Deep observation of the NGC1275 region with MAGIC: search of diffuse gamma-ray emission from cosmic rays in the Perseus cluster

Clusters of galaxies are expected to be reservoirs of cosmic rays (CRs) that should produce diffuse γ-ray emission due to their hadronic interactions with the intra-cluster medium. The nearby Perseus cool-core cluster, identified as the most promising target to search for such an emission, has been observed with the MAGIC telescopes at very-high energies (VHE, E ≥ 100 GeV) for a total of 253 hr from 2009 to 2014. The active nuclei of NGC 1275, the central dominant galaxy of the cluster, and IC 310, lying at about 0.6º from the centre, have been detected as point-like VHE γ-ray emitters during the first phase of this campaign. We report an updated measurement of the NGC 1275 spectrum, which is described well by a power law with a photon index Γ = 3.6 ± 0.2_(stat) ± 0.2_(syst) between 90 GeV and 1200 GeV. We do not detect any diffuse γ-ray emission from the cluster and so set stringent constraints on its CR population. To bracket the uncertainties over the CR spatial and spectral distributions, we adopt different spatial templates and power-law spectral indexes α. For α = 2.2, the CR-to-thermal pressure within the cluster virial radius is constrained to be ≤ 1 − 2%, except if CRs can propagate out of the cluster core, generating a flatter radial distribution and releasing the CR-to-thermal pressure constraint to ≤ 20%. Assuming that the observed radio mini-halo of Perseus is generated by secondary electrons from CR hadronic interactions, we can derive lower limits on the central magnetic field, B_(0), that depend on the CR distribution. For α = 2.2, B_(0) ≥ 5 − 8 µG, which is below the ∼25 µG inferred from Faraday rotation measurements, whereas for α ≤ 2.1, the hadronic interpretation of the diffuse radio emission contrasts with our γ-ray flux upper limits independently of the magnetic field strength.

A spatial explanation for synchrony biases in perceptual grouping: Consequences for the temporal-binding hypothesis

If two images are shown in rapid sequential order, they are perceived as a single, fused image. Despite this, recent studies have revealed that fundamental perceptual processes are influenced by extremely brief temporal offsets in stimulus presentation. Some researchers have suggested that this is due to the action of a cortical temporal-binding mechanism, which would serve to keep multiple mental representations of one object distinct from those of other objects. There is now gathering evidence that these studies should be reassessed. This article describes evidence for sensitivity to fixational eye and head movements, which provides a purely spatial explanation for the earlier results. Taken in conjunction with other studies, the work serves to undermine the current body of behavioral evidence for a temporal-binding mechanism.

The temporal and spatial limits of compensation for fixational eye movements

High-fidelity eye tracking is combined with a perceptual grouping task to provide insight into the likely mechanisms underlying the compensation of retinal image motion caused by movement of the eyes. The experiments describe the covert detection of minute temporal and spatial offsets incorporated into a test stimulus. Analysis of eye motion on individual trials indicates that the temporal offset sensitivity is actually due to motion of the eye inducing artificial spatial offsets in the briefly presented stimuli. The results have strong implications for two popular models of compensation for fixational eye movements, namely efference copy and image-based models. If an efference copy model is assumed, the results place constraints on the spatial accuracy and source of compensation. If an image-based model is assumed then limitations are placed on the integration time window over which motion estimates are calculated. (c) 2006 Elsevier Ltd. All rights reserved.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.

Does spatial distribution of fauna depend on algal belts on intertidal boulder fields of the French Basque coast ?

To determine good ecological status and conservation of the Sub-Marine area of the Bay of Biscay, the implementation of a new rocky intertidal habitats monitoring is needed. A protocol has been adapted from the Brittany protocol for the water body FRFC11 "Basque coast" for the two indicators "intertidal macroalgae" and "subtidal macroalgae" under the Water Framework Directive to qualify the ecological. However no protocol has been validated for fauna in front of meridional characters of the benthic communities. Investigations carried out on macroalgae communities on intertidal area in WFD framework, since 2008, constitute an important working basis. This is the aim of the Bigorno project (Intertidal Biodiversity of the south of the Bay of Biscay and Observation for New search and Monitoring for decision support), financed by the Agency of Marine Protected Areas and the Departmental Council. To implement knowledge, a sampling protocol has been used in 2015 on the boulder fields of Guéthary. This site is part of Natura 2000 area "rocky Basque coast and offshore extension "It constitutes also a Znieff site and restricted fishing area. The sampling strategy considers the heterogeneity of substrates and the presence of intertidal microhabitats. Two main habitats are present: "mediolittoral rock in exposed area habitat" and "boulder fields". Habitat "intertidal pools and permanent ponds" is also present but, it is not investigated. Sampling effort is of 353 quadrats of 0.1 m², drawn randomly according to a spatially stratified sampling plan, defined by habitat and algal belts. Taxa identification and enumeration are done on each quadrat. The objective of this work is to expose results from data collected during 2015 sampling program. The importance of characterizing benthic fauna communities spatial distribution belonging to the Basque coast according to algal belts defines during the WDF survey was highlighted. Concurrently, indicators of biodiversity were studied.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence.