Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, computational memory architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we experimentally demonstrate for the first time, the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize audio signals of alphabets encoded using spikes in the time domain and to generate spike trains at precise time instances to represent the pixel intensities of their corresponding images. Moreover, with a statistical model capturing the experimental behavior of the devices, we investigate architectural and systems-level solutions for improving the training and inference performance of our computational memory-based system. Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems

Aging Pipeline Infrastructure in the United States: How do a changing policy mix, issues of energy justice, and social media communication impact future risk analysis?

Over two and a half million miles of pipeline cross the United States today, half of which is over fifty years old and thus was designed, located, and debated without today’s modern environmental policies in place. Aging pipeline infrastructure, such as the (infamous in Michigan) Enbridge Line 5 pipeline underwater crossing at Michigan’s Straits of Mackinac, has undergone increased public scrutiny and risk analysis this past decade. This has led to the potential for policy changes in the historically stable energy services institution associated with pipeline infrastructure regulation. While policy process literature generally describes how policy changes over time, it is missing research on how new goals and new technology, such as energy justice and social media, impact agenda setting and decisions when added to the policy mix. This dissertation first investigates the evolving federal pipeline regime policy goals through an advanced policy mix analysis. Next, it argues that energy justice research can be advanced through deterministic approaches and analyses. Last, this dissertation uses a social network analysis to explain why aging pipelines are on today’s policy agenda through social network analysis. By understanding how the pipeline policy mix has changed over time, including through the addition of modern topics such as energy justice and modern technologies such as social media, policy and decision makers can improve prioritization of risk analysis for aging pipeline infrastructure

Causal modeling and prediction over event streams

In recent years, there has been a growing need for causal analysis in many modern stream applications such as web page click monitoring, patient health care monitoring, stock market prediction, electric grid monitoring, and network intrusion detection systems. The detection and prediction of causal relationships help in monitoring, planning, decision making, and prevention of unwanted consequences. An event stream is a continuous unbounded sequence of event instances. The availability of a large amount of continuous data along with high data throughput poses new challenges related to causal modeling over event streams, such as (1) the need for incremental causal inference for the unbounded data, (2) the need for fast causal inference for the high throughput data, and (3) the need for real-time prediction of effects from the events seen so far in the continuous event streams. This dissertation research addresses these three problems by focusing on utilizing temporal precedence information which is readily available in event streams: (1) an incremental causal model to update the causal network incrementally with the arrival of a new batch of events instead of storing the complete set of events seen so far and building the causal network from scratch with those stored events, (2) a fast causal model to speed up the causal network inference time, and (3) a real-time top-k predictive query processing mechanism to find the most probable k effects with the highest scores by proposing a run-time causal inference mechanism which addresses cyclic causal relationships. In this dissertation, the motivation, related work, proposed approaches, and the results are presented in each of the three problems

Vehicle-Level Reasoning Systems: Integrating System-Wide Data to Estimate the Instantaneous Health State

At the aircraft level, a Vehicle-Level Reasoning System (VLRS) can be developed to provide aircraft with at least two significant capabilities: improvement of aircraft safety due to enhanced monitoring and reasoning about the aircrafts health state, and also potential cost savings by enabling Condition Based Maintenance (CBM). Along with the benefits of CBM, an important challenge facing aviation safety today is safeguarding against system and component failures and malfunctions. Faults can arise in one or more aircraft subsystem their effects in one system may propagate to other subsystems, and faults may interact

Access to consciousness of briefly presented visual events is modulated by transcranial direct current stimulation of left dorsolateral prefrontal cortex

Adaptive behaviour requires the ability to process goal-relevant events at the expense of irrelevant ones. However, perception of a relevant visual event can transiently preclude access to consciousness of subsequent events — a phenomenon called attentional blink (AB). Here we investigated involvement of the left dorsolateral prefrontal cortex (DLPFC) in conscious access, by using transcranial direct current stimulation (tDCS) to potentiate or reduce neural excitability in the context of an AB task. In a sham-controlled experimental design, we applied between groups anodal or cathodal tDCS over the left DLPFC, and examined whether this stimulation modulated the proportion of stimuli that were consciously reported during the AB period. We found that tDCS over the left DLPFC affected the proportion of consciously perceived target stimuli. Moreover, anodal and cathodal tDCS had opposing effects, and exhibited different temporal patterns. Anodal stimulation attenuated the AB, enhancing conscious report earlier in the AB period. Cathodal stimulation accentuated the AB, reducing conscious report later in the AB period. These findings support the notion that the DLPFC plays a role in facilitating information transition from the unconscious to the conscious stage of processing

Emerging Challenges and Opportunities in Infectious Disease Epidemiology.

Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners

Big continuous data: dealing with velocity by composing event streams

International audienceThe rate at which we produce data is growing steadily, thus creating even larger streams of continuously evolving data. Online news, micro-blogs, search queries are just a few examples of these continuous streams of user activities. The value of these streams relies in their freshness and relatedness to on-going events. Modern applications consuming these streams need to extract behaviour patterns that can be obtained by aggregating and mining statically and dynamically huge event histories. An event is the notification that a happening of interest has occurred. Event streams must be combined or aggregated to produce more meaningful information. By combining and aggregating them either from multiple producers, or from a single one during a given period of time, a limited set of events describing meaningful situations may be notified to consumers. Event streams with their volume and continuous production cope mainly with two of the characteristics given to Big Data by the 5V’s model: volume & velocity. Techniques such as complex pattern detection, event correlation, event aggregation, event mining and stream processing, have been used for composing events. Nevertheless, to the best of our knowledge, few approaches integrate different composition techniques (online and post-mortem) for dealing with Big Data velocity. This chapter gives an analytical overview of event stream processing and composition approaches: complex event languages, services and event querying systems on distributed logs. Our analysis underlines the challenges introduced by Big Data velocity and volume and use them as reference for identifying the scope and limitations of results stemming from different disciplines: networks, distributed systems, stream databases, event composition services, and data mining on traces