Measurement of the Temperature Dependence of the Casimir-Polder Force

We report on the first measurement of a temperature dependence of the Casimir-Polder force. This measurement was obtained by positioning a nearly pure 87-Rb Bose-Einstein condensate a few microns from a dielectric substrate and exciting its dipole oscillation. Changes in the collective oscillation frequency of the magnetically trapped atoms result from spatial variations in the surface-atom force. In our experiment, the dielectric substrate is heated up to 605 K, while the surrounding environment is kept near room temperature (310 K). The effect of the Casimir-Polder force is measured to be nearly 3 times larger for a 605 K substrate than for a room-temperature substrate, showing a clear temperature dependence in agreement with theory.Comment: 4 pages, 4 figures, published in Physical Review Letter

UNBODY: A Poetry Escape Room in Augmented Reality

The integration of augmented reality (AR) technology into personal computing is happening fast, and augmented workplaces for professionals in areas such as Industry 4.0 or digital health can reasonably be expected to form liminal zones that push the boundary of what currently possible. The application potential in the creative industries, however, is vast and can target broad audiences, so with UNBODY, we set out to push boundaries of a different kind and depart from the graphic-centric worlds of AR to explore textual and aural dimensions of an extended reality, in which words haunt and re-create our physical selves. UNBODY is an AR installation for smart glasses that embeds poetry in the user’s surroundings. The augmented experience turns reality into a medium where holographic texts and film clips spill from dayglow billboards and totems. In this paper, we develop a blueprint for an AR escape room dedicated to the spoken and written word, with its open source code facilitating uptake by others into existing or new AR escape rooms. We outline the user-centered process of designing, building, and evaluating UNBODY. More specifically, we deployed a system usability scale (SUS) and a spatial interaction evaluation (SPINE) in order to validate its wider applicability. In this paper, we also describe the composition and concept of the experience, identifying several components (trigger posters, posters with video overlay, word dropper totem, floating object gallery, and a user trail visualization) as part of our first version before evaluation. UNBODY provides a sense of situational awareness and immersivity from inside an escape room. The recorded average mean for the SUS was 59.7, slightly under the recommended 68 average but still above ‘OK’ in the zone of low marginal acceptable. The findings for the SPINE were moderately positive, with the highest scores for output modalities and navigation support. This indicated that the proposed components and escape room concept work. Based on these results, we improved the experience, adding, among others, an interactive word composer component. We conclude that a poetry escape room is possible, outline our co-creation process, and deliver an open source technical framework as a blueprint for adding enhanced support for the spoken and written word to existing or coming AR escape room experiences. In an outlook, we discuss additional insight on timing, alignment, and the right level of personalization

CSI : A nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data

How an organism responds to the environmental challenges it faces is heavily influenced by its gene regulatory networks (GRNs). Whilst most methods for inferring GRNs from time series mRNA expression data are only able to cope with single time series (or single perturbations with biological replicates), it is becoming increasingly common for several time series to be generated under different experimental conditions. The CSI algorithm (Klemm, 2008) represents one approach to inferring GRNs from multiple time series data, which has previously been shown to perform well on a variety of datasets (Penfold and Wild, 2011). Another challenge in network inference is the identification of condition specific GRNs i.e., identifying how a GRN is rewired under different conditions or different individuals. The Hierarchical Causal Structure Identification (HCSI) algorithm (Penfold et al., 2012) is one approach that allows inference of condition specific networks (Hickman et al., 2013), that has been shown to be more accurate at reconstructing known networks than inference on the individual datasets alone. Here we describe a MATLAB implementation of CSI/HCSI that includes fast approximate solutions to CSI as well as Markov Chain Monte Carlo implementations of both CSI and HCSI, together with a user-friendly GUI, with the intention of making the analysis of networks from multiple perturbed time series datasets more accessible to the wider community.1 The GUI itself guides the user through each stage of the analysis, from loading in the data, to parameter selection and visualisation of networks, and can be launched by typing >> csi into the MATLAB command line. For each step of the analysis, links to documentation and tutorials are available within the GUI, which includes documentation on visualisation and interacting with output file

