Joint superexchange--Jahn-Teller mechanism for A-type antiferromagnetism in LaMnO3LaMnO_3

We propose a mechanism for A-type antiferromagnetism in orthorombic LaMnO_3, compatible with the large Jahn-Teller splitting inferred from structural data. Orbital ordering resulting from Jahn-Teller distortions effectively leads to A-type ordering (antiferromagnetic in the c axis and ferromagnetic in the ab plane) provided the in-plane distorsion Q_2 is large enough, a condition generally fulfilled in existing data.Comment: 4 pages Late

The Full-sky Astrometric Mapping Explorer -- Astrometry for the New Millennium

FAME is designed to perform an all-sky, astrometric survey with unprecedented accuracy. It will create a rigid astrometric catalog of 4x10^7 stars with 5 < m_V < 15. For bright stars, 5 < m_V < 9, FAME will determine positions and parallaxes accurate to < 50 microarcseconds, with proper motion errors < 50 microarcseconds/year. For fainter stars, 9 < m_V < 15, FAME will determine positions and parallaxes accurate to < 500 microarcseconds, with proper motion errors < 500 microarcseconds/year. It will also collect photometric data on these 4 x 10^7 stars in four Sloan DSS colors.Comment: 6 pages, 4 figures, to appear in "Working on the Fringe

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Haptic guidance improves the visuo-manual tracking of trajectories

BACKGROUND: Learning to perform new movements is usually achieved by following visual demonstrations. Haptic guidance by a force feedback device is a recent and original technology which provides additional proprioceptive cues during visuo-motor learning tasks. The effects of two types of haptic guidances-control in position (HGP) or in force (HGF)-on visuo-manual tracking ("following") of trajectories are still under debate. METHODOLOGY/PRINCIPALS FINDINGS: Three training techniques of haptic guidance (HGP, HGF or control condition, NHG, without haptic guidance) were evaluated in two experiments. Movements produced by adults were assessed in terms of shapes (dynamic time warping) and kinematics criteria (number of velocity peaks and mean velocity) before and after the training sessions. CONCLUSION/SIGNIFICANCE: These results show that the addition of haptic information, probably encoded in force coordinates, play a crucial role on the visuo-manual tracking of new trajectories

The STAR Silicon Strip Detector (SSD)

The STAR Silicon Strip Detector (SSD) completes the three layers of the Silicon Vertex Tracker (SVT) to make an inner tracking system located inside the Time Projection Chamber (TPC). This additional fourth layer provides two dimensional hit position and energy loss measurements for charged particles, improving the extrapolation of TPC tracks through SVT hits. To match the high multiplicity of central Au+Au collisions at RHIC the double sided silicon strip technology was chosen which makes the SSD a half million channels detector. Dedicated electronics have been designed for both readout and control. Also a novel technique of bonding, the Tape Automated Bonding (TAB), was used to fullfill the large number of bounds to be done. All aspects of the SSD are shortly described here and test performances of produced detection modules as well as simulated results on hit reconstruction are given.Comment: 11 pages, 8 figures, 1 tabl

Molecular and morphological identification of mealybug species (Hemiptera: Pseudococcidae) in Brazilian vineyards.

Mealybugs (Hemiptera: Pseudococcidae) are pests constraining the international trade of Brazilian table grapes. They damage grapes by transmitting viruses and toxins, causing defoliation, chlorosis, and vigor losses and favoring the development of sooty mold. Difficulties in mealybug identification remain an obstacle to the adequate management of these pests. In this study, our primary aim was to identify the principal mealybug species infesting the major table grape-producing regions in Brazil, by morphological and molecular characterization. Our secondary aim was to develop a rapid identification kit based on species-specific Polymerase Chain Reactions, to facilitate the routine identification of the most common pest species. We surveyed 40 sites infested with mealybugs and identified 17 species: Dysmicoccus brevipes (Cockerell), Dysmicoccus sylvarum Williams and Granara de Willink, Dysmicoccus texensis (Tinsley), Ferrisia cristinae Kaydan and Gullan, Ferrisia meridionalis Williams, Ferrisia terani Williams and Granara de Willink, Phenacoccus baccharidis Williams, Phenacoccus parvus Morrison, Phenacoccus solenopsis Tinsley, Planococcus citri (Risso), Pseudococcus viburni (Signoret), Pseudococcus cryptus Hempel, four taxa closely related each of to Pseudococcus viburni, Pseudococcus sociabilis Hambleton, Pseudococcus maritimus (Ehrhorn) and Pseudococcus meridionalis Prado, and one specimen from the genus Pseudococcus Westwood. The PCR method developed effectively identified five mealybug species of economic interest on grape in Brazil: D. brevipes, Pl. citri, Ps. viburni, Ph. solenopsis and Planococcus ficus (Signoret). Nevertheless, it is not possible to assure that this procedure is reliable for taxa that have not been sampled already and might be very closely related to the target species

Strong enhancement of extremely energetic proton production in central heavy ion collisions at intermediate energy

The energetic proton emission has been investigated as a function of the reaction centrality for the system 58Ni + 58Ni at 30A MeV. Extremely energetic protons (EpNN > 130 MeV) were measured and their multiplicity is found to increase almost quadratically with the number of participant nucleons thus indicating the onset of a mechanism beyond one and two-body dynamics.Comment: 5 pages, 2 figures, submitted to Physical Review Letter

Multi-objective reinforcement learning for responsive grids

The original publication is available at www.springerlink.comInternational audienceGrids organize resource sharing, a fundamental requirement of large scientific collaborations. Seamless integration of grids into everyday use requires responsiveness, which can be provided by elastic Clouds, in the Infrastructure as a Service (IaaS) paradigm. This paper proposes a model-free resource provisioning strategy supporting both requirements. Provisioning is modeled as a continuous action-state space, multi-objective reinforcement learning (RL) problem, under realistic hypotheses; simple utility functions capture the high level goals of users, administrators, and shareholders. The model-free approach falls under the general program of autonomic computing, where the incremental learning of the value function associated with the RL model provides the so-called feedback loop. The RL model includes an approximation of the value function through an Echo State Network. Experimental validation on a real data-set from the EGEE grid shows that introducing a moderate level of elasticity is critical to ensure a high level of user satisfaction

A simple but efficient voice activity detection algorithm through Hilbert transform and dynamic threshold for speech pathologies

A simple but efficient voice activity detector based on the Hilbert transform and a dynamic threshold is presented to be used on the pre-processing of audio signals -- The algorithm to define the dynamic threshold is a modification of a convex combination found in literature -- This scheme allows the detection of prosodic and silence segments on a speech in presence of non-ideal conditions like a spectral overlapped noise -- The present work shows preliminary results over a database built with some political speech -- The tests were performed adding artificial noise to natural noises over the audio signals, and some algorithms are compared -- Results will be extrapolated to the field of adaptive filtering on monophonic signals and the analysis of speech pathologies on futures works20th Argentinean Bioengineering Society Congress, SABI 2015 (XX Congreso Argentino de Bioingeniería y IX Jornadas de Ingeniería Clínica)28–30 October 2015, San Nicolás de los Arroyos, Argentin