David Dutwin · 2020
This chapter explores an exercise using self-reported survey data matched onto registration-based samples (RBS) to build model-based variables across the entire RBS datafile and to use the survey data to assess the efficacy of preexisting modeled data in the RBS. It reports on an applied example of using tree-based approaches to build models that predict whether a person in a national voter file is Jewish, and to use predictions to better identify individuals' religious identification for survey research. The SSRS omnibus is a phone survey of 1000 adults in the US, conducted at least once per week, using simple random samples of landlines and cellphones, in English and Spanish across all 50 states. The goal is to develop models for a range of metrics, including specific Hispanic groups, households with children, households that are uninsured or on public assistance, and others, the test again endeavored to model Jewish households.
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Mohammad Daneshzand (5993489), Miad Faezipour (5993492), Buket D. Barkana (5993495) · Figshare · 2018
<p>The LFP is recorded from the center of the STN population and then filtered with a damped oscillator. The result is shifted through a linear delayed feedback block and is used to adjust the DBS current. The frequency of the biphasic DBS signal is adjusted linearly based on the amplitude of . The larger the amplitude of is, the higher the frequency of the DBS biphasic pulses will be.</p>
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Justyna Stasiowska · Jagiellonian University Repository (Jagiellonian University) · 2017
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SpringerReference · 2011
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G. Kenney, Linda M. Engelbrecht · 1996
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Simeon Adejumo (14838827), Tomonori Kikuchi (3277875), Kazuki Tsuji (516626) et al. · Figshare · 2023
<p>A feedback loop between the queen’s patrol behaviour and the reproductive activity of workers.</p>
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Prof. Dr. Yoesoep Edhie Rachmad, DBA, Ph.D · OSF Preprints (OSF Preprints) · 2024
Rachmad, Yoesoep Edhie. 2022. Feedback Loop Theory. León Cuero Publicaciones Internacionales, Edición Especial 2022. https://doi.org/10.17605/osf.io/fsrde Introduced by Yoesoep Edhie Rachmad in 2022, Feedback Loop Theory emerged from the need to understand how continuous feedback can enhance individual and organizational performance and development. In a constantly changing and challenging environment, effective feedback becomes key to adaptation and growth. This theory was developed to provide guidance on how to optimally utilize feedback through a continuous and structured cycle. Feedback Loop Theory defines feedback as the process of providing information about performance or behavior to encourage improvement and learning. The core concept of this theory is that feedback, when given consistently and timely, can create a continuous improvement cycle, allowing individuals and organizations to adapt and enhance their performance. The theory emphasizes the importance of constructive, clear feedback based on accurate data. The theory stems from the phenomenon that many individuals and organizations struggle to give and receive feedback effectively. For example, unstructured or inappropriate feedback can lead to confusion, demotivation, and resistance to change. This phenomenon indicates the need for a systematic approach to providing feedback that helps individuals and organizations understand their strengths and areas for improvement, and develop strategies for continuous enhancement. The working principles of Feedback Loop Theory involve several key steps. It is crucial to create a supportive environment where feedback is openly received and seen as an opportunity for learning. Feedback should be timely and specific, focusing on behaviors or performance that can be changed. Encouraging reflection and action based on received feedback is essential to creating a continuous improvement cycle. Effective communication and positive relationships between feedback givers and receivers are also emphasized. Key indicators of this theory include the frequency and quality of feedback given and received, the level of feedback acceptance, and performance or behavior changes following feedback. Other indicators include satisfaction with the feedback process, engagement in the learning process, and the effectiveness of strategies implemented based on feedback. These indicators can be measured through surveys, interviews, and performance data analysis in various contexts. Operational variables of Feedback Loop Theory encompass the measurement of feedback frequency, communication quality during the feedback process, and changes resulting from feedback application. These variables can be measured through surveys, self-assessments, and performance data analysis. This data provides insights into how the feedback process functions and how interventions can be designed to enhance feedback effectiveness. The theory can be applied across various fields such as Human Resource Management (HRM), Psychology, Education, Social Communication, and Health. In HRM, it helps develop more effective performance evaluation programs and increase employee engagement. In Psychology, it is useful for understanding how feedback influences personal development and behavior change. In Education, it supports the development of teaching methods that are more responsive and adaptive to student needs. In Social Communication, it guides strategies to improve community interaction and engagement. In Health, it aids in designing programs that enhance communication between patients and healthcare providers to improve care outcomes. The success of implementing this theory is determined by several key factors. Support from leaders and a feedback-supportive organizational culture are crucial. Adequate