Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

We present Tweet2Vec, a novel method for generating general-purpose vector representation of tweets. The model learns tweet embeddings using character-level CNN-LSTM encoder-decoder. We trained our model on 3 million, randomly selected English-language tweets. The model was evaluated using two methods: tweet semantic similarity and tweet sentiment categorization, outperforming the previous state-of-the-art in both tasks. The evaluations demonstrate the power of the tweet embeddings generated by our model for various tweet categorization tasks. The vector representations generated by our model are generic, and hence can be applied to a variety of tasks. Though the model presented in this paper is trained on English-language tweets, the method presented can be used to learn tweet embeddings for different languages.Comment: SIGIR 2016, July 17-21, 2016, Pisa. Proceedings of SIGIR 2016. Pisa, Italy (2016

Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images

We address the problem of fine-grained action localization from temporally untrimmed web videos. We assume that only weak video-level annotations are available for training. The goal is to use these weak labels to identify temporal segments corresponding to the actions, and learn models that generalize to unconstrained web videos. We find that web images queried by action names serve as well-localized highlights for many actions, but are noisily labeled. To solve this problem, we propose a simple yet effective method that takes weak video labels and noisy image labels as input, and generates localized action frames as output. This is achieved by cross-domain transfer between video frames and web images, using pre-trained deep convolutional neural networks. We then use the localized action frames to train action recognition models with long short-term memory networks. We collect a fine-grained sports action data set FGA-240 of more than 130,000 YouTube videos. It has 240 fine-grained actions under 85 sports activities. Convincing results are shown on the FGA-240 data set, as well as the THUMOS 2014 localization data set with untrimmed training videos.Comment: Camera ready version for ACM Multimedia 201

Structural engineering of pyrrolo[3,4-: F] benzotriazole-5,7(2 H,6 H)-dione-based polymers for non-fullerene organic solar cells with an efficiency over 12%

In this work, we have synthesized two wide band gap donor polymers based on benzo[1,2-b:4,5-b′]dithiophene (BDT) and pyrrolo[3,4-f]benzotriazole-5,7(2H,6H)-dione (TzBI), namely, PBDT-TzBI and PBDT-F-TzBI and studied their photovoltaic properties by blending them with ITIC as an acceptor. Polymer solar cell devices made from PBDT-TzBI:ITIC and PBDT-F-TzBI:ITIC exhibited power conversion efficiencies (PCEs) of 9.22% and 11.02% and while annealing at 160 \ub0C, improved the device performances to 10.24% and 11.98%, respectively. Upon solvent annealing with diphenyl ether (DPE) (0.5%) and chlorobenzene (CB), the PCE of the PBDT-F-TzBI-based device increased to 12.12%. The introduction of the fluorinated benzodithiophene (BDT-F) moiety on the backbone of PBDT-F-TzBI improved the open circuit voltage, short circuit current and fill factor simultaneously. The high PCEs of the PBDT-F-TzBI:ITIC-based devices were supported by comparison and analysis of the optical and electronic properties, the charge carrier mobilities, exciton dissociation probabilities, and charge recombination behaviors of the devices

Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201

Assessment of Knowledge and Practices Toward COVID-19 Prevention Among Healthcare Workers in Tigray, North Ethiopia

Background: The incidence rate of coronavirus disease 2019 (COVID-19) is increasing in several countries despite that public health measures are put in place. Given that COVID-19 is a newly emerging disease, there is little knowledge about the disease. The present study aims to assess knowledge, perception, and preventive practices toward COVID-19 among health workers in Tigray, North Ethiopia.Materials and Methods: A health facility-based cross-sectional study was conducted among health professionals working in public hospitals. Data were collected between April and May 2020. The researchers included 403 participants and recruited them via a simple random sampling technique. To collect data, the researchers prepared a structured questionnaire guided by the WHO survey questions. Data were entered into Epi-info 7 and exported to SPSS version 20.00 for analysis. The researchers applied descriptive and inferential statistical analyses. Tables and graphs were used to describe data, and multivariate binary logistic regression was used to determine factors affecting knowledge, perception, and practices toward COVID-19 prevention.Results: Among the participants, 79, 88, and 64.3% of them had adequate knowledge, positive perception, and good practice toward preventing COVID-19, respectively. Besides, 92% of the study participants knew that the COVID-19 virus does not have curative treatment and vaccine. The findings revealed that 55% of the respondents did not use the necessary personal protective equipment (PPE) at all times. The result showed that being female [AOR: 2.43, 95% CI (1.50–3.94)] and having a work experience of 2–5 years [AOR: 2.44, 95% CI (1.10–5.39)], news media as a source information [AOR: 7.11, 95% CI (3.07–16.49)], social media as a source information [AOR: 4.59, 95% CI (2.15–9.84)], and governmental website as a source information [AOR: 4.21, 95% CI (2.15–8.27)] were reported as protective factors; and being single [AOR: 0.15, 95% CI (0.03–0.75)] was reported as risk factor toward the prevention of COVID-19.Conclusion: Most health workers had adequate knowledge and positive attitude toward COVID-19; nevertheless, a significant proportion of health workers had poor practice toward the prevention of COVID-19, including the use of PPE. Additionally, some groups of health professional showed poor practices of implementing the public health measures, hence the call for them to improve in the prevention and control of COVID-19

Social networks and contraceptive dynamics in southern Ghana

There is accumulating evidence that social diffusion processes affect the pace of the adoption of modern contraception in societies undergoing fertility transition. In settings where mortality has declined and many other social and economic changes are underway, decisions about contraception are fraught with uncertainty and risk. In such circumstances, couples may rely on other persons for information and guidance. In this paper, we examine the influence of informal social networks on the contraceptive behavior of reproductive-age women, using longitudinal data collected in six communities in southern Ghana. Our results confirm the hypothesis that adoption of modern contraception is strongly affected by the reproductive attitudes and behaviors of social network partners. What might be termed “social contagion” accelerates the adoption of contraception. Finally, our data reveal that social networks are structured along the lines of social, economic, and cultural characteristics, suggesting further pathways by which socioeconomic variables can influence reproductive behavior

Population Genomics of Mycobacterium tuberculosis in Ethiopia Contradicts the Virgin Soil Hypothesis for Human Tuberculosis in Sub-Saharan Africa

Colonial medical reports claimed that tuberculosis (TB) was largely unknown in Africa prior to European contact, providing a "virgin soil" for spread of TB in highly susceptible populations previously unexposed to the disease [1, 2]. This is in direct contrast to recent phylogenetic models which support an African origin for TB [3-6]. To address this apparent contradiction, we performed a broad genomic sampling of Mycobacterium tuberculosis in Ethiopia. All members of the M. tuberculosis complex (MTBC) arose from clonal expansion of a single common ancestor [7] with a proposed origin in East Africa [3, 4, 8]. Consistent with this proposal, MTBC lineage 7 is almost exclusively found in that region [9-11]. Although a detailed medical history of Ethiopia supports the view that TB was rare until the 20(th) century [12], over the last century Ethiopia has become a high-burden TB country [13]. Our results provide further support for an African origin for TB, with some genotypes already present on the continent well before European contact. Phylogenetic analyses reveal a pattern of serial introductions of multiple genotypes into Ethiopia in association with human migration and trade. In place of a "virgin soil" fostering the spread of TB in a previously naive population, we propose that increased TB mortality in Africa was driven by the introduction of European strains of M. tuberculosis alongside expansion of selected indigenous strains having biological characteristics that carry a fitness benefit in the urbanized settings of post-colonial Africa

Connecting wastes to resources for clean technologies in the chlor-alkali industry: a life cycle approach

Our current economic model is experiencing increasing demand and increasing pressure on resource utilisation, as valuable materials are lost as waste. Moving towards a circular economy and supporting efficient resource utilisation is essential for protecting the environment. The chlor-alkali industry is one of the largest consumers of salt, and efforts have been made to reduce its electricity use. Furthermore, KCl mining wastes have received increasing attention because they can be transformed into value-added resources. This work studies the influence of using different salt sources on the environmental sustainability of the chlor-alkali industry to identify further improvement opportunities. Rock salt, solar salt, KCl waste salt, vacuum salt and solution-mined salt were studied. Membrane cells in both bipolar and monopolar configurations were studied and compared to the emergent oxygen-depolarised cathode (ODC) technology. Life cycle assessment was applied to estimate the cradle-to-gate environmental impacts. The natural resource (NR) requirements and the environmental burdens (EBs) to the air and water environments were assessed. The total NR and EB requirements were reduced by 20% when vacuum salt was replaced with KCl. Moreover, the environmental impacts estimated for the monopolar membrane using KCl were comparable to those generated for the bipolar membrane using VS. The difference between the monopolar and bipolar scenarios (17%) was slightly higher than that between the bipolar and ODC technologies (12%). This work demonstrates the importance of studying every life cycle stage in a chemical process and the environmental benefit of applying a circular economy, even in energy intensive industries such as the chlor-alkali industry.This work was funded by the Spanish Ministry of Economy and Competitiveness (MINECO), Project CTM2013-43539-R. The authors are grateful for this funding