Time Dependent Processing in a Parallel Pipeline Architecture

Succession has been a focal point of ecological research for over a century, but thus far has been poorly explored through the lens of modern phylogenetic and trait-based approaches to community assembly. The vast majority of studies conducted to date have comprised static analyses where communities are observed at a single snapshot in time. Long-term datasets present a vantage point to compare established and emerging theoretical predictions on the phylogenetic and functional trajectory of communities through succession. We investigated within, and between, community measures of phylogenetic and functional diversity in a fire-prone heathland along a 21 year time series. Contrary to widely held expectations that increased competition through succession should inhibit the coexistence of species with high niche overlap, plots became more phylogenetically and functionally clustered with time since fire. There were significant directional shifts in individual traits through time indicating deterministic successional processes associated with changing abiotic and/or biotic conditions. However, relative to the observed temporal rate of taxonomic turnover, both phylogenetic and functional turnover were comparatively low, suggesting a degree of functional redundancy among close relatives. These results contribute to an emerging body of evidence indicating that limits to the similarity of coexisting species are rarely observed at fine spatial scales.

Evaluating the social impact indicators of engineered products is crucial to better understanding how products affect individuals’ lives and discover how to design for positive social impact. Most existing methods for evaluating social impact indicators require direct human interaction with users of a product, such as one-on-one interviews. These interactions produce high-fidelity data that are rich in information but provide only a single snapshot in time of the product’s impacts and are less frequently collected due to the significant human resources and cost associated with obtaining them. A framework is proposed that describes how low-fidelity data passively obtained using remote sensors, satellites, and digital technology can be collected and correlated with high-fidelity, low-frequency data using machine learning. Using this framework provides an inexpensive way to continuously monitor the social impact indicators of products by augmenting high-fidelity, low-frequency data with low-fidelity, continuously-collected data using machine learning. We illustrate an application of this framework by demonstrating how it can be used to examine the gender-related social impact indicators of water pumps in Uganda. The provided example uses a deep learning model to correlate pump handle movement (measured via an integrated motion unit) with user type (man, woman, or child) of 1,200 hand pump users.

This study presents a methodology for quantifying the tradeoffs between sampling costs and local concentration estimation errors in an existing groundwater monitoring network. The method utilizes historical data at a single snapshot in time to identify potential spatial redundancies within a monitoring network. Spatially redundant points are defined to be monitoring locations that do not appreciably increase local estimation errors if they are not sampled. The study combines nonlinear spatial interpolation with the nondominated sorted genetic algorithm (NSGA) to identify the tradeoff curve (or Pareto frontier) between sampling costs and local concentration estimation errors. Guidelines are given for using theoretical relationships from the field of genetic and evolutionary computation for population sizing and niching to ensure that the NSGA is competently designed to navigate the problem's decision space. Additionally, both a selection pressure analysis and a niching-based elitist enhancement of the NSGA are presented, which were integral to the algorithm's efficiency in quantifying the Pareto frontier for costs and estimation errors. The elitist NSGA identified 34 of 36 members of the Pareto optimal set attained from enumerating the monitoring application's decision space; this represents a substantial improvement over the standard NSGA, which found at most 21 of 36 members.

Genotyping tumor tissue in search of somatic genetic alterations for actionable information has become routine practice in clinical oncology. Although these sequence alterations are highly informative, sampling tumor tissue has significant inherent limitations; tumor tissue is a single snapshot in time, is subject to selection bias resulting from tumor heterogeneity, and can be difficult to obtain. Cell-free fragments of DNA are shed into the bloodstream by cells undergoing apoptosis or necrosis, and the load of circulating cell-free DNA (cfDNA) correlates with tumor staging and prognosis. Moreover, recent advances in the sensitivity and accuracy of DNA analysis have allowed for genotyping of cfDNA for somatic genomic alterations found in tumors. The ability to detect and quantify tumor mutations has proven effective in tracking tumor dynamics in real time as well as serving as a liquid biopsy that can be used for a variety of clinical and investigational applications not previously possible.

