Modernizing Legacy Physics Applications for Reuse in Web and SOA

This paper concerns the application of recent information technologies for creating a software system for numerical simulations in the domain of plasma physics and in particular metal vapor lasers. The presented work is connected with performing modernization of legacy physics software for reuse on the web and inside a Service-Oriented Architecture environment. Applied and described is the creation of Java front-ends of legacy C++ and FORTRAN codes. Then the transformation of some of the scientific components into web services, as well as the creation of a web interface to the legacy application, is presented. The use of the BPEL language for managing scientific workflows is also considered.

Upgrading legacy long-haul WDM systems through unbalancing the proportion of 1s and 0s in the transmitted data

We present experimental results for wavelength-division multiplexed (WDM) transmission performance using unbalanced proportions of 1s and 0s in pseudo-random bit sequence (PRBS) data. This investigation simulates the effect of local, in time, data unbalancing which occurs in some coding systems such as forward error correction when extra bits are added to the WDM data stream. We show that such local unbalancing, which would practically give a time-dependent error-rate, can be employed to improve the legacy long-haul WDM system performance if the system is allowed to operate in the nonlinear power region. We use a recirculating loop to simulate a long-haul fibre system.

The relationship between spoken language and speech and nonspeech processing in children with autism:a magnetic event-related field study

It has been proposed that language impairments in children with Autism Spectrum Disorders (ASD) stem from atypical neural processing of speech and/or nonspeech sounds. However, the strength of this proposal is compromised by the unreliable outcomes of previous studies of speech and nonspeech processing in ASD. The aim of this study was to determine whether there was an association between poor spoken language and atypical event-related field (ERF) responses to speech and nonspeech sounds in children with ASD (n = 14) and controls (n = 18). Data from this developmental population (ages 6-14) were analysed using a novel combination of methods to maximize the reliability of our findings while taking into consideration the heterogeneity of the ASD population. The results showed that poor spoken language scores were associated with atypical left hemisphere brain responses (200 to 400 ms) to both speech and nonspeech in the ASD group. These data support the idea that some children with ASD may have an immature auditory cortex that affects their ability to process both speech and nonspeech sounds. Their poor speech processing may impair their ability to process the speech of other people, and hence reduce their ability to learn the phonology, syntax, and semantics of their native language.

Legacy and Fate of Mercury and Methylmercury in the Florida Everglades

Mass inventories of total Hg (THg) and methylmercury (MeHg) and mass budgets of Hg newly deposited during the 2005 dry and wet seasons were constructed for the Everglades. As a sink for Hg, the Everglades has accumulated 914, 1138, 4931, and 7602 kg of legacy THg in its 4 management units, namely Water Conservation Area (WCA) 1, 2, 3, and the Everglades National Park (ENP), respectively, with most Hg being stored in soil. The current annual Hg inputs account only for 1−2% of the legacy Hg. Mercury transport across management units during a season amounts to 1% or less of Hg storage, except for WCA 2 where inflow inputs can contribute 4% of total MeHg storage. Mass budget suggests distinct spatiality for cycling of seasonally deposited Hg, with significantly lower THg fluxes entering water and floc in ENP than in the WCAs. Floc in WCAs can retain a considerable fraction (around 16%) of MeHg produced from the newly deposited Hg during the wet season. This work is important for evaluating the magnitude of legacy Hg contamination and for predicting the fate of new Hg in the Everglades, and provides a methodological example for large-scale studies on Hg cycling in wetlands.

