Stream-Close: Fast mining of Closed Frequent Itemsets in high speed data streams

With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window by multiple transactions at a time. Therefore, we propose a novel approach for mining CFIpsilas cumulatively by making sliding width(ges1) over high speed data streams. However, it is nontrivial to mine CFIpsilas cumulatively over stream, because such growth may lead to the generation of exponential number of candidates for closure checking. In this study, we develop an efficient algorithm, stream-close, for mining CFIpsilas over stream by exploring some interesting properties. Our performance study reveals that stream-close achieves good scalability and has promising results.

A fast algorithm for finding frequent episodes in event streams

Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the frequency of an episode is some suitable measure of how often the episode occurs in the data sequence. Recently,we proposed a new frequency measure for episodes based on the notion of non-overlapped occurrences of episodes in the event sequence, and showed that, such a definition, in addition to yielding computationally efficient algorithms, has some important theoretical properties in connecting frequent episode discovery with HMM learning. This paper presents some new algorithms for frequent episode discovery under this non-overlapped occurrences-based frequency definition. The algorithms presented here are better (by a factor of N, where N denotes the size of episodes being discovered) in terms of both time and space complexities when compared to existing methods for frequent episode discovery. We show through some simulation experiments, that our algorithms are very efficient. The new algorithms presented here have arguably the least possible orders of spaceand time complexities for the task of frequent episode discovery.

Lantana camara L. (Verbenaceae) invasion along streams in a heterogeneous landscape

Streams are periodically disturbed due to flooding, act as edges between habitats and also facilitate the dispersal of propagules, thus being potentially more vulnerable to invasions than adjoining regions. We used a landscape-wide transect-based sampling strategy and a mixed effects modelling approach to understand the effects of distance from stream, a rainfall gradient, light availability and fire history on the distribution of the invasive shrub Lantana camara L.(lantana) in the tropical dry forests of Mudumalai in southern India. The area occupied by lantana thickets and lantana stem abundance were both found to be highest closest to streams across this landscape with a rainfall gradient. There was no advantage in terms of increased abundance or area occupied by lantana when it grew closer to streams in drier areas as compared to moister areas. On an average, the area covered by lantana increased with increasing annual rainfall. Areas that experienced greater number of fires during 1989-2010 had lower lantana stem abundance irrespective of distance from streams. In this landscape, total light availability did not affect lantana abundance. Understanding the spatially variable environmental factors in a heterogeneous landscape influencing the distribution of lantana would aid in making informed management decisions at this scale.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

The production of separate streams of pure hydrogen and carbon dioxide from coal via an iron-oxide redox cycle

A chemical looping process using the redox reactions of iron oxide has been used to produce separate streams of pure H2 and CO2 from a solid fuel. An iron oxide carrier prepared using a mechanical mixing technique and comprised of 100wt.% Fe2O3 was used. It was demonstrated that hydrogen can be produced from three representative coals - a Russian bituminous, a German lignite and a UK sub-bituminous coal. Depending on the fuel, pure H2 with [CO] ≲50vol.ppm can be obtained from the proposed process. The cyclic stability of the iron oxide carrier was not adversely affected by contaminants found in syngas which are gaseous above 273K. Stable quantities of H2 were produced over five cycles for all three coals investigated. Independent of the fuel, SO2 was not formed during the oxidation with steam, i.e. the produced H2 was not contaminated with SO2. Since oxidation with air removes contaminants and generates useful heat and pure N2 for purging, it should be included in the operating cycle. Overall, it was demonstrated that the proposed process may be an attractive approach to upgrade crude syngas produced by the gasification of low-rank coals to pure H2, representing a substantial increase in calorific value, whilst simultaneous capturing CO2, a greenhouse gas. © 2010 Elsevier B.V.

High-speed streams from coronal holes and the accelerating mechanism of the solar-wind

In this paper, the general Mach number equation is derived, and the influence of typical energy forms in the solar wind is analysed in detail. It shows that the accelerating process of the solar wind is influenced critically by the form of heating in the corona, and that the transonic mechanism is mainly the result of the adjustment of the variation of the crosssection of flowing tubes and the heat source term.The accelerating mechanism for both the high-speed stream from the coronal hole and the normal solar wind is similar. But, the temperature is low in the lower level of the coronal hole and more heat energy supply in the outside is required, hence the high speed of the solar wind; while the case with the ordinary coronal region is just the opposite, and the velocity of the solar wind is therefore lower. The accelerating process for various typical parameters is calculated, and it is found that the high-speed stream may reach 800 km/sec.

Career Planning in Spain: Do temporary contracts delay marriage and motherhood?

The aim of this paper is to find an empirical connection between the impressive increase in the use of temporary contracts in Spain and the observed increase in age at marriage and maternity. Using a pool of the six available waves of individual information from the European Household Panel for Spain, we find that holding temporary contracts rather than permanent ones delays marriage for males, but not for females. Concerning maternity, the labor market situation of both members of the household affects entry into motherhood. In particular, if woman has a temporary contract rather than a permanent one, motherhoods is delayed independently of the husband's contract. As expected, postponement of maternity is not found for non-working women. These results give strong support to the career planning, motive to delay maternity in Spain, given that an unstable labor market situation of female workers is found to be the main deterrent to entry into motherhood.

Wage differentials between permanent and temporary workers: Firm and occupational segregation

Published as an article in: Moneda y Crédito (2004), 219, pp.: 43-68.

Report on Knowledge Exchange workshop Price of keeping knowledge: financial streams for digital preservation

In contrast to cost modeling activities, the pricing of services must be simple and transparent. Calculating and thus knowing price structures, would not only help identify the level of detail required for cost modeling of individual instititutions, but also help develop a ”public” market for services as well as clarify the division of task and the modeling of funding and revenue streams for data preservation of public institutions. This workshop has built on the results from the workshop ”The Costs and Benefits of Keeping Knowledge” which took place 11 June 2012 in Copenhagen. This expert workshop aimed at: •Identifying ways for data repositories to abstract from their complicated cost structures and arrive at one transparent pricing structure which can be aligned with available and plausible funding schemes. Those repositories will probably need a stable institutional funding stream for data management and preservation. Are there any estimates for this, absolute or as percentage of overall cost? Part of the revenue will probably have to come through data management fees upon ingest. How could that be priced? Per dataset, per GB or as a percentage of research cost? Will it be necessary to charge access prices, as they contradict the open science paradigm? •What are the price components for pricing individual services, which prices are currently being paid e.g. to commercial providers? What are the description and conditions of the service(s) delivered and guaranteed? •What types of risks are inherent in these pricing schemes? •How can services and prices be defined in an all-inclusive and simple manner, so as to enable researchers to apply for specific amount when asking for funding of data-intensive projects?Please

Global pressures, specific responses: effects of nutrient enrichment in streams from different biomes

13 p. + 2 p. (Erratum)

The Affects of acid mine drainage on the water quality, and benthic macroinvertebrate and fish population in streams in Maryland

The purpose of this study was to measure and evaluate relationships between populations of benthic macroinvertebrates and fish, as well as variations in water quality in streams affected by acid Mine drainage. (PDF contains 21 pages)