Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses

Random-effects meta-analyses of observational studies can produce biased estimates if the synthesized studies are subject to unmeasured confounding. We propose sensitivity analyses quantifying the extent to which unmeasured confounding of specified magnitude could reduce to below a certain threshold the proportion of true effect sizes that are scientifically meaningful. We also develop converse methods to estimate the strength of confounding capable of reducing the proportion of scientifically meaningful true effects to below a chosen threshold. These methods apply when a "bias factor" is assumed to be normally distributed across studies or is assessed across a range of fixed values. Our estimators are derived using recently proposed sharp bounds on confounding bias within a single study that do not make assumptions regarding the unmeasured confounders themselves or the functional form of their relationships to the exposure and outcome of interest. We provide an R package, ConfoundedMeta, and a freely available online graphical user interface that compute point estimates and inference and produce plots for conducting such sensitivity analyses. These methods facilitate principled use of random-effects meta-analyses of observational studies to assess the strength of causal evidence for a hypothesis

Coalitions in Cooperative Wireless Networks

Cooperation between rational users in wireless networks is studied using coalitional game theory. Using the rate achieved by a user as its utility, it is shown that the stable coalition structure, i.e., set of coalitions from which users have no incentives to defect, depends on the manner in which the rate gains are apportioned among the cooperating users. Specifically, the stability of the grand coalition (GC), i.e., the coalition of all users, is studied. Transmitter and receiver cooperation in an interference channel (IC) are studied as illustrative cooperative models to determine the stable coalitions for both flexible (transferable) and fixed (non-transferable) apportioning schemes. It is shown that the stable sum-rate optimal coalition when only receivers cooperate by jointly decoding (transferable) is the GC. The stability of the GC depends on the detector when receivers cooperate using linear multiuser detectors (non-transferable). Transmitter cooperation is studied assuming that all receivers cooperate perfectly and that users outside a coalition act as jammers. The stability of the GC is studied for both the case of perfectly cooperating transmitters (transferrable) and under a partial decode-and-forward strategy (non-transferable). In both cases, the stability is shown to depend on the channel gains and the transmitter jamming strengths.Comment: To appear in the IEEE Journal on Selected Areas in Communication, Special Issue on Game Theory in Communication Systems, 200

Killing the Straw Man: Does BICEP Prove Inflation at the GUT Scale?

The surprisingly large value of $r$, the ratio of power in tensor to scalar density perturbations in the CMB reported by the BICEP2 Collaboration, if confirmed, provides strong evidence for Inflation at the GUT scale. While the Inflationary signal remains the best motivated source, a large value of $r$ alone would still allow for the possibility that a comparable gravitational wave background might result from a self ordering scalar field (SOSF) transition that takes place later at somewhat lower energy. We find that even without detailed considerations of the predicted BICEP signature of such a transition, simple existing limits on the isocurvature contribution to CMB anisotropies would definitively rule out a contribution of more than $5\%$ to $r \approx 0.2$,. We also present a general relation for the allowed fractional SOSF contribution to $r$ as a function of the ultimate measured value of $r$. These results point strongly not only to an inflationary origin of the BICEP2 signal, if confirmed, but also to the fact that if the GUT scale is of order $10^{16} GeV$ then either the GUT transition happens before Inflation or the Inflationary transition and the GUT transition must be one and the same.Comment: 3 pages 2 figures, accepted for publication in Physics Letters B . Accepted version revised slightly in response to referee's comment

Manufacture of fiber-epoxy test specimens: Including associated jigs and instrumentation

Experimental work on the manufacture and strength of graphite-epoxy composites is considered. The correct data and thus a true assessment of the strength properties based on a proper and scientifically modeled test specimen with engineered design, construction, and manufacture has led to claims of a very broad spread in optimized values. Such behavior is in the main due to inadequate control during manufacture of test specimen, improper curing, and uneven scatter in the fiber orientation. The graphite fibers are strong but brittle. Even with various epoxy matrices and volume fraction, the fracture toughness is still relatively low. Graphite-epoxy prepreg tape was investigated as a sandwich construction with intermittent interlaminar bonding between the laminates in order to produce high strength, high fracture toughness composites. The quality and control of manufacture of the multilaminate test specimen blanks was emphasized. The dimensions, orientation and cure must be meticulous in order to produce the desired mix

A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks

Transfer Learning (TL) plays a crucial role when a given dataset has insufficient labeled examples to train an accurate model. In such scenarios, the knowledge accumulated within a model pre-trained on a source dataset can be transferred to a target dataset, resulting in the improvement of the target model. Though TL is found to be successful in the realm of image-based applications, its impact and practical use in Natural Language Processing (NLP) applications is still a subject of research. Due to their hierarchical architecture, Deep Neural Networks (DNN) provide flexibility and customization in adjusting their parameters and depth of layers, thereby forming an apt area for exploiting the use of TL. In this paper, we report the results and conclusions obtained from extensive empirical experiments using a Convolutional Neural Network (CNN) and try to uncover thumb rules to ensure a successful positive transfer. In addition, we also highlight the flawed means that could lead to a negative transfer. We explore the transferability of various layers and describe the effect of varying hyper-parameters on the transfer performance. Also, we present a comparison of accuracy value and model size against state-of-the-art methods. Finally, we derive inferences from the empirical results and provide best practices to achieve a successful positive transfer.Comment: 9 pages, 2 figures, accepted in SDM 201

STIRLOCHARGER POWERED BY EXHAUST HEAT FOR HIGH EFFICIENCY COMBUSTION AND ELECTRIC GENERATION

Stirling engines have been in existence since the early 1900s, and have been of little study in the recent years. Stirling engines are low power, heat engines which work on the principle of a temperature differential between the hot and cold sides. This thesis will look into the integration of a Stirling engine onto a turbocompressor for automotive applications calling the device a Stirlocharger