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AI solutions for financial services
Is Artificial Intelligence Permanently Inscrutable?
by Aaron M. Bornstein
Dmitry Malioutov can't say much about what he built.
As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning."
In fact, it may not have helped even if they were machine learning experts. That's because the model was an artificial neural network, a program that takes in a given type of data—in this case, the insurance company's customer records—and finds patterns in them. These networks have been in practical use for over half a century, but lately they've seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.
HIDDEN MEANINGS: In neural networks, data is passed from layer to layer, undergoing simple transformations at each step. Between the input and output layers are hidden layers, groups of nodes and connections that often bear no human-interpretable patterns or obvious connections to either input or output. "Deep" networks are those with many hidden layers.
Michael Nielsen / NeuralNetworksandDeepLearning.com
As exciting as their performance gains have been, though, there's a troubling fact about modern neural networks: Nobody knows quite how they work. And that means no one can predict when they might fail.
Take, for example, an episode recently reported by machine learning researcher Rich Caruana and his colleagues. They described the experiences of a team at the University of Pittsburgh Medical Center who were using machine learning to predict whether pneumonia patients might develop severe complications. The goal was to send patients at low risk for complications to outpatient treatment, preserving hospital beds and the attention of medical staff. The team tried several different methods, including various kinds of neural networks, as well as software-generated decision trees that produced clear, human-readable rules.
The neural networks were right more often than any of the other methods. But when the researchers and doctors took a look at the human-readable rules, they noticed something disturbing: One of the rules instructed doctors to send home pneumonia patients who already had asthma, despite the fact that asthma sufferers are known to be extremely vulnerable to complications.
The model did what it was told to do: Discover a true pattern in the data. The poor advice it produced was the result of a quirk in that data. It was hospital policy to send asthma sufferers with pneumonia to intensive care, and this policy worked so well that asthma sufferers almost never developed severe complications. Without the extra care that had shaped the hospital's patient records, outcomes could have been dramatically different.
Ken Goldberg is nothing if not creative. A distinguished roboticist and researcher at the Automation Sciences Lab at the University of California, Berkeley, he's also an internationally recognized artist. He's the author of more than 150 peer-reviewed papers on algorithms...READ MORE
The hospital anecdote makes clear the practical value of interpretability. "If the rule-based system had learned that asthma lowers risk, certainly the neural nets had learned it, too," wrote Caruana and colleagues—but the neural net wasn't human-interpretable, and its bizarre conclusions about asthma patients might have been difficult to diagnose.1 If there hadn't been an interpretable model, Malioutov cautions, "you could accidentally kill people."
This is why so many are reluctant to gamble on the mysteries of neural networks. When Malioutov presented his accurate but inscrutable neural network model to his own corporate client, he also offered them an alternative, rule-based model whose workings he could communicate in simple terms. This second, interpretable, model was less accurate than the first, but the client decided to use it anyway—despite being a mathematically sophisticated insurance company for which every percentage point of accuracy mattered.
"They could relate to [it] more," Malioutov says. "They really value intuition highly."
Even governments are starting to show concern about the increasing influence of inscrutable neural-network oracles. The European Union recently proposed to establish a "right to explanation," which allows citizens to demand transparency for algorithmic decisions.2 The legislation may be difficult to implement, however, because the legislators didn't specify exactly what "transparency" means. It's unclear whether this omission stemmed from ignorance of the problem, or an appreciation of its complexity.
Some researchers hope to eliminate the need to choose—to let us have our many-layered cake, and understand it, too.
In fact, some believe that such a definition might be impossible. At the moment, though we can know everything there is to know about what neural networks are doing—they are, after all, just computer programs—we can discern very little about how or why they are doing it. The networks are made up of many, sometimes millions, of individual units, called neurons. Each neuron converts many numerical inputs into a single numerical output, which is then passed on to one or more other neurons. As in brains, these neurons are divided into "layers," groups of cells that take input from the layer below and send their output to the layer above.
Neural networks are trained by feeding in data, then adjusting the connections between layers until the network's calculated output matches the known output (which usually consists of categories) as closely as possible. The incredible results of the past few years are thanks to a series of new techniques that make it possible to quickly train deep networks, with many layers between the first input and the final output. One popular deep network called AlexNet is used to categorize photographs—labeling them according to such fine distinctions as whether they contain a Shih Tzu or a Pomeranian. It consists of over 60 million "weights," each of which tell each neuron how much attention to pay to each of its inputs. "In order to say you have some understanding of the network," says Jason Yosinski, a computer scientist affiliated with Cornell University and Geometric Intelligence, "you'd have to have some understanding of these 60 million numbers."
Even if it were possible to impose this kind of interpretability, it may not always be desirable. The requirement for interpretability can be seen as another set of constraints, preventing a model from a "pure" solution that pays attention only to the input and output data it is given, and potentially reducing accuracy. At a DARPA conference early this year, program manager David Gunning summarized the trade-off in a chart that shows deep networks as the least understandable of modern methods. At the other end of the spectrum are decision trees, rule-based systems that tend to prize explanation over efficacy.
WHAT VS. WHY: Modern learning algorithms show a tradeoff between human interpretability, or explainability, and their accuracy. Deep learning is both the most accurate and the least interpretable.
The result is that modern machine learning offers a choice among oracles: Would we like to know what will happen with high accuracy, or why something will happen, at the expense of accuracy? The "why" helps us strategize, adapt, and know when our model is about to break. The "what" helps us act appropriately in the immediate future.
It can be a difficult choice to make. But some researchers hope to eliminate the need to choose—to allow us to have our many-layered cake, and understand it, too. Surprisingly, some of the most promising avenues of research treat neural networks as experimental objects—after the fashion of the biological science that inspired them to begin with—rather than analytical, purely mathematical objects. Yosinski, for example, says he is trying to understand deep networks "in the way we understand animals, or maybe even humans." He and other computer scientists are importing techniques from biological research that peer inside networks after the fashion of neuroscientists peering into brains: probing individual components, cataloguing how their internals respond to small changes in inputs, and even removing pieces to see how others compensate.
Having built a new intelligence from scratch, scientists are now taking it apart, applying to these virtual organisms the digital equivalents of a microscope and scalpel.
Yosinski sits at a computer terminal, talking into a webcam. The data from the webcam is fed into a deep neural net, while the net itself is being analyzed, in real time, using a software toolkit Yosinski and his colleagues developed called the Deep Visualization toolkit. Clicking through several screens, Yosinski zooms in on one neuron in the network. "This neuron seems to respond to faces," he says in a video record of the interaction.3 Human brains are also known to have such neurons, many of them clustered in a region of the brain called the fusiform face area. This region, discovered over the course of multiple studies beginning in 1992,4, 5 has become one of the most reliable observations in human neuroscience. But where those studies required advanced techniques like positron emission tomography, Yosinski can peer at his artificial neurons through code alone.
