In this paper, we will continue to study the model of black holes coupled to the radial flows of dark matter (RDM-stars). According to recent studies, this model well describes the experimental Rotation Curves (RCs) o...
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The question of a possibility of opening a wormhole due to the deformation of the equation of state of the matter caused by quantum gravity effects is considered. As a wormhole environment, the previously considered m...
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The recently formulated model of black holes coupled to the radial flows of dark matter (RDM-stars) is considered and the shape of the galactic rotation curves predicted by the model is evaluated. Under the assumption...
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System-algebraic multigrid (AMG) provides a flexible framework for linear systems in simulation applications that involve various types of physical unknowns. Reservoir-simulation applications, with their driving ellip...
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Background Remote monitoring technologies (RMTs), such as smartphone apps, smartwatches, and in-home sensors, are rapidly changing the way functional and cognitive performance is measured in Alzheimer’s disease (AD) ...
Background Remote monitoring technologies (RMTs), such as smartphone apps, smartwatches, and in-home sensors, are rapidly changing the way functional and cognitive performance is measured in Alzheimer’s disease (AD) patients. Here, we present results from the European RADAR-AD study on the use of multimodal data streams for the identification of functional deficits across all syndromic stages of AD. Method Four study groups (Healthy controls (HC), preclinical AD (pre. AD), prodromal AD (pro. AD), and mild AD) were included in this cross-sectional study. The RMT features (gait measures from Timed up and Go (TUG), Dual Task Effect (DTE) using physilog, acoustic features from Speech task in Mezurio, neurocognitive function using Altoida, managing finances with Banking app) with in-clinic neuropsychological (NP) tests, activities of daily living with Amsterdam IADL and demographics (age, gender, education years) were analyzed for different combinations of the multimodal digital biomarker of disease stage in AD across different pairwise comparisons. The analysis includes data from 175 participants (HC = 67, *** = 26, *** = 50, Mild AD = 32) collected for 8 weeks. An extreme gradient boosting (XGBoost) machine learning model was trained to obtain a multimodal biomarker of AD disease stage with repeated cross-validation (5-fold with 4 repeats) (Figure 1). This investigation is part of the ongoing RADAR-AD study. Result The multimodal combination of RMTs achieved a mean AUC of > 0.60 in all pairwise comparisons with a mean AUC > 0.65 for HC vs (***, *** and Mild) (Figures 2 and 3). The addition of NP tests increases the performance considerably across all pairwise comparisons except HC vs ***. Conclusion Our results highlight the advantage of combining RMTs to identify functional deficits in the early stage of AD. In particular, in prodromal and mild AD patients, a combined signal shows much more strength compared to individual tests. This work has received s
Biological and medical researchers explore the mechanisms of living organisms and tend to gain a better understanding of underlying fundamental biological processes of life. To tackle such complex tasks they constantl...
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Relevance feedback for document retrieval systems is a technique where user feedback is used to improve a query response. In this work we propose a system that uses multiple clusterings and a semi-supervised heuristic...
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The formulation of transport network problems is represented as a translation between two domain specific languages: From a network description language, used by network simulation community, to a problem description ...
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Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads...
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A lot of problems in natural language processing can be interpreted using structures from discrete mathematics. In this paper we will discuss the search query and topic finding problem using a generic context-based ap...
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A lot of problems in natural language processing can be interpreted using structures from discrete mathematics. In this paper we will discuss the search query and topic finding problem using a generic context-based approach. This problem can be described as a Minimum Set Cover Problem with several constraints. The goal is to find a minimum covering of documents with the given context for a fixed weight function. The aim of this problem reformulation is a deeper understanding of both the hierarchical problem using union and cut as well as the non-hierarchical problem using the union. We thus choose a modeling using bipartite graphs and suggest a novel reformulation using an integer linear program as well as novel graph-theoretic approaches.
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