Tag Archives: network

Network scientist publishes incredible visualization of character relationships in The Witcher

A stunning new graphical display shows how characters in The Witcher intertwine throughout the plot.

Andrej Sapkowski’s The Witcher series of books, one which is a favorite among the ZME Science team as well, has become increasingly popular and well-known over the last few years. Several videogames and a Netflix series based on the books definitely helped in this regard. Testament to its popularity is that the latest season of the show was watched for a worldwide total of 2.2 billion minutes in its debut week alone.

The social map of The Witcher. Image credits: Milán Janosov.

Milán Janosov, lead scientist at Datopolis with a Ph.D. in Network Science from Central European University is obviously also a fan of the series. He has recently put together a visual representation of the relationships between the characters of this universe using the magic of network science. In a paper he published on the work, Janosov details the process of creating this representation.

Steel for men, network science for plots

“I started reading “‘The Witcher’ early last year, shortly after I got hooked to the Netflix show, and the storyline just sucked me in,” Janosov told Ingrid Fadelli at TechXplore. “It was a somewhat similar experience to watching ‘Game of Thrones’ a few years ago, which had also inspired one of my research articles. When I was about to finish watching the new season of Witcher, I started to wonder how I could get more out of this.”

His first step was to collect the data needed to create the visualization. Janosov started with the subtitles of the Netflix adaptation of the books, but quickly realized they would not do by themselves, and ended up drawing on the books as well.

“To build a network, I also needed a complete list of the characters who appeared in the series,” Janosov said. “After collecting these initial pieces of information, my job was fairly simple. I wrote a computer program that screened through every single sentence of all the books and took a note every time it matched a character’s name into a sentence.”

The program noted each point where a character’s name would appear. From this, it calculated how close or far apart two characters were, based on how often they were mentioned together and at what interval. For example, whether they were mentioned in the same sentence or two apart. The program tracked these mentions up to five sentences away, which Janosov says is a “pretty good indicator” of whether two characters have actually met or were featured in the same plots”.

In the final visualization, the characters of the narrative are represented as nodes, with the main 50 characters being labeled by name. The size of each mode signifies how central they are to the story, and different colors represent different communities, which not all characters share. The number of links formed between nodes is based on how often two characters were mentioned within a five-sentence span of each other in the novel.

“While context is relatively easy to interpret for humans, for a computer, it is not that simple,” Janosov explained. “So to capture the context of the characters mentioned, I assumed that two characters were mentioned in the same context as they were not mentioned further than five sentences from each other. While the number five is somewhat arbitrary, it was chosen for the sake of simplicity (and OCD-friendliness), because three, four or even six sentence-distances lead to very similar results too, also staying consistent for example with the typical paragraph lengths in written text.”

This paper is an example of how network science can use datasets to spot hidden patterns in unstructured data. While any reader would get a general idea of the relationships in the novels after reading The Witcher series, Janosov’s visualization lays them down in easily-seen detail.

“In our daily lives, we are surrounded by social networks: our friends on social media, our colleagues at work, friends from school, family, sports and hobbies, and many more,” Janosov said. “All these social systems are intertwined by networks of which we almost always only have a partial and subjective understanding. To overcome this lack of knowledge and sparsity of information, network science comes really handy as it provides a set of tools and a framework of thinking that can help us better understand these social networks we participate in daily, just as it helped to clear the fog around ‘The Witcher.'”

Janosov adds that the research could be useful, as similar approaches are already being used, in real-world settings. International trade and communications are a great example of where network science can be brought to bear to uncover hidden trends.

For now, however, it allows us all to enjoy this wonderful illustration and gives fans a great way to immerse themselves further in the universe of The Witcher.

The paper “A Network Map of The Witcher” has been published in the pre-print server arXiv.

4G on the Moon? Yes! NASA and Nokia are already working on it

In a bit to solve internet connectivity issues in space, NASA has partnered with Nokia to set up a 4G network on the Moon, as part of the Tipping Point project. The plan is to first build a 4G network, and eventually transition to 5G, just like on Earth. It will be the first 4G communication system in space.

