Faster optimal univariate microgaggregation

Microaggregation is a method to coarsen a dataset, by optimally clustering data points in groups of at least $k$ points, thereby providing a $k$-anonymity type disclosure guarantee for each point in the dataset. Previous algorithms for univariate microaggregation had a $O(k n)$ time complexity. By rephrasing microaggregation as an instance of the concave least weight subsequence problem, in this work we provide improved algorithms that provide an optimal univariate microaggregation on sorted data in $O(n)$ time and space. We further show that our algorithms work not only for sum of squares cost functions, as typically considered, but seamlessly extend to many other cost functions used for univariate microaggregation tasks. In experiments we show that the presented algorithms lead to real world performance improvements

Neighborhood Structure Configuration Models

We develop a new method to efficiently sample synthetic networks that preserve the d-hop neighborhood structure of a given network for any given d. The proposed algorithm trades off the diversity in network samples against the depth of the neighborhood structure that is preserved. Our key innovation is to employ a colored Configuration Model with colors derived from iterations of the so-called Color Refinement algorithm. We prove that with increasing iterations the preserved structural information increases: the generated synthetic networks and the original network become more and more similar, and are eventually indistinguishable in terms of centrality measures such as PageRank, HITS, Katz centrality and eigenvector centrality. Our work enables to efficiently generate samples with a precisely controlled similarity to the original network, especially for large networks

Drug Hypersensitivity Syndrome toCarbamazepine and Human Herpes Virus 6 Infection: Case Reportand Literature Review

Abstract.: We describe a patient with a drug-induced hypersensitivity syndrome to carbamazepine and a concomitant active infection with human herpes virus 6 (HHV-6). The potential role of HHV-6 regarding the drug-induced hypersensitivity syndrome is discussed and the main clinical features of this potentially fatal adverse drug reaction are highlighte

Oxygen vacancies as active sites for water dissociation on rutile TiO₂(110)

Through an interplay between scanning tunneling microscopy experiments and density functional theory calculations, we determine unambiguously the active surface site responsible for the dissociation of water molecules adsorbed on rutile TiO2(110). Oxygen vacancies in the surface layer are shown to dissociate H2O through the transfer of one proton to a nearby oxygen atom, forming two hydroxyl groups for every vacancy. The amount of water dissociation is limited by the density of oxygen vacancies present on the clean surface exclusively. The dissociation process sets in as soon as molecular water is able to diffuse to the active site

CANreduce-SP—adding psychological support to web-based adherence-focused guided self-help for cannabis users: study protocol for a three-arm randomized control trial

Background: Cannabis is the most-frequently used illicit drug in Europe. Over the last few years in Spain, treatment demand has increased, yet most cannabis users do not seek treatment despite the related problems. A web-based self-help tool, like CANreduce 2.0, could help these users to control their consumption. Methods: This study protocol describes a three-arm randomized controlled trial (RCT) comparing the effectiveness of three approaches, in terms of reducing cannabis use among problematic cannabis users, the first two treatment arms including the Spanish version of CANreduce 2.0 (an adherence-focused, guidance-enhanced, web-based self-help tool) (1) with and (2) without psychological support; and the third group (3) treatment as usual (TAU). Study hypotheses will be tested concerning the primary outcome: change in the number of days of cannabis use over the previous week, comparing assessments at 6 weeks and 3 and 6 months follow-up between groups and against baseline. Secondary outcomes related to cannabis use will be tested similarly. Mental disorders will be explored as predictors of adherence and outcomes. Analyses will be performed on an intention-to-treat basis, then verified by complete case analyses. Discussion: This study will test how effective the Spanish version of CANreduce 2.0 (CANreduce-SP) is at reducing both the frequency and quantity of cannabis use in problematic users and whether adding psychological support increases its effectiveness. Trial registration: This trial is registered with the Clinical Trials Protocol Registration and Results System (PRS) number: NCT04517474 . Registered 18 August 2020, (Archived by archive.is https://archive.is/N1Y64 ). The project commenced in November 2020 and recruitment is anticipated to end by November 2022. Keywords: Adherence; CANreduce; Cannabis use disorder; Cognitive behavioural therapy; Guidance; Psychological support; Randomized controlled trial; Reducing cannabis; Self-help too

Optimizing integrated steelworks process off-gas distribution through Economic Hybrid Model Predictive Control and Echo State Networks

Steel production in integrated steelworks involves the simultaneous production of various byproducts, including process off-gases that are usually exploited for generating electricity in the internal power plant, heat and steam. Their discontinuous production is managed through complex network, gasholders and torches, which must be managed with stringent operational constraints. In this paper we present a supervision and control system designed to optimize the economic management of the distribution of process off-gases that also allows minimizing the environmental impact. The system implements a digital twin based mainly on machine learning techniques, including Echo State Networks, and a hierarchical optimization system, which first level is based on an economic model predictive approach and the second level is based on the economic hybrid model predictive control. This system allows to effectively maximize the use of off-gases while minimizing the environmental impact of their use up to 97%

A Classifier-based approach to identify genetic similarities between diseases

Motivation: Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a large number of SNPs. We separate the diseases for which we have individual genotype data into one reference disease and several query diseases. We train a classifier that distinguishes between individuals that have the reference disease and a set of control individuals. This classifier is then used to classify the individuals that have the query diseases. We can then rank query diseases according to the average classification of the individuals in each disease set, and identify which of the query diseases are more similar to the reference disease. We repeat these classification and comparison steps so that each disease is used once as reference disease