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Frantisek Spalek
1 Comparison of Efficient Production of Small Module Gears
Authors: Robert CEP, Vaclav Musil, Sarka Malotova, Jiri Hajnys, Frantisek Spalek
Abstract:The new designs of satellite gears comprising a number of small gears pose high requirements on the precise production of small module gears. The objective of the experimental activity stated in this article was to compare the conventional rolling gear cutting technology with the modern wire electrical discharge machining (WEDM) technology for the production of small module gear m=0.6 mm (thickness of 2.5 mm and material 30CrMoV9). The WEDM technology lies in copying the profile of gearing from the rendered trajectory which is then transferred to the track of a wire electrode. During the experiment, we focused on the comparison of these production methods. Main measured parameters which significantly influence the lifetime and noise was chosen. The first parameter was to compare the precision of gearing profile in respect to the mathematic model. The second monitored parameter was the roughness and surface topology of the gear tooth side. The experiment demonstrated high accuracy of WEDM technology, but a low quality of machined surface.
Keywords: precision of gearing, small module gears, surface topology, WEDM technologyProcedia PDF Downloads 79


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Bereavement Prayers
God, do not let my feelings overwhelm me. During moments of anguish, touch my heart with courage, my soul with your compassion and with your love to comfort me. Despite my pain, let me know healing is occurring. Let me hear often that you are always with me. God, today is different; I am faced with my challenges. Help me know I am not powerless and that I am surviving. Give me hope for tomorrow. Let me believe in myself and allow others to comfort me.
Death is nothing at all… I have only slipped away into the next room. Whatever we were to each other, that we are still. Call me by my old familiar name, speak to me in the easy way which you always used. Laugh as we always laughed together. Play, smile, think of me, pray for me. Let me name be the household word that is always was. Let it be spoken without effort. Life means all that is ever meant. It is the same as it ever was, there is absolutely unbroken continuity. Why should I be out of your mind because I am out of your sight? I am but waiting for you. For an interval, somewhere very near just around the corner. All is well. Nothing is past; nothing is lost. One brief moment and all with be as it was before…only better. Infinitely happier and forever…we will all be one together with Christ.
It has been a long-standing tradition in the church that we honor with respect the memory of deceased loved ones. Thus, the church has always encouraged us to make cemetery visits, to take care of their places of burial and to offer prayers in their behalf. This enables us to realize that physical death is not the last word on human life, because we as persons are destined for endless life, which finds its fulfillment only in God.
These thoughts flow from the beautiful belief we gave in the communion of saints which is three-fold: those who are already enjoying the presence of God in heaven; those who are still being purified and thus look forward to that divine presence of God in heaven; and those of us on earth who await our personal call into eternity.
Through our prayers, we ask the intercession of those already in heaven to assist us in our daily lives, so that we too will one day enjoy God's presence. We, in turn, are able to assist those in purgatory who are still being prepared for the happiness of heaven, among whom could be some of our own family members or friends, through our frequent remembrance of them when we offer holy mass and in our other prayers.
Visiting their place of burial and praying for our deceased love ones, are certainly ways that we keep their memory alive… it is indeed saying to them: "I'll never forget you".
There is a very special person in heaven with whom we can join in our sorrow. Scripture teaches us that our Blessed Mother Mary "pondered in her heart" the happenings of her son's life. She experienced the drama of Christ's death at the foot of the Cross and then shared in the joy of his resurrection. She, whom we invoke as the Gate of Heaven, can help us to understand the value of remembering our departed, and of keeping ourselves focused on the ultimate goal of our lives, which is the eternal happiness of heaven.
God did not promise
Skies always blue,
Flower strewn pathways,
All our lives through;
God did not promise
Sun without rain,
Joy without sorrow.
Peace without pain.
But God did promise
Strength for the day,
Rest for the labor,
Light for the way,
Grace for the trails,
Help from above,
Unfailing sympathy,
Undying love…
Revelation 21:4

subset: commoncrawl


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Olly's Hotel Review: The Mandarin Oriental Bangkok
January 19, 2012 § 3 Comments
View from our room on the 16th floor
The Mandarin Oriental of Bangkok has to be by far my favorite in City hotel. Starting off with the staff absolutely impeccable, they were super helpful at all times, especially the driver that took us to Ayutthaya, he turned out to be our guide throughout the ruins.
The Room of the hotel where great, we were made to chose also which kind of toiletries we wanted to have for our bathrooms, the choice included : Apreys, Acqua di Parma and a more organic one. We were pampered from the beginning to end of our stay with beautiful fruit arrangements, various snacks and a delicious local tea.
This hotel really took me back to how hotels used to feel when I was traveling as a kid, Milly really appreciated all the effort involved and agrees with me that it was by far the best service seen in a while.
The Food at the hotel was absolutely superb and the breakfast was divine. Im really a big fan of big buffet breakfast and the Mandarin Oriental really had to offer everything one could have wanted. The Restaurant across the river was just divine.
Forgot to mention Milly was shown the room of one of her favorite authors Somerset Maugham, the room was beautiful and really captured the spirit of a passed time.
I strongly recommend staying here!
Style: A Classic
Hotel Philosophy: "For more than 135 years distinguished travelers have followed the legendary Chao Phraya River, through the heart of Bangkok, to the doors of one of the world's most luxurious hotels, Mandarin Oriental, Bangkok. A paragon of contemporary luxury and rich in history, our hotel is filled with stories of the celebrated guests who have considered us home during their world travels"
Pros: Super Helpful Service, Fantastic food, Pretty pool, Romantic Restaurant, Impeccable amenities in-room, great view on the city
Cons: The staff in the boutiques were somewhat rude, and unhelpful when asked about items they were selling.
THE MANDARIN ORIENTAL WEBSITE: http://www.mandarinoriental.com/bangkok/
Our living room
Milly and Olly heading to the Veranda restaurant across the river
§ 3 Responses to Olly's Hotel Review: The Mandarin Oriental Bangkok
From Hive-HQ Cape Town QueenB! loves your Buzz!


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Erik Erikson in his book Vital Involvement in Old Age, written with Joan Erikson and Helen Q. Kivnick, stated:
"Old age is a time for remembering and weaving together many disparate elements and for integrating these incongruities into a comprehensive whole."
Willard Spiegelman is the Hughes Professor of English at Southern Methodist University of Dallas. From 1984 until 2016, he served as editor-in-chief of Southwest Review. And for more than a quarter century, he has been a regular contributor to the Leisure & Arts pages of the Wall Street Journal.
In this creative and wise collection of essays, Spiegelman is right in sync with Erikson's definition of old age. Claiming to have reached his biblical allotment of three score years and ten, the author looks backward and then ahead with frequent nods to all the fun, the pleasures and adventures of his international travels, his urban experiences, the art of conversation, his sensitivity to the subtleties of climate and landscape, his need for varied cuisine, his appreciation of music as the perfect conveyor of nostalgia, his response to reunions, and his love of books, art, and quiet.
We were impressed with Spiegelman's yearning for silence which he calls "as precious and rare as slowness or solitude, clean air, and a star-filled night sky." We, like him, have a special place in our hearts for the Quiet Cars on Amtrak trains. Spiegelman's quest for public silence takes him to many places and spaces but it is all for naught. Perhaps his solution is the best one of all — attend a Quaker meeting for silence-in-company.


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Meet the Staff - Howard Middle
Howard Middle Extended Day Program
Site Manager
Phone: (352) 671-7231
Ms. Britteny - Group Leader
Mr. Pete - Group Leader
Ms. Raven - Group Leader
Ms. Eloise- Group Leader
Ms. Destinee - Group Leader
Activity Time - Extended Day
Extended Day provides opportunities for children to develop socially, emotionally, and physically, within a warm and caring atmosphere. This is accomplished through our focus on leadership, academics, wellness, and S.T.E.A.M. (science, technology, engineering, arts, and math). Please note: we offer a morning only program at Belleview Middle, beginning at 6:30 a.m until school begins.


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Good practices in Modern Tensorflow for NLP
__author__ = 'Guillaume Genthial'
TL;DR Recent changes to TensorFlow make it much easier to develop and productionize deep learning models. We've created a notebook full of code snippets that illustrate the new design patterns and features, including a number of tips and tricks specific to developing NLP models.
The evolution of deep learning libraries
Another year, another contest in the small world of deep learning libraries. In 2016, rising from the ashes of Theano (Montreal University's precursor), Google's TensorFlow quickly imposed itself as the industry leader, gaining popularity for its 'ease of use'.
Today, two years later, a new challenger threatens TensorFlow's hegemony. Born at Facebook, PyTorch is quickly gaining popularity, especially among researchers. If you hang out with CS grad students, chances are that one of them will tell you how they 're-implemented an entire model in PyTorch overnight'.
If you're a firm believer in the positive impacts of (fair) competition, it won't come as a surprise, but this led to major welcome changes and additions to TensorFlow, starting especially at version 1.9.
At Roam, we carefully reviewed those additions from an NLP-oriented perspective (though most of it applies to other areas as well). We compiled a short guide to help engineers quickly bootstrap their deep learning projects. We are making it publicly available in the hope that it will help other engineers and scientists, from academia and industry.
The notebook
Our notebook of code snippets covers three main topics:
- Eager execution (PyTorch-like): yes, you can now print actual values of the graph.
tf.data: a better, faster and stronger way of inputing data to the graph (thanks to Derek Murray et al!)
tf.estimator: a new high-level API for model-building.
For (possibly former) users of TensorFlow who went through the concepts of
tf.placeholder, etc. and had to write their own
Model classes: this belongs to the past. You can now have a state-of-the-art deep learning model up and running with just a few lines of code. Less boilerplate code, better readability.
There are still major differences between PyTorch and TensorFlow, and each has its strengths and weaknesses. The notebook will walk you through some of modern TensorFlow's strengths. For instance,
tf.estimator gives you a lot of things for 'free', including TensorBoard, model serialization, RESTful API serving of your model, and a unified interface that makes reusing others' code easier.
Because NLP has its own needs (vocabularies, tokenization, padding, etc.), we also provide some tips and tricks for using TensorFlow in the right way with text, and try to find the right tradeoff between efficiency and ease-of-implementation. (We prefer to use
tf.data.Dataset.from_generator rather than
tf.data.TextLineDataset, and we also like the
tf.contrib.lookup module for vocabularies, etc.)
The main takeaway of our review is that the latest versions of TensorFlow addressed most of the concerns its users had, making it much more friendly, flexible, and efficient. We found that it successfully reduces the need for high-level wrappers like Keras by allowing fast prototyping that almost immediately converts into productionized models – this, for a startup, is a must-have!
What lies ahead?
It's hard to predict the future, but some of the battles that lie ahead probably involve cloud computing (will Google's TPU force most companies to use TensorFlow?), or weights exchange (ONNX?) and framework compatibility (at least of serialized models). In the meantime, let's try to use the existing tools at their best!


