Spontaneous task composition in urban computing environments based on social, spatial, and temporal aspects

An effective robot trajectory representation should encode all relevant aspects of the desired motion. For kinematic representations, this means that both the spatial course of the trajectory and its speed profile must be specified. The concept of dynamic movement primitives (DMP) provides a kinematic representation that fully specifies these two aspects of motion. They are, however, not separated from each other within the DMP representation. This can be problematic when movements with significant speed variations are compared within movement recognition and skill learning algorithms. In such comparisons it is often important to distinguish between the spatial and temporal aspects of motion. In this paper we propose a new representation based on dynamic movement primitives, where spatial and temporal aspects are well separated. We demonstrate the effectiveness of the proposed representation for statistical learning of robot skills and movement recognition and compare the performance with standard DMPs, where temporal and spatial aspects of motion are intertwined.

A study was conducted in the semi‐arid Mbeere district, Eastern Kenya, to determine spatial and temporal variability of crop yields and climatic factors and their impacts on sustainability of semi‐arid agriculture. Spatial aspects were assessed by conducting a survey of on‐farm crop yields and using a computer model (QUEFTS) to predict maize yields from soil chemical indices. Temporal aspects were studied using time‐series data (rainfall, temperature and crop yields). The study did not find a significant correlation between farmers' actual yields and QUEFTS predictions, and soil nutrients were thus not the only factors influencing maize yields in the study area. Other yield‐reducing factors (climate and management) not accounted for in the QUEFTS model played a role. Grain yields of staple food crops were highly variable, fluctuating in time and space, and suboptimal. Particular aspects of rainfall (e.g. the short rains, for cowpeas and beans) were more important than mean annual rainfall in determining crop yields, and factors other than rainfall and soil fertility apparently played a role. The observed low grain yields, large yield gaps and high rainfall variability challenge the sustainability of these farming systems. Copyright © 2008 John Wiley & Sons, Ltd

Network-on-Chip (NoC) has emerged as an alternative interconnecting paradigm in the state-of-the-art multi-core architectures. Its capability of parallel data transfer over various paths increases the possibility of resource contention and causes network congestion. How to avoid contention, as well as to optimize performance and energy consumption becomes a great challenge for system designers. In this paper, we consider spatial and temporal aspects of communication to avoid contention. In spatial aspect, we propose to utilize overlapped communication paths to estimate the degree of contention, such that they can be minimized in mapping stage. In temporal aspect, we sequentialize data transfer with potential contention by introducing additional latency. To optimize the design concerns with the corresponding constraint model, we further provide an efficient algorithm to search for better solutions for the problem, which integrates a local search process into a genetic algorithm and applies various heuristics in initialization and evolution process. Experimentations from random and real-case benchmarks demonstrate the efficiency of our method in multi-objective optimization and the effectiveness of our techniques in avoiding network contention, while keeping performance and energy consumption optimized.

This paper presents the analysis of South Sumatera hotspots distribution pattern from 2005–2015 based on spatial and temporal aspects. The hotspot distribution data used is FIRMS MODIS Fires data. The spatial aspect is represented by the attribute of latitude and longitude as the location of the occurrence of hotspots and the temporal is represented by the date attribute. DBSCAN clustering is used to analyze spatial aspect. The results show that Tulung Selapan subdistrict is on high fire-prone region. Air Sugihan, Cengal, Bayung Lencir is on medium and the others is low. For temporal aspect, the result shows most hotspots occurred on August, September and October.

As social primates, one of the most important cognitive tasks we conduct, dozens of times a day, is to look at a face and extract the person's identity. During the last decade, the neural basis of face processing has been extensively investigated in humans with event-related potential (ERP) and functional MRI (fMRI). These two methods provide complementary information about the temporal and spatial aspects of the neural response, with ERPs allowing high temporal resolution of milliseconds but low spatial resolution of the neural generator and fMRI displaying a slow hemodynamic response but better spatial localization of the activated regions. Despite the extensive fMRI and ERP research of faces, only a few studies have assessed the relationship between the two methods and no study to date have collected simultaneous ERP and fMRI responses to face stimuli. In the current paper we will try to assess the spatial and temporal aspects of the neural response to faces by simultaneously collecting functional MRI and event-related potentials (ERP) to face stimuli. Our goals are twofold: 1) ERP and fMRI show a robust selective response to faces. In particular, two well-established face-specific phenomena, the RH superiority and the inversion effect are robustly found with both ERP and fMRI. Despite the extensive research of these effects with ERP and fMRI, it is still unknown to what extent their spatial (fMRI) and temporal (ERP) aspects are associated. In Study 1 we will employ an individual differences approach, to assess the relationship between these ERP and fMRI face-specific responses. 2) Face processing involves several stages starting from structural encoding of the face image through identity processing to storage for later retrieval. This representation undergoes several manipulations that take place at different time points and in different brain regions before the final percept is generated. By simultaneously recording ERP and fMRI we hope to gain a more comprehensive understanding of the timecourse that different brain areas participate in the generation of the face representation.

