• AI IN FINANCE VIRTUAL SUMMIT

  • 09:00

    WELCOME & OPENING REMARKS (ALL TIMES EDT)

  • PLENARY SESSION

  • 09:10
    Javier Perez

    The Growth of AI Open-Source Software in Unexpected Platforms

    Javier Perez - Open Source Program Strategist - IBM

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    The Growth of AI Open-Source Software in Unexpected Platforms

    Today Open Source Software (OSS) is more prevalent than in any other era and continues to grow with the latest technologies from AI and Data Science to Blockchain and Autonomous Vehicles. In this session, we are going to review AI open-source in unexpected platforms. Specifically, we are going to cover OSS in the modern mainframe, the platform used by most financial services organizations, including now fintech startups every large financial institution.

    Tensorflow, Python, Spark, and many other widely used OSS have become the building blocks of all AI and ML applications. Open-source is addressing the major trends in the Financial industry: Modernization with AI and big data, regulatory compliance, and DevOps.
    Open Source Software for mainframes is neither widely known nor something new. This session is going to present information on how open source is done for mainframes and how to port existing software to a modern platform available in all Linux distributions.

    Key Takeaways: • Learn about available open source software in AI • Learn about the platform of choice for AI in Financial institutions • Learn how to continue the growth of the open-source ecosystem for AI

    Javier Perez leads the Open Source Program for the IBM Z and LinuxONE ecosystem at IBM. Javier has been in the Open Source, Cloud, SaaS, and Mobile industries for 20+ years. He has been working directly with Open Source Software (OSS) for over 10 years, more recently leading product strategy of the Software Composition Analysis product line at Veracode. Prior to Veracode, Javier was at Axway leading a successful open source project, Appcelerator, and at Red Hat where he was Director of Product Management driving the OpenShift-based Mobile Application Platform offering for developers and enterprises including containerized applications. Javier has had the opportunity to speak at webinars and conferences all over the world covering open source, security, cloud, and application development topics. Javier has held leadership positions in Product Management and Sales Engineering for different startups, leading successful product exits and product integrations post-acquisition. Javier holds an honors degree in Computer Systems and an MBA.

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  • THE CHANGING FINANCIAL LANDSCAPE

  • 09:30
    Harshwardhan Prasad

    Recent Developments in Deep Learning in Finance

    Harshwardhan Prasad - VP - Quant Analytics - Morgan Stanley

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    Recent Developments in Deep Learning in Finance

    This talk aims to provide a literature survey of published use cases and research papers on use of machine learning in finance and how it is helping re-focus the financial sector to its fundamental purpose. The discussion will in particular focus on recent developments in deep learning applications and put a spotlight on some of the relevant research in deep learning and reinforcement learning. Other aspects like generating synthetic data, text analytics, transfer learning and explainability of deep learning models will also be discussed. This talk will conclude with update on evolving regulatory landscape and some ethical questions about use of these models.

    Harsh currently works with Morgan Stanley in Quant Analytics Group. He started his career as a programmer focussed on developing data driven algos in the areas of speech recognition, image processing and bioinformatics. He then moved to financial risk management and over the last 12 years has worked in various roles through the life cycle of models. In these roles, he has been continuously enthusiastic to applying machine learning in problems related to behavioural assumptions, data quality, recommender systems, model benchmarking and text analytics. His current role requires him reviewing all Machine Learning models used by the firm and providing direction to shaping AIML governance framework and strategy. He is also a visiting lecturer with universities and training institutions.

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  • AI TOOLS & TECHNIQUES IN FINANCE

  • 09:50
    Manuel Proissl

    Towards Algorithmic Assurance of Governing Machine Learning Systems at Scale

    Manuel Proissl - Head of Predictive Analytics in Banking Products - UBS

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    Towards Algorithmic Assurance of Governing Machine Learning Systems at Scale

    Over the past years a vast amount of research and guidelines have been published with the aim to pave the way towards 'governance frameworks' of machine learning systems affecting consumers, particularly around adversarial robustness, model transparency, privacy conservation, algorithmic fairness and ethical principles. This presentation focuses on a set of techniques that have shown potential and presumably practical relevance in financial services. Furthermore, the talk attempts to also shed light on opportunities and challenges of embedding third-party APIs that have been developed/trained by global communities.

    Manuel is currently Head of Predictive Analytics in Banking Products at UBS. Previously, he's been a senior advisor and machine learning cloud platform lead at Ernst & Young, developed numerous AI-driven business solutions for global organizations, and held managing roles in cross-border audit & advisory engagements and leading international research collaborations with contributions to AI research, Cognitive Control Systems and Particle Physics.

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  • 10:10
    Vicki Fabian

    AI in Banking on the Cloud

    Vicki Fabian - Senior Risk Manager - Lloyd's Banking Group

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    AI in Banking on the Cloud

    Angela will talk about the drive for Financial Services organisations to move to the cloud and how this may advantage technologies such as AI, Machine Learning, Big Data and Analytics. Angela will talk about how to set up a vision and strategy and drive through implementation.

    Key Takeaways: • Rationale for cloud • Challenges in moving to the cloud  • Get rights for getting to and thriving in the cloud

    Vicki Fabian is a Senior Risk Manager in Lloyds Banking Group’s (LBG) IT Change and New Technologies Risk team. She is responsible for providing insightful and high quality analysis and oversight across all areas of technology risks and controls in support of the execution of effective risk management across LBG. Vicki has over 15 years risk and compliance experience in a financial services environment. Before joining the New Technologies team she worked as a Senior Risk Manager focusing on Digital Banking, Change and Transformation.

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  • 10:30
    Valeria Cortez Vaca Diez

    Detecting Discriminatory Outcomes in Classification Models

    Valeria Cortez Vaca Diez - Senior Data Scientist - Monzo

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    Detecting Discriminatory Outcomes in Classification Models

    Lending, policing, and hiring are some of the many areas where Machine Learning can harm disproportionately the most vulnerable groups in our society. This can lead to discrimination and long-lasting negative impact in society. It is therefore crucial to understand unfair treatment in AI to prevent automated discrimination at scale. The fundamental techniques to analyze and detect bias in Machine Learning decision can be explained through simple metrics applied to model outcomes. The aim of this presentation is to pass this knowledge to empower ML practitioners to challenge how Machine Learning is implemented.

    Valeria recently joined Monzo Bank as Senior Data Scientist. Previously, she worked on the development of Machine Learning solutions for different business areas of Lloyds Banking Group and their customers. During this time, she focused on building tools and processes to detect and mitigate bias in Machine Learning models. Before joining LBG, Valeria started her career in Cambridge researching on the economics of privacy at Microsoft Research and working for TAB, a Fintech startup. Valeria is a strong advocate of ethics and responsibility in AI as well as bringing more diversity into tech teams.

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  • 10:50

    COFFEE

  • 11:30
    Rahul Singhal

    Lessons Learned in Applying AI To Extract Data Points From Derivative Contracts

    Rahul Singhal - Chief Product Leader - Innodata

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    Lessons Learned in applying AI To Extract Data Points From Derivative Contracts

    Innodata has built a world class solution that allows Legal department/Treasury and Portfolio managers to understand key data points and covenants from executed contracts. Rahul will discuss how the solution was created and lessons learned on building a world class AI system and some best practices.

    Rahul Singhal leads product, technology and business development initiatives for Innodata, a data and content transformation company. Prior to joining Innodata, Rahul was Chief Product Officer at Equals 3, an AI marketing platform which won several accolades including Gartner Cool Vendor. Before that, Rahul spent 12 years at IBM, the last three of which he spent leading the product portfolio for the Watson Platform which included a collection of APIs for vision, speech, data and language.

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  • 11:50
    Helen Byrne

    Accelerate your AI Financial Modelling with IPU

    Helen Byrne - Field Applications Engineer - Graphcore

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    Accelerate your AI Financial Modelling with IPU

    In the finance sector, the potential for innovation with advanced machine intelligence is significant. But often, new and complex models are not being fully leveraged due to latency issues and compute restraints. Enter the IPU – a completely new processing architecture designed for machine intelligence, capable of running advanced financial models up to 26x faster. Helen Byrne from Graphcore explains how the IPU’s unique architecture can power such incredible breakthroughs – and what this means for the future of finance and trading.

    Key Takeaways: • Innovation in financial modelling is possible with AI • We need a new processor (the IPU) to do this • We can achieve 26X speedup on real models.

    Helen joined Graphcore in July 2018 as an AI Research Engineer focussed on distributed learning in large-scale machines, before moving to her current role working on customer applications on Graphcore IPUs. She has a BSc in Mathematics and a Master’s degree in Artificial Intelligence. Before joining Graphcore, she was a Maths teacher and worked at an Investment Banking FinTech.

