The NIH Process: Demystifying the Black Box

1995 ◽  
Vol 112 (5) ◽  
pp. P136-P136
Author(s):  
Maureen Hannley
Keyword(s):  
Black Box ◽  
Grant Application ◽  
Educational Objectives ◽  
Know How

Educational objectives: To understand the grant application, review, and funding process and to know how to establish contact with NIH staff to seek advice and assistance.

Stapedectomy with Vein Graft and Teflon Piston

1995 ◽  
Vol 112 (5) ◽  
pp. P67-P67
Author(s):  
John J. Shea
Keyword(s):  
Vein Graft ◽  
Educational Objectives ◽  
Know How ◽  
Teflon Piston

Educational objectives: To know how to recognize otosclerosis, which patients to operate on, and how toper-form a stapedectomy.

Black-Box Protection of Core Competencies in Strategic Alliances

2000 ◽  
Vol 6 (2) ◽  
pp. 32-43 ◽  
Author(s):  
Michael Milgate
Keyword(s):  
Strategic Alliances ◽  
Strategic Alliance ◽  
Joint Venture ◽  
Core Competencies ◽  
Black Box ◽  
Advanced Technology ◽  
Proprietary Information ◽  
Know How ◽  
Sensitive Knowledge ◽  
Cooperative Venture

AbstractThis article presents a conceptual framework that participants in cooperative ventures may use to protect core competencies and proprietary information, while allowing the cooperative venture to benefit from these. While strategic alliances, in various forms, are becoming more common (Beamish and Delios, 1997), a potentially issue that often remains unresolved is how to protect your core competencies, while still cooperating openly with your partner, particularly when advanced technology is involved. It can be difficult for partners in an alliance to cooperate and openly share strategic know-how. Cooperation and openness are necessary, however, if a joint venture is to succeed. Since the success of any strategic alliance is based on cooperation, trust and an open sharing of competencies, potentially sensitive knowledge might be exposed through the joint venture. This is why many executives regard strategic alliances with reservation (Lorenz, 1992). They resist giving away core strategic competencies that might be misused in other contexts.

Conclusions

2021 ◽  
pp. 193-202
Author(s):  
Andrew Geddes
Keyword(s):  
National Level ◽  
Black Box ◽  
Know How ◽  
Global Standards ◽  
Global Norms ◽  
The Relationship

This chapter summarizes the main arguments of the book. It specifies the value of migration governance repertoires as a way to look inside the ‘black box’ of migration governance and to know more about how know-how and expectations about role held by key actors play a powerful role in the outputs and outcomes that emerge from these systems. It is shown that regions play a key role in mediating the relationship between the global and national level. More than this it, is argued that regions play an important role in shaping the ways in which global norms and standards are understood and interpreted. This means that it is not simply a question of adaptation to global standards but, in contrast, how global norms and standards are rendered meaningful at regional and national levels. The chapter also shows how understandings of what is ‘normal’ about migration—of its causes and effects—are the backdrop against which intermittent crises are interpreted.

scholarly journals Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

2021 ◽  
Author(s):  
Rabia Saleem ◽  
Bo Yuan ◽  
Fatih Kurugollu ◽  
Ashiq Anjum
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Process Model ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Black Box ◽  
Decision Making Process ◽  
Deterministic Models ◽  
Know How

Artificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decision- making is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.

scholarly journals The microfoundations of organizational ambidexterity: A systematic review of individual ambidexterity through a multilevel framework

2020 ◽  
pp. 234094442092971 ◽  
Author(s):  
Eva M Pertusa-Ortega ◽  
José F Molina-Azorín ◽  
Juan José Tarí ◽  
Jorge Pereira-Moliner ◽  
María D López-Gamero
Keyword(s):  
Jel Classification ◽  
Black Box ◽  
Future Research ◽  
Organizational Ambidexterity ◽  
Methodological Issues ◽  
Individual Level ◽  
Know How ◽  
Antecedents And Consequences ◽  
The Individual ◽  
Exploration Exploitation

The analysis of ambidexterity at the individual level may help to open the black box of organizational ambidexterity and to address the microfoundations of the exploration/exploitation dilemma in order to know how it can be achieved. The purpose of this article is to examine the main characteristics, antecedents, and consequences of individual ambidexterity, highlighting several key theoretical and methodological issues. Through a systematic literature review, we synthesize and integrate knowledge about individual ambidexterity, identifying opportunities and challenges for future research. We propose a multilevel framework that includes antecedents, consequences, and contingent effects of individual ambidexterity and their relationships. This multilevel framework may help to overcome the micro–macro divide in the management field. We also provide a synthesis of future research proposals. JEL CLASSIFICATION: M10

scholarly journals Water Monitoring and Automatic Feed in Aquarium Based on Microcontroller

2019 ◽  
Vol 9 (2) ◽  
pp. 1738-1743
Keyword(s):  
Ph Value ◽  
Black Box ◽  
Integrated System ◽  
Fish Feed ◽  
Know How ◽  
Long Time ◽  
Aquarium Water ◽  
Original Size ◽  
Automation Tools ◽  
The Impact

technology needs to be applied to all items that are still not fully automated. But in reality still not all automation tools implement all parts working automatically. As with maintaining fish in an aquarium which is a hobby of urban residents who work manually and routinely, such as feeding fish regularly then monitoring pH, etc., therefore transferring the work of caring for fish and aquariums is important to be done automatically. Using a microcontroller and other sensors combined into an integrated system can automate activities such as raising fish in an aquarium. Automatic activities include monitoring the pH of water and feed supplemented with reports in the form of SMS as information for aquarium owners. By using an automatic water and fish feed monitoring system based on a microcontroller, aquarium owners can find out the pH value of aquarium water even though they know how to calculate it and will feel calm when traveling for a long time because the aquarium has been automated, where the bait will be given automatically according to the schedule and predetermined portion. The formation of tools using the waterfall method commonly used to design technology so that research is expected to know the impact of the formation of this waterfall. Microcontroller results are tested using a black box to measure the original size with the size of the results of the microcontroller. the results show that if this automation is done using the waterfall method and tested using a black box successfully produces automation in the aquarium. .

scholarly journals Shining a Light Inside the “Black Box” of NIH Application Submission and Review

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 765-765
Author(s):  
Elia Ortenberg ◽  
Shalanda Bynum
Keyword(s):  
Peer Review ◽  
Black Box ◽  
National Institutes Of Health ◽  
Grant Application ◽  
Scientific Review ◽  
Training Opportunity ◽  
Business Innovation ◽  
Innovation Research ◽  
Grant Applications ◽  
Potential Impact

Abstract What happens to applications after they are submitted to the National Institutes of Health, and how can you better prepare yourself and your application for the process of peer review? The Center for Scientific Review (CSR) works closely with the 24 funding institutes and centers at the National Institutes of Health that provide funding support for projects of high scientific merit and high potential impact. CSR conducts the first level of review for the majority of grant applications submitted to the NIH, which includes 90% of R01s, 85% of Fellowships, and 95% of Small Business Innovation Research (SBIR) applications as well as many other research and training opportunity activities. In this capacity, CSR helps to identify the most meritorious projects, cutting-edge research, and future scientists who will advance the mission of the NIH: to enhance health, lengthen life, and reduce illness and disability. The purpose of this project is to provide an overview of 1) what happens to NIH applications before, during, and after peer review at CSR; 2) a summary of new and current peer review policies and practices that impact investigators and their submitted applications; and 3) strategies for developing a strong NIH grant application. Peer review is the cornerstone of the NIH grant supporting process, and an insider’s view can shine a light inside the “Black Box” of how the most meritorious projects are identified.

scholarly journals What do deep saliency models learn about where we look in scenes?

2021 ◽  
Author(s):  
Taylor R. Hayes ◽  
John M. Henderson
Keyword(s):  
State Of The Art ◽  
Black Box ◽  
Image Features ◽  
Know How ◽  
Mixed Effects Modeling ◽  
Current State ◽  
Saliency Model ◽  
Image Saliency ◽  
High Level ◽  
Scene Features

Abstract Deep saliency models represent the current state-of-the-art for predicting where humans look in real-world scenes. However, for deep saliency models to inform cognitive theories of attention, we need to know how deep saliency models predict where people look. Here we open the black box of deep saliency models using an approach that models the association between the output of 3 prominent deep saliency models (MSI-Net, DeepGaze II, and SAM-ResNet) and low-, mid-, and high-level scene features. Specifically, we measured the association between each deep saliency model and low-level image saliency, mid-level contour symmetry and junctions, and high-level meaning by applying a mixed effects modeling approach to a large eye movement dataset. We found that despite different architectures, training regimens, and loss functions, all three deep saliency models were most strongly associated with high-level meaning. These findings suggest that deep saliency models are primarily learning image features associated with scene meaning.

Choanal Atresia: Historical Perspective and Surgical Management

1995 ◽  
Vol 112 (5) ◽  
pp. P80-P80
Author(s):  
Arthur S. Hengerer
Keyword(s):  
Surgical Management ◽  
Surgical Approach ◽  
Historical Perspective ◽  
Choanal Atresia ◽  
Surgical Techniques ◽  
Educational Objectives ◽  
Know How ◽  
Surgery Technique

Educational objectives: To understand the origin of this congenital problem and its treatment from historical perspective; to know the surgical techniques available to manage or correct the problem; and to know how to choose the correct surgical approach and fine points of surgery technique.

scholarly journals Framework to Interpret Deep Learning-Based Health Management System with Human Interactions

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael H. Azarian ◽  
Namkyoung Lee ◽  
Michael G. Pecht
Keyword(s):  
Deep Learning ◽  
Management System ◽  
Health Management ◽  
System Development ◽  
Black Box ◽  
Remaining Useful Life ◽  
Management Systems ◽  
Know How ◽  
Human Interactions ◽  
Useful Life

Deep learning has shown good performance in detecting a product’s faults and estimating the remaining useful life of a product. However, it is hard to interpret deep learning-based health management systems because deep learning is often regarded as a black box. In order to make a maintenance decision based on the result of the management system, humans need to know how it gave the outcome. This study aims to develop a framework that utilizes human interactions during system development to understand the internal process of deep learning. The study will demonstrate the framework on bearing datasets.

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