Credit Flickr Osde8Info

NASA awarded Nokia $14.1 million to deploy the cellular network on the Moon. The grant is part of $370 million worth of contracts for lunar surface research missions. Most of the funds were given to large space companies such as SpaceX and United Launch Alliance to perfect techniques to make and handle rocket propellant in space.

The project will have to move fast to stay in line with NASA’s goal to have astronauts working at a lunar base by 2028.

“We need power systems that can last a long time on the surface of the moon, and we need habitation capability on the surface,” NASA Administrator Jim Bridenstine said in a statement.

Back in 2018, Nokia and British firm Vodafone had announced their goal for a moon mission. They intended to launch a lander and rover built by Audi, utilizing a SpaceX rocket. They would set down near the Apollo 17 landing site and examine the Luna Roving vehicle astronauts left behind in 1972.

The launch never took place but the new contract with NASA brings Nokia’s plans for moon projects to life. The upcoming 4G network could allow for surface communications at greater distances, increased speeds, and provide more reliability than current standards, NASA explains. This means communication between lunar landers, rovers, habitats, and astronauts would be possible thanks to the service, said Jim Reuter, associate administrator for NASA’s Space Technology Mission Directorate. Nokia will look at how terrestrial technology could be modified for the lunar environment, he adds.

The moon’s cellular network will operate during lunar landings and launches. At the same time, it will be designed to tolerate the particularities on the lunar surface such as radiation, extreme temperature, and vacuum.

The network will allow astronauts to control lunar rovers, stream high-definition videos, transmit data, and have real-time navigation of the lunar geography. While 4G networks on Earth need big cell towers with power generations, Nokia has created small cell technology that is much easier to pack into a rocket ship.

Other technologies funded by NASA include demonstrations of lunar surface power generation and energy storage. Intuitive Machines will develop a hopping robot that could launch and carry small packages from one lunar site to another, while Alpha Space will create a small laboratory that could land on the moon’s surface.

Virtual private networks grow popular under the lockdown

The coronavirus outbreak forced us all into the house — and thus dramatically increased the value of our internet connections.

An internet router.
Image in the Public Domain.

With our online activity rising to such prominence in our lives, many people are looking to protect both their privacy and data. Virtual private networks seem to be one of the more popular choices for doing so. Demand for these ‘VPNs’ in April was between 22% to 36% higher than pre-pandemic levels, companies report. Demand peaked in late March with an increase of 65% above previous levels.

Private browsing

“Online searches for VPN began to surge around the world in mid-March in the days following the World Health Organization’s declaration of a pandemic on March 11,” writes Simon Migliano, a Digital Privacy & Censorship expert for CNet. “We’ve seen demand suddenly double in countries where lockdowns have been announced or expected.”

VPNs work by extending a private network, such as one you’d set up with a few cables between two computers in your home, over the internet. Think of it as a ‘residents only’ lane of the internet. They’re called ‘virutal’ because they aren’t exactly private networks, but use the (public) architecture of the Internet as we know it to create networks that function like private ones.

Machines that are part of this network enjoy a greater degree of privacy and security of data. Robust encryption is one of the key features of popular VPNs like Atlas VPN.

Other popular uses of VPNs are as smokescreens to protect a users’ identity and real-life location (through the use of a proxy server), usually in order to bypass georestrictions, or as a means of avoiding censorship or surveillance.

All in all, powerful tools — but not something that the vast majority of people would have given mich thought to a few months back.

They have gained major interest during the lockdown, however. Both business and private user demand have increased in 75 countries, with Egypt showing the greatest increase, of 224%. Other companies have reported a 36% increase in global users from February to March.

Work, education, entertainment, and social events are all increasingly moving online. Internet traffic as a whole shot up by 50% in the week of March 23, after restrictions were set in place to curb the outbreak in the EU and US. The more time we spend digitally, the more important issues such as net neutrality and access become as well. 

Of brides and men: how the search for a spouse creates social structures

New research from the University of Tokyo (UoT) is looking into how human social networks form and found that they naturally arise from simple, direct-exchange marriage relationships between familiar groups.

Image via Pixabay.

The team developed new mathematical models to study how traditional community structures and conventions arose around the world, including wide-spread taboos such as incest. For their study, they also drew on statistical physics models employed by evolutionary biologists and data on community structures documented by anthropologists around the world.

The original social network

“We think this is the first time cultural anthropology and computer simulations have met in a single research study,” said Professor Kunihiko Kaneko, an expert in theoretical biology and physics from the University of Tokyo Research Center for Complex Systems Biology.

“Anthropologists have documented kinship structures all over the world, but it still remains unclear how those structures emerged and why they have common properties,” said Kenji Itao, a first year master’s degree student in Kaneko’s laboratory, and first author of the study.

The team wanted to find the underlying mechanisms that shape human social networks, and how they lead to the traditional community structures and conventions we see around the world.

Back in the 1960s, cultural anthropologists studied the social networks among indigenous communities around the world, identifying two structures that seemed to naturally arise wherever they went. Among hunter-gatherers, direct-exchange kinship structures were common. These involve women from two different communities changing places when they marry (i.e. an “exchange of brides among more than two clans”). Agrarian societies, meanwhile, develop kinship structures where women move between multiple communities to marry.

“In human society, a family and kinship are formed by marriage and descent. In indigenous societies, families sharing a common ancestor are called a lineage. Lineages form a socially related group, called a clan, in which common culture is shared,” the authors write.

“Social relationships with others, such as cooperation, rivalry, or marriage, are mostly determined by the clans the parties belong to”.

The first social networks were tightly-knit structures formed among (biologically-related) families, the team explains. Such groups would then develop various relationships with other cultural groups in their local area as they interacted.

Itao and Kaneko used computer modeling and simulation to gauge which external factors could drive biologically-related families to organize into larger communities and control the exchange of brides in between lineages (i.e. the development of the incest taboo). They explain that incest is almost universally considered a taboo in human societies; however, the ancient focus of the taboo was on social closeness rather than blood ties — marrying someone born into the same cultural group as you, not necessarily someone you’re related to, was seen as taboo.

While “it is more common for women to move to a new community when they marry”, Itao explains, the model they used for this study didn’t make any distinction based on gender in this regard.

Someone not like me, please

They report that simulated families which shared traits or interests naturally coalesced into distinct cultural groups. However, the traits individual members possessed were different from the ones they desired in a spouse — the simulated actors desired to marry someone who wasn’t similar to themselves. This, they believe, is the underlying cause of community-based incest taboos.

When the model pushed these communities to cooperate, they formed generalized kinship exchange structures. Exactly which shape these structures took mainly depended on how difficult it was to find suitable brides and how much cooperation or conflict with other communities was necessary in order to secure these women.

The findings are based on a simple model that only included how social conflict and cooperation relate to marriage; the team hopes to further expand on it and include economic factors (which they say can cause communities to separate into classes). A better model could be used to expand the research to different communities in the modern world.

“It is rewarding to see that the combination of statistical physics and evolution theory, together with computer simulations, will be relevant to identify universal properties that affect human societies,” said Kaneko.

“I would be glad if perhaps our results can give field anthropologists a hint about universal structures that might explain what they observe in new studies,” Itao adds.

The paper “Evolution of kinship structures driven by marriage tie and competition” has been published in the journal Proceedings of the National Academy of Sciences.

Computers can now read handwriting with 98% accuracy

New research in Tunisia is teaching computers how to read your handwriting.

Image via Pixabay.

Researchers at the University of Sfax in Tunisia have developed a new method for computers to recognize handwritten characters and symbols in online scripts. The technique has already achieved ‘remarkable performance’ on texts written in the Latin and Arabic alphabets.

iRead

“Our paper handles the problem of online handwritten script recognition based on an extraction features system and deep approach system for sequence classification,” the researchers wrote in their paper. “We used an existent method combined with new classifiers in order to attain a flexible system.”

Handwriting recognition systems are, unsurprisingly, computer tools designed to recognize characters and hand-written symbols in a similar way to our brains. They’re similar in form and function with the neural networks that we’ve designed for image classification, face recognition, and natural language processing (NLP).

As humans, we innately begin developing the ability to understand different types of handwriting in our youth. This ability revolves around the identification and understanding of specific characters, both individually and when grouped together, the team explains. Several attempts have been made to replicate this ability in a computer over the last decade in a bid to enable more advanced and automatic analyses of handwritten texts.