RWB and Institutional Change The current version of the NPC is a “representation within bounds” parliament. Deputies convey citizen grievances but shy away from sensitive political issues, and the government in turn displays partial responsiveness to their concerns. Deputies are socialized into this behavioral pattern and are punished or rewarded depending on the quality of their representative behavior. They also enjoy substantial rents for their services. The analysis in this book is focused on a single snapshot in time, the 11th NPC (2008–12) under the Hu-Wen administration. This decision was made largely on the basis of data availability, but it leaves open the question of how the CCP regime arrived at this equilibrium. How has representation in the NPC changed over time? How long has the representation within bounds system been in place, and what precisely spurred its creation? And importantly, can we expect it to continue in the future? The purpose of this chapter is to explore and explain the historical evolution of the National People's Congress. I will use the ideas from the formal theory in Chapter 2 to aid in the presentation and interpretation of the history. This approach has been referred to as an “analytic narrative” or a “theory guided narrative” (Bates et al. 2000; Buthe 2002; Levi 2002; Capoccia and Kelemen 2007), and it can be helpful in assessing the plausibility of a particular framework and identifying relevant actors and trends (Buthe 2002). In line with rational-choice institutionalism, I ascribe to the view that institutions can be manipulated to meet the ends of different political actors (Shepsle 2006; Pepinsky 2014). I will also draw on concepts developed by proponents of historical institutionalism, particularly the idea that institutional development is often the result of rapid change followed by institutional reproduction (Thelen 1999; Capoccia and Kelemen 2007). Recall from the model in Chapter 2 that for the Autocrat, the ideal level of representation is a function of the probability of a revolution being successful, π . At extremely low values of π , the Citizen cannot credibly threaten the regime, and there is no need for policy responsiveness. The Autocrat can get away with minimal representation and prefers the Stable Nonresponsive equilibrium E 2 . 1.

Scorpion venoms are mixtures of proteins, peptides and small molecular compounds with high specificity for ion channels and are therefore considered to be promising candidates in the venoms-to-drugs pipeline. Transcriptomes are important tools for studying the composition and expression of scorpion venom. Unfortunately, studying the venom gland transcriptome traditionally requires sacrificing the animal and therefore is always a single snapshot in time. This paper describes a new way of generating a scorpion venom gland transcriptome without sacrificing the animal, thereby allowing the study of the transcriptome at various time points within a single individual. By comparing these venom-derived transcriptomes to the traditional whole-telson transcriptomes we show that the relative expression levels of the major toxin classes are similar. We further performed a multi-day extraction using our proposed method to show the possibility of doing a multiple time point transcriptome analysis. This allows for the study of patterns of toxin gene activation over time a single individual, and allows assessment of the effects of diet, season and other factors that are known or likely to influence intraindividual venom composition. We discuss the gland characteristics that may allow this method to be successful in scorpions and provide a review of other venomous taxa to which this method may potentially be successfully applied.

Resolving plant development in space and time with single-cell genomics

This chapter focuses on cross-sectional and longitudinal studies. cross-sectional studies involve the analysis of usually quantitative data collected at a single snapshot in time. The unit of observation might be people or countries, and those are measured only once, all at approximately the same time. In contrast, longitudinal studies (also referred to as repeated measures studies) involve analysis on multiple occasions over time, where the same individuals (or countries) — the panel — are measured on each occasion. As such, the unit of observation is occasions, and there are multiple occasions/measures of each individual. A subcategory of longitudinal studies is event-history/survival/duration analysis, where the dependent variable is binary and the focus is on causes of changes between the two states of the outcome. Note that in comparison, time series analysis typically involves fewer individuals (often only one) and a larger number of time points. A third type of study, situated in between longitudinal and cross-sectional studies, is repeated cross-sectional analysis, which involves the analysis of multiple cross-sectional data sets over time, and different individuals are measured in each wave of the survey. Here, the unit of observation is individuals, and there are multiple individuals measured in each survey wave.