BRAIN ACTIVITY: A single neuron in a deep neural net (highlighted by a green box) responds to Yosinski's face, just as a distinct part of the human brain reliably responds to faces (highlighted in yellow).
Left: Jason Yosinski, et al. Understanding Neural Networks Through Deep Visualization. Deep Learning Workshop, International Conference on Machine Learning (ICML) (2015). Right: Maximilian Riesenhuber, Georgetown University Medical Center
This approach lets him map certain artificial neurons to human-understandable ideas or objects, like faces, which could help turn neural networks into intuitive tools. His program can also highlight which aspects of a picture are most important to stimulating the face neuron. "We can see that it would respond even more strongly if we had darker eyes, and rosier lips," he says.
To Cynthia Rudin, professor of computer science and electrical and computer engineering at Duke University, these "post-hoc" interpretations are by nature problematic. Her research focuses on building rule-based machine learning systems applied to domains like prison sentencing and medical diagnosis, where human-readable interpretations are possible—and critically important. But for problems in areas like vision, she says, "Interpretations are completely within the eye of the beholder." We can simplify a network response by identifying a face neuron, but how can we be certain that's really what it's looking for? Rudin's concerns echo the famous dictum that there may be no simpler model of the visual system than the visual system itself. "You could have many explanations for what a complex model is doing," she says. "Do you just pick the one you 'want' to be correct?"
Yosinski's toolkit can, in part, counter these concerns by working backward, discovering what the network itself "wants" to be correct—a kind of artificial ideal. The program starts with raw static, then adjusts it, pixel by pixel, tinkering with the image using the reverse of the process that trained the network. Eventually it finds a picture that elicits the maximum possible response of a given neuron. When this method is applied to AlexNet neurons, it produces caricatures that, while ghostly, unquestionably evoke the labeled categories.
This seems to support his claim that the face neurons are indeed looking for faces, in some very general sense. But there's a catch. To generate those pictures, Yosinski's procedure relies on a statistical constraint (called a natural image prior) that confines it to producing images that match the sorts of structure that one finds in pictures of real-world objects. When he removes those rules, the toolkit still settles on an image that it labels with maximum confidence, but that image is pure static. In fact, Yosinski has shown that in many cases, the majority of images that AlexNet neurons prefer appear to humans as static. He readily admits that "it's pretty easy to figure out how to make the networks say something extreme."
To avoid some of these pitfalls, Dhruv Batra, an assistant professor of electrical and computer engineering at Virginia Tech, takes a higher-level experimental approach to interpreting deep networks. Rather than trying to find patterns in their internal structure—"people smarter than me have worked on it," he demurs—he probes how the networks behave using, essentially, a robotic version of eye tracking. His group, in a project led by graduate students Abhishek Das and Harsh Agrawal, asks a deep net questions about an image, like whether there are drapes on a window in a given picture of a room. Unlike AlexNet or similar systems, Das' network is designed to focus on only a small patch of an image at a time. It moves its virtual eyes around the picture until it has decided it has enough information to answer the question. After sufficient training, the deep network performs extremely well, answering about as accurately as the best humans.
Trained machines are exquisitely well suited to their environment—and ill-adapted to any other.
Das, Batra, and their colleagues then try to get a sense of how the network makes its decisions by investigating where in the pictures it chooses to look. What they have found surprises them: When answering the question about drapes, the network doesn't even bother looking for a window. Instead, it first looks to the bottom of the image, and stops looking if it finds a bed. It seems that, in the dataset used to train this neural net, windows with drapes may be found in bedrooms.
While this approach does reveal some of the inner workings of the deep net, it also reinforces the challenge presented by interpretability. "What machines are picking up on are not facts about the world," Batra says. "They're facts about the dataset." That the machines are so tightly tuned to the data they are fed makes it difficult to extract general rules about how they work. More importantly, he cautions, if you don't know how it works, you don't know how it will fail. And when they do they fail, in Batra's experience, "they fail spectacularly disgracefully."
Some of the obstacles faced by researchers like Yosinski and Batra will be familiar to scientists studying the human brain. Questions over the interpretations of neuroimaging, for example, are common to this day, if not commonly heeded. In a 2014 review of the field, cognitive neuroscientist Martha Farah wrote that "the worry ... is that [functional brain] images are more researcher inventions than researcher observations."6 The appearance of these issues in very different realizations of intelligent systems suggests that they could be obstacles, not to the study of this or that kind of brain, but to the study of intelligence itself.
Is chasing interpretability a fool's errand? Zachary Lipton, of the University of California, San Diego, questions both the enterprise of interpreting neural networks, and the value of building interpretable machine learning models at all. He submitted a provocative paper to a workshop (organized by Malioutov and two of his colleagues) on Human Interpretability at this year's International Conference on Machine Learning (ICML). The paper was titled "The Mythos of Model Interpretability," and it challenged the definitions of interpretability, as well as the reasons that researchers give for seeking it.
Lipton points out that many scholars disagree over the very concept, which suggests to him either that interpretability doesn't exist—or that there are many possible meanings. He argues that the impulse to interpret shouldn't be trusted, and that researchers should, instead, use neural networks to free themselves to "explore ambitious models." Interpretability, he says, keeps the models from reaching their full potential. He argues that one purpose of the field is to "build models which can learn from a greater number of features than any human could consciously account for."
But this ability is both feature and failing: If we don't understand how network output is generated, then we can't know what aspects of the input were necessary, or even what might be considered input at all. Case in point: In 1996, Adrian Thompson of Sussex University used software to design a circuit by applying techniques similar to those that train deep networks today. The circuit was to perform a straightforward task: discriminate between two audio tones. After thousands of iterations, shuffling and rearranging circuit components, the software found a configuration that performed the task nearly perfectly.
Thompson was surprised, however, to discover that the circuit used fewer components than any human engineer would have used—including several that were not physically connected to the rest, and yet were somehow still necessary for the circuit to work properly.
He took to dissecting the circuit. After several experiments, he learned that its success exploited subtle electromagnetic interference between adjacent components. The disconnected elements influenced the circuit by causing small fluctuations in local electrical fields. Human engineers usually guard against these interactions, because they are unpredictable. Sure enough, when Thompson copied the same circuit layout to another batch of components—or even changed the ambient temperature—it failed completely.
The circuit exhibited a hallmark feature of trained machines: They are as compact and simplified as they can be, exquisitely well suited to their environment—and ill-adapted to any other. They pick up on patterns invisible to their engineers; but can't know which of those patterns exist nowhere else. Machine learning researchers go to great lengths to avoid this phenomenon, called "overfitting," but as these algorithms are used in more and more dynamic situations, their brittleness will inevitably be exposed.
