Evidence of Stars’ Catalyzed Fusion Found

The Carbon-Nitrogen-Oxygen energy-production mechanism in the universe is detected

University of Massachusetts Amherst

November 25, 2020 -- AMHERST, Mass. – An international team of about 100 scientists of the Borexino Collaboration, including particle physicist Andrea Pocar at the University of Massachusetts Amherst, report in Nature this week detection of neutrinos from the sun, directly revealing for the first time that the carbon-nitrogen-oxygen (CNO) fusion-cycle is at work in our sun.

The CNO cycle is the dominant energy source powering stars heavier than the sun, but it had so far never been directly detected in any star, Pocar explains.

For much of their life, stars get energy by fusing hydrogen into helium, he adds. In stars like our sun or lighter, this mostly happens through the ‘proton-proton’ chains. However, many stars are heavier and hotter than our sun, and include elements heavier than helium in their composition, a quality known as metallicity. The prediction since the 1930’s is that the CNO-cycle will be dominant in heavy stars.

Neutrinos emitted as part of these processes provide a spectral signature allowing scientists to distinguish those from the ‘proton-proton chain’ from those from the ‘CNO-cycle.’ Pocar points out, “Confirmation of CNO burning in our sun, where it operates at only one percent, reinforces our confidence that we understand how stars work.”

Beyond this, CNO neutrinos can help resolve an important open question in stellar physics, he adds. That is, how the sun’s central metallicity, as can only be determined by the CNO neutrino rate from the core, is related to metallicity elsewhere in a star. Traditional models have run into a difficulty – surface metallicity measures by spectroscopy do not agree with the sub-surface metallicity measurements inferred from a different method, helioseismology observations. 

Pocar says neutrinos are really the only direct probe science has for the core of stars, including the sun, but they are exceedingly difficult to measure. As many as 420 billion of them hit every square inch of the earth’s surface per second, yet virtually all pass through without interacting. Scientists can only detect them using very large detectors with exceptionally low background radiation levels. 

The Borexino detector lies deep under the Apennine Mountains in central Italy at the INFN’s Laboratori Nazionali del Gran Sasso. It detects neutrinos as flashes of light produced when neutrinos collide with electrons in 300-tons of ultra-pure organic scintillator. Its great depth, size and purity make Borexino a unique detector for this type of science, alone in its class for low-background radiation, Pocar says. The project was initiated in the early 1990s by a group of physicists led by Gianpaolo Bellini at the University of Milan, Frank Calaprice at Princeton and the late Raju Raghavan at Bell Labs.

Until its latest detections, the Borexino collaboration had successfully measured components of the ‘proton-proton’ solar neutrino fluxes, helped refine neutrino flavor-oscillation parameters, and most impressively, even measured the first step in the cycle: the very low-energy ‘pp’ neutrinos, Pocar recalls.

Its researchers dreamed of expanding the science scope to also look for the CNO neutrinos – in a narrow spectral region with particularly low background – but that prize seemed out of reach. However, research groups at Princeton, Virginia Tech and UMass Amherst believed CNO neutrinos might yet be revealed using the additional purification steps and methods they had developed to realize the exquisite detector stability required.

Over the years and thanks to a sequence of moves to identify and stabilize the backgrounds, the U.S. scientists and the entire collaboration were successful. “Beyond revealing the CNO neutrinos which is the subject of this week’s Nature article, there is now even a potential to help resolve the metallicity problem as well,” Pocar says.

Before the CNO neutrino discovery, the lab had scheduled Borexino to end operations at the close of 2020. But because the data used in the analysis for the Nature paper was frozen, scientists have continued collecting data, as the central purity has continued to improve, making a new result focused on the metallicity a real possibility, Pocar says. Data collection could extend into 2021 since the logistics and permitting required, while underway, are non-trivial and time-consuming. “Every extra day helps,” he remarks.

Pocar has been with the project since his graduate school days at Princeton in the group led by Frank Calaprice, where he worked on the design, construction of the nylon vessel and the commissioning of the fluid handling system. He later worked with his students at UMass Amherst on data analysis and, most recently, on techniques to characterize the backgrounds for the CNO neutrino measurement.

This work was supported in the U.S. by the National Science Foundation. Borexino is an international collaboration also funded by the Italian National Institute for Nuclear Physics (INFN), and funding agencies in Germany, Russia and Poland.

           vhttps://www.umass.edu/newsoffice/article/neutrinos-yield-first-experimental

Wasps Can Disable Aircraft

George Dvorsky writes in the November 25, 2020 Gizmodo: “Keyhole wasps like to build their nests in tiny holes, including the openings of devices used to measure the speed of aircraft. A recent investigation shows the problem is worse than we realized.”

Full story at this link: The Surprising Way Keyhole Wasps Can Take Down an Airplane (gizmodo.com)

U.S. Dollar Shrinking Fast in 2020

 The Dollar Is Being Systematically Destroyed, And We're On A Path That Inevitably Leads To Hyperinflation

Authored by Michael Snyder via The Economic Collapse blog,

If we keep treating the U.S. dollar like it is toilet paper, it is just a matter of time before our entire financial system goes down the tubes.  At this moment, the dollar is still the primary reserve currency of the world, and the fact that we control it is an absolutely massive advantage for us.  Because the rest of the globe uses dollars to trade with one another, that creates a tremendous amount of artificial demand for our currency, and it keeps the value of our currency elevated at a level that it much higher than it otherwise would be.  But now that we are starting to act like the Weimar Republic in their heyday, it is only going to be a matter of time before everyone else on the planet starts abandoning the U.S. dollar in droves.  We are literally killing our “golden goose”, and most Americans do not even understand what is happening.

The remarks that John Williams made about hyperinflation during a recent interview with Greg Hunter have created quite an uproar, but the truth is that Williams is right on target.

We are on the exact same path that Zimbabwe, Venezuela and so many others have already gone down, and the very foolish decisions that we have been making are only going to end in complete and utter disaster.

To illustrate what I am talking about, I would like to direct your attention to what has happened to M2 during this calendar year.  For those that are not familiar with M2, here is a definition that comes from Investopedia…

M2 is a calculation of the money supply that includes all elements of M1 as well as “near money.” M1 includes cash and checking deposits, while near money refers to savings deposits, money market securities, mutual funds, and other time deposits. These assets are less liquid than M1 and not as suitable as exchange mediums, but they can be quickly converted into cash or checking deposits.

…  the M2 curve has been rising at an exponential pace in 2020.  In fact, since the pandemic started the curve has nearly gone vertical…

If we keep doing this, we won’t be facing a major financial disaster years from now.

Rather, it will just be a matter of months before the wheels start coming off.

But our leaders do not have any intention of changing course now.  During 2020 the Federal Reserve has been pumping money into the financial system at a rate that we have never seen before, and they have indicated that they plan to continue to support the financial markets as we head into 2021.

And Chicago Federal Reserve Bank President Charles Evans just said that he expects that interest rates could continue to be pushed all the way to the floor “perhaps into 2024”…

Chicago Federal Reserve Bank President Charles Evans said Monday there is still “quite a long ways to go” for the U.S. recovery from the coronavirus crisis, adding that he expects the Fed to keep interest rates at their current near-zero level until perhaps into 2024.

Of course the federal government is going to continue to pump out “stimulus package” after “stimulus package” no matter who is in the White House.  This is a point that John Williams made very strongly during his interview with Greg Hunter…

Because they has been so much damage done to the economy, Williams says there will have to be stimulus no matter who eventually makes it into the White House.  Williams contends, “Let’s say Trump gets re-elected.  He’s not going to have any choice but to increase stimulus to try to help the economy and help people.  If Biden takes over, he’s going to have to do the same.  He is already promising massive stimulus.  Where it gets really scary is if the Democrats can take control of the House, the Senate as well as the White House. . . . The stimulus there is going to be unbelievable. . . . The more radical Democrats will just print the money you need and spend whatever you need to spend it on, and don’t worry about it. . . . Whoever gets into power, there is going to be more deficit spending.  It’s just a matter of how radical it will be. . . . There is no way we are escaping massive stimulus for at least the next year and into 2022.”

Virtually everyone likes getting “free money” from the government, but you have probably noticed that the price of just about everything has been going up lately.

And this is just the beginning.  According to Williams, we are literally on the verge of a “hyperinflationary Great Depression”…

Williams expects to see some very large inflation because of all the stimulus coming and predicts, “The more left we go, the more rapid will be the demise of the dollar.  Eventually, it will be a hyperinflation in the United States.  What I am looking at here is this evolving into a hyperinflationary Great Depression.  To save yourself, you have to preserve your wealth, your dollar assets.  To do that, you have to convert your dollars into physical gold and silver, precious metals and just hold them.  They will retain value over time as opposed to paper dollars that will effectively become worthless.  You’ll be getting a lot of money from the government, and they will keep giving you more and more and more, but that’s going to be an environment of rising and rising inflation.  It’s not necessarily going to buy you more. . . . Hyperinflation will bring political disruption. . . . Hyperinflation is a form of default.  Gold is telling us hyperinflation is straight ahead of us.”

Needless to say, what Williams is saying is perfectly consistent with the warnings in my new book.

To protect themselves, a lot of investors have been pouring money into gold, silver and other precious metals.

At the start of this year, the price of gold was sitting at $1,520.55.  As I write this article, the price of gold is at $1824.00.

And actually the rise in the price of silver has been even more dramatic over the course of 2020.

Gold and silver will almost certainly keep rising as the value of the dollar continues to be destroyed, but even those that invest in precious metals are not going to win in the end.

Because the truth is that the complete collapse of our financial system is not going to benefit any of us, and there is going to be no way to avoid such a fate if we keep going down this very dangerous path.

Michael’s new book entitled “Lost Prophecies Of The Future Of America” is now available in paperback and for the Kindle on Amazon.

                            http://theeconomiccollapseblog.com/  [November 23, 2020 entry]

Smallest Atom Memory Unit Created

From The University of Texas at Austin

November 23, 2020 -- Faster, smaller, smarter and more energy-efficient chips for everything from consumer electronics to big data to brain-inspired computing could soon be on the way after engineers at The University of Texas at Austin created the smallest memory device yet. And in the process, they figured out the physics dynamic that unlocks dense memory storage capabilities for these tiny devices.

The research published recently in Nature Nanotechnology builds on a discovery from two years ago, when the researchers created what was then the thinnest memory storage device. In this new work, the researchers reduced the size even further, shrinking the cross section area down to just a single square nanometer.

Getting a handle on the physics that pack dense memory storage capability into these devices enabled the ability to make them much smaller. Defects, or holes in the material, provide the key to unlocking the high-density memory storage capability.

"When a single additional metal atom goes into that nanoscale hole and fills it, it confers some of its conductivity into the material, and this leads to a change or memory effect," said Deji Akinwande, professor in the Department of Electrical and Computer Engineering.

Though they used molybdenum disulfide -- also known as MoS2 -- as the primary nanomaterial in their study, the researchers think the discovery could apply to hundreds of related atomically thin materials.

The race to make smaller chips and components is all about power and convenience. With smaller processors, you can make more compact computers and phones. But shrinking down chips also decreases their energy demands and increases capacity, which means faster, smarter devices that take less power to operate.

"The results obtained in this work pave the way for developing future generation applications that are of interest to the Department of Defense, such as ultra-dense storage, neuromorphic computing systems, radio-frequency communication systems and more," said Pani Varanasi, program manager for the U.S. Army Research Office, which funded the research.

The original device -- dubbed "atomristor" by the research team -- was at the time the thinnest memory storage device ever recorded, with a single atomic layer of thickness. But shrinking a memory device is not just about making it thinner but also building it with a smaller cross-sectional area.

"The scientific holy grail for scaling is going down to a level where a single atom controls the memory function, and this is what we accomplished in the new study," Akinwande said.

Akinwande's device falls under the category of memristors, a popular area of memory research, centered around electrical components with the ability to modify resistance between its two terminals without a need for a third terminal in the middle known as the gate. That means they can be smaller than today's memory devices and boast more storage capacity.

This version of the memristor -- developed using the advanced facilities at the Oak Ridge National Laboratory -- promises capacity of about 25 terabits per square centimeter. That is 100 times higher memory density per layer compared with commercially available flash memory devices.

                       World's smallest atom-memory unit created -- ScienceDaily

Basic Guide to Petrochemicals

Petrochemicals (also known as petroleum distillates; and sometimes abbreviated as petchems) are the chemical products obtained from petroleum by refining.  Some chemical compounds made from petroleum are also obtained from other fossil fuels, such as coal or natural gas, or renewable sources such as maize, palm fruit or sugar cane.

The two most common petrochemical classes are olefins (including ethylene and propylene) and aromatics (including benzene, toluene and xylene isomers).

Oil refineries  produce olefins and aromatics by fluid catalytic cracking of petroleum fractions. Chemical plants produce olefins by steam cracking of natural gas liquids like ethane and propane.  Aromatics are produced by catalytic reforming of naphtha.  Olefins and aromatics are the building-blocks for a wide range of materials such as solvents, detergents, and adhesives. Olefins are the basis for polymers and oligomers used in plastics, resins, fibers, elastomers, lubricants, and gels.

Global ethylene and propylene production are about 115 million tonnes and 70 million tonnes per annum, respectively. Aromatics production is approximately 70 million tonnes. The largest petrochemical industries are located in the USA and Western Europe; however, major growth in new production capacity is in the Middle East and Asia. There is substantial inter-regional petrochemical trade.

Primary petrochemicals are divided into three groups depending on their chemical structure:

• Olefins includes Ethene, Propene, Butenes and butadiene. Ethylene and propylene are important sources of industrial chemicals and plastics products.  Butadiene is used in making synthetic rubber.
• Aromatics includes Benzene, toluene and xylenes, as a whole referred to as BTX and primarily obtained from petroleum refineries by extraction from the reformate produced in catalytic reformers using Naphtha obtained from petroleum refineries. Alternatively, BTX can be produced by aromatization of alkanes.  Benzene is a raw material for dyes and synthetic detergents, and benzene and toluene for isocyanates MDI and TDI used in making polyurethanes. Manufacturers use xylenes to produce plastics and synthetic fibers.
• Synthesis gas is a mixture of carbon monoxide and hydrogen used to make ammonia and methanol.  Ammonia is used to make the fertilizer urea and methanol is used as a solvent and chemical intermediate. Steam crackers are not to be confused with steam reforming plants used to produce hydrogen and ammonia.
• Methane, ethane, propane and butanes obtained primarily from natural gas processing plants.
• Methanol and formaldehyde.

In 2007, the amounts of ethylene and propylene produced in steam crackers were about 115 Mt (megatonnes) and 70 Mt, respectively.  The output ethylene capacity of large steam crackers ranged up to as much as 1.0 – 1.5 Mt per year.

Like commodity chemicals, petrochemicals are made on a very large scale. Petrochemical manufacturing units differ from commodity chemical plants in that they often produce a number of related products. Compare this with specialty chemical and fine chemical manufacture where products are made in discrete batch processes.

Petrochemicals are predominantly made in a few manufacturing locations around the world, for example in Jubail & Yanbu Industrial Cities in Saudi Arabia, Texas & Louisiana in the US, in Teesside in the Northeast of England in the United Kingdom, in Rotterdam in the Netherlands, in Jamnagar, Dahej in Gujarat, India and in Singapore. Not all of the petrochemical or commodity chemical materials produced by the chemical industry are made in one single location but groups of related materials are often made in adjacent manufacturing plants to induce industrial symbiosis as well as material and utility efficiency and other economies of scale. This is known in chemical engineering terminology as integrated manufacturing. Speciality and fine chemical companies are sometimes found in similar manufacturing locations as petrochemicals but, in most cases, they do not need the same level of large scale infrastructure (e.g., pipelines, storage, ports and power, etc.) and therefore can be found in multi-sector business parks.

The large scale petrochemical manufacturing locations have clusters of manufacturing units that share utilities and large scale infrastructure such as power stations, storage tanks, port facilities, road and rail terminals. In the United Kingdom for example, there are 4 main locations for such manufacturing: near the River Mersey in Northwest England, on the Humber on the East coast of Yorkshire, in Grangemouth near the Firth of Forth in Scotland and in Teesside as part of the Northeast of England Process Industry Cluster (NEPIC). To demonstrate the clustering and integration, some 50% of the United Kingdom's petrochemical and commodity chemicals are produced by the NEPIC industry cluster companies in Teesside.