training and resources for developing feedback-giving and receiving skills are also key factors. Success is also influenced by the ability to utilize feedback in decision-making and strategy development. Implementing this theory requires a structured and ongoing approach. The first step is identifying needs and obstacles in the feedback process and developing appropriate programs. Effective strategies include providing training on feedback techniques, developing policies that support the feedback process, and creating mechanisms for continuous feedback and evaluation. Continuous evaluation and strategy adjustment based on feedback data are essential for ensuring long-term success. Challenges in applying this theory include resistance to change, lack of understanding of the importance of feedback, and limited resources for developing and running feedback programs. However, with strong support from leaders and a supportive organizational culture, along with ongoing education, these challenges can be overcome. Success also depends on the ability to create an environment that supports engagement and openness in the feedback process, as well as the ability to manage conflicts and obstacles that may arise. Feedback Loop Theory emphasizes the importance of continuous feedback in achieving optimal performance and development. It provides a comprehensive framework for understanding and facilitating the feedback process across various fields. With proper implementation, this theory can help individuals and organizations improve their ability to give and receive feedback, better face challenges, and create a positive impact on performance and well-being. Table of Contents Feedback Loop Theory By Yoesoep Edhie Rachmad Published by León Cuero Publicaciones Internacionales, Edición Especial 2022 DOI: https://doi.org/10.17605/osf.io/fsrde ________________________________________ Chapter 1: Introduction to Feedback Loop Theory 1.1 The Evolution of Feedback in Modern Contexts............. 1 1.2 The Need for Continuous Improvement............. 15 1.3 Overview of Feedback Loop Theory............. 28 Chapter 2: Defining the Feedback Process 2.1 Feedback as a Catalyst for Growth............. 41 2.2 The Role of Constructive Feedback............. 54 2.3 Understanding Feedback Cycles............. 66 Chapter 3: Barriers to Effective Feedback 3.1 Psychological Resistance to Feedback............. 79 3.2 Organizational Challenges in Feedback Delivery............. 93 3.3 Strategies for Overcoming Feedback Barriers............. 106 Chapter 4: Implementing Structured Feedback Loops 4.1 Establishing Continuous Feedback Cycles............. 119 4.2 Tools for Effective Feedback Collection............. 134 4.3 Encouraging Reflection and Action Based on Feedback............. 148 Chapter 5: Measuring Feedback Effectiveness 5.1 Key Metrics for Feedback Success............. 162 5.2 Tracking Behavioral and Performance Changes............. 176 5.3 Feedback Satisfaction Surveys and Indicators............. 190 Chapter 6: Feedback in Human Resource Management 6.1 Performance Evaluations and Feedback Integration............. 205 6.2 Increasing Employee Engagement through Feedback............. 220 6.3 Leadership’s Role in Facilitating Feedback............. 234 Chapter 7: Psychological Impact of Feedback 7.1 Feedback and Personal Development............. 248 7.2 Feedback-Induced Behavior Change............. 263 7.3 Managing Emotional Responses to Feedback............. 278 Chapter 8: Feedback in Education 8.1 Responsive Teaching: Feedback for Student Success............. 293 8.2 Developing Adaptive Learning Models............. 308 8.3 Building Feedback-Driven Educational Environments............. 323 Chapter 9: Social Communication and Community Engagement 9.1 Enhancing Social Interaction Through Feedback............. 338 9.2 Feedback Loops in Digital and Social Media Platforms............. 352 9.3 Community Feedback Systems for Civic Engagement............. 367 Chapter 10: Feedback in Health and Healthcare Settings 10.1 Improving Patient Care through Feedback............. 382 10.2 Communication Between Healthcare Providers and Patients............. 398 10.3 Developing Patient-Centered Feedback Models............. 414 ________________________________________ Appendices • Appendix A: Glossary of Feedback Terminology............. 430 • Appendix B: Feedback Loop Models Across Different Sectors............. 445 • Appendix C: Sample Feedback Tools and Techniques............. 460 ________________________________________ References .................................................... 480 Index .............................................................. 512 Acknowledgments ........................................ 533 AUTHOR PROFILE In 2016, the author earned the title of Doctor of Humanity, hold a Ph.D. in Information Technology and a DBA in General Management. Since 2016, the author has been teaching at international universities in Malaysia, Singapore, Thailand, and the USA. In 1999, the author founded the Education Training Centre (ETC), an organization dedicated to providing educational services and social support for the underprivileged. This organization offers shelter homes for children in need of a safe place to live and drop-in schools for those who need to continue their education. The ETC is also involved in research aimed at advancing science, which led to the author earning the title of Professor and joining the WPF. Additionally, the author is actively involved in global social development programs through the United Nations. They are a member of the UN Global Compact (id-137635), the UN Global Market (id-709131), and the UN ECOSOC (id-677556). The author has served as a reviewer for several international journals and book chapters, and has written numerous books and articles on a wide range of topics including Philosophy, Economics, Management, Arts and Culture, Anthropology, Law, Psychology, Education, Sociology, Health, Technology, Tourism, and Communication.
