Publications

Abstract not provided.

We use Bayesian data analysis to predict dengue fever outbreaks and quantify the link between outbreaks and meteorological precursors tied to the breeding conditions of vector mosquitos. We use Hamiltonian Monte Carlo sampling to estimate a seasonal Gaussian process modeling infection rate, and aperiodic basis coefficients for the rate of an “outbreak level” of infection beyond seasonal trends across two separate regions. We use this outbreak level to estimate an autoregressive moving average (ARMA) model from which we extrapolate a forecast. We show that the resulting model has useful forecasting power in the 6–8 week range. The forecasts are not significantly more accurate with the inclusion of meteorological covariates than with infection trends alone.

We apply transfer learning techniques to create topically and/or stylistically biased natural language models from small data samples, given generic long short-term memory (LSTM) language models trained on larger data sets. Although LSTM language models are powerful tools with wide-ranging applications, they require enormous amounts of data and time to train. Thus, we build general purpose language models that take advantage of large standing corpora and computational resources proactively, allowing us to build more specialized analytical tools from smaller data sets on demand. We show that it is possible to construct a language model from a small, focused corpus by first training an LSTM language model on a large corpus (e.g., the text from English Wikipedia) and then retraining only the internal transition model parameters on the smaller corpus. We also show that a single general language model can be reused through transfer learning to create many distinct special purpose language models quickly with modest amounts of data.

This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to de- velop a method for analyzing multiple time-series data streams to identify precursors provid- ing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication sub- missions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.

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Social network graph models are data structures representing entities (often people, corpora- tions, or accounts) as "vertices" and their interactions as "edges" between pairs of vertices. These graphs are most often total-graph models -- the overall structure of edges and vertices in a bidirectional or directional graph are described in global terms and the network is gen- erated algorithmically. We are interested in "egocentrie or "agent-based" models of social networks where the behavior of the individual participants are described and the graph itself is an emergent phenomenon. Our hope is that such graph models will allow us to ultimately reason from observations back to estimated properties of the individuals and populations, and result in not only more accurate algorithms for link prediction and friend recommen- dation, but also a more intuitive understanding of human behavior in such systems than is revealed by previous approaches. This report documents our preliminary work in this area; we describe several past graph models, two egocentric models of our own design, and our thoughts about the future direction of this research.

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We present a temporal model of individual-scale social media user behavior, comprising modal activity levels and mode switching patterns. We show that this model can be effectively and easily learned from available social media data, and that our model is sufficiently flexible to capture diverse users’ daily activity patterns. In applications such as electric power load prediction, computer network traffic analysis, disease spread modeling, and disease outbreak forecasting, it is useful to have a model of individual-scale patterns of human behavior. Our user model is intended to be suitable for integration into such population models, for future applications of prediction, change detection, or agent-based simulation.

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It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

We present a novel class of dynamic neural networks that is capable of learning, in an unsupervised manner, attractors that correspond to generalities in a data set. Upon presentation of a test stimulus, the networks follow a sequence of attractors that correspond to subsets of increasing size or generality in the original data set. The networks, inspired by those of the insect antennal lobe, build upon a modified Hopfield network in which nodes are periodically suppressed, global inhibition is gradually strengthened, and the weight of input neurons is gradually decreased relative to recurrent connections. This allows the networks to converge on a Hopfield network's equilibrium within each suppression cycle, and to switch between attractors in between cycles. The fast mutually reinforcing excitatory connections that dominate dynamics within cycles ensures the robust error-tolerant behavior that characterizes Hopfield networks. The cyclic inhibition releases the network from what would otherwise be stable equilibriums or attractors. Increasing global inhibition and decreasing dependence on the input leads successive attractors to differ, and to display increasing generality. As the network is faced with stronger inhibition, only neurons connected with stronger mutually excitatory connections will remain on; successive attractors will consist of sets of neurons that are more strongly correlated, and will tend to select increasingly generic characteristics of the data. Using artificial data, we were able to identify configurations of the network that appeared to produce a sequence of increasingly general results. The next logical steps are to apply these networks to suitable real-world data that can be characterized by a hierarchy of increasing generality and observe the network's performance. This report describes the work, data, and results, the current understanding of the results, and how the work could be continued. The code, data, and preliminary results are included and are available as an archive.

If software is designed so that the software can issue functions that will move that software from one computing platform to another, then the software is said to be 'mobile'. There are two general areas of security problems associated with mobile code. The 'secure host' problem involves protecting the host from malicious mobile code. The 'secure mobile code' problem, on the other hand, involves protecting the code from malicious hosts. This report focuses on the latter problem. We have found three distinct camps of opinions regarding how to secure mobile code. There are those who believe special distributed hardware is necessary, those who believe special distributed software is necessary, and those who believe neither is necessary. We examine all three camps, with a focus on the third. In the distributed software camp we examine some commonly proposed techniques including Java, D'Agents and Flask. For the specialized hardware camp, we propose a cryptographic technique for 'tamper-proofing' code over a large portion of the software/hardware life cycle by careful modification of current architectures. This method culminates by decrypting/authenticating each instruction within a physically protected CPU, thereby protecting against subversion by malicious code. Our main focus is on the camp that believes that neither specialized software nor hardware is necessary. We concentrate on methods of code obfuscation to render an entire program or a data segment on which a program depends incomprehensible. The hope is to prevent or at least slow down reverse engineering efforts and to prevent goal-oriented attacks on the software and execution. The field of obfuscation is still in a state of development with the central problem being the lack of a basis for evaluating the protection schemes. We give a brief introduction to some of the main ideas in the field, followed by an in depth analysis of a technique called 'white-boxing'. We put forth some new attacks and improvements on this method as well as demonstrating its implementation for various algorithms. We also examine cryptographic techniques to achieve obfuscation including encrypted functions and offer a new application to digital signature algorithms. To better understand the lack of security proofs for obfuscation techniques, we examine in detail general theoretical models of obfuscation. We explain the need for formal models in order to obtain provable security and the progress made in this direction thus far. Finally we tackle the problem of verifying remote execution. We introduce some methods of verifying remote exponentiation computations and some insight into generic computation checking.

This report describes the results of research and development in the area of communication among disparate species of software agents. The two primary elements of the work are the formation of ontologies for use by software agents and the means by which software agents are instructed to carry out complex tasks that require interaction with other agents. This work was grounded in the areas of commercial transport and cybersecurity.