Quantum Hole Digging in Magnetic Molecular Clusters

Below 360 mK, Fe8 magnetic molecular clusters are in the pure quantum relaxation regime. We showed recently that the predicted ``square-root time'' relaxation is obeyed, allowing us to develop a new method for watching the evolution of the distribution of molecular spin states in the sample. We measured the distribution P(H) of molecules which are in resonance at the applied field H. Tunnelling initially causes rapid transitions of molecules, thereby ``digging a hole'' in P(H). For small initial magnetisation values, the hole width shows an intrinsic broadening which may be due to nuclear spins. We present here hole digging measurements in the thermal activated regime which may allow to study the effect of spin-phonon coupling.Comment: 3 pages, 2 figures, conference proceedings of LT22 (Helsinki, Finland, August 4-11, 1999

Answer to the comment of Chudnovsky: On the square-root time relaxation in molecular nanomagnets

Answer to the comment of E. Chudnovsky concerning the following papers: (1) N.V. Prokof'ev, P.C.E. Stamp, Phys. Rev. Lett.80, 5794 (1998). (2) W. Wernsdorfer, T. Ohm, C. Sangregorio, R. Sessoli, D. Mailly, C. Paulsen, Phys. Rev. Lett. 82, 3903 (1999).Comment: 1 page

H.E.S.S. observations of the Large Magellanic Cloud

The Large Magellanic Cloud (LMC) is a satellite galaxy of the Milky Way at a distance of approximately 48 kpc. Despite its distance it harbours several interesting targets for TeV gamma-ray observations. The composite supernova remnant N 157B/PSR J05367-6910 was discovered by H.E.S.S. being an emitter of very high energy (VHE) gamma-rays. It is the most distant pulsar wind nebula ever detected in VHE gamma-rays. Another very exciting target is SN 1987A, the remnant of the most recent supernova explosion that occurred in the neighbourhood of the Milky Way. Models for Cosmic Ray acceleration in this remnant predict gamma-ray emission at a level detectable by H.E.S.S. but this has not been detected so far. Fermi/LAT discovered diffuse high energy (HE) gamma-ray emission from the general direction of the massive star forming region 30 Doradus but no clear evidence for emission from either N 157B or SN 1987A has been published. The part of the LMC containing these objects has been observed regularly with the H.E.S.S. telescopes since 2003. With deep observations carried out in 2010 a very good exposure of this part of the sky has been obtained. The current status of the H.E.S.S. LMC observations is reported along with new results on N 157B and SN 1987A.Comment: 4 pages, 3 figures, proceedings of the 32nd Internatioal Cosmic Ray Conference, Beijing 201

Proton Spin Relaxation Induced by Quantum Tunneling in Fe8 Molecular Nanomagnet

The spin-lattice relaxation rate $T_{1}^{-1}$ and NMR spectra of $^1$H in single crystal molecular magnets of Fe8 have been measured down to 15 mK. The relaxation rate $T_1^{-1}$ shows a strong temperature dependence down to 400 mK. The relaxation is well explained in terms of the thermal transition of the iron state between the discreet energy levels of the total spin S=10. The relaxation time $T_1$ becomes temperature independent below 300 mK and is longer than 100 s. In this temperature region stepwise recovery of the $^1$H-NMR signal after saturation was observed depending on the return field of the sweep field. This phenomenon is attributed to resonant quantum tunneling at the fields where levels cross and is discussed in terms of the Landau-Zener transition.Comment: 13 pages, 5 figure

Modelling the transport of oil after a proposed oil spill accident in Barents Sea and its environmental impact on Alke species

OA Green publisher. Can archive pre-print and post-print or publisher's version/PDF. Link to publisher's version: http://doi.org/10.1088/1755-1315/82/1/012010Accidental oil spills can have significant effect on the coastal and marine environment. As the oil extraction and exploration activities increase in the Barents Sea, it is of increasingly importance to investigate the potential oil spill incidents associated with these activities. In this study, the transport and fate of oil after a proposed oil spill incident in Barents Sea was modelled by oil spill contingency and response model OSCAR. The possibility that the spilled oil reach the open sea and the strand area was calculated respectively. The influence area of the incident was calculated by combining the results from 200 simulations. The possibility that the spilled oil reach Alke species, a vulnerable species and on the National Red List of birds in Barents Sea, was analyzed by combining oil spill modelling results and the Alke species distribution data. The results showed that oil is dominated with a probability of 70-100% in the open sea to reach an area in a radius of 20km from the release location after 14 days of release. The probability reduces with the increasing distances from the release location. It is higher possibility that the spilled oil will reach the Alke species in the strand area than in the open sea in the summer. The total influence area of the release is 11 429 km2 for the surface water and 1528 km2 for the coastal area