History

In 1835, Henri Victor Regnault, a French chemist left vinyl chloride in the sun and found white solid at the bottom of the flask which was polyvinyl chloride. In 1839 Eduard Simon discovered polystyrene by accident by distilling storax.  In 1856, William Henry Perkin discovered the first synthetic dye, Mauveine.  In 1888, Friedrich Reinitzer, an Austrian plant scientist observed cholesteryl benzoate had two different melting points.  In 1909, Leo Hendrik Baekeland invented bakelite made from phenol and formaldehyde.  In 1928 synthetic fuels were invented using Fischer-Tropsch process.  In 1929, Walter Bock invented synthetic rubber Buna-S which is made up of styrene and butadiene and used to make car tires.  In 1933, Otto Röhm polymerized the first acrylic glass methyl methacrylate.  In 1935, Michael Perrin invented polyethylene. After World War II, polypropylene was discovered in the early 1950s.  In 1937, Wallace Hume Carothers invented nylon. In 1946, he invented Polyester. Polyethylene terephthalate (PET) bottles are made from ethylene and paraxylene.  In 1938, Otto Bayer invented polyurethane.  In 1941,Roy Plunkett invented Teflon.  In 1949, Fritz Stastny turned polystyrene into foam. In 1965, Stephanie Kwolek invented Kevlar.

                                   https://en.wikipedia.org/wiki/Petrochemical

A Look Back to Tyrants

From Sovereign Man

[Editor’s note: This letter was written by Viktorija, our Sovereign Woman.]

San Juan, Puerto Rico, November 25, 2020 My experience with ridiculous government overreach began literally on the day I was born. It was more than three decades ago, in the tiny Soviet Socialist Republic of Lithuania, back when the Soviet Union still existed. As my mother tells the story, she and my father hurried to the closest hospital when her contractions started. But the hospital administration staff informed them that they were not allowed to give birth there. Apparently we weren’t registered with that municipality. So the bureaucrats turned my parents away and ordered them to drive to the correct hospital that matched their records. Unfortunately, my parents were nearly out of gas. Gasoline was in very short supply at the time and considered a major luxury; the Soviet Union was near collapse, and people routinely had to wait in line for more than a day just to fill up a few liters of gas. So, my parents were without any means to drive to the ‘correct’ hospital. And that’s when they resorted to what most people ended up doing back in Soviet times: bribery. They went back to the hospital that turned them away, and paid off the nurses and administrators to let them give birth there. And poof, shortly thereafter, I came into the world. My mother is full of these stories about Soviet times; in her youth, she worked at a clothing store… and almost all of the inventory was Soviet-made garbage. On rare occasion, though, a new dress would come in that was made in Western Europe. My mom would immediately hide it, and sell it to special customers who were willing to pay much more. That was the only way she could afford to buy enough food that month. Anything foreign, in fact, was considered a major luxury. Vehicles were fairly common in the Soviet Union, but they were all pitiful Soviet brands like Zaporozhet, Moskvitch, or Volga. Even just seeing a Mercedes was a dream come true. Travel was the same. If you were lucky enough to have any money, you were allowed to travel. But only inside the Soviet Union… so you could look forward to a fancy vacation to Azerbaijan. Only big bosses with special connections were allowed to travel outside of the Soviet Union. But for most of us in the proletariat, visiting Paris or London was an unimaginable luxury. It’s funny how the things that we consider luxuries tend to change over time. As children we used to get really excited about a new toy, which, in adulthood, probably seems rather trivial to us now. And I remember the first time I saw someone with a cell phone. It was the size of a suitcase, but I thought he was the wealthiest man in the world. Now everyone has a smartphone; it’s not even close to being a luxury anymore. I’ve been to some of the poorest countries in the world—places like Myanmar and Eswatini (formerly known as Swaziland, in southern Africa). And even there, people have smartphones connected to the Internet. This was inconceivable 15 years ago. Most people still consider ‘luxuries’ to be things that require a lot of money-- private jets, fancy cars, and expensive champagne. But as corny as it may sound, I believe one of the biggest luxuries right now is freedom. Covid-19 lockdowns around the world have taught us how precious freedom is, and how easily simple things like going outside, breathing fresh air, and the ability to travel, can be taken away from us by people who refuse to follow their own rules. Frankly this was another theme of the Soviet Union—the big bosses had one set of rules for themselves, and the rest of us peasants had another set of rules that we had to follow. You see this all over the Western world now, with politicians who can’t be bothered to adhere to their own lockdowns, but require everyone else to isolate from friends and family. It’s easy to be angry about this. But it’s more effective to do something about it. Unlike expensive luxuries like fancy handbags and supercars, freedom doesn’t require suitcases full of cash. It requires rational thinking, the right information, and the will to take action. You might be eligible for a second passport, practically for free, simply because you have ancestors from a certain country (including my native Lithuania!) And having a second passport or second residency is a huge step towards being able to take back your freedom. It means that, no matter what rules are imposed, you’ll at least have another place you can go. That optionality is now more important than ever. To your freedom,

Viktorija, SovereignMan.com

The 2020 Nursing Home Nightmare

The article below is exactly what I never want to have happen to me.  I would rather live alone in an apartment wearing a wristwatch that tracks my vital signs and beeps when they are awry so that I must call an ambulance.  I have had hospital surgeries and recoveries.  I have never been treated like those in the AP story immediately below.

https://apnews.com/article/nursing-homes-neglect-death-surge-3b74a2202140c5a6b5cf05cdf0ea4f32

A Computer's Misinformation or Artifact

When and If Artificial Intelligence Fails Us

From: University of Houston (UH)

November 23, 2020 -- Machine learning has delivered amazing results, but there also have been failures, ranging from the harmless to potentially deadly. New work suggests that common assumptions about the cause behind these supposed malfunctions may be mistaken, information that is crucial for evaluating the reliability of these networks.

