Overview of the gene ontology task at BioCreative IV

Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation

Mobile Services in Hubei: Adoption Model and Empirical Analysis

Mobile Commerce has developed rapidly in China with the characters of ubiquity, location relevance, convenience and personalization. The researches on technology, value chain, business models, user adoption have become a hot topic among academics. Based on the classical Davis’ TAM theory and the expansions of it, and the predecessors\u27 research on perceived enjoyment and perceived cost, this study builds an adoption model of Mobile value-added services in Hubei Province. In the variety of individual mobile value-added business, four most commonly used services are extracted in this study ,including Mobile Instant Message, Multimedia Messaging Service, WAP Web browse and Multi-media Downloads to represent the overall situation. According to the result of empirical analysis based on valid data of questionnaires, perceived enjoyment and perceived cost are the most influential factors. Six of the seven hypotheses in this study are verified

Atatürk, Türk Dili ve Yazı İnkılâbı

Özet: Dünyada çok nadir lider bir ömürde hem savaş alanında hem kültür alanında o kadar çok zafer kazanmıştır. Atatürk her yönüyle o nadir liderlerden biridir. O kendi düşünce sistemiyle, öngörüşüyle ve çevreye zamanında motivasyon vererek faaliyete geçirmesiyle Türkiye’ye ve Türk halkına yeni ufuklar açmıştır. Atatürk, bir fikir adamı olarak, milli kültürün temel direklerinden birinin dil olduğunu biliyordu. Bu sebeple konuşma dili ile yazı dilini ve halkın dili ile aydınların dilini yakınlaştırmak için dil inkılâbını teşvik etti. Dil inkılâbının ilk safhası olarak 1 Kasım 1928’de yazı inkılâbı gerçekleşti ve Latin harfleri kanunla kabul edildi. Böylece, yazı inkılâbı, Türk dilinin ve Türk kültürünün tarihinde bir dönüm noktası oldu ve kısa zamanda Türkçe kendi yumuşaklığı ve ahenkliği ile yeniden bütün güzelliğini meydana çıkardı. Anahtar kelimeler: Atatürk, Türk dili, Türk dilinin Latin alfabesi, Türkçede vokaller, Türkçede konsonantlar

EPISOSE: An Epistemology-based Social Search Framework for Exploratory Information Seeking

Abstract. Search engines are indispensable for locating information in WWW, but encounter great difficulties in handling exploratory information seeking, where precise keywords are hard to be formulated. A viable solution is to improve efficiency and quality of exploratory search by utilizing the wisdom of crowds (i.e., taking advantage of collective knowledge and efforts from a mass of searchers who share common or relevant search interests/goals). In this paper, we present an epistemology-based social search framework for supporting exploratory information seeking, which makes the best of both search engines' immense power of information collection and pre-processing and human users' knowledge of information filtering and post-processing. To validate the feasibility and effectiveness of the framework, we have designed and implemented a prototype system with the guidance of the framework. Our experimental results show that an epistemology-based social search system outperforms a conventional search engine for most exploratory information seeking tasks

Assessment of Pathogens in Flood Waters in Coastal Rural Regions: Case study after Hurricane Michael and Florence

The severity of hurricanes, and thus the associated impacts, is changing over time. One of the understudied threats from damage caused by hurricanes is the potential for cross-contamination of water bodies with pathogens in coastal agricultural regions. Using microbiological data collected after hurricanes Florence and Michael, this study shows a dichotomy in the presence of pathogens in coastal North Carolina and Florida. Salmonella typhimurium was abundant in water samples collected in the regions dominated by swine farms. A drastic decrease in Enterococcus spp. in Carolinas is indicative of pathogen removal with flooding waters. Except for the abundance presence of Salmonella typhimurium, no significant changes in pathogens were observed after Hurricane Michael in the Florida panhandle. We argue that a comprehensive assessment of pathogens must be included in decision-making activities in the immediate aftermath of hurricanes to build resilience against risks of pathogenic exposure in rural agricultural and human populations in vulnerable locations

PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor). In this paper, we propose PIT, a deep-learning compiler for dynamic sparsity. PIT proposes a novel tiling mechanism that leverages Permutation Invariant Transformation (PIT), a mathematically proven property, to transform multiple sparsely located micro-tiles into a GPU-efficient dense tile without changing the computation results, thus achieving both high GPU utilization and low coverage waste. Given a model, PIT first finds feasible PIT rules for all its operators and generates efficient GPU kernels accordingly. At runtime, with the novel SRead and SWrite primitives, PIT rules can be executed extremely fast to support dynamic sparsity in an online manner. Extensive evaluation on diverse models shows that PIT can accelerate dynamic sparsity computation by up to 5.9x (average 2.43x) over state-of-the-art compilers