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Farnam Jahanian, Ph.D.
Next Generation Computing and Big Data Analytics
April 24, 2013
Good morning, Chairman Massie and Chairman Bucshon, Ranking Members Wilson and Lipinski, and members of the Subcommittees. My name is Farnam Jahanian and I am the Assistant Director of the Computer and Information Science and Engineering (CISE) Directorate at the National Science Foundation (NSF). I also serve as the co-chair of the interagency Networking Information Technology Research and Development (NITRD) program, which provides a framework and mechanisms for coordination among 20 Federal agencies that support networking and information technology R&D.
I welcome this opportunity to highlight U.S. investments in advanced computing infrastructure and Big Data research and education â and how they are leading to transformative breakthroughs in all areas of science and engineering, as well as providing enormous societal benefit. The goal is to fund Big Data research at the frontiers of knowledge, to capitalize on the intellectual capacity of both early and experienced investigators in our Nation's academic and research institutions, and to foster partnerships across U.S. government agencies, the private sector and international organizations to effectively leverage these investments.
Innovative information technologies are transforming the fabric of society, and data represent a transformative new currency for science, education, government and commerce. Data are everywhere; they are produced in rapidly increasing volume and variety by virtually all scientific, educational, governmental, societal and commercial enterprises.1
Today we live in an "Era of Data and Information." This era is enabled by modern experimental methods and observational studies; large-scale simulations; scientific instruments, such as telescopes and particle accelerators; Internet transactions, email, videos, images, and click streams; and the widespread deployment of sensors everywhere â in the environment, in our critical infrastructure, such as in bridges and smart grids, in our homes, and even on our clothing! Consider this fact: every day, 2.5 quintillion bytes of data are generated â so much that 90% of the data in the world today has been created in the last two years alone2.
When we talk about Big Data, however, it is important to note that it is not just the enormous volume of data that needs to be emphasized, but also the heterogeneity, velocity, and complexity that collectively create the science and engineering challenges we face today.
In December 2010, the President's Council of Advisors on Science and Technology (PCAST) published a report to the President and Congress entitled, Designing a Digital Future: Federally Funded Research and Development in Networking and Information Technology3. In that report, PCAST pointed to the research challenges involved in large-scale data management and analysis and the critical role of Networking and Information Technology (NIT) in moving from data to knowledge to action, underpinning the Nation's future prosperity, health and security.
Through long-term, sustained investments in foundational computing, communications and computational research, and the development and deployment of large-scale facilities and cyberinfrastructure, Federal agency R&D investments over the past several decades have both helped generate this explosion of data as well as advance our ability to capture, store, analyze and use these data for societal benefit. More specifically, we have seen fundamental advances in machine learning, knowledge representation, natural language processing, information retrieval and integration, network analytics, computer vision, and data visualization, which together have enabled Big Data applications and systems that have the potential to transform all aspects of our lives.
These investments are already starting to pay off, demonstrating the power of Big Data approaches across science, engineering, medicine, commerce, education, and national security, and laying the foundations for U. S. competitiveness for many decades to come. Let me offer three examples:
- Today's homes account for more than 20 percent of the total energy consumption in the Nation,4 and about half of that energy is consumed for heating and cooling5. Each degree cooler a house is kept in the winter or each degree warmer in the summer can mean energy savings of 20%, translating to $200 to $300 in lower energy bills per year â not to mention fewer power plants built and lower carbon emissions6. A team of researchers has pioneered an intelligent thermostat that uses machine learning to transform home heating and cooling. At first, a person may set the thermostat four times in one day â upon getting up, going to work, getting back from work, and going to bed. The thermostat uses those settings daily, but then adapts to further changes. If a person is out of town every other Monday on business, for instance, the thermostat's sensors coupled with machine learning algorithms detect the lack of activity and switch to an "auto away" setting for lower energy use.
- Collectively, Americans spent nearly 630,000 years â 5.52 billion hours â stuck in traffic in 2011, at a cost of $121.2 billion in fuel, maintenance, and lost productivity7. A number of regional ventures â in Los Angeles, the Bay Area, northern New Jersey, and here in the Washington, DC, region â are integrating heterogeneous data sources such as road sensors, traffic cameras, individuals' GPS devices, etc., to develop principles and methods that go beyond real-time traffic data and allow us to do inference over entire cities8,9,10. The aim is to identify hotspots and traffic-sensitive directions to drivers well before a potential traffic jam materializes. In Los Angeles, for example, city planners have synchronized every one of the 4,500 traffic signals across 469 square miles of downtown, and they use a system of sensors in the road measuring traffic flow, live traffic cameras, and a centralized computing platform leveraging data mining and machine learning to make constant, automated adjustments and keep cars running as smoothly as possible11. Under this system, the average speed of traffic across the city has increased by 16%, with delays at major intersections down 12%11.
- Breast cancer is the most common cancer among American women, except for skin cancers, and nearly 40,000 women die from the disease each year12. By extending image analysis techniques to hundreds of breast cancer biopsy images, researchers were able to identify a small subset of cellular features â out of over 6,000 possible features â that was predictive of survival time among breast cancer patients13. This feature set was unique: pathologists had not previously identified it as relevant to cancer prognosis, and the information and insight was above and beyond that of many existing standard measures of cancer severity, such as grade, protein markers, tumor size, and lymph node status. The feature set also resulted in an unexpected finding: the features that were the best predictors of patient survival were not from the cancer tissue itself, but rather from adjacent tissue â something that had gone undetected by pathologists and clinicians. These new discoveries will allow clinicians to better understand the genesis and morphology of breast cancer, enabling personalized treatments that aim to improve survival times among patients.
These kinds of breakthroughs are catalyzing a profound transformation in the culture and conduct of scientific research, requiring new methods to derive knowledge from the data; new infrastructure to manage, curate and serve data to communities; new approaches to education and training; and, finally, new types of collaborations of multi-disciplinary teams and communities that have the potential to solve todayâs most complex science and engineering challenges. Through the NITRD program and its member agencies, the U.S. Government has responded to this Big Data revolution with a bold, sustainable, and comprehensive approach. These agencies support the development of cutting-edge tools and algorithms for all aspects of the data lifecycle, all with the aim of helping scientists to transcend the logistics of data handling and to focus on scientific discovery. Many of these advances leverage capabilities in high performance computing, which shares many of the same technology challenges as data-intensive science and has become a critical tool in analyzing and interpreting scientific data. As part of this overall effort, NSF will also address data access policies to better enable science communities to work together to address science challenges.
Why is Big Data Important?
Big Data is important to all facets of the discovery and innovation ecosystem, including the Nation's academic, government, industrial, entrepreneurial, and investment communities.
First, insights and more accurate predictions from large and complex collections of data have important implications for the economy. Access to information is transforming traditional businesses and is creating opportunities in new markets. Further, Big Data is driving the creation of new IT products and services based on business intelligence and data analytics, and is boosting the productivity of firms that use it to make better decisions and identify new business trends.
Second, advances in our ability to store, integrate, and extract meaning and information from data are critical to accelerate the pace of discovery in almost every science and engineering discipline. From new insights about protein structure, biomedical research and clinical decision-making, and climate modeling, to new ways to mitigate and respond to natural disasters, and develop new strategies for effective learning and education â there are enormous opportunities for data-driven discovery.
Third, Big Data will be a key component to solving the Nation's most pressing challenges â in education, healthcare, medicine, energy, transportation, commerce, disaster prevention and mitigation, and cyber and national security â yielding enormous societal benefit and laying the foundations for U.S. competitiveness.
There are enormous opportunities to harness the increasingly large-scale and diverse data sets, to extract knowledge from them, to provide powerful new approaches to drive discovery and decision-making, and to make increasingly accurate predictions and move toward deeper understandings of causal relationships based on advanced data analysis. These advances will lead to new innovations, job creation, and long-term economic development and prosperity.
New Era of Data and Information
We are now in a new era of observation as well as a new era of data and information14. Today, our scientific tools provide an unprecedented sophistication, resolution and scope. Within the NSF-supported research context, these tools can reach the outer edges of the universe as well as dig deep into the tiniest phenomenon. They can transcend all scales, from the molecular and genetic to the organismal and social. Our ability to gain new knowledge would be impossible without these capabilities.
At the one extreme, advanced research infrastructure â large-scale facilities, experimental tools, and cyberinfrastructure â enables new knowledge at the far reaches of the cosmos. For example, the Large Synoptic Survey Telescope (LSST), jointly funded by NSF and the Department of Energy (DOE), is expected to enter full operations for a 10-year survey beginning in January 2022. This telescope will probe mysterious dark matter and dark energy, map small objects in the solar system, particularly near-Earth asteroids, and detect transient optical events such as novae or supernovae â generating 30 terabytes of data each night that span billions of light years.
At the other end of the spectrum, we see research that explores phenomena at nano, pico, and femto scales. NSF is supporting IceCube, a particle detector drilling three kilometers deep into the Antarctic in search of interactions of a nearly massless subatomic particle called a neutrino, which could reveal the new physical processes associated with the enigmatic origin of the highest energy particles in nature.
Likewise, we can explore the properties of a single neuron in the brain, in response to different stimuli and stresses â and then integrate data about millions of such neurons to begin to understand not just the underlying biology, but also the interconnections of the brain that give rise to the psychology of the human mind.
We have new opportunities with technology as well. For example, with the advent of the Internet and mobile devices, "citizen science" is increasingly leading to new knowledge. Take the iPad, for example. One can envision a middle-school child anywhere in the world accessing data in real time that comes out of a hundred-million-dollar facility funded by NSF. The child could participate in an experiment in which he or she actually gathers data where he or she lives. In another example, last year, citizens distributed across the U.S. using a software application, FoldIt, together resolved the detailed molecular structure of an enzyme that is believed to play a critical role in the spread of the AIDS virus â a breakthrough that had confounded scientists for decades15.
These advances are not only experimental in nature, but also, combined with advances in computational hardware and software to capture and make sense of the data, they are equally computational and theoretical.
What Does this Mean for Scientific Discovery?
Data are motivating a profound transformation in the culture and conduct of scientific research. Data-driven discovery is revolutionizing scientific exploration and engineering innovations. This approach has been called the "fourth paradigm," in contrast to the three earlier approaches to scientific research: empirical observation and experimentation; analytical/theoretical approaches; and computational science and simulation. Such a data-enabled approach to science complements these other earlier approaches, but has the promise to revolutionize science even further.
Indeed, the fourth paradigm has led to improved hypotheses and faster insights. From new knowledge about protein structure paving the way to advances in biomedical research and clinical decision-making, to new ways to mitigate and respond to natural disasters, to new strategies for effective learning and education, there are enormous opportunities for this new form of scientific discovery called "data-driven discovery"!
Data access and analysis are already having enormous impacts. The opportunities for the future are immense. Imagine, if you can:
- Complete health/disease/genome/environmental knowledge bases that enable biomedical discovery and patient-centered therapy. The data can be mined to spot unwanted drug interactions or to predict onset of diseases.
- Companies that, by linking together finance, human resources, supply chain, customer management systems, can use data mining techniques to get a complete picture of their operations â to identify new business trends, operate more efficiently, and improve forecasting.
- Accurate high-resolution models that support forecasting and management of increasingly stressed watersheds and ecosystems.
- Consumers that all have the information they need to make optimal energy consumption decisions in their homes and cars.
- Accurate predictions of natural disasters, such as earthquakes, hurricanes, and tornadoes, that enable life-saving and cost-saving preventative actions.
- A cyber-enabled world that is safe, secure, and private, enabling assured use of our critical infrastructure and on-line commerce.
- Students and researchers who have access to intuitive tools to view, understand, and learn from publicly-available, large scientific data sets on everything from genome sequences to astronomical star surveys, from public health databases to particle accelerator simulations, and teachers and educators who use student performance analytics to improve learning and enhance assessment.
Many R&D challenges remain. Below is a list of Big Data hard problems that the research community is addressing:
- Many data sets are too poorly organized to be usable. Research must come up with new techniques to better organize and retrieve data.
- Many data sets are comprised of unstructured data. Research must develop new data mining tools and/or machine learning techniques to make these data usable. Opening government data to all is one of the first steps to spur innovation.
- Many data sets are heterogeneous in type, structure, semantics, organization, granularity, and accessibility. Research must find novel ways to integrate and customize access to federated data; research must find ways to make heterogeneous data more interoperable and usable.
- The utility of data is limited by our ability to interpret and use it. Research must find better usability techniques to extract and visualize actionable information. Research needs to discover new techniques for evaluating and showing results.
- More data are being collected than we can store. With the right data infrastructure, practitioners could analyze data as it becomes available; they could immediately decide what to archive and what to discard.
- Many data sets are too large to download or send over today's Internet. With the right data infrastructure, practitioners could analyze the data wherever it resides, instead of sending it to data centers.
- Large and linked datasets may be exploited to identify individuals. Research on privacy protection and Big Data is critical; new techniques and analysis could have "built-in" privacy preserving characteristics.
The landscape of open research and development challenges is vast. American scientists must rise to the occasion and seize the opportunities afforded by this new, data-driven revolution. The work we do today will lay the groundwork for new enterprises and long-term economic prosperity.
U.S. Government Response to Big Data R&D Challenges
The December 2010 PCAST report â Designing a Digital Future: Federally Funded Research and Development in Networking and Information Technology16 â recommended several actions to take advantage of Big Data opportunities.
The potential impacts and outcomes for the Nation are huge â on the economy, the pace of discovery in science and engineering, national security, healthcare, education, energy efficiency, real-time labor market information, to name a few national priorities. The Office of Science and Technology Policy (OSTP) in the Executive Office of the President (EOP) responded to these recommendations, in part, by chartering a Big Data Senior Steering Group (BDSSG) that would focus on Big Data R&D under the NITRD umbrella. Currently, NSF and the National Institutes of Health (NIH) co-chair the SSG, and membership is comprised of representatives from the science agencies, such as NSF, NIH, the National Institute of Standards and Technology (NIST), DOE Office of Science, and National Aeronautic and Space Agency (NASA), as well as Departments of Defense, Health and Human Services, Treasury, and Commerce (National Oceanic and Atmospheric Administration).
