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→Program Overview
Mozilla is a part of the Open Source and Free Software movement. We were born out of this movement. We prosper because of the technology and activism which comes from this movement. And we know that Open Source and Free Software remains a key part of the Internet and the online life we seek to build. We have had an ad-hoc grants program for many years. MOSS is a systematic way to provide a new level of support to this community.
The Mozilla Open Source Support program is designed to recognize and celebrate communities who are leading the way with open source projects that contribute to our work and the health of the Web. It encompasses a "give back" element for Open Source and Free Software projects that Mozilla relies on (the "Foundational Technology" track), a "give forward" element for supporting other projects whose goals are in alignment with ours (the "Mission Partners" track), and soon, an element for improving the security of the open source ecosystem (the "Secure Open Source" track).
* MOSS Track 1 - [[MOSS/Foundational Technology|Foundational Technology]]
* MOSS Track 2 - [[MOSS/Mission Partners|Mission Partners]]
* MOSS Track 3 - [[MOSS/Secure Open Source|Secure Open Source]](under development)
Use the links above to find out about each track, including details on how to apply. To stay informed about and involved with MOSS in general, please join the MOSS [https://www.mozilla.org/en-US/about/forums/#moss discussion forum].
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The Board oversees a suite of activities and committees to provide expert advice through prestigious, independent, impartial and influential studies on complex issues in science, technology and public policy.
The Board on Energy and Environmental Systems (BEES) is a unit of the Division on Engineering and Physical Sciences (DEPS) of the National Research Council (NRC). The National Research Council, the principal operating arm of the National Academies is a private, non-profit organization that provides science and technology advice under a congressional charter. Since 1975, the Board (formerly the Energy Engineering Board [EEB]) has conducted a diverse program of studies and related activities (workshops, symposia, etc.) to produce authoritative, independent recommendations about the science and technology aspects of public policy questions in energy, the environment, national security, and defense.
In its work, the Board mobilizes a wide range of expertise in engineering and the physical and social sciences. In pursuit of its goals, the Board develops strategic plans, meets with sponsors and other interested organizations to exchange ideas and information, and provides stewardship of sponsored activities involving studies, briefings, workshops, symposia, and a variety of information dissemination activities.


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Whether you require high profile protection or a low-key solution, 3e will be present by your side or in the background. We will ensure you make the right moves to avoid any potential risks or threats, so you are able to concentrate on doing what you do best.