First-order thermal correction to the quadratic response tensor and rate for second harmonic plasma emission

Three-wave interactions in plasmas are described, in the framework of kinetic theory, by the quadratic response tensor (QRT). The cold-plasma QRT is a common approximation for interactions between three fast waves. Here, the first-order thermal correction (FOTC) to the cold-plasma QRT is derived for interactions between three fast waves in a warm unmagnetized collisionless plasma, whose particles have an arbitrary isotropic distribution function. The FOTC to the cold-plasma QRT is shown to depend on the second moment of the distribution function, the phase speeds of the waves, and the interaction geometry. Previous calculations of the rate for second harmonic plasma emission (via Langmuir-wave coalescence) assume the cold-plasma QRT. The FOTC to the cold-plasma QRT is used here to calculate the FOTC to the second harmonic emission rate, and its importance is assessed in various physical situations. The FOTC significantly increases the rate when the ratio of the Langmuir phase speed to the electron thermal speed is less than about 3.Comment: 11 pages, 2 figures, submitted to Physics of Plasma

Discovering transcriptional modules by Bayesian data integration

Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. Results: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs

Galaxy And Mass Assembly (GAMA) : The mechanisms for quiescent galaxy formation at z<1

© 2016 The Authors. One key problem in astrophysics is understanding how and why galaxies switch off their star formation, building the quiescent population that we observe in the local Universe. From the Galaxy And Mass Assembly and VIsible MultiObject Spectrograph Public Extragalactic Redshift surveys, we use spectroscopic indices to select quiescent and candidate transition galaxies.We identify potentially rapidly transitioning post-starburst (PSB) galaxies and slower transitioning green-valley galaxies. Over the last 8Gyr, the quiescent population has grown more slowly in number density at high masses (M * > 10 11 M ⊙ ) than at intermediate masses (M * > 10 10.6 M ⊙ ). There is evolution in both the PSB and green-valley stellar mass functions, consistent with higher mass galaxies quenching at earlier cosmic times.At intermediatemasses (M * > 10 10.6 M ⊙ ), we find a green-valley transition time-scale of 2.6 Gyr. Alternatively, at z ~ 0.7, the entire growth rate could be explained by fast-quenching PSB galaxies, with a visibility time-scale of 0.5 Gyr. At lower redshift, the number density of PSBs is so low that an unphysically short visibility window would be required for them to contribute significantly to the quiescent population growth. The importance of the fast-quenching route may rapidly diminish at z 10 11 M ⊙ ), there is tension between the large number of candidate transition galaxies compared to the slow growth of the quiescent population. This could be resolved if not all high-mass PSB and green-valley galaxies are transitioning from star forming to quiescent, for example if they rejuvenate out of the quiescent population following the accretion of gas and triggering of star formation, or if they fail to completely quench their star formation

Multi-network-based diffusion analysis reveals vertical cultural transmission of sponge tool use within dolphin matrilines

Behavioural differences among social groups can arise from differing ecological conditions, genetic predispositions and/or social learning. In the past, social learning has typically been inferred as responsible for the spread of behaviour by the exclusion of ecological and genetic factors. This ‘method of exclusion’ was used to infer that ‘sponging’, a foraging behaviour involving tool use in the bottlenose dolphin (Tursiops aduncus) population in Shark Bay, Western Australia, was socially transmitted. However, previous studies were limited in that they never fully accounted for alternative factors, and that social learning, ecology and genetics are not mutually exclusive in causing behavioural variation. Here, we quantified the importance of social learning on the diffusion of sponging, for the first time explicitly accounting for ecological and genetic factors, using a multi-network version of ‘network-based diffusion analysis'. Our results provide compelling support for previous findings that sponging is vertically socially transmitted from mother to (primarily female) offspring. This research illustrates the utility of social network analysis in elucidating the explanatory mechanisms behind the transmission of behaviour in wild animal populations