Effective study of ocean processes requires sampling over the duration of long (weeks to months) oscillation patterns. Such sampling requires persistent, autonomous underwater vehicles that have a similarly, long deployment duration. The spatiotemporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. In this paper, we consider the combination of two methods for reducing navigation and localization error: a predictive approach based on ocean model predictions and a prior information approach derived from terrain-based navigation. The motivation for this work is not only for real-time state estimation but also for accurately reconstructing the actual path that the vehicle traversed to contextualize the gathered data, with respect to the science question at hand. We present an application for the practical use of priors and predictions for large-scale ocean sampling. This combined approach builds upon previous works by the authors and accurately localizes the traversed path of an underwater glider over long-duration, ocean deployments. The proposed method takes advantage of the reliable, short-term predictions of an ocean model, and the utility of priors used in terrain-based navigation over areas of significant bathymetric relief to bound uncertainty error in dead-reckoning navigation. This method improves upon our previously published works by (1) demonstrating the utility of our terrain-based navigation method with multiple field trials and (2) presenting a hybrid algorithm that combines both approaches to bound navigational error and uncertainty for long-term deployments of underwater vehicles. We demonstrate the approach by examining data from actual field trials with autonomous underwater gliders and demonstrate an ability to estimate geographical location of an underwater glider to <100 m over paths of length >2 km. Utilizing the combined algorithm, we are able to prescribe an uncertainty bound for navigation and instruct the glider to surface if that bound is exceeded during a given mission.

 

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation
Andrew Stuntz, Jonathan Scott Kelly and Ryan N. Smith

Front. Robot. AI, 29 April 2016 | http://dx.doi.org/10.3389/frobt.2016.00023

Source: journal.frontiersin.org

Next place prediction algorithms are invaluable tools, capable of increasing the efficiency of a wide variety of tasks, ranging from reducing the spreading of diseases to better resource management in areas such as urban planning and communication networks. In this work we estimate upper and lower limits on the predictability of human mobility to help assess the performance of competing algorithms. We do this using GPS traces from 604 individuals participating in a multiyear long experiment, The Copenhagen Networks study. Earlier works, focusing on the prediction of a participants whereabouts in the next time bin, have found very high upper limits (> 90%). We show that these upper limits, at least for some spatiotemporal scales, are mainly driven by the fact that humans tend to stay in the same place for long periods of time. This leads us to propose a new approach, focusing on the prediction of the next Point of Interest. By removing the trivial parts of human mobility behaviour, we show that the predictability of human mobility is significantly lower than implied by earlier works.

 

From A to B: A new approach to the limits of predictability of human mobility patterns
Edin Lind Ikanovic, Anders Mollgaard

http://arxiv.org/abs/1608.06419

Source: arxiv.org

Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Photos posted by depressed individuals were more likely to be bluer, grayer, and darker. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These findings suggest new avenues for early screening and detection of mental illness.

 

Instagram photos reveal predictive markers of depression
Andrew G. Reece, Christopher M. Danforth

http://arxiv.org/abs/1608.03282

Source: arxiv.org

The mesoscopic level of brain organization, describing the organization and dynamics of small circuits of neurons including from few tens to few thousands, has recently received considerable experimental attention. It is useful for describing small neural systems of invertebrates, and in mammalian neural systems it is often seen as a middle ground that is fundamental to link single neuron activity to complex functions and behavior. However, and somewhat counter-intuitively, the behavior of neural networks of small and intermediate size can be much more difficult to study mathematically than that of large networks, and appropriate mathematical methods to study the dynamics of such networks have not been developed yet. Here we consider a model of a network of firing-rate neurons with arbitrary finite size, and we study its local bifurcations using an analytical approach. This analysis, complemented by numerical studies for both the local and global bifurcations, shows the emergence of strong and previously unexplored finite-size effects that are particularly hard to detect in large networks. This study advances the tools available for the comprehension of finite-size neural circuits, going beyond the insights provided by the mean-field approximation and the current techniques for the quantification of finite-size effects.

 

Fasoli D, Cattani A, Panzeri S (2016) The Complexity of Dynamics in Small Neural Circuits. PLoS Comput Biol 12(8): e1004992. doi:10.1371/journal.pcbi.1004992

Source: journals.plos.org

We study the influence of selfish vs. polite behaviours on the dynamics of a pedestrian evacuation through a narrow exit. To this end, experiments involving about 80 participants with distinct prescribed behaviours are performed; reinjection of participants into the setup allowed us to improve the statistics. Notwithstanding the fluctuations in the instantaneous flow rate, we find that a stationary regime is almost immediately reached. The average flow rate increases monotonically with the fraction c\_s of vying (selfish) pedestrians, which corresponds to a “faster-is-faster” effect in our experimental conditions; it is also positively correlated with the average density of pedestrians in front of the door, up to nearly close-packing. At large c\_s , the flow displays marked intermittency, with bursts of quasi-simultaneous escapes. In addition to these findings, we wonder whether the effect of cooperation is specific to systems of intelligent beings, or whether it can be reproduced by a purely mechanical surrogate. To this purpose, we consider a bidimensional granular flow through an orifice in which some grains are made “cooperative” by repulsive magnetic interactions which impede their mutual collisions.

 

Influence of selfish and polite behaviours on a pedestrian evacuation through a narrow exit: A quantitative characterisation
Alexandre Nicolas, Sebastián Bouzat, Marcelo Kuperman

http://arxiv.org/abs/1608.04863

Source: arxiv.org