Thermoelectric power quantum oscillations in the ferromagnet UGe2_2

We present thermoelectric power and resistivity measurements in the ferromagnet UGe$_2$ as a function of temperature and magnetic field. At low temperature, huge quantum oscillations are observed in the thermoelectric power as a function of the magnetic field applied along the $a$ axis. The frequencies of the extreme orbits are determined and an analysis of the cyclotron masses is performed following different theoretical approaches for quantum oscillations detected in the thermoelectric power. They are compared to those obtained by Shubnikov-de Haas experiments on the same crystal and previous de Haas-van Alphen experiments. The agreement of the different probes confirms thermoelectric power as an excellent probe to extract simultaneously both microscopic and macroscopic information on the Fermi-surface properties. Band-structure calculations of UGe$_2$ in the ferromagnetic state are compared to the experiment.Comment: 10 figures, 12 pages, accepted for publication in Phys. Rev.

Keyboards as a New Model of Computation

We introduce a new formalisation of language computation, called keyboards. We consider a set of atomic operations (writing a letter, erasing a letter, going to the right or to the left) and we define a keyboard as a set of finite sequences of such operations, called keys. The generated language is the set of words obtained by applying some non-empty sequence of those keys. Unlike classical models of computation, every key can be applied anytime. We define various classes of languages based on different sets of atomic operations, and compare their expressive powers. We also compare them to rational, context-free and context-sensitive languages. We obtain a strict hierarchy of classes, whose expressiveness is orthogonal to the one of the aforementioned classical models. We also study closure properties of those classes, as well as fundamental complexity problems on keyboards

Dissolved oxygen dynamics during a phytoplankton bloom in the Ross Sea polynya

The Ross Sea polynya is one of the most productive regions in the Southern Ocean. However, limited access and high spatio-temporal variability of physical and biological processes limit the use of conventional oceanographic methods to measure early season primary productivity. High-resolution observations from two Seagliders provide insights into the timing of a bloom in the southern Ross Sea polynya in December 2010. Changes in chlorophyll and oxygen concentrations are used to assess bloom dynamics. Using a ratio of dissolved oxygen to carbon, net primary production is estimated over the duration of the bloom showing a sensitive balance between net autotrophy and heterotrophy. The two gliders, observing spatially distinct regions during the same period, found net community production rates of -0.9±0.7 and 0.7±0.4 g C m-2 d-1. The difference highlights the spatial variability of biological processes and is probably caused by observing different stages of the bloom. The challenge of obtaining accurate primary productivity estimates highlights the need for increased observational efforts, particularly focusing on subsurface processes not resolved using surface or remote observations. Without an increased observational effort and the involvement of emerging technologies, it will not be possible to determine the seasonal trophic balance of the Ross Sea polynya and quantify the shelf's importance in carbon export

Short Flip Sequences to Untangle Segments in the Plane

A (multi)set of segments in the plane may form a TSP tour, a matching, a tree, or any multigraph. If two segments cross, then we can reduce the total length with the following flip operation. We remove a pair of crossing segments, and insert a pair of non-crossing segments, while keeping the same vertex degrees. The goal of this paper is to devise efficient strategies to flip the segments in order to obtain crossing-free segments after a small number of flips. Linear and near-linear bounds on the number of flips were only known for segments with endpoints in convex position. We generalize these results, proving linear and near-linear bounds for cases with endpoints that are not in convex position. Our results are proved in a general setting that applies to multiple problems, using multigraphs and the distinction between removal and insertion choices when performing a flip.Comment: 19 pages, 10 figure

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou

Polarimetric variations of binary stars. III Periodic polarimetric variations of the Herbig Ae/Be star MWC 1080

We present polarimetric observations of a massive pre-main sequence short-period binary star of the Herbig Ae/Be type, MWC 1080. The mean polarization at 7660 A is 1.60% at 81.6 deg, or 0.6% at 139 deg if an estimate of the interstellar polarization is subtracted. The intrinsic polarization points to an asymmetric geometry of the circumstellar or circumbinary environment while the 139 deg intrinsic position angle traces the axis of symmetry of the system and is perpendicular to the position angle of the outflow cavity. The polarization and its position angle are clearly variable, at all wavelengths, and on time scales of hours, days, months, and years. Stochastic variability is accompanied by periodic variations caused by the orbital motion of the stars in their dusty environment. These periodic polarimetric variations are the first phased-locked ones detected for a pre-main sequence binary. The variations are not simply double-periodic (seen twice per orbit) but include single-periodic (seen once per orbit) and higher-order variations. The presence of single-periodic variations could be due to non equal mass stars, the presence of dust grains, an asymmetric configuration of the circumstellar or circumbinary material, or the eccentricity of the orbit. MWC 1080 is an eclipsing binary with primary and secondary eclipses occurring at phases 0.0 and 0.55. The signatures of the eclipses are seen in the polarimetric observations.Comment: 30 pages, 8 figures, to be published in the Astronomical Journa

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

New Candidate Interstellar Particle in Stardust IS Aerogel Collector: Analysis by STXM and Ptychography

The Stardust Interstellar Preliminary Examination (ISPE) reported in 2014 the discovery of 7 probable contemporary interstellar (IS) particles captured in Stardust IS Collector aerogel and foils. The ISPE reports represented work done over 6 years by more than 60 scientists and >30,000 volunteers, which emphasizes the challenge identifying and analyzing Stardust IS samples was far beyond the primary Stardust cometary collection. We present a new potentially interstellar particle resulting from a continuation of analyses of the IS aerogel collection

Spatial Analysis of Land Use by Cattle Herds in a Village of the Sudanese Zone in Senegal. Application for Grazing System Improvement

Spatial analysis of land use by cattle herds in the sub-humid area of Senegal is conducted through the utilisation of a Geographic Information System. This tool allows us to establish relationships between spatial practices, ruminant nutrition and performances. It gives leads to proposals for the improvement of the extensive ruminant feeding system