Over the course of the year following its establishment, the BDSSG inventoried existing Big Data programs and projects across the agencies and began coordinating their efforts in four main areas: investments in Big Data core techniques and technologies; education and workforce; domain cyberinfrastructure; and challenges and competitions.17 Other critical areas for Big Data were also identified, including privacy issues, open access to government data, and partnerships with industry and not-for-profits.
On March 29, 2012, the Administration launched the National Big Data Research & Development Initiative. Led by OSTP, this initiative seeks to greatly improve the tools and techniques used for Big Data analysis and the human capital needed to move data to knowledge to action.
Examples of agency efforts that are well aligned with this initiative include18:
- The Department of Defense (DOD) launched "Data to Decisions," a series of programs that are harnessing and utilizing massive data in new ways, and bringing together sensing, perception, and decision support to (a) make truly autonomous systems that can maneuver and make decisions on their own, and (b) improve situational awareness to help warfighters and analysts and provide increased support to operations.
- Defense Advanced Research Projects Agency (DARPA) announced the XDATA program to develop computational techniques and software tools for analyzing large volumes of data, both semi-structured (tabular, relational, categorical, and meta-data) and unstructured (text documents, message traffic), particularly in the context of targeted defense applications.
- NIH made available 200 terabytes of data from the 1000 Genomes Project in the cloud through Amazon Web Services (AWS), constituting the world's largest set of data on human genetic variation and enabling genome-wide association studies to understand the genetic contribution to disease.
- Through its Scientific Discovery Through Advanced Computing (SciDAC) program, the DOE Office of Science unveiled a $25 million Scalable Data Management, Analysis, and Visualization Institute, spanning six national laboratories and seven universities, that is developing "new and improved tools to help scientists manage and visualize data" and supporting the scientists in their use.
- The U.S. Geological Survey (USGS) John Wesley Powell Center for Analysis and Synthesis issued awards focused on improving our understanding of earth system science through Big Data, including "species response to climate change, earthquake recurrence rates, and the next generation of ecological indicators."
In addition, a number of other agencies are participating, including the Office of the Director of National Intelligence (ODNI) through its Intelligence Advanced Research Projects Activity (IARPA), the Department of Homeland Security (DHS), Department of Veterans Affairs (VA), Food and Drug Administration (FDA), National Archives and Records Administration (NARA), and National Security Agency (NSA)19.
Anchoring this coordinated effort, NSF and NIH released a joint solicitation, "Core Techniques and Technologies for Advancing Big Data Science & Engineering," or "BIGDATA." This program aims to extract and use knowledge from collections of large data sets in order to accelerate progress in science and engineering research. Foundational research advances in data management, analysis and collaboration promise to change paradigms of research and education, and develop new approaches to addressing national priorities. The goal is new capabilities for data-intensive and data-enabled science to create actionable information that leads to timely and more informed decisions and actions. It will both help to accelerate discovery and innovation in all sciences, engineering and education, as well as support their transition into practice to benefit society.
As we enter the second year of the Big Data Initiative, the BDSSG is encouraging multiple stakeholders, including federal agencies, private industry, academia, state and local government, non-profits and foundations, to develop and participate in Big Data innovation projects across the country. The BDSSG is planning an event to announce these new projects and to emphasize the importance of building multi-stakeholder partnerships in all areas of Big Data science and engineering across the country.
Collectively, these coordinated activities led PCAST to conclude in a January 2013 update to its 2010 report on Networking and Information Technology R&D, "Federal agencies have made significant progress in supporting R&D for data collection, storage, management, and automated large-scale analysis ('big data')"20. PCAST found that Big Data remains a "critical focal point" in 2012 and beyond, and recommended continued emphasis and coordination.
Coordination of Federal Big Data Investments
NSF coordinates its Big Data R&D activities with other Federal agencies, including the NIH, NASA, DOE Office of Science, DARPA, and many others, through the following "mission-bridging" mechanisms:
- The National Science and Technology Council's NITRD Sub-Committee, of which I am co-chair, has played a prominent role in the coordination of the Federal government's Big Data research investments.
- Under the NITRD umbrella, the BDSSG coordinates Big Data R&D across the member agencies by 1) promoting new science and accelerating the progress of discovery through large, heterogeneous data; 2) exploiting the value of Big Data to address areas of national needs, agency missions and societal and economic importance; 3) supporting responsible stewardship and sustainability of Big Data resulting from federally-funded research; and 4) developing and sustaining the infrastructure needed to advance data science.
- Under the auspices of the NITRD program and the BDSSG, various participating agencies collectively sponsor workshops, develop joint programs, and invest in other activities that leverage their complementary missions.
Most multi-disciplinary, cross-agency fields of NIT inquiry in which NSF makes investments are managed in a similar way (cybersecurity, cyberphysical systems, etc.).
A Framework for NSF Investments
At NSF, it is expected that improvements in access, manipulation, data mining, management, analysis, sharing and storing of Big Data will provide new insights, change paradigms of research and education, and create new approaches to addressing national priorities. NSF has identified four major investment areas that address these challenges and promise to serve as the foundations of a comprehensive, long-term agenda. They are:
- Foundational Research in all Areas of Science and Engineering: Advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large, diverse, distributed and heterogeneous data sets. Facilitate the development of new data analytic tools and algorithms; scalable, accessible, and sustainable data infrastructure; and large-scale integrated statistical modeling. This research aims to, among other things, advance our knowledge and understanding of mathematical and physical systems, the science of learning, and human and social processes and interactions.
- Cyberinfrastructure: Provide science, engineering, and education with a comprehensive data infrastructure that will enable the capture, management, curation, analysis, interpretation, archiving and sharing of data of unprecedented scale, parallelism, and complexity in a manner that will stimulate discovery in all areas of inquiry, and from all instruments and facilities, ranging from campus- to national-level investments.
- Education and Workforce Development: Ensure that the future, diverse workforce of scientists, engineers, and educators is equipped with the skills to make use of, and build upon, the next generation of data analytics, modeling, and cyberinfrastructure. Support new approaches to K-16 teaching and learning that takes advantage of new cyberinfrastructure and data-driven approaches, leading to a national learning laboratory.
- Scientific Community Building and Governance: Support transformative interdisciplinary and collaborative research in areas of inquiry stimulated by data through the development of robust, shared resources and partnerships across diverse communities. This development must acknowledge the new challenges surrounding reproducibility, storage, curation, and open dissemination of scientific data in all its forms, and recognize its importance for accelerating fundamental discovery, interdisciplinary research, and innovation in society. Open and shared data can enable new approaches for communities to address complex problems in science and engineering.
NSF is developing a bold and comprehensive approach for this new data-centric world â from fundamental mathematical, statistical and computational approaches needed to understand the data, to infrastructure at a national and international level needed to support and serve our communities, to policy enabling rapid dissemination and sharing of knowledge. Together, these activities will accelerate scientific progress, create new possibilities for education, enhance innovation in society and be a driver for job creation. Everyone will benefit from these activities.
Big Data Research: NSF continues to cast a wide net and let the best ideas surface, rather than pursuing a prescriptive research agenda. It engages the Big Data research community in developing new fundamental ideas, which are then evaluated by the best researchers through the peer review process. This process, which supports the vast majority of unclassified researchers in the United States, has led to innovative and transformative results. Indeed, NSF investments today leverage a long history of Foundation-wide support for data analytics and computational science.
In October 2012, just six months after the Big Data Initiative launch, NSF and NIH announced nearly $15 million in new Big Data fundamental research projects, the first step toward realizing the goals to advance the foundational science and engineering of Big Data. We received over 450 proposals in response to the joint solicitation, spanning a broad spectrum of R&D activities from new scientific techniques for Big Data management, to new data analytic approaches, to e-science collaborations with possible future applications in a variety of fields, such as medicine, physics and economics.
As an example of the new awards that we made, consider the work of Eli Upfal of Brown University, who is leading a project on data analytics. Dr. Upfal and his team plan to develop mathematically well-founded algorithmic and statistical techniques for analyzing large-scale, heterogeneous and so-called "noisy" data. The resultant algorithms will be tested on extensive cancer genome data, contributing to better health and the development of new health information technology.
A second example is an award led by Christos Faloutsos of Carnegie Mellon University and Nikolaos Sidiropoulos of the University of Minnesota, aiming to develop theory and algorithms to tackle the complexity of language processing and to develop methods that approximate how the human brain works in processing language. The research also promises better algorithms for search engines, new approaches for understanding brain activity, and better recommendation systems for the retail sector.
NSF also funds center-scale activities. One project announced at the Big Data Initiative launch in March 2012 was a $10 million award to researchers at the University of California, Berkeley, under the NSF Expeditions in Computing program. The research team will integrate algorithms, machines, and people (AMP) to turn data into knowledge and insight. The objective is to develop new scalable machine-learning algorithms and data management tools that can handle large-scale and heterogeneous datasets (spanning data generated by computers, sensors and scientific instruments; media such as images and video; and free-form tweets, text messages, blogs and documents), novel datacenter-friendly programming models, and an improved computational infrastructure. The team is focusing on key applications of societal importance, including cancer genomics and personalized medicine; large-scale sensing for traffic prediction, environmental monitoring, and urban planning; and network security. Although the project is only in its first year, it has already led to significant contributions, including open source high performance machine learning software, called Spark, that was featured on Siliconangle's list of Top 5 Open Source Projects in Big Data21.
Aside from its investments in fundamental research, NSF also supports development activities beyond the stage of research prototypes through its Small Business Innovative Research (SBIR) and Small Business Technology Transfer (STTR) programs, as well as the Innovation Corps Teams Program (I-Corps), which identifies NSF-funded researchers who will receive additional support â in the form of mentoring and funding â to accelerate innovation that can attract subsequent third-party funding.
Big Data Education and Workforce Development: A report by the McKinsey Global Institute22 estimated, "By 2018 the United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data."
At NSF, investments in Big Data research are accompanied by investments in Big Data education and workforce development. Research undertaken in academia not only engages some of our nation's best and brightest researchers, but because these researchers are also teachers, new generations of students are exposed to the latest thinking from the people who understand it best. And when these students graduate and move into the workplace, they are able to take this knowledge and understanding with them. Moreover, faculty members in this dual role of researchers and teachers have incentives to write textbooks and develop other learning materials that allow dissemination of their work to a wide audience, including teachers and students nationwide.
Over the years, NSF has supplemented its investments in Big Data by giving additional funding to researchers who were willing to bring undergraduates into their labs through the Research Experiences for Undergraduates (REU) program. This program gives many undergraduate students their first hands-on experiences with real science and engineering research projects. In addition, NSF funds up and coming young investigators through the prestigious CAREER program that offers awards in support of junior faculty who are exemplary teacher-scholars. These awardees conduct outstanding research, develop and implement excellent education plans, and integrate education and research within the context of the mission of their organizations.
More recently, NSF used its Integrative Graduate Education and Research Traineeship (IGERT), mechanism to educate and train researchers in data-enabled science and engineering, including 1) core techniques and technologies for advancing big data science and engineering; 2) analyzing and dealing with challenging computational and data-enabled science and engineering (CDS&E) problems; and 3) researching, providing, and using the cyberinfrastructure that makes cutting-edge CDS&E research possible in any and all disciplines.
Finally, the move from face-to-face to online and blended learning, which allows for learning anywhere, anytime, and by anyone, is rapidly transforming education into a data-rich domain. By collecting, analyzing, sharing, and managing the data collected through monitoring learners' use of technology, we can begin to understand how people learn. The result is an ability to advance understanding of how to use technologies and integrate them into new learning environments so that their potential is fulfilled. An anticipated cross-disciplinary effort is participation in an Ideas Lab to explore ways to use Big Data to enhance teaching and learning effectiveness23.
Computational and Data Cyberinfrastructure: NSF has been an international leader in high-performance computing (HPC) deployment, application, research, and education for almost four decades. With the accelerating pace of advances in computing and related technologies, coupled with the exponential growth and complexity of data for the science, engineering, and education enterprise, new approaches are needed to advance and support a comprehensive advanced computing infrastructure that facilitates transformational ideas using new paradigms and approaches. The goal is a complementary, comprehensive, and balanced portfolio of advanced computing infrastructure and programs for research and education to support multidisciplinary computational and data-enabled science and engineering that in turn support the entire scientific, engineering, and education community.
Below, I give a few examples, illustrating the range and scope of today's computational and data cyberinfrastructure.
Advanced Computational Infrastructure. Last October, NSF inaugurated "Yellowstone," one of the world's most powerful supercomputers, based at the National Center for Atmospheric Research (NCAR) in Cheyenne, WY, and this past March, NSF dedicated two advanced computational facilities, "Blue Waters," located at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, and "Stampede," headquartered at the Texas Advanced Computing Center at the University of Texas at Austin.
The three systems will provide the nation's research community with unprecedented computational capabilities, further enhancing the already potent union between technology and the human mind, offering the opportunity to better test and advance great scientific ideas. Each strengthens the other.
Consider, for instance, the extraordinary capability of just one of these systems, Blue Waters. This computing system is equipped with more than 1.5 petabytes of memory, enough to store 300 million images from your digital camera; more than 25 petabytes of disk storage, enough to store all of the printed documents in all of the world's libraries; and up to 500 petabytes of tape storage, enough to store 10% of all the words spoken in the existence of humankind. If you could multiply two numbers together every second, it would take you 32 million years to do what Blue Waters does each second.