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  • CUSTOMER FOCUS

  • 12:10
    Eric Charton

    Using Deep Learning with Word Embeddings to Improve Customer Satisfaction

    Eric Charton - Senior AI Director - National Bank of Canada

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    Using Deep Learning with Word Embeddings to improve Customer Satisfaction

    Understanding customer satisfaction in retail banking requires exploring and comprehending multiple sources of feedback, such as emails, social networks reviews, web feedback, bot interactions, as well as speech-to-text transcripts collected from call centers. Since such a vast amount of textual data can be difficult to leverage with traditional text mining techniques, deep learning and word embeddings can be used to automatically classify and label feedback, and then deeply analyze and understand their content. In this communication we explain how we leverage all those AI techniques to get an in-depth understanding of the opinions and needs of National Bank’s retail customers. We also show how we improve the performance levels of those AI tools using in-house algorithms and data resources to improve the overall capacity of natural language understanding.

    Key Takeaways: • Industrial applications • State of the art classification • Understanding of DL embedding limits

    Eric Charton hold a Master in machine learning applied to voice recognition, and a Ph.D. in machine learning applied to Information extraction and natural language generation. He worked as scientist and research project coordinator in academic context in Europe (University of Avignon) and North America (CRIM, École Polytechnique de Montréal) before becoming head of search engine research and development at Yellow Pages Canada. Since March 2018, he is Senior AI Director at National Bank of Canada.

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  • 12:30
    Marsal Gavalda

    Developing ML-Driven Customer-Facing Product Features

    Marsal Gavalda - Head of Machine Learning - Square

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    Marsal Gavalda is a senior R&D executive with deep expertise in speech, language, and machine learning technologies. Marsal currently heads the Commerce Platform Machine Learning team at Square, where he applies machine learning and automation for Square's overarching purpose of economic empowerment. Marsal holds a PhD in Language Technologies and a MS in Computational Linguistics, both from Carnegie Mellon University, and a BS in Computer Science from BarcelonaTech. Marsal is the author of over thirty technical and literary publications, thirteen issued patents, and is fluent in six languages.

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  • 12:50

    VIDEO ROUNDTABLE NETWORKING MIXER

  • 09:00

    WELCOME & OPENING REMARKS (ALL TIMES EDT)

  • PREVENTING FRAUD

  • 09:10
    Rafał Jasiński

    How Cloud and ML Ops Enable to Detect Frauds Better

    Rafał Jasiński - Senior Business Analyst and Service Owner - PGS Software

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    How Cloud and ML Ops Enable to Detect Frauds Better

    Where there are transactions, there is the potential for fraudulent behavior – and in the digital landscape, it can be all too easy for software weaknesses to be exploited. Recent technological advances have made possible effective fraud detection methods that can prevent that from happening! To make the most of them, you need to use that technology the right way.

    Key Takeaways: • How Cloud and AI can help you detect fraud • What the benefits of a custom fraud detection solution are • How you can develop a fraud detection solution in just few weeks

    Rafał Jasiński is a Senior Business Analyst and Service Owner of PGS Software’s Data Services. In his 10 years of IT industry experience, he has worked on diverse projects involving manufacturing, telecommunications and healthcare with a focus on maximizing value through AI and Machine Learning solutions. A proponent of Digital Twin technology and Agile working methodologies, he sees the solution of modern business challenges in empowerment through technology. My main driver in life is curiosity. I strive to acquire new knowledge, gain new experience and learn new skills. IT is a field where I have plenty of opportunities to do just that, learn. I always encourage people around me to do the same, explore new ideas, have courage to try it in the real-world situations. The worst thing that can happen is they will learn something.

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  • AI APPLICATIONS IN FINANCE

  • 09:30

    PANEL: Tackling AI Explainability, Interpretability & Transparency in Finance - in partnership with IBM

  • Antonio Fazzalari

    Moderator:

    Antonio Fazzalari - Data Science and AI Solutions for Financial Services - IBM Cloud and Cognitive Business Unit

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    Antonio Fazzalari is a visionary leader with over two decades of experience developing, selling and delivering technology-based solutions in financial services, retail/distribution, and healthcare/life sciences. He is passionate about applying his diverse experience to help organizations find new ways to create value, enhance performance and grow through innovation in people and technology systems. Mr. Fazzalari has helped businesses succeed with sophisticated transformational initiatives, including creation of business and technical strategies, operations assessments, systems implementations, and the full-range of organizational capability development and implementation.