Colorectal cancers (CRCs) evolve by a reiterative process of genetic diversification and clonal evolution. The molecular profile of CRC is routinely assessed in surgical or bioptic samples. Genotyping of CRC tissue has inherent limitations; a tissue sample represents a single snapshot in time, and it is subjected to spatial selection bias owing to tumor heterogeneity. Repeated tissue samples are difficult to obtain and cannot be used for dynamic monitoring of disease progression and response to therapy. We exploited circulating tumor DNA (ctDNA) to genotype colorectal tumors and track clonal evolution during treatment with the epidermal growth factor receptor (EGFR)-specific antibodies cetuximab or panitumumab. We identified alterations in ctDNA of patients with primary or acquired resistance to EGFR blockade in the following genes: KRAS, NRAS, MET, ERBB2, FLT3, EGFR and MAP2K1. Mutated KRAS clones, which emerge in blood during EGFR blockade, decline upon withdrawal of EGFR-specific antibodies, indicating that clonal evolution continues beyond clinical progression. Pharmacogenomic analysis of CRC cells that had acquired resistance to cetuximab reveals that upon antibody withdrawal KRAS clones decay, whereas the population regains drug sensitivity. ctDNA profiles of individuals who benefit from multiple challenges with anti-EGFR antibodies exhibit pulsatile levels of mutant KRAS. These results indicate that the CRC genome adapts dynamically to intermittent drug schedules and provide a molecular explanation for the efficacy of rechallenge therapies based on EGFR blockade.

The efficacy of currently available repair techniques has been assessed for a wide variety of defect types encountered on advanced lithographic masks. Focused ion beam (FIB) with gas-assisted etching and deposition, electron beam induced chemical processing (EBIC), and atomic force microscope based nano-machining (RAVE) were among the different methodologies evaluated. Various types of optical phase-shifting masks for the 45nm lithographic node, as well as nano-imprint lithography (NIL) templates, were used as test vehicles. Defect imaging resolution, spatial process confinement, repair edge placement, end-pointing control, sample damage (undesired changes in topographic or optical properties), and future extendibility served as the primary metrics for gauging repair performance. The primary aim of this study was to provide a single snapshot in time of the current development status of each tool for the context of 45nm node mask repair specifications and by no means were there any expectations for a final solution to already be commercially available. However, the results obtained from these tests should provide useful feedback and information to help improve the learning cycle for the development of 45nm lithographic node mask repair systems.

Data streaming has become an important paradigm for the real-time processing of continuous data flows in domains such as finance, telecommunications, networking, Some applications in these domains require to process massive data flows that current technology is unable to manage, that is, streams that, even for a single query operator, require the capacity of potentially many machines. Research efforts on data streaming have mainly focused on scaling in the number of queries or query operators, but overlooked the scalability issue with respect to the stream volume. In this paper, we present StreamCloud a large scale data streaming system for processing large data stream volumes. We focus on how to parallelize continuous queries to obtain a highly scalable data streaming infrastructure. StreamCloud goes beyond the state of the art by using a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. StreamCloud is implemented as a middleware and is highly independent of the underlying data streaming engine. We explore and evaluate different strategies to parallelize data streaming and tackle with the main bottlenecks and overheads to achieve scalability. The paper presents the system design, implementation and a thorough evaluation of the scalability of the fully implemented system.

A checklist of Lichen-forming, Lichenicolous and Allied Fungi of Ecuador is presented with a total of 2599 species, of which 39 are reported for the first time from the country. The names of three species, Hypotrachyna montufariensis, H. subpartita and Sticta hypoglabra, previously not validly published, are validated. Pertusaria oahuensis, originally introduced by Magnusson as ‘ad interim’, is validated as Lepra oahuensis. The form Leucodermia leucomelos f. albociliata is validated. Two new combinations, Fissurina tectigera and F. timida, are made, and Physcia mobergii is introduced as a replacement name for the illegitimate P. lobulata Moberg non (Flörke) Arnold. In an initial step, the checklist was compiled by reviewing literature records of Ecuadorian lichen biota spanning from the late 19th century to the present day. Subsequently, records were added based on vouchers from 56 collections participating in the Consortium of Lichen Herbaria, a Symbiota-based biodiversity platform with particular focus on, but not exclusive to, North and South America. Symbiota provides sophisticated tools to manage biodiversity data, such as occurrence records, a taxonomic thesaurus, and checklists. The thesaurus keeps track of frequently changing names, distinguishing taxa currently accepted from ones considered synonyms. The software also provides tools to create and manage checklists, with an emphasis on selecting vouchers based on occurrence records that can be verified for identification accuracy. Advantages and limitations of creating checklists in Symbiota versus traditional ways of compiling these lists are discussed. Traditional checklists are well suited to document current knowledge as a ‘snapshot in time’. They are important baselines, frequently used by ecologists and conservation scientists as an established naming convention for citing species reported from a country. Compiling these lists, however, requires an immense effort, only to inadequately address the dynamic nature of scientific discovery. Traditional checklists are thus quickly out of date, particularly in groups with rapidly changing taxonomy, such as lichenized fungi. Especially in megadiverse countries, where new species and new occurrences continue to be discovered, traditional checklists are not easily updated; these lists necessarily fall short of efficiently managing immense data sets, and they rely primarily on secondary evidence (i.e. literature records rather than specimens). Ideally, best practices make use of dynamic database platforms such as Symbiota to assess occurrence records based both on literature citations and voucher specimens. Using modern data management tools comes with a learning curve. Systems like Symbiota are not necessarily intuitive and their functionality can still be improved, especially when handling literature records. However, online biodiversity data platforms have much potential in more efficiently managing and assessing large biodiversity data sets, particularly when investigating the lichen biota of megadiverse countries such as Ecuador.

