Tweets as data: Demonstration of TweeQL and TwitInfo

Microblogs such as Twitter are a tremendous repository of user-generated content. Increasingly, we see tweets used as data sources for novel applications such as disaster mapping, brand sentiment analysis, and real-time visualizations. In each scenario, the workflow for processing tweets is ad-hoc, and a lot of unnecessary work goes into repeating common data processing patterns. We introduce TweeQL, a stream query processing language that presents a SQL-like query interface for unstructured tweets to generate structured data for downstream applications. We have built several tools on top of TweeQL, most notably TwitInfo, an event timeline generation and exploration interface that summarizes events as they are discussed on Twitter. Our demonstration will allow the audience to interact with both TweeQL and TwitInfo to convey the value of data embedded in tweets

TwitInfo: Aggregating and Visualizing Microblogs for Event Exploration

Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100% of manually labeled peaks, facilitating a relatively complete view of each event studied

Processing and visualizing the data in tweets

Microblogs such as Twitter provide a valuable stream of diverse user-generated data. While the data extracted from Twitter is generally timely and accurate, the process by which developers extract structured data from the tweet stream is ad-hoc and requires reimplementation of common data manipulation primitives. In this paper, we present two systems for querying and extracting structure from Twitter-embedded data. The first, TweeQL, provides a streaming SQL-like interface to the Twitter API, making common tweet processing tasks simpler. The second, TwitInfo, shows how end-users can interact with and understand aggregated data from the tweet stream, in addition to showcasing the power of the TweeQL language. Together these systems show the richness of content that can be extracted from Twitter

Photodegradation of Selected PCBs in the Presence of Nano-TiO2 as Catalyst and H2O2 as an Oxidant

Photodegradation of five strategically selected PCBs was carried out in acetonitrile/water 80:20. Quantum chemical calculations reveal that PCBs without any chlorine on ortho-positions are closer to be planar, while PCBs with at least one chlorine atoms at the ortho-positions causes the two benzene rings to be nearly perpendicular. Light-induced degradation of planar PCBs is much slower than the perpendicular ones. The use of nano-TiO2 speeds up the degradation of the planar PCBs, but slows down the degradation of the non-planar ones. The use of H2O2 speeds up the degradation of planar PCBs greatly (by >20 times), but has little effect on non-planar ones except 2,3,5,6-TCB. The relative photodegradation rate is: 2,2′,4,4′-TCB > 2,3,5,6-TCB > 2,6-DCB ≈ 3,3′,4,4′-TCB > 3,4′,5-TCB. The use of H2O2 in combination with sunlight irradiation could be an efficient and “green” technology for PCB remediation

Forward genetic screen of homeostatic antibody levels in the Collaborative Cross identifies MBD1 as a novel regulator of B cell homeostasis

Variation in immune homeostasis, the state in which the immune system is maintained in the absence of stimulation, is highly variable across populations. This variation is attributed to both genetic and environmental factors. However, the identity and function of specific regulators have been difficult to identify in humans. We evaluated homeostatic antibody levels in the serum of the Collaborative Cross (CC) mouse genetic reference population. We found heritable variation in all antibody isotypes and subtypes measured. We identified 4 quantitative trait loci (QTL) associated with 3 IgG subtypes: IgG1, IgG2b, and IgG2c. While 3 of these QTL map to genome regions of known immunological significance (major histocompatibility and immunoglobulin heavy chain locus), Qih1 (associated with variation in IgG1) mapped to a novel locus on Chromosome 18. We further associated this locus with B cell proportions in the spleen and identify Methyl-CpG binding domain protein 1 under this locus as a novel regulator of homeostatic IgG1 levels in the serum and marginal zone B cells (MZB) in the spleen, consistent with a role in MZB differentiation to antibody secreting cells

The changing landscape of disaster volunteering: opportunities, responses and gaps in Australia

There is a growing expectation that volunteers will have a greater role in disaster management in the future compared to the past. This is driven largely by a growing focus on building resilience to disasters. At the same time, the wider landscape of volunteering is fundamentally changing in the twenty-first century. This paper considers implications of this changing landscape for the resilience agenda in disaster management, with a focus on Australia. It first reviews major forces and trends impacting on disaster volunteering, highlighting four key developments: the growth of more diverse and episodic volunteering styles, the impact of new communications technology, greater private sector involvement and growing government expectations of and intervention in the voluntary sector. It then examines opportunities in this changing landscape for the Australian emergency management sector across five key strategic areas and provides examples of Australian responses to these opportunities to date. The five areas of focus are: developing more flexible volunteering strategies, harnessing spontaneous volunteering, building capacity to engage digital (and digitally enabled) volunteers, tapping into the growth of employee and skills-based volunteering and co-producing community-based disaster risk reduction. Although there have been considerable steps taken in Australia in some of these areas, overall there is still a long way to go before the sector can take full advantage of emerging opportunities. The paper thus concludes by identifying important research and practice gaps in this area

MLSys: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations withP <5 x 10(-8)in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P <5 x 10(-8)) in the discovery samples. Ten novel SNVs, including rs12616219 nearTMEM182, were followed-up and five of them (rs462779 inREV3L, rs12780116 inCNNM2, rs1190736 inGPR101, rs11539157 inPJA1, and rs12616219 nearTMEM182) replicated at a Bonferroni significance threshold (P <4.5 x 10(-3)) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, inCCDC141and two low-frequency SNVs inCEP350andHDGFRP2. Functional follow-up implied that decreased expression ofREV3Lmay lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.Peer reviewe