Neuromorphometric characterization with shape functionals

This work presents a procedure to extract morphological information from neuronal cells based on the variation of shape functionals as the cell geometry undergoes a dilation through a wide interval of spatial scales. The targeted shapes are alpha and beta cat retinal ganglion cells, which are characterized by different ranges of dendritic field diameter. Image functionals are expected to act as descriptors of the shape, gathering relevant geometric and topological features of the complex cell form. We present a comparative study of classification performance of additive shape descriptors, namely, Minkowski functionals, and the nonadditive multiscale fractal. We found that the proposed measures perform efficiently the task of identifying the two main classes alpha and beta based solely on scale invariant information, while also providing intraclass morphological assessment

Corotating light cylinders and Alfv\'en waves

Exact relativistic force free fields with cylindrical symmetry are explored. Such fields are generated in the interstellar gas via their connection to pulsar magnetospheres both inside and outside their light cylinders. The possibility of much enhanced interstellar fields wound on cylinders of Solar system dimensions is discussed but these are most likely unstable.Comment: 6 pages, 6 figures, accepted by MNRA

Massive stars and globular cluster formation

We first present chemodynamical simulations to investigate how stellar winds of massive stars influence early dynamical and chemical evolution of forming globular clusters (GCs). In our numerical models, GCs form in turbulent,high-density giant molecular clouds (GMCs), which are embedded in a massive dark matter halo at high redshifts. We show how high-density, compact stellar systems are formed from GMCs influenced both by physical processes associated with star formation and by tidal fields of their host halos. We also show that chemical pollution of GC-forming GMCs by stellar winds from massive stars can result in star-to-star abundance inhomogeneities among light elements (e.g., C, N, and O) of stars in GCs. The present model with a canonical initial mass function (IMF) also shows a C-N anticorrelation that stars with smaller [C/Fe] have larger [N/Fe] in a GC. Although these results imply that ``self-pollution'' of GC-forming GMCs by stellar winds from massive stars can cause abundance inhomogeneities of GCs, the present models with different parameters and canonical IMFs can not show N-rich stars with [N/Fe] ~ 0.8 observed in some GCs (e.g., NGC 6752). We discuss this apparent failure in the context of massive star formation preceding low-mass one within GC-forming GMCs (``bimodal star formation scenario''). We also show that although almost all stars (~97%) show normal He abundances (Y) of ~0.24 some stars later formed in GMCs can have Y as high as ~0.3 in some models. The number fraction of He-rich stars with Y >0.26 is however found to be small (~10^-3) for most models.Comment: 10 pages, 8 figures, accepted by Ap

Assessing the ability of Transformer-based Neural Models to represent structurally unbounded dependencies

Filler-gap dependencies are among the most challenging syntactic constructions for com- putational models at large. Recently, Wilcox et al. (2018) and Wilcox et al. (2019b) provide some evidence suggesting that large-scale general-purpose LSTM RNNs have learned such long-distance filler-gap dependencies. In the present work we provide evidence that such models learn filler-gap dependencies only very imperfectly, despite being trained on massive amounts of data. Finally, we compare the LSTM RNN models with more modern state-of-the-art Transformer models, and find that these have poor-to-mixed degrees of success, despite their sheer size and low perplexity

A drive towards thermodynamic efficiency for dissipative structures in chemical reaction networks

Dissipative accounts of structure formation show that the self-organisation of complex structures is thermodynamically favoured, whenever these structures dissipate free energy that could not be accessed otherwise. These structures therefore open transition channels for the state of the universe to move from a frustrated, metastable state to another metastable state of higher entropy. However, these accounts apply as well to relatively simple, dissipative systems, such as convection cells, hurricanes, candle flames, lightning strikes, or mechanical cracks, as they do to complex biological systems. Conversely, interesting computational properties—that characterize complex biological systems, such as efficient, predictive representations of environmental dynamics— can be linked to the thermodynamic efficiency of underlying physical processes. However, the potential mechanisms that underwrite the selection of dissipative structures with thermodynamically efficient subprocesses is not completely understood. We address these mechanisms by explaining how bifurcation-based, work-harvesting processes—required to sustain complex dissipative structures— might be driven towards thermodynamic efficiency. We first demonstrate a simple mechanism that leads to self-selection of efficient dissipative structures in a stochastic chemical reaction network, when the dissipated driving chemical potential difference is decreased. We then discuss how such a drive can emerge naturally in a hierarchy of self-similar dissipative structures, each feeding on the dissipative structures of a previous level, when moving away from the initial, driving disequilibrium

Comparison of Reynolds averaging Navier-Stokes (RANS) turbulent models in predicting wind pressure on tall buildings

This paper presents a detailed comparison of using Reynolds Averaging Navier-Stokes (RANS) approach in predicting wind pressure on a super-tall 406 m slender tower with circular cross-section. The results obtained from wind tunnel tests using a rigid model approach in a boundary layer wind tunnel (BLWT) were compared to that of Computational Fluid Dynamics (CFD) numerical simulations. The main objective of this study is to critically investigate the possibility of using RANS turbulent model based CFD approach in tall building design. Three different RANS turbulence models were compared with the wind tunnel data in predicting flow characteristics. The detailed wind tunnel experimental procedure and numerical approach are discussed and presented. It was shown that the shear stress transport (SST) variant model,could predict pressure coefficients comparable to that of the wind tunnel experiments. The influence of flow separation point on flow characterisation and pressure prediction is highlighted. The improvement that can be made in the near-wall region in the finite volume mesh to achieve an accurate separation point is presented. The effects of Reynolds number produced in the wind tunnel and scaled-down numerical models were compared with the anticipated full-scale flow Reynolds number. Hence, it is shown that a correct modelling technique in CFD using RANS turbulence models can be used as an alternative design approach of super-tall structures to estimate wind-induced pressures.ARC DE150101703, CERDS USy

Implementation of Pedestrian Meso Models in Portugal. VRU-TOO Deliverable 8.

DRIVE I Project "An Intelligent Traffic System for Vulnerable Road Users" created a computer model WLCAN1, which simulated the crossing behaviour of pedestrians on a length of urban street. Furthermore, a car-based assignment model SATURN was used to assess the effect on cars of pedestrian-friendly policies. The attitude towards this modelling work were very "Northern European" in four important senses: - The technical approach was based upon technology developed in Northern Europe for network models of cars, which are used widely throughout Northern Europe. - The behavioural sub-models used in WLCANl and SATURN were all taken from empirical results obtained in Northern Europe. - All data to feed and calibrate the models was collected at Northern European sites. - All the partners in the DRIVE I project were from Northern Europe, and so the model development automatically followed their way of thinking. The objectives of this deliverable are to assess the transferability of the modelling work of VULCANl (and subsequent updating in DRIVE 11) and SATURN to Portugal. These objectives cover both the transferability of empirically created behavioural sub-models as well as the usefulness of the technology to the practical end user