Spatial Distribution of Intracluster Light versus Dark Matter in Horizon Run 5

One intriguing approach for studying the dynamical evolution of galaxy clusters is to compare the spatial distributions among various components, such as dark matter, member galaxies, gas, and intracluster light (ICL). Utilizing the recently introduced Weighted Overlap Coefficient (WOC) \citep{2022ApJS..261...28Y}, we analyze the spatial distributions of components within 174 galaxy clusters ($M_{\rm tot}> 5 \times 10^{13} M_{\odot}$, $z=0.625$) at varying dynamical states in the cosmological hydrodynamical simulation Horizon Run 5. We observe that the distributions of gas and the combination of ICL with the brightest cluster galaxy (BCG) closely resembles the dark matter distribution, particularly in more relaxed clusters, characterized by the half-mass epoch. The similarity in spatial distribution between dark matter and BCG+ICL mimics the changes in the dynamical state of clusters during a major merger. Notably, at redshifts $>$ 1, BCG+ICL traced dark matter more accurately than the gas. Additionally, we examined the one-dimensional radial profiles of each component, which show that the BCG+ICL is a sensitive component revealing the dynamical state of clusters. We propose a new method that can approximately recover the dark matter profile by scaling the BCG+ICL radial profile. Furthermore, we find a recipe for tracing dark matter in unrelaxed clusters by including the most massive satellite galaxies together with BCG+ICL distribution. Combining the BCG+ICL and the gas distribution enhances the dark matter tracing ability. Our results imply that the BCG+ICL distribution is an effective tracer for the dark matter distribution, and the similarity of spatial distribution may be a useful probe of the dynamical state of a cluster.Comment: 23 pages, 12 figures, accepted for publication in Ap

Spatial Distribution of Intracluster Light versus Dark Matter in Horizon Run 5

One intriguing approach for studying the dynamical evolution of galaxy clusters is to compare the spatial distributions among various components such as dark matter, member galaxies, gas, and intracluster light (ICL). Utilizing the recently introduced weighted overlap coefficient (WOC), we analyze the spatial distributions of components within 174 galaxy clusters (M tot > 5 × 1013 M ⊙, z = 0.625) at varying dynamical states in the cosmological hydrodynamical simulation Horizon Run 5. We observe that the distributions of gas and the combination of ICL with the brightest cluster galaxy (BCG) closely resembles the dark matter distribution, particularly in more relaxed clusters, characterized by the half-mass epoch. The similarity in spatial distribution between dark matter and BCG+ICL mimics the changes in the dynamical state of clusters during a major merger. Notably, at redshifts >1, BCG+ICL traced dark matter more accurately than the gas. Additionally, we examined the one-dimensional radial profiles of each component, which show that the BCG+ICL is a sensitive component revealing the dynamical state of clusters. We propose a new method that can approximately recover the dark matter profile by scaling the BCG+ICL radial profile. Furthermore, we find a recipe for tracing dark matter in unrelaxed clusters by including the most massive satellite galaxies together with the BCG+ICL distribution. Combining the BCG+ICL and the gas distribution enhances the dark matter tracing ability. Our results imply that the BCG+ICL distribution is an effective tracer for the dark matter distribution, and the similarity of the spatial distribution may be a useful probe of the dynamical state of a cluster

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression

Machine learning has been successfully used for target localization in wireless sensor networks (WSNs) due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR). The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods

Time-Series Laplacian Semi-Supervised Learning for Indoor Localization

Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localization methods to reduce use of labeled training data. To boost the efficiency and the accuracy of indoor localization, this paper proposes a new time-series semi-supervised learning algorithm. The key aspect of the developed method, which distinguishes it from conventional semi-supervised algorithms, is the use of unlabeled data. The learning algorithm finds spatio-temporal relationships in the unlabeled data, and pseudolabels are generated to compensate for the lack of labeled training data. In the next step, another balancing-optimization learning algorithm learns a positioning model. The proposed method is evaluated for estimating the location of a smartphone user by using a Wi-Fi received signal strength indicator (RSSI) measurement. The experimental results show that the developed learning algorithm outperforms some existing semi-supervised algorithms according to the variation of the number of training data and access points. Also, the proposed method is discussed in terms of why it gives better performance, by the analysis of the impact of the learning parameters. Moreover, the extended localization scheme in conjunction with a particle filter is executed to include additional information, such as a floor plan

A New Methodology for Updating Land Cover Maps in Rapidly Urbanizing Areas of Levying Stormwater Utility Fee

