A spatio-temporal structure-based approach to drought characterisation

Drought characterisation is an intrinsically spatio-temporal problem. A limitation of previous approaches to characterisation is that they discard much of the spatio-temporal information by reducing events to a lower-order subspace. To address this, an explicit 3-dimensional (longitude, latitude, time) structure-based method is described in which drought events are defined by a spatially and temporarily coherent set of points displaying standardised precipitation below a given threshold. Geometric methods can then be used to measure similarity between individual drought structures. Groupings of these similarities provide an alternative to traditional methods for extracting recurrent space-time signals from geophysical data. The explicit consideration of structure encourages the construction of summary statistics which relate to the event geometry. Example measures considered are the event volume, centroid, and aspect ratio. The utility of a 3-dimensional approach is demonstrated by application to the analysis of European droughts (15 °W to 35°E, and 35 °N to 70°N) for the period 1901–2006. Large-scale structure is found to be abundant with 75 events identified lasting for more than 3 months and spanning at least 0.5 × 106 km2. Near-complete dissimilarity is seen between the individual drought structures, and little or no regularity is found in the time evolution of even the most spatially similar drought events. The spatial distribution of the event centroids and the time evolution of the geographic cross-sectional areas strongly suggest that large area, sustained droughts result from the combination of multiple small area (∼106 km2) short duration (∼3 months) events. The small events are not found to occur independently in space. This leads to the hypothesis that local water feedbacks play an important role in the aggregation process.

Temporal PageRank

The our reality is characterized by a constant progress and, to follow that, people need to stay up to date on the events. In a world with a lot of existing news, search for the ideal ones may be difficult, because the obstacles that make it arduous will be expanded more and more over time, due to the enrichment of data. In response, a great help is given by Information Retrieval, an interdisciplinary branch of computer science that deals with the management and the retrieval of the information. An IR system is developed to search for contents, contained in a reference dataset, considered relevant with respect to the need expressed by an interrogative query. To satisfy these ambitions, we must consider that most of the developed IR systems rely solely on textual similarity to identify relevant information, defining them as such when they include one or more keywords expressed by the query. The idea studied here is that this is not always sufficient, especially when it's necessary to manage large databases, as is the web. The existing solutions may generate low quality responses not allowing, to the users, a valid navigation through them. The intuition, to overcome these limitations, has been to define a new concept of relevance, to differently rank the results. So, the light was given to Temporal PageRank, a new proposal for the Web Information Retrieval that relies on a combination of several factors to increase the quality of research on the web. Temporal PageRank incorporates the advantages of a ranking algorithm, to prefer the information reported by web pages considered important by the context itself in which they reside, and the potential of techniques belonging to the world of the Temporal Information Retrieval, exploiting the temporal aspects of data, describing their chronological contexts. In this thesis, the new proposal is discussed, comparing its results with those achieved by the best known solutions, analyzing its strengths and its weaknesses.

Performance on auditory and visual temporal informationprocessing is related to psychometric intelligence

The present study investigated the relationship between psychometric intelligence and temporal resolution power (TRP) as simultaneously assessed by auditory and visual psychophysical timing tasks. In addition, three different theoretical models of the functional relationship between TRP and psychometric intelligence as assessed by means of the Adaptive Matrices Test (AMT) were developed. To test the validity of these models, structural equation modeling was applied. Empirical data supported a hierarchical model that assumed auditory and visual modality-specific temporal processing at a first level and amodal temporal processing at a second level. This second-order latent variable was substantially correlated with psychometric intelligence. Therefore, the relationship between psychometric intelligence and psychophysical timing performance can be explained best by a hierarchical model of temporal information processing.

Adaptive spatio-temporal filter for low-cost camera depth maps

In this paper we present an adaptive spatio-temporal filter that aims to improve low-cost depth camera accuracy and stability over time. The proposed system is composed by three blocks that are used to build a reliable depth map of static scenes. An adaptive joint-bilateral filter is used to obtain consistent depth maps by jointly considering depth and video information and by adapting its parameters to different levels of estimated noise. Kalman filters are used to reduce the temporal random fluctuations of the measurements. Finally an interpolation algorithm is used to obtain consistent depth maps in the regions where the depth information is not available. Results show that this approach allows to considerably improve the depth maps quality by considering spatio-temporal information and by adapting its parameters to different levels of noise.

Efficient spatio-temporal hole filling strategy for Kinect depth maps

In this paper we present an efficient hole filling strategy that improves the quality of the depth maps obtained with the Microsoft Kinect device. The proposed approach is based on a joint-bilateral filtering framework that includes spatial and temporal information. The missing depth values are obtained applying iteratively a joint-bilateral filter to their neighbor pixels. The filter weights are selected considering three different factors: visual data, depth information and a temporal-consistency map. Video and depth data are combined to improve depth map quality in presence of edges and homogeneous regions. Finally, the temporal-consistency map is generated in order to track the reliability of the depth measurements near the hole regions. The obtained depth values are included iteratively in the filtering process of the successive frames and the accuracy of the hole regions depth values increases while new samples are acquired and filtered

Context-sensitive synaptic plasticity and temporal-to-spatial transformations in hippocampal slices

Hippocampal slices are used to show that, as a temporal input pattern of activity flows through a neuronal layer, a temporal-to-spatial transformation takes place. That is, neurons can respond selectively to the first or second of a pair of input pulses, thus transforming different temporal patterns of activity into the activity of different neurons. This is demonstrated using associative long-term potentiation of polysynaptic CA1 responses as an activity-dependent marker: by depolarizing a postsynaptic CA1 neuron exclusively with the first or second of a pair of pulses from the dentate gyrus, it is possible to “tag” different subpopulations of CA3 neurons. This technique allows sampling of a population of neurons without recording simultaneously from multiple neurons. Furthermore, it reflects a biologically plausible mechanism by which single neurons may develop selective responses to time-varying stimuli and permits the induction of context-sensitive synaptic plasticity. These experimental results support the view that networks of neurons are intrinsically able to process temporal information and that it is not necessary to invoke the existence of internal clocks or delay lines for temporal processing on the time scale of tens to hundreds of milliseconds.

Recognizing and tagging temporal expressions in Spanish

This paper shows a system about the recognition of temporal expressions in Spanish and the resolution of their temporal reference. For the identification and recognition of temporal expressions we have based on a temporal expression grammar and for the resolution on an inference engine, where we have the information necessary to do the date operation based on the recognized expressions. For further information treatment, the output is proposed by means of XML tags in order to add standard information of the resolution obtained. Different kinds of annotation of temporal expressions are explained in another articles [WILSON2001][KATZ2001]. In the evaluation of our proposal we have obtained successful results.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

Field-oriented assessment of agricultural crops through temporal segmentation of modis VI data.

Monitoring agricultural crops constitutes a vital task for the general understanding of land use spatio-temporal dynamics. This paper presents an approach for the enhancement of current crop monitoring capabilities on a regional scale, in order to allow for the analysis of environmental and socio-economic drivers and impacts of agricultural land use. This work discusses the advantages and current limitations of using 250m VI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for this purpose, with emphasis in the difficulty of correctly analyzing pixels whose temporal responses are disturbed due to certain sources of interference such as mixed or heterogeneous land cover. It is shown that the influence of noisy or disturbed pixels can be minimized, and a much more consistent and useful result can be attained, if individual agricultural fields are identified and each field's pixels are analyzed in a collective manner. As such, a method is proposed that makes use of image segmentation techniques based on MODIS temporal information in order to identify portions of the study area that agree with actual agricultural field borders. The pixels of each portion or segment are then analyzed individually in order to estimate the reliability of the temporal signal observed and the consequent relevance of any estimation of land use from that data. The proposed method was applied in the state of Mato Grosso, in mid-western Brazil, where extensive ground truth data was available. Experiments were carried out using several supervised classification algorithms as well as different subsets of land cover classes, in order to test the methodology in a comprehensive way. Results show that the proposed method is capable of consistently improving classification results not only in terms of overall accuracy but also qualitatively by allowing a better understanding of the land use patterns detected. It thus provides a practical and straightforward procedure for enhancing crop-mapping capabilities using temporal series of moderate resolution remote sensing data.