To Sanjeev Arora, a professor of computer science at Princeton University, this fact is the primary motivation to seek interpretable models that allow humans to intervene and adjust the networks. Arora points to two problems that could represent hard limits on the capabilities of machines in the absence of interpretability. One is "composability"—when the task at hand involves many different decisions (as with Go, or self-driving cars), networks can't efficiently learn which are responsible for a failure. "Usually when we design things, we understand the different components and then we put them together," he says. This allows humans to adjust components that aren't appropriate for a given environment.
The other problem with leaving interpretability unsolved is what Arora calls "domain adaptability"—the ability to flexibly apply knowledge learned in one setting to another. This is a task human learners do very well, but machines can fail at in surprising ways. Arora describes how programs can be catastrophically incapable of adjusting to even subtle contextual shifts of the sort that humans handle with ease. For instance, a network trained to parse human language by reading formal documents, like Wikipedia, can fail completely in more vernacular settings, like Twitter.
By this view, interpretability seems essential. But do we understand what we mean by the word? Pioneering computer scientist Marvin Minsky coined the phrase "suitcase word" to describe many of the terms—such as "consciousness" or "emotion"—we use when we talk about our own intelligence.7 These words, he proposed, reflect the workings of many different underlying processes, which are locked inside the "suitcase." As long as we keep investigating these words as stand-ins for the more fundamental concepts, the argument went, our insight will be limited by our language. In the study of intelligence, could interpretability itself be such a suitcase word?
While many of the researchers I spoke to are optimistic that theorists will someday unlock the suitcase and discover a single, unifying set of principles or laws that govern machine (and, perhaps, human) learning, akin to Newton's Principia, others warn that there is little reason to expect this. Massimo Pigliucci, a professor of philosophy at City University of New York, cautions that "understanding" in the natural sciences—and, by extension, in artificial intelligence—might be what Ludwig Wittgenstein, anticipating Minsky, called a "cluster concept," one which could admit many, partly distinct, definitions. If "understanding" in this field does come, he says, it could be of the sort found not in physics, but evolutionary biology. Rather than Principia, he says, we might expect Origin of the Species.
This doesn't mean, of course, that deep networks are a harbinger of some new kind of autonomous life. But they could turn out to be just as difficult to understand as life. The field's incremental, experimental approaches and post-hoc interpretations may not be some kind of desperate feeling around in the dark, hoping for theory to shine a light. They could, instead, be the only kind of light we can expect. Interpretability may emerge piecemeal, as a set of prototypic examples of "species" arranged in a taxonomy defined by inference and contingent, context-specific explanations.
At the ICML workshop's close, some of the presenters appeared on a panel to try and define "interpretability." There were as many responses as there were panelists. After some discussion the group seemed to find consensus that "simplicity" was necessary for a model to be interpretable. But, when pressed to define simplicity, the group again diverged. Is the "simplest" model the one that relies on the fewest number of features? The one that makes the sharpest distinctions? Is it the smallest program? The workshop closed without an agreed-upon answer, leaving the definition of one inchoate concept replaced by another.
As Malioutov puts it, "Simplicity is not so simple."
Aaron M. Bornstein is a researcher at the Princeton Neuroscience Institute. His research investigates how we use memories to make sense of the present and plan for the future.
1. Caruana, R., et. al Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1721-1730 (2015).
2. Metz, C. Artificial Intelligence Is Setting Up the Internet for a Huge Clash with Europe. Wired.com (2016).
3. Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., & Lipson, H. Understanding neural networks through deep visualization. arXiv:1506.06579 (2015).
4. Sergent, J., Ohta, S., & MacDonald, B. Functional neuroanatomy of face and object processing. A positron emission tomography study. Brain 115, 15–36 (1992).
5. Kanwisher. N., McDermott, J., & Chun, M.M. The fusiform face area: A module in human extrastriate cortex specialized for face perception. The Journal of Neuroscience 17, 4302–4311 (1997).
6. Farah, M.J. Brain images, babies, and bathwater: Critiquing critiques of functional neuroimaging. Interpreting Neuroimages: An Introduction to the Technology and Its Limits 45, S19-S30 (2014).
7. Brockman, J. Consciousness Is a Big Suitcase: A talk with Marvin Minsky. Edge.org (1998).
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Gethin Gibbon
John Gibbon
Birth Abt 1860 England
Family Gwenllyn Parker
1. Gethin Gibbon, b. 28 Aug 1882, Pittsburgh, Pennsylvania
Gwenllyn Parker
Family John Gibbon, b. Abt 1860, England
Maude E. Sprake
Birth 14 Aug 1895 Pennsylvania
Died 13 Mar 1976 Wilkes-Barre, Pennsylvania, USA
Father James Hewlett Sprake, b. 10 Mar 1859, Blaenavon, England
Mother Mary Ann Evans, b. 20 Jul 1865, Danville, Pennsylvania, USA
Family Gethin Gibbon, b. 28 Aug 1882, Pittsburgh, Pennsylvania
Married 25 Nov 1931
James Hewlett Sprake
Birth 10 Mar 1859 Blaenavon, England
Died 19 Dec 1937 Plymouth PA
Father James Hewlett Sprake, b. 2 Apr 1828, Herefordshire, England, United Kingdom
Mother Elizabeth Samuel, b. 16 Dec 1831, Blaenavon, Monmouthshire, Wales
Married 1852 Abergavenny, Monmouthshire, Wales
Family Mary Ann Evans, b. 20 Jul 1865, Danville, Pennsylvania, USA
1. Maude E. Sprake, b. 14 Aug 1895, Pennsylvania
2. Stella Louise Sprake, b. 25 Dec 1886, Plymouth Luz, Pennsylvania
3. Emily Blanche Sprake, b. 16 Sep 1889, Pennsylvania
4. Edna Mae Sprake, b. Abt 1902, Pennsylvania
5. Cora May Sprake, b. 18 Sep 1884, Plymouth, Pennsylvania
6. Jennie Sprake, b. Dec 1896
Personal Information | Notes | Sources | All
Name Gethin Gibbon [1, 2, 3, 4, 5]
Born 28 Aug 1882 Pittsburgh, Pennsylvania [1, 2, 3, 4, 5]
Died 7 Sep 1934 Wilkes-Barre, Luzerne, Pennsylvania, USA [4]
Father John Gibbon, b. Abt 1860, England
Mother Gwenllyn Parker
Family Maude E. Sprake, b. 14 Aug 1895, Pennsylvania , d. 13 Mar 1976, Wilkes-Barre, Pennsylvania, USA (Age 80 years)
Newspaper Obituary - September 7, 1934 Wilkes-Barre Times Leader Evening News - Well Known Contractor Passes Away - Gethin Gibbon Dies at Hazle Street Home; Had Been Employed With County - Gethin Gibbon - Gethin Gibbon, 52, well known contractor and prominent in city politics for many years, died at 2:40 o'clock this morning at his home, 285 Hazle Street, following several month illness of complications. He was a member of the contracting firm of Gibbon and Kohl and for the last two years was employed as forman in the Luzerne County Department of roads and bridges. Mr. Gibbon served as committeeman in the Thirteenth Ward and was at one time a candidate for Wilkes-Barre School Director. Deceased was born in Pittsburgh in 1882, son of the late John C. Gibbon, a stone contractor. Mr. Gibbon moved to this city many years ago and since had been active in civic and political affairs. The contracting firm, with which he was identified, also enjoyed considerable success for a number of years. Mr. Gibbon was a member of First Baptist Church, Fidelity Lodge No. 655, F & A. M., Keystone Consistory and Order of Mystic Shrine. Surviving are his widow, the former Maude Sprake of Plymouth; two children, John and Laura; his mother, Mrs. John C. Gibbon, all at home. The funeral will take place on Monday afternoon at 2:30 o'clock from the late home. Rev. Gomer Evans of Bethel Baptist Church will officiate and interment will be in Nanticoke Cemetery.
[S850] 1930 United States Federal Census, Ancestry.com, (Name: Ancestry.com Operations Inc; Location: Provo, UT, USA; Date: 2002;), Year: 1930; Census Place: Wilkes-Barre, Luzerne, Pennsylvania; Roll: 2073; Page: 2B; Enumeration District: 0249; Image: 638.0; FHL microfilm: 2341807.
[S840] 1900 United States Federal Census, Ancestry.com, (Name: Ancestry.com Operations Inc; Location: Provo, UT, USA; Date: 2004;), Year: 1900; Census Place: Wilkes Barre Ward 13, Luzerne, Pennsylvania; Roll: 1436; Page: 1B; Enumeration District: 0178; FHL microfilm: 1241436.
[S1994] 1920 United States Federal Census, Ancestry.com, (Name: Ancestry.com Operations, Inc.; Location: Provo, UT, USA; Date: 2010;), Year: 1920; Census Place: Wilkes-Barre Ward 13, Luzerne, Pennsylvania; Roll: T625_1597; Page: 4A; Enumeration District: 250; Image: 791.
[S2009] Pennsylvania, Death Certificates, 1906-1964, Ancestry.com, (Name: Ancestry.com Operations, Inc.; Location: Provo, UT, USA; Date: 2014;).
[S848] U.S., World War I Draft Registration Cards, 1917-1918, Ancestry.com, (Name: Ancestry.com Operations Inc; Location: Provo, UT, USA; Date: 2005;), Registration State: Pennsylvania; Registration County: Luzerne; Roll: 1927078; Draft Board: 3.

