The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.Comment: 12 pages, and 8 figures. This works was presented in IBERAMIA 201

Geranien nach biologischen Grundsätzen pflegen

Das Merkblatt informiert über die richtige Pflanzerde, die Bewässerung, die Düngung, die Überwinterung und die Krankheiten von Geranien

Construction of an integrated consensus map of the Apple genome based on four mapping populations

An integrated consensus genetic map for apple was constructed on the basis of segregation data from four genetically connected crosses (C1¿=¿Discovery × TN10-8, C2¿=¿Fiesta × Discovery, C3¿=¿Discovery × Prima, C4¿=¿Durello di Forli × Fiesta) with a total of 676 individuals using CarthaGene® software. First, integrated female¿male maps were built for each population using common female¿male simple sequence repeat markers (SSRs). Then, common SSRs over populations were used for the consensus map integration. The integrated consensus map consists of 1,046 markers, of which 159 are SSR markers, distributed over 17 linkage groups reflecting the basic chromosome number of apple. The total length of the integrated consensus map was 1,032 cM with a mean distance between adjacent loci of 1.1 cM. Markers were proportionally distributed over the 17 linkage groups (¿ 2¿=¿16.53, df¿=¿16, p¿=¿0.41). A non-uniform marker distribution was observed within all of the linkage groups (LGs). Clustering of markers at the same position (within a 1-cM window) was observed throughout LGs and consisted predominantly of only two to three linked markers. The four integrated female¿male maps showed a very good colinearity in marker order for their common markers, except for only two (CH01h01, CH05g03) and three (CH05a02z, NZ02b01, Lap-1) markers on LG17 and LG15, respectively. This integrated consensus map provides a framework for performing quantitative trait locus (QTL) detection in a multi-population design and evaluating the genetic background effect on QTL expression

Different Target Modalities Improve the Single Probe Protocol of the Response Time-Based Concealed Information Test

To detect if someone hides specific knowledge (called “probes”), the response time-based Concealed Information Test (RT-CIT) asks the examinee to classify items into two categories (targets/non-targets). Within the non-targets, slower RTs to the probes reveal recognition of concealed information. The preferred protocol examines one piece of information per test block (single probe protocol), but its validity is suboptimal. The aim of this study was to improve the validity of the single probe protocol by presenting the information in multiple modalities. In a preregistered study (N = 388) participants were instructed to try to hide their nationality. The items referring to the nationality were presented as words, flags, and maps. Increasing the number of modalities of the targets (BF10 = 37), but not of the probes and irrelevants (BF01 = 6), increased the CIT-effect.</p

Entropy/IP: Uncovering Structure in IPv6 Addresses

In this paper, we introduce Entropy/IP: a system that discovers Internet address structure based on analyses of a subset of IPv6 addresses known to be active, i.e., training data, gleaned by readily available passive and active means. The system is completely automated and employs a combination of information-theoretic and machine learning techniques to probabilistically model IPv6 addresses. We present results showing that our system is effective in exposing structural characteristics of portions of the IPv6 Internet address space populated by active client, service, and router addresses. In addition to visualizing the address structure for exploration, the system uses its models to generate candidate target addresses for scanning. For each of 15 evaluated datasets, we train on 1K addresses and generate 1M candidates for scanning. We achieve some success in 14 datasets, finding up to 40% of the generated addresses to be active. In 11 of these datasets, we find active network identifiers (e.g., /64 prefixes or `subnets') not seen in training. Thus, we provide the first evidence that it is practical to discover subnets and hosts by scanning probabilistically selected areas of the IPv6 address space not known to contain active hosts a priori.Comment: Paper presented at the ACM IMC 2016 in Santa Monica, USA (https://dl.acm.org/citation.cfm?id=2987445). Live Demo site available at http://www.entropy-ip.com

Aspects of the FM Kondo Model: From Unbiased MC Simulations to Back-of-an-Envelope Explanations

Effective models are derived from the ferromagnetic Kondo lattice model with classical corespins, which greatly reduce the numerical effort. Results for these models are presented. They indicate that double exchange gives the correct order of magnitude and the correct doping dependence of the Curie temperature. Furthermore, we find that the jump in the particle density previously interpreted as phase separation is rather explained by ferromagnetic polarons.Comment: Proceedings of Wandlitz Days of Magnetism 200

Molecular dynamics simulations and free energy calculations of netropsin and distamycin binding to an AAAAA DNA binding site

Molecular dynamics simulations have been performed on netropsin in two different charge states and on distamycin binding to the minor groove of the DNA duplex d(CGCGAAAAACGCG)·d(CGCGTTTTTCGCG). The relative free energy of binding of the two non-covalently interacting ligands was calculated using the thermodynamic integration method and reflects the experimental result. From 2 ns simulations of the ligands free in solution and when bound to DNA, the mobility and the hydrogen-bonding patterns of the ligands were studied, as well as their hydration. It is shown that even though distamycin is less hydrated than netropsin, the loss of ligand-solvent interactions is very similar for both ligands. The relative mobilities of the ligands in their bound and free forms indicate a larger entropic penalty for distamycin when binding to the minor groove compared with netropsin, partially explaining the lower binding affinity of the distamycin molecule. The detailed structural and energetic insights obtained from the molecular dynamics simulations allow for a better understanding of the factors determining ligand-DNA bindin