30,055 research outputs found
Ibraheem Sayed Interview
No full textIbraheem Sayed (Class of 2022) was interviewed by Laura Narvaez on December 16, 2023 via the Zoom internet-based video conferencing software. Sayed was born in Boston, MA in 1999 and attended SMU from 2018 to 2022, where he majored in Accounting. He chose to attend SMU after being blown away by the campus during a high school tour and lived in Armstrong Commons from freshman through senior year, where he also served as a Residential Advisor and was active in the Armstrong Commons Council. Sayed was very focused on academics and discussed his COVID-19 learning experience
Interview of Sayed Z. El-Sayed by Brian Shoemaker
No full textDr. Hussein Fausi, pp. 2
Professor Abdel Fatah Mohammed, pp. 2
Dr. Richard Van Cleef, pp. 3
Dr. Richard Flemming, pp. 3
Haupt ______, pp. 3
Ravel _______, pp. 3
Shepard ______, pp. 3
Claude du Bear, pp. 3
Walter Monk, pp. 3
Peter Ray, pp. 3
Captain Luis R. Capurro, pp. 6-7
Byunig Don Lee, pp. 8, 22
Dr. Martin Johnson, pp. 9
Captain Canepa, pp. 9-10
Larry Gould, pp. 10
______Zumberg, pp. 10
Lee Washbrun, pp. 10
George Llano, pp. 10, 15, 23-24, 30
Professor Mosby, pp. 12
Richard Thornton, pp. 13
_______Allsion McQueeny, pp. 15
Dr. Morita, pp. 15, 23
Claude Zumell, pp. 16
Holm Henson, pp. 16, 23
Larry Weber, pp. 19
_______Filchner, pp. 22
_______Shackelton, pp. 22
Mary Alice ________, pp. 22-23
George Knox, pp. 28
Dr. Numoto, pp. 29
Lou de Galle, pp. 29
Dick Laws, pp. 29, 37, 42, 64, 66
Joe Farnham, pp. 34, 63
Carol ________, pp. 35
Todd ________, pp. 40
Lubimora ______, pp. 40
Professor Bogdanor, pp. 41
__________ Kryzechevski, pp. 42
Barry Heywood, pp. 42, 65
David Drury, pp. 43
Martin Johnson, pp. 46
Carl Stegan, pp. 54
Sherwood Roland, pp. 55
Mario Mornina, pp. 55
Paul Ramsey, pp. 55-56
Bob Stephenson, pp. 60
Paul Skelly Powers, pp. 60
Charlie Inge, pp. 60
_________ Hovis, pp. 60
Emil Anderson, pp. 61
Admiral Bill Ramsey, pp. 62
Dean Stockwell, pp. 62
Bernard Stonehouse, pp. 64
Bob Abel, pp. 67Dr. El-Sayed was born in Alexandria, Egypt. After secondary school, he went to the University of Alexandria for his B.S. (1949) in Oceanography. After his M.S., he went to the Scripps Institute of Oceanography on a Fulbright Fellowship. He received his PhD from the University of Washington. As professor emeritus at Texas A & M, he directs a project with the Cooperative Marine Research Program in the Middle East. A friend asked him to work on a biological project on Drake Passage, Antarctica.
He worked for several years on vessels from Argentina and was later assigned to a ship for the study of krill. The science team included specialists interested in different aspects of the ecosystem. This was the first of many trips, including those on the Atlantic Ocean, Pacific Ocean, and Indian Ocean. He wrote the book “The Historical Perspective of the Antarctic Marine Research.” This book addresses the studies on the productivity of krill, in addition to phytoplankton and how solar radiation, nutrients, and the depletion of the ozone affected the marine ecosystem. The UVB radiation had a deleterious effect on the survival of the phytoplankton and nanoplankton.
Dr. El-Sayed describes his associations with SCAR, BIMASS, SCORE, and other research organizations. He summarizes the phasing out of CFCs production. Because some phytoplankton are inhibited by solar radiation, the maximum concentration of chlorophyll is between 10 and 20 meters. As a member of the Nimbus Experimental Team, Dr. El-Sayed used the coastal zone color scanner to study the krill ecosystem.
