2,614 research outputs found
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
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
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
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
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