Deep neural networks, multilayered systems built to process images and other data through the use of mathematical modeling, are a cornerstone of artificial intelligence.

They are capable of seemingly sophisticated results, but they can also be fooled in ways that range from relatively harmless -- misidentifying one animal as another -- to potentially deadly if the network guiding a self-driving car misinterprets a stop sign as one indicating it is safe to proceed.

A philosopher with the University of Houston suggests in a paper published in Nature Machine Intelligence that common assumptions about the cause behind these supposed malfunctions may be mistaken, information that is crucial for evaluating the reliability of these networks.

As machine learning and other forms of artificial intelligence become more embedded in society, used in everything from automated teller machines to cybersecurity systems, Cameron Buckner, associate professor of philosophy at UH, said it is critical to understand the source of apparent failures caused by what researchers call "adversarial examples," when a deep neural network system misjudges images or other data when confronted with information outside the training inputs used to build the network. They're rare and are called "adversarial" because they are often created or discovered by another machine learning network -- a sort of brinksmanship in the machine learning world between more sophisticated methods to create adversarial examples and more sophisticated methods to detect and avoid them.

"Some of these adversarial events could instead be artifacts, and we need to better know what they are in order to know how reliable these networks are," Buckner said.

In other words, the misfire could be caused by the interaction between what the network is asked to process and the actual patterns involved. That's not quite the same thing as being completely mistaken.

"Understanding the implications of adversarial examples requires exploring a third possibility: that at least some of these patterns are artifacts," Buckner wrote. " ... Thus, there are presently both costs in simply discarding these patterns and dangers in using them naively."

Adversarial events that cause these machine learning systems to make mistakes aren't necessarily caused by intentional malfeasance, but that's where the highest risk comes in.

"It means malicious actors could fool systems that rely on an otherwise reliable network," Buckner said. "That has security applications."

A security system based upon facial recognition technology could be hacked to allow a breach, for example, or decals could be placed on traffic signs that cause self-driving cars to misinterpret the sign, even though they appear harmless to the human observer.

Previous research has found that, counter to previous assumptions, there are some naturally occurring adversarial examples -- times when a machine learning system misinterprets data through an unanticipated interaction rather than through an error in the data. They are rare and can be discovered only through the use of artificial intelligence.

But they are real, and Buckner said that suggests the need to rethink how researchers approach the anomalies, or artifacts.

These artifacts haven't been well understood; Buckner offers the analogy of a lens flare in a photograph -- a phenomenon that isn't caused by a defect in the camera lens but is instead produced by the interaction of light with the camera.

The lens flare potentially offers useful information -- the location of the sun, for example -- if you know how to interpret it. That, he said, raises the question of whether adverse events in machine learning that are caused by an artifact also have useful information to offer.

Equally important, Buckner said, is that this new way of thinking about the way in which artifacts can affect deep neural networks suggests a misreading by the network shouldn't be automatically considered evidence that deep learning isn't valid.

"Some of these adversarial events could be artifacts," he said. "We have to know what these artifacts are so we can know how reliable the networks are."

Misinformation or artifact: A new way to think about machine learning: A researcher considers when - and if - we should consider artificial intelligence a failure -- ScienceDaily

Signal Temporal Logic Beats Algorithms

Showing robots how to drive a car...in just a few easy lessons

From the University of Southern California

November 19, 2020 --- Imagine if robots could learn from watching demonstrations: you could show a domestic robot how to do routine chores or set a dinner table. In the workplace, you could train robots like new employees, showing them how to perform many duties. On the road, your self-driving car could learn how to drive safely by watching you drive around your neighborhood.

Making progress on that vision, USC researchers have designed a system that lets robots autonomously learn complicated tasks from a very small number of demonstrations -- even imperfect ones. The paper, titled Learning from Demonstrations Using Signal Temporal Logic, was presented at the Conference on Robot Learning (CoRL), Nov. 18.

The researchers' system works by evaluating the quality of each demonstration, so it learns from the mistakes it sees, as well as the successes. While current state-of-art methods need at least 100 demonstrations to nail a specific task, this new method allows robots to learn from only a handful of demonstrations. It also allows robots to learn more intuitively, the way humans learn from each other -- you watch someone execute a task, even imperfectly, then try yourself. It doesn't have to be a "perfect" demonstration for humans to glean knowledge from watching each other.

"Many machine learning and reinforcement learning systems require large amounts of data and hundreds of demonstrations -- you need a human to demonstrate over and over again, which is not feasible," said lead author Aniruddh Puranic, a Ph.D. student in computer science at the USC Viterbi School of Engineering.

"Also, most people don't have programming knowledge to explicitly state what the robot needs to do, and a human cannot possibly demonstrate everything that a robot needs to know. What if the robot encounters something it hasn't seen before? This is a key challenge."

Learning from demonstrations

Learning from demonstrations is becoming increasingly popular in obtaining effective robot control policies -- which control the robot's movements -- for complex tasks. But it is susceptible to imperfections in demonstrations and also raises safety concerns as robots may learn unsafe or undesirable actions.

