Tracking physical events on social media

Social media platforms have emerged as the widely accessed form of communication channel on the world wide web in the modern day. The first ever social networking website came into existence in the year 2002 and currently there are about 2.08 billion social media users around the globe. The participation of users within a social network can be considered as an act of sensing where they are interacting with the physical world and recording the corresponding observations in the form of texts, pictures, videos, etc. This phenomenon is termed as Social Sensing and motivates us to develop robust techniques which can estimate the physical state from the human observations. This dissertation addresses a set of problems related to detection and tracking of real-world events. The term ‘event’ refers to an entity that can be characterized by spatial and temporal properties. With the help of these properties we design novel mathematical models that help us with our goals. We first focus on a simple event detection technique using ‘Twitter’ as the source of information. The method described in this work allow us to perform detection in a completely language independent and unsupervised fashion. We next extend the event detection problem to a different type of social media, ‘Instagram’, which allows users to share pictorial information of nearby observations. With the availability of geotagged data we solve two different subproblems - the first one is to detect and geolocalize the instance of an event and the second one is to estimate the path taken by an event during its course. The next problem we look at is related to improving the quality of event localization with the help of text and metadata information. Twitter, in general, has less volume of geotagged data available in comparison to Instagram, which demands us to design methods that explore the supplementary information available from the detected events. Finally, we take a look at both the social networks at the same time in order to utilize the complementary advantages and perform better than the methods designed for the individual networks

Social Fusion: Integrating Twitter and Instagram for Event Monitoring

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On Localizing Urban Events with Instagram

This paper develops an algorithm that exploits picture-oriented social networks to localize urban events. We choose picture-oriented networks because taking a picture requires physical proximity, thereby revealing the location of the photographed event. Furthermore, most modern cell phones are equipped with GPS, making picture location, and time metadata commonly available. We consider Instagram as the social network of choice and limit ourselves to urban events (noting that the majority of the world population lives in cities). The paper introduces a new adaptive localization algorithm that does no require the user to specify manually tunable parameters. We evaluate the performance of our algorithm for various real-world datasets, comparing it against a few baseline methods. The results show that our method achieves the best recall, the fewest false positives, and the lowest average error in localizing urban events.Ope

Clarifying sensor anomalies using social network feeds

The explosive growth in social networks that publish real-time content begs the question of whether their feeds can complement traditional sensors to achieve augmented sensing capabilities. One such capability is to explain anomalous sensor readings. Towards that end, in this work, we build an automated anomaly clarification service, called ClariSense. It explains sensor anomalies using social network feeds. Explanation goes beyond detection. When a sensor network detects anomalous conditions, our system automatically suggests hypotheses that explain the likely causes of the anomaly to a human by identifying unusual social network feeds that seem to be correlated with the sensor anomaly in time and in space. To evaluate this service, we use real-time data feeds from the California traffic system that shares vehicle count and traffic speed on major California highways at 5 minute intervals. When anomalies are detected, our system automatically diagnoses their root cause by correlating the anomaly with feeds on Twitter. The identified cause is then compared to official traffic and incident reports, showing a great correspondence with ground truth

Positive temperature coefficient and structural relaxations in selectively localized MWNTs in PE/PEO blends

The dispersion state of multiwall carbon nanotubes (MWNTs) in melt mixed polyethylene/polyethylene oxide (PE/PEO) blends has been assessed by both surface and volume electrical conductivity measurements and the structural relaxations have been assessed by broadband dielectric spectroscopy. The selective localization of MWNTs in the blends was controlled by the flow characteristics of the components, which led to their localization in the energetically less favored phase (PE). The electrical conductivity and positive temperature co-efficient (PTC) measurements were carried out on hot pressed samples. The neat blends exhibited only a negative temperature coefficient (NTC) effect while the blends with MWNTs exhibited both a PTC and a NTC at the melting temperatures of PE and PEO respectively. These phenomenal changes were corroborated with the different crystalline morphology in the blends. It was deduced that during compression molding, the more viscous PEO phase spreads less in contrast to the less viscous PE phase. This has further resulted in a gradient in morphology as well as the distribution state of the MWNTs in the samples and was supported by scanning electron and scanning acoustic microscopy (SAM) studies and contact angle measurements. SAM from different depths of the samples revealed a gradient in the microstructure in the PE/PEO blends which is contingent upon the flow characteristics of the components. Interestingly, the surface and volume electrical conductivity was different due to the different dispersion state of the MWNTs at the surface and bulk. The observed surface and volume electrical conductivity measurements were corroborated with the evolved morphology during processing. The structural relaxations in both PE and PEO were discerned from broadband dielectric spectroscopy. The segmental dynamics below and above the melting temperature of PEO were significantly different in the presence of MWNTs