The new paper presents two systems based on deep neural networks: an online handwriting segmentation and recognition system that uses a long short-term memory network (OnHSR-LSTM) and an online handwriting recognition system composed of a convolutional long short-term memory network (OnHR-covLSTM).

The first is based on the theory that our own brains work to transform language from the graphical marks on a piece of paper into symbolic representations. This OnHSR-LSTM works by detecting common properties of symbols or characters and then arranging them according to specific perceptual laws, for instance, based on proximity, similarity, etc. Essentially, it breaks down the script into a series of strokes, that is then turned into code, which is what the program actually ‘reads’.

“Finally, [the model] attempts to build a representation of the handwritten form based on the assumption that the perception of form is the identification of basic features that are arranged until we identify an object,” the researchers explained in their paper.

“Therefore, the representation of handwriting is a combination of primitive strokes. Handwriting is a sequence of basic codes that are grouped together to define a character or a shape.”

The second system, the convolutional long short-term memory network, is trained to predict both characters and words based on what it read. It is particularly well-suited for processing and classification of long sequences of characters and symbols.

Both neural networks were trained then evaluated using five different databases of handwritten scripts in the Arabic and Latin alphabets. Both systems achieved recognition rates of over 98%, which is ‘remarkable’ according to the team. Both systems, they explained, performed similarly to human subjects at the task.

“We now plan to build on and test our proposed recognition systems on a large-scale database and other scripts,” the researchers wrote.

The paper “Neural architecture based on fuzzy perceptual representation for online multilingual handwriting recognition” has been published in the preprint server arXiv.

Meet your new organ: the interstitium

Doctors have identified a previously unknown feature of human anatomy with many implications for the functions of most organs and tissues, and for the mechanisms of most major diseases.

Structural evaluation of the interstitial space. (A) Transmission electron microscopy shows collagen bundles (asterisks) that are composed of well-organized collagen fibrils. Some collagen bundles have a single flat cell along one side (arrowheads). Scale bar, 1 μm. (B) Higher magnification shows that cells (arrowhead) lack features of endothelium or other types of cells and have no basement membrane. Scale bar, 1 μm. (C) Second harmonics generation imaging shows that the bundles are fibrillar collagen (dark blue). Cyan-colored fibers are from autofluorescence and are likely elastin, as shown by similar autofluorescence in the elastic lamina of a nearby artery (inset) (40×). (D) Elastic van Gieson stain shows elastin fibers (black) running along collagen bundles (pink) (40×).

A new paper published on March 27th in Scientific Reports, shows that layers of the body long thought to be dense, connective tissues — below the skin’s surface, lining the digestive tract, lungs, and urinary systems, and surrounding arteries, veins, and the fascia between muscles — are instead interconnected, fluid-filled spaces.

Scientists named this layer the interstitium — a network of strong (collagen) and flexible (elastin) connective tissue fibers filled with fluids, that acts like a shock absorber to keep tissues from rupturing while organs, muscles, and vessels constantly pump and squeeze throughout the day.

This fluid layer that surrounds most organs may explain why cancer spreads so easily. Scientists think this fluid is the source of lymph, the highway of the immune system.

In addition, cells that reside in the interstitium and collagen bundles they line, change with age and may contribute to the wrinkling of skin, the stiffening of limbs, and the progression of fibrotic, sclerotic and inflammatory diseases.

Scientists have long known that more than half the fluid in the body resides within cells, and about a seventh inside the heart, blood vessels, lymph nodes, and lymph vessels. The remaining fluid is “interstitial,” and the current paper is the first to define the interstitium as an organ in its own right and, the authors write, one of the largest of the body, the authors write.

A team of pathologists from NYU School of Medicine thinks that no one saw these spaces before because of the medical field’s dependence on the examination of fixed tissue on microscope slides. Doctors examine the tissue after treating it with chemicals, slicing it thinly, and dyeing it in various colorations. The “fixing” process allows doctors to observe vivid details of cells and structures but drains away all fluid. The team found that the removal of fluid as slides are made makes the connective protein meshwork surrounding once fluid-filled compartments to collapse and appear denser.

“This fixation artifact of collapse has made a fluid-filled tissue type throughout the body appear solid in biopsy slides for decades, and our results correct for this to expand the anatomy of most tissues,” says co-senior author Neil Theise, MD, professor in the Department of Pathology at NYU Langone Health. “This finding has potential to drive dramatic advances in medicine, including the possibility that the direct sampling of interstitial fluid may become a powerful diagnostic tool.”

Researchers discovered the interstitium by using a novel medical technology — Probe-based confocal laser endomicroscopy. This new technology combines the benefits of endoscopy with the ones of lasers. The laser lights up the tissues, sensors analyze the reflected fluorescent patterns, offering a microscopic real-time view of the living tissues.

When probing a patient’s bile duct for cancer spread, endoscopists and study co-authors Dr. David Carr-Locke and Dr. Petros Benias observed something peculiar — a series of interconnected spaces in the submucosa level that was never described in the medical literature.

Baffled by their findings, they asked Dr. Neil Theise, professor in the Department of Pathology at NYU Langone Health and co-author of the paper for help in resolving the mystery. When Theise made biopsy slides out of the same tissue, the reticular pattern found by endomicroscopy vanished. The pathology team would later discover that the spaces seen in biopsy slides, traditionally dismissed as tears in the tissue, were instead the remnants of collapsed, previously fluid-filled, compartments.

Researchers collected tissues samples of bile ducts from 12 cancer patients during surgery. Before the pancreas and the bile duct were removed, patients underwent confocal microscopy for live tissue imaging. After recognizing this new space in images of bile ducts, the team was able to quickly spot it throughout the body.

Theise believes that the protein bundles seen in the space are likely to generate electrical current as they bend with the movements of organs and muscles, and may play a role in techniques like acupuncture.

Another scientist involved in the study was first author Rebecca Wells of the Perelman School of Medicine at the University of Pennsylvania, who determined that the skeleton in the newfound structure was comprised of collagen and elastin bundles.

Artificial synapse brings us one step closer to brain-like computers

Researchers have created a working artificial, organic synapse. The new device could allow computers to mimic some of the brain’s inner workings and improve their capacity to learn. Furthermore, a machine based on these synapses would be much more energy efficient that modern computers.

It may not look like much, but this device could revolutionize our computers forever.
Image credits Stanford University.

As far as processors go, the human brain is hands down the best we’ve ever seen. Its sheer processing power dwarfs anything humans have put together, for a fraction of the energy consumption, and it does it with elegance. If you allow me a car analogy, the human brain is a formula 1 race car that somehow uses almost no fuel and our best supercomputer… Well, it’s an old, beat-down Moskvich.

And it misfires.
Image credits Sludge G / Flickr.

So finding a way to emulate the brain’s hardware has understandably been high on the wishlist of computer engineers. A wish that may be granted sooner than they hoped. Researchers Stanford University and Sandia National Laboratories have made a breakthrough that could allow computers to mimic one element of the brain — the synapse.

 

 

 

“It works like a real synapse but it’s an organic electronic device that can be engineered,” said Alberto Salleo, associate professor of materials science and engineering at Stanford and senior author of the paper.

“It’s an entirely new family of devices because this type of architecture has not been shown before. For many key metrics, it also performs better than anything that’s been done before with inorganics.”

Copycat

The artificial synapse is made up of two thin, flexible films holding three embedded terminals connected by salty water. It works similarly to a transistor, with one of the terminals dictating how much electricity can flow between the other two. This behavior allowed the team to mimic the processes that go on inside the brain — as they zap information from one another, neurons create ‘pathways’ of sorts through which electrical impulses can travel faster. Every successful impulse requires less energy to pass through the synapse. For the most part, we believe that these pathways allow synapses to store information while they process it for comparatively little energy expenditure.

Because the artificial synapse mimics the way synapses in the brain respond to signals, it removes the need to separately store information after processing — just like in our brains, the processing creates the memory. These two tasks are fulfilled simultaneously for less energy than other versions of brain-like computing. The synapse could allow for a much more energy-efficient class of computers to be created, addressing a problem that’s becoming more and more poignant in today’s world.

Modern processors need huge fans because they use a lot of energy, giving off a lot of heat.

One application for the team’s synapses could be more brain-like computers that are especially well suited to tasks that involve visual or auditory signals — voice-controlled interfaces or driverless cars, for example. Previous neural networks and artificially intelligent algorithms used for these tasks are impressive but come nowhere near the processing power our brains hold in their tiny synapses. They also use a lot more energy.

“Deep learning algorithms are very powerful but they rely on processors to calculate and simulate the electrical states and store them somewhere else, which is inefficient in terms of energy and time,” said Yoeri van de Burgt, former postdoctoral scholar in the Salleo lab and lead author of the paper.

“Instead of simulating a neural network, our work is trying to make a neural network.”

The team will program these artificial synapses the same way our brain learns — they will progressively reinforce the pathways through repeated charge and discharge. They found that this method allows them to predict what voltage will be required to get a synapse to a specific electrical state and hold it with only 1% uncertainty. Unlike traditional hard drives where data has to be stored or lost when the machine shuts down, the neural network can just pick up where it left off without the need for any data banks.

One of a kind

Right now, the team has only produced one such synapse. Sandia researchers have taken some 15,000 measurements during various tests of the device to simulate the activity of a whole array of them. This simulated network was able to identify handwritten digits (between 0-9) with 93 to 97% accuracy — which, if you’ve ever used the recognize handwriting feature, you’ll recognize as an incredible success rate.

“More and more, the kinds of tasks that we expect our computing devices to do require computing that mimics the brain because using traditional computing to perform these tasks is becoming really power hungry,” said A. Alec Talin, distinguished member of technical staff at Sandia National Laboratories in Livermore, California, and senior author of the paper.

“We’ve demonstrated a device that’s ideal for running these type of algorithms and that consumes a lot less power.”

One of the reasons these synapses perform so well is the numbers of states they can hold. Digital transistors (such as the ones in your computer/smartphone) are binary — they can either be in state 1 or 0. The team has been able to successfully program 500 states in the synapse, and the higher the number the more powerful a neural network computational model becomes. Switching from one state to another required roughly a tenth of the energy modern computing system drain to move data from processors to memory storage.

Still, this means that the artificial synapse is currently 10,000 times less energy efficient than its biological counterpart. The team hopes they can tweak and improve the device after trials in working devices to bring this energy requirement down.

Another exciting possibility is the use of these synapses in-vivo. The devices are largely composed of organic elements such as hydrogen or carbon, and should be fully compatible with the brain’s chemistry. They’re soft and flexible, and use the same voltages as those of human neurons. All this raises the possibility of using the artificial synapse in concert with live neurons in improved brain-machine interfaces.

Before they considering any biological applications, however, the team wants to test a full array of artificial synapses.

The full paper “A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing” has been published in the journal Nature Materials.

 

 

Mathematicians show who is the the real main character in Game of Thrones

It’s probably not who you think it is – but this small guy casts a big shadow.

The books by George Martin and HBO’s subsequent series adaptation have stolen our hearts. Game of Thrones has become one of the most popular series of all time, with millions of people eagerly awaiting to see the developments of the new season. The narrative has several innovative features, including several major plot line, with numerous character arcs and intricate relationships. Most of the time, you can’t really tell who or what the story is about, because there’s just so much going on. With that in mind, two mathematicians, Andrew Beveridge and Jie Shan conducted a network analysis of the characters to see who stands out the most. Three characters stand out:

“Tyrion, Jon, and Sansa,” Beveridge and Shan write.

This in itself is a bit surprising. Jon is regarded by most people as the main character, despite his… let’s say precarious situation at the end of the last season. Tyrion is also well connected, but Sansa came as quite a surprise. Other fan favorites like Arya or Daenerys are a bit isolated from the main action, so they can’t be regarded as the main ones. So, who’s the main character after all?

“Acting as the Hand of the King, Tyrion is thrust into the center of the political machinations of the capitol city. Our analysis suggests that he is the true protagonist of the book. Meanwhile, Jon Snow is uniquely positioned in the network, with connections to highborn lords, the Night’s Watch militia, and the savage wildlings beyond the Wall. The real surprise may be the prominence of Sansa Stark, a de facto captive in King’s Landing. However, other players are aware of her value as a Stark heir and they repeatedly use her as a pawn in their plays for power. If she can develop her cunning, then she can capitalize on her network importance to dramatic effect.”

