Exploring youporn categories, tags, and nicknames for pleasant recommendations

YouPorn is one of the largest providers of adult content on the web. Being free of charge, the video portal allows users - besides watching - to upload, categorize, and comment on pornographic videos. With this position paper, we point out the challenges of analyzing the textual data offered with the videos. We report on first experiments and problems with our YouPorn dataset , which we extracted from the non-graphical content of the YP website. To gain some insights, we performed association rule mining on the video categories and tags, and investigated preferences of users based on their nickname. Hoping that future research will be able to build upon our initial experiences, we make the ready-to-use YP dataset publicly available

Knowledge graph exploration for natural language understanding in web information retrieval

In this thesis, we study methods to leverage information from fully-structured knowledge bases (KBs), in particular the encyclopedic knowledge graph (KG) DBpedia, for different text-related tasks from the area of information retrieval (IR) and natural language processing (NLP). The key idea is to apply entity linking (EL) methods that identify mentions of KB entities in text, and then exploit the structured information within KGs. Developing entity-centric methods for text understanding using KG exploration is the focus of this work. We aim to show that structured background knowledge is a means for improving performance in different IR and NLP tasks that traditionally only make use of the unstructured text input itself. Thereby, the KB entities mentioned in text act as connection between the unstructured text and the structured KG. We focus in particular on how to best leverage the knowledge as contained in such fully-structured (RDF) KGs like DBpedia with their labeled edges/predicates – which is in contrast to previous work on Wikipedia-based approaches we build upon, which typically relies on unlabeled graphs only. The contribution of this thesis can be structured along its three parts: In Part I, we apply EL and semantify short text snippets with KB entities. While only retrieving types and categories from DBpedia for each entity, we are able to leverage this information to create semantically coherent clusters of text snippets. This pipeline of connecting text to background knowledge via the mentioned entities will be reused in all following chapters. In Part II, we focus on semantic similarity and extend the idea of semantifying text with entities by proposing in Chapter 5 a model that represents whole documents by their entities. In this model, comparing documents semantically with each other is viewed as the task of comparing the semantic relatedness of the respective entities, which we address in Chapter 4. We propose an unsupervised graph weighting schema and show that weighting the DBpedia KG leads to better results on an existing entity ranking dataset. The exploration of weighted KG paths turns out to be also useful when trying to disambiguate the entities from an open information extraction (OIE) system in Chapter 6. With this weighting schema, the integration of KG information for computing semantic document similarity in Chapter 5 becomes the task of comparing the two KG subgraphs with each other, which we address by an approximate subgraph matching. Based on a well-established evaluation dataset for semantic document similarity, we show that our unsupervised method achieves competitive performance similar to other state-of-the-art methods. Our results from this part indicate that KGs can contain helpful background knowledge, in particular when exploring KG paths, but that selecting the relevant parts of the graph is an important yet difficult challenge. In Part III, we shift to the task of relevance ranking and first study in Chapter 7 how to best retrieve KB entities for a given keyword query. Combining again text with KB information, we extract entities from the top-k retrieved, query-specific documents and then link the documents to two different KBs, namely Wikipedia and DBpedia. In a learning-to-rank setting, we study extensively which features from the text, theWikipedia KB, and the DBpedia KG can be helpful for ranking entities with respect to the query. Experimental results on two datasets, which build upon existing TREC document retrieval collections, indicate that the document-based mention frequency of an entity and the Wikipedia-based query-to-entity similarity are both important features for ranking. The KG paths in contrast play only a minor role in this setting, even when integrated with a semantic kernel extension. In Chapter 8, we further extend the integration of query-specific text documents and KG information, by extracting not only entities, but also relations from text. In this exploratory study based on a self-created relevance dataset, we find that not all extracted relations are relevant with respect to the query, but that they often contain information not contained within the DBpedia KG. The main insight from the research presented in this part is that in a query-specific setting, established IR methods for document retrieval provide an important source of information even for entity-centric tasks, and that a close integration of relevant text document and background knowledge is promising. Finally, in the concluding chapter we argue that future research should further address the integration of KG information with entities and relations extracted from (specific) text documents, as their potential seems to be not fully explored yet. The same holds also true for a better KG exploration, which has gained some scientific interest in recent years. It seems to us that both aspects will remain interesting problems in the next years, also because of the growing importance of KGs for web search and knowledge modeling in industry and academia

Second WAW Quantum Computing: Introductory Talk

This talk sketches how quantum computers are build and the current state of the art. In a second part we discuss possibilities and limitations of quantum computers

Second WAW Quantum Computing: Introductory Talk

This talk sketches how quantum computers are build and the current state of the art. In a second part we discuss possibilities and limitations of quantum computers

Ranking Entities in a Large Semantic Network

Abstract. We present two knowledge-rich methods for ranking entities in a semantic network. Our approach relies on the DBpedia knowledge base for acquiring fine-grained information about entities and their semantic relations. Experiments on a benchmarking dataset show the viability of our approach

DOTA-PESIN, a DOTA-conjugated bombesin derivative designed for the imaging and targeted radionuclide treatment of bombesin receptor-positive tumours

Purpose: We aimed at designing and developing a novel bombesin analogue, DOTA-PEG4-BN(7-14) (DOTA-PESIN), with the goal of labelling it with 67/68Ga and 177Lu for diagnosis and radionuclide therapy of prostate and other human cancers overexpressing bombesin receptors. Methods: The 8-amino acid peptide bombesin (7-14) was coupled to the macrocyclic chelator DOTA via the spacer 15-amino-4,7,10,13-tetraoxapentadecanoic acid (PEG4). The conjugate was complexed with Ga(III) and Lu(III) salts. The GRP receptor affinity and the bombesin receptor subtype profile were determined in human tumour specimens expressing the three bombesin receptor subtypes. Internalisation and efflux studies were performed with the human GRP receptor cell line PC-3. Xenografted nude mice were used for biodistribution. Results: [GaIII/LuIII]-DOTA-PESIN showed good affinity to GRP and neuromedin B receptors but no affinity to BB3. [67Ga/177Lu]-DOTA-PESIN internalised rapidly into PC-3 cells whereas the efflux from PC-3 cells was relatively slow. In vivo experiments showed a high and specific tumour uptake and good retention of [67Ga/177Lu]-DOTA-PESIN. [67Ga/177Lu]-DOTA-PESIN highly accumulated in GRP receptor-expressing mouse pancreas. The uptake specificity was demonstrated by blocking tumour uptake and pancreas uptake. Fast clearance was found from blood and all non-target organs except the kidneys. High tumour-to-normal tissue ratios were achieved, which increased with time. PET imaging with [68Ga]-DOTA-PESIN was successful in visualising the tumour at 1h post injection. Planar scintigraphic imaging showed that the 177Lu-labelled peptide remained in the tumour even 3days post injection. Conclusion: The newly designed ligands have high potential with regard to PET and SPECT imaging with 68/67Ga and targeted radionuclide therapy with 177L

Finding relevant relations in relevant documents

This work studies the combination of a document retrieval and a relation extraction system for the purpose of identifying query-relevant relational facts. On the TREC Web collection, we assess extracted facts separately for correctness and relevance. Despite some TREC topics not being covered by the relation schema, we find that this approach reveals relevant facts, and in particular those not yet known in the knowledge base DBpedia. The study confirms that mention frequency, document relevance, and entity relevance are useful indicators for fact relevance. Still, the task remains an open research problem

Novel Schizophrenia Risk Gene TCF4 Influences Verbal Learning and Memory Functioning in Schizophrenia Patients

Background: Recently, a role of the transcription factor 4 (TCF4) gene in schizophrenia has been reported in a large genome-wide association study. It has been hypothesized that TCF4 affects normal brain development and TCF4 has been related to different forms of neurodevelopmental disorders. Schizophrenia patients exhibit strong impairments of verbal declarative memory (VDM) functions. Thus, we hypothesized that the disease-associated C allele of the rs9960767 polymorphism of the TCF4 gene led to impaired VDM functioning in schizophrenia patients. Method: The TCF4 variant was genotyped in 401 schizophrenia patients. VDM functioning was measured using the Rey Auditory Verbal Learning Test (RAVLT). Results: Carriers of the C allele were less impaired in recognition compared to those carrying the AA genotype (13.76 vs. 13.06; p = 0.049). Moreover, a trend toward higher scores in patients with the risk allele was found for delayed recall (10.24 vs. 9.41; p = 0.088). The TCF4 genotype did not influence intelligence or RAVLT immediate recall or total verbal learning. Conclusion: VDM function is influenced by the TCF4 gene in schizophrenia patients. However, the elevated risk for schizophrenia is not conferred by TCF4-mediated VDM impairment. Copyright (C) 2011 S. Karger AG, Base

Developing and testing a Corona VaccinE tRiAL pLatform (COVERALL) to study Covid-19 vaccine response in immunocompromised patients

BACKGROUND The rapid course of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic calls for fast implementation of clinical trials to assess the effects of new treatment and prophylactic interventions. Building trial platforms embedded in existing data infrastructures is an ideal way to address such questions within well-defined subpopulations. METHODS We developed a trial platform building on the infrastructure of two established national cohort studies: the Swiss human immunodeficiency virus (HIV) Cohort Study (SHCS) and Swiss Transplant Cohort Study (STCS). In a pilot trial, termed Corona VaccinE tRiAL pLatform (COVERALL), we assessed the vaccine efficacy of the first two licensed SARS-CoV-2 vaccines in Switzerland and the functionality of the trial platform. RESULTS Using Research Electronic Data Capture (REDCap), we developed a trial platform integrating the infrastructure of the SHCS and STCS. An algorithm identifying eligible patients, as well as baseline data transfer ensured a fast inclusion procedure for eligible patients. We implemented convenient re-directions between the different data entry systems to ensure intuitive data entry for the participating study personnel. The trial platform, including a randomization algorithm ensuring balance among different subgroups, was continuously adapted to changing guidelines concerning vaccination policies. We were able to randomize and vaccinate the first trial participant the same day we received ethics approval. Time to enroll and randomize our target sample size of 380 patients was 22 days. CONCLUSION Taking the best of each system, we were able to flag eligible patients, transfer patient information automatically, randomize and enroll the patients in an easy workflow, decreasing the administrative burden usually associated with a trial of this size

DAOA/G72 predicts the progression of prodromal syndromes to first episode psychosis

The genetic factors determining the progression of prodromal syndromes to first episode schizophrenia have remained enigmatic to date. In a unique prospective multicentre trial, we assessed whether variants at the d-amino acid oxidase activator (DAOA)/G72 locus influence progression to psychosis. Young subjects with a prodromal syndrome were observed prospectively for up to 2 years to assess the incidence of progression to schizophrenia or first episode psychosis. Of the 82 probands with a prodromal syndrome, 21 probands experienced progression to psychosis within the observation period. Assessment of nine common variants in the DAOA/G72 locus yielded two variants with the predictive value for symptom progression: all four probands with the rs1341402 CC genotype developed psychosis compared with 17 out of 78 probands with the TT or CT genotypes (χ2 = 12.348; df = 2; p = 0.002). The relative risk for progression to psychosis was significantly increased in the CC genotype: RR = 4.588 (95% CI = 2.175–4.588). Similarly, for rs778294, 50% of probands with the AA genotype, but only 22% of probands with a GG or GA genotype progressed to psychosis (χ2 = 7.027; df = 2; p = 0.030). Moreover, haplotype analysis revealed a susceptibility haplotype for progression to psychosis. This is one of the first studies to identify a specific genetic factor for the progression of prodromal syndromes to schizophrenia, and further underscores the importance of the DAOA/G72 gene for schizophrenia