Also, not all demonstrations are equal: some demonstrations are a better indicator of desired behavior than others and the quality of the demonstrations often depends on the expertise of the user providing the demonstrations.

To address these issues, the researchers integrated "signal temporal logic" or STL to evaluate the quality of demonstrations and automatically rank them to create inherent rewards.

In other words, even if some parts of the demonstrations do not make any sense based on the logic requirements, using this method, the robot can still learn from the imperfect parts. In a way, the system is coming to its own conclusion about the accuracy or success of a demonstration.

"Let's say robots learn from different types of demonstrations -- it could be a hands-on demonstration, videos, or simulations -- if I do something that is very unsafe, standard approaches will do one of two things: either, they will completely disregard it, or even worse, the robot will learn the wrong thing," said co-author Stefanos Nikolaidis, a USC Viterbi assistant professor of computer science.

"In contrast, in a very intelligent way, this work uses some common sense reasoning in the form of logic to understand which parts of the demonstration are good and which parts are not. In essence, this is exactly what also humans do."

Take, for example, a driving demonstration where someone skips a stop sign. This would be ranked lower by the system than a demonstration of a good driver. But, if during this demonstration, the driver does something intelligent -- for instance, applies their brakes to avoid a crash -- the robot will still learn from this smart action.

Adapting to human preferences

Signal temporal logic is an expressive mathematical symbolic language that enables robotic reasoning about current and future outcomes. While previous research in this area has used "linear temporal logic," STL is preferable in this case, said Jyo Deshmukh, a former Toyota engineer and USC Viterbi assistant professor of computer science .

"When we go into the world of cyber physical systems, like robots and self-driving cars, where time is crucial, linear temporal logic becomes a bit cumbersome, because it reasons about sequences of true/false values for variables, while STL allows reasoning about physical signals."

Puranic, who is advised by Deshmukh, came up with the idea after taking a hands-on robotics class with Nikolaidis, who has been working on developing robots to learn from YouTube videos. The trio decided to test it out. All three said they were surprised by the extent of the system's success and the professors both credit Puranic for his hard work.

"Compared to a state-of-the-art algorithm, being used extensively in many robotics applications, you see an order of magnitude difference in how many demonstrations are required," said Nikolaidis.

The system was tested using a Minecraft-style game simulator, but the researchers said the system could also learn from driving simulators and eventually even videos. Next, the researchers hope to try it out on real robots. They said this approach is well suited for applications where maps are known beforehand but there are dynamic obstacles in the map: robots in household environments, warehouses or even space exploration rovers.

"If we want robots to be good teammates and help people, first they need to learn and adapt to human preference very efficiently," said Nikolaidis. "Our method provides that."

"I'm excited to integrate this approach into robotic systems to help them efficiently learn from demonstrations, but also effectively help human teammates in a collaborative task."

University of Southern California. "Showing robots how to drive a car...in just a few easy lessons." ScienceDaily. ScienceDaily, 19 November 2020. www.sciencedaily.com/releases/2020/11/201119153956.htm

Longing for a Political Conspiracy

Why America Is Ripe for Election Conspiracy Theorizing

By Brian Gallagher, Nautilus

November 17, 2020 – One day in 1787, Benjamin Franklin emerged from the Constitutional Convention in Philadelphia, where the founders were debating the shape of a new government. He was confronted by Elizabeth Willing Powel, a society figure and wife of the Philadelphia mayor. “Doctor, what have we got? A republic or a monarchy?” she asked. “A republic,” Franklin responded. “If you can keep it.”

At least that’s how the story goes. Historians aren’t certain of the wording or where Powel asked Franklin the question. On the streets of Philadelphia? In Powel’s home? But the anecdote quickens our attention on a crisis in America today. If we can keep our republic, it will require dampening the allure of conspiracy theories, particularly about the fraudulence of elections.

The new conspiracism, fomented by Trump, doesn’t bother with a facade of theory.

A 2017 study, based on surveys administered before and after the 2012 election, found that “belief in election fraud is a common and predictable consequence of both underlying conspiratorial thinking and motivated partisan reasoning.” Presumably since then this problem has gotten, and will continue to get, worse, because “motivated partisan reasoning”—most of all in the United States—is becoming more fanatical. 

An impressive group of psychologists and political scientists made this clear in a recent paper in Science. Political life is increasingly sectarian, unhinged from the reality and magnitude of policy disagreements. More and more we view opposing partisans as alien to ourselves, dislike and distrust them, and see them as iniquitous. “Viewing opposing partisans as different, or even as dislikable or immoral, may not be problematic in isolation,” the researchers write. “But when all three converge, political losses can feel like existential threats that must be averted—whatever the cost.” Which of course includes alleging that elections are rigged. 

This sort of conspiratorial thinking poisons the roots of democracy, researchers have found. The authors of a recent study, which was based on a survey experiment conducted the day before and the morning of the 2016 election, were expecting “vote rigging conspiracy theories to make people less confident in elections, less likely to accept the results, and less likely to say that the loser should concede.” And that’s what they found. “This study has shown conspiratorial rhetoric around elections leaves Americans unsettled in their emotional reactions and, depending on whether their party is helped or hurt, less committed to the idea that political candidates must accept election outcomes.” The results, the authors note, confirm what Russell Muirhead and Nancy Rosenblum argue in their 2019 book, A Lot of People Are Saying: The New Conspiracism and the Assault on Democracy. Their idea is that a new conspiracism, fomented by Trump, doesn’t bother with a facade of theory, instead relying on repeating lies until they seem true.

Can people become less susceptible to believing conspiracy theories? Perhaps. One of the things that underlie conspiratorial thinking is a teleological bias, the tendency to see intention or planning where it doesn’t exist. A 2018 study found that this bias, “a resilient ‘default’ component of early cognition” that shapes adult intuitions, is associated with both creationist and conspiracist beliefs. Both of these, the researchers wrote, “entail the distant and hidden involvement of a purposeful and final cause to explain complex worldly events.”

Aleksandra Cichocka, a political psychologist at the University of Kent, says the psychological need for understanding the world is joined by two other needs that underlie conspiracism—feeling safe, and belonging to social groups that affirm or encourage self-respect. “Those who feel defensive about themselves are more likely than others to embrace conspiracy theories, perhaps to deflect blame for their shortcomings,” she wrote recently in Nature. “Conspiracy beliefs have also been linked to feelings of powerlessness, anxiety, isolation and alienation. Those who feel that they are insignificant cogs in the political machinery tend to assume that there are nefarious influences at play.” 

A 2016 paper, “The dark side of meaning-making: How social exclusion leads to superstitious thinking,” explains that a person who feels excluded very often doubles down on anxiety and isolation and “searches for like-minded individuals who further reinforce these beliefs, until they become entrenched.”

What makes people susceptible to conspiracy theories isn’t healthy skepticism, a sensitivity to evidence joined to a sense of proportion. It’s a skepticism that’s abetted by political sectarianism and, as Cichocka explains, exacerbated by society-deranging events like the onset of COVID-19. It’s “created a perfect storm for vulnerability to conspiracy narratives,” she wrote. “Uncertainty and anxiety are high. Lockdown and social distancing bring isolation. People struggling to understand this unprecedented time might reach for extraordinary explanations.” 

The way forward, she thinks, can’t be to just correct conspiracy beliefs on a case by case basis, because research shows compelling false claims spread faster than their refutations. Ultimately her solution goes deeper. We’ve got to be better about promoting “a sense of common identity, to boost feelings of belonging and meaning.” 

This seems to have worked in New Zealand. “Prime Minister Jacinda Ardern stressed solidarity and transparent decision-making, and offered people a sense of purpose,” Cichocka wrote. “Early data suggest that despite an increase in distress during lockdown, New Zealanders showed no increase in conspiracy thinking, and more trust in science.” 

When considering the U.S.’s large and coarsening culture of political zealotry, this doesn’t seem quite like enough. Joe Biden says he plans to govern in the interest of all Americans. That’s a start. But according to the authors of the Science paper, that “will require multifaceted efforts to change leadership, media, and democratic systems in ways that are sensitive to human psychology. There are no silver bullets.”

Brian Gallagher is an associate editor at Nautilus. Follow him on Twitter @bsgallagher.

Why America Is Ripe for Election Conspiracy Theorizing - Facts So Romantic - Nautilus

 

Origin of Modern Homo Sapiens

Writer Luke Taylor has an interesting article in the November 18, 2020, Discover magazine about the different theories surrounding the geographical origin of modern humans.  Lively candidates for the honor include Morocco, Ethiopia and Botswana.  A hybrid origin has also been suggested.  It remains a question open to new bones and new ideas.  See https://www.discovermagazine.com/planet-earth/where-was-the-birthplace-of-modern-humans

Will Havoc Rule In the 2020s?

The Atlantic has been the gold standard of liberal thought in America for at least sixty years.  At its best it breaks ground by studying new approaches and methodologies of importance.  In the current issue of December 2020, it analyzes the use of mathematics to find patterns of history, a technique repudiated for generations by most historians.  A unique and key leader in the field of mathematical history is Peter Turchin from the University of Connecticut, Storrs.  For The Atlantic, Graeme Wood writes this about him:

“In 2010, he predicted that the unrest would get serious around 2020, and that it wouldn’t let up until those social and political trends reversed. Havoc at the level of the late 1960s and early ’70s is the best-case scenario; all-out civil war is the worst. 

The fundamental problems, he says, are a dark triad of social maladies: a bloated elite class, with too few elite jobs to go around; declining living standards among the general population; and a government that can’t cover its financial positions.”

The full article is available at:  https://www.theatlantic.com/magazine/archive/2020/12/can-history-predict-future/616993/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=RI+Free&utm_content=RI+Weekly%3A+The+Ecology+of+Doom&utm_campaign=20201119

The Blog Author Explains Trump Defeat

Comments in the internet version of The Christian Science Monitor

Edward Binns

College educated suburbanites are the heart and soul of the Republican Party. Drumpf has betrayed them and they said, "You're fired!"

Jimmy Stock

 - why would you say that ?

Edward Binns reply:

Why we college-educated suburbanites fired Trump

He lied when he pretended to be a deficit hawk in 2016

He committed multiple felony counts of obstruction of justice

He acted like a pig in front of the Queen of England

He weakened NATO and that risks American military security

He  abandoned and thus double-crossed the Kurds

He almost certainly cheated on his New York state income taxes

He challenges other debaters on personality rather than advocating sound policy

Personal conduct: he cheated on all three of his trophy wives

He proudly leads by fear and never by personal example or maturity

He doesn’t understand rule of law: attorneys are mere fixers in his mind

He tried to get us into a lame duck war with Iran in January and again last week

He stiffed and ignored the senators who got him elected in 2016 (Rubio, Burr, Johnson and Toomey)

He offered to buy Greenland from Denmark

He fires his most competent cabinet members and staff advisors

He told fellow Americans to drink toxic solvents to avoid infection by the COVID-19 virus

His own family has warned the public about his character

He can’t take direction from experts and professionals.  He never listens to the pros.