Now, for those of you who only watched the series, it has to be said that this study was conducted based only on the books – on the third book, to be more precise (A Storm of Swords). The action in the series has been slowly but surely diverging from that in the books, so the same might not stand for the series.

They laid out a diagram of how all the characters in the book were connected to each other, and if they were mentioned within 15 words of each other, they’re connected, regardless of how they feel about each other. They then mapped out all the relationships thusly defined, creating a type of “Page Rank“, similar in concept to the one used by Google. The “degree centrality” of the characters was measured based on how many connections they had and how many interactions within those connections. Researchers acknowledge that their work is trivial in purpose, but the technique can be applied in a myriad of other, more important areas.

“We have considered a fanciful application of network science to give an enticing taste of its capabilities. More serious applications abound, and network science promises to be invaluable in understanding our modern networked life.”

Network science is an academic field which studies complex networks… yeah, I know. It basically studies the relationships between things, whether it’s people, money transactions, biological relationships or anything else. It’s a growing field of data science, employing methods and elements from numerous other fields, including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology.

The study was published in the April issue of the Mathematical Association of America’s journal Math Horizons.

The Science Behind Network Cabling Connectivity

For businesses or apartments, network cabling can help to connect a variety of different gadgets to each other on one specific cable network. This is one of the best ways to hard-wire all of the gadgets within the business or home, ensuring that everyone is able to easily access the Internet or cable that they have chosen. Learning about what network cabling is and how to have it installed will help you to decide if it is the right option for you.

What is Network Cabling?

The Science Behind Network Cabling Connectivity

Network cabling basically connects a variety of gadgets to one main source, such as a modem or other channel outlet. For instance, you might want to connect a printer, fax machine and a computer all to the same source so that it can pick up the Internet without necessarily going through WiFi. Because of this, you will be able to have better access to the Internet without any major problems. This is a great option for individuals who have businesses or very large homes or apartments that need to be connected to the same source.

Network cabling has been around for many years now and many people use it for their homes and businesses. It also allows you to save money because you are using one main source to receive your network cable capabilities. This is great for just about anyone and can be the ideal choice for individuals who have very large businesses with a lot of different gadgets. It pays to look into having this type of cabling network installed by the experts in order to begin making use of it in your very own life. Be sure to learn as much as possible about network cabling and the companies that provide this service before choosing it for your self.

Getting a Professional to Install the Network Cables

Installing network cabling can be somewhat dangerous because you are using a lot of electricity and this can be a problem if you are unfamiliar with how to do this type of work. The best thing for you to do is to consider hiring an expert for structured cabling installation so that the work is done properly without any major problems that can come from it. Electricity issues can sometimes be problematic because a lot of business and homeowners do not know how to actually install a network cable.

The best thing that you can do for your business or home is to contact a local company who will be able to get the network cable installed for you. They will charge a fee for their services, but it is well worth it because the cabling will be done correctly and it is going to provide you with the connection that you both need and want. Make sure that you do adequate research to find a local company with which you can work in order to have this type of system installed for you at a reasonable price.

Getting the right type of network cabling connection is ideal for the average business or homeowner. You will be able to connect a wide range of gadgets to the same network, enabling you to save some money. Considering the dangers of installing this type of wiring yourself, it is best to leave it to the experts.

 

Influential few predict behavior of the many – on all scales

As Niels Bohr once pointed out, to fully understand how a living organism works, you’d have to take it apart in the smallest of parts; since this is not something you can actually do, organisms, which represent systems of very high complexity, are impossible to track and understand in all their details.

The few and the many

chemical networkBut by using some very creative mathematics that reveal complex systems by tracking a selected few of their components network-theory researchers seem to come up with some really unique solutions. For example, say you wanted to track all the biological markers that associate some people with a certain disease. You can track down all the genes that are expressed differently in people with the disease and create a network that shows their interactions, but how do you pick the ones connected to the disease from the ones coincidentally different?

