10 research outputs found
New Sequential and Parallel Division Free Methods for Determinant of Matrices
A determinant plays an important role in many applications of linear algebra. Finding determinants using non division free methods will encounter problems if entries of matrices
are represented in rational or polynomial expressions, and also when floating point errors arise. To overcome this problem, division free methods are used instead. The
two commonly used division free methods for finding determinant are cross multiplication and cofactor expansion. However, cross multiplication which uses the Sarrus Rule only works for matrices of order less or equal to three, whereas cofactor expansion requires lengthy and tedious computation when dealing with large matrices. This research, therefore, attempts to develop new sequential and parallel methods for finding determinants of matrices. The research also aims to generalise the Sarrus Rule for any order of square matrices based on permutations which are derived using starter sets. Two strategies were introduced to generate distinct starter sets namely the circular and the exchanging of two elements operations. Some theoretical works and mathematical properties for generating permutation and determining determinants were also constructed to support the research. Numerical results indicated that the new proposed methods performed better
than the existing methods in term of computation times. The computation times in the newly developed sequential methods were dominated by generating starter sets. Therefore, two parallel strategies were developed to parallelise this algorithm so as to reduce the computation times. Numerical results showed that the parallel methods were able to
compute determinants faster than the sequential counterparts, particularly when the tasks were equally allocated. In conclusion, the newly developed methods can be used as viable alternatives for finding determinants of matrices
Systematic Design Methods for Efficient Off-Chip DRAM Access
Typical design flows for digital hardware take, as their input, an abstract description
of computation and data transfer between logical memories. No existing commercial
high-level synthesis tool demonstrates the ability to map logical memory inferred from
a high level language to external memory resources. This thesis develops techniques for
doing this, specifically targeting off-chip dynamic memory (DRAM) devices. These are
a commodity technology in widespread use with standardised interfaces. In use, the
bandwidth of an external memory interface and the latency of memory requests asserted
on it may become the bottleneck limiting the performance of a hardware design. Careful
consideration of this is especially important when designing with DRAMs, whose latency
and bandwidth characteristics depend upon the sequence of memory requests issued by
a controller.
Throughout the work presented here, we pursue exact compile-time methods for designing
application-specific memory systems with a focus on guaranteeing predictable performance
through static analysis. This contrasts with much of the surveyed existing work,
which considers general purpose memory controllers and optimized policies which improve
performance in experiments run using simulation of suites of benchmark codes.
The work targets loop-nests within imperative source code, extracting a mathematical
representation of the loop-nest statements and their associated memory accesses, referred
to as the âPolytope Modelâ. We extend this mathematical representation to represent the
physical DRAM ârowâ and âcolumnâ structures accessed when performing memory transfers.
From this augmented representation, we can automatically derive DRAM controllers
which buffer data in on-chip memory and transfer data in an efficient order. Buffering
data and exploiting âreuseâ of data is shown to enable up to 50Ă reduction in the quantity
of data transferred to external memory. The reordering of memory transactions exploiting
knowledge of the physical layout of the DRAM device allowing to 4Ă improvement in
the efficiency of those data transfers
Knowledge-driven entity recognition and disambiguation in biomedical text
Entity recognition and disambiguation (ERD) for the biomedical domain are notoriously difficult problems due to the variety of entities and their often long names in many variations. Existing works focus heavily on the molecular level in two ways. First, they target scientific literature as the input text genre. Second, they target single, highly specialized entity types such as chemicals, genes, and proteins. However, a wealth of biomedical information is also buried in the vast universe of Web content. In order to fully utilize all the information available, there is a need to tap into Web content as an additional input. Moreover, there is a need to cater for other entity types such as symptoms and risk factors since Web content focuses on consumer health. The goal of this thesis is to investigate ERD methods that are applicable to all entity types in scientific literature as well as Web content. In