Abstract-We consider the scenario where a robot is tasked with sending a fixed number of given bits of information to a remote station, in a limited operation time, as it travels along a pre-defined trajectory, and while minimizing its motion and communication energy costs. We propose a co-optimization framework that allows the robot to plan its motion speed, transmission rate and stop time, based on its probabilistic prediction of the channel quality along the trajectory. We show that in order to save energy, the robot should move faster (slower) and send less (more) bits at the locations that have worse (better) predicted channel qualities. We furthermore prove that if the robot must stop, it should then stop only once and at the location with the best predicted channel quality. We also prove some properties for two special scenarios: the heavy-task load and the light-task load cases. We also propose an additional stop-time online adaptation strategy to further fine tune the stop location as the robot moves along its trajectory and measures the true value of the channel. Finally, our simulation results show that our proposed framework results in a considerable performance improvement.

An embedding is a function that maps entities from one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping relational data or text into vector spaces where they can be used for machine learning, similarity search, or similar tasks. We address the problem of finding vector space embeddings for theories in the Description Logic EL ++ that are also models of the TBox. To find such embeddings, we define an optimization problem that characterizes the model-theoretic semantics of the operators in EL ++ within R n , thereby solving the problem of finding an interpretation function for an EL ++ theory given a particular domain ∆. Our approach is mainly relevant to large EL ++ theories and knowledge bases such as the ontologies and knowledge graphs used in the life sciences. We demonstrate that our method can be used for improved prediction of protein-protein interactions when compared to semantic similarity measures or knowledge graph embeddings.

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Abstract. We used the recently developed commercially available Delta Ray isotope ratio infrared spectrometer (IRIS) to continuously measure the CO 2 concentration c and its isotopic composition δ 13 C and δ 18 O in a managed beech forest in central Germany. Our objectives are (a) to characterize the Delta Ray IRIS and evaluate its internal calibration procedure and (b) to quantify the seasonal variability of c, δ 13 C, δ 18 O and the isotopic composition of nighttime net ecosystem CO 2 exchange (respiration) R 13 eco C and R 18 eco O derived from Keeling plot intercepts. The analyzer's minimal Allan deviation (as a measure of precision) was below 0.01 ppm for the CO 2 concentration and below 0.03 ‰ for both δ values. The potential accuracy (defined as the 1σ deviation from the respective linear regression that was used for calibration) was approximately 0.45 ppm for c, 0.24 ‰ for 13 C and 0.3 ‰ for 18 O. For repeated measurements of a target gas in the field, the long-term standard deviation from the mean was 0.3 ppm for c and below 0.3 ‰ for both δ values. We used measurements of nine different inlet heights to evaluate the isotopic compositions of nighttime net ecosystem CO 2 exchange R 13 eco C and R 18 eco O in a 3-month measurement campaign in a beech forest in autumn 2015. During this period, an early snow and frost event occurred, coinciding with a change in the observed characteristics of both R 13 eco C and R 18 eco O. Before the first snow, R 13 eco C correlated significantly (p < 10 −4 ) with time-lagged net radiation R n , a driver of photosynthesis and photosynthetic discrimination against 13 C. This correlation became insignificant (p > 0.1) for the period after the first snow, indicating a decoupling of δ 13 C of respiration from recent assimilates. For 18 O, we measured a decrease of 30 ‰ within 10 days in R 18 eco O after the snow event, potentially reflecting the influence of 18 O depleted snow on soil moisture. This decrease was 10 times larger than the corresponding decrease in δ 18 O in ambient CO 2 (below 3 ‰) and took 3 times longer to recover (3 weeks vs. 1 week). In summary, we conclude that (1) the new Delta Ray IRIS with its internal calibration procedure provides an opportunity to precisely and accurately measure c, δ 13 C and δ 18 O at field sites and (2) even short snow or frost events might have strong effects on the isotopic composition (in particular 18 O) of CO 2 exchange on an ecosystem scale.

Abstract-In this paper, we consider the problem of robotic router formation, where two nodes need to maintain their connectivity over a large area by using a number of mobile routers. We are interested in the robust operation of such networks in realistic communication environments that naturally experience path loss, shadowing, and multipath fading. We propose a probabilistic router formation and motion-planning approach by integrating our previously proposed stochastic channel learning framework with robotic router optimization. We furthermore consider power constraints of the network, including both communication and motion costs, and characterize the underlying tradeoffs. Instead of taking the common approach of formation optimization through maximization of the Fiedler eigenvalue, we take a different approach and use the end-to-end bit error rate (BER) as our performance metric. We show that the proposed framework results in a different robotic configuration, with a considerably better performance, as compared with only considering disk models for communication and/or maximizing the Fielder eigenvalue. Finally, we show the performance with a simple preliminary experiment, with an emphasis on the impact of localization errors. Along this line, we show interesting interplays between the localization quality and the channel correlation/learning quality.

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Wi-Fi based indoor localization system has attracted considerable attention due to the growing need for location based service (LBS) and the rapid development of mobile phones. However, most existing Wi-Fi based indoor positioning systems suffer from the low accuracy due to the dynamic variation of indoor environment and the time delay caused by the time consumption to provide the position. In this paper, we propose an indoor localization system using the affinity propagation (AP) clustering algorithm and the particle swarm optimization based artificial neural network (PSO-ANN). The clustering technique is adopted to reduce the maximum location error and enhance the prediction performance of PSO-ANN model. And the strong learning ability of PSO-ANN model enables the proposed system to adapt to the complicated indoor environment. Meanwhile, the fast learning and prediction speed of the PSO-ANN would greatly reduce the time consumption. Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed.

Abstract. Flood hazard is increasing in frequency and magnitude in major South East Asian metropolitan areas due to fast urban development and changes in climate, threatening people's property and life. Typically, flood management actions are mostly focused on large-scale defences, such as river embankments or discharge channels or tunnels. However, these are difficult to implement in town centres without affecting the value of their heritage districts and might not provide sufficient mitigation. Therefore, urban heritage buildings may become vulnerable to flood events, even when they were originally designed and built with intrinsic resilient measures, based on the local knowledge of the natural environment and its threats at the time. Their aesthetic and cultural and economic values mean that they can represent a proportionally high contribution to losses in any event. Hence it is worth investigating more localized, tailored mitigation measures. Vulnerability assessment studies are essential to inform the feasibility and development of such strategies. In this study we propose a multilevel methodology to assess the flood vulnerability and risk of residential buildings in an area of Kuala Lumpur, Malaysia, characterized by traditional timber housing. The multiscale flood vulnerability model is based on a wide range of parameters, covering building-specific parameters, neighbourhood conditions and catchment area conditions. The obtained vulnerability index shows the ability to reflect different exposure by different building types and their relative locations. The vulnerability model is combined with high-resolution fluvial and pluvial flood maps providing scenario events with 0.1 % annual exceedance probability (AEP). A damage function of generic applicability is developed to compute the economic losses at individual building and sample levels. The study provides evidence that results obtained for a small district can be scaled up to the city level, to inform both generic and specific protection strategies.