Online Local Learning via Semidefinite Programming

In many online learning problems we are interested in predicting local information about some universe of items. For example, we may want to know whether two items are in the same cluster rather than computing an assignment of items to clusters; we may want to know which of two teams will win a game rather than computing a ranking of teams. Although finding the optimal clustering or ranking is typically intractable, it may be possible to predict the relationships between items as well as if you could solve the global optimization problem exactly. Formally, we consider an online learning problem in which a learner repeatedly guesses a pair of labels (l(x), l(y)) and receives an adversarial payoff depending on those labels. The learner's goal is to receive a payoff nearly as good as the best fixed labeling of the items. We show that a simple algorithm based on semidefinite programming can obtain asymptotically optimal regret in the case where the number of possible labels is O(1), resolving an open problem posed by Hazan, Kale, and Shalev-Schwartz. Our main technical contribution is a novel use and analysis of the log determinant regularizer, exploiting the observation that log det(A + I) upper bounds the entropy of any distribution with covariance matrix A.Comment: 10 page

Design and standalone characterisation of a capacitively coupled HV-CMOS sensor chip for the CLIC vertex detector

The concept of capacitive coupling between sensors and readout chips is under study for the vertex detector at the proposed high-energy CLIC electron positron collider. The CLICpix Capacitively Coupled Pixel Detector (C3PD) is an active High-Voltage CMOS sensor, designed to be capacitively coupled to the CLICpix2 readout chip. The chip is implemented in a commercial $180$ nm HV-CMOS process and contains a matrix of $128\times128$ square pixels with $25$ $\mu$m pitch. First prototypes have been produced with a standard resistivity of $\sim20$ $\Omega$cm for the substrate and tested in standalone mode. The results show a rise time of $\sim20$ ns, charge gain of $190$ mV/ke$^{-}$ and $\sim40$ e$^{-}$ RMS noise for a power consumption of $4.8$ $\mu$W/pixel. The main design aspects, as well as standalone measurement results, are presented.Comment: 13 pages, 13 figures, 2 tables. Work carried out in the framework of the CLICdp collaboratio

In-Space Propulsion, Logistics Reduction, and Evaluation of Steam Reformer Kinetics: Problems and Prospects

Human space missions generate waste materials. A 70-kg crewmember creates a waste stream of 1 kg per day, and a four-person crew on a deep space habitat for a 400+ day mission would create over 1600 kg of waste. Converted into methane, the carbon could be used as a fuel for propulsion or power. The NASA Advanced Exploration Systems (AES) Logistics Reduction and Repurposing (LRR) project is investing in space resource utilization with an emphasis on repurposing logistics materials for useful purposes and has selected steam reforming among many different competitive processes as the preferred method for repurposing organic waste into methane. Already demonstrated at the relevant processing rate of 5.4 kg of waste per day, high temperature oxygenated steam consumes waste and produces carbon dioxide, carbon monoxide, and hydrogen which can then be converted into methane catalytically. However, the steam reforming process has not been studied in microgravity. Data are critically needed to understand the mechanisms that allow use of steam reforming in a reduced gravity environment. This paper reviews the relevant literature, identifies gravity-dependent mechanisms within the steam gasification process, and describes an innovative experiment to acquire the crucial kinetic information in a small-scale reactor specifically designed to operate within the requirements of a reduced gravity aircraft flight. The experiment will determine if the steam reformer process is mass-transport limited, and if so, what level of forced convection will be needed to obtain performance comparable to that in 1-g

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Extraction and Characterization of Lipids from Salicornia virginica and Salicornia europaea

The lipid content from Salicornia virginica and Salicornia europaea is investigated. The plants are leafless halophytes with seeds contained in terminal nodes. The lipids, in the form of cell membranes and oil bodies that come directly from the node cells, are observed using fluorescence microscopy. Two extraction methods as well as the results of extracting from the seeds and from the entire nodes are described. Characterization of the fatty acid components of the lipids using Gas Chromatography in tandem with Mass Spectroscopy is also described. Comparisons are made between the two methods and between the two plant materials as lipid sources

Development of microsatellite markers in the toxic dinoflagellate Alexandrium minutum (Dinophyceae)

Author Posting. © Blackwell, 2006. This is the author's version of the work. It is posted here by permission of Blackwell for personal use, not for redistribution. The definitive version was published in Molecular Ecology Notes 6 (2006): 756-758, doi:10.1111/j.1471-8286.2006.01331.x.Outbreaks of paralytic shellfish poisoning caused by the toxic dinoflagellate Alexandrium minutum (Dinophyceae) are a worldwide concern from both the economic and human health points of view. For population genetic studies of A. minutum distribution and dispersal, highly polymorphic genetic markers are of great value. We isolated 12 polymorphic microsatellites from this cosmopolitan, toxic dinoflagellate species. These loci provide one class of highly variable genetic markers, as the number of alleles ranged from 4 to 12, and the estimate of gene diversity was from 0.560 to 0.862 across the 12 microsatellites; these loci have the potential to reveal genetic structure and gene flow among A. minutum populations.Support for this research provided in part (to DMA) by U.S. National Science Foundation grants OCE-0136861 and OCE-0430724, and the National Institute of Environmental Health Sciences Grant 1 P50 ES012742-01

Chemical Processing of Non-Crop Plants for Jet Fuel Blends Production

The use of Biofuels has been gaining in popularity over the past few years due to their ability to reduce the dependence on fossil fuels. Biofuels as a renewable energy source can be a viable option for sustaining long-term energy needs if they are managed efficiently. We describe our initial efforts to exploit algae, halophytes and other non-crop plants to produce synthetics for fuel blends that can potentially be used as fuels for aviation and non-aerospace applications. Our efforts have been dedicated to crafting efficient extraction and refining processes in order to extract constituents from the plant materials with the ultimate goal of determining the feasibility of producing biomass-based jet fuel from the refined extract. Two extraction methods have been developed based on communition processes, and liquid-solid extraction techniques. Refining procedures such as chlorophyll removal and transesterification of triglycerides have been performed. Gas chromatography in tandem with mass spectroscopy is currently being utilized in order to qualitatively determine the individual components of the refined extract. We also briefly discuss and compare alternative methods to extract fuel-blending agents from alternative biofuels sources

Microstructural and Mechanical Characterization of a Dispersion Strengthened Medium Entropy Alloy Produced Using Selective Laser Melting

High entropy alloys (HEAs) are an interesting new class of alloys which have been shown to exhibit both notable strength and ductility for a wide range of temperature and stresses. In addition, the remarkably small difference between the solvus and liquidus temperatures for many face centered cubic HEAs makes them an excellent candidate for selective laser melting fabrication. In this study, the microstructure and mechanical properties of a dispersion strengthened equiatomic NiCoCr alloy successfully produced using selective laser melting are explored. The effect laser speed, laser power, and powder recyclability have on final part density and microstructural segregation are analyzed through both x-ray diffraction and high resolution scanning electron microscopy. These results are further validated and compared to stable phase predictions produced using a commercially available high entropy alloy mobility database. Lastly, the tensile strengths resulting from different heat treatment pathways are detailed