NNNLO correction to the toponium and bottomonium wave-functions at the origin

We report new results of the NNNLO correction to the S-wave quarkonium wave-functions at the origin, which also provide an estimate of the resonance cross section in t-tbar threshold production at the ILC.Comment: 5 pages, 2 figures, Proceedings of 2007 International Linear Collider Workshop: LCWS07 and ILC07, Hamburg, Germany, 30 May - 3 Jun 200

Multiple Avalanches Across the Metal-Insulator Transition of Vanadium Oxide Nano-scaled Junctions

The metal insulator transition of nano-scaled $VO_2$ devices is drastically different from the smooth transport curves generally reported. The temperature driven transition occurs through a series of resistance jumps ranging over 2 decades in amplitude, indicating that the transition is caused by avalanches. We find a power law distribution of the jump amplitudes, demonstrating an inherent property of the $VO_2$ films. We report a surprising relation between jump amplitude and device size. A percolation model captures the general transport behavior, but cannot account for the statistical behavior.Comment: 4 papers and 4 figures submitted to PR

Initial phases of massive star formation in high infrared extinction clouds. II. Infall and onset of star formation

The onset of massive star formation is not well understood because of observational and theoretical difficulties. To find the dense and cold clumps where massive star formation can take place, we compiled a sample of high infrared extinction clouds, which were observed previously by us in the 1.2 mm continuum emission and ammonia. We try to understand the star-formation stages of the clumps in these high extinction clouds by studying the infall and outflow properties, the presence of a young stellar object (YSO), and the level of the CO depletion through a molecular line survey with the IRAM 30m and APEX 12m telescopes. Moreover, we want to know if the cloud morphology, quantified through the column density contrast between the clump and the clouds, has an impact on the star formation occurring inside it. We find that the HCO+(1-0) line is the most sensitive for detecting infalling motions. SiO, an outflow tracer, was mostly detected toward sources with infall, indicating that infall is accompanied by collimated outflows. The presence of YSOs within a clump depends mostly on its column density; no signs of YSOs were found below 4E22 cm-2. Star formation is on the verge of beginning in clouds that have a low column density contrast; infall is not yet present in the majority of the clumps. The first signs of ongoing star formation are broadly observed in clouds where the column density contrast between the clump and the cloud is higher than two; most clumps show infall and outflow. Finally, the most evolved clumps are in clouds that have a column density contrast higher than three; almost all clumps have a YSO, and in many clumps, the infall has already halted. Hence, the cloud morphology, based on the column density contrast between the cloud and the clumps, seems to have a direct connection with the evolutionary stage of the objects forming inside

Non-Arrhenius ionic conductivities in glasses due to a distribution of activation energies

Previously observed non-Arrhenius behavior in fast ion conducting glasses [\textit{Phys.\ Rev.\ Lett.}\ \textbf{76}, 70 (1996)] occurs at temperatures near the glass transition temperature, $T_{g}$, and is attributed to changes in the ion mobility due to ion trapping mechanisms that diminish the conductivity and result in a decreasing conductivity with increasing temperature. It is intuitive that disorder in glass will also result in a distribution of the activation energies (DAE) for ion conduction, which should increase the conductivity with increasing temperature, yet this has not been identified in the literature. In this paper, a series of high precision ionic conductivity measurements are reported for $0.5{Na}_{2}{S}+0.5[x{GeS}_{2}+(1-x){PS}_{5/2}]$ glasses with compositions ranging from $0 \leq x \leq 1$. The impact of the cation site disorder on the activation energy is identified and explained using a DAE model. The absence of the non-Arrhenius behavior in other glasses is explained and it is predicted which glasses are expected to accentuate the DAE effect on the ionic conductivity.Comment: 2 figure

A caloritronics-based Mott neuristor

Machine learning imitates the basic features of biological neural networks to efficiently perform tasks such as pattern recognition. This has been mostly achieved at a software level, and a strong effort is currently being made to mimic neurons and synapses with hardware components, an approach known as neuromorphic computing. CMOS-based circuits have been used for this purpose, but they are non-scalable, limiting the device density and motivating the search for neuromorphic materials. While recent advances in resistive switching have provided a path to emulate synapses at the 10 nm scale, a scalable neuron analogue is yet to be found. Here, we show how heat transfer can be utilized to mimic neuron functionalities in Mott nanodevices. We use the Joule heating created by current spikes to trigger the insulator-to-metal transition in a biased VO2 nanogap. We show that thermal dynamics allow the implementation of the basic neuron functionalities: activity, leaky integrate-and-fire, volatility and rate coding. By using local temperature as the internal variable, we avoid the need of external capacitors, which reduces neuristor size by several orders of magnitude. This approach could enable neuromorphic hardware to take full advantage of the rapid advances in memristive synapses, allowing for much denser and complex neural networks. More generally, we show that heat dissipation is not always an undesirable effect: it can perform computing tasks if properly engineered

Acquisition of an In-House X-ray Scattering Facility for Nanostructure Characterization and Student Training

This equipment grant was specifically dedicated to the development of a "state of the art" x-ray scattering facility..