Tree Mortality following Prescribed Fire and a Storm Surge Event in Slash Pine (Pinus elliottii var. densa) Forests in the Florida Keys, USA

In fire-dependent forests, managers are interested in predicting the consequences of prescribed burning on postfire tree mortality. We examined the effects of prescribed fire on tree mortality in Florida Keys pine forests, using a factorial design with understory type, season, and year of burn as factors. We also used logistic regression to model the effects of burn season, fire severity, and tree dimensions on individual tree mortality. Despite limited statistical power due to problems in carrying out the full suite of planned experimental burns, associations with tree and fire variables were observed. Post-fire pine tree mortality was negatively correlated with tree size and positively correlated with char height and percent crown scorch. Unlike post-fire mortality, tree mortality associated with storm surge from Hurricane Wilma was greater in the large size classes. Due to their influence on population structure and fuel dynamics, the size-selective mortality patterns following fire and storm surge have practical importance for using fire as a management tool in Florida Keys pinelands in the future, particularly when the threats to their continued existence from tropical storms and sea level rise are expected to increase.

Effects of an extreme temperature event on the behavior and age structure of an estuarine top predator, Carcharhinus leucas

The frequency of extreme environmental events is predicted to increase in the future. Understanding the short- and long-term impacts of these extreme events on large-bodied predators will provide insight into the spatial and temporal scales at which acute environmental disturbances in top-down processes may persist within and across ecosystems. Here, we use long-term studies of movements and age structure of an estuarine top predator—juvenile bull sharks Carcharhinus leucas—to identify the effects of an extreme ‘cold snap’ from 2 to 13 January 2010 over short (weeks) to intermediate (months) time scales. Juvenile bull sharks are typically year-round residents of the Shark River Estuary until they reach 3 to 5 yr of age. However, acoustic telemetry revealed that almost all sharks either permanently left the system or died during the cold snap. For 116 d after the cold snap, no sharks were detected in the system with telemetry or captured during longline sampling. Once sharks returned, both the size structure and abundance of the individuals present in the nursery had changed considerably. During 2010, individual longlines were 70% less likely to capture any sharks, and catch rates on successful longlines were 40% lower than during 2006−2009. Also, all sharks caught after the cold snap were young-of-the-year or neonates, suggesting that the majority of sharks in the estuary were new recruits and several cohorts had been largely lost from the nursery. The longer-term impacts of this change in bull shark abundance to the trophic dynamics of the estuary and the importance of episodic disturbances to bull shark population dynamics will require continued monitoring, but are of considerable interest because of the ecological roles of bull sharks within coastal estuaries and oceans.

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Discharge measurements and storm event chemistry at the San Francisco River station (Planta) in 2007-2008

To improve our knowledge of the influence of land-use on solute behaviour and export rates in neotropical montane catchments we investigated total organic carbon (TOC), Ca, Mg, Na, K, NO3 and SO4 concentrations during April 2007-May 2008 at different flow conditions and over time in six forested and pasture-dominated headwaters (0.7-76 km2) in Ecuador. NO3 and SO4 concentrations decreased during the study period, with a continual decrease in NO3 and an abrupt decrease in February 2008 for SO4. We attribute this to changing weather regimes connected to a weakening La Niña event. Stream Na concentration decreased in all catchments, and Mg and Ca concentration decreased in all but the forested catchments during storm flow. Under all land-uses TOC increased at high flows. The differences in solute behaviour during storm flow might be attributed to largely shallow subsurface and surface flow paths in pasture streams on the one hand, and a predominant origin of storm flow from the organic layer in the forested streams on the other hand. Nutrient export rates in the forested streams were comparable to the values found in literature for tropical streams. They amounted to 6-8 kg/ha/y for Ca, 7-8 kg/ha/y for K, 4-5 kg/ha/y for Mg, 11-14 kg/ha/y for Na, 19-22 kg/ha/y for NO3 (i.e. 4.3-5.0 kg/ha/y NO3-N) and 17 kg/ha/y for SO4. Our data contradict the assumption that nutrient export increases with the loss of forest cover. For NO3 we observed a positive correlation of export value and percentage forest cover.

