Mode Repulsion and Mode Coupling in Random Lasers

We studied experimentally and theoretically the interaction of lasing modes in random media. In a homogeneously broadened gain medium, cross gain saturation leads to spatial repulsion of lasing modes. In an inhomogeneously broadened gain medium, mode repulsion occurs in the spectral domain. Some lasing modes are coupled through photon hopping or electron absorption and reemission. Under pulsed pumping, weak coupling of two modes leads to synchronization of their lasing action. Strong coupling of two lasing modes results in anti-phased oscillations of their intensities.Comment: 13 pages, 4 figure

Influence of Spatial Correlations on the Lasing Threshold of Random Lasers

The lasing threshold of a random laser is computed numerically from a generic model. It is shown that spatial correlations of the disorder in the medium (i.e., dielectric constant) lead to an increase of the decay rates of the eigenmodes and of the lasing threshold. This is in conflict with predictions that such correlations should lower the threshold. While all results are derived for photonic systems, the computed decay rate distributions also apply to electronic systems

A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed by compressing the internal data representation with discovered substructures. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp 566-581. Supplementary material can be downloaded from http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd

In-Situ Nuclear Magnetic Resonance Investigation of Strain, Temperature, and Strain-Rate Variations of Deformation-Induced Vacancy Concentration in Aluminum

Critical strain to serrated flow in solid solution alloys exhibiting dynamic strain aging (DSA) or Portevin–LeChatelier effect is due to the strain-induced vacancy production. Nuclear magnetic resonance (NMR) techniques can be used to monitor in situ the dynamical behavior of point and line defects in materials during deformation, and these techniques are nondestructive and noninvasive. The new CUT-sequence pulse method allowed an accurate evaluation of the strain-enhanced vacancy diffusion and, thus, the excess vacancy concentration during deformation as a function of strain, strain rate, and temperature. Due to skin effect problems in metals at high frequencies, thin foils of Al were used and experimental results correlated with models based on vacancy production through mechanical work (vs thermal jogs), while in situ annealing of excess vacancies is noted at high temperatures. These correlations made it feasible to obtain explicit dependencies of the strain-induced vacancy concentration on test variables such as the strain, strain rate, and temperature. These studies clearly reveal the power and utility of these NMR techniques in the determination of deformation-induced vacancies in situ in a noninvasive fashion.

Extracellular nanomatrix-induced self-organization of neural stem cells into miniature substantia nigra-like structures with therapeutic effects on Parkinsonian rats

Substantia nigra (SN) is a complex and critical region of the brain wherein Parkinson's disease (PD) arises from the degeneration of dopaminergic neurons. Miniature SN‐like structures (mini‐SNLSs) constructed from novel combination of nanomaterials and cell technologies exhibit promise as potentially curative cell therapies for PD. In this work, a rapid self‐organization of mini‐SNLS, with an organizational structure and neuronal identities similar to those of the SN in vivo, is achieved by differentiating neural stem cells in vitro on biocompatible silica nanozigzags (NZs) sculptured by glancing angle deposition, without traditional chemical growth factors. The differentiated neurons exhibit electrophysiological activity in vitro. Diverse physical cues and signaling pathways that are determined by the nanomatrices and lead to the self‐organization of the mini‐SNLSs are clarified and elucidated. In vivo, transplantation of the neurons from a mini‐SNLS results in an early and progressive amelioration of PD in rats. The sculptured medical device reported here enables the rapid and specific self‐organization of region‐specific and functional brain‐like structures without an undesirable prognosis. This development provides promising and significant insights into the screening of potentially curative drugs and cell therapies for PD

Distributed flow optimization and cascading effects in weighted complex networks

We investigate the effect of a specific edge weighting scheme $\sim (k_i k_j)^{\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficiency upon the correlations between the topology and weights. By studying the severity of cascades for different control parameter $\beta$, we find that network resilience to cascading overloads and network throughput is optimal for the same value of $\beta$ over the range of node capacities and available bandwidth

Measurement of the Mass Splittings between the bbˉχb,J(1P)b\bar{b}\chi_{b,J}(1P) States

We present new measurements of photon energies and branching fractions for the radiative transitions: Upsilon(2S)->gamma+chi_b(J=0,1,2). The masses of the chi_b states are determined from the measured radiative photon energies. The ratio of mass splittings between the chi_b substates, r==(M[J=2]-M[J=1])/(M[J=1]-M[J=0]) with M the chi_b mass, provides information on the nature of the bbbar confining potential. We find r(1P)=0.54+/-0.02+/-0.02. This value is in conflict with the previous world average, but more consistent with the theoretical expectation that r(1P)<r(2P); i.e., that this mass splittings ratio is smaller for the chi_b(1P) triplet than for the chi_b(2P) triplet.Comment: 11 page postscript file, postscript file also available through http://w4.lns.cornell.edu/public/CLN

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types