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Past seminars & Recordings
Click to see abstract and bio. Recorded seminars can be replayed below.
Oct 29, 2020: Percy Liang, Stanford University
Surprises in the Quest for Robust Machine Learning
Percy's Abstract: Standard machine learning produces models that are accurate on average but degrade dramatically on when the test distribution of interest deviates from the training distribution. We consider three settings where this happens: when test inputs are subject to adversarial attacks, when we are concerned with performance on minority subpopulations, and when the world simply changes (classic domain shift). Our aim is to produce methods that are provably robust to such deviations. In this talk, I will (attempt to) summarize all the work my group has done on this topic over the last three years. We have found many surprises in our quest for robustness: for example, that the "more data" and "bigger models" strategy that works so well for average accuracy sometimes fails out-of-domain. On the other hand, we have found that certain tools such as analysis of linear regression and use of unlabeled data (e.g., robust self-training) have reliably delivered promising results across a number of different settings.
Percy's Bio: Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
Zoom registration: https://us02web.zoom.us/webinar/register/WN_-M5Y9REHTMS2tf7e1X1-4w
YouTube live-stream and recording: https://www.youtube.com/watch?v=jCEo8PRJ9NA
Nov 12, 2020 Rising Star Spotlights: Irene Chen and Arpita Biswas
Irene Chen, MIT. Ethical Machine Learning for Healthcare
Arpita Biswas, Indian Institute of Science. Two-Sided Fairness Guarantees for Recommendation Systems
Irene's Abstract: Machine learning (ML) has demonstrated the potential to fundamentally improve healthcare because of its ability to find latent patterns in large observational datasets and scale insights rapidly. However, the use of ML in healthcare also raises numerous ethical concerns, especially as models can amplify existing health inequities. In this talk, I briefly outline two approaches to characterize inequality in ML and adapt models for patients without reliable access to healthcare. First, I decompose cost-based metrics of discrimination in supervised learning into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Second, I describe a deep generative model for disease subtyping while correcting for patient misalignment in disease onset time. I conclude with a pipeline for ethical machine learning in healthcare, ranging from problem selection to post-deployment considerations, and recommendations for future research.
Irene's Bio: Irene Chen is a computer science PhD student at MIT, advised by David Sontag. Her research focuses on machine learning methods to improve clinical care and deepen our understanding of human health, with applications in areas such as heart failure and intimate partner violence. Her work has been published in both machine learning conferences (NeurIPS) and medical journals (Nature Medicine, AMA Journal of Ethics), and covered by media outlets including MIT Tech Review, NPR/WGBH, and Stat News. Prior to her PhD, Irene received her AB in applied math and SM in computational engineering from Harvard University.
Arpita's Abstract: Major B2C eCommerce websites (such as Amazon, Spotify, etc.) are two-sided platforms, with customers on one side and producers on the other. Traditionally, recommendation protocols of these platforms are customer-centric---focusing on maximizing customer satisfaction by tailoring the recommendation according to the personalized preferences of individual customers. However, this may lead to unfair distribution of exposure among the producers and adversely impact their well-being. As more and more people depend on such platforms to earn a living, it is important to strike a balance between fairness among the producers and customer satisfaction. The problem of two-sided fairness in recommendation can be formulated as a hierarchically constrained fair allocation problem. This problem naturally captures a number of other resource-allocation applications, including budgeted course allocation and allocation of cloud computing resources. Our main contribution is to develop a polynomial time algorithm for the problem. In this talk, I'll discuss the constrained fair allocation problem, and show how the solution can be applied to ensure a two-sided fair recommendation.
Arpita's Bio: Arpita Biswas completed her Ph.D. from the Department of Computer Science and Automation, Indian Institute of Science (IISc). During her Ph.D., she was a recipient of Google Ph.D. Fellowship award. Her Ph.D. dissertation provides algorithms and provable guarantees for fair decision making in resource allocation, recommendation, and classification domains. After completing her Ph.D., she joined Google Research as a Visiting Researcher, where she worked closely with a non-profit organization that aims at improving maternal health among low-income households in India by carrying out a free call-based program for spreading maternal care information. She is joining Harvard University as a Postdoctoral Research Fellow, starting from November 2020. Her primary areas of interest include Algorithmic Game Theory, Optimization, and Machine Learning---in particular, multi-agent learning, incentive mechanisms, market algorithms, scheduling, etc. Thus far, she has worked on problems arising from real-world scenarios like online crowd-sourcing, resource allocation, healthcare, dynamic pricing in transportation, ride-sharing, etc.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_mOjYGQYcS2y8y9qYiPpNIQ
YouTube live-stream and recording: https://youtu.be/KM2vwajbasU
Nov 19, 2020: Ayanna Howard, Georgia Tech
Making the World Better with AI
Ayanna's Abstract: At 27, Dr. Ayanna Howard was hired by NASA to lead a team designing a robot for future Mars exploration missions that could "think like a human and adapt to change." Her accomplishments since then include being named as one of 2015's most powerful women engineers in the world and as one of Forbes' 2018 U.S. Top 50 Women in Tech. From creating robots to studying the impact of global warming on the Antarctic ice shelves to founding a company that develops STEM education and therapy products for children and those with varying needs, Professor Howard focuses on our role in being responsible global citizens. In this talk, Professor Howard will delve into the implications of recent advances in robotics and AI and explain the critical importance of ensuring diversity and inclusion at all stages to reduce the risk of unconscious bias and ensuring robots are designed to be accessible to all. Throughout the talk, Professor Howard will weave in her own experience on developing new AI technologies through her technical leadership roles at NASA, Georgia Tech, and in technology startups.
Ayanna's Bio: Dr. Ayanna Howard is Chair of the School of Interactive Computing at the Georgia Institute of Technology. She also serves on the Board of Directors for Autodesk and the Partnership on AI. Prior to Georgia Tech, Dr. Howard was at NASA's Jet Propulsion Laboratory where she functioned as Deputy Manager in the Office of the Chief Scientist. To date, Dr. Howard's unique accomplishments have been highlighted through a number of awards and articles, including being recognized as one of the 23 most powerful women engineers in the world by Business Insider and one of the Top 50 U.S. Women in Tech by Forbes. She regularly advises on issues concerning robotics, AI, and workforce development. Howard also serves on the board of CRA-WP, a nonprofit dedicated to broadening participation in computing research and education, as well as AAAS COOS, a board-appointed committee with the mandate to advise the Association on matters related to diversity in science, engineering, and related fields.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_hlQKlJ52S0qCNMT_mXnDCg
YouTube live-stream and recording: This seminar will not be live-streamed or recorded.
Dec 3, 2020: Jenn Wortman Vaughan, Microsoft Research
Intelligibility Throughout the Machine Learning Life Cycle
Jenn's Abstract: People play a central role in the machine learning life cycle. Consequently, building machine learning systems that are reliable, trustworthy, and fair requires that relevant stakeholders—including developers, users, and the people affected by these systems—have at least a basic understanding of how they work. Yet what makes a system "intelligible" is difficult to pin down. Intelligibility is a fundamentally human-centered concept that lacks a one-size-fits-all solution. I will explore the importance of evaluating methods for achieving intelligibility in context with relevant stakeholders, ways of empirically testing whether intelligibility techniques achieve their goals, and why we should expand our concept of intelligibility beyond machine learning models to other aspects of machine learning systems, such as datasets and performance metrics.
Jenn's Bio: Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to human-centered approaches to transparency, interpretability, and fairness in machine learning as part of MSR's FATE group and co-chair of Microsoft's Aether Working Group on Transparency. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_tFiL3Oc0S4qUEqd6_g2hng
YouTube live-stream and recording: https://youtu.be/bogHfN-RkaA
Dec 17, 2020: Pin-Yu Chen, IBM Research
Practical Backdoor Attacks and Defenses in Machine Learning Systems
Pin-Yu's Abstract: Backdoor attack is a practical adversarial threat to modern machine learning systems, especially for deep neural networks. It is a training-time adversarial attack that embeds Trojan patterns to a well-trained model for gaining the ability to manipulate machine decision-making at the testing phase. In this talk, I will start by providing a comprehensive overview of adversarial robustness in the lifecycle of machine learning systems. Then, I will delve into recent backdoor attacks and practical defenses in different scenarios, including standard training and federated learning. The defenses include methods to detect and repair backdoored models. I will also cover a novel application of transfer learning with access-limited models based on the lessons learned from backdoor attacks.
Pin-Yu's Bio: Dr. Pin-Yu Chen is a research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. degree in electrical engineering and computer science from the University of Michigan, Ann Arbor, USA, in 2016. Dr. Chen's recent research focuses on adversarial machine learning and robustness of neural networks. His long-term research vision is building trustworthy machine learning systems. He has published more than 30 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at CVPR'20, ECCV'20, ICASSP'20, KDD'19, and Big Data'18, and organized several workshops for adversarial machine learning. He received a NeurIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper Award.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_3wXz3lgJTcSvGtC0re7NKA
YouTube live-stream and recording: https://youtu.be/RY8j_2zIvPY
Jan 7, 2021 Rising Star Spotlights: Lizzie Kumar and Amirata Ghorbani
Lizzie Kumar, University of Utah. Epistemic values in feature importance methods: Lessons from feminist epistemology
Amirata Ghorbani, Stanford University. Equitable Valuation of Data
Lizzie's Abstract: As the public seeks greater accountability and transparency from machine learning algorithms, the research literature on methods to explain algorithms and their outputs has rapidly expanded. Feature importance, or the practice of assigning quantitative importance values to the input features of a machine learning model, form a popular class of such methods. Much of the research on feature importance rests on formalizations that attempt to capture universally desirable properties. We investigate the ways in which epistemic values are implicitly embedded in these methods and analyze the ways in which they conflict with ideas from feminist philosophy. We offer some suggestions on how to conduct research on explanations that respects feminist epistemic values, taking into account the importance of social context, the epistemic privileges of subjugated knowers, and adopting more interactional ways of knowing.
Lizzie's Bio: Lizzie Kumar is a second-year Computing Ph.D. student advised by Suresh Venkatasubramanian at the University of Utah where her work has previously been supported by the ARCS Foundation. She is interested in the practice of analyzing the social impact of machine learning systems and developing responsible AI law and policy. Previously, she developed risk models on the Data Science team at MassMutual while completing her M.S. in Computer Science at the University of Massachusetts, and also holds a B.A. in Mathematics from Scripps College.
Amirata's Abstract: As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this talk, we discuss a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on a number of data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation. We introduce Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets. We then briefly discuss the notion of distributional Shapley, where the value of a point is defined in the context of underlying data distribution.
Amirata's Bio: Amirata Ghorbani is a fifth year Ph.D. student at Stanford University working with James Zou. His research is focused on problems in machine learning including equitable methods for data valuation, algorithms to interpret machine learning models, ways to make existing ML predictors more interpretable and fair, and creating ML systems for healthcare applications such as cardiology and dermatology. He has also done work as a research intern in Google Brain, Google Brain Medical, and Salesforce Research. Before joining Stanford, he got his bachelor's degree in Electrical Engineering from Sharif University of Technology after doing some work in Signal Processing and Game Theory.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_mwddSBuHRROcDmuqDl-q7A
YouTube live-stream and recording: https://youtu.be/_vL1Gy_6m-A
Jan 21, 2021 Zachary Lipton, Carnegie Mellon University
Prediction and Data-Driven Decision-Making in Real World Environments
Zack's Abstract: Most machine learning methodology is developed to address prediction problems under restrictive assumptions and applied to drive decisions in environments where those assumptions are violated. This disconnect between our methodological frameworks and their application has caused confusion both among researchers (who often lack the right formalism to tackle these problems coherently) and practitioners (who have developed a folks tradition of ad hoc practices for deploying and monitoring systems). In this talk I'll discuss some of the critical disconnects plaguing the application of machine learning and our fledgling efforts to bridge some of these gaps.
Zack's Bio: Zachary Chase Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University and a Visiting Scientist at Amazon AI. His research spans core machine learning methods and their social impact and addresses diverse application areas, including clinical medicine and natural language processing. Current research focuses include robustness under distribution shift, breast cancer screening, the effective and equitable allocation of organs, and the intersection of causal thinking and the messy high-dimensional data that characterizes modern deep learning applications. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. Find on Twitter (@zacharylipton) or GitHub (@zackchase).
Zoom registration: https://us02web.zoom.us/webinar/register/WN_HCip_6VzQSOucLtL97Crng
YouTube live-stream and recording: https://youtu.be/fvL6MSzsQ6Q
Feb 4, 2021 Steven Wu, Carnegie Mellon University
Involving Stakeholders in Building Fair ML Systems
Steven's Abstract: Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions. However, we still lack a comprehensive understanding of how to develop machine learning systems with fairness criteria that reflect relevant stakeholders' nuanced viewpoints in real-world contexts. This talk will cover our recent work that aims to address this gap. We will first discuss an algorithmic framework that enforces the individual fairness criterion through interactions with a human auditor, who can identify fairness violations without enunciating a fairness (similarity) measure. We then discuss an empirical study on how to elicit stakeholders' fairness notions in the context of a child maltreatment predictive system.
Steven's Bio: Steven Wu is an Assistant Professor in the School of Computer Science at Carnegie Mellon University. His research focuses on (1) how to make machine learning better aligned with societal values, especially privacy and fairness, and (2) how to make machine learning more reliable and robust when algorithms interact with social and economic dynamics. In 2017, he received his Ph.D. in computer science at the University of Pennsylvania, where his doctoral dissertation received Penn's Morris and Dorothy Rubinoff Award for best thesis. After spending one year as a post-doc researcher at Microsoft Research-New York City, he was an Assistant Professor at the University of Minnesota from 2018 to 2020. His research is supported by an Amazon Research Award, a Facebook Research Award, a Mozilla research grant, a Google Faculty Research Award, a J.P. Morgan Research Faculty Award, and the National Science Foundation.
Zoom registration: https://us02web.zoom.us/webinar/register/WN__GqfnrkaRJOaGiSP_vOAfA
YouTube live-stream and recording: https://youtu.be/gV8f9ZEQxb8
Feb 18, 2021 Celia Cintas, IBM Research Africa
A tale of adversarial attacks & out-of-distribution detection stories
Celia's Abstract: Most deep learning models assume ideal conditions and rely on the assumption that test/production data comes from the in-distribution samples from the training data. However, this assumption is not satisfied in most real-world applications. Test data could differ from the training data either due to adversarial perturbations, new classes, noise, or other distribution changes. These shifts in the input data can lead to classifying unknown types, classes that do not appear during training, as known with high confidence. On the other hand, adversarial perturbations in the input data can cause a sample to be incorrectly classified. We will discuss approaches based on group-based and individual subset scanning methods from the anomalous pattern detection domain and how they can be applied over off-the-shelf DL models.
Celia's Bio: Celia Cintas is a Research Scientist at IBM Research Africa - Nairobi, Kenya. She is a member of the AI Science team at the Kenya Lab. Her current research focuses on the improvement of ML techniques to address challenges on Global Health in developing countries and exploring subset scanning for anomaly detection under generative models. Previously, grantee from National Scientific and Technical Research Council (CONICET) working on Deep Learning and Geometrics Morphometrics for populations studies at LCI-UNS and IPCSH-CONICET (Argentina) as part of the Consortium for Analysis of the Diversity and Evolution of Latin America (CANDELA). During her PhD, she was a visiting student at the University College of London (UK). She was also a Postdoc researcher visitor at Jaén University (Spain) applying ML to Heritage and Archeological studies. She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). Co-chair of several Scipy Latinamerica conferences and happy member of LinuxChix Argentina. Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019) and Diversity Co-Chair for SciPy 2020.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_s9rGThypSBuHRuRT3wHLoA
YouTube live-stream and recording: https://youtu.be/XWaaWWvPwDA
Mar 4, 2021 Rising Star Spotlights
Shibani Santurkar, MIT. How Do ML Models Make Decisions?
Victor Farias, Universidade Federal do Ceará, Brazil. Differential Privacy for Non-numeric Queries via Local Sensitivity
Shibani's Abstract: Machine learning models today attain impressive accuracy on many benchmark tasks. Yet, these models remain remarkably brittle---small perturbations of natural inputs can completely degrade their performance. Why is this the case? In this talk, we take a closer look at this brittleness, and examine how it can, in part, be attributed to the fact that our models often make decisions very differently to humans. Viewing neural networks as feature extractors, we study how these extracted features may diverge from those used by humans. We then take a closer look at the building blocks of the ML pipeline to identify potential sources of this divergence and discuss how we can make progress towards mitigating it.
Shibani's Bio: Shibani Santurkar is a PhD student in the MIT EECS Department, advised by Aleksander Mądry and Nir Shavit. Her research revolves around two broad themes: developing a precise understanding of widely-used deep learning techniques; and identifying avenues to make machine learning be robust and reliable. Prior to joining MIT, she received a bachelor's degree in electrical engineering from IIT Bombay, India. She is a recipient of the Google Fellowship.
Victor's Abstract: Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for privately releasing the noisy output of non-numeric queries (i.e., queries that produce discrete outputs) by perturbing the output of the query. Those mechanisms use the notion of global sensitivity to calibrate the amount of noise one should inject to cover the individuals' identity. A related notion named local sensitivity has been used in many numeric queries (i.e., queries that produce numeric outputs) to reduce the noise injected however it has not been used for non-numeric queries. In this talk, we discuss how to adapt the notion of local sensitivity for non-numeric queries and present a generic approach to apply it. We illustrate the effectiveness of this approach by applying it to two diverse problems: influential node analysis and decision tree induction.
Victor's Bio: Victor is a fifth year PhD student at Universidade Federal do Ceará - Brazil advised by Prof. Javam Machado. His research interests include Differential Privacy, Machine Learning and Databases. His thesis is on applying local sensitivity on differentially private selection with applications on graph analysis and tree induction algorithms. This work has been carried out in collaboration with Divesh Srivastava at AT&T Labs Research. Victor completed a Masters in Computer Science from the department of computer Science in Universidade Federal do Ceará – Brazil where he worked on elasticity for distributed databases using machine learning with a research visit period at Télécom SudParis - France.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_JjYVljUhRqOB9Oj59uA4ZA
YouTube live-stream and recording: https://youtu.be/_s-Li0I18vU
Apr 1, 2021 Gautam Kamath, University of Waterloo
CoinPress: Practical Private Estimation
Gautam's Abstract: We introduce a simple framework for differentially private estimation. As a case study, we will focus on mean estimation for sub-Gaussian data. In this setting, our algorithm is highly effective both theoretically and practically, matching state-of-the-art theoretical bounds, and concretely outperforming all previous methods. Specifically, previous estimators either have weak empirical accuracy at small sample sizes, perform poorly for multivariate data, or require the user to provide strong a priori estimates for the parameters. No knowledge of differential privacy will be assumed. Based on joint work with Sourav Biswas, Yihe Dong, and Jonathan Ullman.
Gautam's Bio: Dr. Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, and a faculty affiliate at the Vector Institute. He is mostly interested in principled methods for statistics and machine learning, with a focus on settings which are common in modern data analysis (high-dimensions, robustness, and privacy). He was a Microsoft Research Fellow at the Simons Institute for the Theory of Computing for the Fall 2018 semester program on Foundations of Data Science and the Spring 2019 semester program on Data Privacy: Foundations and Applications. Before that, he completed his Ph.D. at MIT, affiliated with the Theory of Computing group in CSAIL.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_X9QB0ynaSTWwDoa77i7zTQ
YouTube live-stream and recording: https://youtu.be/OqYjRT4Z8M8
Apr 15, 2021 Suresh Venkatasubramanian, University of Utah
The limits of Shapley values as a method for explaining the predictions of an ML system
Suresh's Abstract: One of the more pressing concerns around the deployment of ML systems is explainability: can we understand why an ML system made the decision that it did. This question can be unpacked in a variety of ways, and one approach that has become popular is the idea of feature influence: that we can assign a score to features that represents their (relative) influence in an outcome (either locally for particular input, or globally). One of the most influential of such approaches has been one based on cooperative game theory, where features are modeled as "players" and feature influence is captured as "player contribution" via the Shapley value of a game. The argument is that the axiomatic framework provided by Shapley values is well-aligned with the needs of an explanation system. But is it? I'll talk about two pieces of work that nail down mathematical deficiencies of Shapley values as a way of estimating feature influence and quantify the limits of Shapley values via a fascinating geometric interpretation that comes with interesting algorithmic challenges.
Suresh's Bio: Suresh Venkatasubramanian is a professor at the University of Utah. His background is in algorithms and computational geometry, as well as data mining and machine learning. His current research interests lie in algorithmic fairness, and more generally the impact of automated decision-making systems in society. Suresh was the John and Marva Warnock Assistant Professor at the U, and has received a CAREER award from the NSF for his work in the geometry of probability, as well as a test-of-time award at ICDE 2017 for his work in privacy. His research on algorithmic fairness has received press coverage across North America and Europe, including NPR's Science Friday, NBC, and CNN, as well as in other media outlets. He is a member of the Computing Community Consortium Council of the CRA, a member of the board of the ACLU in Utah, and a member of New York City's Failure to Appear Tool (FTA) Research Advisory Council, as well as the Research Advisory Council for the First Judicial District of Pennsylvania.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_FJBqBy7qSS2B_PJbDJEr_A
YouTube live-stream and recording: https://youtu.be/5izWQN3SKQs
May 13, 2021 Alexander D'Amour, Google Brain
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander's Abstract: ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains. We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics. Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain.
Alexander's Bio: Alexander D'Amour is a Senior Research Scientist at Google Brain in Cambridge, MA. Alex's work is primarily focused on the interface between machine learning and causality; both on how machine learning techniques can be used to estimate causal effects more effectively, and on how core concepts from causal inference can be used to improve real-world outcomes when machine learning is deployed. In research and in consulting, he has worked on applications in fairness, social network analysis, sports, healthcare, education, marketing, finance, microfinance, and entertainment. Formerly, he was a Neyman Visiting Assistant Professor in the Department of Statistics at UC Berkeley. Alex earned his PhD in the Department of Statistics at Harvard University.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_8SP2pFsOQ8aIrVe8pveF9g
YouTube live-stream and recording: https://youtu.be/lGeUtmKCmKs
June 10, 2021 Katherine Heller and Jessica Schrouff, Google
Integrating predictions from Electronic Health Records in the clinical setting
Abstract: Predicting patient deterioration has been considered as a high-yield goal by clinical systems: can we detect when the patient's health starts deteriorating such that we can avoid the worst-case scenario? To answer this question, many research works have developed machine learning models based on Electronic Health Records (EHRs, the time series of the patient's interactions with the clinical system). In this talk, we will highlight two models predicting patient deterioration: one predicting sepsis (Futoma et al., 2017), and another predicting Acute Kidney Injury (AKI, Tomasev et al., 2020). We will then discuss how integrating these models into clinical practice is not a straightforward operation (Elish et al., 2021), with consequences on the clinical staff's workflow. We will also discuss how providing explanations from these EHR models can be challenging (Mincu et al., 2021), with many technical and human factors still to be investigated.
Katherine's Bio: Katherine is a research scientist in Google Brain and a member of Brain Health Research. She works at the boundary of Machine Learning (ML) and Healthcare, particularly focusing on fairness and ethics in the ML+Health space, and the development of inclusive mobile health technology. Prior to joining Google, she was Statistical Science faculty at Duke University, where she developed a sepsis detection system now in use at Duke University Hospital, and a nationally released iOS app which tries to complete the picture of peoples' Multiple Sclerosis course between clinic visits. Katherine received a BS in CS and Applied Math from SUNY Stony Brook, an MS in CS from Columbia University, and a PhD in Machine Learning from the Gatsby Computational Neuroscience Unit at UCL. She was then a postdoc on an EPSRC fellowship in Engineering at the University of Cambridge, and an NSF postdoc fellow in Brain and Cognitive Sciences at MIT.
Jessica's Bio: Jessica is a research scientist at Google Research working on machine learning for healthcare. Before joining Google in 2019, she was a Marie Curie post-doctoral fellow at University College London (UK) and Stanford University (USA), developing machine learning techniques for neuroscience discovery and clinical predictions. Throughout her career, Jessica's interests have lied not only in the technical advancement of machine learning methods, but also in critical aspects of their deployment such as their credibility, fairness, robustness and interpretability. She has published papers in both science and machine learning venues, and released open-source software now used by 300+ neuroscience teams across the world. She is also involved in DEI initiatives, such as Women in Machine Learning (WiML) and founded the Women in Neuroscience Repository (www.winrepo.org).
Zoom registration: https://us02web.zoom.us/webinar/register/WN_9KGcGA51Q1uQMo1swzN9JA
YouTube live-stream and recording: https://youtu.be/gH0yQN4Yprs
June 24, 2021 Industry Spotlights: Rumman Chowdhury and Jiahao Chen
Rumman Chowdhury, Twitter
Jiahao Chen, JP Morgan
Rumman and Jiahao will talk about the unique practical challenges of deploying trustworthy ML in industry.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_pscCi8cjQQCC_--ss2js1A
July 8, 2021 Cynthia Rudin, Duke University
Do Simpler Models Exist?
Abstract: Many data science problems admit accurate models that are surprisingly simple. We have a preliminary hypothesis for why this occurs, which is that many datasets admit a lot of almost-equally-accurate models, and among these are simple models. I will present some interesting experiments illustrating that large "Rashomon" sets of almost-equally-accurate models correlate with good performance across different algorithms. This experiment leads to an easy calculation to check for the possibility of a simpler-yet-equally-accurate model before finding one. I will briefly discuss work on "variable importance diagrams" that help us visualize the "Rashomon set" of approximately-equally-good models.
Bio: Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University, and directs the Prediction Analysis Lab, whose main focus is interpretable machine learning. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award and a fellow of the American Statistical Association and the Institute of Mathematical Statistics.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_Ler3QWPTRxqpeGO_AftjGw
YouTube live-stream and recording: https://youtu.be/Jy_AgSVSJMA
July 22, 2021 Marinka Zitnik, Harvard Medical School
Graph Representation Learning for Biomedical Discovery
Abstract: Graphs are pervasive in science and medicine, from maps of interactions between molecules in a cell to dependencies between diseases in a person, all the way to relationships between individuals in a population. First, I will describe our efforts in learning deep graph representations that are actionable and allow users to receive robust predictions that can be interpreted meaningfully. These methods optimize graph transformation functions to represent graphs in compact vector spaces such that performing algebraic operations in the space reflects the graph topology. Second, I will describe applications in drug development and precision medicine. Our methods enabled repurposing of drugs for an emerging disease where predictions were experimentally confirmed in human cells, and explanations gave insights leading to the discovery of a new class of drugs. The methods also enabled discovering dozens of drug combinations safer for patients than today's treatments. Last, I will highlight Therapeutics Data Commons (https://tdcommons.ai), a resource with ML-ready datasets and tasks that we are developing to facilitate algorithmic innovation in the broad area of drug discovery and development.
Bio: Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Broad Institute of MIT and Harvard. Dr. Zitnik investigates machine learning, focusing on challenges brought forward by interconnected data in science, medicine, and health. Her research recently won best paper and research awards from the International Society for Computational Biology, the Bayer Early Excellence in Science Award, Amazon Faculty Research Award, a Rising Star Award in EECS, and a Next Generation Recognition in Biomedicine, being the only young scientist who received such recognition in both EECS and Biomedicine.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_jJ69jgJVS6OrLfsfzCsgAg
YouTube live-stream and recording: https://youtu.be/CoOCcVPk2gs
August 5, 2021 Hoda Heidari, Carnegie Mellon University
A Discussion of Mathematical Formulations of Fairness through the Lens of Equality of Opportunity
Abstract: I begin by presenting a simple mapping between existing mathematical notions of fairness for Machine Learning and models of Equality of opportunity (EOP)—an extensively studied ideal of fairness in political philosophy. Through this conceptual mapping, I will argue that many existing definitions of fairness, such as predictive value parity and equality of odds, can be interpreted as special cases of EOP. In this respect, the EOP interpretation serves as a unifying framework for understanding the normative assumptions underlying existing notions of fairness. Additionally, the EOP view provides a systematic approach for defining new, context-aware mathematical formulations of fairness. I will conclude with a discussion of limitations and directions for future work.
Bio: Hoda Heidari is currently an Assistant Professor in Machine Learning and Societal Computing at the School of Computer Science, Carnegie Mellon University. Her research is broadly concerned with the societal and economic aspects of Artificial Intelligence, and in particular, unfairness and opaqueness through Machine Learning. Her work has won a best-paper award at the ACM Conference on Fairness, Accountability, and Transparency (FAccT) and an exemplary track award at the ACM Conference on Economics and Computation (EC). She has organized several academic events around responsible and trustworthy AI, including a tutorial at the Web Conference (WWW), and workshops at the Neural and Information Processing Systems (NeurIPS) conference, and the International Conference on Learning Representations (ICLR).
Zoom registration: https://us02web.zoom.us/webinar/register/WN_ThLzR0KfQCiwFYWi1o6nbA
YouTube live-stream and recording: This seminar will not be live-streamed or recorded
August 19, 2021 Rising Star Spotlights
Shiori Sagawa, Stanford. Improving Robustness to Distribution Shifts: Methods and Benchmarks
Vihari Piratla, IIT Bombay. Machine Learning as a Service: The next million users
Shiori's Abstract: Machine learning models deployed in the real world constantly face distribution shifts, yet current models are not robust to these shifts; they can perform well when the train and test distributions are identical, but still have their performance plummet when evaluated on a different test distribution. In this talk, I will discuss methods and benchmarks for improving robustness to distribution shifts. First, we consider the problem of spurious correlations and show how to mitigate it with a combination of distributionally robust optimization (DRO) and controlling model complexity---e.g., through strong L2 regularization, early stopping, or underparameterization. Second, we present WILDS, a curated and diverse collection of 10 datasets with real-world distribution shifts, that aims to address the under-representation of real-world shifts in the datasets widely used in the ML community today. We observe that existing methods fail to mitigate performance drops due to these distribution shifts, underscoring the need for new training methods that produce models which are more robust to the types of distribution shifts that arise in practice.
Shiori's Bio: Shiori Sagawa is a third-year PhD student at Stanford University, advised by Percy Liang. She studies robustness to distribution shifts, and to this end, she has developed methods based on distributionally robust optimization, analyzed these algorithms in the context of deep learning models, and recently built a benchmark on distribution shifts in the wild. She is a recipient of Apple Scholars in AI/ML PhD Fellowship and Herbert Kunzel Stanford Graduate Fellowship.
Vihari's Abstract: Increasing compute and memory requirements of ML models led to the broad adoption of Machine Learning (ML) as a Service APIs. However, the one-size-fits-all paradigm of ML Services is naive when catering to millions of users; We will look at two specific challenges.
ML Services should report performance as a surface over the combination of environment-characterizing attributes instead of a single aggregate. Surface mapping is non-trivial since labelling data spanning combinatorially large attribute combinations is practically impossible. We will look at an efficient estimation technique that handles the challenge.
ML Services magnify the prevailing performance gaps (in ML systems) between domains, such as demographic groups. We will discuss a potential cause of such performance gaps and their fix.
Vihari's Bio: Vihari Piratla is a PhD student at IIT Bombay, advised by Prof. Sunita Sarawagi and Prof. Soumen Chakrabarty. His research focuses on problems that could enable wider reception of ML systems, such as their evaluation, generalization, and adaptation to new deploy environments. He is a recipient of the Google PhD Fellowship.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_zUsV30_rTtGP9J3U7ITQRA
YouTube live-stream and recording: https://www.youtube.com/watch?v=PZvE-DEudsA
September 2, 2021 Sara Magliacane, University of Amsterdam
Causality-inspired ML: what can causality do for ML? The domain adaptation case
Abstract: Applying machine learning to real-world cases often requires methods that are trustworthy and robust w.r.t. heterogeneity, missing not at random or corrupt data, selection bias, non i.i.d. data etc. and that can generalize across different domains. Moreover, many tasks are inherently trying to answer causal questions and gather actionable insights, a task for which correlations are usually not enough. Several of these issues are addressed in the rich causal inference literature. On the other hand, often classical causal inference methods require either a complete knowledge of a causal graph or enough experimental data (interventions) to estimate it accurately.
Recently, a new line of research has focused on causality-inspired machine learning, i.e. on the application ideas from causal inference to machine learning methods without necessarily knowing or even trying to estimate the complete causal graph. In this talk, I will present an example of this line of research in the unsupervised domain adaptation case, in which we have labelled data in a set of source domains and unlabelled data in a target domain ("zero-shot"), for which we want to predict the labels. In particular, given certain assumptions, our approach is able to select a set of provably "stable" features (a separating set), for which the generalization error can be bound, even in case of arbitrarily large distribution shifts. As opposed to other works, it also exploits the information in the unlabelled target data, allowing for some unseen shifts w.r.t. to the source domains. While using ideas from causal inference, our method never aims at reconstructing the causal graph or even the Markov equivalence class, showing that causal inference ideas can help machine learning even in this more relaxed setting.
Bio: Sara Magliacane is an assistant professor in the Informatics Institute at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab. She received her PhD at the VU Amsterdam on logics for causal inference under uncertainty in 2017, focusing on learning causal relations jointly from different experimental settings, especially in the case of latent confounders and small samples. After a year in IBM Research NY as a postdoc, she joined the MIT-IBM Watson AI Lab in 2019 as a Research Scientist, where she has been working on methods to design experiments that would allow one to learn causal relations in a sample-efficient and intervention-efficient way. Her current focus is on causality-inspired machine learning, i.e. applications of causal inference to machine learning and especially transfer learning, and formally safe reinforcement learning.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_2YwbW4O-QH6Zp1tTEFokOA
YouTube live-stream and recording: https://youtu.be/X8foHlW-Dsw
September 16, 2021 Sherri Rose, Stanford University
Fair Machine Learning for Continuous Outcomes in Risk Adjustment
Abstract: It is well-known in health policy that financing changes can lead to improved health outcomes and gains in access to care. More than 50 million people in the U.S. are enrolled in an insurance product that risk adjusts payments, and this has huge financial implications—hundreds of billions of dollars. Unfortunately, current risk adjustment formulas are known to undercompensate payments to health insurers for certain marginalized groups of enrollees (by underpredicting their spending). This incentivizes insurers to discriminate against these groups by designing their plans such that individuals in undercompensated groups will be less likely to enroll, impacting access to health care for these groups. We will discuss new fair statistical machine learning methods for continuous outcomes designed improve risk adjustment formulas for undercompensated groups. Then, we combine these tools with other approaches (e.g., leveraging variable selection to reduce health condition upcoding) for simplifying and improving the performance of risk adjustment systems, while centering fairness. Lastly, we discuss the paucity of methods for identifying marginalized groups in risk adjustment, and more broadly in the algorithmic fairness literature, including groups defined by multiple intersectional attributes. Extending the concept of variable importance, we construct a new measure of "group importance" to identify groups defined by multiple attributes. This work provides policy makers with a tool to uncover incentives for selection in insurance markets and a path towards more equitable health coverage. (Joint work with Anna Zink, Harvard & Tom McGuire, Harvard.)
Bio: Sherri Rose, Ph.D. is an Associate Professor and Co-Director of the Health Policy Data Science Lab at Stanford University. Her methodological research focuses on machine learning for prediction and causal inference. Within health policy, Dr. Rose works on risk adjustment, ethical algorithms in health care, comparative effectiveness research, and health program evaluation. In 2011, Dr. Rose coauthored the first book on machine learning for causal inference, with a sequel text released in 2018. She is a fellow of the American Statistical Association and her other honors include the Bernie J. O'Brien New Investigator Award, an NIH Director's New Innovator Award, and the Mortimer Spiegelman Award, which recognizes the statistician under age 40 who has made the most significant contributions to public health statistics. Dr. Rose comes from a low-income background and is committed to increasing justice, equity, diversity and inclusion in the mathematical and health sciences.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_x8nHs8OTQlODVbecZ5mWow
YouTube live-stream and recording: https://youtu.be/mTqQbsvgQSw
September 30, 2021 Sanmi Koyejo, University of Illinois at Urbana-Champaign
Towards Algorithms for Measuring and Mitigating ML Unfairness
Abstract: It is increasingly evident that widely-deployed machine learning (ML) models can lead to discriminatory outcomes and exacerbate group disparities. The renewed interest in measuring and mitigating (un)fairness has led to various metrics and mitigation strategies. Nevertheless, the measurement problem remains challenging, as existing metrics may not capture tradeoffs relevant to the context at hand, and different fairness definitions can lead to incompatible outcomes. To this end, I will outline metric elicitation as a framework for addressing this metric selection problem -- by efficiently estimating implicit preferences from stakeholders via interactive feedback. Towards mitigation, I will briefly outline some new approaches for overlapping groups, unknown sensitive attributes, and other scenarios beyond the most widely studied settings.
Bio: Sanmi (Oluwasanmi) Koyejo is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo also spends time at Google as a part of the Google Brain team. Koyejo's research interests are in developing the principles and practice of trustworthy machine learning. Additionally, Koyejo focuses on applications to neuroscience and healthcare. Koyejo completed his Ph.D. in Electrical Engineering at the University of Texas at Austin, advised by Joydeep Ghosh, and completed postdoctoral research at Stanford University. His postdoctoral research was primarily with Russell A. Poldrack and Pradeep Ravikumar. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence (UAI), a Skip Ellis Early Career Award, a Sloan Fellowship, an NSF CAREER award, a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves as the president of the Black in AI organization.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_jaEkcA8PQom3qqupt-AAcw
YouTube live-stream and recording: https://youtu.be/QgeZyuo6eD4
Nov. 11, 2021 Rising Star Spotlights
Biwei Huang: Learning and Using Causal Knowledge: Opportunities and Challenges
Aahlad Puli: Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations
Biwei's Abstract: Understanding causal relationships is a fundamental problem in scientific research, and recently, causal analysis has also attracted much interest in computer science and statistics. One focus of this talk is on how to find causal relationships from observational data, which is known as causal discovery. It serves as an appropriate alternative to interventions and randomized experiments in practice and is able to identify causal structure and quantitative models. Specifically, I will introduce recent methodological developments in causal discovery in complex environments with distribution shifts and unobserved confounders. In addition, I will also discuss the challenges we face towards more reliable causal discovery. Besides learning causality, another problem of interest is how causality can help understand and advance machine learning and artificial intelligence. Specifically, I will show what and how we can leverage from causal understanding to facilitate efficient, effective, and interpretable generalizations in transfer-learning tasks.
Biwei's Bio: Biwei Huang is a final-year Ph.D. candidate at Carnegie Mellon University. Her research interests are mainly in three aspects: (1) automated causal discovery in complex environments with theoretical guarantees, (2) advancing machine learning from the causal perspective, and (3) scientific applications of causal discovery approaches. Her research contributions have been published in JMLR, ICML, NeurIPS, KDD, AAAI, IJCAI, and UAI. She successfully led a NeurIPS'20 workshop on causal discovery and causality-inspired machine learning and co-organizes the first Conference on Causal Learning and Reasoning (CLeaR 2022). She is a recipient of the Presidential Fellowship at CMU and the Apple Scholar in AI/ML.
Aahlad's Abstract: In many prediction problems, spurious correlations are induced by a changing relationship between the label and a nuisance variable that is also correlated with the covariates. For example, in classifying animals in natural images, the background, which is the nuisance, can predict the type of animal. This nuisance-label relationship does not always hold, and the performance of a model trained under one such relationship may be poor on data with a different nuisance-label relationship. In this talk, I will describe an algorithm, Nuisance-Randomized Distillation (NuRD ), for building predictive models that perform well regardless of the nuisance-label relationship. NuRD constructs representations that distill out the influence of the nuisances while maximizing its information with the label. We evaluate NuRD on several tasks including chest X-ray classification where, using non-lung patches as the nuisance, NuRD produces models that predict pneumonia under strong spurious correlations.
Aahlad's Bio: Aahlad is a fourth-year Ph.D. student in Computer Science at NYU, advised by Prof. Rajesh Ranganath. He currently works on developing algorithms for predictive modeling in the presence of spurious correlations. His prior work has tackled causal effect estimation and survival analysis. He has a master's degree in Computer Science from NYU and bachelor's and master's degrees in Electrical Engineering from IIT Madras.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_PLbzQyxTSyWHDL-s_GY4iQ
YouTube live-stream and recording: https://youtu.be/YUIBmOlzG7M
November 11, 2021 Nicholas Carlini, Google Brain
Extracting training data from neural networks
Abstract: Trustworthy machine learning models must respect the privacy of their training datasets, especially when training on sensitive or personal data. Unfortunately, we have found that current models are not private. Given access to a pre-trained language model, we show that it is possible to extract individual examples from the training dataset that were used to train the model. We then investigate various heuristic approaches to improve privacy, and show that many defenses can be easily attacked. We conclude with potential next steps that might allow us to better understand and control the ways in which models memorize their training data.
Bio: Nicholas Carlini is a research scientist at Google Brain. He studies the security and privacy of machine learning, and for this has received best paper awards at ICML, USENIX Security and IEEE S&P. He obtained his PhD from the University of California, Berkeley in 2018.
Zoom registration: https://us02web.zoom.us/webinar/register/WN_XGRuyFUWRmeZ2kzhOJpqvQ
YouTube live-stream and recording: https://www.youtube.com/watch?v=2Xl2B2R7_1M
Trustworthy ML Initiative. trustworthyml@gmail.com