Major Topics
The University of Alexandria
The Scripps Institute of Oceanography
The University of Washington
Texas A & M University
Cooperative Marine Research Program in the Middle East
Drake Passage in Antarctica
Phytoplankton and nanoplankton on the Filchner Ice Shelf
Water currents in the Weddell Sea
Changes in the krill population
The formation of SCAR’s Marine Committee
The Antarctic marine ecosystem
Establishment of the first two International BIMASS experiments
The use of satellite images to study marine ecologyFunded by a grant from the National Science Foundation
Phyllotetranychus Sayed 1938
No full textGenus <i>Phyllotetranychus</i> Sayed, 1938 <p> <b>Type species:</b> <i>Phyllotetranychus aegyptium</i> Sayed, 1938</p> <p> <b>Diagnosis:</b> Full complement of 16 dorsal setae; dorsal setae large, broadly orbicular to ovate, leaf-like and with pseudovenation; setae <i> h 2</i> not flagellate; anterior margin of prodorsum with two pairs of prodorsal projections; palps two-segmented (tibio-tarsus with one eupathidium (<i>ul'ζ</i>) and two setae, femorogenu with one seta (<i>d</i>)); two pairs of pseudanal setae <i> ps 1–2</i> ; ventral, genital and anal plates not sclerotised or developed.</p>Published as part of <i>Mahdavi, Sayed Mosayeb, Latifi, Malihe & Asadi, Mahdieh, 2019, A new species of Phyllotetranychus (Acari: Tenuipalpidae) from Iran, pp. 566-578 in Zootaxa 4565 (4)</i> on page 567, DOI: 10.11646/zootaxa.4565.4.10, <a href="http://zenodo.org/record/2591261">http://zenodo.org/record/2591261</a>
Graph Learning Over Partially Observed Diffusion Networks: Role of Degree Concentration
No full textThis work examines the problem of learning the topology of a network from the samples of a diffusion process evolving at the network nodes, under the restriction that a limited fraction thereof is probed (partial observability). We provide the following main contributions. Given an estimator of the combination matrix (i.e., the matrix that quantifies the pairwise interaction between nodes), we introduce the notion of identifiability gap, a minimum separation between the entries of the estimated matrix that is critical to enable discrimination between connected and unconnected node pairs. Then we focus on the popular Granger estimator. First, we prove that this matrix estimator, followed by a universal clustering algorithm inspired by the k-means algorithm, learns faithfully the probed subgraph as the network size increases. This result is proved for the case where the network topology is obtained through an Erdős-Rényi random graph under statistical concentration of the node degrees, and the combination matrix is symmetric with nonzero entries bounded in terms of the reciprocal of the maximal and minimal degree. The analysis explores different connectivity regimes, including the dense regime where the probed nodes are influenced by many connections coming from the latent (hidden) part of the graph. Second, we answer a sample complexity question and establish that the number of samples for the Granger estimator scales almost quadratically with the expected graph degree. We also propose three other estimators that are proved to achieve faithful graph learning, and compare them to the Granger estimator, gaining nontrivial insights especially for the case of directed graphs
Graph Learning under Partial Observability
No full textMany optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There is a large body of literature examining the effect of the graph structure on the performance of decentralized processing strategies. In this article, we examine the inverse problem and consider the reverse question: How much information does observing the behavior at the nodes of a graph convey about the underlying topology? For large-scale networks, the difficulty in addressing such inverse problems is compounded by the fact that usually only a limited fraction of the nodes can be probed, giving rise to a second important question: Despite the presence of unobserved nodes, can partial observations still be sufficient to discover the graph linking the probed nodes? The article surveys recent advances on this challenging learning problem and related questions
A simplified method for computing phase behavior of crude oil-carbon dioxide mixtures
No full textTypescript (photocopy).Phase behavior plays a fundamental role in oil recovery processes, ranging from the production of gas condensate and volatile oil reservoirs to the injection of CO2 and N2 for enhanced oil recovery processes. In phase behavior methods, equilibrium ratios are used to predict compositional changes in the reservoir fluids, particularly when using compositional simulators. Literature search and experience in the phase behavior of CO2 - reservoir oil systems have shown that equations of state and available correlations give acceptable results in some areas, but in general, they are not satisfactory due to lack of accuracy, large computational time, or sometimes yielding trivial solutions. Therefore, accurate, faster and more reliable new methods are needed, particularly for actual compositional studies. In this study, a K-value method is developed. This method, expressed in a simple mathematical form, relates the equilibrium ratios of each component with its boiling temperature, critical temperature and pressure, and the mixture's pressure, convergence pressure and overall compositional changes. This method uses some experimental data for the mixture under study to adjust the form of the K-value expression. These experimental data are obtained from some the routine PVT laboratory tests. A least squares-linear programming optimization routine is adopted to adjust the correlation to match the actual behavior of the mixtures with the calculated. Nine reservoir fluid samples were simulated, four retrograde gas condensate and five oil systems. The K-value method demonstrated good matches with the experimental data for all systems, including crude oil-carbon dioxide mixtures. This method worked well compared to Peng-Robinson and Soave-Redlich-Kwong equations of state for matching the saturation pressures and swollen volumes of four cases of CO2 and N2 injections into crude oil systems. The K-value method was faster than the equations of state by a factor of 7-20. In addition, the K-value method required less computer memory, less input data and fewer parameters to adjust than the equations of state..
