A Computational Model of the Development of Separate Representations of Facial Identity and Expression in the Primate Visual System

Experimental studies have provided evidence that the visual processing areas of the primate brain represent facial identity and facial expression within different subpopulations of neurons. For example, in non-human primates there is evidence that cells within the inferior temporal gyrus (TE) respond primarily to facial identity, while cells within the superior temporal sulcus (STS) respond to facial expression. More recently, it has been found that the orbitofrontal cortex (OFC) of non-human primates contains some cells that respond exclusively to changes in facial identity, while other cells respond exclusively to facial expression. How might the primate visual system develop physically separate representations of facial identity and expression given that the visual system is always exposed to simultaneous combinations of facial identity and expression during learning? In this paper, a biologically plausible neural network model, VisNet, of the ventral visual pathway is trained on a set of carefully-designed cartoon faces with different identities and expressions. The VisNet model architecture is composed of a hierarchical series of four Self-Organising Maps (SOMs), with associative learning in the feedforward synaptic connections between successive layers. During learning, the network develops separate clusters of cells that respond exclusively to either facial identity or facial expression. We interpret the performance of the network in terms of the learning properties of SOMs, which are able to exploit the statistical indendependence between facial identity and expression

Identifying RR lyrae variable stars in six years of the dark energy survey

We present a search for RR Lyrae stars using the full six-year data set from the Dark Energy Survey covering ∼5000 deg2 of the southern sky. Using a multistage multivariate classification and light-curve template-fitting scheme, we identify RR Lyrae candidates with a median of 35 observations per candidate. We detect 6971 RR Lyrae candidates out to ∼335 kpc, and we estimate that our sample is >70% complete at ∼150 kpc. We find excellent agreement with other wide-area RR Lyrae catalogs and RR Lyrae studies targeting the Magellanic Clouds and other Milky Way satellite galaxies. We fit the smooth stellar halo density profile using a broken-power-law model with fixed halo flattening (q = 0.7), and we find strong evidence for a break at = - R 32.1+ kpc 0 0.9 1.1 with an inner slope of = - - n 2.54+ 1 0.09 0.09 and an outer slope of = - - n 5.42+ 2 0.14 0.13. We use our catalog to perform a search for Milky Way satellite galaxies with large sizes and low luminosities. Using a set of simulated satellite galaxies, we find that our RR Lyrae-based search is more sensitive than those using resolved stellar populations in the regime of large (rh 500 pc), low-surface-brightness dwarf galaxies. A blind search for large, diffuse satellites yields three candidate substructures. The first can be confidently associated with the dwarf galaxy Eridanus II. The second has a distance and proper motion similar to the ultrafaint dwarf galaxy Tucana II but is separated by ∼5 deg. The third is close in projection to the globular cluster NGC 1851 but is ∼10 kpc more distant and appears to differ in proper motion. © 2021 Institute of Physics Publishing. All rights reserved

Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification

Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag) in a single exposure; over 5000 deg2) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (median 4 observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~31% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets

A conceptual model for action and design research

Organizational research has a pattern of special characteristics which make a clear distinction from other research paradigms. When using these approaches – based on Action and Design – the Interpretivist, Constructivist, and Participatory perspectives dominate. They have already proven to have strong foundations, which turn these paradigmatic approaches into effective ways for getting knowledge, doing things, and promoting change within organizational settings. It combines the traditional scientific, engineering, and organization development approaches, depicting how an organization can, simultaneously, solve multidimensional problems and produce actionable knowledge, effective change and useful artifacts. It has been developed using a Design Science Research approach, tested in a major organizational change program (Henriques, 2015; Henriques & ONeill, 2014), and successfully used to teach research methods essentials to Master and DBA students.info:eu-repo/semantics/publishedVersio

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

A Randomized Controlled Trial Comparing the Effects of Counseling and Alarm Device on HAART Adherence and Virologic Outcomes

Michael Chung and colleagues show that intensive early adherence counseling at HAART initiation resulted in sustained, significant impact on adherence and virologic treatment failure, whereas use of an alarm device had no effect

Y Chromosome Lineages in Men of West African Descent

The early African experience in the Americas is marked by the transatlantic slave trade from ∼1619 to 1850 and the rise of the plantation system. The origins of enslaved Africans were largely dependent on European preferences as well as the availability of potential laborers within Africa. Rice production was a key industry of many colonial South Carolina low country plantations. Accordingly, rice plantations owners within South Carolina often requested enslaved Africans from the so-called “Grain Coast” of western Africa (Senegal to Sierra Leone). Studies on the African origins of the enslaved within other regions of the Americas have been limited. To address the issue of origins of people of African descent within the Americas and understand more about the genetic heterogeneity present within Africa and the African Diaspora, we typed Y chromosome specific markers in 1,319 men consisting of 508 west and central Africans (from 12 populations), 188 Caribbeans (from 2 islands), 532 African Americans (AAs from Washington, DC and Columbia, SC), and 91 European Americans. Principal component and admixture analyses provide support for significant Grain Coast ancestry among African American men in South Carolina. AA men from DC and the Caribbean showed a closer affinity to populations from the Bight of Biafra. Furthermore, 30–40% of the paternal lineages in African descent populations in the Americas are of European ancestry. Diverse west African ancestries and sex-biased gene flow from EAs has contributed greatly to the genetic heterogeneity of African populations throughout the Americas and has significant implications for gene mapping efforts in these populations

Representing Where along with What Information in a Model of a Cortical Patch

Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

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