|From: Tom Clarke||4/30/2020 1:48:08 PM|
|>>At a funeral during the Roman Republic, there were grave formalities to be observed: The procession was led by musicians, followed by sons with veiled heads and bareheaded daughters. Men wore the masks of their ancestors and put on the garments that typified the high offices held by those ancestors. According to Plutarch and Polybius, an effigy of the dead man wearing his own mask was carried on a funeral bed.|
For Romans (and other traditional peoples), the dead have not entirely gone away. They may, in the manner of Greek heroes, bless their descendants and people in the neighborhood, or, if they are offended, they may prove troublesome. Once a year at the Parentalia (nine days in February), Roman families honored their ancestors, whose shades (Manes) were brought offerings, and exorcised any malevolent intentions that might have been provoked. Ovid tells the traditional tale (which he does not believe) that when on one occasion the rites had not been observed, ghosts left their tombs and threatened Rome. [Fasti II 533 ff.] In May they celebrated the Lemuria, whose rites were generally aimed at exorcizing evil spirits. Traditional Catholics preserve this understanding of the awesome dead by celebrating All Saints and All Souls.
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|From: Glenn Petersen||5/10/2020 4:39:27 PM|
The New Indiana Jones? AI. Here’s How It’s Overhauling Archaeology
By Peter Rejcek
May 07, 2020
Archaeologists have uncovered scores of long-abandoned settlements along coastal Madagascar that reveal environmental connections to modern-day communities. They have detected the nearly indiscernible bumps of earthen mounds left behind by prehistoric North American cultures. Still other researchers have mapped Bronze Age river systems in the Indus Valley, one of the cradles of civilization.
All of these recent discoveries are examples of landscape archaeology. They’re also examples of how artificial intelligence is helping scientists hunt for new archaeological digs on a scale and at a pace unimaginable even a decade ago.
“AI in archaeology has been increasing substantially over the past few years,” said Dylan Davis, a PhD candidate in the Department of Anthropology at Penn State University. “One of the major uses of AI in archaeology is for the detection of new archaeological sites.”
The near-ubiquitous availability of satellite data and other types of aerial imagery for many parts of the world has been both a boon and a bane to archaeologists. They can cover far more ground, but the job of manually mowing their way across digitized landscapes is still time-consuming and laborious. Machine learning algorithms offer a way to parse through complex data far more quickly.
AI Gives Archaeologists a Bird’s Eye View
Davis developed an automated algorithm for identifying large earthen and shell mounds built by native populations long before Europeans arrived with far-off visions of skyscrapers and superhighways in their eyes. The sites still hidden in places like the South Carolina wilderness contain a wealth of information about how people lived, even what they ate, and the ways they interacted with the local environment and other cultures.
In this particular case, the imagery comes from LiDAR, which uses light pulses that can penetrate tree canopies to map forest floors. The team taught the computer the shape, size, and texture characteristics of the mounds so it could identify potential sites from the digital 3D datasets that it analyzed.
“The process resulted in several thousand possible features that my colleagues and I checked by hand,” Davis told Singularity Hub. “While not entirely automated, this saved the equivalent of years of manual labor that would have been required for analyzing the whole LiDAR image by hand.”
In Madagascar—where Davis is studying human settlement history across the world’s fourth largest island over a timescale of millennia—he developed a predictive algorithm to help locate archaeological sites using freely available satellite imagery. His team was able to survey and identify more than 70 new archaeological sites—and potentially hundreds more—across an area of more than 1,000 square kilometers during the course of about a year.
Machines Learning From the Past Prepare Us for the Future
One impetus behind the rapid identification of archaeological sites is that many are under threat from climate change, such as coastal erosion from sea level rise, or other human impacts. Meanwhile, traditional archaeological approaches are expensive and laborious—serious handicaps in a race against time.
“It is imperative to record as many archaeological sites as we can in a short period of time. That is why AI and machine learning are useful for my research,” Davis said.