By all measures, these computers, with their high speed and storage capacity, and the ability to produce high-resolution simulations, will have a significant impact on the pace of scientific progress. They will expand the range of data-intensive computationally-challenging science and engineering applications that can be tackled with current national resources. They will allow today's scientists to better understand the workings of Earth and beyond, for example, by helping to trace the evolution of distant galaxies, by providing data that contribute to the design of new materials, and by supporting researchers trying to forecast tornadoes, hurricanes and other severe storms, and even space weather, such as solar eruptions.
Sloan Digital Sky Survey. The Sloan Digital Sky Survey (SDSS) â one of the most ambitious and influential surveys in the history of astronomy â was launched in 2000. It collected more data in its first few weeks of operation than had been amassed in the entire previous history of astronomy. Within a decade, over 140 terabytes of information were collected, representing 35% of the sky. The final dataset includes 230 million celestial objects detected in 8,400 square degrees of imaging, and spectra of 930,000 galaxies, 120,000 quasars, and 225,000 stars.
NSF and NASA jointly support this project, together with the Alfred P. Sloan Foundation, DOE, and international partners in Japan and Germany. The protocols developed in this cyberinfrastructure underpin astronomical archives the world over, including the Panoramic Survey Telescope and Rapid Response System project, now about to issue its first data release, and the planned LSST, which will produce approximately the same amount of data as the first decade of SDSS, every single night of its operation. A recent survey of literature citations has listed SDSS as the most influential, most cited observatory.
iPlant. iPlant, a plant science cyberinfrastructure collaborative led by the University of Arizona, utilizes new computer, computational science and cyberinfrastructure solutions to address an evolving array of grand challenges in the plant sciences. This center is a community-driven effort, involving plant biologists, computer and information scientists and engineers, as well as experts from other disciplines, all working in integrated teams.
An important grand challenge that iPlant is attempting to address (i.e., bridging the divide between genotype and phenotype) involves integrating many types of data, including DNA sequences, trait data, and geographical occurrence information. The latter is particularly useful, as a large proportion of variation in phenotype is due to environmental influences. iPlant's computational capabilities have enabled species range models for over 88,000 species across North and South America; this will help set the baseline for biodiversity. Catalyzed in part by iPlant efforts, large agricultural datasets have been released from Monsanto and Syngenta for use in modeling crop performance under existing and predicted climate regimes.
NEON. A Major Research Equipment and Facilities Construction (MREFC) project, the National Ecological Observatory Network (NEON) is a continental-scale observatory designed to gather and provide 30 years of ecological data24. By making all its data freely available, NEON is providing infrastructure to facilitate hypothesis-driven basic biological and ecological research, enabling the development of a predictive understanding of the direct effects and feedbacks between environmental change and biological processes.
NEON is unique in its continental reach and longitudinal data collection over several decades, delivering and curating a multimodal stream of never-before-available regional and continental scale ecological datasets to the scientific community and the Nation. Just as NEON researchers will benefit from access to data from federal agency networks, federal agencies will benefit from the techniques, sensors and knowledge gained through NEON-enabled activities. NEON's systems engineering-guided design, construction and operations plans, and formalized transition to operations are defining a new standard for research infrastructure deployment and operations. Other Federal Agencies (e.g., US Department of Agriculture, NASA) and international groups (e.g., European Union, Australian Terrestrial Observing Network) are emulating the standards established by NEON, Inc.
Big Data Community Building and Partnerships: NSF seeks to enable research communities to develop new visions, teams, and capabilities dedicated to creating new, large-scale, next-generation data resources and relevant analytic techniques to advance fundamental research across all areas of science and engineering as well as to transition discoveries into practice.
An example of successful community building is EarthCube, which focuses on the development of community-guided cyberinfrastructure to integrate big data across geosciences and ultimately change how geosciences research is conducted. Integrating data from disparate locations and sources with eclectic structures and formats that has been stored as well as captured in real time will expedite the delivery of geoscience knowledge.
In 2013 EarthCube released a solicitation to engage all stakeholders, from geoscientists to computer scientists, industry, academia and government, to build on the momentum and enthusiasm generated in the past year. The different components of the call will allow these stakeholders to participate in developing the next stage of EarthCube. This includes coordination networks to help geoscientists develop standards and policies, demonstrations of promising technologies for integrating across geosciences data, and activities to plan innovative architectures across the whole enterprise.
In recent years, the transition of Big Data research results to the private sector has helped bring innovative Big Data solutions and technologies to the marketplace, fuel job growth, and promote economic growth and improved health and quality of life. Some of the examples I noted earlier in this testimony speak to this transition of discoveries into practice. By promoting strong connections between academia and industry, NSF further enhances its research portfolio in Big Data with foundational concepts and new ideas that are directly relevant to the commercial sector.
Data Access Policy
As investments in research, education, and cyberinfrastructure further Big Data science and engineering, there is also recognition of the importance of enabling rapid dissemination and sharing of new knowledge, tools, and expertise. In February 2013, OSTP issued a memo directing Federal agencies to develop plans to support increased access to results from federally-funded research25. The memo focuses on two particular elements. First, peer-reviewed publications should be stored for "long-term preservation and publicly accessible to search, retrieve, and analyze in ways that maximize the impact and accountability of the Federal research investment." Second, digitally formatted scientific data resulting from unclassified research "should be stored and publicly accessible to search, retrieve and analyze."
In my testimony today, I have tried to illustrate how we find ourselves in the midst of a new era of data and information, driven by innovative information technologies that are at the center of an ongoing societal transformation in terms of how we live, work, learn, play, and communicate. I have outlined the enormous volume, velocity, heterogeneity, and complexity of data being generated through modern experimental methods and observational studies, large-scale simulations, Internet transactions, and the pervasive use of sensor-based technologies. I have indicated how the U.S. government has responded to this new era through a coordinated, multi-agency National Big Data Research & Development Initiative, and, in particular, described NSF's role in support of Big Data research, education, and cyberinfrastructure. Finally, I have shared with you how these investments are starting to pay off; much progress has been made and, in turn, the power of Big Data approaches is evident in nearly all sectors of society and across all national priority areas. With robust sustained support for fundamental research, education, and infrastructure in the area of Big Data in both the executive and legislative branches of our government, there is a unique and enormous opportunity to position the Nation at the forefront of advances in science and engineering, job creation, and economic development for decades to come. This concludes my remarks. I appreciate the opportunity to have this dialogue with members of the Subcommittees on these very important topics, and I would be happy to answer any questions at this time.
1. "Dealing with Data," Science Magazine, Volume 331, February 11, 2011.
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2. See http://www-01.ibm.com/software/data/bigdata/.
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3. For example, some new technologies include smartphones, eBook readers, and game consoles; corporate data-centers, cloud services and scientific supercomputers; digital photography and photo editing, MP3 music players, streaming media, GPS navigation; robot vacuum cleaners, adaptive cruise control in cars and real-time control systems in hybrid vehicles, robot vehicles on and above the battlefield; the Internet and the World Wide Web; email, search engines, eCommerce, and social networks; medical imaging, computer-assisted surgery, and large-scale data analysis enabling evidence-based healthcare and the new biology; and rapidly improving speech recognition. Our world today relies to an astonishing degree on systems, tools, and services that belong to a vast and still growing domain known as NIT.
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4. U.S. Energy Information Administration (EIA). Annual Energy Outlook 2013: Market Trends â U.S. Energy Demand. See http://www.eia.gov/forecasts/aeo/ MT_energydemand.cfm#indus_comm.
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5. See http://www.energystar.gov/index.cfm?c=heat_cool.pr_hvac.
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6. See http://www.nest.com/saving-energy/.
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7. "2012 Urban Mobility Report," Texas A&M Transportation Institute, December 2012, http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/mobility-report-2012.pdf.
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8. See http://www-03.ibm.com/press/us/en/pressrelease/34261.wss.
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9. "Trapping 'Big Data' to Fill Potholes: Start-Ups Help States and Municipalities Track Effects of Car Speeds, Other Variables on Traffic," The Wall Street Journal, June 12, 2012.
10. See http://www.cattlab.umd.edu/?portfolio=ritis.
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11. "To Fight Gridlock, Los Angeles Synchronizes Every Red Light," The New York Times, April 1, 2013.
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12. See http://www.cancer.org/cancer/breastcancer/detailedguide/breast-cancer-key-statistics.
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13. Beck, A.H., Sangoi, A.R., Leung, S., Marinelli, R.J., Nielsen, T.O., van de Vijver, M., West, R.B., van de Rijn, M., and Koller, D. 2011. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3: 108ra113.
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14. "Vision from the National Science Foundation," A presentation by Subra Suresh, Director, at the National Academy of Sciences Symposium on Science, Innovation, and Partnerships for Sustainability Solutions, Washington, DC, May 16, 2012: http://www.nsf.gov/news/speeches/suresh/12/ss120516_nas_symposium.jsp.
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15. See http://cosmiclog.nbcnews.com/_news/2011/09/16/7802623-gamers- solve-molecular-puzzle-that-baffled-scientists.
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16. President's Council of Advisors on Science and Technology (PCAST). Report to the President and Congress: Designing a Digital Future: Federally Funding Research and Development in Networking and Information Technology. December 2010. Executive Office of the President. http://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ pcast-nitrd-report-2010.pdf
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17. See http://www.nitrd.gov/nitrdgroups/index.php?title=Big_Data_(BD_SSG)#title.
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18. See http://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ big_data_press_release_final_2.pdf.
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19. See http://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/big_data_fact_sheet_final_2.pdf.
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20. See http://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/pcast-nitrd2013.pdf.
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21. See http://siliconangle.com/blog/2013/02/04/top-5-open-source-projects-in-big-data- breaking-analysis/.
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22. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A.H. 2011. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute: http://www.mckinsey.com/insights/business_technology/big_data_the_next_ frontier_for_innovation.
Return to testimony.
23. See http://www.nsf.gov/pubs/2012/nsf12060/nsf12060.jsp.
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24. See http://www.neoninc.org/.
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25. See http://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ostp_public_access_ memo_2013.pdf.
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A few days ago, I told Sylvia we're about to run out of the pre-cooked rice-packets we use for lunch.
- Several weeks ago, the grocery store we work in had lots of them in stock. So we bought all of them.
Every day that we work, I look through the store, to see if more have arrived (to see if they've "manifested" yet). Even just before leaving work yesterday, there were none in the store.
We were just about to leave when I noticed 2 employees unpacking a palette of new products for the freezer. I looked-over what they were stocking and saw 2 cases of "Amy's" veggie burgers.
- Sylvia and I have been vegetarians since that late 80s.
That grocery store, being a "salvage" store, almost never gets a quality brand like "Amy's" in stock. So we bought 1 whole case.
- They were discounted from what they sell for in other stores AND we get an "employee's discount". So buying an entire "case" is a very good idea.
Sylvia and I actually spent several hours working on 1 of the songs for our album today. (It was our day off.)
I think I mentioned this song a couple days ago. I referred to it as "Song 2" on the album, just after the 1st song, which is totally chaotic.
We worked on the lyrics today and, right now, it looks as though this 2nd song will be called: "Guided-Aligned". (We still haven't written any portion of the 1st song but we know the structure of the album.)
The music is "chaotic" on its own and when the female voice of "Cyber Diva" sings, there's even more "clashing" of sonic textures a musical styles. It still has a way to go but at 1 point I told Sylvia:
- "This is NOT as chaotic as the 1st song? How twisted is that opening song going to be?"
This is today's Posting by "Gaia Portal":
- "Flares of recognition are in place."
- "Standards of the past are dropped."
- "Wrigglings cease."
- "Heaven Sents are viewed with delight."
- "Sustenance follows."
Question everything.
Here's the link:
This is Benjamin Fulford's weekly update. Here are some excerpts:
- "On all of these fronts a lot has been happening. There was a lot of controversy generated over the report on this newsletter last week that the Federal Reserve Board had been nationalized. This claim was not just based on the (disputed by some) notion that the Federal Reserve Board had changed its home page to .gov from .org indicating it was now part of the government. The report was based on information provided by US military intelligence. In any case, the fact is that in analogue paper phone books, that cannot be altered (as the internet can), the Fed is not listed as a government institution. In future phone books in the United States at least, it now will be."
- "Sources at the Pentagon say that since the Fed has now fallen under government control it will soon start issuing funds to the government at 0% interest rate to finance a massive rebuilding program for decrepit US infrastructure. Other nationalized Fed funds will pay for university scholarships, scientific research, nature conservation, the domestic operations of the US military and much more. This can now be done with zero debt and zero taxation. There will also be asset backed money made available to rebuild US industry.
- "In any case, a massive campaign to cut off all remaining sources of Nazionist finance is continuing. The Taliban in Afghanistan is aiming to shut down their heroin business. The South American drug lords are no longer supplying funds to the Nazionists, according to Vatican sources. Visible proof of this was seen last week when top Bush and Nazionist agent in Honduras, Jaime Rosenthal, was indicted by the Treasury Department for laundering drug money."
- "Pentagon and CIA white hat sources are saying that both Citibank and the Bank of America will be "taken down" for laundering Bush CIA money."
- "In the Middle East, meanwhile, revenue from stolen oil, the other main Nazionist source of financing, continues to be shut down. Bush CIA and ISIS drug, weapons and slave bases in Syria and Iraq continue to be destroyed even as their oil facilities are being confiscated. This is the ongoing work of a Pentagon, Iranian, Russian, Syrian and Yemeni etc. coalition."
- "The cleansing continues: last week a missile destroyed 17 Saudi F-15 aircraft and 9 Apache helicopters as well as killing 66 high ranking officers and injuring 300."
- "Here is some good advice for the Saud royal family: invest in camels because that is all you are going to be left with unless you surrender."
- "The Rothschild family, the original founders of the misbegotten project to send brainwashed Europeans to the Middle East, have already jumped ship and are busy currying favour with the Chinese."
Question everything.
Here's the link:
This next one's a 'bonus' (something you should know but it doesn't directly provide "me" with any feedback of "when" me and My Sylvia will be together).
This 1-hour, 15-minute video is called: "Stewart Swerdlow - CERN, Blue Beam and the Portal to Hell".
So of this information is helpful and some of it is based on a "negative" TimeLine… in "my" opinion. I'm including this so you can use your own Heart to discern and take from it what you will.
Here are a few things he talks about:
- Alien species
- Time travel
- Kuiper Belt Beings
- The hole at the North and South Poles (Hollow Earth)
- Nazis of today
- Project Blue Beam
Question everything.
Here's the link:


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In this classic article from 2010 in City Journal, the relation between the two is explored.
An excerpt.
For the first two-thirds of the twentieth century, American liberals distinguished themselves from conservatives by what Lionel Trilling called "a spiritual orthodoxy of belief in progress." Liberalism placed its hopes in human perfectibility. Regarding human nature as essentially both beneficent and malleable, liberals, like their socialist cousins, argued that with the aid of science and given the proper social and economic conditions, humanity could free itself from its cramped carapace of greed and distrust and enter a realm of true freedom and happiness. Conservatives, by contrast, clung to a tragic sense of man's inherent limitations. While acknowledging the benefits of science, they argued that it could never fundamentally reform, let alone transcend, the human condition. Most problems don't have a solution, the conservatives maintained; rather than attempting Promethean feats, man would do best to find a balanced place in the world.
In the late 1960s, liberals appeared to have the better of the argument. Something approaching the realm of freedom seemed to have arrived. American workers, white and black, achieved hitherto unimagined levels of prosperity. In the nineteenth century, only utopian socialists had imagined that ordinary workers could achieve a degree of leisure; in the 1930s, radicals had insisted that prosperity was unattainable under American capitalism; yet these seemingly unreachable goals were achieved in the two decades after World War II.
Why, then, did American liberalism, starting in the early 1970s, undergo a historic metanoia, dismissing the idea of progress just as progress was being won? Multiple political and economic forces paved liberalism's path away from its mid-century optimism and toward an aristocratic outlook reminiscent of the Tory Radicalism of nineteenth-century Britain; but one of the most powerful was the rise of the modern environmental movement and its recurrent hysterias.
If one were to pick a point at which liberalism's extraordinary reversal began, it might be the celebration of the first Earth Day, in April 1970. Some 20 million Americans at 2,000 college campuses and 10,000 elementary and secondary schools took part in what was the largest nationwide demonstration ever held in the United States. The event brought together disparate conservationist, antinuclear, and back-to-the-land groups into what became the church of environmentalism, complete with warnings of hellfire and damnation. Senator Gaylord Nelson of Wisconsin, the founder of Earth Day, invoked "responsible scientists" to warn that "accelerating rates of air pollution could become so serious by the 1980s that many people may be forced on the worst days to wear breathing helmets to survive outdoors. It has also been predicted that in 20 years man will live in domed cities."
Thanks in part to Earth Day's minions, progress, as liberals had once understood the term, started to be reviled as reactionary. In its place, Nature was totemized as the basis of the authenticity that technology and affluence had bleached out of existence. It was only by rolling in the mud of primitive practices that modern man could remove the stain of sinful science and materialism. In the words of Joni Mitchell's celebrated song "Woodstock": "We are stardust / We are golden / And we got to get ourselves back to the garden."
Retrieved April 23, 2015 from http://www.city-journal.org/2010/20_3_american-liberalism.html