A tanulmány a transznacionális migráció jelenségét vizsgálja egy eddig kevésbé feltárt perspektívából, a gyerekek szemszögéből. Jóllehet az általános vélekedések szerint a gyerekek a leginkább kitettek a családi migrációs döntéseknek, az újabb kutatási eredmények árnyalják a változásokat passzívan megélő, a migráció hatásait csupán elszenvedő gyerek képét, és a hangsúlyt egyre inkább az élményeikre, az egyéni adaptációs stratégiáikra, azaz az ágenciájukra helyezik.A két esettanulmány a Magyarországról külföldre irányuló munkavállalási célú migráció helyi társadalmakra gyakorolt hatásai című OTKA-kutatás keretében a migrációban érintett tanulókkal felvett interjúk, asszociációk, valamint gyerekrajzok dinamikus elemzésén alapul. A különféle módszerek közötti triangulációt a gyermeki perspektíva megszólaltatásának kihívásai mellett az egyes megközelítések korlátai, valamint a migráció jelenségének komplexitása indokolják.A mélyfúrásszerű elemzés – más projektív technikákon alapuló elemzésekhez hasonlóan – problémafeltáró jellegű, következésképpen nem reprezentatív és nem általánosítható. Az elemzés célja, hogy felvillantson különféle attitűdöket, egyéni megküzdési módokat, valamint ezek kognitív, érzelmi, térhasználatbeli aspektusait és reflektáljon a kombinatív módszertan alkalmazására.A migrációs tapasztalatok ambivalenciáját a különféle módszerekkel feltárt élmények más-más megvilágításba helyezik. A különböző projektív technikák használatával a pusztán verbális információszerzésnél sokszínűbb, összetettebb kép rajzolódik ki a gyermekek migrációs élményeiről. A módszerek közötti trianguláció közvetíti az idegenség tapasztalatának összetettségét, térbeli és társas vetületeit: a beilleszkedés kihívásait, valamint az idegen tér tapasztalatát és annak kiismerésével megszerzett kontrollt.

This work proposes that the construction of indicators of biological diversity incorporate the social and spatial aspects.For this, the methodology called Social and Spatial Analysis -ASE was first applied in the analysis of Brazilian biodiversity targets and indicators presented in the national reports for the Convention on Biological Diversity for the years 2011 and 2015, in addition to the national set of indicators of biodiversity of PainelBio presented in 2016.The ASE was then applied to investigate how Brazil is in relation to countries considered advanced in their biodiversity conservation policies, in terms of incorporating the social and spatial aspects into their indicators.The results indicate that Brazil has advanced in its methodological process, but this progress is still fragile, especially in relation to the traceability of information presented in official documents.Countries, in addition to incorporating biodiversity indicators into their strategies, should ensure that such indicators, index and targets are methodologically robust, that the needs of different social groups are represented, and that indicators are appropriate instruments for assessing the policy to which they relate to.

Organizational and spatial features of managing the innovative development of smart cities are a key aspect of their successful transformation. The article examines the organisational and spatial characteristics of innovation management in smart city development. It identifies the role of the cities as centres of innovative development that accumulate intellectual and financial resources, thus facilitating technological and social progress. The research emphasises the challenges of urbanisation that necessitate new development models integrating economic, social, and spatial aspects. The concept of a smart city is analysed as a contemporary approach to urban development management in the digital era. The evolution of this concept from its inception to the present day is examined in part, along with its role in enhancing the efficiency of urban systems and improving citizens’ quality of life. Particular emphasis is placed on key operational mechanisms of smart cities, including integration of ICT infrastructure, analytical processing of collected data, development of interactive citizen engagement, and ensuring sustainable development. The research methodology is based on a systems approach, utilising methods of structural analysis, comparative analysis, and modelling of urban processes. Special attention is devoted to spatial and organisational aspects of fostering innovative development in smart cities under various operational conditions. A significant component of the study is the comparative analysis of the models of spatial innovation development in urban studies. The research highlights the tendency towards combining elements from different models in urban development practice. The primary factors in forming innovative environments in cities are identified. The article examines the approaches to overcoming limitations of basic elements in digital city management through establishing appropriate innovation infrastructure within existing cities, around universities or research centres, or concurrent with the construction of new cities.