With a steady increase in impervious surfaces due to urbanization in Korea, there is a growing burden and an urgent need to fund better management of nonpoint sources of pollution and stormwater. A prerequisite for securing the necessary financial resources is the determination of basic data for the accurate calculation of impervious surfaces as the basis for estimating the costs of nonpoint source pollution control and billing of stormwater utility fees. This requires the extraction of landcover information in a Geographic Information System (GIS) environment and development of a landcover map that accurately delineates pervious surfaces within impervious surface areas. However, since landcover maps in Korea are generated and updated at irregular intervals, it is difficult to properly track land use changes. To address this problem, this study has developed a new method for the detailed updating of landcover maps in developing urban areas to facilitate the calculation of stormwater utility fees. Sejong City was selected as the study site because it has experienced large-scale land use changes due to the recent relocation of the national administrative capital and continuous urbanization. The methodology proposed in this study is based on various spatial data such as aerial photographs and digital topographic maps and follows four process steps: preprocessing, first and second updates of landcover information, and quality assurance. In a test of this method, a total of 19,049 reclassified items were generated in the first and second updates, affecting a total area of 26.49 km2 within the original landcover map. The accuracy of these updates reached 99.78%, considering the changed areas and rate of change. This study provides fundamental data for further application of a stormwater utility fee policy in Korea. However, further research is required to automate the generation of accurate pervious/impervious maps and develop pertinent guidelines so that individual municipal and provincial governments can generate and update their own pervious/impervious maps as a basis for calculating the impervious surfaces in their regions

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning

A New Methodology for Updating Land Cover Maps in Rapidly Urbanizing Areas of Levying Stormwater Utility Fee

With a steady increase in impervious surfaces due to urbanization in Korea, there is a growing burden and an urgent need to fund better management of nonpoint sources of pollution and stormwater. A prerequisite for securing the necessary financial resources is the determination of basic data for the accurate calculation of impervious surfaces as the basis for estimating the costs of nonpoint source pollution control and billing of stormwater utility fees. This requires the extraction of landcover information in a Geographic Information System (GIS) environment and development of a landcover map that accurately delineates pervious surfaces within impervious surface areas. However, since landcover maps in Korea are generated and updated at irregular intervals, it is difficult to properly track land use changes. To address this problem, this study has developed a new method for the detailed updating of landcover maps in developing urban areas to facilitate the calculation of stormwater utility fees. Sejong City was selected as the study site because it has experienced large-scale land use changes due to the recent relocation of the national administrative capital and continuous urbanization. The methodology proposed in this study is based on various spatial data such as aerial photographs and digital topographic maps and follows four process steps: preprocessing, first and second updates of landcover information, and quality assurance. In a test of this method, a total of 19,049 reclassified items were generated in the first and second updates, affecting a total area of 26.49 km2 within the original landcover map. The accuracy of these updates reached 99.78%, considering the changed areas and rate of change. This study provides fundamental data for further application of a stormwater utility fee policy in Korea. However, further research is required to automate the generation of accurate pervious/impervious maps and develop pertinent guidelines so that individual municipal and provincial governments can generate and update their own pervious/impervious maps as a basis for calculating the impervious surfaces in their regions

Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks

Tracking the locations and identities of moving targets in the surveillance area of wireless sensor networks is studied. In order to not rely on high-cost sensors that have been used in previous researches, we propose the integrated localization and classification based on semi-supervised learning, which uses both labeled and unlabeled data obtained from low-cost distributed sensor network. In our setting, labeled data are obtained by seismic and PIR sensors that contain information about the types of the targets. Unlabeled data are generated from the RF signal strength by applying Gaussian process, which represents the probability of predicted target locations. Finally, by using classified unlabeled data produced by semi-supervised learning, identities and locations of multiple targets are estimated. In addition, we consider a case when the labeled data are absent, which can happen due to fault or lack of the deployed sensor nodes and communication failure. We overcome this situation by defining artificial labeled data utilizing characteristics of support vector machine, which provides information on the importance of each training data point. Experimental results demonstrate the accuracy of the proposed tracking algorithm and its robustness to the absence of the labeled data thanks to the artificial labeled data

Semisupervised Location Awareness in Wireless Sensor Networks Using Laplacian Support Vector Regression

Supervised machine learning has been widely used in context-aware wireless sensor networks (WSNs) to discover context descriptions from sensor data. However, collecting a lot of labeled training data in order to guarantee good performance requires much cost and time. For this reason, the semisupervised learning has been recently developed due to its superior performance despite using only a small amount of the labeled data. In this paper, we extend the standard support vector regression (SVR) to the semisupervised SVR by employing manifold regularization, which we call Laplacian SVR (LapSVR). The LapSVR is compared with the standard SVR and the semisupervised least square algorithm that is another recently developed semisupervised regression algorithm. The algorithms are evaluated for location awareness of multiple mobile robots in a WSN. The experimental results show that the proposed algorithm yields more accurate location estimates than the other algorithms