With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple's Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches.

Microring resonators (MRRs) can be used to generate optical millimetre‐wave solitons with a broadband frequency of 40–60 GHz. Non‐linear light behaviours within MRRs, such as chaotic signals, can be used to generate logic codes (digital codes). The soliton signals can be multiplexed and modulated with the logic codes using an orthogonal frequency division multiplexing (OFDM) technique to transmit the data via a network system. OFDM uses overlapping subcarriers without causing inter‐carrier interference. It provides both a high data rate and symbol duration using frequency division multiplexing over multiple subcarriers within one channel. The results show that MRRs support both single‐carrier and multi‐carrier optical soliton pulses, which can be used in an OFDM based on whether fast Fourier transform or discrete wavelet transform transmission/receiver system. Localised ultra‐short soliton pulses within frequencies of 50 and 52 GHz can be seen at the throughput port of the panda system with respect to full‐width at half‐maximum (FWHM) and free spectrum range of 5 MHz and 2 GHz, respectively. The soliton pulses with FWHMs of 10 MHz could be generated at the drop port. Therefore, transmission of data information can be performed via a communication network using soliton pulse carriers and an OFDM technique.