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Using Custom Fonts with Sencha ArchitectPosted on in
Lee Boonstra
Back in the days when websites were restricted to typical fonts such as Arial, Georgia, Times and so on, fonts were delivered from the user's machine. Fortunately today, because of HTML5 and CSS3, browsers support embedded web fonts.
This time, I will explain how to use custom fonts with Sencha Architect. For this tutorial, I will use one of the Sencha Touch starter apps, with the default theme. However, you can also use these tips for other Sencha Touch or Ext JS apps.


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Based on the best-selling pair of memoirs from father and son David and Nic Sheff, Beautiful Boy chronicles the heartbreaking and inspiring experience of survival, relapse, and recovery in a family coping with addiction over many years.
Sorry! None of the critics have had a chance to review this movie yet.


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Pre-ordered Crash from EB yesterday. They are taking any 2 PS4 games + $20 for it. I traded in Singstar (cost me $6) and Infamous First Light (got it on PS+ now). So not a bad deal at all.
Horizon Zero Dawn and Wipeout at Target for $39 each starting Thursday.
In the event anyone here has their Plus membership renewal soon
So who has picked up Watch Dogs 2? Is it dramatically better than the first (e.g. doesn't look like a ps3 game, doesn't have an arsehole main character, bug free and so on)
The Metacritic score is decent so I'm thinking about getting it during Target's Black Friday deals.
I presume everyone has seen this by now. I love it so much.
Sup gang?
Been a bit quiet lately as I've been in Europe for 3 weeks. 1 week of work in the office in London, then took some time off to go to Paris and Rome before going back to London.
Here's a couple of pics of myself there:
Gat out of Hell in July's PS+ freebies. I'm expecting everyone to download and play it that hasn't!
If you don't own this already go get it. $30.95 is a bargain.
Picked up Mad Max off the PS Store for $20.95. Seems like, gameplay wise, that it's going to be pretty similar to Shadow of Mordor so it should be decent.
Nice. That's on my list to check out at some point. I got darksiders 2, transformers devastation and assassins creed chronicles (disc) last week though, so I wont be getting anymore for a while. They're all pretty good, especially the first 2 - but I need to somehow tear myself away from rocket league to actually play them...
Witcher 2 is free on the Xbox marketplace atm. Will work on both the 360 and Xbone - marketplace.xbox.com/en-AU/Product/The-Witcher-2/…
Just pulled the trigger on this - ozbargain.com.au/node/224555
Dark side here I come.
EB Games online sale today
Fallout 4 is $59 through pre-order on JB Hifi for those that are interested.
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On behalf of our client, one of the largest financial institution in Zürich, we are looking for an outstanding Quantitative Developer (C# .NET).
For this 12 month contract position based in Zürich, our client is looking for an English speaking candidate, who can start as soon as possible.
Are you looking for an exciting new opportunity with a market leading global company?
Then please have a look at the details:
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Who I am looking for:
Are you interested in this opportunity? Kindly send us your CV today through the link in the advert. In case of any questions, please contact Doris Wen on (see below)
Experis IT is Europe's leading IT&T recruitment agency.