redpajama_set_name: RedPajamaCommonCrawl


I'm doing a lot of teaching over the next few months!
A quick look at my calendar shows that I'm gonna be busy over the next few months! As I add new events to my calendar I am also adding them to the EVENTS page on my site. That way you can always check up on where I will be. The events page is easily found in the top list of links right under my header image.
Here are my Upcoming Events for the next few months.
I hope you can join me at one of them!
Visit Quiltmaker's site for more information.
What: Teaching 3 Classes. Giving a lecture & hosting a book signing.
Quiltique's site for more information.
For more information on any of the classes please contact the shop, guild, or event sponsor.
Good for you, Julie! You'll be busy, but I hope you have fun!
Lucky students! You're close but still too far for me to attend, maybe some day. Enjoy, I love your patterns and taste.
Congrats on all of your success' Julie! I'm so happy for you and to see that you are so popular is wonderful!
I just made Brick from Skip the Borders for my daughter. The perfect fabrics, Chloe, came into The Round Bobbin and I had to make her a quilt and Bricks was perfect!
Keep up your hard work designing!
Your quilts are just so great! I'll enjoy your book since I'm not able to join your classes.


"ISIS poses a greater threat than 9/11. This is way beyond anything we have ever seen. We must prepare for everything. Get ready!" US Secretary of Defense, Chuck Hagel.
Whoa, Chuckie...back off a bit, here. Just who do you think it was that helped create the Islamic State of Iraq and the Levant, aka ISIS, fund them, train them, and provide the best weapons for them? Can you spell U S, as in a part of your title description as Secretary of Defense? Are you really telling America, and the world, that you are actually that clueless?
Nobel Peace Prize recipient and primary world warmonger, Barack Obama, has been itching to start a war in Syria. Why? To stop Russia from achieving its flow of LNG from East to West, a fact to which Obama will not openly admit. Turns out, bloggers on the internet can accept some credit for exposing Obama's false flag excuses for attempting to overthrow Assad. How did Obama's war machine, [mostly CIA-led], plan to oust Assad? By backing ISIS in every way possible: funds, training and weapons. ISIS is as much of an outgrowth of Western [US] meddling in that part of the Middle East as anything else.
Is this a story familiar to most Americans? Hardly. Beyond watching reality TV programs, almost all of Americans watch mainstream media for their "news," those bought-and-paid- for organizations that spew whatever the administration wants the pablum-fed public to hear, and nothing else. The truth is, an American is 100% more likely to be killed by some local militarized cop than any ISIS member. Not only is that truth, it is a stark reality.
Several articles have appeared about the slaying of unarmed Michael Brown from as many responses written by Afghan and Iraqi veterans that admit while engaged in actual war, they were not as heavily armed and geared as Ferguson local cops, those same local militarized cops purposefully pointing their weapons at an unarmed, non-threatening public while perched atop a 14 ton armored vehicle, or while walking on the street, aiming at anyone within their range.
"Get off the streets and go home! We are not violating your Constitutional Rights!"
Say what?! You just did. Where is the outage, America? This is happening right in front of your eyes. Everywhere across this country, hundreds of local police have received free military vehicles and weapons from the Department of Defense, vehicles whose sole purpose was to engage in war. What is happening in Ferguson will eventually happen in a neighborhood near you, if not your own.
Do you think there is no connection with the Department of Homeland Security buying 3 billion rounds of hollow-point bullets and the ongoing militarization of local police departments? Did you know that even the elite's world organization, the United Nations, has outlawed hollow-point bullets? Did you know the IRS [NOT a part of the corporate Federal government], also has IRS agents now armed and supplied with bullets? All the while, Obama, and elite wannabe former NY mayor Bloomberg want to see all Americans disarmed. If you want to better understand why, reflect more on events at Ferguson.
"Uncle Sam Wants YOU!" Just not in the way you thought.
The NSA has spent billions of fiat dollars, on your tab, and spies on every American [not only Americans], but admits it has not stopped a single terrorist threat or unveiled a single plot. Again, local militarized police are a proven greater threat to Americans than any other source, and Ferguson is proof positive that Americans have NO rights when push comes to shove. Fascism is alive and well in the US, but US citizens find that almost impossible to believe.
The elites have planned the takeover of the US since the early 1800's. They think in terms of generations in executing their plans, gradually over time so that the effect is hardly noticed, like the gradual elimination of specie-backed United States Treasury Notes, and replacing them with worthless fiat Federal Reserve Notes issued by a private banking cartel. What citizen of the United States knows anything other than fiat Federal Reserve Notes as "money?"
People, on the other hand, think in terms of what is impacting their lives for the immediate term, and that is why we discussed ISIS, Ferguson, lack of Rights, a well-armed Department of Homeland Security and IRS agents, among others, the militarization of all police forces, fiat Federal Reserve Notes issued by the elite's private banking cartel, almost entirely foreign-owned.
There are many other situations that could have been addressed. The presentation of these seemingly unrelated events is not random. Instead, each is symptomatic of the overall plans of the elites to maintain control over every aspect of US citizens without said citizens being aware of how the seemingly disparate dots connect.
The end will be devastating for almost all Americans who remain unaware of being unaware. The end will also take longer than most believe possible, until it all unravels seemingly "overnight."
If you think the reasons for owning physical silver and gold are a function of the number of coin sales, the ratio of paper demand per available physical ounce, how many tonnes China, Russia, India, Turkey, and other countries own is why you should hold either or both metals, think again. The elite's drive for a New World Order is all but done, and once they have total control, if you do not have any gold or silver, you got nothing, and you will be under the total subjugation of a federal government that will make Ferguson look like a walk in the park.
Buy physical gold and silver at any price. Once the window closes, based on the ongoing and never-ending developments you read about, and they are not as random as you might think, that window will be closed to you for your lifetime and your children's lifetime. It is not just about the imminent collapse of the fiat Federal Reserve "Dollar." If you think you have personal freedom, it is an illusion. Try moving large amounts of cash around, try speaking your mind against the Federal government, local cops, try asserting what you may think are your rights in a court of law.
The end is nearer and nearer, just not in sight, but for those who can exercise even a minimal amount of foresight, taking self-direction has never been more important. The charts give no indication of panic by those in control of the PM market through the use of derivatives. The fact that China is endeavoring to become the next gold trade center, in control of a legitimate pricing mechanism has done nothing to alter the chart read.
Gold has almost disappeared from the headlines this past week, and the fact that it cannot make a move out of its protracted TR explains why. There is nothing apparent over which one can be enthused for the prospects of higher prices, but one must always be prepared for events when they do happen.
What we are viewing is the current status of the price of gold, and manipulated or not, the charts are not related to the need[s] for owning the physical. We mentioned earlier in the year, the second half of 2014 may not be any different from the first half, and until we see concrete signs of change, expect more of the same in the weeks/months ahead.
The chart comments are observations of how the market has been [under]performing, but the bottom line is, not much is happening to turn this market around. We all get to deal with what is, and this is what is, for now.
Without giving a definitive reason as to why, we are of the mind that silver could be the first metal to turn the trend, or eventually lead when the trend does turn. The gold/silver ration is around 66, after languishing around 63+ for some time. The turn in the ratio is much stronger if/when it nears 75-80:1.
The very small weekly range could produce a buyer's upside reaction because sellers were unable to extend the range lower.
We see that same evidence of a lack of sellers to press price lower. Both Thursday and Friday did show evidence of buyers, more likely short-covering than net new longs. It takes more than that to turn a trend, but a turn in trend starts with one step at a time.
No apparent change here. Buy the physical metal for more reasons than a seemingly low price, but stay away from the long side of the paper market.
© 2014 Copyright Michael Noonan - All Rights Reserved Disclaimer: The above is a matter of opinion provided for general information purposes only and is not intended as investment advice. Information and analysis above are derived from sources and utilising methods believed to be reliable, but we cannot accept responsibility for any losses you may incur as a result of this analysis. Individuals should consult with their personal financial advisors.


