Spin Effects in High Energy Fragmentation Processes

Recent measurements, in particular those on $\Lambda$ polarization and spin alignment of vector mesons in $e^+e^-$ annihilation at LEP, and those on the azimuthal asymmetry at HERA, have attracted much attention on the spin effects in high energy fragmentation processes. In this talk, we make a brief introduction to the different topics studied in this connection and a short summary of the available data. After that, we present a short summary of the main theoretical results that we obtained in studying these different topics. The talk was mainly based on the publications [5-9] which have been finished in collaboration with C.Boros, Liu Chun-xiu and Xu Qing-hua.Comment: Plenary talk given at the 3rd Circum-Pan-Pacifc Symposium on High Energy Spin Physics, October 2001, 8 pages, 4 figure

Neutrino factory in stages: Low energy, high energy, off-axis

We discuss neutrino oscillation physics with a neutrino factory in stages, including the possibility of upgrading the muon energy within the same program. We point out that a detector designed for the low energy neutrino factory may be used off-axis in a high energy neutrino factory beam. We include the re-optimization of the experiment depending on the value of theta_13 found. As upgrade options, we consider muon energy, additional baselines, a detector mass upgrade, an off-axis detector, and the platinum (muon to electron neutrino) channels. In addition, we test the impact of Daya Bay data on the optimization. We find that for large theta_13 (theta_13 discovered by the next generation of experiments), a low energy neutrino factory might be the most plausible minimal version to test the unknown parameters. However, if a higher muon energy is needed for new physics searches, a high energy version including an off-axis detector may be an interesting alternative. For small theta_13 (theta_13 not discovered by the next generation), a plausible program could start with a low energy neutrino factory, followed by energy upgrade, and then baseline or detector mass upgrade, depending on the outcome of the earlier phases.Comment: 23 pages, 10 (color) figures. Minor clarifications and changes. Final version to appear in PR

Hidden Charged Dark Matter and Chiral Dark Radiation

In the light of recent possible tensions in the Hubble constant $H_0$ and the structure growth rate $\sigma_8$ between the Planck and other measurements, we investigate a hidden-charged dark matter (DM) model where DM interacts with hidden chiral fermions, which are charged under the hidden SU(N) and U(1) gauge interactions. The symmetries in this model assure these fermions to be massless. The DM in this model, which is a Dirac fermion and singlet under the hidden SU(N), is also assumed to be charged under the U(1) gauge symmetry, through which it can interact with the chiral fermions. Below the confinement scale of SU(N), the hidden quark condensate spontaneously breaks the U(1) gauge symmetry such that there remains a discrete symmetry, which accounts for the stability of DM. This condensate also breaks a flavor symmetry in this model and Nambu-Goldstone bosons associated with this flavor symmetry appear below the confinement scale. The hidden U(1) gauge boson and hidden quarks/Nambu-Goldstone bosons are components of dark radiation (DR) above/below the confinement scale. These light fields increase the effective number of neutrinos by $\delta N_{\rm eff}\simeq 0.59$ above the confinement scale for $N=2$, resolving the tension in the measurements of the Hubble constant by Planck and Hubble Space Telescope if the confinement scale is $\lesssim 1$ eV. DM and DR continuously scatter with each other via the hidden U(1) gauge interaction, which suppresses the matter power spectrum and results in a smaller structure growth rate. The DM sector couples to the Standard Model sector through the exchange of a real singlet scalar mixing with the Higgs boson, which makes it possible to probe our model in DM direct detection experiments. Variants of this model are also discussed, which may offer alternative ways to investigate this scenario.Comment: 20 pages, 4 figures; v2: version accepted for publication in PL

Shear Force Fiber Spinning: Process Parameter and Polymer Solution Property Considerations

For application of polymer nanofibers (e.g., sensors, and scaffolds to study cell behavior) it is important to control the spatial orientation of the fibers. We compare the ability to align and pattern fibers using shear force fiber spinning, i.e. contacting a drop of polymer solution with a rotating collector to mechanically draw a fiber, with electrospinning onto a rotating drum. Using polystyrene as a model system, we observe that the fiber spacing using shear force fiber spinning was more uniform than electrospinning with the rotating drum with relative standard deviations of 18% and 39%, respectively. Importantly, the approaches are complementary as the fiber spacing achieved using electrospinning with the rotating drum was ~10 microns while fiber spacing achieved using shear force fiber spinning was ~250 microns. To expand to additional polymer systems, we use polymer entanglement and capillary number. Solution properties that favor large capillary numbers (\u3e50) prevent droplet breakup to facilitate fiber formation. Draw-down ratio was useful for determining appropriate process conditions (flow rate, rotational speed of the collector) to achieve continuous formation of fibers. These rules of thumb for considering the polymer solution properties and process parameters are expected to expand use of this platform for creating hierarchical structures of multiple fiber layers for cell scaffolds and additional applications

Building a diversity featured search system by fusing existing tools

This paper describes our diversity featured retrieval system which are built for the task of ImageCLEFPhoto 2008. Two existing tools are used: Solr and Carrot. We have experimented with different settings of the system to see how the performance changes. The results suggest that the system can indeed increase diversity of the retrieved results and keep the precision about the same

Creating a test collection to evaluate diversity in image retrieval

This paper describes the adaptation of an existing test collection for image retrieval to enable diversity in the results set to be measured. Previous research has shown that a more diverse set of results often satisfies the needs of more users better than standard document rankings. To enable diversity to be quantified, it is necessary to classify images relevant to a given theme to one or more sub-topics or clusters. We describe the challenges in building (as far as we are aware) the first test collection for evaluating diversity in image retrieval. This includes selecting appropriate topics, creating sub-topics, and quantifying the overall effectiveness of a retrieval system. A total of 39 topics were augmented for cluster-based relevance and we also provide an initial analysis of assessor agreement for grouping relevant images into sub-topics or clusters

Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

Robust clustering of high-dimensional data is an important topic because, in many practical situations, real data sets are heavy-tailed and/or asymmetric. Moreover, traditional model-based clustering often fails for high dimensional data due to the number of free covariance parameters. A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed by including a penalty term in the likelihood constraining the parameters resulting in a flexible model for high dimensional data and a meaningful interpretation. An analytically feasible EM algorithm is developed by placing a gamma-Lasso penalty constraining the concentration matrix. The proposed methodology is investigated through simulation studies and two real data sets

Low-energy behavior of spin-liquid electron spectral functions

We calculate the electron spectral function for a spin-liquid with a spinon Fermi surface and a Dirac spin-liquid. Calculations are based upon the slave-rotor mean-field theory. We consider the effect of gauge fluctuations using a simple model and find the behavior is not strongly modified. The results, distinct from conventional Mott insulator or band theory predictions, suggest that measuring the spectral function e.g. via ARPES could help in the experimental verification and characterization of spin liquids.Comment: 7 pages, 7 figure

Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder

Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process. As a result, the variational lower bound of the joint likelihood can be optimized via a conditional variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was motivated by two real-world applications in computational sustainability: one studies the spatial correlation among multiple bird species using the eBird data and the other models multi-dimensional landscape composition and human footprint in the Amazon rainforest with satellite images. We show that MEDL_CVAE captures rich dependency structures, scales better than previous methods, and further improves on the joint likelihood taking advantage of very large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall