Text and spatial data mining

Parcellation of the human brain Parcellation of the human brain by combining text mining and spatial data mining within a neuroinformatics database. Text mining: Analysis of scientific abstracts. Spatial data mining: Modeling of the distribution of Talairach coordinates. Seek communality between the the text representation and spatial representation by multivariate analysis

Semi-Supervised Kernel PCA

We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets

Databasing Molecular Neuroimaging

Molecular neuroimaging Most molecular imaging studies relies on analysis of values from brain regions and report descriptive statistics for these values. There are two significant difficulties when comparing molecular neuroimaging studies: 1. Regions differ between studies: E.g., some include values for “temporal cortex ” others do not. 2. Measured and reported values differ between studies and they are not comparable: Tracers and receptors; transport rates (e.g., K1), distribution volume, binding potentials; different methods to compute the values

Neuroinformatics in Functional Neuroimaging

This Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database. Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular “known ” subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data. Talairach foci from the BrainMap ™ database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap ™ database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap ™ database constituted among others: Entry errors, errors in the article and unusual terminology

Conceptual Knowledge Representation and Reasoning

One of the main areas in knowledge representation and logic-based artificial intelligence concerns logical formalisms that can be used for representing and reasoning with concepts. For almost 30 years, since research in this area began, the issue of intensionality has had a special status in that it has been considered to play an important role, yet it has not been precisely established what it means for a logical formalism to be intensional. This thesis attempts to set matters straight. Based on studies of the main contributions to the issue of intensionality from philosophy of language, in particular the works of Gottlob Frege and Rudolf Carnap, we start by defining when a logical formalism is intensional. We then examine whether the current formalizations of concepts are intensional. The result is negative in the sense that none of the prevalent formalizations are intensional. This motivates the development of intensional logics for concepts. Our main contribution is the presentation of such an intensional concept logic

Internet Safety and Security Surveys - A Review

www.imm.dtu.d

The contribution of pre-symptomatic infection to the transmission dynamics of COVID-2019.

Background: Pre-symptomatic transmission can be a key determinant of the effectiveness of containment and mitigation strategies for infectious diseases, particularly if interventions rely on syndromic case finding. For COVID-19, infections in the absence of apparent symptoms have been reported frequently alongside circumstantial evidence for asymptomatic or pre-symptomatic transmission. We estimated the potential contribution of pre-symptomatic cases to COVID-19 transmission. Methods: Using the probability for symptom onset on a given day inferred from the incubation period, we attributed the serial interval reported from Shenzen, China, into likely pre-symptomatic and symptomatic transmission. We used the serial interval derived for cases isolated more than 6 days after symptom onset as the no active case finding scenario and the unrestricted serial interval as the active case finding scenario. We reported the estimate assuming no correlation between the incubation period and the serial interval alongside a range indicating alternative assumptions of positive and negative correlation. Results: We estimated that 23% (range accounting for correlation: 12 - 28%) of transmissions in Shenzen may have originated from pre-symptomatic infections. Through accelerated case isolation following symptom onset, this percentage increased to 46% (21 - 46%), implying that about 35% of secondary infections among symptomatic cases have been prevented. These results were robust to using reported incubation periods and serial intervals from other settings. Conclusions: Pre-symptomatic transmission may be essential to consider for containment and mitigation strategies for COVID-19

Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China.

Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R 0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R 0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R 0 and k (95% CrIs: R 0 1.4-12; k 0.04-0.2); however, the upper bound of R 0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events