    Mr. Fazzalari has worked at IBM over seventeen years, during which he has held jobs in business solutions consulting, offering management, and market development. Twice he served as IBM business unit executive (industry solutions sales and business value engineering) and has received IBM leadership awards for outstanding achievement in solutions development and deployment.

    Mr. Fazzalari leads Americas financial services sector Data and AI solutions for the IBM Cloud and Cognitive Software business. He develops, sells and deploys data and analytics solutions and industry-aligned architecture patterns for banking and financial markets businesses. He previously served as senior contributor for the IBM blockchain business within the industry platforms unit focused on the platform for cross-border payments solution, a universal network that combined messaging, clearing and near real-time settlement of payments. He was responsible for developing and growing the IBM Blockchain cross-border payments platform and expanding impact across the financial services ecosystem, as well as identifying and building partnerships with financial institutions. Mr. Fazzalari also served as core member of the IBM cognitive solutions business and helped financial services companies improve results applying IBM analytic solutions, commerce and business consulting services. Mr. Fazzalari has also worked as business consultant, for IBM and other firms, advising businesses in financial services, retail/distribution, life sciences, and consumer electronics.

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  • Swapna Malekar

    Panelist:

    Swapna Malekar - Product Lead - R&D for Cards & Payments tech - RBC

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    Swapna is a Product Lead at Royal Bank of Canada, leading fintech innovation and R&D efforts in digital payments, cards and emerging technologies. Prior to this role, she led experiences and products in the financial planning & investments space.

    Prior to RBC, Swapna was the Head of Product at a UK-based SaaS Data intelligence company, Klood, that provided insights to enterprise clients through artificial intelligence.

    She started her career with Accenture, and is a technology buff. Her current passion is identifying innovative usecases for digital identity and AI in the fintech/banking domain.

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  • Chris Merz

    Panelist

    Chris Merz - Vice President Security and Decision Products - Mastercard

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    Chris Merz has been with Mastercard for 18 years. He leads several teams of data scientists that provide an AI layer to such security and decision products as Decision Intelligence, Safety Net, Smart Authentication, AI Express, Stand In, AML, and other R&D efforts. Chris has also spent time in Mastercard Labs, Loyalty Solutions, and Advisors, applying machine learning techniques to engage cardholders. Before joining Mastercard, he worked at a startup for online personalization and performed machine learning research at the NASA COE for AI, and the McDonnell Douglas Research Lab. He has a PhD in Machine Learning from the University of California, a Master of Science in Computer Science from Missouri University of Science and Technology, and a Bachelor of Science in Computer Science from the University of Missouri, St. Louis.

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  • Natalia Bailey

    Panelist:

    Natalia Bailey - Policy Advisor, Digital Finance - Institute of International Finance

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    Natalia Bailey is an Policy Advisor, Digital Finance at the IIF, where she focuses on the digital transformation of the financial system, particularly the application of new technologies such as Machine Learning to the domain of risk management, and financial sector supervision.

    In her prior role she focused on banking prudential regulation where she reviewed the modeling practices in banks’ internal RWA models, and helped develop a multi-pronged approach to enhance internal model based capital approaches.

    Natalia holds a MPP from George Mason University, and a BA in Economics from Hollins University, where she attended on an IIE-Fulbright Scholarship.

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  • 10:30

    COFFEE

  • 11:15
    Jessica Lennard

    Putting the Customer at the Heart of AI in Payments

    Jessica Lennard - Senior Director, Global Data and AI Initiatives - Visa Data Science Lab

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    Putting the Customer at the Heart of AI in Payments

    Payment transactions provide one the largest, richest data sources on the planet. As the world’s leading payments provider, Visa is driving advances in data science and AI for the benefit of customers, the businesses that serve them, and society more broadly. As well as discussing some of the key use cases, this session will also look at what businesses need to do to empower consumers and engender public trust in these new technologies and use cases of data. Key Takeaways: • AI is flourishing in the payments sector across a wide variety of applications • Use of transaction data delivers tangible benefits to consumers, businesses and society • Visa takes an ethical, responsible approach to data and AI, placing the consumer at the heart of what we do.

