Diving Beneath the Sea of Stellar Activity: Chromatic Radial Velocities of the Young AU Mic Planetary System

We present updated radial-velocity (RV) analyses of the AU Mic system. AU Mic is a young (22 Myr) early-M dwarf known to host two transiting planets—P b ∼ 8.46 days, R b = 4.38 − 0.18 + 0.18 R ⊕ , P c ∼ 18.86 days, R c = 3.51 − 0.16 + 0.16 R ⊕ . With visible RVs from Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical echelle Spectrographs (CARMENES)-VIS, CHIRON, HARPS, HIRES, Minerva-Australis, and Tillinghast Reflector Echelle Spectrograph, as well as near-infrared (NIR) RVs from CARMENES-NIR, CSHELL, IRD, iSHELL, NIRSPEC, and SPIRou, we provide a 5σ upper limit to the mass of AU Mic c of M c ≤ 20.13 M ⊕ and present a refined mass of AU Mic b of M b = 20.12 − 1.57 + 1.72 M ⊕ . Used in our analyses is a new RV modeling toolkit to exploit the wavelength dependence of stellar activity present in our RVs via wavelength-dependent Gaussian processes. By obtaining near-simultaneous visible and near-infrared RVs, we also compute the temporal evolution of RV “color” and introduce a regressional method to aid in isolating Keplerian from stellar activity signals when modeling RVs in future works. Using a multiwavelength Gaussian process model, we demonstrate the ability to recover injected planets at 5σ significance with semi-amplitudes down to ≈10 m s−1 with a known ephemeris, more than an order of magnitude below the stellar activity amplitude. However, we find that the accuracy of the recovered semi-amplitudes is ∼50% for such signals with our model.

Interrogating theoretical models of neural computation with emergent property inference

A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon -- whether behavioral or a pattern of neural activity -- and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example of parameter inference in the stomatogastric ganglion. EPI is then shown to allow precise control over the behavior of inferred parameters and to scale in parameter dimension better than alternative techniques. In the remainder of this work, we present novel theoretical findings in models of primary visual cortex and superior colliculus, which were gained through the examination of complex parametric structure captured by EPI. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

УПРАВЛІННЯ ІННОВАЦІЙНИМ КЛАСТЕРНИМ РОЗВИТКОМ ТУРИСТИЧНОГО ПІДПРИЄМНИЦТВА: ОСОБЛИВОСТІ ТА ТЕНДЕНЦІЇ

The article discusses the contemporary issues related to the specifics of managing the innovative development of cluster entrepreneurship in the tourism sector. High dynamism of the external environment, rapidly growing consumer market demands for the quality of goods and services, intensified competition and a range of other factors urge local governments to make effective management decisions aimed at supporting further development of tourism business clusters that have to rely on compliance and well-reasoned application of methods, principles and functions of strategic management based on modern economic and mathematical modeling toolkit. The study presents the results of research on managing innovative cluster development of tourism entrepreneurship along with providing step-by-step assessment of innovation environment and exploring the sources of possible barriers to transformation processes. In addition, the intensity of transformation dynamics has been measured. The findings suggest an algorithm to run the diagnostics for the sensitivity of tourism companies within integrated clusters to innovative transformations. It is argued that the management framework, with innovative cluster development pattern of tourism entrepreneurship as its structural element, is capable to ensure integrated congruence of interests of all market actors, non-for-profit institutions and consumers, which allows to develop and implement effective policies for tourism and recreational services sector, subject to availability of appropriate management structures, powers and levers of influence. A conclusion is made that the primary function in managing the innovative cluster development of tourism entrepreneurship is the ability to balance the interests of various regional market actors and implement the strategic socioeconomic priorities of the tourism and recreation services sector in the context of sustainable development.

Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits

Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.

Brain Modeling ToolKit: an Open Source Software Suite for Multiscale Modeling of Brain Circuits

Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.