Subset Feature Learning for Fine-Grained Category Classification

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy

How well will ton-scale dark matter direct detection experiments constrain minimal supersymmetry?

Weakly interacting massive particles (WIMPs) are amongst the most interesting dark matter (DM) candidates. Many DM candidates naturally arise in theories beyond the standard model (SM) of particle physics, like weak-scale supersymmetry (SUSY). Experiments aim to detect WIMPs by scattering, annihilation or direct production, and thereby determine the underlying theory to which they belong, along with its parameters. Here we examine the prospects for further constraining the Constrained Minimal Supersymmetric Standard Model (CMSSM) with future ton-scale direct detection experiments. We consider ton-scale extrapolations of three current experiments: CDMS, XENON and COUPP, with 1000 kg-years of raw exposure each. We assume energy resolutions, energy ranges and efficiencies similar to the current versions of the experiments, and include backgrounds at target levels. Our analysis is based on full likelihood constructions for the experiments. We also take into account present uncertainties on hadronic matrix elements for neutralino-quark couplings, and on halo model parameters. We generate synthetic data based on four benchmark points and scan over the CMSSM parameter space using nested sampling. We construct both Bayesian posterior PDFs and frequentist profile likelihoods for the model parameters, as well as the mass and various cross-sections of the lightest neutralino. Future ton-scale experiments will help substantially in constraining supersymmetry, especially when results of experiments primarily targeting spin-dependent nuclear scattering are combined with those directed more toward spin-independent interactions.Comment: 53 pages, 19 figures; typos corrected; number of plots reduced and some discussions added in response to referee's comments; matches published versio

Church Leadership Personalities: A Comparative Study of the Personality Components of Senior and Executive Pastors

The purpose of this quantitative correlational research was to understand the relationship between the personality components of Senior and Executive Pastors. This study collected data in a quantitative form using a personality assessment called The Birkman Method. The data was then analyzed to understand the relationship between the personality types of Senior and Executive Pastors. Research was needed to understand the relationship of personality types of Senior and Executive Pastors. Research questions derived from the problem statement and research purpose are presented as they provide the structure for the dissertation. These questions led to the gathering of information related to personality components to identify if a correlation existed between the personalities of Executive and Senior Pastors, and if one was found, what that correlation was. The population for this study was 11 Senior and Executive Pastors who had served in their current positions in Oklahoma Southern Baptist churches that had an average Sunday morning worship attendance of 500 or more. This study will add to the literature related to the development of church and ministry staff and will benefit churches and ministries in their hiring practices. Further, this research will significantly help those who work together in executive leadership positions in a hierarchical reporting structure

The Small Stellated Dodecahedron Code and Friends

We explore a distance-3 homological CSS quantum code, namely the small stellated dodecahedron code, for dense storage of quantum information and we compare its performance with the distance-3 surface code. The data and ancilla qubits of the small stellated dodecahedron code can be located on the edges resp. vertices of a small stellated dodecahedron, making this code suitable for 3D connectivity. This code encodes 8 logical qubits into 30 physical qubits (plus 22 ancilla qubits for parity check measurements) as compared to 1 logical qubit into 9 physical qubits (plus 8 ancilla qubits) for the surface code. We develop fault-tolerant parity check circuits and a decoder for this code, allowing us to numerically assess the circuit-based pseudo-threshold.Comment: 19 pages, 14 figures, comments welcome! v2 includes updates which conforms with the journal versio

A FRAMEWORK FOR EFFECTIVE INDUSTRY STRATEGIC PLANNING

As agricultural commodity industries strategically plan for their future, they need to consider the systemic and synergistic effects of such factors as changing government regulations, demand expansion or contraction, globalized markets, increased competitive pressures, and greater customer quality requirements. This article discusses a framework developed to help industries strategically plan within the context of these dynamic factors. This framework, based upon relevant theory and an accumulation of experiences with this type of strategic planning, provides one possible approach for addressing the strategic needs of an entire industry. In this way, a commodity industry as a whole can identify and address key industrywide strategic issues to maintain and enhance its competitiveness, profitability, or at the very least, its survival in increasingly global markets.framework, industry, strategic planning, Agribusiness,

Toward quantum opto-mechanics in a gram-scale suspended mirror interferometer

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 137-153).A new generation of interferometric gravitational wave detectors, currently under construction, will closely approach the fundamental quantum limits of measurement, serving as a prominent example of quantum mechanics at the macroscale. Simultaneously, numerous experiments involving micro-mechanical oscillators are beginning to explore the quantum regime, with the help of optical cooling techniques. We discuss the approach to the quantum regime in a gram-scale opto-mechanical experiment, and in large-scale gravitational wave detectors. The gram-scale experiment is designed so that radiation pressure forces completely dominate the dynamics of the mechanical mirror suspensions. We review a series of optical trapping and cooling techniques that we have demonstrated using this apparatus. A variant of these techniques is applied to a gravitational wave interferometer -- yielding an effective temperature of 1.4 microkelvin and a phonon occupation number of 234 in a kilogram-scale oscillator. Then we analyze the displacement noise spectrum in the gram-scale system, which is currently limited by thermally driven fluctuations of the mirror suspensions. We identify methods for improving the suspension, in order to reveal the quantum fluctuations attributable to back-action of a displacement measurement. Finally, we propose a scheme for exploiting the opto-mechanical coupling in this system to generate optical entanglement.by Christopher Wipf.Ph.D

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

The XENON1T Data Distribution and Processing Scheme

The XENON experiment is looking for non-baryonic particle dark matter in the universe. The setup is a dual phase time projection chamber (TPC) filled with 3200 kg of ultra-pure liquid xenon. The setup is operated at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. We present a full overview of the computing scheme for data distribution and job management in XENON1T. The software package Rucio, which is developed by the ATLAS collaboration, facilitates data handling on Open Science Grid (OSG) and European Grid Infrastructure (EGI) storage systems. A tape copy at the Center for High Performance Computing (PDC) is managed by the Tivoli Storage Manager (TSM). Data reduction and Monte Carlo production are handled by CI Connect which is integrated into the OSG network. The job submission system connects resources at the EGI, OSG, SDSC's Comet, and the campus HPC resources for distributed computing. The previous success in the XENON1T computing scheme is also the starting point for its successor experiment XENONnT, which starts to take data in autumn 2019.Comment: 8 pages, 2 figures, CHEP 2018 proceeding