Periodic Pattern Mining a Algorithms and Applications

Owing to a large number of applications periodic pattern mining has been extensively studied for over a decade Periodic pattern is a pattern that repeats itself with a specific period in a give sequence Periodic patterns can be mined from datasets like biological sequences continuous and discrete time series data spatiotemporal data and social networks Periodic patterns are classified based on different criteria Periodic patterns are categorized as frequent periodic patterns and statistically significant patterns based on the frequency of occurrence Frequent periodic patterns are in turn classified as perfect and imperfect periodic patterns full and partial periodic patterns synchronous and asynchronous periodic patterns dense periodic patterns approximate periodic patterns This paper presents a survey of the state of art research on periodic pattern mining algorithms and their application areas A discussion of merits and demerits of these algorithms was given The paper also presents a brief overview of algorithms that can be applied for specific types of datasets like spatiotemporal data and social network

Coherent network analysis technique for discriminating gravitational-wave bursts from instrumental noise

Existing coherent network analysis techniques for detecting gravitational-wave bursts simultaneously test data from multiple observatories for consistency with the expected properties of the signals. These techniques assume the output of the detector network to be the sum of a stationary Gaussian noise process and a gravitational-wave signal, and they may fail in the presence of transient non-stationarities, which are common in real detectors. In order to address this problem we introduce a consistency test that is robust against noise non-stationarities and allows one to distinguish between gravitational-wave bursts and noise transients. This technique does not require any a priori knowledge of the putative burst waveform.Comment: 18 pages, 11 figures; corrected corrupted figur

Learning structure and schemas from heterogeneous domains in networked systems: a survey

The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible to manually organize such documents. Additionally, most of the document sexist in an unstructured form and do not follow any schemas. Therefore, research efforts in this direction are being dedicated to automatically infer structure and schemas. This is essential in order to better organize huge collections as well as to effectively and efficiently retrieve documents in heterogeneous domains in networked system. This paper presents a survey of the state-of-the-art methods for inferring structure from documents and schemas in networked environments. The survey is organized around the most important application domains, namely, bio-informatics, sensor networks, social networks, P2Psystems, automation and control, transportation and privacy preserving for which we analyze the recent developments on dealing with unstructured data in such domains.Peer ReviewedPostprint (published version

Detection And Microhabitat Use Of The Cryptic Patch-nosed Salamander (Urspelerpes Brucei) In Three Occupied Streams

Patch-nosed Salamanders (Urspelerpes brucei) are tiny (25.76 ± 0.17 mm [SE] snout-vent length) plethodontids endemic to headwater streams in a small (29 km2 at the time of our study) geographic region of northeast Georgia (GA) and northwest South Carolina (SC). Due to its secretive nature and recent discovery (2007), little is known about U. bruceihabitat, life history, or potential threats. Though environmental DNA (eDNA), aquatic leaf litter bags, and opportunistic active searches are successful detection techniques for U. brucei, and other factors influencing detection are unknown. Additionally, while occupied U. brucei streams have been characterized as shallow and steep-walled, previous work has not identified factors that influence U. brucei microhabitat use among patches within a single occupied stream. In Chapter 1, we applied six survey methods in three streams where U. brucei occur using a randomized complete block design and replicated each survey technique six times between August 2021 and June 2022. We used occupancy models to simultaneously investigate factors influencing U. brucei occupancy within a 5-m stream segment while estimating detection probability for each survey method and across a range of other survey-specific conditions. We found aquatic and terrestrial area-unconstrained surveys were at least 3.25 times better at detecting U. brucei than aquatic area-constrained surveys, while cloudless skies, recent rainfall amounts (\u3e 5 cm in the previous week), and higher soil temperatures (≥ 20 °C) were positively associated with detection. Chapter 2 describes our finer scale analysis where we used occupancy models to investigate factors influencing U. brucei microhabitat use at the scale of a 31 x 31 cm patch. Notably, we used a subset of field data collected in Chapter 1 for analysis in Chapter 2. We found leaf litter accumulationrepresented the primary driver of microhabitat use for both U. brucei larvae and adults. Specifically, for larvae, shallow stream depths (≤ 4 mm) and deep leaf litter (≥ 70 mm) was the best predictor for microhabitat use, whereas adults had the highest probability of using microhabitat consisting of leaf litter depths ≥ 111 mm. Collectively, our findings emphasize the success of area-unconstrained surveys and leaf litter bag surveys under ideal weather conditions when detecting both larval and adult U. brucei. Additionally, we highlight how U. brucei distribution within streams can be highly variable, presumably because of variation in stream depth and leaf litter depth. We also found U. brucei seem to be rarer in SC relative to GA for reasons we do not fully understand. Moreover, our findings advance our understanding of effective U. brucei survey protocols, identifying microhabitat use, and baseline occupancy estimates that can be used to inform future research aiming to ascertain population demographics, further investigate distribution within occupied streams, and prioritize essential habitat to protect

Habitat use of young-of-year Arctic Grayling (Thymallus arcticus) in Barrenland streams of central Nunavut, Canada

Arctic Grayling, a species within the family Salmonidae that is valued by sport fishers and Indigenous communities, is distributed throughout a diversity of northern landscapes. While Arctic Grayling are known to be sensitive to perturbations in habitat and water quality, our understanding of constraints on their distribution is incomplete, particularly in the vast subarctic Barrenlands region. Understanding the habitat requirements and distribution of Barrenland populations of Arctic Grayling is necessary to develop effective conservation policies, avoid or mitigate potential impacts of mining and other development, and evaluate population distribution trends over time. Barrenland populations of Arctic Grayling rely on seasonally connected networks of lakes and streams to migrate, spawn, and rear. Knowledge of stream conditions and characteristics that are suitable for rearing young-of-year Arctic Grayling is critical for understanding and predicting variability in recruitment, and thus to ensuring the continued persistence of Barrenland populations. In summer 2019, visual surveys assessing the presence/absence of young-of-year Arctic Grayling were conducted at 49 streams in the Barrenlands region near Baker Lake, Nunavut. Occupancy modeling was used to relate a comprehensive suite of stream habitat (e.g., depth, velocity, water temperature) and landscape (e.g., land cover, contributing upstream lake area) variables to the presence/absence of young-of-year Arctic Grayling. Quantification of detection efficiency, and variables that affect detection efficiency, allowed for improved inferences on species-habitat relationships. While detection efficiency was negatively influenced by water depth and water velocity, the best predictors of young-of-year grayling occupancy were the total area of contributing upstream lakes and the landcover (upland/lowland) of the stream basin. These results suggest that the position of streams within Barrenland landscapes is related to reliability of stream connectivity, and thus suitability for young-of-year. Both explanatory variables are important in promoting hydrologic connectivity throughout the summer rearing period and facilitating the migration of young-of-year to overwintering lakes prior to freeze up. Contributing upstream lake area and land classification data may be obtained remotely, which allows for preliminary predictions of stream suitability to be conducted with minimal financial and logistic effort, and more spatially focused field operations. The occupancy model developed here can be used as a valuable predictive tool for Arctic Grayling young-of-year stream use in the Barrenlands, and will facilitate regulators, scientists, resource managers, and industry in developing more effective conservation and mitigation plans for fish and fish habitat in areas of resource development