Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Passive Visual Analytics of Social Media Data for Detection of Unusual Events

Now that social media sites have gained substantial traction, huge amounts of un-analyzed valuable data are being generated. Posts containing images and text have spatiotemporal data attached as well, having immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, the S.M.A.R.T system provides the analyst with an interactive visual spatiotemporal analysis and spatial decision support environment that assists in evacuation planning and disaster management. S.M.A.R.T fetches data from various social media sources and arranges them in a perceivable manner, which is visually appealing. This in turn is a huge aid in finding and understanding abnormal events. Introducing a passive mode makes the tool more efficient, where it automatically detects idle time and gives a summary of all the anomalies encountered in the inactive period as soon as the analyst resumes monitoring. Using the tool, the analyst can first extract major topics from a set of selected messages and rank them probabilistically. The case studies in the past show improved situational awareness by using the methods mentioned before

Social Media Analytics Reporting Toolkit

With the fast growth of social media services, vast amount of user-generated content with time-space stamps are produced everyday. Considerable amount of these data are publicly available online, some of which collectively convey information that are of interest to data analysts. Social media data are dynamic and unstructured by nature, which makes it very hard for analysts to efficiently and effectively retrieve useful information. Social Media Analytics Reporting Toolkit (SMART), a system developed at Purdue VACCINE lab, aims to support such analyzing. The current framework collects real-time Twitter messages and visualizes volume densities on a map. It uses Latent Dirichilet Allocation (LDA) to extract regional topics and can optionally apply Seasonal-Trend decomposition using Loess (STL) to detect abnormal events. While Twitter has a fair amount of active users, they account for a small portion of total active social media users. Data generated by many other social media services are not currently utilized by SMART. Therefore, my work focused on expanding data sources of SAMRT system by creating means to collect data from other sources such as Facebook and Instagram. During a test run using a collection of 88 specified keywords in search, over two million Facebook posts were collected in one week. Besides, current SMART framework utilizes only one topic model, i.e. LDA, which is considered to be slower than Non-negative Matrix Factorization (NMF) model, thus I also put my effort into integrating NMF algorithm into the system. The improved SMART system can be used to fulfill a variety of analyzing tasks such as monitoring regional social media responses from different sources in disastrous events, detecting user reported crimes and so on. SMART is currently an ongoing and promising project that can be further improved by integrating new features

Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.Comment: Submitted to IEEE Big Dat

Negative regulation of floral transition in Arabidopsis by HOS15-PWR-HDA9 complex

Arabidopsis HOS15/PWR/HDA9 repressor complex, which is similar to the TBL1/NcoR1/HDAC complex in animals, plays a well-known role in epigenetic regulation. PWR and HDA9 have been reported to interact with each other and modulate the flowering time by repressing AGL19 expression, whereas HOS15 and HDA9, together with the photoperiodic evening complex, regulate flowering time through repression of GI transcription. However, the role of the HOS15/PWR/HDA9 core repressor complex as a functional unit in the regulation of flowering time is yet to be explored. In this study, we reported that the loss-of-function hos15-2/pwr/hda9 triple mutant accumulates higher transcript levels of AGL19 and exhibits an early flowering phenotype similar to those of hos15, pwr, and hda9 single mutants. Interestingly, the accumulation of HOS15 in the nucleus was drastically reduced in pwr and hda9 mutants. As a result, HOS15 could not perform its role in histone deacetylation or interaction with H3 in the nucleus. Furthermore, HOS15 is also associated with the same region of the AGL19 promoter known for PWR-HDA9 binding. The acetylation level of the AGL19 promoter was increased in the hos15-2 mutant, similar to the pwr and hda9 mutants. Therefore, our findings reveal that the HOS15/PWR/HDA9 repressor complex deacetylates the promoter region of AGL19, thereby negatively regulating AGL19 transcription, which leads to early flowering in Arabidopsis

Visual analytics for investigative analysis of hoax distress calls using social media

A hoax distress call is a serious concern for the U.S. Coast Guard. Hoax calls not only put the Coast Guard rescue personnel in potentially dangerous situations, but also waste valuable assets that should be used for real emergency situations. However, conventional approaches do not provide enough information for investigating hoax calls and callers. As social media has played a pervasive role in the way people communicate, such data opens new opportunities and solutions to a wide range of challenges. In this paper, we present social media visual analytics solutions for supporting the investigation for hoax distress calls. We not only provide a set of comprehensive keyword collections, but also resolve the lack of social media data for the investigation. Our framework allows investigators to identify suspicious Twitter users and provide a visual analytics environment designed to examine geo-tagged tweets and Instagram messages in the context of hoax distress calls

Analyzing High-dimensional Multivariate Network Links with Integrated Anomaly Detection, Highlighting and Exploration

This paper focuses on the integration of a family of visual analytics techniques for analyzing high-dimensional, multivariate network data that features spatial and temporal information, network connections, and a variety of other categorical and numerical data types. Such data types are commonly encountered in transportation, shipping, and logistics industries. Due to the scale and complexity of the data, it is essential to integrate techniques for data analysis, visualization, and exploration. We present new visual representations, Petal and Thread, to effectively present many-to-many network data including multi-attribute vectors. In addition, we deploy an information-theoretic model for anomaly detection across varying dimensions, displaying highlighted anomalies in a visually consistent manner, as well as supporting a managed process of exploration. Lastly, we evaluate the proposed methodology through data exploration and an empirical study

PWR/HDA9/ABI4 Complex Epigenetically Regulates ABA Dependent Drought Stress Tolerance in Arabidopsis

Drought stress adversely affects plant growth and development and significantly reduces crop productivity and yields. The phytohormone abscisic acid (ABA) rapidly accumulates in response to drought stress and mediates the expression of stress-responsive genes that help the plant to survive dehydration. The protein Powerdress (PWR), which interacts with Histone Deacetylase 9 (HDA9), has been identified as a critical component regulating plant growth and development, flowering time, floral determinacy, and leaf senescence. However, the role and function of PWR and HDA9 in abiotic stress response had remained elusive. Here we report that a complex of PWR and HDA9 interacts with ABI4 and epigenetically regulates drought signaling in plants. T-DNA insertion mutants of PWR and HDA9 are insensitive to ABA and hypersensitive to dehydration. Furthermore, the expression of ABA-responsive genes (RD29A, RD29B, and COR15A) is also downregulated in pwr and hda9 mutants. Yeast two-hybrid assays showed that PWR and HDA9 interact with ABI4. Transcript levels of genes that are normally repressed by ABI4, such as CYP707A1, AOX1a and ACS4, are increased in pwr. More importantly, during dehydration stress, PWR and HDA9 regulate the acetylation status of the CYP707A1, which encodes a major enzyme of ABA catabolism. Taken together, our results indicate that PWR, in association with HDA9 and ABI4, regulates the chromatin modification of genes responsible for regulation of both the ABA-signaling and ABA-catabolism pathways in response to ABA and drought stressPeer reviewe