Tag Archives: networks

Skinny seals and hungry cod point to trouble in the Baltic Sea

Not all is well in the Baltic Sea, new research suggests — the local food networks are in trouble.

Baltic sea sky.

Image credits Michal Jarmoluk.

The top predators of the area, gray seals and cod, are losing weight, the study reports. This development is linked to the worsening health of the cornerstones of the Baltic’s local food networks: bottom-living crustaceans, isopods, and amphipods.

Sinking food stocks

“It is important that you understand how the food web works when managing a fishery. It is not enough to manage how the fish and fisheries are changing. The availability and quality of food is at least as important,” explains Lena Bergström, researcher at the Department of Aquatic Resources at the Swedish Agricultural University and the study’s corresponding author.

The study, a collaboration between several universities, looked at the health and abundance of key species over the last two decades in the Bothnian Sea and the Baltic Proper. Seal, cod, herring, sprat, isopods, amphipods, and zooplankton all made the object of this study, as they are important players at different levels of the local food webs. These networks are very complex, the team writes, and the same species can be both prey and predator — for example, herrings eat zooplankton and bottom fauna while being hunted by cod and seals in turn.

The authors show that there is a link between the health of cod and seals, the top predators in this ecosystem, and that of bottom-dwelling species, which are the lowest rung on the ladder. Seals are indirectly linked to these bottom-feeders, as they dine on herrings (who in turn dine on the bottom-dwelling species). The worsening health of both cod and seals, the authors explain, is tied to climate change and eutrophication. Eutrophication is an excess of nutrients in a body of water, frequently due to run-off from land, which causes a dense growth of bacteria and algae.

“Oxygen levels in Baltic Sea have reduced since the 1990s, in big part due to eutrophication, creating vast oxygen-free areas. This leads to less living space for the bottom-living prey animals,” says Agnes Karlsson, lead author and researcher at the Department of Ecology, Environment, and Plant Sciences (DEEP) at Stockholm University.

“This has, among other things, led to the fact that the isopods have become fewer and smaller, making them a poorer food choice for cod.”

The team explains that, while the mean weight and fat content of herring in the Bothnian Sea have recently been on the uptick — made possible by an increase in the quantity of bottom-living amphipods — this isn’t an improvement; it’s a recovery. These crustaceans were almost wiped out by a period of extremely heavy rains in the early 2000s which changed the quality of local waters.

“The upturn is relative, because the amphipod in the Bothnian Sea collapsed in the early 2000s and what we now see are signs of a recovery,” Karlsson adds.

“With climate change it is likely that we will see similar extreme events more frequently in the future,” Bergström adds. “If activities that lead to eutrophication are not reduced, oxygen shortage in the Baltic Sea will likely continue, leading to further reductions in the numbers of bottom-living animals. This can have far reaching effects for the economy, with reference to the fish species that are important commercially. To manage a fishery, we must also manage the environment and the food web.”

The paper “Linking consumer physiological status to food-web structure and prey food value in the Baltic Sea” has been published in the journal Ambio.

Scientific citation.

AI developed to tackle physics problems is really good at summarizing research papers

New research from MIT and elsewhere is making an AI that can read scientific papers and generate a plain-English summary of one or two sentences.

Scientific citation.

Image credits Mike Thelwall, Stefanie Haustein, Vincent Larivière, Cassidy R. Sugimoto (paper). Finn Årup Nielsen (screenshot).

A big part of our job here at ZME Science is to trawl through scientific journals for papers that look particularly interesting or impactful. They’re written in dense, technical jargon, which we then take and present in a (we hope) pleasant and easy to follow way that anybody can understand, regardless of their educational background.

MIT researchers are either looking to make my job easier or get me unemployed, I’m not sure exactly sure which yet. A novel neural network they developed, along with other computer researchers, journalists, and editors, can read scientific papers and render a short, plain-English summary.

Autoread

“We have been doing various kinds of work in AI for a few years now,” says Marin Soljačić, a professor of physics at MIT and co-author of the research.