Local Tomography of Large Networks under the Low-Observability Regime
No full textThis article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, where only a small fraction of the agents can be feasibly observed. The goal is to infer the underlying subnetwork of interactions and we refer to this problem as local tomography. In order to study the large-scale setting, we adopt a proper stochastic formulation where the unobserved part of the network is modeled as an Erdős-Rényi random graph, while the observable subnetwork is left arbitrary. The main result of this work is to establish that, under this setting, local tomography is actually possible with high probability, provided that certain conditions on the network model are met (such as stability and symmetry of the network combination matrix). Remarkably, such conclusion is established under the low-observability regime, where the cardinality of the observable subnetwork is fixed, while the size of the overall network scales to infinity
Consistent tomography under partial observations over adaptive networks
No full textThis paper studies the problem of inferring whether an agent is directly influenced by another agent over a network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update its online learning algorithm. The solution of this inference task is challenging for two main reasons. First, only the output of the learning algorithm is available to the external observer that must perform the inference based on these indirect measurements. Second, only output measurements from a fraction of the network agents is available, with the total number of agents itself being also unknown. The main focus of this paper is ascertaining under these demanding conditions whether consistent tomography is possible, namely, whether it is possible to reconstruct the interaction profile of the observable portion of the network, with negligible error as the network size increases. We establish a critical achievability result, namely, that for symmetric combination policies and for any given fraction of observable agents, the interacting and non-interacting agent pairs split into two separate clusters as the network size increases. This remarkable property then enables the application of clustering algorithms to identify the interacting agents influencing the observations. We provide a set of numerical experiments that verify the results for finite network sizes and time horizons. The numerical experiments show that the results hold for asymmetric combination policies as well, which is particularly relevant in the context of causation
Estimation and Detection Over Adaptive Networks
No full textIn this chapter, we review the foundations of statistical inference over adaptive networks by considering two canonical problems: distributed estimation and distributed detection. In the former setting, agents cooperate to estimate a model of interest while in the second setting, the agents cooperate to detect a state of nature. We focus on adaptive learning solutions where agents are able to track drifts in the underlying models, and examine performance limits under both estimation and detection formulations. Special attention is paid to the detailed characterization of the steady-state performance. Certain universal laws are highlighted and compared against known laws for estimation and detection in traditional (centralized or decentralized, nonadaptive) inferential systems
Decision Learning and Adaptation over Multi-Task Networks
No full textThis paper studies the operation of multi-agent networks engaged in multi-task decision problems under the paradigm of simultaneous learning and adaptation. Two scenarios are considered:one in which a decision must be taken among multiple states of nature that are known but can vary over time and space, and another in which there exists a known 'normal' state of nature and the task is to detect unpredictable and unknown deviations from it. In both cases the network learns from the past and adapts to changes in real time in a multi-task scenario with different clusters of agents addressing different decision problems. The system design takes care of challenging situations with clusters of complicated structure, and the performance assessment is conducted by computer simulations. A theoretical analysis is developed to obtain a statistical characterization of the agents' status at steady-state, under the simplifying assumption that clustering is made without errors. This provides approximate bounds for the steady-state decision performance of the agents. Insights are provided for deriving accurate performance prediction by exploiting the derived theoretical results
- …
