The constrained E6SSM

We discuss the predictions of a constrained version of the exceptional supersymmetric standard model (cE6SSM), with a universal high energy soft scalar mass, soft trilinear coupling and soft gaugino mass. The spectrum includes a light gluino, a light wino-like neutralino and chargino pair and a light bino-like neutralino, with other sparticle masses except the lighter stop being much heavier. We also discuss scenarios with an extra light exotic colour triplet of fermions and scalars and a TeV scale Z', which lead to early exotic physics signals at the LHC.Comment: To appear in proceedings of The 2009 Europhysics Conference on High Energy Physics, 16-22 July 2009 Krakow, Poland; 4 page

Resonant optical control of the structural distortions that drive ultrafast demagnetization in Cr2_2O3_3

We study how the color and polarization of ultrashort pulses of visible light can be used to control the demagnetization processes of the antiferromagnetic insulator Cr$_2$O$_3$. We utilize time-resolved second harmonic generation (SHG) to probe how changes in the magnetic and structural state evolve in time. We show that, varying the pump photon-energy to excite either localized transitions within the Cr or charge transfer states, leads to markedly different dynamics. Through a full polarization analysis of the SHG signal, symmetry considerations and density functional theory calculations, we show that, in the non-equilibrium state, SHG is sensitive to {\em both} lattice displacements and changes to the magnetic order, which allows us to conclude that different excited states couple to phonon modes of different symmetries. Furthermore, the spin-scattering rate depends on the induced distortion, enabling us to control the timescale for the demagnetization process. Our results suggest that selective photoexcitation of antiferromagnetic insulators allows fast and efficient manipulation of their magnetic state.Comment: 7 pages, 5 figure

Phylogeny structures species' interactions in experimental ecological communities

Species' traits and interactions are products of evolutionary history. Despite the long-standing hypothesis that closely related species possess similar traits, and thus experience stronger competition, measuring the effect of evolutionary history on the ecology of natural communities remains challenging. We propose a novel framework to test whether phylogeny influences patterns of coexistence and abundance of species assemblages. In our approach, phylogenetic trees are used to parameterize species' interactions, which in turn determine the abundance of species in a given assemblage. We use likelihoods to score models parameterized with a given phylogeny, and contrast them with models built using random trees, allowing us to test whether phylogenetic information helps to predict species' abundances. Our statistical framework reveals that interactions are indeed structured by phylogeny in a large set of experimental plant communities. Our results confirm that evolutionary history can help predict, and potentially manage or conserve, the structure and function of complex ecological communities

Does player specialization predict player actions? Evidence from penalty kicks at FIFA World Cup and UEFA Euro Cup

Penalty-kicks are analysed in the literature as `real life experiments' for assessing the use of rational mixed strategies by professional players. However, each penalty kick cannot be considered a repetition of the same event because of the varying background conditions, in particular the heterogeneous ability of different players. Consequently, aggregate statistics over datasets composed of a large number of penalty kicks mediate the behaviour of the players in \emph{different} games, and the properties of optimal mixed strategies cannot be tested directly because of \emph{aggregation bias}. In this paper we model the heterogeneous ability of players. We then test the hypothesis that differently talented players randomise over different actions. To this aim, we study a dataset that collects penalties kicked during shootout series in the last editions of FIFA World-Cup and UEFA Euro-Cup (1994-2012) where kickers are categorized as specialists and non-specialists. The results support our theoretical prediction

Gamma Rays from Star Formation in Clusters of Galaxies

Star formation in galaxies is observed to be associated with gamma-ray emission. The detection of gamma rays from star-forming galaxies by the Fermi Large Area Telescope (LAT) has allowed the determination of a functional relationship between star formation rate and gamma-ray luminosity (Ackermann et. al. 2012). Since star formation is known to scale with total infrared (8-1000 micrometers) and radio (1.4 GHz) luminosity, the observed infrared and radio emission from a star-forming galaxy can be used to quantitatively infer the galaxy's gamma-ray luminosity. Similarly, star forming galaxies within galaxy clusters allow us to derive lower limits on the gamma-ray emission from clusters, which have not yet been conclusively detected in gamma rays. In this study we apply the relationships between gamma-ray luminosity and radio and IR luminosities derived in Ackermann et. al. 2012 to a sample of galaxy clusters from Ackermann et. al. 2010 in order to place lower limits on the gamma-ray emission associated with star formation in galaxy clusters. We find that several clusters have predicted lower limits on gamma-ray emission that are within an order of magnitude of the upper limits derived in Ackermann et. al. 2010 based on non-detection by Fermi-LAT. Given the current gamma-ray limits, star formation likely plays a significant role in the gamma-ray emission in some clusters, especially those with cool cores. We predict that both Fermi-LAT over the course of its lifetime and the future Cherenkov Telescope Array will be able to detect gamma-ray emission from star-forming galaxies in clusters.Comment: 17 pages, 2 figures, 2 tables. Minor revisions made to match version accepted to Ap

Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of a smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper showing the variability of noise-related distractions, thermal comfort, and associated context. The results show people experience at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants' needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.Comment: Accepted at the CISBAT 2023 The Built Environment in Transition, Hybrid International Conference, EPFL, Lausanne, Switzerland, 13-15 September 202

A faint red stellar halo around an edge-on disc galaxy in the Hubble Ultra Deep Field

We analyse the detailed structure of a highly-inclined (i>~80 degrees) disc galaxy which lies within the Hubble Ultra Deep Field (UDF). The unprecedented depth of the UDF data allow disc and extraplanar emission to be reliably traced to surface brightness levels of mu_{V,i,z}~29-30 mag/arcsec^2 (corresponding to rest-frame equivalents of mu_{g,r,i}~28-29 mag/arcsec^2) in this redshift z=0.32 system. We detect excess emission above the disc which is characterised by a moderately-flattened (b/a~0.6) power-law (I proportional to R^(-2.6)). The structure and colour of this component are very similar to the stellar halo detected in an SDSS stacking analysis of local disc galaxies (Zibetti, White and Brinkmann 2004) and lend support to the idea that we have detected a stellar halo in this distant system. Although the peculiar colours of the halo are difficult to understand in terms of normal stellar populations, the consistency found between the UDF and SDSS analyses suggests that they cannot be easily discounted.Comment: 5 pages, 5 figures, accepted for publication in MNRAS Letters. Figure 1 substantially degraded, full resolution version available at http://www.mpa-garching.mpg.de/~zibetti/UDFhalo.pd

Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training