So is 觉得 also for how someone feels? Because I remember learning in school that it was only how someone feels for a subject. Also what's the difference between 觉得 and the 感觉 given in the hovertext?
Firstly, I want to discuss about the English sentence. Does "She feels alright." also mean "She feels that it is alright."? I am not native English speaker so I can't tell, but I doubt it.
觉得 and 感觉 can often be used interchangeably, but there is a subtle difference. Comparatively, 觉得 is more objective, assertive and determined, and 感觉 is more subjective and uncertain.
When we make a judgement we often would use 觉得, e.g. 我觉得他的书毫无价值/I think his book is worthless. 感觉 is not very suitable here, for the same reason that we tend not to say I feel his book is worthless. Such statement would appear very harsh and direct. In such case using 感觉 instead would make us more like an observer, e.g. 我感觉他的书没有什么价值/I feel that his book doesn't have much value.
Keith, thanks for clarifying more! To answer your question: "She feels alright" does not mean "she feels that it is alright."
"She feels alright" refers to her state of wellbeing. "She feels that it is alright" states her opinion about something (most likely an action for this particular phrase).
I also would have used 感觉 here. I also did not know that 觉得 could be used without reference to a subject matter.
But, about the difference: 觉得 can mean "think" or "feel." 感觉 is more squarely "feel." I don't think it can mean "think" (ei, it's emotions and perceptions, not logic).
Alright is misspelled. It should be all right.
"She is a celebrity in China."


text:
ID: High Commissioner Update on Ukraine - 34th Meeting, 41st Regular Session Human Rights Council
10 Jul 2019 - Interactive dialogue on:
- Regular periodic update on the situation of human rights in Ukraine
Under Agenda Item 10: Technical assistance and capacity-building
41st Regular Session of the Human Rights Council.
Ms. Kate Gilmore, Deputy High Commissioner for Human Rights, OHCHR (Presentation of oral report)
Ukraine (Country Concerned), Mr. Yurii Klymenko
European Union, Mr. Carl Hallergard
Slovenia, Mr. Klemen Ponikvar
Canada, Mr. Curtis Peters
Russian Federation, Ms. Kristina Sukacheva
UNICEF, Ms. Anne Grandjean
Lithuania, Mr. Andrius Krivas
Sweden, Ms. Clara Molin
Australia, Mr. Kevin Plyaford
Czech Republic, Mr. Jan Kára
Croatia, Ms. Vesna Batistic Kos
Montenegro, Mr. Milorad Šcepanovic
Spain, Mr. Pablo Nuño Garcia
Netherlands, Ms. Monique T.G. van Daalen
France, Ms. Clarisse Gerardin
Denmark, Mr. Morten Jespersen
Hungary, Mr. András Szorényi
Switzerland, Ms. Jasna Lazarevic
Bulgaria, Ms. Deyana Kostadinova
Poland, Mr. Zbigniew Czech
Ms. Kate Gilmore, Deputy High Commissioner for Human Rights, OHCHR (Presentation of oral report) 00:00:33
Ukraine (Country Concerned), Mr. Yurii Klymenko 00:12:18
European Union, Mr. Carl Hallergard 00:17:59
Slovenia, Mr. Klemen Ponikvar 00:19:58
Canada, Mr. Curtis Peters 00:21:36
Russian Federation, Ms. Kristina Sukacheva 00:23:47
UNICEF, Ms. Anne Grandjean 00:25:50
Lithuania, Mr. Andrius Krivas 00:27:46
Sweden, Ms. Clara Molin 00:29:44
Australia, Mr. Kevin Plyaford 00:31:39
Czech Republic, Mr. Jan Kára 00:35:40
Croatia, Ms. Vesna Batistic Kos 00:37:50
Spain, Mr. Pablo Nuño Garcia 00:41:50
Netherlands, Ms. Monique T.G. van Daalen 00:45:38
France, Ms. Clarisse Gerardin 00:47:30
Denmark, Mr. Morten Jespersen 00:49:18
Hungary, Mr. András Szorényi 00:51:08
Latvia 00:53:25
Switzerland, Ms. Jasna Lazarevic 00:55:21
Poland, Mr. Zbigniew Czech 00:59:23
ID: High Commissioner Update on Ukraine (Cont'd) - 35th Meeting, 41st Regular...
ID: High Commissioner Update on Ukraine - 52nd eeting, 34th Regular Session...
ID: High Commissioner Update on Ukraine - 49th Meeting, 37th Regular Session...
ID: High Commissioner Update on Ukraine - 31st Meeting, 33rd Regular Session...
ID: High Commissioner Update on Ukraine - 33rd Meeting, 42nd Regular Session...


Derrick Harriell is a poet who was born and raised in Milwaukee, Wisconsin. Since 2011 he has been a professor at the University of Mississippi in Oxford, Mississippi. He earned an MFA from Chicago State University and a Ph.D. at the University of Wisconsin-Milwaukee in 2012. He was a poetry editor for The Cream City Review in Milwaukee and Third World Press. He has been nominated twice for the Pushcart Prize. His two works of poetry are Ropes and Cotton.


I am a creative and open-minded individual seeking an accepting environment in which to express myself in front of a camera. Most of my experience is with casual wear, but I am open to other types of fashion/attire.
I was born biologically male, but have started the male to female transition process. So naturally, I prefer photographers that are respectful of transgender models.
Thank you for taking the time to look at my portfolio.


Earlier this year we were delighted to be a part of Singapore influencer Mongchin Yeoh (@mongabong) and Matthias' surprise marriage proposal.
Fast forward several months later and we're happy to report that Mong is well on her way to her wedding. The beautiful bride-to-be will be opening up to The Wedding Scoop in a series of diary entries so sit back, relax and enjoy this sneak peek into her thoughts on her planning journey and wedding vision.
What do you want your wedding to look and feel like?
Mong: I want it to be something simple, small, homely and comfortable as I want my guests to enjoy themselves and the food.
Do you want it formal or relaxed?
Mong: I want it to be really relaxed and in fact, I want it to be as normal as possible. I do not want it to be too extravagant. The most important thing is that I want everyone to enjoy themselves.
How do you envision your gown, décor, and venue?
Mong: I am going to be getting married in a church, so the venue is already set. The dinner banquet will be in a hotel that serves really good food but at the same time it will not be too extravagant for my guests. For my gown, I already have a few designs in my mind, but mostly it will be chic, modern and minimal which will be very simple and classy.
What sort of food do you want to feed your guests?
Mong: We will be feeding our guests with food which we hope they like. Matt and I are very particular about food so that is why we have only chosen vendors that we really like and know we can trust. So hopefully our guests will like the food too!
Thank you Mong, we love the sound of your wedding already. Till next time!
Wedding Scoopers, if you need help keeping track of your big day, check out our wedding planning schedule.