Informational constraints on the emergence of passing direction in the team sport of futsal

This study investigated compound spatial and temporal measures of interpersonal interactions purported to constrain the emergence of affordances for passing direction in the team sport of futsal. For this purpose, attacker–defender interactions in 37 sequences of play from a futsal competition in which 24 male professional players participated (M=30.04 years, SD=4.10) were filmed and analysed using TACTO software. Relative angle data were used as measures to study coordination tendencies that emerged between players during performance. Results showed that the direction for a pass emerged from relative angles between: (1) the vector from a ball carrier to ball receiver and the vector from the ball carrier to the nearest defender (70°) (p<0.01) and (2) the vector from a ball carrier to ball receiver and the vector from the ball carrier to a ball receiver's nearest defender (31°) (p < 0.01). Furthermore, passing direction was also constrained by temporal information from the emergence of both angles, since the pass was performed to attacker–defender dyads with the highest velocities of these angles (p < 0.05). Results suggested that decisions on selecting the direction of a pass in the team sport of futsal emerged at critical values of these key compound motion measures.

Time-aware topic recommendation based on micro-blogs

Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com.

Towards a visually enhanced medical search engine

This paper presents the prototype of an information retrieval system for medical records that utilises visualisation techniques, namely word clouds and timelines. The system simplifies and assists information seeking tasks within the medical domain. Access to patient medical information can be time consuming as it requires practitioners to review a large number of electronic medical records to find relevant information. Presenting a summary of the content of a medical document by means of a word cloud may permit information seekers to decide upon the relevance of a document to their information need in a simple and time effective manner. We extend this intuition, by mapping word clouds of electronic medical records onto a timeline, to provide temporal information to the user. This allows exploring word clouds in the context of a patient’s medical history. To enhance the presentation of word clouds, we also provide the means for calculating aggregations and differences between patient’s word clouds.

Neural bases for individual differences in the subjective experience of short durations (less than 2 seconds)

The current research was designed to establish whether individual differences in timing performance predict neural activation in the areas that subserve the perception of short durations ranging between 400 and 1600 milliseconds. Seventeen participants completed both a temporal bisection task and a control task, in a mixed fMRI design. In keeping with previous research, there was increased activation in a network of regions typically active during time perception including the right supplementary motor area (SMA) and right pre-SMA and basal ganglia (including the putamen and right pallidum). Furthermore, correlations between neural activity in the right inferior frontal gyrus and SMA and timing performance corroborate the results of a recent meta-analysis and are further evidence that the SMA forms part of a neural clock that is responsible for the accumulation of temporal information. Specifically, subjective lengthening of the perceived duration were associated with increased activation in both the right SMA (and right pre-SMA) and right inferior frontal gyrus.

Enhancing human action recognition with region proposals

Deep convolutional network models have dominated recent work in human action recognition as well as image classification. However, these methods are often unduly influenced by the image background, learning and exploiting the presence of cues in typical computer vision datasets. For unbiased robotics applications, the degree of variation and novelty in action backgrounds is far greater than in computer vision datasets. To address this challenge, we propose an “action region proposal” method that, informed by optical flow, extracts image regions likely to contain actions for input into the network both during training and testing. In a range of experiments, we demonstrate that manually segmenting the background is not enough; but through active action region proposals during training and testing, state-of-the-art or better performance can be achieved on individual spatial and temporal video components. Finally, we show by focusing attention through action region proposals, we can further improve upon the existing state-of-the-art in spatio-temporally fused action recognition performance.

Improving the agent selection, release and evaluation process: the role of bioclimatic modelling

The value of CLIMEX models to inform biocontrol programs was assessed, including predicting the potential distribution of biocontrol agents and their subsequent population dynamics, using bioclimatic models for the weed Parkinsonia aculeata, two Lantana camara biocontrol agents, and five Mimosa pigra biocontrol agents. The results showed the contribution of data types to CLIMEX models and the capacity of these models to inform and improve the selection, release and post release evaluation of biocontrol agents. Foremost among these was the quality of spatial and temporal information as well as the extent to which overseas range data samples the species’ climatic envelope. Post hoc evaluation and refinement of these models requires improved long-term monitoring of introduced agents and their dynamics at well selected study sites. The authors described the findings of these case studies, highlighted their implications, and considered how to incorporate models effectively into biocontrol programs.