Graph Neural Networks and Applied Linear Algebra

Sparse matrix computations are ubiquitous in scientific computing. With the recent interest in scientific machine learning, it is natural to ask how sparse matrix computations can leverage neural networks (NN). Unfortunately, multi-layer perceptron (MLP) neural networks are typically not natural for either graph or sparse matrix computations. The issue lies with the fact that MLPs require fixed-sized inputs while scientific applications generally generate sparse matrices with arbitrary dimensions and a wide range of nonzero patterns (or matrix graph vertex interconnections). While convolutional NNs could possibly address matrix graphs where all vertices have the same number of nearest neighbors, a more general approach is needed for arbitrary sparse matrices, e.g. arising from discretized partial differential equations on unstructured meshes. Graph neural networks (GNNs) are one approach suitable to sparse matrices. GNNs define aggregation functions (e.g., summations) that operate on variable size input data to produce data of a fixed output size so that MLPs can be applied. The goal of this paper is to provide an introduction to GNNs for a numerical linear algebra audience. Concrete examples are provided to illustrate how many common linear algebra tasks can be accomplished using GNNs. We focus on iterative methods that employ computational kernels such as matrix-vector products, interpolation, relaxation methods, and strength-of-connection measures. Our GNN examples include cases where parameters are determined a-priori as well as cases where parameters must be learned. The intent with this article is to help computational scientists understand how GNNs can be used to adapt machine learning concepts to computational tasks associated with sparse matrices. It is hoped that this understanding will stimulate data-driven extensions of classical sparse linear algebra tasks

Ant-infecting Ophiocordyceps genomes reveal a high diversity of potential behavioral manipulation genes and a possible major role for enterotoxins

Much can be gained from revealing the mechanisms fungal entomopathogens employ. Especially intriguing are fungal parasites that manipulate insect behavior because, presumably, they secrete a wealth of bioactive compounds. To gain more insight into their strategies, we compared the genomes of five ant-infecting Ophiocordyceps species from three species complexes. These species were collected across three continents, from five different ant species in which they induce different levels of manipulation. A considerable number of (small) secreted and pathogenicity-related proteins were only found in these ant-manipulating Ophiocordyceps species, and not in other ascomycetes. However, few of those proteins were conserved among them, suggesting that several different methods of behavior modification have evolved. This is further supported by a relatively fast evolution of previously reported candidate manipulation genes associated with biting behavior. Moreover, secondary metabolite clusters, activated during biting behavior, appeared conserved within a species complex, but not beyond. The independent co-evolution between these manipulating parasites and their respective hosts might thus have led to rather diverse strategies to alter behavior. Our data indicate that specialized, secreted enterotoxins may play a major role in one of these strategies

Genome sequence of the button mushroom Agaricus bisporus reveals mechanisms governing adaptation to a humic-rich ecological niche

Agaricus bisporus is the model fungus for the adaptation, persistence, and growth in the humic-rich leaf-litter environment. Aside from its ecological role, A. bisporus has been an important component of the human diet for over 200 y and worldwide cultivation of the "button mushroom" forms a multibillion dollar industry. We present two A. bisporus genomes, their gene repertoires and transcript profiles on compost andduringmushroomformation.The genomes encode a full repertoire of polysaccharide-degrading enzymes similar to that of wood-decayers. Comparative transcriptomics of mycelium grown on defined medium, casing-soil, and compost revealed genes encoding enzymes involved in xylan, cellulose, pectin, and protein degradation aremore highly expressed in compost. The striking expansion of heme-thiolate peroxidases and β-etherases is distinctive from Agaricomycotina wood-decayers and suggests a broad attack on decaying lignin and related metabolites found in humic acid-rich environment. Similarly, up-regulation of these genes together with a lignolytic manganese peroxidase, multiple copper radical oxidases, and cytochrome P450s is consistent with challenges posed by complex humic-rich substrates. The gene repertoire and expression of hydrolytic enzymes in A. bisporus is substantially different from the taxonomically related ectomycorrhizal symbiont Laccaria bicolor. A common promoter motif was also identified in genes very highly expressed in humic-rich substrates. These observations reveal genetic and enzymatic mechanisms governing adaptation to the humic-rich ecological niche formed during plant degradation, further defining the critical role such fungi contribute to soil structure and carbon sequestration in terrestrial ecosystems. Genome sequence will expedite mushroom breeding for improved agronomic characteristics