    Jessica is a Senior Director in Visa’s Global Strategic Initiatives team leading on Data and Artificial Intelligence. Her work focuses on AI (policy, regulation and ethics); privacy, data protection and data sharing; and consumer empowerment. She has over ten years’ corporate affairs experience across highly regulated, technology-driven sectors including digital and cyber, data and AI, telecoms, cleantech and fintech. During this time, Jessica has advised political parties, businesses (start-up to large scale global enterprises), consultancies, trade bodies, think tanks and NGOs. Jessica sits on the Board of TechUK, as well as data and AI advisory boards and committees at UKFinance, the All Party Parliamentary Group for AI, and the World Federation of Advertisers. She is an enthusiastic member of campaigning group ‘Women Leading in AI’, which advocates for ethics, diversity and inclusion in AI. She previously trained as a capital markets solicitor at Linklaters, having completed an undergraduate degree in Law from Oxford University and a Masters degree in Political Theory from the LSE.

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  • 11:35
    Yuanyuan Liu

    Trends for AI in Investments

    Yuanyuan Liu - Director, Statistical Machine Learning - AIG

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    Trends for AI in Investments

    According to World Economic Forum, 76% of banking CXOs agree that adopting AI will be critical to their organisation’s ability to differentiate in the market. In the meantime, we have seen 48% CAGR in AI investment through 2021; while the global AI investment has exceeded $50 billion influencing more than $1 trillion market up until today. Unlike past 'AI Springs', the science and practice of AI appears poised to continue an unprecedented multi-decade run of advancement. Key financial services opportunities enabled by AI ranging from conservative improvements to bold bets on new capabilities.

    Dr Yuanyuan Liu joined AIG in 2013, and is currently leading the machine learning division within AIG’s Investments AI department. During the past 7 years, he has initiated and led multiple global projects such as SME loss-risk analysis, client lifetime value modelling, and opportunity map, etc. Most recently, he is working on AIG’s innovative R&D projects to apply edge-cutting deep learning algorithms in insurance and investment, using generative model, sequential model, and reinforcement learning. Yuanyuan’s team has published a series of papers in NeurIPS, ICML, AAAI, ICASSP for granular and accurate insurance pricing, equity volatility forecasting, efficient multi-mode data samplings etc. Yuanyuan graduated from the University of Oxford with a DPhil in Statistical Machine Learning and a MSc in Applied Statistics. Prior to that, he studied a Mathematics with Statistics major in the University of Bristol.

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  • 11:55
    Sophie Chen

    From Wall Street to Main Street: The Challenges and Opportunities for Leveraging Lessons from Financial Market

    Sophie Chen - Data Scientist (Machine Intelligence Lab) - Nasdaq

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    From Wall Street to Main Street: The Challenges and Opportunities for Leveraging Lessons from Financial Market

    It takes over 400 years for the stock market to evolve into a semi-efficient market today. What lessons can be transferred beyond the capital market? Nasdaq has been working on bringing the experience and tools we had for the financial markets to broader use cases. This talk will demonstrate our journey to improve the efficiency in non-financial markets by leveraging market data, market architecture, and machine learning models. The talk will also cover different challenges we had along with some potential solutions.

    Key Takeaways: • The challenges of building a market place • The potential technology people can use for building a market place • The use cases of the market data

    Shihui (Sophie) Chen is a Data Scientist at NASDAQ’s Machine Intelligence Lab, a group dedicated to leveraging AI to improve financial markets and solutions. Her previous projects covered alternative data research, risk management, portfolio construction, and optimization. She holds a Masters of Finance degree from MIT.

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  • 12:15
    Pearl Lieberman

    Stop Operating Your Models in the Dark! Lessons Learned From the Field

    Pearl Lieberman - Head of Product Marketing - Superwise.ai

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    Stop Operating Your Models in the Dark! Lessons Learned From the Field

    As AI is becoming ubiquitous, machine learning practitioners are faced with a new challenge: the day after production. As ML systems are inherently data-dependent, trying to ensure their proper behaviour “in production” can be thorny: from drifts to bias or data quality issues, through missing labels.

    In this session, we will share best practices to monitor AI in production in the financial sector, and maximize the value of your AI program for all stakeholders.

    Key Takeaways: • To stop the "black box effect" of models in production you need to monitor them • AI assurance is about monitoring metrics and empowering data scientists as well as operations stakeholders • You can't scale your AI activities without proper assurance

    Pearl Lieberman is the head of product marketing at superwise.ai, the startup devoted to assuring the health of AI models in production. With over 10 years experience translating sophisticated technology into valuable business benefits for the financial sector, Pearl has a track record of bringing together teams across the enterprise to advance and scale innovation.

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  • 12:30

    VIDEO ROUNDTABLE NETWORKING MIXER

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