Studying the rise and fall of past civilizations can also teach modern humans a thing or two about how to grapple with these current challenges.
Researchers at the Institut Català d’Arqueologia Clàssica (ICAC) turned to machine-learning algorithms to reconstruct more than 20,000 kilometers of paleo-rivers along the Indus Valley civilization of what is now part of modern Pakistan and India. Such AI-powered mapping techniques wouldn’t be possible using satellite images alone.
That effort helped locate many previously unknown archaeological sites and unlocked new insights into those Bronze Age cultures. However, the analytics can also assist governments with important water resource management today, according to Hèctor A. Orengo Romeu, co-director of the Landscape Archaeology Research Group at ICAC.
“Our analyses can contribute to the forecasts of the evolution of aquifers in the area and provide valuable information on aspects such as the variability of agricultural productivity or the influence of climate change on the expansion of the Thar desert, in addition to providing cultural management tools to the government,” he said.
Leveraging AI for Language and Lots More
While landscape archaeology is one major application of AI in archaeology, it’s far from the only one. In 2000, only about a half-dozen scientific papers referred to the use of AI, according to the Web of Science, reputedly the world’s largest global citation database. Last year, more than 65 papers were published concerning the use of machine intelligence technologies in archaeology, with a significant uptick beginning in 2015.
AI methods, for instance, are being used to understand the chemical makeup of artifacts like pottery and ceramics, according to Davis. “This can help identify where these materials were made and how far they were transported. It can also help us to understand the extent of past trading networks.”
Linguistic anthropologists have also used machine intelligence methods to trace the evolution of different languages, Davis said. “Using AI, we can learn when and where languages emerged around the world.”
In other cases, AI has helped reconstruct or decipher ancient texts. Last year, researchers at Google’s DeepMind used a deep neural network called PYTHIA to recreate missing inscriptions in ancient Greek from damaged surfaces of objects made of stone or ceramics.
Named after the Oracle at Delphi, PYTHIA “takes a sequence of damaged text as input, and is trained to predict character sequences comprising hypothesised restorations of ancient Greek inscriptions,” the researchers reported.
In a similar fashion, Chinese scientists applied a convolutional neural network (CNN) to untangle another ancient tongue once found on turtle shells and ox bones. The CNN managed to classify oracle bone morphology in order to piece together fragments of these divination objects, some with inscriptions that represent the earliest evidence of China’s recorded history.
“Differentiating the materials of oracle bones is one of the most basic steps for oracle bone morphology—we need to first make sure we don’t assemble pieces of ox bones with tortoise shells,” lead author of the study, associate professor Shanxiong Chen at China’s Southwest University, told Synced, an online tech publication in China.
AI Helps Archaeologists Get the Scoop…
And then there are applications of AI in archaeology that are simply … interesting. Just last month, researchers published a paper about a machine learning method trained to differentiate between human and canine paleofeces.
The algorithm, dubbed CoproID, compares the gut microbiome DNA found in the ancient material with DNA found in modern feces, enabling it to get the scoop on the origin of the poop.
Also known as coprolites, paleo-feces from humans and dogs are often found in the same archaeological sites. Scientists need to know which is which if they’re trying to understand something like past diets or disease.
“CoproID is the first line of identification in coprolite analysis to confirm that what we’re looking for is actually human, or a dog if we’re interested in dogs,” Maxime Borry, a bioinformatics PhD student at the Max Planck Institute for the Science of Human History, told Vice.
…But Machine Intelligence Is Just Another Tool
There is obviously quite a bit of work that can be automated through AI. But there’s no reason for archaeologists to hit the unemployment line any time soon. There are also plenty of instances where machines can’t yet match humans in identifying objects or patterns. At other times, it’s just faster doing the analysis yourself, Davis noted.
“For ‘big data’ tasks like detecting archaeological materials over a continental scale, AI is useful,” he said. “But for some tasks, it is sometimes more time-consuming to train an entire computer algorithm to complete a task that you can do on your own in an hour.”