redpajama_set_name: RedPajamaCommonCrawl


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Stay Informed on NSAIDs with NSAIDs List
bishop's peak women's health center
Bishop's Peak Women's Health Center is a nonprofit organization dedicated to helping the women and their families in the communities where they live. Its mission is to "support the health and well being" of women living in underserved and diverse communities.
Bishop's Peak Women's Health Center is not just about improving health for women living in underserved and diverse communities, it's also about improving the health of women in their community. Bishop's Peak Womens Health Center is the first center to be built in Chicago and is the hub for the entire healthcare system in the city. It is the only one of its kind in Chicago.
Bishop's Peak (a.k.a. the "The Women's Health Center") is a women's health organization that aims to provide a safe haven and service for women living in underserved and diverse communities. It is the only one of its kind in Chicago and the first center to be built in the city. Bishop's Peak has been in operation since 2010 and it has helped over 100,000 women.
The center is named after the bishop of Chicago, Thomas W. Burke. His vision was to build an organization that would serve women in all parts of the city and he is most famous for his efforts to build the Chicago Women's Health Center, which opened in the summer of 1892. Burke's namesake center was the first facility of its kind in the Midwest and the first in the United States.
Bishop's peak was built in the 1930s as part of the Chicago Womens Health Center's new construction program. The buildings were designed by John D. and Charles S. Mathers and the tower was constructed by Thomas W. Burke. Bishop's peak was named after the bishop at Chicago, Thomas W. Burke.
Bishop's Peak's new building will be the first woman-owned health center in the Midwest in the 1920s. It's housed in an old school building that was designed in the Georgian Revival style by Chicago architect Charles S. Mathers. It was built in the 1930s in the style of the Chicago architectural firm of John D. and Charles S. Mathers.
Bishop's Peak is so new that it has yet to be filled with people. I personally hope that it turns into a place where local women, and their friends and family, can get all of their health needs met.
I'm actually impressed with the design of this health center. It could be an ideal place to pick up a few bottles of wine or to get something to eat. I'd also like to see more of the old school classroom concept of the health center with its big windows.
I think that this place could do a really good job of filling up with patients, and I am happy to see that bishop's Peak is a health center you can visit. Bishop's Peak women's health center is located in the heart of the Twin Cities, in the city's south side, and it is a great place to get a health checkup in addition to other services.
If you have an idea yet, you can go ahead and give it a try. I'm sure you'll give it a try at some point because I'm just starting out with the game.
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Tag Archives: Tim Sale
Today's Super Comics — Superman For All Seasons #1-4 (1998)
Less is often more. Superman For All Seasons, a four-issue miniseries by frequent collaborators Jeph Loeb and Tim Sale, distills Superman into his key elements and zeroes in on his most super quality—he can do almost anything, but he chooses to help people.
The book is set in Superman's early days, and as the title suggests, it's structured around the four seasons. A different character narrates each issue: Jonathan Kent, Lois Lane, Lex Luthor, and Lana Lang.
Pa Kent talks about a young man who was raised right and wants to do right. Lois talks about a dashing man who's too good to be true, and yet he is that good. Luthor talks about a rival for the affection of Metropolis, a lonely man who can't save everyone no matter how good his intentions are. And Lana talks about Clark Kent, the kind boy she grew up with who's still there inside that costume.
Together, the issues form a nice arc, guiding us from Clark's initial desire to use his abilities to help the world, to his initial successes, to his first real defeat, to his acceptance that though he can't do everything, he can still do everything he can do.
Superman has definitive origin details, but he doesn't have a definitive origin story. Nothing about Krypton informs who Clark Kent is as a person. No traumatic event motivates him to become Superman. By virtue of his upbringing, he's intrinsically motivated to do good.
What's interesting, then, is how he grows into the role and his responsibilities, how he adjusts to the burden that he has freely chosen, how he sticks with it despite any setbacks. That's what Superman For All Seasons examines, and that's why it succeeds in instilling a sense of grandeur on nearly every page. To understand the super, you have to understand the man.
In issue #4, two pages are devoted to a single panel of Superman flying over Smallville and looking down as the town is flooding. The only words on the page are Superman saying, "All right, Lana. I'll make things safe." It's a perfect summation of who Superman is.
Writer: Jeph Loeb
Artist: Tim Sale
How to Read It: back issues; Comixology; included in Superman For All Seasons (TPB)
Posted in comic books, Today's Super Comic | Tagged DC Comics, Jeph Loeb, Lana Lang, Lex Luthor, Lois Lane, Superman, Superman For All Seasons, Tim Sale
Today's Super Comic — Solo #1 (2004)
Posted on February 11, 2017 by drsherrier
Anthology series are a tough sell. It's much easier to get invested in ongoing sagas than short stories (and comic book short stories are super-short). I'm plenty guilty of overlooking them, even knowing full well the gems that may be hidden within.
But I actually did pick up one anthology book when it was new—the first issue of DC Comics' Solo. The series was designed to spotlight the talents of renowned comics artists, and each issue "starred" a single such artist. Tim Sale headlined issue #1, joined by writers Darwyn Cooke, Diana Schutz, Jeph Loeb, and Brian Azzarello (and Sale did some of the writing himself).
The issue's stories span genres, from superhero to noir to ordinary slice-of-life, but they're all love stories in their own way. Catwoman takes Batman on a "date" by having him chase her across Gotham, though she's actually chasing him. Supergirl recalls her first love. Martha Kent narrates a story about Clark trying to be a good person on his prom night. A hitman remembers a dead lover and his current loneliness. And so on.
Throughout the book, Sale demonstrates the range of his talents, bringing kinetic energy to Catwoman and Batman's "dance" across the city, innocence and sadness to Supergirl, quiet grandeur to a young Clark Kent, pervasive bleakness to a hitman, and more.
"Solo" may be a misnomer, given all the talent helping out. Name aside, though, it's a solid anthology that allows you to appreciate not only the storytelling possibilities of the artist, but of the comic book medium in general.
Of course, foolish me, I never picked up another issue, and DC cancelled it after #12. (Clearly it's all my fault…or DC's for setting the price tag at $4.95. Probably the latter.)
Writers: Darwyn Cooke, Diana Schutz, Jeph Loeb, and Brian Azzarello
How to Read It: back issues; Comixology; included in Solo: The Deluxe Edition (HC)
Posted in comic books, Today's Super Comic | Tagged Batman, Catwoman, Darwyn Cooke, DC Comics, Jeph Loeb, Solo, Supergirl, Superman, Tim Sale
Today's Super Comics — Batman: The Long Halloween #1-13 (1996-97)
Posted on October 29, 2016 by drsherrier
Seems to be an appropriate time of year for Batman: The Long Halloween, though anytime near a major holiday would work. This Batman story is, as the title implies, long in scope, spanning a full year early in the Dark Knight's career. A serial killer is targeting gangsters, but only on the holidays, giving Batman a mystery to haunt him for a full 365 days.
It's a busy year in which we see many of our favorite Bat-villains, including quite a bit of Harvey Dent as he transitions into Two-Face. Early on, Harvey, Batman, and Commissioner Gordon make a vow to bring down the crime lord Carmine Falcone, a.k.a. the Roman—perhaps with bending some rules, but never breaking any, Gordon insists. You can spot the DNA of the excellent Dark Knight movie in that and other moments throughout. In Gotham City, doing the right thing takes a toll—but it still needs to be done.
Certain writer/artist teams seem to bring out the best in each other, and the quintessential example is frequent collaborators Jeph Loeb and Tim Sale, the writer and artist here. Sale's panels are big, uncluttered, and consistently a little rough, which suits Batman's world rather well, and Loeb writes a lean, efficient script that covers up a minimum of the artwork. Interestingly, the story has plenty of room to breathe over thirteen issues, but it still feels stripped down to its essential components.
And another plus—the story requires Batman to be a detective. That facet of him tends to get overlooked sometimes, particularly in other mediums.
The miniseries deserves its status as a classic. I wouldn't call it the best Batman story or anything like that, but it certainly is something special.
How to Read It: back issues; Comixology; Batman: The Long Halloween (TPB)
Posted in comic books, Today's Super Comic | Tagged Batman, Batman: The Long Halloween, Commissioner Gordon, DC Comics, Jeph Loeb, Tim Sale, Two-Face


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Mallireddy Ajay Kumar (born 12 December 1989) is an Indian cricketer. He made his Twenty20 debut for Andhra in the 2015–16 Syed Mushtaq Ali Trophy on 10 January 2016.

References

External links

1989 births
Living people
Indian cricketers
Andhra cricketers
People from East Godavari district

redpajama_set_name: RedPajamaWikipedia


Orvelina Melendez witnessed her husband's murder. Now on US-bound caravan, she hopes to provide for family, sick son.
Work is suspended on a statue of Gandhi in Malawi, after campaigners argue he used racial slurs.
The agency says there will be cloudy and sunny, hot dry weather conditions with chances of thunderstorms over most parts of the country.
The post What weather will look like on Thursday – NiMet appeared first on Premium Times Nigeria.
The government says N3,000 grant would be given monthly to all pregnant women for their transport to hospitals and clinics.
The post 1000 pregnant women get grants for free ante-natal care in Bayelsa appeared first on Premium Times Nigeria.
The board says the examination will enable them practice in Nigeria.
The post JAMB conducts professional exam for 903 foreign medical, dental graduates appeared first on Premium Times Nigeria.
The former Lagos governor also speaks on the crises in the APC.
The post 2019: Tinubu speaks on Atiku's chance, Oshiomhole's fate as APC chairman appeared first on Premium Times Nigeria.
Three children of Kate's great-grandfather were killed during the war.
The post UK's Duchess, Kate, sees letters of relatives killed in World War 1 appeared first on Premium Times Nigeria.
An American model, Angela Renèe, popularly known as Blac Chyna, is set to visit Nigeria for a pool party in November.
In a post on her Instagram page on Wednesday, the mother of one who was formerly married to Rob Kardashian, a brother to reality TV star, Kim Kardashian described Nigeria as "motherland".
Alongside a flyer of the event, she wrote ""Hello Nigeria, I am visiting the motherland finally to party with the playnetworkafr in Abuja. Shout out to Charlesofplay and hit him up for invites".
Confirming her visit, Arafat Balogun, the manager of Play Network Africa revealed that the deal cost $30,000 aside travel plans and accommodation.
Speaking with NAN, she said the model would be in Abuja for the party.
She said: "A deal in the excess of N10 million has been agreed, excluding travel plans and accommodation. It is going to be a private event at an undisclosed location.
Black Chyna began stripping to pay for her college tuition, but dropped out after falling asleep in class severally due to the nature of her work.
The Islamic Movement in Nigeria (IMN) has accused the Nigerian Army and the Nigeria Police Force of killing 46 of their members.
The group also said 107 members sustained injuries, while 1,000 members were still missing after a clash with security operatives during the three-day religious gathering tagged: 'Arbaeen Symbolic Trek' in Abuja.
The statement read: "The public will recall that on Saturday, 27 October, 2018 the followers of Sheikh Ibraheem Zakzaky (H) publicly announced the commencement of three (3) days religious gathering (Arbaeen Symbolic Trek) and protest for unconditional release of our leader, Sheikh Zakzaky (H). Unfortunately, during the programme, the Military and Police killed 46 mourners and protesters, injured 107 and 1,000 got missing. The worst part of the crime, the Police are now blackmailing us to cover their atrocities.
He also noted that the struggle to demand for justice and unconditional release of their spiritual leader, Sheikh Ibraheem Zakzaky.
Members of National Air Traffic Communicators Association of Nigeria (NACAN) have cried out over non-functional radio equipment in some stations and airports across the country.
Speaking in an interview at the just concluded fifth Annual General Meeting held in Benin City, Edo State, President of NACAN, Comrade Nkambo George, disclosed that it is difficult for some stations to read one another beyond 9am and 10am in some cases.
According to Nkambo, the situation has led to the use of telephone lines, which ought to be the back-up, as the primary source.
He said the southern network with the High Frequency (HF) radios deployed as a stopgap to assist HF, modulates from frequency to frequency and causes delay in messages as long as the network is bad.
"We have resorted to telephones. We use this close user group telephone line, which ought to be the back-up. It's more or less the major equipment we are using now in most communication centres, except for Lagos, Kano, Abuja and Port Harcourt where Alt is still functional.
"Normally, messages are not supposed to be delayed; when you receive a message, it ought to go. The International Civil Aviation Organisation (ICAO) recommends that three minutes or at most five minutes as you receive a message, let it be sent out. So you receive backlog of messages in some centres as a result of equipment malfunction," he said.
He noted that in some centres, the radios were not functioning at all, stating that it is not good for the image of the association.
However, on manpower, Nkambo commended the management of the Nigerian Airspace Management Agency (NAMA) for the transformation in the system through training of its personnel since Captain Fola Akinkuotu took over as the helmsman of the agency.
Nkambo revealed that 40 of its members were undergoing training at the Nigerian College of Aviation in Zaria, while 16 graduated in August this year. He, however, said 28 members were yet to be trained.
According to the NACAN boss, the non-employment into the department in the past two decades had negatively affected communicators and called on the government to commence the recruitment of between 20 and 50 personnel every year to fill the gap. He disclosed that 27 staff would be retiring in January next year, which he declared would also affect the department as the newly recruited personnel had no job experience.
Meanwhile, the management of NAMA has said that the provision of relevant technology is important for professionals to perform their roles.
Ayodele Otitolaye, who represented the Managing Director of NAMA at the event, gave the assurance that the management would continue to provide modern facilities required to perform the task of air navigational services.
According to Otitolaye, the issue of inadequate manpower due to aging workforce is being addressed gradually across all sections of the agency.