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Yoesoep Edhie Rachmad · OSF Preprints (OSF Preprints) · 2024
Rachmad, Yoesoep Edhie. 2022. Feedback Loop Theory. León Cuero Publicaciones Internacionales, Edición Especial 2022. https://doi.org/10.17605/osf.io/fsrde\n\n \nIntroduced by Yoesoep Edhie Rachmad in 2022, Feedback Loop Theory emerged from the need to understand how continuous feedback can enhance individual and organizational performance and development. In a constantly changing and challenging environment, effective feedback becomes key to adaptation and growth. This theory was developed to provide guidance on how to optimally utilize feedback through a continuous and structured cycle.\nFeedback Loop Theory defines feedback as the process of providing information about performance or behavior to encourage improvement and learning. The core concept of this theory is that feedback, when given consistently and timely, can create a continuous improvement cycle, allowing individuals and organizations to adapt and enhance their performance. The theory emphasizes the importance of constructive, clear feedback based on accurate data.\nThe theory stems from the phenomenon that many individuals and organizations struggle to give and receive feedback effectively. For example, unstructured or inappropriate feedback can lead to confusion, demotivation, and resistance to change. This phenomenon indicates the need for a systematic approach to providing feedback that helps individuals and organizations understand their strengths and areas for improvement, and develop strategies for continuous enhancement.\nThe working principles of Feedback Loop Theory involve several key steps. It is crucial to create a supportive environment where feedback is openly received and seen as an opportunity for learning. Feedback should be timely and specific, focusing on behaviors or performance that can be changed. Encouraging reflection and action based on received feedback is essential to creating a continuous improvement cycle. Effective communication and positive relationships between feedback givers and receivers are also emphasized.\nKey indicators of this theory include the frequency and quality of feedback given and received, the level of feedback acceptance, and performance or behavior changes following feedback. Other indicators include satisfaction with the feedback process, engagement in the learning process, and the effectiveness of strategies implemented based on feedback. These indicators can be measured through surveys, interviews, and performance data analysis in various contexts.\nOperational variables of Feedback Loop Theory encompass the measurement of feedback frequency, communication quality during the feedback process, and changes resulting from feedback application. These variables can be measured through surveys, self-assessments, and performance data analysis. This data provides insights into how the feedback process functions and how interventions can be designed to enhance feedback effectiveness.\nThe theory can be applied across various fields such as Human Resource Management (HRM), Psychology, Education, Social Communication, and Health. In HRM, it helps develop more effective performance evaluation programs and increase employee engagement. In Psychology, it is useful for understanding how feedback influences personal development and behavior change. In Education, it supports the development of teaching methods that are more responsive and adaptive to student needs. In Social Communication, it guides strategies to improve community interaction and engagement. In Health, it aids in designing programs that enhance communication between patients and healthcare providers to improve care outcomes.\nThe success of implementing this theory is determined by several key factors. Support from leaders and a feedback-supportive organizational culture are crucial. Adequate training and resources for developing feedback-giving and receiving skills are also key factors. Success is also influenced by the ability to utilize feedback in decision-making and strategy development.\nImplementing this theory requires a structured and ongoing approach. The first step is identifying needs and obstacles in the feedback process and developing appropriate programs. Effective strategies include providing training on feedback techniques, developing policies that support the feedback process, and creating mechanisms for continuous feedback and evaluation. Continuous evaluation and strategy adjustment based on feedback data are essential for ensuring long-term success.\nChallenges in applying this theory include resistance to change, lack of understanding of the importance of feedback, and limited resources for developing and running feedback programs. However, with strong support from leaders and a supportive organizational culture, along with ongoing education, these challenges can be overcome. Success also depends on the ability to create an environment that supports engagement and openness in the feedback process, as well as the ability to manage conflicts and obstacles that may arise.\nFeedback Loop Theory emphasizes the importance of continuous feedback in achieving optimal performance and development. It provides a comprehensive framework for understanding and facilitating the feedback process across various fields. With proper implementation, this theory can help individuals and organizations improve their ability to give and receive feedback, better face challenges, and create a positive impact on performance and well-being. \n
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Paweł D. Domański · 2025
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Firas Shama, Roey Mechrez, Alon Shoshan et al. · arXiv (Cornell University) · 2019
Thanks to their remarkable generative capabilities, GANs have gained great popularity, and are used abundantly in state-of-the-art methods and applications. In a GAN based model, a discriminator is trained to learn the real data distribution. To date, it has been used only for training purposes, where it's utilized to train the generator to provide real-looking outputs. In this paper we propose a novel method that makes an explicit use of the discriminator in test-time, in a feedback manner in order to improve the generator results. To the best of our knowledge it is the first time a discriminator is involved in test-time. We claim that the discriminator holds significant information on the real data distribution, that could be useful for test-time as well, a potential that has not been explored before. The approach we propose does not alter the conventional training stage. At test-time, however, it transfers the output from the generator into the discriminator, and uses feedback modules (convolutional blocks) to translate the features of the discriminator layers into corrections to the features of the generator layers, which are used eventually to get a better generator result. Our method can contribute to both conditional and unconditional GANs. As demonstrated by our experiments, it can improve the results of state-of-the-art networks for super-resolution, and image generation.