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Ethnobotanist Fiona Hamersley Chambers
Friday, January 29, 2016 2:55 PM
Wise Woman
Interview with Fiona Hamersley Chambers. Fiona is an ethnobotanist whose research focuses on how northwest coast First Peoples managed and co-evolved with their plant resources. Her PhD work, which is supervised by internationally-acclaimed ethnobotanist Dr. Nancy Turner, on BC's central coast investigates how key berry plants were in cultivated and owned in 'gardens'. Ultimately, the hope is for this applied research to act as a link between traditional knowledge and practice and the needs of First Nations communities as well as our larger society today. Fiona is a long-time organic farmer and commercial seed grower specializing in organically-grown and locally-adapted food plants. Fiona spent her childhood in the First Nations communities of Nitnaht Lake and Penelakut Island, where she maintains close ties. She also volunteers her time to help communities create food gardens.
this weeks Q&A includes:
• symptoms of panic and heart attack are the same- motherwort tincture for panic attack- the lion hearted...
• chickweed tincture dissolves ovarian cysts...
• after surgery, what can you do to make it up to your body? massage...
• wild yam tea or tincture is specific for treating digestive problems having to do with gallbladder complaints..
• difference of making tinctures from fresh and dried plants- dried and powdered herbs release more poisons into alcohol...
• essential oils kill bacteria and once introduced into the environment they do not go away...
• food not supplements!!!
• no such thing as whole food vitamins!
• no increase of mercury or radioactivity in seaweed...
• do not use french press when making infusions..
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Concordia's COVID-19 updates
Concordia continues to monitor COVID-19 response measures and follow the advice of public health agencies.
Key facts and dates
- Concordia's campuses remain closed until the end of August, with services to students — including for the Library — accessible online.
- Employees currently working remotely will continue to do so until further notice.
- As outlined on Concordia's research site, the university is re-opening research labs in phases. A pilot phase comprising a small number of research labs began on May 25. Since then, the university has re-opened more than 150 labs in sciences, engineering, fine arts and humanities. Read more about Concordia's gradual return-to-campus plan.
- Those designated to resume or support research activities will be notified by their manager/supervisor. More information is available on Cspace.
- Summer term 2020 courses will be delivered remotely. There are no in-person courses for the summer term(s).
- Fall 2020 admissions continue as planned, with courses delivered remotely.
- Tuition fee deadlines have been extended.
- The university is unable to host any in-person conferences and events until August 31.
Messages to the community
Here are the latest updates about our COVID-19 response efforts:
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What we advocate instead of animal experimentation
We advocate scientifically viable and non-harmful research, testing and teaching methods for humans and animals.
For people these include:
- Human relevant research for finding cures and treatments for humans as well as for testing the safety of products and substances for humans (i.e. epidemiology, in vitro research using human tissue, in silico research, and micro-dosing).
- Human relevant teaching methods for training people how to help people.
- Non-harmful teaching methods.
For animals these include:
- Non-harmful research, testing or teaching that is done to improve the quality of health for the individual involved in the study - like Dr Jane Goodall's historic and ground-breaking, correctly named 'animal research'. Non-invasive generally means non-surgical. Interventions should be non-invasive except where necessary for an animal's recovery or improvement in health.
- Scientifically viable and non-harmful teaching methods - these can be non-animal based for all teaching methods apart from training veterinarians and vet nurses and other animal care professions as there will be a point in the training process that students need to be exposed to animals, just like human doctors and surgeons. Using animals for veterinary purposes should – and can – match the use of human patients in research that will benefit humans: ethically and without harming any individual.
- Observational or behavioural research on free-living and sanctuary animals where participation is voluntary. This would involve the voluntary or passive participation of the subject where research methods are compatible with the subject's natural habits (ethology).
- Research, testing or teaching that is done for conservation purposes of the species involved in the study. These methods will exclude harmful interventions which would result in pain, injury, or death.
What are the non-animal based methods for research, testing and teaching?
There are already many different types of viable non-animal based research, testing and teaching methods but there is also a strong need for more of these methods to be developed!
Animals are not appropriate models for predicting the human response so when it comes to finding viable research methods for finding medical advancements, we need to make sure our start point is accurate — if we want to find cures and treatments for humans, we need to be using human-relevant research.
Human-based research and test methods
This doesn't mean jumping straight to risky tests on human volunteers instead of animals, despite the common misconception.
The truth is that human-based research and test methods involve a broad range of bio-technologies that are human-relevant. They use advanced and current biological knowledge of the human species to study human diseases and develop drugs which are safe and effective in humans.
Examples include:
Toxicology/ regulatory testing
Testing chemicals such as drugs, household products, pesticides and cosmetics on animals is time-consuming and expensive, and the results can generally not be applied accurately to humans.
Predicting the potential risks that a chemical has to human health or the environment can easily be done without the use of animals.
Testing can start with in-silico methods and high-throughput screening to find patterns of activity that can then be followed up with targeted testing using cell or tissue models. Large groups of untested chemicals can also be assessed in this way.
In Silico Methods
These are sophisticated computer models. These can help provide information about chemicals and their health effects. Automated decisions trees can be built using knowledge of hundreds of chemicals and predict how likely it is that a new chemical will cause a certain kind of toxicity.
Chemicals made up of similar structure fragments can be grouped together and compared, allowing toxicologists to "read across" from a chemical with data to one without. And healthy virtual cells or even entire organs can be created and then used to simulate what might happen when exposed to a particular chemical.
- High-Throughput Screening
High-Throughput Screening can perform many different tests—maximizing cell types, or amounts or mixtures of chemicals—very quickly: These assays usually test chemical activity at the molecular level.1
Medical research
Human-based research will always be the most relevant and reliable when trying to find treatments and cures for human disease.
Some researchers believe that animals have to be used in the early stages of developing a treatment or cure. However, this is only making the research flawed from the very beginning.
Many human diseases are also not found naturally in animals so they are artificially induced. This creates a whole set of new problems as the disease being studied is slightly (or dramatically) different from that found in humans.
Some researchers will also argue that they need to test on an entire living system. This is correct to a degree; it does need to be tested in a living system but that system also needs to be of the same species. Using a battery of human-based tests would be more predictive of human outcomes, compared to using an entirely different species.
A more accurate and relevant method would involve using a battery of human-based tests before moving into clinical trials. These human-based tests could be:
- Scanning technologies
There is a wide range of scanning technologies that can reveal processes in living humans. The images produced are now truly remarkable and are especially useful in neurodegenerative conditions like Alzheimer's.
- Micro-dosing
Microdosing involves giving a very small amount of a substance - less than one-hundredth of the quantity expected to have a noticeable effect - to a volunteer or patient. Body fluids are then analysed to see how the body has responded, or PET imaging is employed to ascertain how the substance behaves in specific organs. This technique has already been used successfully to test drugs for cardiovascular disease, pain, Alzheimer's disease and gastrointestinal disorders.
- Microfluidic devices
Microfluidic devices contain human tissue samples in tiny chambers linked by microchannels. Fluids and chemicals flow in a natural way between different compartments, simulating conditions in the human body. The technology can help scientists to understand how cancers spread, for example. Microfluidics can investigate human tissues and organ systems, with the creation of 'bioreactor' designed to supply nutrients and remove waste products.
For example Human-on a chip:
"Human-on-a-chip" models use real human cells inside tiny silicon chips. Several chips with different types of human cells inside can be linked together so that scientists can see how the mini "organs" react together and communicate with one another.
There are continuous developments with this technology. The Harvard University's Wyss Institute has recently developed lung-on-a-chip, which can mimic the complicated mechanical and biochemical behaviours of a human lung.
- Isolated organs
Ethically-sourced human organs can be used in experiments; for example, it is common to test potential asthma triggers or therapies with isolated tracheas.
- Sophisticated computer programs
These can simulate complex human systems, form a single organ to the entire body. These computer systems use data from real humans. Computer simulations have been developed, for example, to predict the behaviour of a drug in the digestive system. These simulations are likely to predict such effects in humans more accurately than animal models and in a much more efficient way.
- Human tissues or organ systems
Human tissues or organ systems can also be recreated in laboratories. For example, Human lymph nodes have been created in the laboratory and can be employed to test vaccines and biologically-based drugs, like the TGN1412 monoclonal antibody, which - having been passed as safe on the basis of tests on monkeys - went on to cause catastrophic injuries to human trial subjects.
- Epidemiology
Epidemiology involves the study of significant numbers of people over a period of years, comparing their lifestyles, genes, medical interventions, environments, social status, etc. It remains a powerful tool with huge potential and has already produced enormously valuable findings, including the link between smoking and lung cancer. 2
Plus many more other methods! Read more about how some of these human-based methods are used in research into human diseases, such as cancer and Parkinson's disease here
Other resources on non-animal based research and test methods that we recommend checking out:
Tail of Toxics - Improving Chemical Safety Without Animals by Physicians Committee for Responsible Medicine.
There are many different types of non-animal based teaching methods that include replacements for dissections. These include synthetic animal models, digitalised animal models, virtual organs as well as many other methods.
These are available for a wide range of teaching types including training veterinary and medical students. The availability of these is so big that currently, 100% of medical schools in the US and Canada have ended live animal use for medical student training. These medical schools now use non-animal based methods such as lifelike interactive and programmable simulators that better replicate human anatomy and physiology.
See more of these non-harmful teaching methods at:
It is important to clarify that:
We are fully opposed to and aim to abolish the use of animals to try and predict the human response to drugs and disease.
We don't support research that significantly harms individuals, even when a conservation benefit may accrue. These or other benefits should be pursued through non-harmful research.
The use of animals for the purpose of enhancing the animal agricultural industry is also something that we don't support. Rather than doing research and testing on animals to try and make them more productive for alleged human benefit, we should focus our efforts on eliminating the exploitation of animals.
- Physicians Committee for Responsible Medicine(PCRM), http://www.pcrm.org/
- Speaking of human-based research, http://www.speakingofhumanbasedresearch.com/
- Header image source: The Animals in Science Policy Institute