This gift measures 10 inches long by 9 inches wide and 16 inches tall and weighs 5 pound complete. This get well gift comes wrapped in cellophane and topped with a handmade get well bow. It is shipped from MIssouri for $14.95 and cannot be hand delivered.


text:
This Week In Landscape | 17 March 2013
Landscape Links for the week 10-17 March 2013
Charles Birnbaum on the future of landscape architecture | Charles Birnbaum | Dwell
Landscape architecture, however, has fared worse. In fact, fewer than 2,500 of the 80,000-plus National Register sites boast any significant landscape design.
Adapting to climate change on the Mississippi | Washington University in St.Louis
In the political realm, climate change remains a point of debate. But for architects, engineers, urban designers and others charged with managing its effects—the storms and floods followed, whiplash style, by drought and water scarcity—the evidence is in.
The City in 2050: Bridging the Gaps and Bringing into Focus the Future of Cities | Carla Guerrera | Stantec Is
The next four decades are full of opportunity for growth but require adaptability, and resiliency.
Los Angeles 2013 | LA Times
On April 3, 1988, the Los Angeles Times Magazine published a 25-year look ahead to 2013.
A New Movement for The New City: Reallocating Space Away from the Car | Bruce McVean | This Big City
If the private car's time is up, the age of the bicycle is just beginning. Bikes, the ultimate form of private urban transport, are space efficient, genuinely zero emissions, healthy, sociable, affordable and fun.
Citizens are key to shaping the city | Marian Scott | The Gazette
Citizens need to do whatever it takes to make their voices heard, Lambert said. "Take to the streets with placards and petitions. Because when you get the public involved, just look at the Old Port," she said.
Camden Amphitheatre & Public Library Receive National Historic Landmark Status | Alice McFadden | The Free Press
The Camden Amphitheatre and Public Library is one of the few public projects of Fletcher Steele, one of America's premier practitioners of 20th-century landscape design.
urban design planning


text:
est une ville située dans la sous-préfecture d'Iburi, à Hokkaidō au Japon.

Toponymie
Le toponyme « Noboribetsu » vient de l'aïnu « nupurpet », qui signifie « rivière sale ».

Géographie

Situation
Noboribetsu est située dans le sud-ouest d'Hokkaidō, au bord de l'océan Pacifique.

Démographie
En , Noboribetsu comptait , répartis sur une superficie de .

Histoire
Le village de Horobetsu a été fondé en 1919. Il est élevé au statut de bourg en 1951. En 1961, le bourg prend le nom de Noboribetsu et obtient le statut de ville en 1970.

Transports
La ville est desservie par la ligne principale Muroran de la JR Hokkaido.

Jumelage
Noboribetsu est jumelée avec :
 ,
 ,
 .

Symboles municipaux
L'arbre symbole de Noboribetsu est le platane, sa fleur symbole le chrysanthème.

Notes et références

Voir aussi

Liens externes
  .

Ville au Japon
Station thermale au Japon


text:
Посёлок Апатито-нефелиновой фабрики № 2, Апатито-нефелиновой фабрики № 2, посёлок АНОФ-2, Новый Город — упразднённый посёлок в пригородной зоне города Кировска Мурманской области РСФСР. В августе 1959 преобразован, вместе с населёнными пунктами Молодёжный, Белореченский, в рабочий пос. Молодёжный. С 1966 года года территория посёлка находится в микрорайоне Молодёжный города Апатиты.

История
Посёлок возник во 2‑й половине 1950‑х годов, вблизи разработок хибинских апатитов, при строительстве второй апатито-нефелиновой фабрики (АНОФ-2).

21 ноября 1955 года на техническом совещании при заместителе председателя Кировского горисполкома было рассмотрено проектное задание по строительству поселка АНОФ-2.

В марте 1958 года трест «Апатитстрой» предъявил к сдаче первый жилой дом в населённом пункте посёлок АНОФ-2 (Новый Город).

Решением Мурманского облисполкома от 13 августа 1959 года населённые пункты Молодёжный, Белореченский и АНОФ-2 были отнесены к категории рабочего посёлка с присвоением этому посёлку наименования «Молодежный».

7 июля 1966 года, в соответствии с Указом Президиума Верховного Совета РСФСР, рабочие посёлки Апатиты и Молодёжный пригородной зоны Кировска преобразованы в город областного подчинения Апатиты. После образования города Апатиты в 1966 году территория упразднённого посёлка вошла в его состав.

Примечания

Упразднённые населённые пункты городского округа город Апатиты
населённые пункты, упразднённые в 1959 году


text:
Q: System.Diagnostics - Eventsource with empty message I'm using System.Diagnostics to get some messages using EventSource, to trace them as a log. It appears that some informations are missing, as for example the EventMessage and the Message (they are empty), while the EventId and the Level are correctly set. The WriteEvent method is correct (The number and types of arguments passed to every event method exactly match what is passed to WriteEvent()).
In particular, I am using WAD on a ServiceFabric Cluster to collect traces on an Azure Table Storage.
Any idea?
Thanks!

A: The parameters of the Event must match to the parameters of the method which you call. Eventsource builds the manifest based on the parameters of the method.

Event methods must match exactly the types of the WriteEvent overload it calls, in particular you should avoid implicit scalar conversions; they are dangerous because the manifest is generated based on the signature of the ETW event method, but the values passed to ETW are based on the signature of the WriteEvent overload.

A: I have had this problem ever since we upgraded to any version of the .NET Framework greater than 4.5.2. I finally figured out my problem and hopefully, this will help you too. I know this is an old post, but maybe it will help someone else as well.
It seems Event Tracing for Windows (ETW) handles errors and the only indication you get is what you're seeing where the logged message is null. I am using a custom formatter derived from IEventTextFormatter. (the EventData.FormattedMessage is null). Setting a breakpoint in this method showed the EventData.FormattedMessage was null. I also noticed the following messages in the Output/debug window in Visual Studio:
The parameters to the Event method do not match the parameters to the WriteEvent method. This may cause the event to be displayed incorrectly.
EventSourceException while processing event "WriteLogDefault": System.IndexOutOfRangeException:Index was outside the bounds of the array.
A first chance exception of type 'System.NullReferenceException' occurred in NEAM.App.ProfileDataMart.BL.dll

I traced my problem to the following method:
[Event(1, Message = "{2}", Level = EventLevel.Informational, Keywords = Keywords.Informational)]
private void WriteLogDefault(int p_EventId, string p_AssemblyName, int p_Key, string p_Message)
{
    WriteEvent(p_EventId, p_AssemblyName, p_Key, p_Message);
}

Based on the remarks in the docs on MSDN, the call to WriteEvent should include the EventId followed by all the arguments passed to your method. Evidently, the above code did not throw any exceptions in .NET Framework 4.5.2 or earlier.

When you implement a method that is identified as an ETW event in an EventSource-derived class. You must call the base class WriteEvent method passing the EventId and the same arguments as the implemented method.

