Spontaneous Resonances and the Coherent States of the Queuing Networks

We present an example of a highly connected closed network of servers, where the time correlations do not go to zero in the infinite volume limit. This phenomenon is similar to the continuous symmetry breaking at low temperatures in statistical mechanics. The role of the inverse temperature is played by the average load.Comment: 3 figures added, small correction

Oscillations of a rapidly rotating annular Bose-Einstein condensate

A time-dependent variational Lagrangian analysis based on the Gross-Pitaevskii energy functional serves to study the dynamics of a metastable giant vortex in a rapidly rotating Bose-Einstein condensate. The resulting oscillation frequencies of the core radius reproduce the trends seen in recent experiments [Engels et al., Phys. Rev. Lett. 90, 170405 (2003)], but the theoretical values are smaller by a factor approximately 0.6-0.8.Comment: 7 pages, revtex

Inkjet Printing of Materials with Resistance to Bacterial Attachment

Biofilm formation on the surface of medical devices is a major source of health-care associated infections. The discovery of new materials that inherently avoid formation of such biofilms on their surface points the way to the fabrication of biofilm resistant devices, with the consequent reduction in the incidence rate of device centred infections and therefore a reduction in suffering and costs for health-care systems. Drop on Demand (DOD) Three Dimensional (3D) Inkjet Printing presents higher versatility than common techniques for printing biomaterials. One of the main representations of this enhanced versatility is polymerisation post-jetting, which provides a great range of printable polymers. The combination of such materials with inkjet printing could revolutionise the biomedical industry. In this paper, the printability of four acrylates with resistance to bacterial attachment was assessed using the printability indicator or Z parameter. Three of the materials showed a value of Z within the printability range. The remainder displayed a Z value higher than the maximum suggested. However, this material was ejected with stability using a complex waveform designed for low viscosity inks. Drop spacing was optimised for each ink using PET and glass as substrates. The combination of printability optimisation together with ideal drop spacing allowed the construction of 3D structures of three of the four inks that were tested.Mechanical Engineerin

Small-amplitude normal modes of a vortex in a trapped Bose-Einstein condensate

We consider a cylindrically symmetric trap containing a small Bose-Einstein condensate with a singly quantized vortex on the axis of symmetry. A time-dependent variational Lagrangian analysis yields the small-amplitude dynamics of the vortex and the condensate, directly determining the equations of motion of the coupled normal modes. As found previously from the Bogoliubov equations, there are two rigid dipole modes and one anomalous mode with a negative frequency when seen in the laboratory frame.Comment: 4 pages, no figures, Revte

Charged Randall-Sundrum black holes and N=4 super Yang-Mills in AdS(2)xS(2)

We obtain some exact results for black holes in the Randall-Sundrum model with a single brane. We consider an extreme black hole charged with respect to a Maxwell field on the brane. The near-horizon geometry is determined. The induced metric on the brane and the black hole entropy are compared with the predictions of 4d General Relativity. There is good agreement for large black holes, with calculable subleading corrections. As a separate application, the bulk solution provides a gravitational dual for (strongly coupled, large N) N=4 SYM in AdS(2)xS(2) for arbitrary relative size of AdS(2) and S(2).Comment: 13 page

Fourier Acceleration of Langevin Molecular Dynamics

Fourier acceleration has been successfully applied to the simulation of lattice field theories for more than a decade. In this paper, we extend the method to the dynamics of discrete particles moving in continuum. Although our method is based on a mapping of the particles' dynamics to a regular grid so that discrete Fourier transforms may be taken, it should be emphasized that the introduction of the grid is a purely algorithmic device and that no smoothing, coarse-graining or mean-field approximations are made. The method thus can be applied to the equations of motion of molecular dynamics (MD), or its Langevin or Brownian variants. For example, in Langevin MD simulations our acceleration technique permits a straightforward spectral decomposition of forces so that the long-wavelength modes are integrated with a longer time step, thereby reducing the time required to reach equilibrium or to decorrelate the system in equilibrium. Speedup factors of up to 30 are observed relative to pure (unaccelerated) Langevin MD. As with acceleration of critical lattice models, even further gains relative to the unaccelerated method are expected for larger systems. Preliminary results for Fourier-accelerated molecular dynamics are presented in order to illustrate the basic concepts. Possible extensions of the method and further lines of research are discussed.Comment: 11 pages, two illustrations included using graphic

A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei

Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimaging do not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the subnuclei of the amygdala. To tackle this challenging task, we developed a dual-branch dilated residual 3D fully convolutional network with parallel convolutions to extract more global context and alleviate the class imbalance issue by maintaining a small receptive field that is just the size of the regions of interest (ROIs). We also conduct multi-scale feature fusion in both parallel and series to compensate the potential information loss during convolutions, which has been shown to be important for small objects. The serial feature fusion enabled by residual connections is further enhanced by a proposed top-down attention-guided refinement unit, where the high-resolution low-level spatial details are selectively integrated to complement the high-level but coarse semantic information, enriching the final feature representations. As a result, the segmentations resulting from our method are more accurate both volumetrically and morphologically, compared with other deep learning based approaches. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala. We also demonstrated the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to harmonize multi-site MRI data, and show that our method generalizes well to challenging traumatic brain injury (TBI) datasets collected from multiple centers. This appears to be a promising strategy for image segmentation for multiple site studies and increased morphological variability from significant brain pathology

Vortices and dynamics in trapped Bose-Einstein condensates

I review the basic physics of ultracold dilute trapped atomic gases, with emphasis on Bose-Einstein condensation and quantized vortices. The hydrodynamic form of the Gross-Pitaevskii equation (a nonlinear Schr{\"o}dinger equation) illuminates the role of the density and the quantum-mechanical phase. One unique feature of these experimental systems is the opportunity to study the dynamics of vortices in real time, in contrast to typical experiments on superfluid $^4$He. I discuss three specific examples (precession of single vortices, motion of vortex dipoles, and Tkachenko oscillations of a vortex array). Other unusual features include the study of quantum turbulence and the behavior for rapid rotation, when the vortices form dense regular arrays. Ultimately, the system is predicted to make a quantum phase transition to various highly correlated many-body states (analogous to bosonic quantum Hall states) that are not superfluid and do not have condensate wave functions. At present, this transition remains elusive. Conceivably, laser-induced synthetic vector potentials can serve to reach this intriguing phase transition.Comment: Accepted for publication in Journal of Low Temperature Physics, conference proceedings: Symposia on Superfluids under Rotation (Lammi, Finland, April 2010

Natural variation in Arabidopsis thaliana reveals shoot ionome, biomass, and gene expression changes as biomarkers for zinc deficiency tolerance

Zinc (Zn) is an essential nutrient for plants, with a crucial role as a cofactor for many enzymes. Approximately one-third of the global arable land area is Zn deficient, leading to reduced crop yield and quality. To improve crop tolerance to Zn deficiency, it is important to understand the mechanisms plants have adopted to tolerate suboptimal Zn supply. In this study, physiological and molecular aspects of traits related to Zn deficiency tolerance were examined in a panel of 19 Arabidopsis thaliana accessions. Accessions showed a larger variation for shoot biomass than for Zn concentration, indicating that they have different requirements for their minimal Zn concentration required for growth. Accessions with a higher tolerance to Zn deficiency showed an increased expression of the Zn deficiency-responsive genes ZIP4 and IRT3 in comparison with Zn deficiency-sensitive accessions. Changes in the shoot ionome, as a result of the Zn treatment of the plants, were used to build a multinomial logistic regression model able to distinguish plants regarding their Zn nutritional status. This set of biomarkers, reflecting the A. thaliana response to Zn deficiency and Zn deficiency tolerance, can be useful for future studies aiming to improve the performance and Zn status of crop plants grown under suboptimal Zn concentrations

Network e-Volution

Modern society is a network society permeated by information technology (IT). As a result of innovations in IT, enormous amounts of information can be communicated to a larger number of recipients faster than ever before. The evolution of networks is heavily influenced by the extensive use of IT, which has enabled co-evolving advanced quantitative and qualitative forms of networking. Although several networks have been formed with the aim to reduce or deal with uncertainty through faster and broader access to information, it is in fact IT that has created new kinds of uncertainty. For instance, although digital information integration in supply chains has made production planning more robust, it has at the same time intensified mutual dependencies, thereby actually increasing the level of uncertainty. The aim of this working paper is to investigate the aspects of evolving networks and uncertainty in networks at the cutting edges of different types of networks and from the perspective of different layers defining these networks