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The concept of culturomics was born out of the availability of massive amounts of textual data and the interest to make sense of cultural and language phenomena over time. Thus far however, culturomics has only made use of, and shown the great potential of, statistical methods. In this paper, we present a vision for a knowledge-based culturomics that complements traditional culturomics. We discuss
The aim of this thesis was to reconstruct the glaciodynamics, deglacial landforms, isostatic uplift and to date the deglaciation. Glaciodynamics and deglacial landforms were focused on to provide a process and depositional model for De Geer moraine and Niemisel moraine and to reveal their internal and spatial relationship, based on detailed sedimentological and structural investigations. The isost
Radio based localization and tracking usually require multiple receivers/transmitters or a known floor plan. This paper presents a method for anchor free indoor positioning based on single antenna ultra wideband (UWB) measurements. By using time of arrival information from multipath propagation components stemming from scatterers with different, but unknown, positions we estimate the movement of t
In this paper, we describe a passage retrieval component for a questioning answering system and we evaluate its performance on Swedish documents. We used a corpus of questions and answers transcribed from the Swedish board game Kvitt eller dubbelt and, as source for the passages, we used the articles of the Swedish version of Wikipedia. We show that Wikipedia is a suitable knowledge source to answ
The program system MOLCAS is a package for calculations of electronic and structural properties of molecular systems in gas, liquid, or solid phase. It contains a number of modern quantum chemical methods for studies of the electronic structure in ground and excited electronic states. A macromolecular environment can be modeled by a combination of quantum chemistry and molecular mechanics. It is f
This thesis explores methods for generating proposition databases in a large-scale and multilingual setting. Our methods are centered on using semantic role labeling for extracting predicate-argument structures, and the subsequent transformation of such structures for knowledge base population and generation. By extending semantic role labeling with entity detection, we demonstrate how predicate-a
In this paper, we describe KOSHIK, an end-to-end framework to process the unstructured natural language content of multilingual documents. We used the Hadoop distributed computing infrastructure to build this framework as it enables KOSHIK to easily scale by adding inexpensive commodity hardware. We designed an annotation model that allows the processing algorithms to incrementally add layers of a
Inthispaper,wedescribeanovelsystemthatidentifiesrelationsbetweentheobjectsextractedfromanimage. We started from the idea that in addition to the geometric and visual properties of the image objects, we could exploit lexical and semantic information from the text accompanying the image. As experimental set up, we gathered a corpus of images from Wikipedia as well as their associated articles. We ext
Most question answering systems feature a step to predict an expected answer type given a question. Li and Roth \cite{li2002learning} proposed an oft-cited taxonomy to the categorize the answer types as well as an annotated data set. While offering a framework compatible with supervised learning, this method builds on a fixed and rigid model that has to be updated when the question-answering domai
The massive amounts of text data made available through the Google Books digitization project have inspired a new field of big-data textual research. Named culturomics, this field has attracted the attention of a growing number of scholars over recent years. However, initial studies based on these data have been criticized for not referring to relevant work in linguistics and language technology.
This paper describes a set of methods to link entities across images and text. As a corpus, we used a data set of images, where each image is commented by a short caption and where the regions in the images are manually segmented and labeled with a category. We extracted the entity mentions from the captions and we computed a semantic similarity between the mentions and the region labels. We also
We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences),
The Learning with Errors (LWE) problem is one of the main mathematical foundations of post-quantum cryptography. One of the main groups of algorithms for solving LWE is the Blum-Kalai-Wasserman (BKW) algorithm. This paper presents new improvements for BKW-style algorithms for solving LWE instances. We target minimum concrete complexity and we introduce a new reduction step where we partially reduc
Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggr
Cryptography as it is used today relies on a foundational level on the assumptionthat either the Integer Factoring Problem (IFP) or the DiscreteLogarithm Problem (DLP) is computationally intractable. In the 1990s PeterShor developed a quantum algorithm that solves both problems in polynomialtime. Since then alternative foundational mathematical problems to replace IFPand DLP have been suggested. T
Things such as organizations, persons, or locations are ubiquitous in all texts circulating on the internet, particularly in the news, forum posts, and social media. Today, there is more written material than any single person can read through during a typical lifespan. Automatic systems can help us amplify our abilities to find relevant information, where, ideally, a system would learn knowledge
The present study investigates goal side selection in soccer penalty kicking. Given the task of choosing which side of a soccer goal to best score, participants viewed realistic images of a soccer goal and goalkeeper. The goalkeeper’s position was systematically displaced along the goal line, and the lateral position of the goalmouth was systematically displaced in each image – to simulate changes
