3,880 research outputs found

    Learning dynamic representations of the functional connectome in neurobiological networks

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    The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps. First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior. Code is available at https://github.com/dyballa/dynamic-connectomes.Comment: Accepted at ICLR 2

    Magnetic field generation in finite beam plasma system

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    For finite systems boundaries can introduce remarkable novel features. A well known example is the Casimir effect [1, 2] that is observed in quantum electrodynamic systems. In classical systems too novel effects associated with finite boundaries have been observed, for example the surface plasmon mode [3] that appears when the plasma has a finite extension. In this work a novel instability associated with the finite transverse size of a beam owing through a plasma system has been shown to exist. This instability leads to distinct characteristic features of the associated magnetic field that gets generated. For example, in contrast to the well known unstable Weibel mode of a beam plasma system which generates magnetic field at the skin depth scale, this instability generates magnetic field at the scales length of the transverse beam dimension [4]. The existence of this new instability is demonstrated by analytical arguments and by simulations conducted with the help of a variety of Particle - In - Cell (PIC) codes (e.g. OSIRIS, EPOCH, PICPSI). Two fluid simulations have also been conducted which confirm the observations. Furthermore, laboratory experiments on laser plasma system also provides evidence of such an instability mechanism at work

    Performance Limits of Stochastic Sub-Gradient Learning, Part II: Multi-Agent Case

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    The analysis in Part I revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization when gradient noise is present. These algorithms are used when the risk functions are non-smooth and involve non-differentiable components. They have been long recognized as being slow converging methods. However, it was revealed in Part I that the rate of convergence becomes linear for stochastic optimization problems, with the error iterate converging at an exponential rate αi\alpha^i to within an O(μ)−O(\mu)-neighborhood of the optimizer, for some α∈(0,1)\alpha \in (0,1) and small step-size μ\mu. The conclusion was established under weaker assumptions than the prior literature and, moreover, several important problems (such as LASSO, SVM, and Total Variation) were shown to satisfy these weaker assumptions automatically (but not the previously used conditions from the literature). These results revealed that sub-gradient learning methods have more favorable behavior than originally thought when used to enable continuous adaptation and learning. The results of Part I were exclusive to single-agent adaptation. The purpose of the current Part II is to examine the implications of these discoveries when a collection of networked agents employs subgradient learning as their cooperative mechanism. The analysis will show that, despite the coupled dynamics that arises in a networked scenario, the agents are still able to attain linear convergence in the stochastic case; they are also able to reach agreement within O(μ)O(\mu) of the optimizer

    Solar wind collisional heating

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    To properly describe heating in weakly collisional turbulent plasmas such as the solar wind, inter-particle collisions should be taken into account. Collisions can convert ordered energy into heat by means of irreversible relaxation towards the thermal equilibrium. Recently, Pezzi et al. (Phys. Rev. Lett., vol. 116, 2016, p. 145001) showed that the plasma collisionality is enhanced by the presence of fine structures in velocity space. Here, the analysis is extended by directly comparing the effects of the fully nonlinear Landau operator and a linearized Landau operator. By focusing on the relaxation towards the equilibrium of an out of equilibrium distribution function in a homogeneous force-free plasma, here it is pointed out that it is significant to retain nonlinearities in the collisional operator to quantify the importance of collisional effects. Although the presence of several characteristic times associated with the dissipation of different phase space structures is recovered in both the cases of the nonlinear and the linearized operators, the influence of these times is different in the two cases. In the linearized operator case, the recovered characteristic times are systematically larger than in the fully nonlinear operator case, this suggesting that fine velocity structures are dissipated slower if nonlinearities are neglected in the collisional operator

    Locus model for space-time fabric and quantum indeterminacies

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    A simple locus model for the space-time fabric is presented and is compared with quantum foam and random walk models. The induced indeterminacies in momentum are calculated and it is shown that these space-time fabric indeterminacies are, in most cases, negligible compared with the quantum mechanical indeterminacies. This result restricts the possibilities of an experimental observation of the space-time fabric

    Some Remarks about the Complexity of Epidemics Management

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    Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems

    Ferromagnetic and insulating behavior of LaCoO3 films grown on a (001) SrTiO3 substrate. A simple ionic picture explained ab initio

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    This paper shows that the oxygen vacancies observed experimentally in thin films of LaCoO3 subject to tensile strain are thermodynamically stable according to ab initio calculations. By using DFT calculations, we show that oxygen vacancies on the order of 6 % forming chains perpendicular to the (001) direction are more stable than the stoichiometric solution. These lead to magnetic Co2+ ions surrounding the vacancies that couple ferromagnetically. The remaining Co3+ cations in an octahedral environment are non magnetic. The gap leading to a ferromagnetic insulating phase occurs naturally and we provide a simple ionic picture to explain the resulting electronic structure.Comment: 7 pages, 7 figure

    Wave-like Decoding of Tail-biting Spatially Coupled LDPC Codes Through Iterative Demapping

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    For finite coupling lengths, terminated spatially coupled low-density parity-check (SC-LDPC) codes show a non-negligible rate-loss. In this paper, we investigate if this rate loss can be mitigated by tail-biting SC-LDPC codes in conjunction with iterative demapping of higher order modulation formats. Therefore, we examine the BP threshold of different coupled and uncoupled ensembles. A comparison between the decoding thresholds approximated by EXIT charts and the density evolution results of the coupled and uncoupled ensemble is given. We investigate the effect and potential of different labelings for such a set-up using per-bit EXIT curves, and exemplify the method for a 16-QAM system, e.g., using set partitioning labelings. A hybrid mapping is proposed, where different sub-blocks use different labelings in order to further optimize the decoding thresholds of tail-biting codes, while the computational complexity overhead through iterative demapping remains small.Comment: presentat at the International Symposium on Turbo Codes & Iterative Information Processing (ISTC), Brest, Sept. 201
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