Evaluating epistemic uncertainty under incomplete assessments

The thesis of this study is to propose an extended methodology for laboratory based Information Retrieval evaluation under incomplete relevance assessments. This new methodology aims to identify potential uncertainty during system comparison that may result from incompleteness. The adoption of this methodology is advantageous, because the detection of epistemic uncertainty - the amount of knowledge (or ignorance) we have about the estimate of a system's performance - during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections. Across a series of experiments we demonstrate how this methodology can lead towards a finer grained analysis of systems. In particular, we show through experimentation how the current practice in Information Retrieval evaluation of using a measurement depth larger than the pooling depth increases uncertainty during system comparison

Xenon depresses aEEG background voltage activity whilst maintaining cardiovascular stability in sedated healthy newborn pigs

Changes in electroencephalography (EEG) voltage range are used to monitor the depth of anaesthesia, as well as predict outcome after hypoxia-ischaemia in neonates. Xenon is being investigated as a potential neuroprotectant after hypoxic-ischaemic brain injury, but the effect of Xenon on EEG parameters in children or neonates is not known. This study aimed to examine the effect of 50% inhaled Xenon on background amplitude-integrated EEG (aEEG) activity in sedated healthy newborn pigs.Five healthy newborn pigs, receiving intravenous fentanyl sedation, were ventilated for 24 h with 50%Xenon, 30%O2 and 20%N2 at normothermia. The upper and lower voltage-range of the aEEG was continuously monitored together with cardiovascular parameters throughout a 1 h baseline period with fentanyl sedation only, followed by 24 h of Xenon administration.The median (IQR) upper and lower aEEG voltage during 1 h baseline was 48.0 μV (46.0-50.0) and 25.0 μV (23.0-26.0), respectively. The median (IQR) aEEG upper and lower voltage ranges were significantly depressed to 21.5 μV (20.0-26.5) and 12.0 μV (12.0-16.5) from 10 min after the onset of 50% Xenon administration (p=0.002). After the initial Xenon induced depression in background aEEG voltage, no further aEEG changes were seen over the following 24h of ventilation with 50% xenon under fentanyl sedation. Mean arterial blood pressure and heart rate remained stable.Mean arterial blood pressure and heart rate were not significantly influenced by 24h Xenon ventilation. 50% Xenon rapidly depresses background aEEG voltage to a steady ~50% lower level in sedated healthy newborn pigs. Therefore, care must be taken when interpreting the background voltage in neonates also receiving Xenon

Fentanyl induces cerebellar internal granular cell layer apoptosis in healthy newborn pigs

Background: Opioids like fentanyl are regularly used in neonates for analgesia and sedation. So far, they have been reported to be safe and eligible to use. The cerebellum has become a focus of neurodevelopmental research within the last years, as it is known to play an important role in long-lasting motor, cognitive, and other behavioral changes. The cerebellar cortex is of major importance in the coordinative role of the cerebellum and highly vulnerable to injury and impaired growth. Objective: This study was performed to evaluate the apoptotic effect of intravenous fentanyl infusion on the cerebellum in healthy newborn pigs. Methods: Thirteen healthy pigs ( < median 12 h old) were randomized into (1) 24 h of intravenous fentanyl at normothermia (NTFe, n = 6) or (2) non-ventilated controls at normothermia (NTCTR, n = 7). Cerebellar sections were morphologically assessed after staining with hematoxylin–eosin. In addition, paired sections were immuno-stained for cell death [Cleaved caspase-3 and terminal deoxynucleotidyl transferase-mediated deoxyuridine-triphosphate nick-end labeling (TUNEL)], and positive cells were counted in defined areas of the internal granular cell layer. In total, cells in three cerebellar gyri were counted. Results: We found that there was an increase in cells with apoptotic morphology in the internal granular cell layer in the NTFe group. For quantification, we found a significant increase in cell death in group (1) [median (range) number of caspase-3-positive cell group (1) 8 (1–22) vs. group (2) 1 (1–6) and TUNEL-positive cells (1) 6 (1–10) vs. (2) 1 (0–4)]. In both groups, there was no difference in the number of Purkinje cells. Both groups had comparable and stable physiological parameters throughout the 24 h period. Conclusion: Twenty-four hours of continuous intravenous fentanyl infusion increased apoptosis in the internal granular cell layer in the cerebellum of healthy newborn pigs

Exploiting syntactic relations for question answering

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 61-66).Recently there has been a resurgent interest in syntax-based approaches to information access, as a means of overcoming the limitations of keyword-based approaches. So far attempts to use syntax have been ad hoc, choosing to use some syntactic information but still ignoring most of the tree structure. This thesis describes the design and implementation of SMARTQA, a proof-of-concept question answering system that compares syntactic trees in a principled manner. Specifically, SMARTQA uses a tree edit-distance algorithm to calculate the similarity between unordered, unrooted syntactic trees. The general case of this problem is NP-complete; in practice, SMARTQA demonstrates that an optimized implementation of the algorithm can be feasibly used for question answering applications.by Daniel Loreto.M.Eng

Query expansion strategies for laypeople-centred health information retrieval

One of the most common activities on the web is the research for health information. This activity has been gaining popularity among users, but the majority of them have no training in health care, which leads to difficulties in understanding the terminology and contents of documents.In the field of health information retrieval various investigations have been carried out, which resulted in methodologies that offer solutions to improve the quality of the retrieval documents. One of the most covered techniques in this area is the query expansion, that solves one of the biggest difficulties for users in the search of health information: the limited knowledge of medical terminology. This lack of knowledge influence the formulation of queries and the expectations of the retrieval documents. The query expansion complements the original query with additional terms, making it more reliable. These new terms can be obtained through thesaurus containing several terms associated with a medical concept.The amount of research conducted on the issue of readability of the documents is greatly reduced, the most developed subject is relevance, but if a document is relevant and the user does not comprehend it's contents it ceases to be useful.In this thesis it will be proposed a methodology to improve the quality of the retrieval documents, using methods to improve the users queries, such as the query expansion, and it will be used Readability formulas to determine the level of education required to understand a document. Will be conducted several tests to determine if the source to be used in the query expansion and the readability will have an effect in the retrieval process. These tests will be evaluated with precision and NDCG in the case of relevance, and in the case of readability it will be used uRBP

The Second International Nurse Rostering Competition

This paper reports on the Second International Nurse Rostering Competition (INRC-II). Its contributions are (1) a new problem formulation which, differently from INRC-I, is a multi-stage procedure, (2) a competition environment that, as in INRC-I, will continue to serve as a growing testbed for search approaches to the INRC-II problem, and (3) final results of the competition. We discuss also the competition environment, which is an infrastructure including problem and instance definitions, testbeds, validation/simulation tools and rules. The hardness of the competition instances has been evaluated through the behaviour of our own solvers, and confirmed by the solvers of the participants. Finally, we discuss general issues about both nurse rostering problems and optimisation competitions in general.PostprintPeer reviewe

Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in natural language processing (NLP), information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading