Collection selection has been a research issue for years. Typically, in related work, precomputed statistics are employed in order to estimate the expected result quality of each collection, and subsequently the collections are ranked accordingly. Our thesis is that this simple approach is insufficient for several applications in which the collections typically overlap. This is the case, for example, for the collections built by autonomous peers crawling the web. We argue for the extension of existing quality measures using estimators of mutual overlap among collections and present experiments in which this combination outperforms CORI, a popular approach based on quality estimation. We outline our prototype implementation of a P2P web search engine, coined MINERVA 1 , that allows handling large amounts of data in a distributed and self-organizing manner. We conduct experiments which show that taking overlap into account during collection selection can drastically decrease the number of collections that have to be contacted in order to reach a satisfactory level of recall, which is a great step toward the feasibility of distributed web search.
Objective Transcranial direct current stimulation (tDCS) has been shown to improve pain symptoms in fibromyalgia (FM), a central pain syndrome; the underlying mechanisms are not well understood. Our objective was to explore the neurochemical action of tDCS in the FM brain using proton magnetic resonance spectroscopy (1H-MRS). Methods Twelve patients with FM underwent sham tDCS over the left motor (anode) and contralateral supraorbital cortices (cathode) (M1-SO) for 5 consecutive days, a 7 day washout period, and then active M1-SO tDCS for 5 consecutive days. The subjects had clinical pain assessment and 1H-MRS testing at baseline, the week following post-sham tDCS trial, and the week following post-active tDCS trial. Results There was a significant decrease in clinical pain scores between baseline and active tDCS time-points (P=0.04). There was a significant decrease in Glx (glutamate and glutamine) in the anterior cingulate (P=0.013) and a trend towards decreased Glx in the thalami (P=0.056) for the sham-active tDCS comparison. For the baseline-sham tDCS comparison, there was a significant increase in N-acetylaspartate (NAA) levels in the posterior insula (P=0.015). There was a trend towards increased γ-aminobutyric acid (GABA) in the anterior insula for the baseline-active tDCS comparison (P=0.064). There were significant linear regression coefficients between anterior cingulate Glx levels at baseline and the clinical pain scale changes between the baseline-sham tDCS comparison (β1=1.31;P<0.001) and the baseline-active tDCS comparison (β1=1.87;P<0.001). Conclusion Our findings suggest that GABA, Glx and NAA play an important role in the pathophysiology of FM and its modulation by tDCS.
BackgroundAlthough population studies have greatly improved our understanding of migraine, they have relied on retrospective self-reports that are subject to memory error and experimenter-induced bias. Furthermore, these studies also lack specifics from the actual time that attacks were occurring, and how patients express and share their ongoing suffering.ObjectiveAs technology and language constantly evolve, so does the way we share our suffering. We sought to evaluate the infodemiology of self-reported migraine headache suffering on Twitter.MethodsTrained observers in an academic setting categorized the meaning of every single “migraine” tweet posted during seven consecutive days. The main outcome measures were prevalence, life-style impact, linguistic, and timeline of actual self-reported migraine headache suffering on Twitter.ResultsFrom a total of 21,741 migraine tweets collected, only 64.52% (14,028/21,741 collected tweets) were from users reporting their migraine headache attacks in real-time. The remainder of the posts were commercial, re-tweets, general discussion or third person’s migraine, and metaphor. The gender distribution available for the actual migraine posts was 73.47% female (10,306/14,028), 17.40% males (2441/14,028), and 0.01% transgendered (2/14,028). The personal impact of migraine headache was immediate on mood (43.91%, 6159/14,028), productivity at work (3.46%, 486/14,028), social life (3.45%, 484/14,028), and school (2.78%, 390/14,028). The most common migraine descriptor was “Worst” (14.59%, 201/1378) and profanity, the “F-word” (5.3%, 73/1378). The majority of postings occurred in the United States (58.28%, 3413/5856), peaking on weekdays at 10:00h and then gradually again at 22:00h; the weekend had a later morning peak.ConclusionsTwitter proved to be a powerful source of knowledge for migraine research. The data in this study overlap large-scale epidemiological studies, avoiding memory bias and experimenter-induced error. Furthermore, linguistics of ongoing migraine reports on social media proved to be highly heterogeneous and colloquial in our study, suggesting that current pain questionnaires should undergo constant reformulations to keep up with modernization in the expression of pain suffering in our society. In summary, this study reveals the modern characteristics and broad impact of migraine headache suffering on patients’ lives as it is spontaneously shared via social media.
Assertive community treatment offers significant advantages over standard case management models in reducing homelessness and symptom severity in homeless persons with severe mental illness.
Peer-to-Peer (P2P) search requires intelligent decisions for query routing: selecting the best peers to which a given query, initiated at some peer, should be forwarded for retrieving additional search results. These decisions are based on statistical summaries for each peer, which are usually organized on a per-keyword basis and managed in a distributed directory of routing indices. Such architectures disregard the possible correlations among keywords. Together with the coarse granularity of per-peer summaries, which are mandated for scalability, this limitation may lead to poor search result quality. This paper develops and evaluates two solutions to this problem, sk-STAT based on single-key statistics only, and mk-STAT based on additional multi-key statistics. For both cases, hash sketch synopses are used to compactly represent a peer's data items and are efficiently disseminated in the P2P network to form a decentralized directory. Experimental studies with Gnutella and Web data demonstrate the viability and the trade-offs of the approaches.
Abstract. We consider a collaboration of peers autonomously crawling the Web. A pivotal issue when designing a peer-to-peer (P2P) Web search engine in this environment is query routing: selecting a small subset of (a potentially very large number of relevant) peers to contact to satisfy a keyword query. Existing approaches for query routing work well on disjoint data sets. However, naturally, the peers' data collections often highly overlap, as popular documents are highly crawled. Techniques for estimating the cardinality of the overlap between sets, designed for and incorporated into information retrieval engines are very much lacking. In this paper we present a comprehensive evaluation of appropriate overlap estimators, showing how they can be incorporated into an efficient, iterative approach to query routing, coined Integrated Quality Novelty (IQN). We propose to further enhance our approach using histograms, combining overlap estimation with the available score/ranking information. Finally, we conduct a performance evaluation in MINERVA, our prototype P2P Web search engine.
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