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13th International Conference on Data Science, Technology and Applications, DATA 2024

作者： Park, Jaeheon Lee, Suan Lee, Woncheol Kim, Jinho Kangwon Joint Program of Software Convergence Course Kangwon National University Chuncheon Korea Republic of School of Computer Science Semyung University Jecheon Korea Republic of SeedsSoft Co. Ltd. Chuncheon Korea Republic of Dept. of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of

ISBN: (纸本)9789897587078

This study introduces a novel recommendation system aimed at enhancing university career counseling by adapting it to more accurately align with students' interests and career trajectories. Recognizing the challenges students face in selecting courses that complement their career goals, our research explores the efficacy of employing both collaborative filtering and a hybrid model approach in the development of this system. Uniquely, this system utilizes a company-course recommendation method, diverging from the traditional student-course paradigm, to generalize company-course relationships, thereby enhancing the system's recommendation precision. Through meticulous feature engineering, we improved the performance of the NeuMF model. Our experiments demonstrate that the proposed method outperforms other models by 10% to 79% based on the mAP metric, suggesting that the proposed model can effectively recommend courses for employment. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Collaborative learning

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Bridging the Gap: Towards an Expanded Toolkit for AI-Driven Decision-Making in the Public Sector

arXiv

引用

arXiv 2023年

作者： Fischer-Abaigar, Unai Kern, Christoph Barda, Noam Kreuter, Frauke Dept. of Statistics LMU Munich Germany Munich Center for Machine Learning Germany Joint Program in Survey Methodology University of Maryland United States Dept. of Software and Information Systems Engineering Dept. of Epidemiology Biostatistics and Community Health Sciences Ben-Gurion University of the Negev Israel

AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. In this paper, we examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side. Our findings suggest that standard ML methods often rely on assumptions that do not fully account for these complexities, potentially leading to unreliable and harmful predictions. To address this, we propose a shift in modeling efforts from focusing solely on predictive accuracy to improving decision-making outcomes. We offer guidance for selecting appropriate modeling frameworks, including counterfactual prediction and policy learning, by considering how the model estimand connects to the decision-maker’s utility. Additionally, we outline technical methods that address specific challenges within each modeling approach. Finally, we argue for the importance of external input from domain experts and stakeholders to ensure that model assumptions and design choices align with real-world policy objectives, taking a step towards harmonizing AI and public sector objectives. © 2023, CC BY-NC-ND.

关键词： Crime

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AI-augmented Automation for Real Driving Prediction: an Industrial Use Case

arXiv

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arXiv 2024年

作者： Eramo, Romina Salman, Hamzeh Eyal Spezialetti, Matteo Stern, Darko Quinton, Pierre Cicchetti, Antonio Dept. of Communication Science University of Teramo Italy Dept. of Software Engineering IT Faculty Mutah University Jordan Dept. of Inf. Eng. Comp. Science and Math. University of L'Aquila Italy Dept. of Research Program Management AVL Austria Dept. of Methodology and R&D AVL Austria Dept. of IDT Mälardalen University Sweden

The risen complexity of automotive systems requires new development strategies and methods to master the upcoming challenges. Traditional methods need thus to be changed by an increased level of automation, and a faster continuous improvement cycle. In this context, current vehicle performance tests represent a very time-consuming and expensive task due to the need to perform the tests in real driving conditions. As a consequence, agile/iterative processes like DevOps are largely hindered by the necessity of triggering frequent tests. This paper reports on a practical experience of developing an AI-augmented solution - based on Machine Learning and Model-based engineering - to support continuous vehicle development and testing. In particular, historical data collected in real driving conditions is leveraged to synthesize a high-fidelity driving simulator and hence enable performance tests in virtual environments. Based on this practical experience, this paper also proposes a conceptual framework to support predictions based on real driving behavior. © 2024, CC BY.

关键词： Machine learning

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Scaling Optimizations for Large-Scale Distributed Data with Lightweight Coresets

Scaling Optimizations for Large-Scale Distributed Data with ...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Daniel N. Pinheiro Samuel Xavier-de-Souza Daniel Aloise Grad. Program of Electrical and Computer Engineering Univ. Federal do Rio Grande do Norte Natal Brazil Dept. of Computer Engineering and Automation Univ. Federal do Rio Grande do Norte Natal Brazil Dept. of Computer and Software Engineering Polytechnique Montreal Québec Canada

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Lightweight coresets are compact representations of data sets such that clustering methods present competitive results in relation to the complete data set. They are constructed by sampling important points from the complete set. We propose a fast method to approximate the sampling of lightweight coresets from very large data sets which are distributed among multiple machines. We show that the proposed method is much faster and scalable, reaching results 48 times faster than the original lightweight coresets, while holding similar properties.