He got himself re-nominated in 2020 with no platform; he stands firmly for nothing except himself

He was a draft dodger who, as president, asked if it was OK to award himself a Congressional Medal of Honor!

               So we fired the clown.  And awarded Biden gridlock to stymie the socialists.

Existing Diseases May Surge

Large, delayed outbreaks of endemic diseases possible following COVID-19 controls

From: Princeton University

November 9, 2020 -- Measures to reduce the spread of COVID-19 through non-pharmaceutical interventions (NPIs) such as mask wearing and social distancing are a key tool in combatting the impact of the ongoing coronavirus pandemic. These actions also have greatly reduced incidence of many other diseases, including influenza and respiratory syncytial virus (RSV).

Current reductions in these common respiratory infections, however, may merely postpone the incidence of future outbreaks, according to a study by Princeton University researchers published Nov. 9 in the Proceedings of the National Academy of Sciences.

"Declines in case numbers of several respiratory pathogens have been observed recently in many global locations," said first author Rachel Baker, an associate research scholar at the High Meadows Environmental Institute (HMEI) at Princeton University.

"While this reduction in cases could be interpreted as a positive side effect of COVID-19 prevention, the reality is much more complex," Baker said. "Our results suggest that susceptibility to these other diseases, such as RSV and flu, could increase while NPIs are in place, resulting in large outbreaks when they begin circulating again."

Baker and her co-authors found that NPIs could lead to a future uptick in RSV -- an endemic viral infection in the United States and a leading cause of lower respiratory-tract infections in young infants -- but that the same effect was not as pronounced for influenza.

"Although the detailed trajectory of both RSV and influenza in the coming years will be complex, there are clear and overarching trends that emerge when one focuses on some essential effects of NPIs and seasonality on disease dynamics," said co-author Gabriel Vecchi, Princeton professor of geosciences and the High Meadows Environmental Institute.

The researchers used an epidemiological model based on historic RSV data and observations of the recent decline in RSV cases to examine the possible impact of COVID-19 NPIs on future RSV outbreaks in the United States and Mexico.

They found that even relatively short periods of NPI measures could lead to large future RSV outbreaks. These outbreaks were often delayed following the end of the NPI period, with peak cases projected to occur in many locations in winter 2021-22. "It is very important to prepare for this possible future outbreak risk and to pay attention to the full gamut of infections impacted by COVID-19 NPIs," Baker said.

The authors also considered the implications of COVID-19 NPIs for seasonal influenza outbreaks and found results qualitatively similar to RSV. The dynamics of influenza are much harder to project due to viral evolution, however, which drives uncertainty over future circulating strains and the efficacy of available vaccines.

"For influenza, vaccines could make a big difference," Baker said. "In addition, the impact of NPIs on influenza evolution is unclear but potentially very important."

"The decrease in cases of influenza and RSV -- as well as the possible future increase we project -- is arguably the broadest global impact of NPIs across a variety of human diseases that we've seen," said co-author Bryan Grenfell, the Kathryn Briger and Sarah Fenton Professor of Ecology and Evolutionary Biology and Public Affairs, who is associated faculty in HMEI.

"NPIs could have unintended longer-term impacts on the dynamics of other diseases that are similar to the impact on susceptibility we projected for RSV," he said.

A similar effect of pandemic-related NPIs on other pathogens was observed following the 1918 influenza pandemic. Historic measles data from London show a shift from annual cycles to biennial outbreaks following a period of control measures implemented at that time.

Co-author C. Jessica Metcalf, associate professor of ecology and evolutionary biology and public affairs and an associated faculty member in HMEI, said that directly evaluating the associated risks of NPIs by developing and deploying tools such as serology that would better measure susceptibility is an important public health and policy direction. "The future repercussions of NPIs revealed by this paper hinge on how these measures change the landscape of immunity and susceptibility," Metcalf said.

Additional authors on the paper include Wenchang Yang, an associate research scholar in geosciences, and Sang Woo Park, a Ph.D. candidate in ecology and evolutionary biology.

Many of the authors are affiliated with the Climate Change and Infectious Disease initiative funded by HMEI and the Princeton Institute for International and Regional Studies (PIIRS). The current study built on work by the same team published in December 2019 that examined how climate conditions affect RSV outbreaks in the US and Mexico. Another study by the team, published earlier this year, evaluated the impact of the climate and susceptibility on the trajectory of the COVID-19 pandemic.

The paper, "The impact of COVID-19 non-pharmaceutical interventions on the future dynamics of endemic infections" was published online Nov. 9 by the Proceedings of the National Academy of Sciences. This work was supported by the Cooperative Institute for Modeling the Earth System (CIMES), the High Meadows Environmental Institute, and the Princeton Institute for International and Regional Studies (PIIRS).

Journal Reference:

1. Rachel E. Baker, Sang Woo Park, Wenchang Yang, Gabriel A. Vecchi, C. Jessica E. Metcalf, Bryan T. Grenfell. The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections. Proceedings of the National Academy of Sciences, 2020; 202013182 DOI: 10.1073/pnas.2013182117

Princeton University. "Large, delayed outbreaks of endemic diseases possible following COVID-19 controls." ScienceDaily. ScienceDaily, 9 November 2020. www.sciencedaily.com/releases/2020/11/201109184906.htm