280 research outputs found
Biba T-Shirt
No full textBiba T-shirt; black, with gold BIBA logo to centre front. Label (to back neck): SJM Sportswear L Printed in USA 100% Cotton Made in Venezuela. Maker: S,J,M, Sportswear. Date: 1970 - 1979 - from the The Betty Smithers Design Collection at Staffordshire University.
Protein Fold Recognition using Markov Logic Networks
No full textProtein fold recognition is the problem of determining whether a given protein sequence folds into a
previously observed structure. An uncertainty complication is that it is not always true that the structure
has been previously observed. Markov logic networks (MLNs) are a powerful representation that combines
first-order logic and probability by attaching weights to first-order formulas and using these as templates
for features of Markov networks. In this chapter, we describe a simple temporal extension of MLNs that
is able to deal with sequences of logical atoms. We also propose iterated robust tabu search (IRoTS) for
maximum a posteriori (MAP) inference and Markov Chain-IRoTS (MC-IRoTS) for conditional inference
in the new framework. We show how MC-IRoTS can also be used for discriminative weight learning. We
describe how sequences of protein secondary structure can be modeled through the proposed language and
show through some preliminary experiments the promise of our approach for the problem of protein fold
recognition from these sequences
BIBA onderzoek 2004 : Management rapportage : Beheer, Integratie, Bestuur en Architectuur van content
No full textHet Platform ICT en Organisatie van SURF heeft in het kader van het thema Portals het BIBA
project uitgevoerd. Doelstelling van dit project was om door middel van een onderzoek hulpmiddelen
en kennis aan te dragen voor Beheer, Integratie, Bestuur en Architectuur (BIBA) van content
management systemen en portals in het hoger onderwijs.
M&I/Argitek heeft in samenwerking met vertegenwoordigers van Hogeschool Avans, Universiteit
van Amsterdam en Vrije Universiteit Amsterdam, hiertoe een onderzoek uitgevoerd in het
eerste halfjaar van 2004. Een klankbordgroep begeleidde het onderzoek. De uitkomsten van het
onderzoek zijn op 6 oktober 2004 in de Jaarbeurs te Utrecht aan ruim 200 geïnteresseerden gepresenteerd.
Op de SURF-website zijn de presentaties van deze bijeenkomst geplaatst, alsook de
kwantitatieve resultaten van de pakketvergelijking. Deze rapportage bevat een kwalitatieve rapportage
gericht op managers, waar de inhoudelijke conclusies nader wordt toegelicht
Knowledge discovery from noisy relational databases with statistical relational machine learning
No full text
Intelligent Text Processing Techniques for Textual-Profile Gene Characterization
No full textWe present a suite ofMachine Learning and knowledge-based
components for textual-profile based gene prioritization. Most genetic
diseases are characterized by many potential candidate genes that can
cause the disease. Gene expression analysis typically produces a large
number of co-expressed genes that could be potentially responsible for
a given disease. Extracting prior knowledge from text-based genomic
information sources is essential in order to reduce the list of potential
candidate genes to be then further analyzed in laboratory. In this paper
we present a suite of Machine Learning algorithms and knowledge-based
components for improving the computational gene prioritization process.
The suite includes basic Natural Language Processing capabilities,
advanced text classification and clustering algorithms, robust information
extraction components based on qualitative and quantitative keyword
extraction methods and exploitation of lexical knowledge bases for
semantic text processing
Boosting learning and inference in Markov logic through metaheuristics
No full textMarkov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for structure learning in ML, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. We show through real-world experiments that the algorithm improves accuracy and learning time over the state-of-the-art algorithms. On the other side MAP and conditional inference for ML are hard computational tasks. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) metaheuristic. The first algorithm performs MAP inference and we show through extensive experiments that it improves over the state-of-the-art algorithm in terms of solution quality and inference time. The second algorithm combines IRoTS steps with simulated annealing steps for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality
BIBA, BIAS und IFAM gründen Rapid Prototyping Zentrum Bremen
No full textS.32A new initiative called Rapid Prototyping Zentrum Bremen is established between the Bremer Institut für Betriebstechnik und angewandte Arbeitswissenschaften (BIBA), Bremer Institut für angewandte Strahltechnik (BIAS) and the Fraunhofer-Institut für Angewandte Materialforschung (IFAM). This co-operation between three institutes combining information, material, laser and process know-how offers services from technology transfer, seminars and training up to multidisziplinary projects.Nr.1
Modelling and Searching of Combinatorial Spaces Based on Markov Logic Networks
No full textMarkov Logic Networks (MLNs) combine Markov networks (MNs) and firstorder logic by attaching weights to first-order formulas and using these as templates for features of MNs. Learning the structure of MLNs is performed by state-of-the-art methods by maximizing the likelihood of a relational database.
This leads to suboptimal results for prediction tasks due to the mismatch between the objective function (likelihood) and the task of classification
(maximizing conditional likelihood (CL)).
In this paper we propose two algorithms for learning the structure of MLNs.
The first maximizes the CL of query predicates instead of the joint likelihood of
all predicates while the other maximizes the area under the Precision-Recall
curve (AUC). Both algorithms set the parameters by maximum likelihood and choose structures by maximizing CL or AUC. For each of these algorithms we develop two different searching strategies. The first is based on Iterated Local
Search and the second on Greedy Randomized Adaptive Search Procedure.We compare the performances of these randomized search approaches on realworld datasets and show that on larger datasets, the ILS-based approaches
perform better, both in terms of CLL and AUC, while on small datasets, ILS and
RBS approaches are competitive and RBS can also lead to better results for AUC
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