Report of conference evaluation committee

A general classification is made of a number of approaches used for the prediction of turbulent shear flows. The sensitivity of these prediction methods to parameter values and initial data are discussed in terms of variable density, pressure fluctuation, gradient diffusion, low Reynolds number, and influence of geometry

Probability of local bifurcation type from a fixed point: A random matrix perspective

Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.Comment: 21 pages, 19 figure

Lineage tree analysis of immunoglobulin variable-region gene mutations in autoimmune diseases: chronic activation, normal selection

Autoimmune diseases show high diversity in the affected organs, clinical manifestations and disease dynamics. Yet they all share common features, such as the ectopic germinal centers found in many affected tissues. Lineage trees depict the diversification, via somatic hypermutation (SHM), of immunoglobulin variable-region (IGV) genes. We previously developed an algorithm for quantifying the graphical properties of IGV gene lineage trees, allowing evaluation of the dynamical interplay between SHM and antigen-driven selection in different lymphoid tissues, species, and disease situations. Here, we apply this method to ectopic GC B cell clones from patients with Myasthenia Gravis, Rheumatoid Arthritis, and Sjögren’s Syndrome, using data scaling to minimize the effects of the large variability due to methodological differences between groups. Autoimmune trees were found to be significantly larger relative to normal controls. In contrast, comparison of the measurements for tree branching indicated that similar selection pressure operates on autoimmune and normal control clones

Methodological Standardization for the Pre-Clinical Evaluation of Renal Sympathetic Denervation

Transcatheter ablation of renal autonomic nerves is a viable option for the treatment of resistant arterial hypertension; however, structured pre-clinical evaluation with standardization of analytical procedures remains a clear gap in this field. Here we discuss the topics relevant to the pre-clinical model for the evaluation of renal denervation (RDN) devices and report methodologies and criteria toward standardization of the safety and efficacy assessment, including histopathological evaluations of the renal artery, periarterial nerves, and associated periadventitial tissues. The pre-clinical swine renal artery model can be used effectively to assess both the safety and efficacy of RDN technologies. Assessment of the efficacy of RDN modalities primarily focuses on the determination of the depth of penetration of treatment-related injury (e.g., necrosis) of the periarterial tissues and its relationship (i.e., location and distance) and the effect on the associated renal nerves and the correlation thereof with proxy biomarkers including renal norepinephrine concentrations and nerve-specific immunohistochemical stains (e.g., tyrosine hydroxylase). The safety evaluation of RDN technologies involves assessing for adverse effects on tissues local to the site of treatment (i.e., on the arterial wall) as well as tissues at a distance (e.g., soft tissue, veins, arterial branches, skeletal muscle, adrenal gland, ureters). Increasing experience will help to create a standardized means of examining all arterial beds subject to ablative energy and in doing so enable us to proceed to optimize the development and assessment of these emerging technologies

Affine Subspace Representation for Feature Description

This paper proposes a novel Affine Subspace Representation (ASR) descriptor to deal with affine distortions induced by viewpoint changes. Unlike the traditional local descriptors such as SIFT, ASR inherently encodes local information of multi-view patches, making it robust to affine distortions while maintaining a high discriminative ability. To this end, PCA is used to represent affine-warped patches as PCA-patch vectors for its compactness and efficiency. Then according to the subspace assumption, which implies that the PCA-patch vectors of various affine-warped patches of the same keypoint can be represented by a low-dimensional linear subspace, the ASR descriptor is obtained by using a simple subspace-to-point mapping. Such a linear subspace representation could accurately capture the underlying information of a keypoint (local structure) under multiple views without sacrificing its distinctiveness. To accelerate the computation of ASR descriptor, a fast approximate algorithm is proposed by moving the most computational part (ie, warp patch under various affine transformations) to an offline training stage. Experimental results show that ASR is not only better than the state-of-the-art descriptors under various image transformations, but also performs well without a dedicated affine invariant detector when dealing with viewpoint changes.Comment: To Appear in the 2014 European Conference on Computer Visio

Lateralization of face processing in the human brain

Are visual face processing mechanisms the same in the left and right cerebral hemispheres? The possibility of such ‘duplicated processing’ seems puzzling in terms of neural resource usage, and we currently lack a precise characterization of the lateral differences in face processing. To address this need, we have undertaken a three-pronged approach. Using functional magnetic resonance imaging, we assessed cortical sensitivity to facial semblance, the modulatory effects of context and temporal response dynamics. Results on all three fronts revealed systematic hemispheric differences. We found that: (i) activation patterns in the left fusiform gyrus correlate with image-level face-semblance, while those in the right correlate with categorical face/non-face judgements. (ii) Context exerts significant excitatory/inhibitory influence in the left, but has limited effect on the right. (iii) Face-selectivity persists in the right even after activity on the left has returned to baseline. These results provide important clues regarding the functional architecture of face processing, suggesting that the left hemisphere is involved in processing ‘low-level’ face semblance, and perhaps is a precursor to categorical ‘deep’ analyses on the right.John Merck FundSimons FoundationJames S. McDonnell FoundationNational Eye Institute (NIH, grant number R21-EY015521

On the possibility to supercool molecular hydrogen down to superfluid transition

Recent calculations by Vorobev and Malyshenko (JETP Letters, 71, 39, 2000) show that molecular hydrogen may stay liquid and superfluid in strong electric fields of the order of $4\times 10^7 V/cm$. I demonstrate that strong local electric fields of similar magnitude exist beneath a two-dimensional layer of electrons localized in the image potential above the surface of solid hydrogen. Even stronger local fields exist around charged particles (ions or electrons) if surface or bulk of a solid hydrogen crystal is statically charged. Measurements of the frequency shift of the $1 \to 2$ photoresonance transition in the spectrum of two-dimensional layer of electrons above positively or negatively charged solid hydrogen surface performed in the temperature range 7 - 13.8 K support the prediction of electric field induced surface melting. The range of surface charge density necessary to stabilize the liquid phase of molecular hydrogen at the temperature of superfluid transition is estimated.Comment: 5 pages, 2 figure

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics