Lessons Learned from a Full-Scale Real World Wind Noise Modeling Project

2009 ◽  
Author(s):  
Thomas N. Ramsay ◽  
Annie Boh
Keyword(s):  
Real World ◽  
Full Scale ◽  
Lessons Learned ◽  
Wind Noise ◽  
Noise Modeling

Vertical Boilerplate

2017 ◽  
Author(s):  
James Gibson
Keyword(s):  
Market Power ◽  
Real World ◽  
Empirical Work ◽  
Full Scale ◽  
Market Mechanism ◽  
Law School ◽  
Information Cost ◽  
Information Costs ◽  
Policy Proposals ◽  
Radical Solution

Despite what we learn in law school about the “meeting of the minds,” most contracts are merely boilerplate—take-it-or-leave-it propositions. Negotiation is nonexistent; we rely on our collective market power as consumers to regulate contracts’ content. But boilerplate imposes certain information costs because it often arrives late in the transaction and is hard to understand. If those costs get too high, then the market mechanism fails. So how high are boilerplate’s information costs? A few studies have attempted to measure them, but they all use a “horizontal” approach—i.e., they sample a single stratum of boilerplate and assume that it represents the whole transaction. Yet real-world transactions often involve multiple layers of contracts, each with its own information costs. What is needed, then, is a “vertical” analysis, a study that examines fewer contracts of any one kind but tracks all the contracts the consumer encounters, soup to nuts. This Article presents the first vertical study of boilerplate. It casts serious doubt on the market mechanism and shows that existing scholarship fails to appreciate the full scale of the information cost problem. It then offers two regulatory solutions. The first works within contract law’s unconscionability doctrine, tweaking what the parties need to prove and who bears the burden of proving it. The second, more radical solution involves forcing both sellers and consumers to confront and minimize boilerplate’s information costs—an approach I call “forced salience.” In the end, the boilerplate experience is as deep as it is wide. Our empirical work should reflect that fact, and our policy proposals should too.

Electro-Dewatering Waste Activated Sludge: Lessons Learned from Real World Cases

2008 ◽  
Vol 2008 (3) ◽  
pp. 226-235
Author(s):  
Roger Paradis ◽  
Abderrahmane Dermoune ◽  
Scott F. McKay ◽  
Dany Sarrazin Sullivan
Keyword(s):  
Activated Sludge ◽  
Real World ◽  
Lessons Learned ◽  
Waste Activated Sludge

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

Lessons Learned From Designing a Security Architecture for Real-World Government Agencies

2021 ◽  
pp. 2-11
Author(s):  
Amy Dettmer ◽  
Hamed Okhravi ◽  
Kevin Perry ◽  
Nabil Schear ◽  
Richard Shay ◽  
...  
Keyword(s):  
Real World ◽  
Lessons Learned ◽  
Security Architecture ◽  
Government Agencies ◽  
World Government

Results of carotid artery stenting. Lessons learned in a Belgian ‘real world’ practice

2021 ◽  
pp. 1-6
Author(s):  
Laura Kerselaers ◽  
Sarah Gallala ◽  
Dimitri Aerden ◽  
Karl von Kemp ◽  
Erik Debing
Keyword(s):  
Carotid Artery ◽  
Real World ◽  
Carotid Artery Stenting ◽  
Lessons Learned

857 TRIPLE THERAPY FOR HEPATITIS C VIRUS (HCV) INFECTION IN PATIENTS WITH COMPENSATED LIVER CIRRHOSIS: LESSONS LEARNED FROM THE FIRST REAL-WORLD EXPERIENCE

2013 ◽  
Vol 58 ◽  
pp. S351
Author(s):  
B. Maasoumy ◽  
K. Port ◽  
A.A. Markova ◽  
B. Calle Serrano ◽  
L. Sollik ◽  
...  
Keyword(s):  
Liver Cirrhosis ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Triple Therapy ◽  
Real World ◽  
Lessons Learned ◽  
Hcv Infection

Learning to Become Researchers: Lessons Learned from a Research Experience for Undergraduates (REU) Program Focused on Research in the Psychological Sciences with Real-World Implications

2021 ◽  
Vol 4 (4) ◽  
pp. 10-22
Author(s):  
Patricia R. DeLucia ◽  
Amanda L. Woods ◽  
Jeong-Hee Kim ◽  
Ngan Nguyen ◽  
Eugene W. Wang ◽  
...  
Keyword(s):  
Homeland Security ◽  
Real World ◽  
National Science Foundation ◽  
Research Study ◽  
Psychological Research ◽  
Lessons Learned ◽  
Research Experience ◽  
Rigorous Research ◽  
National Science ◽  
National Science Foundation Research

This research study at a National Science Foundation Research Experience for Undergraduates site focuses on psychological research with applications to the real world. Two cohorts of undergraduates engaged in rigorous research projects on, e.g., driving, homeland security, relationships, human-computer interaction, language comprehension and production, discrimination, and health psychology. Results indicated that students and mentors perceived an improvement in the students' research skills.

scholarly journals Hobbit: Providing Fall Detection and Prevention for the Elderly in the Real World

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Markus Bajones ◽  
David Fischinger ◽  
Astrid Weiss ◽  
Daniel Wolf ◽  
Markus Vincze ◽  
...  
Keyword(s):  
Real World ◽  
Independent Living ◽  
Low Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Field Trials ◽  
Lessons Learned ◽  
Human Robot Interaction ◽  
Service Robot ◽  
Emergency Situations

We present the robot developed within the Hobbit project, a socially assistive service robot aiming at the challenge of enabling prolonged independent living of elderly people in their own homes. We present the second prototype (Hobbit PT2) in terms of hardware and functionality improvements following first user studies. Our main contribution lies within the description of all components developed within the Hobbit project, leading to autonomous operation of 371 days during field trials in Austria, Greece, and Sweden. In these field trials, we studied how 18 elderly users (aged 75 years and older) lived with the autonomously interacting service robot over multiple weeks. To the best of our knowledge, this is the first time a multifunctional, low-cost service robot equipped with a manipulator was studied and evaluated for several weeks under real-world conditions. We show that Hobbit’s adaptive approach towards the user increasingly eased the interaction between the users and Hobbit. We provide lessons learned regarding the need for adaptive behavior coordination, support during emergency situations, and clear communication of robotic actions and their consequences for fellow researchers who are developing an autonomous, low-cost service robot designed to interact with their users in domestic contexts. Our trials show the necessity to move out into actual user homes, as only there can we encounter issues such as misinterpretation of actions during unscripted human-robot interaction.

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