关键词： Clustering algorithms Approximation algorithms Distributed databases Optimization Big Data Random access memory Linear programming

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Managed software Evolution 1

引用

1000年

作者： Ralf Reussner Jan Keim Michael Goedicke Wilhelm Hasselbring Birgit Vogel-Heuser Lukas Märtin

ISBN: (数字)9783030134990

ISBN: (纸本)9783030134983;9783030135010

This open access book presents the outcomes of the “Design for Future – Managed software Evolution” priority program 1593, which was launched by the German Research Foundation (“Deutsche Forschungsgemeinschaft (DFG)”) to develop new approaches to software engineering with a specific focus on long-lived software systems. The different lifecycles of software and hardware platforms lead to interoperability problems in such systems. Instead of separating the development, adaptation and evolution of software and its platforms, as well as aspects like operation, monitoring and maintenance, they should all be integrated into one overarching process.;Accordingly, the book is split into three major parts, the first of which includes an introduction to the nature of software evolution, followed by an overview of the specific challenges and a general introduction to the case studies used in the project. The second part of the book consists of the main chapters on knowledge carrying software, and cover tacit knowledge in software evolution, continuous design decision support, model-based round-trip engineering for software product lines, performance analysis strategies, maintaining security in software evolution, learning from evolution for evolution, and formal verification of evolutionary changes. In turn, the last part of the book presents key findings and spin-offs. The individual chapters there describe various case studies, along with their benefits, deliverables and the respective lessons learned. An overview of future research topics rounds out the coverage.;The book was mainly written for scientific researchers and advanced professionals with an academic background. They will benefit from its comprehensive treatment of various topics related to problems that are now gaining in importance, given the higher costs for maintenance and evolution in comparison to the initial development, and the fact that today, most software is not developed from scratch, but as part of a c

关键词： software engineering software Management Management of Computing and Information Systems Theory of Computation

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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Transient stability improvement by fuzzy logic-controlled braking resistor

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International Journal of Power and Energy Systems 2005年第3期25卷 143-149页

作者： Ali, M.H. Soma, Y. Murata, T. Tamura, J. Department of Electrical and Electronic Engineering Kitami Institute of Technology 165 Koen-cho Kitami Hokkaido 090-8507 Japan EEE Dept. RUET Bangladesh JSPS Post-doctoral Fellowship Program Kitami Institute of Technology Japan IEB Japan IEE of Japan Japan IEEE Power Engineering Society Taiwan Japan Technical Software Corporation Japan Kitami Institute of Technology Japan IEE of Japan Society of Instrument and Control Engineers Japan Kitami Institute of Technology Japan

The authors present the results of analyses about transient stability augmentation by the fuzzy logic-controlled braking resistor. Following a major disturbance in electric power systems, variable rotor speed of the synchronous generator is measured, and then the current through the braking resistor is controlled by the firing-angle of the thyristor switch, which is controlled by the fuzzy logic. Thus, the braking resistor controls the accelerating power in generators and makes the system transiently stable. Simulations are performed by using EMTP (Electro-Magnetic Transients program). Through the simulation results of both balanced (3LG: Three-phase-to-ground) and unbalanced (2LG: Double line-to-ground;2LS: Line-to-line;and 1LG: Single line-to-ground) faults, the effectiveness of the fuzzy controlled braking resistor is demonstrated and the optimal conductance value of the braking resistor in enhancing the transient stability is investigated.

关键词： Fuzzy sets

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-dept. literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Abstracts Of Shellfish Technical Papers Presented at the joint meeting of the Northeast Aquaculture Conference and Exposition Milford Aquaculture Seminar and the International Conference on Shellfish Restoration Groton, Connecticut December 12–15, 2012

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Journal of Shellfish Research 2013年第2期32卷 571-612页

