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Symons, Christopher T

Publications and source records attributed to Symons, Christopher T.

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Error-Bounded Graph Construction for Semi-supervised Manifold Learning

Graphs are commonly used in semi-supervised learning to represent a manifold on which the data reside in a high-dimensional ambient space. The graph can then be utilized in different ways, typically via the Laplacian of the graph, in order to leverage associations among the unlabeled data to improve learning. One common way to leverage the graph Laplacian is as a regularization term, where models that would disagree with the graph are penalized. More often the spectrum of the graph Laplacian is used to find a lower dimensional embedding in which neighboring relations encoded via the graph are preserved. Most manifold-based methods of semi-supervised learning depend upon geometric structure in the ambient feature space in order to construct a graph whose edges encode similarity that should be useful in selecting a model. A critical assumption is that some standard measure of similarity applied to the ambient space can be used to construct a graph that is error-free or of low error, meaning that examples (i.e., vertices) from distinct classes are not connected. However, this assumption often precludes the use of such methods in noisy or complex feature spaces, even though such spaces often arise in problems that can most benefit from structure that might be uncovered within the unlabeled data. This paper presents a method of graph construction for manifold-based semi-supervised learning that respects the manifold assumptions underlying these methods and bounds the error on the graph itself, which then permits bounds on the overall generalization error of the learning algorithms without relying on assumptions that do not hold in many modern problem domains.

Symons, Christopher T↗
DOE OSTI2016

BEAM: A Computational Workflow System for Managing and Modeling Material Characterization Data in HPC Environments

Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now—with the rise of multimodal acquisition systems and the associated processing capability—the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalable data analysis and simulation via an intuitive, cross-platform client user interface. This framework delivers authenticated, “push-button” execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing the converged compute-and-data infrastructure at Oak Ridge National Laboratory's (ORNL) Compute and Data Environment for Science (CADES) and HPC environments like Titan at the Oak Ridge Leadership Computing Facility (OLCF). In this work we address the underlying HPC needs for characterization in the material science community, elaborate how BEAM's design and infrastructure tackle those needs, and present a small sub-set of user cases where scientists utilized BEAM across a broad range of analytical techniques and analysis modes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Big Data and Deep data in scanning and electron microscopies: functionality from multidimensional data sets

The development of electron, and scanning probe microscopies in the second half of the twentieth century have produced spectacular images of internal structure and composition of matter with, at nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition and analysis. The progress in imaging technologies in the beginning of the twenty first century has opened the proverbial floodgates of high-veracity information on structure and functionality. High resolution imaging now allows information on atomic positions with picometer precision, allowing for quantitative measurements of individual bond length and angles. Functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this data into physically and chemically relevant information from imaging data.

36 MATERIALS SCIENCE↗
DOE OSTI2013

A Learning System for Discriminating Variants of Malicious Network Traffic

Modern computer network defense systems rely primarily on signature-based intrusion detection tools, which generate alerts when patterns that are pre-determined to be malicious are encountered in network data streams. Signatures are created reactively, and only after in-depth manual analysis of a network intrusion. There is little ability for signature-based detectors to identify intrusions that are new or even variants of an existing attack, and little ability to adapt the detectors to the patterns unique to a network environment. Due to these limitations, the need exists for network intrusion detection techniques that can more comprehensively address both known unknown networkbased attacks and can be optimized for the target environment. This work describes a system that leverages machine learning to provide a network intrusion detection capability that analyzes behaviors in channels of communication between individual computers. Using examples of malicious and non-malicious traffic in the target environment, the system can be trained to discriminate between traffic types. The machine learning provides insight that would be difficult for a human to explicitly code as a signature because it evaluates many interdependent metrics simultaneously. With this approach, zero day detection is possible by focusing on similarity to known traffic types rather than mining for specific bit patterns or conditions. This also reduces the burden on organizations to account for all possible attack variant combinations through signatures. The approach is presented along with results from a third-party evaluation of its performance.

Computer Network Defense↗
DOE OSTI2013

Massively Scalable Near Duplicate Detection in Streams of Documents using MDSH

In a world where large-scale text collections are not only becoming ubiquitous but also are growing at increasing rates, near duplicate documents are becoming a growing concern that has the potential to hinder many different information filtering tasks. While others have tried to address this problem, prior techniques have only been used on limited collection sizes and static cases. We will briefly describe the problem in the context of Open Source Intelligence (OSINT) along with our additional constraints for performance. In this work we propose two variations on Multi-dimensional Spectral Hash (MDSH) tailored for working on extremely large, growing sets of text documents. We analyze the memory and runtime characteristics of our techniques and provide an informal analysis of the quality of the near-duplicate clusters produced by our techniques.

Bogen, Paul Logasa↗
DOE OSTI2012

Guided Text Analysis Using Adaptive Visual Analytics

This paper demonstrates the promise of augmenting interactive visualizations with semi-supervised machine learning techniques to improve the discovery of significant associations and insight in the search and analysis of textual information. More specifically, we have developed a system called Gryffin that hosts a unique collection of techniques that facilitate individualized investigative search pertaining to an ever-changing set of analytical questions over an indexed collection of open-source publications related to national infrastructure. The Gryffin client hosts dynamic displays of the search results via focus+context record listings, temporal timelines, term- frequency views, and multiple coordinated views. Furthermore, as the analyst interacts with the display, the interactions are recorded and used to label the search records. These labeled records are then used to drive semi-supervised machine learning algorithms that re-rank the unlabeled search records such that potentially relevant records are moved to the top of the record listing. Gryffin is described in the context of the daily tasks encountered at the Department of Homeland Securitys Fusion Centers, with whom we are collaborating in its development. The resulting system is capable of addressing the analysts information overload that can be directly attributed to the deluge of information that must be addressed in search and investigative analysis of textual information.

machine learning↗
DOE OSTI2012

A Text Analysis Approach to Motivate Knowledge Sharing via Microsoft SharePoint

Creating incentives for knowledge workers to share their knowledge within an organization continues to be a challenging task. Strong, innate behaviors of the knowledge worker, such as self-preservation and self- advancement, are difficult to overcome, regardless of the level of knowledge. Many incentive policies simply focus on providing external pressure to promote knowledge sharing. This work describes a technical approach to motivate sharing. Utilizing text analysis and machine learning techniques to create an enhanced knowledge sharing experience, a prototype system was developed and tested at Oak Ridge National Laboratory that reduces the overhead cost of sharing while providing a quick, positive payoff for the knowledge worker. This work describes the implementation and experiences of using the prototype in a corporate production environment.

99 GENERAL AND MISCELLANEOUS↗