Located in China, Juno is a 17-country collaboration that will try to detect neutrinos and antineutrinos to learn more about their mass. Juno's sphere (bottom left) and photomultipliers (top right) for neutrino detection. Located 700 meters underground near the city of Jiangmen in southern China, a giant sphere--35 meters in diameter and filled with more than 20,000 tons of liquid--has just started a mission that will last for decades. This is Juno, the Jiangmen Underground Neutrino Observatory, a new, large-scale experiment studying some of the most mysterious and elusive particles known to science. Neutrinos are the most abundant particles in the universe with mass.

Learning in zero-sum games studies a situation where multiple agents competitively learn their strategy. In such multi-agent learning, we often see that the strategies cycle around their optimum, i.e., Nash equilibrium. When a game periodically varies (called a ``periodic'' game), however, the Nash equilibrium moves generically. How learning dynamics behave in such periodic games is of interest but still unclear. Interestingly, we discover that the behavior is highly dependent on the relationship between the two speeds at which the game changes and at which players learn. We observe that when these two speeds synchronize, the learning dynamics diverge, and their time-average does not converge. Otherwise, the learning dynamics draw complicated cycles, but their time-average converges. Under some assumptions introduced for the dynamical systems analysis, we prove that this behavior occurs. Furthermore, our experiments observe this behavior even if removing these assumptions. This study discovers a novel phenomenon, i.e., synchronization, and gains insight widely applicable to learning in periodic games.

How do honeybees land on flowers or avoid obstacles? One would expect such questions to be mostly of interest to biologists. However, the rise of small electronics and robotic systems has also made them relevant to robotics and Artificial Intelligence (AI). For example, small flying robots are extremely restricted in terms of the sensors and processing that they can carry onboard. If these robots are to be as autonomous as the much larger self-driving cars, they will have to use an extremely efficient type of artificial intelligence – similar to the highly developed intelligence possessed by flying insects.

We humans are governed by our free will to choose and make decisions. But, in the world of technology and data brokers, those who can predict and drive one to make decisions, are influencing free will. They are operating behind screens, constantly collecting private information to build a persona and facilitate targeted marketing. Following the recent Cambridge Analytica scandal, the market is under increased vigilance from regulators. With data harvesting becoming popular, the companies are now choosing to use Artificial intelligence and Machine learning that run in the background to collect data and understand one's behaviour. Many credit rating agencies and technology-based enterprises are leading data harvesters who are now being investigated.

Max-cut, clustering, and many other partitioning problems that are of significant importance to machine learning and other scientific fields are NP-hard, a reality that has motivated researchers to develop a wealth of approximation algorithms and heuristics. Although the best algorithm to use typically depends on the specific application domain, a worst-case analysis is often used to compare algorithms. This may be misleading if worst-case instances occur infrequently, and thus there is a demand for optimization methods which return the algorithm configuration best suited for the given application's typical inputs. We address this problem for clustering, max-cut, and other partitioning problems, such as integer quadratic programming, by designing computationally efficient and sample efficient learning algorithms which receive samples from an application-specific distribution over problem instances and learn a partitioning algorithm with high expected performance. Our algorithms learn over common integer quadratic programming and clustering algorithm families: SDP rounding algorithms and agglomerative clustering algorithms with dynamic programming. For our sample complexity analysis, we provide tight bounds on the pseudodimension of these algorithm classes, and show that surprisingly, even for classes of algorithms parameterized by a single parameter, the pseudo-dimension is superconstant. In this way, our work both contributes to the foundations of algorithm configuration and pushes the boundaries of learning theory, since the algorithm classes we analyze consist of multi-stage optimization procedures and are significantly more complex than classes typically studied in learning theory.

A new study suggests the answer is ... kind of. Researchers from the University of Oregon in Eugene have replicated some of the same brain patterns exhibited by human meditators in the brains of mice -- no tiny meditation cushions or squeaky "oms" required. Still, experiments show that the "meditating mice" were more relaxed and less stressed than those with no rodent meditation training. The authors say the work, published Monday in PNAS, provides a proof of concept that will allow them to learn more about how meditation affects the brain. Previous research has shown that just one month of mindful meditation can have a significant impact on humans both physically and psychologically.

The proof given here shows that for any finite, discrete transfer function used by the units of an RCC network, there are finite-state automata (FSA) that the network cannot model, no matter how many units are used. The proof also applies to continuous transfer functions with a finite number of fixed-points, such as sigmoid and radial-basis functions.

The proof given here shows that for any finite, discrete transfer function used by the units of an RCC network, there are finite-state automata (FSA) that the network cannot model, no matter how many units are used. The proof also applies to continuous transfer functions with a finite number of fixed-points, such as sigmoid and radial-basis functions.

The proof given here shows that for any finite, discrete transfer function used by the units of an RCC network, there are finite-state automata (FSA) that the network cannot model, no matter how many units are used. The proof also applies to continuous transfer functions with a finite number of fixed-points, such as sigmoid and radial-basis functions.

This paper relates the computational power of Fahlman' s Recurrent Cascade Correlation (RCC) architecture to that of fInite state automata (FSA). While some recurrent networks are FSA equivalent, RCC is not. The paper presents a theoretical analysis of the RCC architecture in the form of a proof describing a large class of FSA which cannot be realized by RCC. 1 INTRODUCTION Recurrent networks can be considered to be defmed by two components: a network architecture, and a learning rule. The former describes how a network with a given set of weights and topology computes its output values, while the latter describes how the weights (and possibly topology) of the network are updated to fIt a specifIc problem. It is possible to evaluate the computational power of a network architecture by analyzing the types of computations a network could perform assuming appropriate connection weights (and topology).