Evidence of a structural anomaly at 14 K in polymerised CsC60

We report the results of a high-resolution synchrotron X-ray powder diffraction study of polymerised CsC$_{60}$ in the temperature range 4 to 40 K. Its crystal structure is monoclinic (space group I2/m), isostructural with RbC$_{60}$. Below 14 K, a spontaneous thermal contraction is observed along both the polymer chain axis, $a$ and the interchain separation along [111], $d_1$. This structural anomaly could trigger the occurrence of the spin-singlet ground state, observed by NMR at the same temperature.Comment: 8 pages, 5 figures, submitte

Bootstrapping navigation and path planning using human positional traces

Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework. © 2013 IEEE

Variable temperature study of the crystal and magnetic structures of the giant magnetoresistant materials LMnAsO (L=La, Nd)

Peer reviewedPublisher PD

Spin-driven Phase Transitions in ZnCr2_2Se4_4 and ZnCr2_2S4_4 Probed by High Resolution Synchrotron X-ray and Neutron Powder Diffraction

The crystal and magnetic structures of the spinel compounds ZnCr$_2$S$_4$ and ZnCr$_2$Se$_4$ were investigated by high resolution powder synchrotron and neutron diffraction. ZnCr$_2$Se$_4$ exhibits a first order phase transition at $T_N=21$ K into an incommensurate helical magnetic structure. Magnetic fluctuations above $T_N$ are coupled to the crystal lattice as manifested by negative thermal expansion. Both, the complex magnetic structure and the anomalous structural behavior can be related to magnetic frustration. Application of an external magnetic field shifts the ordering temperature and the regime of negative thermal expansion towards lower temperatures. Thereby, the spin ordering changes into a conical structure. ZnCr$_2$S$_4$ shows two magnetic transitions at $T_{N1}=15$ K and $T_{N2}=8$ K that are accompanied by structural phase transitions. The crystal structure transforms from the cubic spinel-type (space group $Fd$\={3}$m$) at high temperatures in the paramagnetic state, via a tetragonally distorted intermediate phase (space group $I4_1$ / $amd$) for $T_{N2} < T < T_{N1}$ into a low temperature orthorhombic phase (space group $I m m a$) for $T < T_{N2}$. The cooperative displacement of sulfur ions by exchange striction is the origin of these structural phase transitions. The low temperature structure of ZnCr$_2$S$_4$ is identical to the orthorhombic structure of magnetite below the Verwey transition. When applying a magnetic field of 5 T the system shows an induced negative thermal expansion in the intermediate magnetic phase as observed in ZnCr$_2$Se$_4$.Comment: 11 pages, 13 figures, to be published in PR

Spring School on Language, Music, and Cognition: Organizing Events in Time

The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session. Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations

Action at a distance: Dependency sensitivity in a New World primate

Sensitivity to dependencies (correspondences between distant items) in sensory stimuli plays a crucial role in human music and language. Here, we show that squirrel monkeys (Saimiri sciureus) can detect abstract, non-adjacent dependencies in auditory stimuli. Monkeys discriminated between tone sequences containing a dependency and those lacking it, and generalized to previously unheard pitch classes and novel dependency distances. This constitutes the first pattern learning study where artificial stimuli were designed with the species' communication system in mind. These results suggest that the ability to recognize dependencies represents a capability that had already evolved in humans' last common ancestor with squirrel monkeys, and perhaps before. © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited

Linear fuzzy gene network models obtained from microarray data by exhaustive search

BACKGROUND: Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. RESULTS: We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes. CONCLUSION: Biological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems