Discovering the Importance of Mesoscale Cloud Organization Through Unsupervised Classification

The representation of shallow trade wind convective clouds in climate models dominates the uncertainty in climate sensitivity estimates. In particular the radiative impact of cloud spatial organization is poorly understood. This work presents the first unsupervised neural network model which autonomously discovers cloud organization regimes in satellite images. Trained on 10,000 GOES‐16 satellite images (tropical Atlantic and boreal winter) the regimes found are shown to exist in a hierarchy of organizational scales, with sub‐clusters having distinct radiative properties. The model requires no time‐consuming and subjective hand‐labeled data based on predefined structures allowing for objective study of very large data sets. The model enables the study of environmental conditions in different organizational regimes and in transitions between regimes and objective comparisons of model behavior with observations through cloud structures emerging in both. These abilities enable the discovery of previously unknown physical relationships in cloud processes, enabling better representation of clouds in weather and climate simulations

Using data network metrics, graphics, and topology to explore network characteristics

Yehuda Vardi introduced the term network tomography and was the first to propose and study how statistical inverse methods could be adapted to attack important network problems (Vardi, 1996). More recently, in one of his final papers, Vardi proposed notions of metrics on networks to define and measure distances between a network's links, its paths, and also between different networks (Vardi, 2004). In this paper, we apply Vardi's general approach for network metrics to a real data network by using data obtained from special data network tools and testing procedures presented here. We illustrate how the metrics help explicate interesting features of the traffic characteristics on the network. We also adapt the metrics in order to condition on traffic passing through a portion of the network, such as a router or pair of routers, and show further how this approach helps to discover and explain interesting network characteristics.Comment: Published at http://dx.doi.org/10.1214/074921707000000058 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Gene Therapy for Cardiovascular Disease

The last decade has seen substantial advances in the development of gene therapy strategies and vector technology for the treatment of a diverse number of diseases, with a view to translating the successes observed in animal models into the clinic. Perhaps the overwhelming drive for the increase in vascular gene transfer studies is the current lack of successful long-term pharmacological treatments for complex cardiovascular diseases. The increase in cardiovascular disease to epidemic proportions has also led many to conclude that drug therapy may have reached a plateau in its efficacy and that gene therapy may represent a realistic solution to a long-term problem. Here, we discuss gene delivery approaches and target diseases

Application of Neural Networks for Energy Reconstruction

The possibility to use Neural Networks for reconstruction of the energy deposited in the calorimetry system of the CMS detector is investigated. It is shown that using feed - forward neural network, good linearity, Gaussian energy distribution and good energy resolution can be achieved. Significant improvement of the energy resolution and linearity is reached in comparison with other weighting methods for energy reconstruction.Comment: 18 pages, 13 figures, LATEX, submitted to: Nuclear Instruments & Methods

Game Change: What Have We Learned? Pt. 2

Share Knowledge. Change Lives. Transform our Community. Our Mission: The Lincy Institute at UNLV conducts and supports research that focuses on improving Nevada\u27s health, education, and social services. Our Research Areas: Education, Health, Social Services, Information Technolog

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets. Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses. Availability: The methods outlined in this article have been implemented in Matlab and are available on request

Neural Filters for Jet Analysis

We study the efficiency of a neural-net filter and deconvolution method for estimating jet energies and spectra in high-background reactions such as nuclear collisions at the relativistic heavy-ion collider and the large hadron collider. The optimal network is shown to be surprisingly close but not identical to a linear high-pass filter. A suitably constrained deconvolution method is shown to uncover accurately the underlying jet distribution in spite of the broad network response. Finally, we show that possible changes of the jet spectrum in nuclear collisions can be analyzed quantitatively, in terms of an effective energy loss with the proposed method. {} {Dong D W and Gyulassy M 1993}{Neural filters for jet analysis} {(LBL-31560) Physical Review E Vol~47(4) pp~2913-2922}Comment: 21 pages of Postscript, (LBL-31560

Consolidated science and user requirements for a next generation gravity field mission

In an internationally coordinated initiative among the main user communities of gravity field products the science and user requirements for a future gravity field mission constellation (beyond GRACE-FO) have been reviewed and defined. This activity was realized as a joint initiative of the IAG (International Association of Geodesy) Sub-Commissions 2.3 and 2.6, the GGOS (Global Geodetic Observing System) Working Group on Satellite Missions, and the IUGG (International Union of Geodesy and Geophysics). After about one year of preparation, in a user workshop that was held in September 2014 consensus among the user communities of hydrology, ocean, cryosphere, solid Earth and atmosphere on consolidated science requirements could be achieved. The consolidation of the user requirements became necessary, because several future gravity field studies have resulted in quite different performance numbers as a target for a future gravity mission (2025+). Based on limited number of mission scenarios which took also technical feasibility into account, a consolidated view on the science requirements among the international user communities was derived, research fields that could not be tackled by current gravity missions have been identified, and the added value (qualitatively and quantitatively) of these scenarios with respect to science return has been evaluated. The resulting document shall form the basis for further programmatic and technological developments. In this contribution, the main results of this initiative will be presented. An overview of the specific requirements of the individual user groups, the consensus on consolidated requirements as well as the new research fields that have been identified during this process will be discussed

Characterising the shape, size, and orientation of cloud‐feeding coherent boundary‐layer structures

Two techniques are presented for characterisation of cloud-feeding coherent boundary-layer structures through analysis of large-eddy simulations of shallow cumulus clouds, contrasting conditions with and without ambient shear. The first technique is a generalisation of the two-point correlation function, where the correlation length-scale as well as the orientation can be extracted. The second technique identifies individual coherent structures and decomposes their vertical transport by the shape, size, and orientation of these objects. The bulk-correlation technique is shown to capture the elongation and orientation of coherence by ambient wind, but is unable to characterise individual coherent structures. Using the object-based approach, it is found that the individual structures dominating the vertical flux are plume-like in character (extending from the surface into cloud) rather than thermal-like, show small width/thickness asymmetry, and rise near-vertically in the absence of ambient wind. The planar stretching and tilting of boundary-layer structures caused by the introduction of ambient shear is also quantified, demonstrating the general applicability of the techniques for future study of other boundary-layer patterns