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To train well-performing generalizing neural networks, sufficiently large and diverse datasets are needed. Collecting data while adhering to privacy legislation becomes increasingly difficult and annotating these large datasets is both a resource-heavy and time-consuming task. An approach to overcome these difficulties is to use synthetic data since it is inherently scalable and can be automatical
The practice of code review is widely adopted in industry and hasbeen studied to an increasing degree in the research community.However, the developer experience of code review has receivedlimited attention. Here, we report on initial results from a mixed-method exploratory study of the developer experience.
This study evaluates the applicability of scaled reassigned spectrograms (ReSTS) on ultrasound radio frequency data obtained with a clinical linear array ultrasound transducer. The ReSTS's ability to resolve axially closely spaced objects in a phantom is compared to the classical cross-correlation method with respect to the ability to resolve closely spaced objects as individual reflectors using u
Rank and cardinality penalties are hard to handle in optimization frameworks due to non-convexity and discontinuity. Strong approximations have been a subject of intense study and numerous formulations have been proposed. Most of these can be described as separable, meaning that they apply a penalty to each element (or singular value) based on size, without considering the joint distribution. In t
LSTM architectures (Hochreiter and Schmidhuber, 1997) have become standard to recognize named entities (NER) in text (Lample et al., 2016; Chiu and Nichols, 2016). Nonetheless, Zhang et al. (2015) recently proposed an approach based on fixed-size ordinally forgetting encoding (FOFE) to translate variable-length contexts into fixed-length features. This encoding method can be used with feed-forward
The Fourier slice theorem for the standard Radon transform generalizes to a Laplace counterpart when considering the exponential Radon transform. We show how to use this fact in combination with algorithms for the unequally spaced fast Laplace transform to construct fast and accurate methods for computing both the forward exponential Radon transform and the corresponding back-projection operator.
Quality assurance has been identified as an important part of radiation protection and safety for a considerable time period. A rational expansion and improvement of quality assurance is to integrate radiation protection and safety in a management system. The aim of this study was to explore factors influencing the implementing strategy when introducing a management system including radiation prot
Reducing train delays important in many countries, even in those like Japan, where punctuality is already high. There is a clearpattern across the literature that the delays typically occur at stations and are recovered on line sections. Previous work has shownthat one explanation for this is that trains interact at stations. When trains have different speeds or stopping patterns, overtakes areimp
The recently introduced method of `signal apparition' offers a fundamentally different approach to separation of multiple interfering sources, by using a periodic sequence of source signatures along one source line. This leads to exact separation of signals in diamond-shaped regions of the frequency-wavenumber domain which are twice as large compared to those recovered by other methods. In this pa
We study a scenario where a monitor is interested in the freshest possible update from a remote sensor. The monitor also seeks to minimize the number of updates that exceed a certain freshness threshold, beyond which, the information is deemed to be too old. Previous work has presented results for First Come First Served (FCFS) systems. However, it has been shown that Last Come First Served (LCFS)We study a scenario where a monitor is interested in the freshest possible update from a remote sensor. The monitor also seeks to minimize the number of updates that exceed a certain freshness threshold, beyond which, the information is deemed to be too old. Previous work has presented results for First Come First Served (FCFS) systems. However, it has been shown that Last Come First Served (LCFS)
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
Denna artikel diskuterar en ny form av snabbt expanderande praktiker från tidigt 2000-tal och som erbjuds av privata företag eller entreprenörer (lekmannaterapeuter). I fokus står självförverkligande och helande. Jag har med hjälp av verktyg från Ritual Studies valt att analysera dessa praktiker som en form av individ-centrerade riter som designas för att lindra smärta och lågt självförtroende förThis article discusses a new form of practices which are offered by private companies or entrepreneurs (layman therapists) whose practices focusing on self-development and healing rapidly expanded in the early 2000s in Sweden. I have chosen to analyze these practices from a Ritual Studies perspective as individual-centered rites designed to remedy ill health and low self-esteem and to build new, p
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
Finding a suitable way to represent information in a dataset is one of the fundamental problems in Artificial Inelegance. With limited labeled information, unsupervised learning algorithms help to discover useful representations. One of the applications of such models is imputation, where missing values are estimated by learning the underlying correlations in a dataset. This thesis explores two ofFinding a suitable way to represent information in a dataset is one of the fundamental problems in Artificial Intelligence. With limited labeled information, unsupervised learning algorithms help to discover useful representations. One of the applications of such models is imputation, where missing values are estimated by learning the underlying correlations in a dataset. This thesis explores two
This article offers an actionable framework for dealing with trust violations in R&D partnerships: it explains how to turn around a conflicted R&D partnership, repair trust, and learn from the experience. As innovation becomes more open, firms increasingly find themselves involved in R&D collaborations with suppliers, customers or even competitors. Trust plays a fundamental role in suc
Linguistic awareness is seen as a development of linguistic abilities beyond what is needed for speech perception and production. According to another way of thinking, linguistic awareness is closely tied to cognitive development and reflects the change in cognitive functioning that is observed in children during middle childhood, approximately from four to eight years of age. Several suggestions
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
In this paper, we describe a system that jointly extracts entities appearing in images and mentioned in their ac- companying captions. As input, the entity linking pro- gram takes a segmented image together with its cap- tion. It consists of a sequence of processing steps: part- of-speech tagging, dependency parsing, and coreference resolution that enables us to identify the entities as well as po
