Automatic Localization of Passive Infra-Red Binary Sensors in Home: from Dense to Scattered Network

International audienceLocation of residents in a household is one of the critical information to provide context-aware services. Passive Infra-Red (PIR) binary motion sensors have become the de facto standard technology used in the home by tracking systems due to their low energy consumption and their wide range of coverage. However, installing and managing this network of PIR sensors is difficult for typical residents, such as older adults, with low technical skill. To enable easy deployment of such a system by anybody, we present an extension of a method to automatically identify the location of multiple PIR sensors in a house from the observed motion detection event sequences. Thanks to a floor plan given as prior knowledge, the method estimates the distance between pairs of sensors and identifies particular patterns in the observations to predict the rooms where those sensors are most likely located. The method, which was designed to deal with dense sensors network is adapted to the case of scattered sensors which correspond to most traditional houses. Experimental results on a realistic home show that our method can estimate the location of sensors placed close to the anchor locations with only a few confusions. The experiments also revealed challenges to be addressed to make this method scale to various house configurations

One Model Fits All: Cross-Region Taxi-Demand Forecasting

The growing demand for ride-hailing services has led to an increasing need for accurate taxi demand prediction. Existing systems are limited to specific regions, lacking generalizability to unseen areas. This paper presents a novel taxi demand forecasting system that leverages a graph neural network to capture spatial dependencies and patterns in urban environments. Additionally, the proposed system employs a region-neutral approach, enabling it to train a model that can be applied to any region, including unseen regions. To achieve this, the framework incorporates the power of Variational Autoencoder to disentangle the input features into region-specific and region-neutral components. The region-neutral features facilitate cross-region taxi demand predictions, allowing the model to generalize well across different urban areas. Experimental results demonstrate the effectiveness of the proposed system in accurately forecasting taxi demand, even in previously unobserved regions, thus showcasing its potential for optimizing taxi services and improving transportation efficiency on a broader scale.Comment: Accepted to The 31st ACM International Conference on Advances in Geographic Information Systems(SIGSPATIAL '23) as a short paper in the Research, Systems and Industrial Experience Papers trac

Phospholipase D Family Member 4, a Transmembrane Glycoprotein with No Phospholipase D Activity, Expression in Spleen and Early Postnatal Microglia

BACKGROUND: Phospholipase D (PLD) catalyzes conversion of phosphatidylcholine into choline and phosphatidic acid, leading to a variety of intracellular signal transduction events. Two classical PLDs, PLD1 and PLD2, contain phosphatidylinositide-binding PX and PH domains and two conserved His-x-Lys-(x)(4)-Asp (HKD) motifs, which are critical for PLD activity. PLD4 officially belongs to the PLD family, because it possesses two HKD motifs. However, it lacks PX and PH domains and has a putative transmembrane domain instead. Nevertheless, little is known regarding expression, structure, and function of PLD4. METHODOLOGY/PRINCIPAL FINDINGS: PLD4 was analyzed in terms of expression, structure, and function. Expression was analyzed in developing mouse brains and non-neuronal tissues using microarray, in situ hybridization, immunohistochemistry, and immunocytochemistry. Structure was evaluated using bioinformatics analysis of protein domains, biochemical analyses of transmembrane property, and enzymatic deglycosylation. PLD activity was examined by choline release and transphosphatidylation assays. Results demonstrated low to modest, but characteristic, PLD4 mRNA expression in a subset of cells preferentially localized around white matter regions, including the corpus callosum and cerebellar white matter, during the first postnatal week. These PLD4 mRNA-expressing cells were identified as Iba1-positive microglia. In non-neuronal tissues, PLD4 mRNA expression was widespread, but predominantly distributed in the spleen. Intense PLD4 expression was detected around the marginal zone of the splenic red pulp, and splenic PLD4 protein recovered from subcellular membrane fractions was highly N-glycosylated. PLD4 was heterologously expressed in cell lines and localized in the endoplasmic reticulum and Golgi apparatus. Moreover, heterologously expressed PLD4 proteins did not exhibit PLD enzymatic activity. CONCLUSIONS/SIGNIFICANCE: Results showed that PLD4 is a non-PLD, HKD motif-carrying, transmembrane glycoprotein localized in the endoplasmic reticulum and Golgi apparatus. The spatiotemporally restricted expression patterns suggested that PLD4 might play a role in common function(s) among microglia during early postnatal brain development and splenic marginal zone cells