Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin's behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin's own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin's consumption more exploratory than the culture's production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.

Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering history of sequential recombination jet algorithms is like the parsing of a sentence. Our approach works directly with the four-momenta of a variable-length set of particles, and the jet-based tree structure varies on an event-by-event basis. Our experiments highlight the flexibility of our method for building task-specific jet embeddings and show that recursive architectures are significantly more accurate and data efficient than previous image-based networks. We extend the analogy from individual jets (sentences) to full events (paragraphs), and show for the first time an event-level classifier operating on all the stable particles produced in an LHC event.

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

An axiomatic approach to signal reconstruction is formulated, involving a sample consistent set and a guiding set, describing desired reconstructions. New frame-less reconstruction methods are proposed, based on a novel concept of a reconstruction set, defined as a shortest pathway between the sample consistent set and the guiding set. Existence and uniqueness of the reconstruction set are investigated in a Hilbert space, where the guiding set is a closed subspace and the sample consistent set is a closed plane, formed by a sampling subspace. Connections to earlier known consistent, generalized, and regularized reconstructions are clarified. New stability and reconstruction error bounds are derived, using the largest nontrivial angle between the sampling and guiding subspaces. Conjugate gradient iterative reconstruction algorithms are proposed and illustrated numerically for image magnification.

Mechanical masterminds have spawned the Bat Bot, a soaring, sweeping and diving robot that may eventually fly circles around other drones. Because it mimics the unique and more flexible way bats fly, this 3-ounce prototype could do a better and safer job getting into disaster sites and scoping out construction zones than bulky drones with spinning rotors, said the three authors of a study released Wednesday in the journal Science Robotics. For example, it would have been ideal for going inside the damaged Fukushima nuclear plant in Japan, said study co-author Seth Hutchinson, an engineering professor at the University of Illinois. Bat Bot, a three-ounce flying robot can be more agile at getting into treacherous places than standard drones. The flying robot weighs just three ounces, and is equipped with nine joints. It measures about 8 inches from head to tail, and has a super-thin membrane that stretches to about a foot and a half.

With cyber criminals constantly adapting to industry defenses, creating new ways to commit cybercrimes, the cybersecurity industry is increasingly looking toward machine learning and artificial intelligence to help provide better deterrents, according to a new study from ABI Research. That increased reliance on automatic, intelligent processes for deterring cyber criminals will result in an increase in big data, intelligence and analytics spending, to the tune of $96 billion by 2021, according to the report. "We are in the midst of an artificial intelligence security revolution," says Dimitrios Pavlakis, industry analyst at ABI Research. "This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management (SIEM) and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years." User and Entity Behavioral Analytics (UEBA), and "deep learning" algorithm designs are becoming two of the more prominent technologies in cybersecurity solutions, their research found.

Islamic State (IS) is now using drones to wreak havoc amongst Iraqi soldiers in Mosul, Iraq according to a report by the Associated Press. Iraqi security forces first reported seeing IS drones in 2015, but the sightings have become more frequent in recent months. Investigators from the AP conducted a search of a warehouse in Mosul earlier this week and uncovered parts of drones, receipts of supplies purchased and reports on IS missions. Islamic State appears to have an open budget, spending thousands of dollars a month on drone materials according to the AP report. It has purchased drones from stores and advanced their technology to fit their requirements or even bought supplies to make their own.

Researchers have unveiled what they say is the first practical blueprint for the'holy grail' of computing - a quantum computer. Researchers from the University of Sussex led the team from around the world, including a team from Google, and say their work has the potential to revolutionise industry, science and commerce on a similar scale as the invention of ordinary computers. If it works, it will be a real-life version of Deep Thought, the supercomputer programmed to solve the'ultimate question of life, the universe, and everything' in The Hitchhiker's Guide To The Galaxy. Researchers from around the world, including a team from Google, have unveiled what they say is the first practical blueprint for the'holy grail' of computing - a quantum computer. Pictured, the prototype of the core of a trapped ion quantum computer, which the team now says could be operational within two years.

FILE - This May 13, 2014, file photo shows a row of Google self-driving Lexus cars at a Google event outside the Computer History Museum in Mountain View, Calif. California regulators release safety reports filed by 11 companies that have been testing self-driving car prototypes on public roads on Wednesday, Feb. 1, 2017. The papers report the number of times in 2016 that human backup drivers took control from the cars' self-driving software, though companies argue such "disengagements" don't always reflect something going wrong. FILE - This May 13, 2014, file photo shows a row of Google self-driving Lexus cars at a Google event outside the Computer History Museum in Mountain View, Calif. California regulators release safety reports filed by 11 companies that have been testing self-driving car prototypes on public roads on Wednesday, Feb. 1, 2017.

Artificial skin as heat-sensitive as pit vipers--the most sensitive heat detectors in nature--could one day help prosthetics and robot limbs detect subtle changes in temperature, a new study finds. Many research groups around the world are developing flexible electronic skin for prosthetic limbs that can help replicate the sensory capabilities of real skin. When it comes to temperature, existing flexible sensors recognize changes of less than one-tenth of a degree C, but only within temperature ranges of less than 5 degrees C. Other flexible devices can work in wider temperature ranges, but are many times less sensitive. Now scientists have developed an electronic skin that is sensitive to changes as little as one-hundredth of a degree C over a 45-degree range, from 5 C to 50 C. This sensitivity is comparable to that of pit vipers such as rattlesnakes, the researchers say.