Still, there’s no telling what the future will hold for studying the past using artificial intelligence.
“We have already started to see real improvements in the accuracy and reliability of these approaches, but there is a lot more to do,” Davis said. “Hopefully, we start to see these methods being directly applied to a variety of interesting questions around the world, as these methods can produce datasets that would have been impossible a few decades ago.”
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|From: Tom Clarke||5/10/2020 9:35:26 PM|
by D. K. Hamlin (Author), Hyperion Knight (Author)
The Manuscript is an extraordinary tale of love, faith, power and the battle between good and evil. It’s a humorous and engrossing read about a book that cannot be read.Pandemic arrives from Asia and decimates medieval Italy. A selfless monk, mired in the suffering around him, labors in disguise to write a manual for mankind to guide them through the crisis. Centuries later, his book, now known as the Voynich Manuscript, makes its way to America and becomes a carefully guarded treasure at Yale University. But there’s a catch: the notorious Voynich Manuscript is in a language unknown to mankind, defying all attempts at translation. A sinister secret society, determined to solve its riddles, ensnares an ordinary New Yorker, Emma Novak, in its plotting. Emma, an aspiring artist who has been unlucky in love, soon finds herself entangled in a romantic triangle with larger than life characters.Set against the exciting backdrop of New York with all its charms, The Manuscript peers into a spiritual world usually hidden from view. This epic tale, spanning half a millennium, culminates in titanic forces revealing themselves in a high stakes contest for humanity.
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|From: isopatch||5/16/2020 6:51:39 PM|
|Identifying Denisovan remains from protein analysis when DNA isn't present. Article from May of 2019.|
<Biggest Denisovan fossil yet spills ancient human’s secrets
Jawbone from China reveals that the ancient human was widespread across the world — and lived at surprising altitude.
A Denisovan jawbone was discovered on Tibetan Plateau at an altitude of more than 3,000 metres.Credit: Dongju Zhang, Lanzhou University
Scientists have uncovered the most complete remains yet from the mysterious ancient-hominin group known as the Denisovans. The jawbone, discovered high on the Tibetan Plateau and dated to more than 160,000 years ago, is also the first Denisovan specimen found outside the Siberian cave in which the hominin was uncovered a decade ago — confirming suspicions that Denisovans were more widespread than the fossil record currently suggests.
The research marks the first time an ancient human has been identified solely through the analysis of proteins. With no usable DNA, scientists examined proteins in the specimen’s teeth, raising hopes that more fossils could be identified even when DNA is not preserved.
Siberia’s ancient ghost clan starts to surrender its secrets “This is fantastic work,” says Katerina Douka, an archaeologist at the Max Planck Institute for the Science of Human History in Jena, Germany, who runs a separate project aiming to uncover Denisovan fossils in Asia. “It tells us that we are looking at the right area.”
Hunting for DenisovansUntil now, everything scientists have learnt about Denisovans has come from a handful of teeth and bone fragments from Denisova Cave in Russia’s Altai Mountains. DNA from these remains revealed that the Denisovans were a sister group to Neanderthals, both descending from a population that split away from modern humans about 550,00–765,000 years ago. And at Denisova Cave, the two groups seem to have met and interbred: a bone fragment described last year belonged an ancient-human hybrid individual who had a Denisovan father and Neanderthal mother.
But many expected that it was only a matter of time before researchers found evidence of Denisovans elsewhere. Some modern humans in Asia and Oceania carry traces of Denisovan DNA, raising the possibility that the hominin lived far away from Siberia. And some researchers think that unclassified hominin fossils from China could be Denisovan.
The latest specimen, described in Nature 1, consists of half a lower jaw, with two complete teeth. A monk found it in Baishiya Karst Cave in China in 1980, and passed on to Lanzhou University. But it wasn’t until the 2010s that archaeologist Dongju Zhang and her colleagues began studying the bone.
