Larsson, Joel Markström, Klas

The basic random k‐SAT problem is: given a set of n Boolean variables, and m clauses of size k picked uniformly at random from the set of all such clauses on our variables, is the conjunction of these clauses satisfiable? Here we consider a variation of this problem where there is a bias towards variables occurring positive—that is, variables occur...

Elliott, Tyler A. Heitkam, Tony Hubley, Robert Quesneville, Hadi Suh, Alexander Wheeler, Travis J.
Published in
Mobile DNA

Transposable elements (TEs) play powerful and varied evolutionary and functional roles, and are widespread in most eukaryotic genomes. Research into their unique biology has driven the creation of a large collection of databases, software, classification systems, and annotation guidelines. The diversity of available TE-related methods and resources...

Eriksson, Anton Edler, Daniel Rojas, Alexis de Domenico, Manlio Rosvall, Martin
Published in
Communications Physics

Real-world networks are typically characterised by a non-trivial organization at the mesoscale, such that groups of nodes are preferentially connected within distinguishable network regions known as communities. In this work the authors define unipartite, bipartite, and multilayer network representations of hypergraph flows to extract the community...

Nilsson, Mattias Liwicki, Foteini Sandin, Fredrik

Spiking neurons can perform spatiotemporal feature detection by nonlinear synaptic and dendritic integration of presynaptic spike patterns. Multicompartment models of nonlinear dendrites and related neuromorphic circuit designs enable faithful imitation of such dynamic integration processes, but these approaches are also associated with a relativel...

Morger, Andrea Svensson, Fredrik Arvidsson McShane, Staffan Gauraha, Niharika Norinder, Ulf Spjuth, Ola Volkamer, Andrea
Published in
Journal of Cheminformatics

Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the query samples have drifted from the training data’s descriptor space. Thus, the assumption for applying machine learning algorithms, that trai...

Gharaee, Zahra

In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks.Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowle...

Auer, Florian Lenarduzzi, Valentina Felderer, Michael Taibi, Davide

Context. Re-architecting monolithic systems with Microservices-based architecture is a common trend. Various companies are migrating to Microservices for different reasons. However, making such an important decision like re-architecting an entire system must be based on real facts and not only on gut feelings. Objective. The goal of this work is to...

Schaller, David Geiß, Manuela Chávez, Edgar González Laffitte, Marcos López Sánchez, Alitzel Stadler, Bärbel M. R. Valdivia, Dulce I. Hellmuth, Marc Hernández Rosales, Maribel Stadler, Peter F.
...
Published in
Journal of Mathematical Biology

Two errors in the article Best Match Graphs (Geiß et al. in JMB 78: 2015–2057, 2019) are corrected. One concerns the tacit assumption that digraphs are sink-free, which has to be added as an additional precondition in Lemma 9, Lemma 11, Theorem 4. Correspondingly, Algorithm 2 requires that its input is sink-free. The second correction concerns an a...

Björklund, Henrik Drewes, Frank Ericson, Petter Starke, Florian

It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even under seemingly harsh restrictions. This means that the parsing problem is difficult even in the non-uniform setting, in which the grammar is considered to be fixed rather than being part of the input. Little is known about restrictions under which tr...

Lundén, Daniel Borgström, Johannes Broman, David

Probabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic programming languages (PPLs) employ different inference algorithms, such as sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), or variational methods. Existing rese...