“We use AI to help with our research, basically to do physics better. And as we got to be more familiar with AI, we would notice that every once in a while there is an opportunity to add to the field of AI because of something that we know from physics — a certain mathematical construct or a certain law in physics. We noticed that hey, if we use that, it could actually help with this or that particular AI algorithm.”

It’s far from perfect at what it does right now — in fact, the neural network’s abilities are quite limited. Even so, it could prove to be a powerful resource in helping editors, writers, and scientists scan a large number of studies for a quick idea of their contents. The system could also find applications in a variety of other areas besides language processing one day, including machine translation and speech recognition.

The team didn’t set out to create the AI for the purpose described in this paper. In fact, they were working to create new AI-based approaches to tackle physics problems. During development, however, the team realized the approach they were working on could be used to solve other computational problems — such as language processing — much more efficiently than existing neural network systems.

“We can’t say this is useful for all of AI, but there are instances where we can use an insight from physics to improve on a given AI algorithm,” Soljačić adds.

Neural networks generally attempt to mimic the way our brains learn new information. The computer is fed many different examples of a particular object or concept to help it ‘learn’ what the key, underlying patterns of that element are. This makes neural networks our best digital tool for pattern recognition, for example identifying objects in photographs. However, they don’t do nearly so well when it comes to correlating information from hefty items of data, such as a research paper.

Various tricks have been used to improve their capability in that latter area, including techniques known as long short-term memory (LSTM) and gated recurrent units (GRU). All in all, however, classical neural networks are still ill-equipped for any sort of real natural-language processing, the authors say.

So, what they did was to base their neural network on mathematical vectors, instead of on the multiplication of matrices (which is classical neural-network approach). This is very deep math territory but, essentially, the system represents each word in the text by a vector — lines of a certain length, orientation, and direction — created and altered in a multidimensional space. Encyclopaedia Britannica defines “vectors, in mathematics, as quantities that have both magnitude and direction but not position,” listing velocity and acceleration as examples.

The network used each vector subsequently, as words were being read, to modify a starting vector. The final vector or set of vectors is then translated back into a string of words. The name the team gave this approach, thankfully, is much easier to wrap your head around: RUM (rotational unit of memory).

“RUM helps neural networks to do two things very well,” says Preslav Nakov, a senior scientist at the Qatar Computing Research Institute and paper co-author. “It helps them to remember better, and it enables them to recall information more accurately.”

RUM was developed to help physicists study phenomena such as the behavior of light in complex engineered materials, the team explains. However, the team soon realized that “one of the places where […] this approach could be useful would be natural language processing.”

Artificial summaries

Soljačić, says he recalls a conversation with Mićo Tatalović, a former Knight Science Journalism fellow at MIT, a former editor at New Scientist magazine, and co-author of the study, who said that such a tool would be useful for his work as an editor trying to decide which papers to write about. Tatalović was, at the time, exploring AI in science journalism as his Knight fellowship project.

“And so we tried a few natural language processing tasks on it,” Soljačić says. “One that we tried was summarizing articles, and that seems to be working quite well.”

As a proof-of-concept, the team ran the same research paper through a conventional (LSTM-based) neural network and through their RUM-based system, asking them to produce short summaries. The end results were dramatically different. RUM can read through an entire research paper, not just it’s abstract, and summarise its content. The team even ran the present study through RUM (they were probably just showing off at this point).

Here’s the summary produced by the LSTM system:

‘”Baylisascariasis,” kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences. This infection, termed “baylisascariasis,” kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences. This infection, termed “baylisascariasis,” kills mice, has endangered the allegheny woodrat.”

Here’s the one the RUM system produced:

“Urban raccoons may infect people more than previously assumed. 7 percent of surveyed individuals tested positive for raccoon roundworm antibodies. Over 90 percent of raccoons in Santa Barbara play host to this parasite.”

Here’s the neural network’s summary of the study we’re discussing:

“Researchers have developed a new representation process on the rotational unit of RUM, a recurrent memory that can be used to solve a broad spectrum of the neural revolution in natural language processing.”

You guys like my coverage better, though, right? Right…?

The paper “Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications” has been published in the journal Transactions of the Association for Computational Linguistics.

Pottery fragments.

Native American societies had their own brand of ‘social media’

Societies in America’s southern Appalachian mountains shared art and technologies through regional networks reminiscent of today’s social media, a new study reports.

Pottery fragments.

Examples of pottery shards used in the study. Symbols were stamped into the clay while it was still wet. Each design and the various characteristics of the clay were used to reconstruct social networks among Native American communities.
Image credits Jacob Lulewicz, (2019), PNAS.

Native American villages established social and political connections well before European explorers came a-knocking, new research reveals. These systems — which functioned similarly to today’s platforms such as MySpace or Facebook, the author notes — laid the groundwork for local political systems as far back as 600 A.D.

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“Just as we have our own networks of ‘friends’ and ‘followers’ on platforms like Facebook and Twitter, societies that existed in North America between 1,200 and 350 years ago had their own information sharing networks,” said Jacob Lulewicz, the study’s author and a lecturer of archaeology in the Department of Anthropology in Arts & Sciences.

“Our study found a way to reconstruct these indigenous communication networks.”

The study used social network analysis techniques to map out the social and political relationships established between dozens of Native American villages in the studied region. The data came from messages embedded not in bytes, but in bits of pottery unearthed at sites throughout southern Appalachia clustered around the site of Etowah in Bartow County, Georgia (belonging to the so-called Mississippian culture). This included 276,626 sherds from 43 sites across eastern Tennessee and 88,705 sherds from 41 sites across northern Georgia. All the pottery dates between 800 and 1650 A.D., a period that saw the gradual emergence and subsequent decline of powerful chiefdoms that controlled wide networks of villages in the region.

Each fragment of pottery was analyzed to help Lulewicz understand how the technology used to make pottery and the symbols used to decorate them evolved over time. Armed with this rough timeline, Lulewicz then looked at how both elements disseminated among different villages or communities over time — in broad lines, this gave him a rough indication of how intensely they communicated.

Etowah served as the regional seat of social, political, economic and religious power across the region. This influence reached its peak between 1050 to 1325 A.D. and was still running in 1540 A.D. when the Spanish explorer Hernando de Soto first reached this area. De Soto accounts how the villages in this area were loosely bound to the influence of a single chief who resided in the town of Coosa (northern Georgia)

Lulewicz argues — based on his findings — that these political elites could emerge because of the social networks he describes. Their political power and centralized leadership, as well as the religious movements and inequality associated with their rule, were built on top of these wider, pre-existing social networks of common people. And, in the end, these networks would prove to be more stable and durable than any interactions dictated by elite chiefs.

“What I show in the paper is that while we see things like the emergence of super powerful chiefs and the rise of major economic inequalities, the very foundations of society — especially relationships and networks of kinship and family and reciprocity — remained virtually unchanged over 1,000 years,” Lulewicz said.

“That is, even though elite interests and political strategies waxed and waned and collapsed and flourished, very basic relationships and networks were some of the strongest, most durable aspects of society.”

Lulewicz argues that these findings show how important social connections between individuals are in guarding communities against unpredictable (or incompetent) leaders and the extended ruling class. He says it mirrors how digital social networks function today, and their role in contemporary revolutions or protest movements. Modern states are often quick to monitor, censor, or even shut down access to these virtual networks, he adds, which shows how valuable such social instruments are even today.

“This is super interesting — at least to me as a social scientist — for understanding how political movements actually play out,” he said. “It doesn’t come down to any particular, innate attribute of leaders and elites. What is comes down to is how those individuals are able to leverage the networks in which they are embedded.”

“Even though chiefs emerge at about 1000 A.D., over the next 650 years, chiefs actually shift their strategies of political and economic control. They tap into different parts of their networks, or leverage their connections in very different ways throughout time.”

“Because these very basic networks were so durable, they allowed these societies — especially common people — to buffer against and mediate the uncertainties associated with major political and economic change. They may have said, ‘You go live on top of that huge mound and do your sacred rituals, and we will go about life as usual for the most part.’ These communication networks served as a social constant for these people and allowed their cultures to persist for thousands of years even across transformations that could have been catastrophic.”

The paper “The social networks and structural variation of Mississippian sociopolitics in the southeastern United States” has been published in the journal PNAS.

Book review: ‘The Power of Networks: Six Principles that connect our Lives’

The Power of Networks: Six Principles that Connect Our Lives
By Christopher G. Brinton & Mung Chiang
Princeton University Press, 328 pp | Buy on Amazon

Ever wondered how Netflix seems to know you better than you do when it recommends new series? Well, it does so thanks to a framework that’s common in other situations — like how Google sorts search results or how WiFI directs bandwidth. In their book, authors Christopher G. Brinton & Mung Chiang explain how networks work and how these affect our lives based on six core principles.

Networks have always existed, today much more so than ever thanks to devices that enable us to connect to the largest network in the world — the internet. Building up from a massive open online course presented by the pair a few years back, The Power of Networks aims to demystify the complex structure of rules, standards, and processes which networks use today.

The book is divided into six chapters, each with its corresponding theme or ‘principle’: sharing resource, ranking and ordering, the collective wisdom and folly of crowds, routing, and management. Along the way, the authors also include interviews they made with renowned experts such as former Google CEO Eric Schmidt, former Verizon Wireless CEO Dennis Strigl or Vint Cerf and Bob Kahn, the founders of the great internet itself.

Using clear language and familiar analogies, the authors take turns in explaining some very big ideas. For instance, one analogy that pops up on more than one occasion is that of the crowded cocktail party. If everyone talked simultaneously, it would be very difficult for anyone to engage in a meaningful conversation. A host might decide to solve this capacity issue by asking guests to speak at separate times (analogous to how TDMA or 2G allowed mobile phone users to share the spectrum). Alternatively, the host might ask every guest to speak in a different language and then they can all talk simultaneously since each pair listening for one language in particular (analogous to the CDMA system). Things get a lot more exciting when the authors explain 3G and 4G networks.

If your job demands it or if you’re simply interested in learning about how networks function under the hood, this is a great introduction. That’s not to say that the subjects and content tackled are superficial. You’ll get a great overview as a non-specialist but each chapter also dives in deep into its treated subject — again, in a manner that simplifies highly complex topics.

It’s my impression that you’ll get a much better understanding of the ubiquitous networks that bind our digital lives together after reading this book.

Man sues neighbor for irritating his ‘electromagnetic allergies’

There are weird lawsuits you can understand, and then there are just weird lawsuits. If you find this sort of things interesting, you gotta listen to this: a man from Santa Fe filed a half a million dollars trial against his neighbor for using and iPhone and other wireless devices that trigger his ‘electrocmegnetic allergies’.

Wi Fi - the new yin and yang

Wi Fi - the new yin and yang

Yahoo News reports that Arthur F., the plaintiff has been sleeping at his friends or in his car in order to avoid the electromagnetic waves created by the Wi-Fi devices from the nearby house. He allegedly suffers Electromagnetic Sensitivity, with symptoms that include “nausea, vertigo, diarrhea, ringing in the ears, severe headaches and body aches, crippling joint pains, insomnia, impaired vision, impaired muscular control”, as well as others, even worse.

Even more, he’s not alone in his battle. Apparently there’s a whole group in Santa Fe that intends to remove all Wi-Fi hot spots because people are suffering from this sort of allergy. But wait, it’s not even an allergy; they want to classify it as a disability and are claiming Americans with Disabilities Act. What’s your take on this? If you ask me, it’s just a bunch of people trying to make some fuss and money where they shouldn’t but… I may be wrong.