The team faced a problem. The Denisova Cave remains had all been identified because they still contained some DNA, which could be compared with genetic sequences from other ancient humans. But there was no DNA left in the jawbone.
Instead, the scientists looked for ancient proteins, which tend to last longer than DNA. In dentine from the teeth, they found collagen proteins suitable for analysis. The team compared these with equivalent proteins in groupsincluding Denisovans and Neanderthals, and found that they lined up closest with sequences from Denisovans.
The team were also able to piece together other snippets of information about the individual. One of the teeth was still erupting, for example, leading the authors to speculate that the jawbone belonged to an adolescent.
A virtual reconstruction of the jawbone.Credit: Jean-Jacques Hublin, MPI-EVA, Leipzig
Previous research 2 identified Neanderthal remains using both proteins and DNA — but the success of the latest study could lead to a greater emphasis on getting ancient proteins out of fossils that haven’t yielded DNA, says Chris Stringer, a palaeoanthropologist at the Natural History Museum in London. The method could prove particularly useful for older samples or those from southeast Asia and other warm climates, where DNA degrades quickest.
But the field is still in its early stages, Stringer adds, and ancient-protein analysis currently has a smaller sample of early hominins for comparison than does DNA analysis. “Although it’s certainly very suggestive of a link with the Denisovans, I think I’d like to see bigger samples to really pin that down more,” he says.
Douka agrees: for now, ancient DNA analysis remains the “gold standard” for this kind of work, she says. Although there is no genetic material in the jawbone, Douka wonders whether researchers could still find DNA in the Tibetan cave — perhaps in sediment.
The Roof of the WorldThe altitude of the new Denisovan’s home — 3,280 metres above sea level — surprised researchers, and helps to solve a mystery about Denisovans’ genetic contribution to modern Tibetans (see ‘Denisovan hang-outs’). “It is astonishing that any ancient humans were at that altitude,” says Stringer.
Some Tibetans have a variant of a gene called EPAS1 that reduces the amount of the oxygen-carrying protein haemoglobin in their blood, enabling them to live at high altitudes with low oxygen levels. Researchers 3 had thought that this adaptation came from Denisovans, but this was difficult to reconcile with Denisova Cave’s relatively low altitude of 700 metres. The latest study suggests that Denisovans evolved the adaptation on the Tibetan Plateau and passed it to Homo sapiens when the species arrived around 30,000–40,000 years ago, says co-author Frido Welker, a molecular anthropologist at the University of Copenhagen. If Denisovans in Asia were adapted to high altitudes, similar sites could harbour more of their remains.
Mum’s a Neanderthal, Dad’s a Denisovan: First discovery of an ancient-human hybrid He points to Sel’Ungur cave in Kyrgyzstan, about 2,000 metres above sea level, where a hominin child’s arm bone was found but did not yield any DNA. “Now I ask myself — maybe that specimen is also a Denisovan and not a Neanderthal, like we usually assume,” says Bence Viola, a palaeoanthropologist at the University of Toronto in Canada.
Re-evaluating fossils? And the fossil is likely to prompt scientists to reconsider the classification of other remains. “We can kind of work ourselves through the fossil record, and link up more and more specimens with the Denisovans,” says Viola.
One? candidate is a jawbone known as Penghu 1, which was caught in a fishing net near Taiwan and has many similarities to the latest mandible. Welker and his colleagues hypothesize that this jaw could be Denisovan — but the ultimate proof will come from DNA or protein analysis, says Welker.
Sampling any remains for proteins or DNA is by its nature destructive, so there must good justification for doing so, he adds. “It’s not a light-hearted decision to make.”
Nature 569, 16-17 (2019)
1.Chen, F. et al. Nature doi.org (2019).
Article Google Scholar2.Welker, F. et al. Proc. Natl Acad. Sci. USA 113, 11162–11167 (2016).
PubMed Article Google Scholar3. Huerta-Sánchez, E. et al. Nature 512, 194–197 (2014).>
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