Spatiotemporal Periodical Pattern Mining in Traffic Data

The widespread use of road sensors has generated huge amount of traffic data, which can be mined and put to various different uses. Finding frequent trajectories from the road network of a big city helps in summarizing the way the traffic behaves in the city. It can be very useful in city planning and traffic routing mechanisms, and may be used to suggest the best routes given the region, road, time of day, day of week, season, weather, and events etc. Other than the frequent patterns, even the events that are not so frequent, such as those observed when there is heavy snowfall, other extreme weather conditions, long traffic jams, accidents, etc. might actually follow a periodic occurrence, and hence might be useful to mine. This problem of mining the frequent patterns from road traffic data has been addressed in previous works using the context knowledge of the road network of the city. In this paper, we have developed a method to mine spatiotemporal periodic patterns in the traffic data and use these periodic behaviors to summarize the huge road network. The first step is to find periodic patterns from the speed data of individual road sensor stations, and use their periods to represent the station's periodic behavior using probability distribution matrices. Then, we use density-based clustering to cluster the sensors on the road network based on the similarities between their periodic behavior as well as their geographical distance, thus combining similar nodes to form a road network with larger but fewer nodes

Chitosan Immobilized Porous Polyolefin As Sustainable and Efficient Antibacterial Membranes

Polyolefinic membranes have attracted a great deal of interest owing to their ease of processing and chemical inertness. In this study, porous polyolefin membranes were derived by selectively etching PEO from PE/PEO (polyethylene/poly(ethylene oxide)) blends. The hydrophobic polyolefin (low density polyethylene) was treated with UV-ozone followed by dip coating in chitosan acetate solution to obtain a hydrophilic-antibacterial surface. The chitosan immobilized PE membranes were further characterized by Fourier transform infrared spectroscope (FTIR) and X-ray photoelectron spectroscope (XPS). It was found that surface grafting of chitosan onto PE membranes enhanced the surface roughness and the concentration of nitrogen (or amine) scaled with increasing concentration of chitosan (0.25 to 2% wt/vol), as inferred from Kjeldahl nitrogen analysis. The pure water flux was almost similar for chitosan immobilized PE membranes as compared to membranes without chitosan. The bacterial population, substantially reduced for membranes with higher concentration of chitosan. For instance, 90 and 94% reduction in Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) colony forming unit respectively was observed with 2% wt/vol of chitosan. This study opens new avenues in designing polyolefinic based antibacterial membranes for water purification

Improving antifouling ability by site-specific silver decoration on polyethylene ionomer membranes for water remediation: assessed using 3D micro computed tomography, water flux and antibacterial studies

Blending immiscible polymer blends often results in coarse microstructures due to interfacial driven coarsening. However, by introducing specific interactions between the constituents, the evolving microstructure can be tailor-made. Herein, water insoluble poly(ethylene-co-methacrylic acid) zinc salt (Surlyn) was blended with water soluble polyethylene oxide (PEO) in 50/50 (wt/wt) ratio to construct co-continuous structures that were not possible by blending PE and PEO at the same fraction. By selectively etching the water soluble phase (PEO), porous membranes can be designed with well-defined microstructure as assessed using X-ray micro-computed tomography and the pure water flux across the membranes was studied systematically. In order to impart an antibacterial surface, silver was directly reduced on the membrane surface utilizing the un-neutralized carboxylic acid moieties present in Surlyn as the reducing sites. This led to uniform decoration of silver on the surface which enhanced the antibacterial and antifouling properties. The presence of silver on the membrane was confirmed by X-ray photoelectron spectroscopy (XPS). The distribution of silver and the morphology of the porous Surlyn membrane was evaluated by field emission scanning electron microscopy (FESEM) coupled with EDAX analysis. The antibacterial activity was assessed using a standard plate count method wherein the bacterial cells were in direct contact with the silver decorated membranes. The content of silver present on the surface and the sustained release from the membrane surface was monitored using inductively coupled plasma optical emission spectrometry. The present study opens new avenues in designing efficient and scalable antibacterial membranes

Aminothiazoles : Part 1— Syntheses and pharmacological evaluation of 4-[isobutylphenyl] -2-substitutedaminothiazoles

1279-1281Unreported p-isobutyl phenacyl chloride has been prepared by the reaction of chloroacetyl chloride on isobutyl benzene. It is condensed with thiourea and its derivatives to get the title compounds. The products obtained are characterized by their IR, 1H NMR and mass spectral analysis