponentially since the second half of the 20th century in all areas of human history. A wide range of both domestic and foreign studies allows authors to assert that creativity is a personality trait, inherent to one degree or another. Whereas the development of such trait becomes an urgent necessity in the new reality. The entire evolutionary process of the social development illustrates its dependence on personal and collective creativity. The aim of this research is to study the phenomenon of creativity through the perspective of axiology, i.e. the science of values. Axiology allows us to consider the realities of the modern world from the perspective of not only external factors, circumstances and situations, but also of deep value foundations. Creativity has been studied quite deeply from the point of view of psychology: the special characteristics of a creative person, stages of the creative process, the relationship between creative and critical thinking, creativity and intelligence. Some psychologists emphasize motivation, creative skills, interdisciplinary knowledge, and the creative environment as the main components that contribute to the development of creativity. The authors of the article argue that values and value orientations towards cognition, creativity, self-realization and self-expression are the drivers of creativity. In a broad sense, values as a matrix of culture determine the attitude of society to creativity, to the development of creativity of the individual and the creative class, and to how economically successful a given society will be. Since innovation and entrepreneurship are embodied creativity. Thus, the study of creativity from the perspective of axiology combines the need for a deep study of this phenomenon and the subjective significance of creativity in the context of new realities

Training Standards for Career Child Psychiatrists

1982 ◽  
Vol 27 (7) ◽  
pp. 543-545
Author(s):  
Philip Barker
Keyword(s):  
Full Range ◽  
Clinical Work ◽  
Psychiatric Training ◽  
Current State ◽  
Child Psychiatric ◽  
Wide Range ◽  
Training Standards ◽  
Special Expertise ◽  
Mentally Retarded Children ◽  
Main Components

The two main components of child psychiatric training should be supervised clinical work of high quality and training in the questing, scientific approach to the subject. These should be combined so that residents consider the assessment and management of all their clinical cases in a critical way, at the same time looking critically also at the pertinent literature. Management and treatment methods should be selected in the context of discussion of the current state of knowledge in the area. Trainees should see and treat children and adolescents of all ages and with the full range of psychiatric disorders. Ten percent of their caseload should consist of mentally retarded children. It may be necessary to teach about some rare syndromes by the use of videotapes. Residents should be familiar with the uses, and drawbacks, of a wide range of therapies, including residential treatment, but can only be expected to develop special expertise in a few. Didactic teaching unrelated to clinical work is probably of limited value.

scholarly journals Innovative Proposal Concerning the Human Factors Shaping Institutional Responses to Domestic Violence – the IMPRODOVA Project

2021 ◽  
Author(s):  
Joachim Kersten ◽  
Catharina Vogt ◽  
Branko Lobnikar
Keyword(s):  
Domestic Violence ◽  
Social Work ◽  
Human Factors ◽  
High Impact ◽  
Wide Range ◽  
Institutional Responses ◽  
Components Analysis ◽  
Main Components ◽  
Collaborative Training

The introductory chapter of this book presents the book's structure as a whole and gives a brief overview of its single chapters and their interrelatedness. The aim of IMPRODOVA - Improving Frontline Responses toHigh Impact Domestic Violence was to deliver recommendations, toolkits and collaborative training for European police organisations and medical and social work professionals to improve and integrate theinstitutional response to high-impact domestic violence. IMPRODOVA had two main components: analysis of current institutional responses to high-impact domestic violence and the development of effectivesolutions to improve those responses. Efforts were made to avoid a one-size-fits-all approach and contextualise our solutions, tools and guidelines to make them applicable to a wide range of societies.

scholarly journals Applications of Microbial β-Mannanases

2020 ◽  
Vol 8 ◽  
Author(s):  
Aneesa Dawood ◽  
Kesen Ma
Keyword(s):  
Catalytic Properties ◽  
Animal Feed ◽  
Plant Tissues ◽  
Degrading Enzymes ◽  
Wide Range ◽  
Potential Applications ◽  
Paper And Pulp ◽  
Main Components ◽  
Temperature Applications

Mannans are main components of hemicellulosic fraction of softwoods and they are present widely in plant tissues. β-mannanases are the major mannan-degrading enzymes and are produced by different plants, animals, actinomycetes, fungi, and bacteria. These enzymes can function under conditions of wide range of pH and temperature. Applications of β-mannanases have therefore, been found in different industries such as animal feed, food, biorefinery, textile, detergent, and paper and pulp. This review summarizes the most recent studies reported on potential applications of β-mannanases and bioengineering of β-mannanases to modify and optimize their key catalytic properties to cater to growing demands of commercial sectors.

scholarly journals Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities

2020 ◽  
Author(s):  
Thijs Dhollander ◽  
Adam Clemente ◽  
Mervyn Singh ◽  
Frederique Boonstra ◽  
Oren Civier ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
State Of The Art ◽  
Specific Information ◽  
Analysis Framework ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
P Diffusion ◽  
Modelling Techniques ◽  
The Rich

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organisation. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "fixel-based analysis" (FBA) framework that implements bespoke solutions to this end, and has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to fixel-based analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of current fixel-based analysis studies (until August 2020), categorised across a broad range of neuroscientific domains, listing key design choices and summarising their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the fixel-based analysis framework, and outline some directions and future opportunities.

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