State of the art treatment for gastric cancer: future directions

2004 ◽  
Vol 2 (7) ◽  
pp. 40-47 ◽  
Author(s):  
A. Cervantes ◽  
V. Georgoulias ◽  
A. Falcone
Keyword(s):  
Gastric Cancer ◽  
State Of The Art ◽  
Future Directions

Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

2016 ◽  
Vol 224 (2) ◽  
pp. 62-70 ◽  
Author(s):  
Thomas Straube
Keyword(s):  
Anxiety Disorders ◽  
Neuronal Plasticity ◽  
Functional Neuroimaging ◽  
State Of The Art ◽  
Methodological Issues ◽  
Future Directions ◽  
Activation Patterns ◽  
New Learning ◽  
Analytical Strategies ◽  
Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

Targeting Cystathionine Beta-Synthase Misfolding in Homocystinuria by Small Ligands: State of the Art and Future Directions

2016 ◽  
Vol 17 (13) ◽  
pp. 1455-1470 ◽  
Author(s):  
Tomas Majtan ◽  
Angel L. Pey ◽  
June Ereño-Orbea ◽  
Luis Alfonso Martínez-Cruz ◽  
Jan P. Kraus
Keyword(s):  
State Of The Art ◽  
Future Directions ◽  
Cystathionine Beta Synthase ◽  
Small Ligands

Poly(hydroxyurethane) Adhesives and Coatings: State-of-the-Art and Future Directions

2021 ◽  
Author(s):  
Alvaro Gomez-Lopez ◽  
Satyannarayana Panchireddy ◽  
Bruno Grignard ◽  
Inigo Calvo ◽  
Christine Jerome ◽  
...  
Keyword(s):  
State Of The Art ◽  
Future Directions

Interpreting testing and assessment: A state-of-the-art review

2021 ◽  
pp. 026553222110361
Author(s):  
Chao Han
Keyword(s):  
State Of The Art ◽  
Professional Certification ◽  
Automatic Assessment ◽  
Prospective Students ◽  
Assessment Practice ◽  
High Stakes ◽  
Future Directions ◽  
Assessment Tasks ◽  
Interpreter Education ◽  
Selection Of

Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the selection of prospective students, the certification of interpreters, and the confirmation/refutation of research hypotheses. However, few reviews exist providing a comprehensive mapping of relevant practice and research. The present article therefore aims to offer a state-of-the-art review, summarizing the existing literature and discovering potential lacunae. In particular, the article first provides an overview of interpreting ability/competence and relevant research, followed by main testing and assessment practice (e.g., assessment tasks, assessment criteria, scoring methods, specificities of scoring operationalization), with a focus on operational diversity and psychometric properties. Second, the review describes a limited yet steadily growing body of empirical research that examines rater-mediated interpreting assessment, and casts light on automatic assessment as an emerging research topic. Third, the review discusses epistemological, psychometric, and practical challenges facing interpreting testers. Finally, it identifies future directions that could address the challenges arising from fast-changing pedagogical, educational, and professional landscapes.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Enterprise search: A state of the art

2019 ◽  
Vol 36 (2) ◽  
pp. 60-69
Author(s):  
Paul H Cleverley ◽  
Simon Burnett
Keyword(s):  
Oil And Gas ◽  
Search Task ◽  
State Of The Art ◽  
Feedback Loops ◽  
Future Directions ◽  
Business Outcomes ◽  
Technological Advances ◽  
Enterprise Search ◽  
Level Model ◽  
Systems Thinking Approach

Enterprise search is changing. The explosion of information within organizations, technological advances and availability of free OpenSource machine learning libraries offer many possibilities. Eighteen informants from practice, academia, search technology vendors and large organizations (Oil and Gas, Governments, Pharmaceuticals, Aerospace and Retail) were interviewed to assess challenges and future directions. The findings confirmed the existence of the ‘Google Habitus’, technology propaganda and a need to transcend disciplines for a Systems thinking approach toward enterprise search. This encompasses information management, user search literacy, governance, learning feedback loops as well as technology. A novel four-level model for enterprise search use cases is presented, covering search as a utility, search as an answer machine, search task apps and a discovery engine. This could be used to reframe enterprise search perceptions, expanding possibilities and improving business outcomes.

Cloud forensics: State-of-the-art and future directions

2016 ◽  
Vol 18 ◽  
pp. 77-78 ◽  
Author(s):  
Kim-Kwang Raymond Choo ◽  
Martin Herman ◽  
Michaela Iorga ◽  
Ben Martini
Keyword(s):  
State Of The Art ◽  
Future Directions ◽  
Cloud Forensics
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