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Michael Cassutt · 2008
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2022
This seminal text demystifies the terminology of working in the fashion industry today, providing definitions of processes, techniques, features, and even some historical terms that you need to know. The dictionary now includes coverage of sustainability, smart materials, new technologies and processes. This book has been reorganized in a purely alphabetical order for easy reference. Lavishly illustrated with over 800 illustrations capturing the styles and details of fashion, this reference work is a must have for students, designers, fashion merchandisers, historians, and fashion enthusiasts.
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Nello Cristianini · 2023
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Jo Stephenson · PubMed · 2007
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Tom Chen · IEEE Communications Magazine · 2006
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Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho et al. · 2013
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Banerjee, Rebekah · Open Science Framework · 2019
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James Gibson · 2009
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Ankit Gupta (211785), Corentin Briat (589208), Mustafa Khammash (252133) · Figshare · 2015
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Pedro Escaleira · Zenodo (CERN European Organization for Nuclear Research) · 2026
MoFaaS LLM Feedback Loop This repository holds the service that transforms the code from one version to a different version of the same FaaS function. The repository is organized into two branches: main: Contains the functional blocks of the LLM Feedback Loop. evaluation: Includes tests and results obtained during the evaluation phase. Components backend: Related to the LLM Agent in the paper. code-verifier: Related to the Syntax Agent. create-test-service: Related to the Building Agent. runtime-verifier: Related to the Functional Testing Agent. frontend: Related to the Entry Point. The system is designed as a feedback loop, with the backend (LLM Agent) commanding all other components. Therefore, the backend needs to be set up with the URLs of all other agents. Installation Backend For any execution, you will need to access the backend directory. At the moment, the image is using the LLM model phi-4-Q4_K_M. You must download it to the models directory: mkdir models cd models wget https://huggingface.co/bartowski/phi-4-GGUF/resolve/main/phi-4-Q4_K_M.gguf You will have to change the environment variables in the docker-compose.yaml according to your environment. You should set: CODE_VERIFIER_URL: the URL for the service code-verifier CREATE_TEST_URL: the URL for the service create-test-service CODE_TESTER_URL: the URL for the service runtime-verifier For the backend, you have two options: Run in a machine with NVidia and CUDA: You will need to install the CUDA Container Toolkit in the host. Then, simply run the Docker Compose with: docker compose -f docker-compose.yaml -f docker-compose-gpu.override.yaml up Run in a machine without GPU: Run the following in your terminal: docker compose -f docker-compose.yaml up Helm Chart Everything but the backend is packaged under an Helm chart. This Helm Chart needs to be launched in a Kubernetes cluster with Knative installed (refer to the MoFaaS main repository for instructions on how to install Knative): cd chart helm install version-generation . All containers are already provided in the repository. However, the backend repository might need to be built for the specific machine where it will be run, as the llm-cpp-python is a compiled library. Contributing We welcome contributions! Please open an issue or submit a pull request. Licensing This repository is licensed under the GNU GENERAL PUBLIC LICENSE 3.0. For the complete license text, see the file LICENSE. This license applies to all files in this distribution, except as noted below. The Open Chat Playground in the frontend directory is licensed under the MIT license. See the frontend/LICENSE.md for the complete license text.
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