Attentional capture by meaning: A multi-level modelling study

We present a computational study of attentional capture by meaning, based on Barnard et al's key-distractor attentional blink task. We highlight a sequence of models, from an abstract black-box to a structurally detailed white-box model. Each of these models reproduces the major findings from the key-distractor blink task. We argue that such multi-level modelling gives greater confidence in the theoretical position encapsulated by these models

pRb-mediated control of epithelial cell proliferation and Indian Hedgehog expression in mouse intestinal development

BACKGROUND: Self-renewal of the epithelium of the small intestine is a highly regulated process involving cell proliferation and differentiation of stem cells or progenitor cells located at the bottom of the crypt, ending ultimately with extrusion of the terminally differentiated cells at the tip of villus. RESULTS: Here, we utilized the Cre/loxP system to investigate the function of the retinoblastoma protein, pRb in intestinal epithelium. pRb null mice displayed a profoundly altered development of the intestine with increased proliferation and abnormal expression of differentiation markers. Loss of pRb induces cell hyperproliferation in the proliferative region (crypt) as well as in the differentiated zone (villi). The absence of pRb further results in an increase in the population of enterocytes, goblet, enteroendocrine and Paneth cells. In addition, differentiated enteroendocrine cells failed to exit the cell cycle in the absence of pRb. These proliferative changes were accompanied by increased expression of Indian hedgehog and activation of hedgehog signals, a known pathway for intestinal epithelial cell proliferation. CONCLUSION: Our studies have revealed a unique function of pRb in intestine development which is critical for controlling not only the proliferation of a stem cell or progenitor cell population but that of terminally differentiated cells as well

A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices

Abstract Land price studies typically employ hedonic analysis to identify the impact of land characteristics on price. Owing to the spatial fixity of land, however, the question of possible spatial dependence in agricultural land prices arises. The presence of spatial dependence in agricultural land prices can have serious consequences for the hedonic model analysis. Ignoring spatial autocorrelation can lead to biased estimates in land price hedonic models. We propose using a flexible quantile regression-based estimation of the spatial lag hedonic model allowing for varying effects of the characteristics and, more importantly, varying degrees of spatial autocorrelation. In applying this approach to a sample of agricultural land sales in Northern Ireland we find that the market effectively consists of two relatively separate segments. The larger of these two segments conforms to the conventional hedonic model with no spatial lag dependence, while the smaller, much thinner market segment exhibits considerable spatial lag dependence. Un mod�le h�donique � r�gression quantile spatiale des prix des terrains agricoles R�sum� Les �tudes sur le prix des terrains font g�n�ralement usage d'une analyse h�donique pour identifier l'impact des caract�ristiques des terrains sur le prix. Toutefois, du fait de la fixit� spatiale des terrains, la question d'une �ventuelle d�pendance spatiale sur la valeur des terrains agricoles se pose. L'existence d'une d�pendance spatiale dans le prix des terrains agricoles peut avoir des cons�quences importantes sur l'analyse du mod�le h�donique. En ignorant cette corr�lation s�rielle, on s'expose au risque d'�valuations biais�es des mod�les h�doniques du prix des terrains. Nous proposons l'emploi d'une estimation � base de r�gression flexible du mod�le h�donique � d�calage spatial, tenant compte de diff�rents effets des caract�ristiques, et surtout de diff�rents degr�s de corr�lations s�rielles spatiales. En appliquant ce principe � un �chantillon de ventes de terrains agricoles en Irlande du Nord, nous d�couvrons que le march� se compose de deux segments relativement distincts. Le plus important de ces deux segments est conforme au mod�le h�donique traditionnel, sans d�pendance du d�calage spatial, tandis que le deuxi�me segment du march�, plus petit et beaucoup plus �troit, pr�sente une d�pendance consid�rable du d�calage spatial. Un modelo hed�nico de regresi�n cuantil espacial de los precios del terreno agr�cola Resumen T�picamente, los estudios del precio de la tierra emplean un an�lisis hed�nico para identificar el impacto de las caracter�sticas de la tierra sobre el precio. No obstante, debido a la fijeza espacial de la tierra, surge la cuesti�n de una posible dependencia espacial en los precios del terreno agr�cola. La presencia de dependencia espacial en los precios del terreno agr�cola puede tener consecuencias graves para el modelo de an�lisis hed�nico. Ignorar la autocorrelaci�n espacial puede conducir a estimados parciales en los modelos hed�nicos del precio de la tierra. Proponemos el uso de una valoraci�n basada en una regresi�n cuantil flexible del modelo hed�nico del lapso espacial que tenga en cuenta los diversos efectos de las caracter�sticas y, particularmente, los diversos grados de autocorrelaci�n espacial. Al aplicar este planteamiento a una muestra de ventas de terreno agr�cola en Irlanda del Norte, descubrimos que el mercado consiste efectivamente de dos segmento relativamente separados. El m�s grande de estos dos segmentos se ajusta al modelo hed�nico convencional sin dependencia del lapso espacial, mientras que el segmento m�s peque�o, y mucho m�s fino, muestra una dependencia considerable del lapso espacial.Spatial lag, quantile regression, hedonic model, C13, C14, C21, Q24,