Assessing the legacy of the pine point mine: post-industrial land cover and land use

While mining is a major component of the northern Canadian economy, including the contemporary mixed economy of Aboriginal communities, it often leaves legacies of environmental and economic transformation that persist after closure. The legacies of historical mines in northern Canada challenge industry claims of sustainability. This thesis addresses how industrial mineral development and closure continue to affect local environments and economies after abandonment. The abandoned Pine Point mine in the Northwest Territories provides a case study for explaining the ongoing relationships among land cover, land use, and the post-industrial landscape. Drawing from landscape ecology and micropolitical ecology, I adopt an interdisciplinary approach to examine environmental and socioeconomic changes in the wake of industrial development and closure at Pine Point. The results show that passive reclamation is not sufficient for restoring ecological function in a subarctic environment. Land use, however, persists as land users adapt to the post-industrial landscape despite grave concern about its environmental condition. If mining is to be considered sustainable, decommissioning and reclamation must explicitly account for long-term environmental transformation as well as ongoing post-industrial land use, particularly in Aboriginal contexts.

Dynamic fault tree analysis with PEWMA modeling of event rates and Bayesian updating of material balance parameters using MCMC

This research explores Bayesian updating as a tool for estimating parameters probabilistically by dynamic analysis of data sequences. Two distinct Bayesian updating methodologies are assessed. The first approach focuses on Bayesian updating of failure rates for primary events in fault trees. A Poisson Exponentially Moving Average (PEWMA) model is implemnented to carry out Bayesian updating of failure rates for individual primary events in the fault tree. To provide a basis for testing of the PEWMA model, a fault tree is developed based on the Texas City Refinery incident which occurred in 2005. A qualitative fault tree analysis is then carried out to obtain a logical expression for the top event. A dynamic Fault Tree analysis is carried out by evaluating the top event probability at each Bayesian updating step by Monte Carlo sampling from posterior failure rate distributions. It is demonstrated that PEWMA modeling is advantageous over conventional conjugate Poisson-Gamma updating techniques when failure data is collected over long time spans. The second approach focuses on Bayesian updating of parameters in non-linear forward models. Specifically, the technique is applied to the hydrocarbon material balance equation. In order to test the accuracy of the implemented Bayesian updating models, a synthetic data set is developed using the Eclipse reservoir simulator. Both structured grid and MCMC sampling based solution techniques are implemented and are shown to model the synthetic data set with good accuracy. Furthermore, a graphical analysis shows that the implemented MCMC model displays good convergence properties. A case study demonstrates that Likelihood variance affects the rate at which the posterior assimilates information from the measured data sequence. Error in the measured data significantly affects the accuracy of the posterior parameter distributions. Increasing the likelihood variance mitigates random measurement errors, but casuses the overall variance of the posterior to increase. Bayesian updating is shown to be advantageous over deterministic regression techniques as it allows for incorporation of prior belief and full modeling uncertainty over the parameter ranges. As such, the Bayesian approach to estimation of parameters in the material balance equation shows utility for incorporation into reservoir engineering workflows.

A Methodology to Emulate Single Event Upsets in Flip-Flops using FPGAs through Partial Reconfiguration and Instrumentation

This paper presents a methodology to emulate Single Event Upsets (SEUs) in FPGA flip-flops (FFs). Since the content of a FF is not modifiable through the FPGA configuration memory bits, a dedicated design is required for fault injection in the FFs. The method proposed in this paper is a hybrid approach that combines FPGA partial reconfiguration and extra logic added to the circuit under test, without modifying its operation. This approach has been integrated into a fault-injection platform, named NESSY (Non intrusive ErrorS injection SYstem), developed by our research group. Finally, this paper includes results on a Virtex-5 FPGA demonstrating the validity of the method on the ITC’99 benchmark set and a Feed-Forward Equalization (FFE) filter. In comparison with other approaches in the literature, this methodology reduces the resource consumption introduced to carry out the fault injection in FFs, at the cost of adding very little time overhead (1.6 �μs per fault).

Ms. Magazine Mile track event at Madison Square Garden with Pat Carbine, Donna Da Varona, Althea Gibson, Bella Abzug, and Wilma Rudolph

General note: Title and date provided by Bettye Lane.

Herb and tea seller at Women's Event in Las Cruces, New Mexico

General note: Title and date provided by Bettye Lane.