170 resultados para Temporal Parts


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Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date tweets such that their locations are close to a user specified location and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “food poisoning vomiting.” We consider the Temporal Spatial-Keyword Top-k Subscription (TaSK) query. Given a TaSK query, we continuously maintain up-to-date top-k most relevant results over a stream of geo-textual objects (e.g., geo-tagged Tweets) for the query. The TaSK query takes into account text relevance, spatial proximity, and recency of geo-textual objects in evaluating its relevance with a geo-textual object. We propose a novel solution to efficiently process a large number of TaSK queries over a stream of geotextual objects. We evaluate the efficiency of our approach on two real-world datasets and the experimental results show that our solution is able to achieve a reduction of the processing time by 70-80% compared with two baselines.

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Understanding the dietary consumption and selection of wild populations of generalist herbivores is hampered by the complex array of factors. Here, we determine the influence of habitat, season, and animal density, sex, and age on the diet consumption and selection of 426 red deer (Cervus elaphus scoticus) culled in Fiordland National Park, New Zealand. Our site differs from studies elsewhere both in habitat (evergreen angiosperm-dominated forests) and the intensity of hunting pressures. We predicted that deer would not consume forage in proportion to its relative availability, and that dietary consumption would change among and within years in response to hunting pressures that would also limit opportunities for age and sex segregation. Using canonical correspondence analysis, we evaluated the relative importance of different drivers of variation in diet consumption assessed from gut content and related these to available forage in the environment. We found that altitude explained the largest proportion of variation in diet consumption, reflecting the ability of deer to alter their consumption and selection in relation to their foraging grounds. Grasses formed a high proportion of the diet consumption, even for deer culled several kilometres from the alpine grasslands. In the winter months, when the alpine grasslands were largely inaccessible, less grass was eaten and deer resorted to woody plants that were avoided in the summer months. Surprisingly, there were no significant dietary differences between adults and juveniles and only subtle differences between the sexes. Sex-based differences in diet consumption are commonly observed in ungulate species and we suggest that they may have been reduced in our study area owing to decreased heterogeneity in available forage as the diversity of palatable species decreased under high deer browsing pressures, or by intense hunting pressure. © 2009 The Authors. Journal compilation © 2009 Ecological Society of Australia.

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The density and composition of stream bed metal deposits are affected by physical, chemical and biological processes. In this paper we investigate the importance of these processes and their relation to algal and non-photosynthetic detrital (NPD) biomass in a set of upland streams in Northern Ireland. Deposit density and Fe, Mn, Al and P concentrations varied with stream pH across sites but not seasonally. No effects of stream bed erosion or photoreduction were detected on deposit densities. Seasonal variation in stream water metal concentrations was correlated with rainfall. NPD biomass was a significant predictor of both spatial and seasonal variation in deposit concentrations. There were strong, non-linear, relations between NPD biomass and deposit metal concentrations, with Fe and Mn becoming relatively more important and algal biomass declining above threshold deposit/NPD densities. The results suggest that NPD biomass influences deposit density and reduces the biomass of photosynthetic autotrophs above a threshold deposit density.

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High Efficiency Video Coding (HEVC) is the most recent video codec coming after currently most popular H.264/MPEG4 codecs and has promising compression capabilities. It is conjectured that it will be a substitute for current video compression standards. However, to the best knowledge of the authors, none of the current video steganalysis methods designed or tested with HEVC video. In this paper, pixel domain steganography applied on HEVC video is targeted for the first time. Also, its the first paper that employs accordion unfolding transformation, which merges temporal and spatial correlation, in pixel domain video steganalysis. With help of the transformation, temporal correlation is incorporated into the system. Its demonstrated for three different feature sets that integrating temporal dependency substantially increased the detection accuracy.

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We propose a spatio-temporal rich model of motion vector planes as a part of a full steganalytic system against motion vector based steganography. Superior detection accuracy of the rich model over the previous methods has been lately demonstrated for digital images in both spatial and DCT domain. It has not been heretofore used for detection of motion vector steganography. We also introduced a transformation so as to extend the feature set with temporal residuals. We carried out the tests along with most recent motion vector steganalysis and steganography methods. Test results show that the proposed model delivers an outstanding performance compared to the previous methods.

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The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

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Although interactions between seaweeds and sponges have been studied in detail, general information concerning the whole epibiontic algal assemblage associated with a sponge species is virtually non-existent. We present here the first study in which the macroalgal community associated with a sponge, Haliclona indistincta (Bowerbank), was examined in detail. In the period October 2009-September 2010, the seaweed assemblage epibiontic on H. indistincta at a site of the Irish West coast was composed of 66 algal taxa (48 red algae, 7 green algae, 11 brown algae). The red algae Gelidium spinosum and Rhodothamniella floridula were the only epibionts associated with H. indistincta for the whole annual cycle. Most of the algal epibionts were filamentous species, which colonized the surface of the sponge and did not penetrate deeply into it. The algal assemblage was most abundant and species-diverse in the period late winter-spring; multivariate analyses revealed a significant variation of the community on the temporal scale of season and sampling date (weeks to months). The results indicate that the algal communities associated with sponges may be very diverse, showing that this type of assemblage deserves further detailed studies. © 2012 Elsevier B.V.

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Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

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Temporal overlapping of ultra-short and focussed laser pulses is a particularly challenging task, as this timescale lies orders of magnitude below the typical range of fast electronic devices. Here we present an optical technique that allows for the measurement of the temporal delay between two focussed and ultra-short laser pulses. This method is virtually applicable to any focussing geometry and relative intensity of the two lasers. Experimental implementation of this technique provides excellent quantitative agreement with theoretical expectations. The proposed technique will prove highly beneficial for high-power multiple-beam laser experiments.