作者： Walter Blogoslawski Christopher Davis Dorothy Leonard Lisa Milke Steven Allen Peter Bergstrom A.C. Carpenter Mark Luckenbach Kennedy Paynter Angela Sowers Eric Weissberger James Wesson Stephanie Westby Steven Allen Stephanie Westby Claire O'Neil Angela Sowers Eric Weisberrger Oliver Bender Peter Boyce Gabriel Betty Mark Borrelli Anamarija Frankic Curtis Felix Jennifer Wilson Peter Boyce Oliver Bender Suzanne Bricker Tessa Getchis Cary Chadwick Julie Rose Cori Rose Kelsey Brockett Jessica Peterman David Bushek Daphne Munroe Eric Powell Lisa Calvo Barney Hollinger William Riggin William Shadel Jenny Paterno Ryan Carnegie David Bushek Carole Cyr Lisandre G. Solomon Francoise Bourque Tyler Carrier Stephen Eddy Sarah Redmond James Cervino Rand Weeks Jason Shorr Carmen Lin Dajana Gjoza Thomas J. Goreau Erik Chapman Derrick Chelikowsky Diane Kapareiko Dorothy Jefferess Gary Wikfors Carmela Cuomo Joseph Choromanski Sheila Stiles Dorothy Jeffress Haley Ladeau Loren Coen Boze Hancock Lesley Baggett Sean Powers Rob Brumbaugh Summer Morlock Bryan DeAngelis Anne Cohen Daniel McCorkle Gail Schwietermann Kathryn Rose Dave Conley SaiGiridhar Dasika April Croxton Soren Dahl Debra Barnes Bassem Allam Jonathan Davis Brian Allen Betsy Peabody Jonathan Davis Julie Davis William Walton Donatella Del Piero Michael Devin Molly Flanagan Paul Rawson Chauncey Devin Dana Morse Mark S. Dixon Genevieve Bernatchez Kelsey Boeff Eve Galimany Yaqin Li Aynur Lok Shannon L. Meseck Marguerite Petit Sean Dixon Sam Lee Dennis O'Connor Kelly Drake Shelley Edmundson Elizabeth Fairchild Melissa Evans Allison Fitzgerald Chester Zarnoch William Wallace Amy Fitzpatrick Gef Flimlin Sandra Macfarlane Gef Flimlin Thomas Foca David Bushek Timothy Visel Walter Blogoslawski Deborah A. Bouchard Joseph Buttner John Ewart Ann Faulds George E. Flimlin Doris Hicks Craig Hollingsworth Andrew Lazur Dale Leavitt Dennis McIntosh Dana L. Morse Tom Rippen Gregg Rivara Roxanna Smolowitz Dan Terlizzi Don Webster Michael Chambers Diane C. Murphy Robert Pomeroy Josh Reitsma Mic U.S. Department of Commerce National Oceanic and Atmospheric Administration National Marine Fisheries Service Northeast Fisheries Science Center Milford Laboratory 212 Rogers Avenue Milford CT 06460 Maine Aquaculture Innovation Center 5717 Corbett Hall Room 436 Orono ME 04469 Ocean Equities LLC 776 Rolling View Drive Annapolis MD 21409. Oyster Recovery Partnership 1805A Virginia St Annapolis MD 21401 NOAA Chesapeake Bay Office 410 Severn Ave Suite 207-A Annapolis MD 21403 Potomac River Fisheries Commission 222 Taylor St Colonial Beach VA 22443 Virginia Institute of Marine Science Eastern Shore Laboratory PO Box 350 Wachapreague VA 23480 1213 HJ Patterson Hall University of Maryland College Park MD 20742 U. S. Army Corps of Engineers Baltimore District P.O. Box 1715 Baltimore MD 21203 Maryland Department of Natural Resources 580 Taylor Ave Annapolis MD 21401 Virginia Marine Resources Commission 2600 Washington Ave Newport News VA 23607 NOAA Restoration Center 410 Severn Ave Annapolis MD 21403. Oyster Recovery Partnership 1805A Virginia St Annpolis MD 21401 National Oceanographic and Atmospheric Administration 410 Severn Ave Suite 107-A Annapolis MD 21403 USA Army Corps of Engineers 10 South Howard St Baltimore MD 21201 Maryland Department of Natural Resources 580 Taylor Ave Tawes State Office Building Annapolis MD 21401. Maria Mitchell Association 4 Vestal St Nantucket MA 02554 Maria Mitchell Association 65 Meadowview Drive Nantucket MA 02554. University of Rhode Island 620 Main St. Hopkinton RI 02832. Provincetown Center for Coastal Studies 5 Holway Avenue Provincetown MA 02657 University of Massachusetts Boston 100 Morrissey Blvd Boston Massachusetts Boston MA Town of Wellfleet 300 Main Street Wellfleet Massachusetts MA 02667. Maria Mitchell Association 65 Meadowview Dr Nantucket MA 02554. NOAA National Center for Coastal Ocean Science 1305 East West Highway Room 8110 Silver Spring MD 20910 Connecticut Sea Grant University o

It was with great excitement that we planned the combined meetings of Northeast Aquaculture Conference and Exposition (NACE) and the International Conference on Shellfish Restoration (ICSR) with the 33rd Milford Aquaculture Seminar (MAS). Bringing these different but complementary audiences together resulted in a meeting that combined many different aspects of fisheries restoration and aquaculture under one *** hundred attendees including government representatives, research scientists, industry, and academia at both the university and vocational high school levels attended this event. The meeting commenced on Wednesday December 12th 2012, with over eighty people attending seven field trips to area aquaculture farms and research facilities. The formal program began on Thursday, December 13th with a plenary session including invited speakers Eric Schwabb, Acting Assistant Secretary for Conservation and Management at NOAA; Sebastian Belle, Executive Director of the Maine Aquaculture Association and Boze Hancock from the Nature Conservancy, who discussed the role of aquaculture in fisheries restoration. John Bullard, the Northeast Regional Administrator of NOAA Fisheries Service, addressed the group during the luncheon the following day. The technical papers and workshops presented were divided into 35 sessions over three days of the meeting and included topics such as the history of aquaculture, aquaculture hatchery innovations, siting and planning issues, risk management, aquaculture business management, ocean acidification, climate change, as well as aquaculture disease issues and potential remedies. Having persons present who have experienced problems and successes, along with those in manufacturing and government responsible for addressing concerns and sharing best practices, was invaluable. Discussions among this cross-section of persons who represent different aspects of aquaculture were as important as the well-presented and interesting formal papers

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