Who's the most important person in your business? The answer is the customer of course. Your customers are what keep you in business, and if your customers are happy, then you're happy. It's certainly important to keep your customers in your good graces- and thinking about your business and your products or services as much as possible. With professional San Antonio social media marketing help, we at Local Customer Search can help your business maintain and improve the relationship you have with your customers- making it that much easier for your business to enjoy even greater success down the road.
Here at Local Customer Search we're proud to offer top-quality social media marketing services for local San Antonio businesses. Whether it's through Facebook, Twitter, or another social media site, it's certainly true that social media can be a powerful tool which can help your business improve its reputation and get information about products and services to your customers in a more effective and efficient way.
If you want to stay ahead of the competition in 2016, then be sure to consider the power found in effective San Antonio social media marketing. You can learn more about how we at Local Customer Search help our clients maintain positive relationships through social media marketing when you browse through our website, www.localcustomersearch.com. If you'd like to learn more, then just give us a call today at 210-912-5659 or fill out the simple contact form found on our website.


Articles and reports on medical analysis and health, wellbeing and different medication. If you suppose you or your shopper could possibly be eligible for help from BHBH, obtain and fill in the referral type This can then have to be signed by a health or social care professional to verify eligibility for the scheme. Please examine along with your health heart for more information on what you will want to submit. Be taught extra about how digital health is reworking care and empowering patients.
In case you are obese or are experiencing well being problems linked to obese or obesity, ask your well being care provider if a modest weight reduction would be helpful. First, using a sturdy evidence and information-led approach to policy making that considers the health impacts of non-well being policies. To assist make health care more inexpensive and to make health care prices easier to understand, we developed a tool to measure the price of care.
Advocacy organisations, then, have a difficult activity – but one the place even small successes can have far-reaching advantages for public well being. Get a well being book and browse it. Higher health isn't hard to accomplish however it is a learning course of. Better Well being recruits medical doctors and supplies career options which have confirmed profitable in attracting high calibre young GPs. The irony is that this disproportionate impact inside the population marginalises the problem of vitamin D deficiency, creating an ethnicity-associated health inequality.
The NIDDK interprets and disseminates research findings by way of its clearinghouses and teaching programs to increase knowledge and understanding about health and illness amongst patients, well being professionals, and the general public. Observe us on Fb , LinkedIn and Twitter to learn about new developments in digital well being and to listen to more tales about how digital health is reworking care. The campaign is centered on a Council of Storytellers" which features Canadians and clinicians who share their tales by means of videos featured on the Higher Health Collectively website and varied social media channels. This helps children with prosopagnosia to know that some youngsters of his or her class or group look very related. Sign as much as obtain e-alerts and newsletters on the health coverage matters you care about most. Digital health has already had a positive affect on the lives of many Canadians.
Gateway supplies major, specialty and urgent care protection to uninsured adults in St. Louis City and County, aged 19-64, via a network of group providers and is sponsored by the Missouri Department of Social Companies in partnership with the St. Louis Regional Health Fee.


text:
Game Insights
Facts and figures: Andreescu's US Open breakthrough
Published by Matt Trollope
Bianca Andreescu (R) and Serena Williams during the US Open 2019 final presentation (Getty Images)
We put Bianca Andreescu's stunning US Open victory into context after the 19-year-old re-wrote the record books.
The statistical context surround this year's US Open women's singles final between Bianca Andreescu and Serena Williams was already absorbing enough, even without the result that followed.
When Williams broke through in New York, 20 years ago in 1999, to win her first Grand Slam singles title, Andreescu wasn't even born.
And when the 19-year-old Andreescu lined up against the 37-year-old Williams, it marked the biggest age gap — 18 years and 263 days — between major finalists in Open Era history.
Andreescu's subsequent 6-3 7-5 win meant the young Canadian re-wrote the record books.
Here are some of the remarkable facts and figures about Andreescu's breakthrough victory — and what it has meant for her season, and career.
Andreescu is now 8-0 against top-10 ranked opponents in her career, with all of those wins being achieved in 2019.
The victory over Williams improves her win-loss record this year to 45-4 — and she's won her last 14 straight.
She owns the most hard-court victories (31) of any player on tour.
Andreescu is projected to rise to world No.5 on Monday; the week of 22 October 2018 she was ranked No.243, and begun 2019 ranked No.152
According to the WTA note, there are some examples if you consider players starting with no ranking.
But if we consider only ranked players, the lowest was Kim Clijsters: n.134 on 6th March 2005, top-10 in mid-August of the same year, YE n.2 and n.1 at beginning of March 2006.
— Diego Barbiani (@Diego_Barbiani) September 4, 2019
She equals Monica Seles as the player winning her first major title the "fastest"; this was just her fourth appearance in a Grand Slam main draw.
She was the first player in 22 years to reach the US Open in her main-draw debut — but unlike Venus Williams in 1997, she won the title.
Andreescu is the first teenager in 13 years to win a Grand Slam singles title — the last was Maria Sharapova at the 2006 US Open. She was the first teenager since Caroline Wozniacki in 2009 to reach a US Open final.
Her victory marks the first time a teenager has ever beaten Serena Williams at the US Open.
She becomes the first player born in the 2000s to win a Grand Slam singles title.
Her previous best showing at a major tournament was the second round of Australian Open 2019; she had never been beyond the first round of US Open qualifying in two previous visits.
Catching up with … Bianca Andreescu
October Money List: Osaka wins big in Beijing
WTA Player of the Year: Barty, Osaka or Andreescu?
September Money List: Andreescu, Nadal win big
Power playing: Make your muscle matter
Comparing tennis today to the game of the past is like comparing a boxer and fencer. There... More
ATP: the biggest strength and weakness of every top 10 playe...
As the first Grand Slam of the season fast approaches, top-10 players are leading the char... More
The history of the most common words in tennis
Tennis is a funny old game. People love you one minute and then want to drop you the next;... More
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Negative thoughts and self-doubt can sometimes prove to be tougher opponents than the pers... More
© 2021 Tennis Australia


