402 research outputs found
SQUWALS: A Szegedy QUantum WALks Simulator
Szegedy's quantum walk is an algorithm for quantizing a general Markov chain.
It has plenty of applications such as many variants of optimizations. In order
to check its properties in an error-free environment, it is important to have a
classical simulator. However, the current simulation algorithms require a great
deal of memory due to the particular formulation of this quantum walk. In this
paper we propose a memory-saving algorithm that scales as
with the size of the graph. We provide additional procedures for simulating
Szegedy's quantum walk over mixed states and also the Semiclassical Szegedy
walk. With these techniques we have built a classical simulator in Python
called SQUWALS. We show that our simulator scales as in both
time and memory resources. This package provides some high-level applications
for algorithms based on Szegedy's quantum walk, as for example the quantum
PageRank.Comment: RevTex 4.2, 16 pages, 9 color figure
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
Toward conceptual indexing using automatic assignment of descriptors
Indexing techniques have reached a well maturated state. Digital libraries and other digital collections make an intense use of these algorithms to store and retrieve documents. In the other side, we have browsing techniques, which lets the user to gather the information. Current approaches are not yet advanced enough in order to satisfy the user. At CERN we are working in a indexer based on thesaurus descriptors. With a collection of documents related to thesaurus, user can manipulate them in a more conceptual way. Here we describe the core of this system, the automatic descriptor assigner
BibRank: Automatic Keyphrase Extraction Platform Using~Metadata
Automatic Keyphrase Extraction involves identifying essential phrases in a
document. These keyphrases are crucial in various tasks such as document
classification, clustering, recommendation, indexing, searching, summarization,
and text simplification. This paper introduces a platform that integrates
keyphrase datasets and facilitates the evaluation of keyphrase extraction
algorithms. The platform includes BibRank, an automatic keyphrase extraction
algorithm that leverages a rich dataset obtained by parsing bibliographic data
in BibTeX format. BibRank combines innovative weighting techniques with
positional, statistical, and word co-occurrence information to extract
keyphrases from documents. The platform proves valuable for researchers and
developers seeking to enhance their keyphrase extraction algorithms and advance
the field of natural language processing.Comment: 12 pages , 4 figures, 8 table
Self-Evaluation Applied Mathematics 2003-2008 University of Twente
This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008
Implications of Computational Cognitive Models for Information Retrieval
This dissertation explores the implications of computational cognitive modeling for information retrieval. The parallel between information retrieval and human memory is that the goal of an information retrieval system is to find the set of documents most relevant to the query whereas the goal for the human memory system is to access the relevance of items stored in memory given a memory probe (Steyvers & Griffiths, 2010).
The two major topics of this dissertation are desirability and information scent. Desirability is the context independent probability of an item receiving attention (Recker & Pitkow, 1996). Desirability has been widely utilized in numerous experiments to model the probability that a given memory item would be retrieved (Anderson, 2007). Information scent is a context dependent measure defined as the utility of an information item (Pirolli & Card, 1996b). Information scent has been widely utilized to predict the memory item that would be retrieved given a probe (Anderson, 2007) and to predict the browsing behavior of humans (Pirolli & Card, 1996b).
In this dissertation, I proposed the theory that desirability observed in human memory is caused by preferential attachment in networks. Additionally, I showed that documents accessed in large repositories mirror the observed statistical properties in human memory and that these properties can be used to improve document ranking. Finally, I showed that the combination of information scent and desirability improves document ranking over existing well-established approaches
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