In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of the robot position. The discrete Kalman filter, which usually is used for prediction and detection of signals in communication and control problems has become a commonly used method to reduce the effect of uncertainty from the sensor data. However, due to the special domain of robot navigation, the Kalman approach is very limited. The use of total least squares filter has been proposed (Boley and Sutherland, 1993) which is capable of converging with many fewer readings and achieving greater accuracy than the classical Kalman filter. The main disadvantage of those approaches is that they can not deal with the case where the noise subspace is of dimension higher than one. Here a parallel Krylov subspace method on parallel distributed memory computers which uses the Lanczos bidiagonalization process with updating techniques is proposed which is more computationally attractive to solve the total least squares problems. The parallel algorithm is derived such that all inner products of a single iteration step are independent. Therefore, the cost of global communication which represents the bottleneck of the parallel performance on parallel distributed memory computers can be significantly reduced. This filter is very promising for very large data information and from our very preliminary experiments we can obtain more precise accuracy and better speedup.

Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an important part of the IoMT because it will dictate in which platform analytical tasks should run in the future. Data flows are usually a sequence of out-of-order tuples with a high data input rate, and mobility analytics requires a real-time flow of data in both directions, from the edge to the cloud, and vice-versa. Before pulling the data streams to the cloud, edge data stream processing is needed for detecting missing, broken, and duplicated tuples in addition to recognize tuples whose arrival time is out of order. Analytical tasks such as data filtering, data cleaning and low-level data contextualization can be executed at the edge of a network. In contrast, more complex analytical tasks such as graph processing can be deployed in the cloud, and the results of ad-hoc queries and streaming graph analytics can be pushed to the edge as needed by a user application. Graphs are efficient representations used in mobility analytics because they unify knowledge about connectivity, proximity and interaction among moving things. This poster describes the preliminary results from our experimental prototype developed for supporting transit systems, in which edge and cloud computing are combined to process transit data streams forwarded from fog nodes into a cloud. The motivation of this research is to understand how to perform meaningfulness mobility analytics on transit feeds by combining cloud and fog computing architectures in order to improve fleet management, mass transit and remote asset monitoring

Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc. The primary approach for this type of detection in previous researches is based on principal score (PS) of divided batches or sliding windows by computing top eigenvalues of the correlation matrix, e.g. the Lanczos algorithm. However, this paper brings up the phenomenon of principal score degeneration for large data set, and then mathematically and practically prove current PS-based methods are likely to fail for CAD on large-scale streaming data even if the number of correlated anomalies grows with the data size at a reasonable rate; in reality, anomalies tend to be the minority of the data, and this issue can be more serious. We propose a framework with two novel randomized algorithms rPS and gPS for better detection of correlated anomalies from large streaming data of various correlation strength. The experiment shows high and balanced recall and estimated accuracy of our framework for anomaly detection from a large server log data set and a U.S. stock daily price data set in comparison to direct principal score evaluation and some other recent group anomaly detection algorithms. Moreover, our techniques significantly improve the computation efficiency and scalability for principal score calculation.