addition, we focus on under-explored aspects of the biomedical ERD problems -- scalability, long noun phrases, and out-of-knowledge base (OOKB) entities. This thesis makes four main contributions, all of which leverage knowledge in UMLS (Unified Medical Language System), the largest and most authoritative knowledge base (KB) of the biomedical domain. The first contribution is a fast dictionary lookup method for entity recognition that maximizes throughput while balancing the loss of precision and recall. The second contribution is a semantic type classification method targeting common words in long noun phrases. We develop a custom set of semantic types to capture word usages; besides biomedical usage, these types also cope with non-biomedical usage and the case of generic, non-informative usage. The third contribution is a fast heuristics method for entity disambiguation in MEDLINE abstracts, again maximizing throughput but this time maintaining accuracy. The fourth contribution is a corpus-driven entity disambiguation method that addresses OOKB entities. The method first captures the entities expressed in a corpus as latent representations that comprise in-KB and OOKB entities alike before performing entity disambiguation.Die Erkennung und Disambiguierung von EntitĂ€ten fĂŒr den biomedizinischen Bereich stellen, wegen der vielfĂ€ltigen Arten von biomedizinischen EntitĂ€ten sowie deren oft langen und variantenreichen Namen, groĂe Herausforderungen dar. Vorhergehende Arbeiten konzentrieren sich in zweierlei Hinsicht fast ausschlieĂlich auf molekulare EntitĂ€ten. Erstens fokussieren sie sich auf wissenschaftliche Publikationen als Genre der Eingabetexte. Zweitens fokussieren sie sich auf einzelne, sehr spezialisierte EntitĂ€tstypen wie Chemikalien, Gene und Proteine. Allerdings bietet das Internet neben diesen Quellen eine Vielzahl an Inhalten biomedizinischen Wissens, das vernachlĂ€ssigt wird. Um alle verfĂŒgbaren Informationen auszunutzen besteht der Bedarf weitere Internet-Inhalte als zusĂ€tzliche Quellen zu erschlieĂen. AuĂerdem ist es auch erforderlich andere EntitĂ€tstypen wie Symptome und Risikofaktoren in Betracht zu ziehen, da diese fĂŒr zahlreiche Inhalte im Internet, wie zum Beispiel Verbraucherinformationen im Gesundheitssektor, relevant sind. Das Ziel dieser Dissertation ist es, Methoden zur Erkennung und Disambiguierung von EntitĂ€ten zu erforschen, die alle EntitĂ€tstypen in Betracht ziehen und sowohl auf wissenschaftliche Publikationen als auch auf andere Internet-Inhalte anwendbar sind. DarĂŒber hinaus setzen wir Schwerpunkte auf oft vernachlĂ€ssigte Aspekte der biomedizinischen Erkennung und Disambiguierung von EntitĂ€ten, nĂ€mlich Skalierbarkeit, lange Nominalphrasen und fehlende EntitĂ€ten in einer Wissensbank. In dieser Hinsicht leistet diese Dissertation vier HauptbeitrĂ€ge, denen allen das Wissen von UMLS (Unified Medical Language System), der gröĂten und wichtigsten Wissensbank im biomedizinischen Bereich, zu Grunde liegt. Der erste Beitrag ist eine schnelle Methode zur Erkennung von EntitĂ€ten mittels Lexikonabgleich, welche den Durchsatz maximiert und gleichzeitig den Verlust in Genauigkeit und Trefferquote (precision and recall) balanciert. Der zweite Beitrag ist eine Methode zur Klassifizierung der semantischen Typen von Nomen, die sich auf gebrĂ€uchliche Nomen von langen Nominalphrasen richtet und auf einer selbstentwickelten Sammlung von semantischen Typen beruht, die die Verwendung der Nomen erfasst. Neben biomedizinischen können diese Typen auch nicht-biomedizinische und allgemeine, informationsarme Verwendungen behandeln. Der dritte Beitrag ist eine schnelle Heuristikmethode zur Disambiguierung von EntitĂ€ten in MEDLINE Kurzfassungen, welche den Durchsatz maximiert, aber auch die Genauigkeit erhĂ€lt. Der vierte Beitrag ist eine korpusgetriebene Methode zur Disambiguierung von EntitĂ€ten, die speziell fehlende EntitĂ€ten in einer Wissensbank behandelt. Die Methode wandelt erst die EntitĂ€ten, die in einem Textkorpus ausgedrĂŒckt aber nicht notwendigerweise in einer Wissensbank sind, in latente Darstellungen um und fĂŒhrt anschlieĂend die Disambiguierung durch
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An investigation into the application of machine learning in information retrieval
There is an increasing variety of online databases available which are also evergrowing in size. In retrieving information from these sources, it is important not only to have effective and efficient retrieval techniques but also to enable some form of adaptation to usersâ specific needs. Frequent users, in particular, should be able to benefit from their high use of the information retrieval system. A machine learning approach can be applied to help the system adapt to usersâ specific needs.
It is argued that users have a particular context within which their queries are formed. It is likely that consecutive queries for a particular user will be related in that they will be part of the same context. Thus, a context learner is proposed.
In this investigation, the context learner is used for enhancing document ordering in partial match systems