» Ergoloid Mesylates Sublingual Tablets contain not less than 90.0 percent and not more than 110.0 percent of the labeled amount of ergoloid mesylates, consisting of not less than 30.3 percent and not more than 36.3 percent of the methanesulfonate salt of each of the individual alkaloids (dihydroergocristine, dihydroergocornine, and dihydroergocryptine). The ratio of alpha- to beta-dihydroergocryptine mesylate is not less than 1.5:1.0 and not more than 2.5:1.0.
Labeling— Label the Tablets to indicate that they are intended for sublingual administration.
Identification— Mix a small amount of powdered Sublingual Tablets, equivalent to about 5 mg of ergoloid mesylates, with 5 mL of water and 5 mL of a mixture of equal volumes of glacial acetic acid and sulfuric acid, and add 1 drop of freshly prepared ferric chloride solution (1 in 20): a violet-blue color develops within 5 minutes.
Assay preparation— Weigh and finely powder not fewer than 20 Sublingual Tablets. Transfer an accurately weighed portion of the powder, equivalent to about 5 mg of ergoloid mesylates, to a 50-mL centrifuge tube. Add 15.0 mL of Internal standard solution, insert the stopper into the tube, and shake for about 15 minutes. Centrifuge, filter if necessary, and use the clear supernatant.
Chromatographic system (see Chromatography 621 )— The liquid chromatograph is equipped with a 280-nm detector and a 4.6-mm × 15-cm column that contains packing L1. [note—Use an L1 column capable of handling pH values greater than 11.] The flow rate is about 1.5 mL per minute. Chromatograph the Standard preparation, and record the peak responses as directed for Procedure: the column efficiency determined for the dihydro- -ergocryptine mesylate peak is not less than 1000 theoretical plates; the tailing factor for the dihydro- -ergocryptine mesylate peak is not more than 2.0; the resolution, R, between the dihydro- -ergocryptine mesylate and dihydroergocristine mesylate peaks is not less than 2.0, and between dihydroergocristine and dihydro- -ergocryptine peaks is not less than 2.0; and the relative standard deviation of the ratio of the sum of the four peaks to the internal standard for replicate injections is not more than 1.5%.


text:
The records stop
four hundred years in the future,
after the lines shut, and the corporation withdrew. What we know of how we got here is pieced together by the fragments of the rail maps and corporate orders that survive.
After numerous waves of social collapse, Glasgow, a once prosperous city, had run to waste in lawless ruin.
The city passed into the hands of
a German-speaking conglomerate, who incorporated the territory into a new civic entity: Neurealm. In an attempt to revive some sense of order, the conglomerate embarked on an ambitious program of infrastructural development centered on the construction of a large overland rail network, which they named
Caledonia New Realm.
Three lines; the Nordwest, New Gàrradh
and Árdkaserne were built, and
construction began on a crossrail tunnel,
the Zentraal Line.
It would never open.
Any momentum towards recovery
was wiped out when Klaw appeared.
There are no records of when the drug arrived in Neurealm, but we know where it took hold; in the dismal communities on the southern banks of the now-dry riverbed that cut the city from West to East.
A vicious narcotic that abused its users without mercy or restraint, Klaw gripped hundreds of thousands of the destitute masses that made the enormous squat between Pollockshail and Pollocksgaith their home. Very soon, stricken dependents turned to crime to feed their habits, running raids across the city from their labyrinthine base. The warren would come to be known as Heaven's Tilt; and the waves of junkie scum the first major gang of Neurealm;
the Dark Leviathan Krew.
Chaos flowed out from Heaven's Tilt.
The newly built rail network provided arteries into the city, junkies swarmed and looted, with the Dark Leviathan Krew claiming as their own fiefdom the territory of the New Gàrradh Line. Within weeks its surfaces were covered in the scrawls of the addicts; the carriages tattooed top to bottom in an arcane, knotted and wretched script.
The New Gàrradh line teemed with contagion. From the trains the dead-eyed spilled. Houses, whole neighbourhoods, were looted in zombified stupor. Food and water became scarce. Economic activity ground to a halt. The Dark Leviathan fell into a narcotic half-life spent in feudal squalor and euphoric pain. Whether by mismanagement, incompetence, or simply indifference, the ruling council of the conglomerate failed to act in any meaningful way to limit it.
Not everyone stood idle. With the council absent, the remnants of the police and security forces seized their opportunity. Fortifications were erected along the Nordwest Line, and among the former aristocratic enclaves of the West End. Tactical weaponry, riot gear, even military ordinances were turned on the citizens. A brutal repression began; the paramilitary force renouncing any sense of duty to the city itself, instead formed its own gang;
the Onslaught Ash Krew.
The Ash squads, no longer bound by law, instead operated out of pure contempt.
They spent their days hunting and killing junkies; bursting into raves in full tactical gear and murdering for sport. Wanton in their cruelty they sat, enthroned, joyless, in the once-esteemed great houses of the west of the city, venturing from their reinforced strongholds in Georgeskreuz, Eldonzirkus, and Kelvinhain
to strike with brutal force.
Whilst clashes between Onslaught Ash troops and Dark Leviathan adherents spread across the Nordwest and New Gàrradh lines, the remaining Árdkaserne Line became itself increasingly fortified; the only passage of refuge for those terrorized by the Klaw addicted junkie masses and the Ash squads. Having lost control of all three branches of the rail network, the council announced its full withdrawal from the region, giving the order of the indefinite shutdown of Caledonia New Realm.
Neurealm, spurned by its government and people alike, enters The Fall; a grim ravestate of factional warfare and ruin.
In the chaotic collapse, as infrastructure was abandoned and food became scarce, an evacuation of the last citizens began towards the north along the Árdkaserne Line. Their hope was to establish a simple life of farming colonies in the woods above.
The cold hardship of the frigid wilderness took its toll. Colonies became cults, and as food supplies dwindled, the dream of a new world in the forests turned to something much darker. From the third line was born the third of Neurealm's gangs, and with it a culture of new horrors; pagan ceremonies in the woods, witchcraft, blood sacrifice, whispers of cannibalism.
They called themselves the Cold Eye Krew.
All three lines were now segregated.
All crossing points were killing-fields.
With Caledonia New Realm construction halted indefinitely, a no-mans land emerged around its municipal headquarters in Parkzicht, the enormous culture complex that had been built in the center of the city.
This is our setting;
Neurealm, After The Fall.
In the chaos of the ruins, there is only one form of anything resembling culture left: the enormous, multi day raves that take place inside Parkzicht's shell, in the monumental caverns of the abandoned palace of art.
Underneath Parkzicht run the forbidden tunnels of the Zentraal Line, the never-completed Subway that was to service the heart of the city, long-abandoned, dangerous, impenetrable.
It is here – the rumour persists – that somewhere in the labyrinths and catacombs below resides a figure who it is claimed will bring an end to this age of darkness.
An avenging angel, a Joan of Arc, a rave goddess somehow untouched by the anguish of the surface. It's unclear if the remaining citizens hope that she saves or destroys them. It is unclear if she even truly exists, or if she is some collective dream, a necessary madness to envisage some kind of terminus point for this misery.
Her name is Eclipser.
All music by Clouds. All graphics by David Rudnick. Site developed by Jack Clarke.


Currently appearing in a Wells Fargo commercial, actor, writer, and spiritual psychologist Luann Chou advocates tenacity as well as letting go. Showcase recently spoke to her about the merits of both and how they apply to creativity.
Showcase: You just did a piece about the power of saying no for "Heal Your Life" newsletter. Give an example of how harnessing "no" would benefit an artist or writer.
Showcase: As a certified spiritual psychologist, you've facilitated past life regressions. What have they revealed?
Chou: I coached a client who was Joan of Arc in a past life. This may seem outlandish but in my mind, "Somebody was Joan of Arc so...." Past lives can be an important healing tool. My general philosophy focuses on the present and the thoughts we think today. I am a big believer that our thoughts direct our lives. It doesn't matter what you've been thinking and for how long. You can redirect your thoughts and your life anytime you choose.
Showcase: When is it better to hang on tight?
Chou: I allow everyone the freedom to process as they choose. I don't force people to let go or forgive. We all have our own timing. My hope is to get to letting go but more importantly I am an advocate for self honoring choices. If you feel someone has wronged you, take time to acknowledge that pain. Some hurts are very deep. Write about it. Talk about it to someone trustworthy. Honor the person you were when you went through the hurts, even if it was yesterday.
Showcase: What sparked your interest in acting and writing?
Showcase: Is there anything you want to share with the world now?
Chou: Honestly I always have so much to share. My passion is Luann's Corner – Sitting with Spirit, my Youtube channel, where I share different spiritual topics from my own life.


