Biologists have long been wary of applying quantum theory to their own field. But, as Jim Al-Khalili and Johnjoe McFadden reveal, it might explain much natural phenomena.
More than a dozen U.S. utilities that were targets in a recent wave of cyberattacks have been identified by The Wall Street Journal. Some of the utilities, most of which are relatively small, are located near dams, locks and other critical infrastructure.
These electricity providers were singled out in a hacking campaign that was brought to light in August by researchers at a Silicon Valley cybersecurity company. But little was known about the attacks until now.
Uncovering trolls and malicious or spammy accounts on social media is increasingly difficult as the miscreants find more and more ways to camouflage themselves as seemingly legitimate. Writing in the International Journal of Intelligent Engineering Informatics, researchers in India have developed an algorithm based on ant-colony optimization that can effectively detect accounts that represent a threat to normal users.
Asha Kumari and Balkishan Department of Computer Science and Applications at Maharshi Dayanand University, in Rohtak, India, explain that the connections between twitter users are analogous to the pheromone chemical communication between ants and this can be modeled in an algorithm based on how ant colonies behave to reveal the strongest connections in the twitter network and so uncover the accounts that one might deem as threatening to legitimate users.
The team’s tests on their system were successful in terms of precision, recall, f-measure, true-positive rate, and false-positive rate based on 26 features examined by the system played against almost 41,500 user accounts attracted to honeypots. Moreover, they report that the approach is superior to existing techniques. The team adds that they hope to be able to improve the system still further by adding so-called machine learning into the algorithm so that it can be trained to better identify threatening accounts based on data from known threats and legitimate accounts.
Scientists have solved the structure of one of the key components of photosynthesis, a discovery that could lead to photosynthesis being ‘redesigned’ to achieve higher yields and meet urgent food security needs.
The study, led by the University of Sheffield and published today in the journal Nature, reveals the structure of cytochrome b6f — the protein complex that significantly influences plant growth via photosynthesis.
Photosynthesis is the foundation of life on Earth providing the food, oxygen and energy that sustains the biosphere and human civilisation.
The quest to develop the understanding for time crystalline behaviour in quantum systems has taken a new, exciting twist.
Physics experts from the Universities of Exeter, Iceland, and ITMO University in St. Petersburg, have revealed that the existence of genuine time crystals for closed quantum systems is possible.
Different from other studies which to date considered non-equilibrium open quantum systems, where the presence of a drive induces time-periodic oscillations, researchers have theoretically found a quantum system where time correlations survive for an infinitely long time.
Over the last few years, rapid progress in AI has enabled our smartphones, social networks, and search engines to understand our voice, recognize our faces, and identify objects in our photos with very good accuracy. These dramatic improvements are due in large part to the emergence of a new class of machine learning methods known as Deep Learning.
Animals and humans can learn to see, perceive, act, and communicate with an efficiency that no Machine Learning method can approach. The brains of humans and animals are “deep”, in the sense that each action is the result of a long chain of synaptic communications (many layers of processing). We are currently researching efficient learning algorithms for such “deep architectures”. We are currently concentrating on unsupervised learning algorithms that can be used to produce deep hierarchies of features for visual recognition. We surmise that understanding deep learning will not only enable us to build more intelligent machines but will also help us understand human intelligence and the mechanisms of human learning. http://www.cs.nyu.edu/~yann/research/deep/
Most clams are happy to make their burrow in a nice, soft bed of sand or mud. Not this mollusc. A recently uncovered relative of the shipworm puts the hard into hardcore, chewing holes into rocks and excreting the debris as sand.
Lithoredo abatanica joins a short list of freshwater animals capable of literally weathering the landscape and creating real estate for other species to hide in, while potentially affecting the course of their river ecosystem.
The innovative technology used to settle on the Red Planet will prove to be very useful on our own homeworld.