My code now looks like this and works as expected. Good luck!
    [Event(1, Message = "{2}", Level = EventLevel.Informational, Keywords = Keywords.Informational)]
    public void WriteLogDefault(string p_AssemblyName, int p_Key, string p_Message)
    {
        WriteEvent(1, p_AssemblyName, p_Key, p_Message);
    }

redpajama_set_name: RedPajamaStackExchange


Denise is an intuitive reader and established the Tea and Tarot as the centre we know and love today.
Her readings are accurate and insightful and have been of great help to people of all walks of life including e.g. directors of companies, well known celebrities and those special people that keep our society working.
Denise works with her guides and connects with yours and sometimes those of loved ones who have passed to bring to you personal readings to assist in decisions and important situations you need to address.
Her work is accessible from all over the world as she is able to work in proxy for you if you are unable to visit in person.
All readings are strictly confidential and no record of your visit is ever kept.
Denise also teaches the Tarot and heads the Weekly Guided Meditations at the Tea and Tarot.
I am enquiring about the cost of a reading please? I live in Claremont.
Please can I book a reading for 14:00 on Friday 21 October 2016 at Tea and Tarot?
Hi Denise , how much do you charge for a session?


In California, Oakley discovers a sort of safety and freedom in Aunt Jo's beach house. Once they're settled, Mom hands her a notebook full of letters addressed to her—from Lucas. As Oakley reads one each day, she realizes how much he loved her, and each letter challenges her to be better and to continue to enjoy her life. He wants her to move on.


text:
Q: Eclipse Maven: no "Checkout Maven project from SCM" option I have to compile and run a Maven project that some colleagues of mine developed some years ago. I'm using Eclipse Helios on Microsoft Windows Server 2008.
In their documentation about how to compile it with Eclipse, I read that I have to install on my Eclipse:

*

*Maven plugin (from http://m2eclipse.sonatype.org/sites/m2e )

*Maven plugin extra (from http://m2eclipse.sonatype.org/sites/m2e-extras )

*Subclipse plugin ( http://subclipse.tigris.org )

After installed these plugins, their document says to select the option "Checkout Maven Project from SCM" and other operations.
I've tried to install these plugins, but, first, I realized that they are antiquated.
I discovered that Maven Plugin changed name and address (now is Maven Integration for Eclipse 1.2), and that Maven Plugin Extra does not exist anymore.
Subclipse, instead, is available.
So I installed these plugins, but, afterwords, no  "Checkout Maven Project from SCM" functionality was present on my Eclipse.
Then I searched online to solve this problem, and I found this thread on StackOverflow.com. It suggested to install the Maven SCM handler for Subclipse.
I've searched it on the plugin repository of Elcipse but it does not exist anymore.
Which Eclipse plugin may I use to get the "Checkout Maven Project from SCM" functionality on my Eclipse IDE ?
Thanks a lot

A: When you go to Window -> Preferences -> Maven -> Discovery, you have a button "Open Catalog", which opens a plugin marketplace specific to the m2e plugin. There you'll find a plugin called m2e-subclipse, which should give you the appropriate options:

A: Change the installed sub version to have m2 plugin. then check out as maven shows up.
Eclipse Marketplace -> Installed -> Subversive SVN -> Change -> Subversive SVN Integration for the M2E Project (Optional) and install it.
see images below.

A: I have recently run into same issue and found solution, as preferences trick mentioned in this post did not work for me.
All you need to do is to go back to your SVN installation (tested for subversive, guess similar for subclipse) and check m2e integration plug-in.
In my case: Eclipse Marketplace -> Installed -> Subversive SVN -> Change -> Subversive SVN Integration for the M2E Project (Optional) and install it.

A: I have a workaround , since I was not able to find m2e-subclipse in Window -> Preferences -> Maven -> Discovery.You need to check out the project.Once done, right click on the Project Explorer and Import->Maven->Existing Maven Project and select the checked out project and click Finish.

A: Just a heads up for anybody seeing this again for future versions of Eclipse.
Sonatype is no longer maintaining this:
You can either use the fork currently being maintainted in github [https://github.com/subclipse/m2e]:
https://dl.bintray.com/subclipse/releases/m2e/1.0.x/
Or you can use the eclipse maintained link for the eclipse version:
https://download.eclipse.org/technology/m2e/releases/1.9/1.9.1.20180912-1601/
Install either one by using the "install new software" link in Eclipse above "Eclipse Marketplace"

A: I used eclipse version 2022-12 from this website https://mergedoc.osdn.jp/
After I face this problem and try many ways. Finally, I got the solution.

*

*Right-click on repo

*Select Find/Check Out As...

*Choose Check out as a project with the name specified, then input your project name alias

*After checkout is finished. Right-click on the project.

*Select Configure > Convert to Maven Project.


A. If He fell in the hand of Herod, and Herod could not kill Him, he would have thought that His flesh was a phantom.
B. The second reason was to bless the people of Egypt by being among them, so that the prophecy of Hosea (11:1) could be fulfilled which says: '... out of Egypt I called My Son.' And also the prophecy of Isaiah (19:1) be fulfilled: 'Behold, the Lord rides on a swift cloud, and will come into Egypt, the idols of Egypt will totter at His presence, and the heart of Egypt will melt in its midst.' The Lord Jesus came to Egypt with Joseph, and His Virgin mother, and Salome. They passed in an estate called 'Basta' where the Holy Family drank from a well, and the water of the well became a curing water for all sicknesses. Then they went to 'Meniet Samannoud' and crossed the Nile to the western side. In this place, the Lord Jesus put His foot on a stone, and the trace of His foot appeared, and the stone was called 'Picha Isos' in Coptic (or The heal of Jesus). From there, they went westward to Wadi El-Natroun, and St. Mary blessed the place, for her knowledge that many monasteries would be built there. Then they went to 'Al-Ashmoneen' where they settled for several days, after which they went to Mount 'Qosqam', where the monastery of St. Mary was later built, which has been known with the name 'El-Moharrak monastery'. When Herod died, the angel of the Lord appeared to Joseph in a dream saying: 'Arise, take the young Child and His mother, and go to the land of Israel, for those who sought the young Child's life are dead.' (Mt. 2:20-21). They came back to Cairo, and on their return they stayed in a cave which is today located in the church of Abu-Serga in Old Cairo. Then they passed by El-Mataryia, and they washed there from a well, and the well has been blessed since, and a tree of Balsam grew beside it, from its oil the Chrism (El-Myroun) is made which is used to consecrate new churches. From there, the Holy Family walked to El-Mahama (Mustorod), and from there to Israel. We ought to celebrate this feast spiritually with joy, for Our Lord Jesus honored our land.
Glory to His Holy Name for ever. Amen.
On this day also, the prophet Habakkuk departed. He was one of the twelve minor prophets, and he was from the tribe of Levi. He was a singer using stringed musical instruments as it is indicated in this verse: 'The Lord God is my strength; He will make my feet like deer's feet, and He will make me walk on my high hills. To the chief musician. With my stringed instruments.' (Habakkuk 3:19). He prophesied at the time of king Jehoiakim. He lived long enough to see the return of the children of Israel from exile, and he prayed saying: (Habakkuk 3:2) He prophesied about the Incarnation of Christ and His birth, saying: (Habakkuk 3:3). He finished his course, and departed in peace. A church after his name was built in Bartsa - El-Behera in Egypt at the time of the Christian Emperor Anastasius, and consecrated on the twenty fourth of Bashans.
May the prayers of this prophet be with us. Amen.
On this day also, of the year 880 A.M. (May 19th., 1164 A.D.), St. Bashnouna was martyred. He was a monk in St. Macarius (Abu Maquar) monastery. During the fighting between the men of prince Dergham and the men of El-Wazir Shawer in the Fatimid rule, the monk was arrested. He was threatened with death if he did not convert to Islam, but he refused. They burned him alive, and he received the crown of martyrdom. The faithful took his bones and carried them to the church of Abu Serga in Old Cairo, in the place of wax, and they buried him there. May his prayers be with us, and glory be to God forever. Amen.


text:
<!DOCTYPE html>

This test ensures that the focus ring is not shown on the anchor after blurring and focusing the window.

Anchor

anchor was focused
anchor was clicked
anchor was blurred
window was blurred
window was focused
anchor was focused

redpajama_set_name: RedPajamaGithub


text:
Home » District of Columbia
District of ColumbiaAlex2019-10-02T11:23:25-04:00
Sitemap to all pages covering the District of Columbia. Photography contributions by Alex Nitzman, Andy Field, Brent Ivy and Carter Buchanan.
City / Area Guides
Other D.C. Roads
U.S. 1
U.S. 29
E Street Expressway
About the District of Columbia
Population (2013 estimate): 646,449
Nickname: D.C.
Official Flower: American Beauty Rose
Official Bird: Wood Thrush
Official Tree: Scarlet Oak
Total Interstate Mileage: 12.27
Land Area: 61.05 square miles
Highest Point: Reno Hill, 409 feet
Lowest Point: Anacostia and Potomac Rivers, sea level
Founded: July 16, 1790
Major League Sports Franchises
D.C. United (MLS)
Washington Capitals (NHL)
Washington Nationals (MLB)
Washington Redskins (NFL) – moved to Landover, Maryland in 1997.
Washington Wizards (NBA)
Washington Caps (ABA), the Oakland Oaks relocated to Washington and became the Caps in 1969. Playing just one season the franchise moved south to become the Virginia Squires due to territorial rights with the Baltimore Bullets.
Washington Nationals / Senators (MLB), the American League Washington Nationals, renamed officially to the Senators in 1955, left in 1960 to become the Minnesota Twins. A National League expansion franchise, also named the Senators, played in D.C. between 1961 and 1972 before moving to Dallas-Fort Worth to become the Texas Rangers.