Haydeeite: a spin-1/2 kagome ferromagnet

The mineral haydeeite, alpha-MgCu3(OD)6Cl2, is a S=1/2 kagome ferromagnet that displays long-range magnetic order below TC=4.2 K with a strongly reduced moment. Our inelastic neutron scattering data show clear spin-wave excitations that are well described by a Heisenberg Hamiltonian with ferromagnetic nearest-neighbor exchange J1=-38 K and antiferromagnetic exchange Jd=+11 K across the hexagons of the kagome lattice. These values place haydeeite very close to the quantum phase transition between ferromagnetic order and non-coplanar twelve-sublattice cuboc2 antiferromagnetic order. Diffuse dynamic short-range ferromagnetic correlations observed above TC persist well into the ferromagnetically ordered phase with a behavior distinct from critical scattering

Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures

A method of simultaneously optimizing both the structure of neural networks and the connection weights in a single training loop can reduce the enormous computational cost of neural architecture search. We focus on the probabilistic model-based dynamic neural network structure optimization that considers the probability distribution of structure parameters and simultaneously optimizes both the distribution parameters and connection weights based on gradient methods. Since the existing algorithm searches for the structures that only minimize the training loss, this method might find overly complicated structures. In this paper, we propose the introduction of a penalty term to control the model complexity of obtained structures. We formulate a penalty term using the number of weights or units and derive its analytical natural gradient. The proposed method minimizes the objective function injected the penalty term based on the stochastic gradient descent. We apply the proposed method in the unit selection of a fully-connected neural network and the connection selection of a convolutional neural network. The experimental results show that the proposed method can control model complexity while maintaining performance.Comment: Accepted as a conference paper at the 28th International Conference on Artificial Neural Networks (ICANN 2019). The final authenticated publication will be available in the Springer Lecture Notes in Computer Science (LNCS). 13 page

An age-dependent branching process model for the analysis of CFSE-labeling experiments

<p>Abstract</p> <p>Background</p> <p>Over the past decade, flow cytometric CFSE-labeling experiments have gained considerable popularity among experimentalists, especially immunologists and hematologists, for studying the processes of cell proliferation and cell death. Several mathematical models have been presented in the literature to describe cell kinetics during these experiments.</p> <p>Results</p> <p>We propose a multi-type age-dependent branching process to model the temporal development of populations of cells subject to division and death during CFSE-labeling experiments. We discuss practical implementation of the proposed model; we investigate a competing risk version of the process; and we identify the classes of cellular dependencies that may influence the expectation of the process and those that do not. An application is presented where we study the proliferation of human CD8+ T lymphocytes using our model and a competing risk branching process.</p> <p>Conclusions</p> <p>The proposed model offers a widely applicable approach to the analysis of CFSE-labeling experiments. The model fitted very well our experimental data. It provided reasonable estimates of cell kinetics parameters as well as meaningful insights into the processes of cell division and cell death. In contrast, the competing risk branching process could not describe the kinetics of CD8+ T cells. This suggested that the decision of cell division or cell death may be made early in the cell cycle if not in preceding generations. Also, we show that analyses based on the proposed model are robust with respect to cross-sectional dependencies and to dependencies between fates of linearly filiated cells.</p> <p>Reviewers</p> <p>This article was reviewed by Marek Kimmel, Wai-Yuan Tan and Peter Olofsson.</p

Vesignieite: a S=12S = \frac{1}{2} kagome antiferromagnet with dominant third-neighbor exchange

The spin-$\frac{1}{2}$ kagome antiferromagnet is an archetypal frustrated system predicted to host a variety of exotic magnetic states. We show using neutron scattering measurements that deuterated vesignieite BaCu$_{3}$V$_{2}$O$_{8}$(OD)$_{2}$, a fully stoichiometric $S=1/2$ kagome magnet with $<$1% lattice distortion, orders magnetically at $T_{\mathrm{N}}=9$K into a multi-k coplanar variant of the predicted triple-k octahedral structure. We find this structure is stabilized by a dominant antiferromagnetic 3$^{\mathrm{rd}}$-neighbor exchange $J_3$ with minor 1$^{\mathrm{st}}$- or 2$^{\mathrm{nd}}$--neighbour exchange. The spin-wave spectrum is well described by a $J_3$-only model including a tiny symmetric exchange anisotropy

Absence of strong magnetic fluctuations in the iron phosphide superconductors LaFePO and Sr2ScO3FeP

We report neutron inelastic scattering measurements on polycrystalline LaFePO and Sr2ScO3FeP, two members of the iron phosphide families of superconductors. No evidence is found for any magnetic fluctuations in the spectrum of either material in the energy and wavevector ranges probed. Special attention is paid to the wavevector at which spin-density-wave-like fluctuations are seen in other iron-based superconductors. We estimate that the magnetic signal, if present, is at least a factor of four (Sr2ScO3FeP) or seven (LaFePO) smaller than in the related iron arsenide and chalcogenide superconductors. These results suggest that magnetic fluctuations are not as influential on the electronic properties of the iron phosphide systems as they are in other iron-based superconductors.Comment: 7 pages, 5 figure

BioJazz : In silico evolution of cellular networks with unbounded complexity using rule-based modeling

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx