Reports of the DAS02 Working Groups

This document is a collection of four working group reports in the areas of digital libraries, document image retrieval, layout analysis, and Web document analysis. These reports were the outcome of discussions by participants at the Fifth IAPR International Workshop on Document Analysis Systems held in Princeton, NJ on 19-21 August 2002

Ontology guided financial knowledge extraction from semistructured information sources

Intermedium has an agent searching the Web for financial articles defined by certain criteria, for instance an industrial domain of interest. A portal service for reading and searching these articles, are available for the customers. The sources searched among are secondary sources, like online newspapers. Secondary sources publish information more frequently, and other information than can be found in annual reports etc, like predictions. Finding and comparing financial figures in the articles are often time consuming and hard to compare with each other. Having the financial figures, and what these applies for, presented in an application where information could be easy reviewed and compared, would apply valuable information for decision makers in bigger companies. Web documents are usually semi-structured, and therefore almost impossible to query for information. Only keyword searches are supported by the computers because of the lack of understanding. Advanced extraction processes of the information needs to be performed. This thesis evaluates an ontology guided approach for extracting financial information from semi-structured information sources. A financial ontology has been constructed based on an investigation of 50 articles gathered from Intermedium’s agent. Instances with synonyms, the words to extract from the text, and relations between the instances have been defined. The ontology language RDF has been chosen and used as ontology language through the entire thesis. A prototype application has been developed to perform the extraction process. Articles are loaded from XML files; words to extract from the text are found by query the ontology using the query language RDQL; NLP and NLTK are used to do the extraction based on the words found in the ontology; Velocity template is used to get the proper structure in the output files RDF and XBRL instance document. The ontology is providing the application with knowledge in the extraction process. When a synonym is found in one instance, a query for reference to other instances is performed, and synonyms of these instances are searched for in the text. If a text does not contain any interesting information, the application does not waste time with trying to match all words in the ontology with the ones in the text. The result is presented with semantic tagging in RDF syntax. A part of the information extracted is also shown as an example of how the financial standard XBRL can be given. The advantage of XBRL is that it can be used directly by supporting tools; RDF has to be processed by a more intelligent application. Financial information has in both these formats been added knowledge with computer processable semantic tagging

Nuclear Fusion Programme: Annual Report of the Association Karlsruhe Institute of Technology/EURATOM ; January 2013 - December 2013 (KIT Scientific Reports ; 7671)

The Karlsruhe Institute of Technology (KIT) is working in the framework of the European Fusion Programme on key technologies in the areas of superconducting magnets, microwave heating systems (Electron-Cyclotron-Resonance-Heating, ECRH), the deuterium-tritium fuel cycle, He-cooled breeding blankets, a He-cooled divertor and structural materials, as well as refractory metals for high heat flux applications including a major participation in the preparation of the international IFMIF project

Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data

In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality. We employ a sophisticated deep learning structure to develop an advanced system for ambient air quality prediction. We utilize three publicly available databases and real-world data to obtain accurate air quality measurements. These four datasets undergo a data cleaning to yield a consolidated, cleaned dataset. Subsequently, the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA)—a dual optimization method combining the Eurasian Oystercatcher Optimizer (EOO) and Pathfinder Algorithm (PFA)—is applied. This method aids in selecting weighted features, optimizing weights, and choosing the most relevant attributes for optimal results. These optimal features are then incorporated into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for the ambient Air Quality Prediction (AQP) process. The variables within MDS-ATCN are further refined using the proposed FEO-PFA to enhance predictive accuracy. An empirical analysis is performed to compare the efficacy of our proposed model with traditional methods, underscoring the superior effectiveness of our approach. The average cost function is reduced to 5.5%, the MAE to 28%, and the RMSE to 14% by the suggested method, according to the performance research conducted with regard to all datasets

URBAN CORPORIS X - UNEXPECTED

Starting from the emergency provoked by the Sars-Cov2 that affected the whole world, the book brings the contributions of researchers and artists from all over the world discussing the theme of the \u201cunexpected\u201d, its implication, and inter-action with everyday life. The book presents a series of essays divided into three parts: Living unexpectedly, Missing interactions, and Different sociality. These three categories bring together authors who have had a reading of the unexpected emergency that occurred, pointing out different perspectives upon dynamics and relation caused by this situation, underlining how the isolation period has affected both the domestic and the urban sphere. Moreover, through drawings, photomontages and photographs, several authors gave a visual interpretation of the changed lives, spaces, and routines. All these contributions don\u2019t want to answer to the enormous problems brought by the pandemic. Rather they synthesize an interpretation of the shifting condition that occurred, showing both the great reactive capacity and the fragility of the no longer present reality

Exercise Limitation in Aortic Stenosis

Symptomatic (Sy) aortic stenosis (AS) is a malignant condition with a five year survival of 25mmHg) and 20 matched controls. Methods; Full echocardiography, cardiopulmonary exercise testing, skeletal muscle strength and endurance, arm and leg ergoreflex activation, brain natriuretic peptide (BNP) and endothelin-1 (ET-1) before and after maximal exercise, were measured. AS was classified by both disease (mild/severe) severity and symptomatic (Sy/Asy) status. Echocardiographic, anthropometric and exercise variables were examined as predictors of aerobic exercise capacity by univariate and multivariate regression analysis. Results: AS and control subjects were well matched for age, body size and sex. Sy patients (67.9+/-11, n=19) were older than Asy AS (47.6+/-19, n=18) and controls (50.4+/-17 years), p=0.001. Exercise capacity (% predicted) was reduced in Sy AS (52+/-27%) compared to Asy AS (86+/-28%) and controls (126+/-17%), p<0.0001. Exercise VEA/CO2 was increased in severe (34.5+/-8) and Sy AS (35.1+/-7) v mild (30.2+5), Asy AS (29.8+/-5) and controls (28.6+/-4) p<0.01. SBP response to exercise was reduced in severe and Sy AS v controls and Asy/mild AS (P<0.001). Isometric quadriceps strength (Sym 470+/-169N v Asy AS 564+/-251N V controls 567+/-199N) and isokinetic endurance (Sym 83.4+/-16% v Asy AS 71.9+/-14% V controls 76.1+/-7%) were not significantly different between AS and controls. Arm ergoreflex activation was not significantly increased in AS. Leg ergoreflex activation (ventilation, % peak) was enhanced in Sym AS (67+/-50) v Asy AS (20+/-46) and controls (17+/-48), p=0.01 but did not predict exercise capacity or VEA/CO2. BNP was increased in severe and Sym AS v mild/Asy AS and controls, p<0.0001. ET-1 was comparable at rest and after exercise in AS and controls. LV mass/mass index and BNP were independent predictors of exercise capacity in AS. Conclusions: Several variables have been identified which differ in symptomatic and asymptomatic AS. These (BNP, exercise capacity and SBP response ) should be assessed for prognostic value in a prospective study of AS