Conditional Random Fields as Recurrent Neural Networks

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. One central issue in this methodology is the limited capacity of deep learning techniques to delineate visual objects. To solve this problem, we introduce a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To this end, we formulate mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks. This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a deep network that has desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF modelling with CNNs, making it possible to train the whole deep network end-to-end with the usual back-propagation algorithm, avoiding offline post-processing methods for object delineation. We apply the proposed method to the problem of semantic image segmentation, obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark.Comment: This paper is published in IEEE ICCV 201

Feedback first: the surprisingly weak effects of magnetic fields, viscosity, conduction, and metal diffusion on galaxy formation

Using high-resolution simulations with explicit treatment of stellar feedback physics based on the FIRE (Feedback in Realistic Environments) project, we study how galaxy formation and the interstellar medium (ISM) are affected by magnetic fields, anisotropic Spitzer-Braginskii conduction and viscosity, and sub-grid metal diffusion from unresolved turbulence. We consider controlled simulations of isolated (non-cosmological) galaxies but also a limited set of cosmological "zoom-in" simulations. Although simulations have shown significant effects from these physics with weak or absent stellar feedback, the effects are much weaker than those of stellar feedback when the latter is modeled explicitly. The additional physics have no systematic effect on galactic star formation rates (SFRs) . In contrast, removing stellar feedback leads to SFRs being over-predicted by factors of $\sim 10 -100$. Without feedback, neither galactic winds nor volume filling hot-phase gas exist, and discs tend to runaway collapse to ultra-thin scale-heights with unphysically dense clumps congregating at the galactic center. With stellar feedback, a multi-phase, turbulent medium with galactic fountains and winds is established. At currently achievable resolutions and for the investigated halo mass range $10^{10}-10^{13} M_{\odot}$, the additional physics investigated here (MHD, conduction, viscosity, metal diffusion) have only weak ($\sim10\%$-level) effects on regulating SFR and altering the balance of phases, outflows, or the energy in ISM turbulence, consistent with simple equipartition arguments. We conclude that galactic star formation and the ISM are primarily governed by a combination of turbulence, gravitational instabilities, and feedback. We add the caveat that AGN feedback is not included in the present work

NASA's Planned Return to the Moon: Global Access and Anytime Return Requirement Implications on the Lunar Orbit Insertion Burns

Lunar orbit insertion LOI is a critical maneuver for any mission going to the Moon. Optimizing the geometry of this maneuver is crucial to the success of the architecture designed to return humans to the Moon. LOI burns necessary to meet current NASA Exploration Constellation architecture requirements for the lunar sortie missions are driven mainly by the requirement for global access and "anytime" return from the lunar surface. This paper begins by describing the Earth-Moon geometry which creates the worst case (delta)V for both the LOI and the translunar injection (TLI) maneuvers over the full metonic cycle. The trajectory which optimizes the overall (delta)V performance of the mission is identified, trade studies results covering the entire lunar globe are mapped onto the contour plots, and the effects of loitering in low lunar orbit as a means of reducing the insertion (delta)V are described. Finally, the lighting conditions on the lunar surface are combined with the LOI and TLI analyses to identify geometries with ideal lighting conditions at sites of interest which minimize the mission (delta)V

But What About... Cosmic Rays, Magnetic Fields, Conduction, & Viscosity in Galaxy Formation

We present a suite of high-resolution cosmological simulations, using the FIRE-2 feedback physics together with explicit treatment of magnetic fields, anisotropic conduction and viscosity, and cosmic rays (CRs) injected by supernovae (including anisotropic diffusion, streaming, adiabatic, hadronic and Coulomb losses). We survey systems from ultra-faint dwarf ($M_{\ast}\sim 10^{4}\,M_{\odot}$, $M_{\rm halo}\sim 10^{9}\,M_{\odot}$) through Milky Way masses, systematically vary CR parameters (e.g. the diffusion coefficient $\kappa$ and streaming velocity), and study an ensemble of galaxy properties (masses, star formation histories, mass profiles, phase structure, morphologies). We confirm previous conclusions that magnetic fields, conduction, and viscosity on resolved ($\gtrsim 1\,$pc) scales have small effects on bulk galaxy properties. CRs have relatively weak effects on all galaxy properties studied in dwarfs ($M_{\ast} \ll 10^{10}\,M_{\odot}$, $M_{\rm halo} \lesssim 10^{11}\,M_{\odot}$), or at high redshifts ($z\gtrsim 1-2$), for any physically-reasonable parameters. However at higher masses ($M_{\rm halo} \gtrsim 10^{11}\,M_{\odot}$) and $z\lesssim 1-2$, CRs can suppress star formation by factors $\sim 2-4$, given relatively high effective diffusion coefficients $\kappa \gtrsim 3\times10^{29}\,{\rm cm^{2}\,s^{-1}}$. At lower $\kappa$, CRs take too long to escape dense star-forming gas and lose energy to hadronic collisions, producing negligible effects on galaxies and violating empirical constraints from $\gamma$-ray emission. But around $\kappa\sim 3\times10^{29}\,{\rm cm^{2}\,s^{-1}}$, CRs escape the galaxy and build up a CR-pressure-dominated halo which supports dense, cool ($T\ll 10^{6}$ K) gas that would otherwise rain onto the galaxy. CR heating (from collisional and streaming losses) is never dominant.Comment: 35 pages, 23 figures. Updated to match published (MNRAS) versio