The motion of social insects constitute beautiful examples of adaptive collective dynamics born out of apparent purposeless individual behavior. In this paper we revisit the topic of the ruling laws behind burst of activity in ants. The analysis, done over previously reported data, reconsider the proposed causation arrows, not finding any link between the duration of the ants activity and its moving speed. Secondly, synthetic trajectories created from steps of different ants, demonstrate that an additive stochastic process can explain the previously reported speed shape profile. Finally we show that as more ants enter the nest, the faster they move, which implies a collective property. Overall these results provides a mechanistic explanation for the reported behavioral laws, and suggest a formal way to further study the collective properties in these scenarios.

The expected indefinite causal structure in quantum gravity poses a challenge to the notion of entanglement: If two parties are in an indefinite causal relation of being spacelike and timelike, can they still be entangled? If so, how does one measure the amount of entanglement? We propose to generalize the notions of entanglement and entanglement measure to address these questions. Incidentally but importantly, the generalization opens the path to study quantum entanglement of states, channels, networks and processes with definite or indefinite causal structure in a unified fashion, e.g., we show that the entanglement distillation capacity of a state, the quantum communication capacity of a channel, and the entanglement generation capacity of a network or a process are different manifestations of one and the same entanglement measure.

In this paper, we develop an effective approach to simplify two-time-scale Markov chains with infinite state spaces by removal of states with fast leaving rates, which improves the simplification method of finite Markov chains. We introduce the concept of fast transition paths and show that the effective transitions of the reduced chain are the superposition of the direct transitions and the indirect transitions via all the fast transition paths. Furthermore, we apply our simplification approach to the standard Markov model of single-cell stochastic gene expression and provide a mathematical theory of random gene expression bursts. We also give the precise mathematical conditions for mRNAs and proteins to yield random bursts. It turns out the the random bursts exactly correspond to the fast transition paths of the Markov model. This helps us gain a better understanding of the physics behind random bursts as an emergent behavior from the complex biochemical reaction kinetics.

Blackbody radiation, emitted from a furnace and described by a Planck spectrum, contains (on average) an entropy of $3.9\pm 2.5$ bits per photon. Since normal physical burning is a unitary process, this amount of entropy is compensated by the same amount of "hidden information" in correlations between the photons. The importance of this result lies in the posterior extension of this argument to the Hawking radiation from black holes, demonstrating that the assumption of unitarity leads to a perfectly reasonable entropy/information budget for the evaporation process. In order to carry out this calculation we adopt a variant of the "average subsystem" approach, but consider a tripartite pure system that includes the influence of the rest of the universe, and which allows "young" black holes to still have a non-zero entropy; which we identify with the standard Bekenstein entropy.

To realize long-distance quantum communication and quantum network, it is required to have multiplexed quantum memory with many memory cells. Each memory cell needs to be individually addressable and independently accessible. Here we report an experiment that realizes a multiplexed DLCZ-type quantum memory with 225 individually accessible memory cells in a macroscopic atomic ensemble. As a key element for quantum repeaters, we demonstrate that entanglement with flying optical qubits can be stored into any neighboring memory cells and read out after a programmable time with high fidelity. Experimental realization of a multiplexed quantum memory with many individually accessible memory cells and programmable control of its addressing and readout makes an important step for its application in quantum information technology.

Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing techniques such as filtering and drift removal, we extracted few statistical features from the acquired plant electrical signals. Using these features, combined with different classification algorithms, we used a decision tree based multi-class classification strategy to identify the three different external chemical stimuli. We here present our exploration to obtain the optimum set of ranked feature and classifier combination that can separate a particular chemical stimulus from the incoming stream of plant electrical signals. The paper also reports an exhaustive comparison of similar feature based classification using the filtered and the raw plant signals, containing the high frequency stochastic part and also the low frequency trends present in it, as two different cases for feature extraction. The work, presented in this paper opens up new possibilities for using plant electrical signals to monitor and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.

Jul 25 2017

cs.NI arXiv:1707.07534v1

Many use cases of unmanned aerial vehicles (UAVs) require beyond visual line-of-sight (LOS) communications. Mobile networks offer wide area, high speed, and secure wireless connectivity, which can enhance control and safety of UAV operations and enable beyond visual LOS use cases. In this article, we share some of our experience in Long-Term Evolution (LTE) connectivity for low altitude small UAVs. We first identify the typical airborne connectivity requirements and characteristics, highlight the different propagation conditions for UAVs and mobiles on the ground with measurement and ray tracing results, and present simulation results to shed light on the feasibility of providing LTE connectivity for UAVs. We also present several ideas on potential enhancements for improving LTE connectivity performance and identify fruitful avenues for future research.

Gwilym Williams, Matthew Hunt, Benedikt Boehm, Andrew May, Michael Taverne, Daniel Ho, Sean Giblin, Dan Read, John Rarity, Rolf Allenspach, Sam Ladak Ferromagnetic materials have been utilised as recording media within data storage devices for many decades. Confinement of the material to a two dimensional plane is a significant bottleneck in achieving ultra-high recording densities and this has led to the proposition of three dimensional (3D) racetrack memories that utilise domain wall propagation along nanowires. However, the fabrication of 3D magnetic nanostructures of complex geometry is highly challenging and not easily achievable with standard lithography techniques. Here, by using a combination of two-photon lithography and electrochemical deposition, we show a new approach to construct 3D magnetic nanostructures of complex geometry. The magnetic properties are found to be intimately related to the 3D geometry of the structure and magnetic imaging experiments provide evidence of domain wall pinning at a 3D nanostructured junction.

Jul 25 2017

gr-qc arXiv:1707.07542v1

Time is a parameter playing a central role in our most fundamental modelling of natural laws. Relativity theory shows that the comparison of times measured by different clocks depends on their relative motion and on the strength of the gravitational field in which they are embedded. In standard cosmology, the time parameter is the one measured by fundamental clocks (i.e., clocks at rest with respect to the expanding space). This proper time is assumed to flow at a constant rate throughout the whole history of the universe. We make the alternative hypothesis that the rate at which the cosmological time flows depends on the dynamical state of the universe. In thermodynamics, the arrow of time is strongly related to the second law, which states that the entropy of an isolated system will always increase with time or, at best, stay constant. Hence, we assume that the time measured by fundamental clocks is proportional to the entropy of the region of the universe that is causally connected to them. Under that simple assumption, we find it possible to build toy cosmological models that present an acceleration of their expansion without any need for dark energy while being spatially closed and finite, avoiding the need to deal with infinite values.

This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive polar of all words. After that, we construct the features of news based on the positive polar of their words. In consideration of sequential stock prices and continuous news effects, we propose a recurrent neural network model to help predict stock prices. Compared to SVM classifier with price features, we find our proposed method has an over 5% improvement on stock prediction accuracy in experiments.