Yang-Yu Liu of Northeastern University in Boston and his colleagues believe they have found an answer. To prove their technique, they analyzed the entire human metabolic network and found that using concentrations of about 10% of the body’s 2,763 metabolites could in fact predict the levels of all the rest. But as many applications as this could have in medicine, the possibilities are way more vast. The same technique could be applied to identify the people whose opinions determine everyone else’s, helping in political predictions, or in environmental issues, helping ecologists single out the particular species to track to follow changes in an entire ecosystem. The potential applications are virtually limitless.

Needling around

To imagine how this works, say for example we have a very simple network, with two chemicals: A and B. Chemical A becomes chemical B. Because any changes in B are exclusively determined by A, monitoring B over time will also enable you to determine the state of A. The same would not be true if you monitored only A: Without knowing the initial level of B, changes in A aren’t enough to determine the level of B.

chemical network2

All is simple and fine – but real life networks are nowhere near as simple. Liu’s team tackled the problem by examining clusters of strongly connected components in a network, representing them with arrows (in the previous example, the arrow would lead from B to A, but not vice versa). Their results were pretty amazing: they found that most of the time (and almost always in real-world networks), these selected nodes alone are sufficient to determine the state of every other node in the network. From a theoretical standpoint, it would be possible (although extremely hard) to reconstruct the entire network from these nodes. But as the team explains, this is not really necessary:

“This paper shows how you can reduce a network to the really important component parts that drive the system’s behaviour,” says Joseph Loscalzo of Harvard Medical School in Boston, Massachusetts. “It begins to make the system more tractable,” adds Loscalzo, who would like to apply the technique to medicine.

Via Nature

Datatransfer

Fastest network data transfer in the world – 186 GB/s

DatatransferScientists from CalTech University have set a new world record for data transfer, after they successfully reached a combined rate of 186 GB/s, in both direction. Their work was presented at the recent SuperComputing 2011 (SC11) conference in Seattle.

To put things into perspective 186 GBps would roughly mean 100,000 transferred Blue Ray disks in a single day, or you could use it to download the current version of the Internet in 1,3 years – the type of speed  which can only be tracked by the highest quality network performance monitor. This extraordinary advancement will pave the way for the next generation of high-tech optical fiber networks, capable of transfering high volumes of information across oceans and continents.

The researchers used a 100-GB/s network circuit between University of Victoria Computing Centre located in Victoria, British Columbia, and the Washington State Convention Centre in Seattle, all set up by Canada’s Advanced Research and Innovation Network (CANARIE) and BCNET, a non-profit, shared IT services organization. Using this high-tech network array, the researchers were able to achieve a staggering efficiency –  data was transferred at a constant rate of 98 Gbps.

[RELATED] Fastest single laser transmission achieved – 26 terabytes/second

When the researchers opted for a simultaneous data rate, in both directions, they successfully managed to reach  a sustained two-way data rate of 186 Gbps between the two data centers – a new world record.

“Our group and its partners are showing how massive amounts of data will be handled and transported in the future,” says Harvey Newman, professor of phsycis at California Institute of Technology (Caltech) and head of the high-energy physics (HEP) team.

“Having these tools in our hands allows us to engage in realizable visions others do not have. We can see a clear path to a future others cannot yet imagine with any confidence.”

But why is this important for me, the average internet flicking Joe whose monthly bandwidth amounts to a few iTunes albums and some Netflix streaming?

Well, high transfer rates is of capital importance for researchers today, especially those working at the CERN experiment. So far, more than 100 petabytes (100,000 terabytes) of data have been processed, distributed, and analyzed using a global grid of 300 computing and storage facilities located at laboratories and universities around the world, and these figures are only set to increase tenfold as new particle collision data needs to be crunched in the future.

“Enabling scientists anywhere in the world to work on the LHC data is a key objective, bringing the best minds together to work on the mysteries of the universe,” says David Foster, the deputy IT department head at CERN.

“The 100-Gbps demonstration at SC11 is pushing the limits of network technology by showing that it is possible to transfer petascale particle physics data in a matter of hours to anywhere around the world,” adds Randall Sobie, a research scientist at the Institute of Particle Physics in Canada and team member.


source

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.