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Tuesday, September 24, 2019, 4:30pm to 5:30pm
OCS, 54 Dunster St, Cambridge, MA 02138
Considering a career related to environmental causes, energy, or sustainability? Interested in climate change or field work? Get a jump on the job and internship search by coming to this session focusing on pathways and resources that will get you on the right track.


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(LEAD) Unheralded midfielder responds to coach's faith with timely debut goal
(ATTN: ADDS comments at bottom)
ASHGABAT, Turkmenistan, Sept. 10 (Yonhap) -- It all happened so quickly. But when the opportunity came, Na Sang-ho didn't miss it.
Na, a 22-year-old midfielder for South Korea, netted his first international goal in the team's 2-0 victory over Turkmenistan in the second round of the Asian World Cup qualifying tournament here on Tuesday.
The goal came in the 13th minute of a match that South Korea dominated from the opening kickoff. Before the goal, the visiting side had already threatened a few dangerous opportunities, including one by Na in the 10th minute from a tough angle. But with Turkmenistan locking down on the defense harder by the minute, the match was teetering on the brink of becoming frustrating for South Korea. If Na hadn't scored when he did, South Korea could well have lost the momentum they had built early on.
Na capitalized on a lucky bounce to score that goal. Fullback Lee Yong sent a cross from the right wing in the general direction toward the net, and the ball deflected off defender Mekan Saparow's foot and went right to unmarked Na. In one motion, Na fired it home to put South Korea ahead 1-0.
Na has been one of the mainstays in midfield for head coach Paulo Bento, who took over South Korea in August 2018. Na made his international debut in a friendly against Australia last November, and also earned call-ups for two friendlies each in March and June.
Na's credentials don't jump off the page. He'd been playing for Gwangju FC in the second-tier K League 2 when he got Bento's call for the first time last year, and he joined FC Tokyo in the top Japanese circuit this year with little fanfare. But Bento, who has been blind to players' club affiliations as long as he thinks they can contribute to the national team, has always liked Na's nose for the goal. And the player finally had his breakthrough on Tuesday.
It was one of the few bright spots in a match that South Korea failed to translate their possession dominance into more goals.
Na said afterward he was glad to get the proverbial monkey off his back with his first goal in such an important match, but lamented his and the team's failure to pile on even more.
"I am happy to have scored in a World Cup qualifying match, but I am also disappointed that we didn't score even more goals," he said. "We all tried to sneak behind the defense and exploit space behind their wall, and I think we executed that well. But we may have taken our foot off the pedal a bit after getting that first goal."


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57 VotesYes55.88%
25 VotesNo24.51%
20 VotesDon't have opinion19.61%
(Togo First) - The Kara agro-hub will have poultry slaughterhouses with a processing capacity of 1,000t-1,500t per hour, at peak production levels.
The agro-hub which is yet to be operational will also be equipped with poultry transformation and conditioning facilities. The hub's hatcheries are forecast to produce nearly three million broiler chicks per year.
At its cruise phase, yearly processing capacities at Kara's agropole are projected at 90,000 tons, 15,000 tons and 10,000 tons, for rice, maize feed and soybeans, said Essowè Batana, acting director general of the Agency for the Promotion and Development of Agropoles in Togo – APRODAT. Sesame and cashew will also be processed at a later period. For the first, 10,000t/y is expected to be processed while for cashew, 20,000t should be processed on a yearly basis. The forecasts' effective implementation depend on investors' decision.
The Kara agropole which is expected to be completed by the end of 2022 is the pilot of a larger project aimed at structurally transforming Togolese agriculture, with a focus on exporting locally-processed products.
Séna Akoda


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Now showing items 1-10 of 20
Habermas, Mouffe and political communication. A case for theoretical eclecticism
(European Institute for Communication and Culture/Faculty of Social Sciences, University of Ljubljana, 2008)
Much of the research on communication and democracy continues to lean on Jürgen Habermas's work. However, many aspects of his approach have been intensely criticised in recent debates, both in communication studies and ...
Television, Digitalisation and Flow: Questioning the Promises of Viewer Control
(Facultat de Ciències de la Communicació Blanquerna, Universitat Ramon Llull, 2005)
The hype surrounding the marketing of and writing about digital television has, since its advent in the 1990s, focused extensively on freedom and control for the viewer: new technology will render channels superfluous, ...
Nettmedier og offentligheten. Vox Publicas netthøring om NRK-plakaten
(Universitetsforlaget, 2010-02-24)
Nettutviklingen har åpnet muligheter for en mer mangfoldig offentlighet der flere kan delta. Med utgangspunkt i erfaringer fra et forsøk med en åpen netthøring om NRKs framtid, diskuterer denne artikkelen hvordan ...
Commercial Services, Enclosure and Legitimacy: Comparing Contexts and Strategies for Public Service Media Funding and Development
(Nordicom, 2008)
European public broadcasters have long since ceased to be solely publicly financed. But the inherently controversial licence fee on television sets has remained a key source of funding. Facing an increasingly complex digital ...
Digitaliseringen av fjernsyn og allmennkringkastingens skjebne
(Universitetet i Bergen, Institutt for medievitenskap, 2003)
Rapporten har spådommene om kringkastingens oppløsning som utgangspunkt, og spør: Står vi virkelig midt oppe i en teknologisk revolusjon som bestemmer utviklingen, eller er det mulig å forklare prosessene ved hjelp av andre ...
From Fermentation to Maturity? Reflections on Media and Communication Studies: An Interview with Todd Gitlin, Jostein Gripsrud & Michael Schudson
(USC Annenberg, School for Communication, 2009)
Twenty-six years after the Journal of Communication published a special issue entitled "Ferment in the Field," Professors Todd Gitlin, Jostein Gripsrud and Michael Schudson reflect on the state of the field of media and ...
Sentrale utfordringer for medieforskningen i dag
(Universitetsforlaget, 2009-09-29)
The Challenges of Comparing Media Systems — An Interview with Daniel C. Hallin
(Marquette Journals, 2008)
In this interview, Daniel C. Hallin offers hindsight concerning his collaborative project with Paolo Mancini — Comparing Media Systems — Three Models of Media and Politics (2004). Hallin discusses methodological difficulties ...
Public Broadcasters, the Internet, and Democracy. Comparing Policy and Exploring Public Service Media Online
(The University of Bergen, 2009-02)
This thesis is a study of public service broadcasting facing a digital media system. Its focus is on internet services since the mid-1990s. With a comparative design, the thesis discusses how public service broadcasters ...
Media Institutions as a Research Field: Three Phases of Norwegian Broadcasting Research
(Nordicom, 2007)
The article discusses the background and origins of research on media institutions as a field, and especially assesses the development and status of Norwegian research on broadcasting institutions. It is demonstrated how ...