Passively-generated location data have the potential to augment mobility and transportation research, as demonstrated by a decade of research. A common trait of these data is a high proportion of missingness. Naïve handling, including list-wise deletion of subjects or days, or linear interpolation across time gaps, has the potential to bias summary results. On the other hand, it is unfeasible to collect mobility data at frequencies high enough to reflect all possible movements. In this paper, we describe the relationship between the temporal and spatial aspects of these data gaps, and illustrate the impact on measures of interest in the field of mobility. We propose a method to deal with missing location data that combines a so-called top-down ratio segmentation method with simple linear interpolation. The linear interpolation imputes missing data. The segmentation method transforms the set of location points to a series of lines, called segments. The method is designed for relatively short gaps, but is evaluated also for longer gaps. We study the effect of our imputation method for the duration of missing data using a completely observed subset of observations from the 2018 Statistics Netherlands travel study. We find that long gaps demonstrate greater downward bias on travel distance, movement events and radius of gyration as compared to shorter but more frequent gaps. When the missingness is unrelated to travel behavior, total sparsity can reach levels of up to 20% with gap lengths of up to 10 min while maintaining a maximum 5% downward bias in the metrics of interest. Temporal aspects can increase these limits; sparsity occurring in the evening or night hours is less biasing due to fewer travel behaviors.

Social groups barely change the speed-density relationship in unidirectional pedestrian flow, but affect operational behaviours

Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.

The transition to low-carbon fuel in the United States has spatial, temporal and economic aspects. Much of the economic literature on this topic has focused on aspects of the cost effectiveness of competing fuels. We expand this literature by simultaneously considering spatial, temporal and economic aspects in an optimization framework that integrates geographic information system (GIS) tools, network analysis, technology choice pathways and a vehicle demand choice model. We focus on natural gas fuel as a low-carbon alternative to oil-based diesel fuel in the heavy-duty sector primarily because of the recent cost benefits relative to diesel fuel and the high vehicle turnover rate in heavy-duty trucks. We find that the level of profitability of natural gas fueling infrastructure depends more on volume of traffic flows rather than proximity to natural gas supply.

Energy monitoring using statistical process control (SPC) methods makes it more straightforward to identify patterns and trends to decrease energy consumption more effectively. The literature review of energy consumption monitoring with SPC techniques generally focuses on the temporal aspect of variation. However, due to the spatial nature of energy data, enhancing these methods to incorporate temporal and spatial aspects would improve the accuracy of the diagnostic information, underscoring simultaneous detection of the time and location of changes. Thus, the main novelty of this work is the spatial modeling and spatiotemporal monitoring of electricity consumption. For this purpose, the study used actual electricity consumption data from eight western cities of Mazandaran province in the north of Iran for spatial modeling using spatial regression models and a geographically weighted regression (GWR) model. The prediction performance evaluation of spatial models showed GWR as an appropriate model, whose coefficients were monitored through a generalized likelihood ratio (GLR) chart in phase II. The GLR chart detected two changes in consumption, and its performance was confirmed based on the statements from electricity experts relying on meteorological information and floating population data. Furthermore, the performance of the GLR chart was evaluated using out-of-control average run length (ARL1) across three different scenarios. The findings indicate that the GLR chart can effectively detect any sizes of shifts (δ), ranging from 5% to 100% of the model's parameter value. Additionally, with larger values of δ, the ARL1 decreases, resulting in faster detection of changes in the model.

The article ventures a critique of the logic of ‘temporal othering’ in contemporary International Relations (IR) theory. Originally articulated in the field of European integration, this logic presupposes a possibility for a political community to constitute its identity without any spatial delimitation by means of casting as Other its own past, whose repetition in the future it seeks to avoid. While the image of contemporary Europe as ‘othering’ its own past has been subjected to empirical criticism, this article makes a conceptual argument for the indissociability of temporal and spatial aspects in any act of othering. Drawing on Alexandre Kojève's reading of Hegel, I argue that any historical action is necessarily spatiotemporal, combining the abstraction of temporal negation with the concrete actuality of a negated spatial being. Alternatives to the logic of sovereign territoriality are therefore not to be sought in the temporal aspect of othering, but rather by pursuing the possibility of self-constitution in the absence of any negating action whatsoever. The article concludes with an outline of such an alternative ethos, developed on the basis of Giorgio Agamben's reconstruction of the Hegelian-Kojèvian problematic of the end of history and his theory of the subject.

ABSTRACTPlanning activities of harvest teams (harvesting and forwarding) and transportation is critical for efficient procurement of roundwood from forests to mills. The planning process involves many integrated decisions that consider process, spatial and temporal aspects. The spatial aspect concerns which area to harvest, which machine team to use, the mill to which the timber should be allocated and where to store the timber. The process decisions involve which bucking instruction to use. The temporal aspect concerns when to harvest, when to transport in order to meet specific demand at mills, and when to store the timber. Temporal decisions also include determining a detailed schedule for each harvest team. Such a schedule includes starting time and movement time between harvest areas. This is complicated by the harvest team having different home bases and different machine systems with their specific performance description and capacities. The overall planning problem can be formulated into one optimization model, but such a model is too large for practical use and cannot be solved in a reasonable time. We propose a decomposition scheme where a sequence of aggregated models, or limited parts of the model, is solved to find high-quality solutions quickly. We test the scheduling in cases involving two large Swedish forest companies.