Now open at the elegant DiXmiX Gallery, curated by Dixmix Source, is Cerebral Frame, an exhibition of photographs by Isa Messioptra. The thirteen beautifully-wrought images on display vary from monochrome to full color, and represent work that extends back over the last year and a half.
"She's a story telling photographer," said Dixmix as we talked about Isa's images. "I can enter in her pictures and tell myself a story — I love that kind of photography." As for the title of the exhibition, "It's the title of one of my pics that isn't up here," laughed Isa, "but it is unlike many of my older pics that were straight linear stories ... while the last couple of years they are more feeling and ambiguous ... like a spark ... and hopefully you feel something from them and it takes you someplace." All of Isa's works are available for purchase, and contributions toward support of the gallery are also welcome.


Scholarships with the value of $5,000 (universities) or $2,500 (colleges) each.
Any IEEE student members, registered in their penultimate year at a university or college with a McNaughton Resource Learning Centre, who has been active in the Centre for a minimum of one year.
To encourage awareness of and participation in the educational programs supported by the IEEE.
One scholarship each year, value $1,500.
All IEEE student members in full time attendance at either the University of Alberta or the University of Calgary, who are in their penultimate year and whose program of study includes courses in electric power engineering.
One scholarship each year, value $1,000.
The nominee must be an undergraduate IEEE Student Member in good standing, registered in their penultimate year of an accredited Electrical Engineering, Computer Engineering or Engineering Science/Engineering Physics undergraduate program offered in a Canadian university, a citizen or permanent resident of Canada, enrolled as a full-time student.
Purpose: To encourage leadership, creativity and enthusiasm for the engineering profession.
One prize each year, value $1,000.
All female IEEE Canada members who received their first professional degree within the last ten years and who are active in IEEE activities.
To recognize excellence in young women engineering professionals and to support the Women In Engineering programme within IEEE in Canada.
Eligibility: Canadian IEEE Student Branches.
To enhance the learning experiences of IEEE student members at Canadian Universities and Colleges, as well as encouraging IEEE membership and activities, and interaction between the IEEE Student Branch and the local IEEE Section.
Deadline: March 15, August 15, November 15.
Deadline: March 15, August 15, and November 15.
The IEEE Canadian Foundation, the philanthropic arm of the IEEE in Canada, is ready to accept your contributions when you renew your IEEE membership in the fall each year.
Please note that this method enables you to make a contribution to the IEEE Canadian Foundation General Fund only. To make a contribution to one or more of our other funds, please consider donating online or donating by mail. Charitable tax receipts are issued to Canadians.


Friends of April Kostenski, a candidate committee, reported receiving $5,150 from Jan. 1 through March 31, according to its first quarter report to the Illinois State Board of Elections.
April Kostenski is a Democrat.
The committee reported $5,150 in individual contributions.
Want to get notified whenever we write about Friends of April Kostenski ?
Next time we write about Friends of April Kostenski, we'll email you a link to the story. You may edit your settings or unsubscribe at any time.


This iOS Developer job description template is optimized for posting on online job boards or careers pages and is easy to customize for your company.
- Collaborating with cross-functional teams to define, design, and ship new features.
- Unit-testing code for robustness, including edge cases, usability, and general reliability.


text:
Power of Play Blog
Powerful Play
Minnesota Children's Museum Delays Opening of Expanded St. Paul Location
by Sara Kerr | Feb 9, 2017 | Press Release
Minnesota Children's Museum, which closed its downtown St. Paul location in December to complete a major expansion and renovation project, will reopen in June, two months later than originally planned.
The delay stems from a disruption in the construction of The Scramble, a four-story climbing adventure that will occupy a glass addition along West Seventh Street.
The Scramble, a one-of-a-kind structure, will feature two climbing towers, a spiral slide and a netted catwalk suspended more than 40 feet high. The vendor fabricating The Scramble went out of business, causing a delay as the museum made arrangements for another company to construct the remaining parts.
"The Scramble is going to be a fun, safe and challenging experience for children and adults alike," said Dianne Krizan, president of Minnesota Children's Museum. "We're working hard to ensure that this experience will be everything we've dreamed it would be, while maintaining the highest standards of quality and safety."
The museum will announce dates for grand opening events in the coming weeks. A Minnesota Children's Museum location at the Mall of America® and Minnesota Children's Museum of Rochester remain open with regular hours.
Museum members with a Passport level or above membership get a two-month extension at no cost to make up for the delay. Membership benefits include free admission to the Mall of America® and Rochester locations.
The newly renovated museum in St. Paul will feature 10 new exhibits plus one gallery for travelling exhibits. The new experiences include:
The Scramble: Four-story climbing adventure with towers, slide and catwalk 40-feet high
Forces at Play | 3M Gallery: Engineer ping pong ball launchers and get scrubbing in the car wash
Sprouts: Wonder, discover and move with joy in a toddler-sized land of enchantment
Creativity Jam | Target Gallery: A lively and energetic space where creativity roams free
The Studio | Target Gallery: Tinker, make, explore and create with real tools
Super Awesome Adventures: Move and groove in an action adventure
Our World | Best Buy Gallery: Updated pretend town with fire station, post office and food truck
Imaginopolis: Build your own story in this abstract play space
Tip Top Terrace | Ecolab Gallery: On the rooftop, experience the sights and sounds of the city
Backyard: New outdoor space; sensory activities with natural materials
Visitors also will enjoy a new café and store, an additional elevator, more bathrooms and an additional entrance on the skyway level.
Construction on the $30 million project began in December 2015. The work is the first major renovation of the museum since it moved from Bandana Square to downtown St. Paul in 1995.
Information about visiting the Mall of America® or Rochester locations can be found at mcm.org.
About Minnesota Children's Museum
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text:
Ravenstein är en stad i Neckar-Odenwald-Kreis i regionen Rhein-Neckar i Regierungsbezirk Karlsruhe i förbundslandet Baden-Württemberg i Tyskland.
Kommunen bildades 1 december 1971 genom en sammanslagning av kommunerna  Erlenbach, Hüngheim, Merchingen, Oberwittstadt och Unterwittstadt samt sataden Ballenberg. Kommenen blev stad 1 april 1974.

Staden ingår i kommunalförbundet Osterburken tillsammans med staden Ravenstein och kommunen Rosenberg.

Referenser

Kommuner och städer i Neckar-Odenwald-Kreis

redpajama_set_name: RedPajamaWikipedia


text:
Home Web Series
Karishma Sharma goes topless in first look of 'Ragini MMS 2.2'!
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ये है बॉलीवुड के सबसे कंजूस सितारे, जिन्होंने अपनी शादी नहीं...
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I know I must be crazy but this winter I will be Freezin' for a Reason when I take the Polar Plunge for Special Olympics Wisconsin. The Polar Plunge is a cool event where supporters raise pledges and jump into frozen bodies of water. Money raised in the Polar Plunge provides funding for nearly 10,000 athletes with cognitive disabilities to reach for the gold.