Christopher T. Symons

Scalable Parallel Algorithms for FPT Problems

Algorithmic methods based on the theory of fixed-parameter tractability are combined with powerful computational platforms to launch systematic attacks on combinatorial problems of significance. As a case study, optimal solutions to very large instances of the NP-hard vertex cover problem are computed. To accomplish this, an efficient sequential algorithm and various forms of parallel algorithms are devised, implemented, and compared. The importance of maintaining a balanced decomposition of the search space is shown to be critical to achieving scalability. Target problems need only be amenable to reduction and decomposition. Applications in high throughput computational biology are also discussed.

Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets

The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition, and analysis. Advances in imaging technology in the beginning of the twenty-first century have opened the proverbial floodgates on the availability of high-veracity information on structure and functionality. From the hardware perspective, high-resolution imaging methods now routinely resolve atomic positions with approximately picometer precision, allowing for quantitative measurements of individual bond lengths and angles. Similarly, 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 multidimensional structural and functional data into physically and chemically relevant information.

High performance computational tools for Motif discovery

Summary form only given. We highlight a fruitful interplay between biology and computation. The sequencing of complete genomes from multiple organisms has revealed that most differences in organism complexity are due to elements of gene regulation that reside in the non protein coding portions of genes. Both within and between species, transcription factor binding sites and the proteins that recognize them govern the activity of cellular pathways that mediate adaptive responses and survival. Experimental identification of these regulatory elements is by nature a slow process. The availability of complete genomic sequences, however, opens the door for computational methods to predict binding sites and expedite our understanding of gene regulation at a genomic level. Just as with traditional experimental approaches, the computational identification of the molecular factors that control a genes expression level has been problematic. As a case in point, the identification of putative motifs is a challenging combinatorial task. For it, powerful new motif finding algorithms and high performance implementations are described. Heavy use is made of graph algorithms, some of which are exceedingly computationally intensive and involve the use of emergent mathematical methods. An approach to fully dynamic load balancing is developed in order to make effective use of highly parallel platforms.

A New Approach and Faster Exact Methods for the Maximum Common Subgraph Problem

The Maximum Common Subgraph (MCS) problem appears in many guises and in a wide variety of applications. The usual goal is to take as inputs two graphs, of order m and n, respectively, and find the largest induced subgraph contained in both of them. MCS is frequently solved by reduction to the problem of finding a maximum clique in the order mn association graph, which is a particular form of product graph built from the inputs. In this paper a new algorithm, termed “clique branching,” is proposed that exploits a special structure inherent in the association graph. This structure contains a large number of naturally-ordered cliques that are present in the association graph’s complement. A detailed analysis shows that the proposed algorithm requires O((m+1)n) time, which is a superior worst-case bound to those known for previously-analyzed algorithms in the setting of the MCS problem.

Nonparametric semi-supervised learning for network intrusion detection: combining performance improvements with realistic in-situ training

A barrier to the widespread adoption of learning-based network intrusion detection tools is the in-situ training requirements for effective discrimination of malicious traffic. Supervised learning techniques necessitate a quantity of labeled examples that is often intractable, and at best cost-prohibitive. Recent advances in semi-supervised techniques have demonstrated the ability to generalize well based on a significantly smaller set of labeled samples. In network intrusion detection, placing reasonable requirements on the number of training examples provides realistic expectations that a learning-based system can be trained in the environment where it will be deployed. This in-situ training is necessary to ensure that the assumptions associated with the learning process hold, and thereby support a reasonable belief in the generalization ability of the resulting model. In this paper, we describe the application of a carefully selected nonparametric, semi-supervised learning algorithm to the network intrusion problem, and compare the performance to other model types using feature-based data derived from an operational network. We demonstrate dramatic performance improvements over supervised learning and anomaly detection in discriminating real, previously unseen, malicious network traffic while generating an order of magnitude fewer false alerts than any alternative, including a signature IDS tool deployed on the same network.

Multi-Criterion Active Learning in Conditional Random Fields

Conditional random fields (CRFs), which are popular supervised learning models for many natural language processing (NLP) tasks, typically require a large collection of labeled data for training. In practice, however, manual annotation of text documents is quite costly. Furthermore, even large labeled training sets can have arbitrarily limited performance peaks if they are not chosen with care. This paper considers the use of multi-criterion active learning for identification of a small but sufficient set of text samples for training CRFs. Our empirical results demonstrate that our method is capable of reducing the manual annotation costs, while also limiting the retraining costs that are often associated with active learning. In addition, we show that the generalization performance of CRFs can be enhanced through judicious selection of training examples

Detecting magnetic ordering with atomic size electron probes

Although magnetism originates at the atomic scale, the existing spectroscopic techniques sensitive to magnetic signals only produce spectra with spatial resolution on a larger scale. However, recently, it has been theoretically argued that atomic size electron probes with customized phase distributions can detect magnetic circular dichroism. Here, we report a direct experimental real-space detection of magnetic circular dichroism in aberration-corrected scanning transmission electron microscopy (STEM). Using an atomic size-aberrated electron probe with a customized phase distribution, we reveal the checkerboard antiferromagnetic ordering of Mn moments in LaMnAsO by observing a dichroic signal in the Mn L-edge. The novel experimental setup presented here, which can easily be implemented in aberration-corrected STEM, opens new paths for probing dichroic signals in materials with unprecedented spatial resolution.

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 and unknown network-based 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.

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

Abstract 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 Laboratorys (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 BEAMs 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.

A Combinatorial Approach to the Analysis of Differential Gene Expression Data

Combinatorial methods are studied in an effort to gauge their potential utility in the analysis of differential gene expression data. Patient and gene relationships are modeled using edge-weighted graphs. Two algorithms with different, but complementary approaches are devised and implemented. One is based on finding optimal cliques within general graphs, the other on isolating near-optimal dominating sets within bipartite graphs. A main goal is to develop methodologies for training algorithms on patient populations with known disease profiles, so that they can be employed to classify and predict the likelihood of disease in patient populations whose profiles are not known. These novel strategies are in marked contrast with Bayesian and other wellknown techniques. Encouraging results are reported.