Selfish Network Creation with Non-Uniform Edge Cost

Network creation games investigate complex networks from a game-theoretic point of view. Based on the original model by Fabrikant et al. [PODC'03] many variants have been introduced. However, almost all versions have the drawback that edges are treated uniformly, i.e. every edge has the same cost and that this common parameter heavily influences the outcomes and the analysis of these games. We propose and analyze simple and natural parameter-free network creation games with non-uniform edge cost. Our models are inspired by social networks where the cost of forming a link is proportional to the popularity of the targeted node. Besides results on the complexity of computing a best response and on various properties of the sequential versions, we show that the most general version of our model has constant Price of Anarchy. To the best of our knowledge, this is the first proof of a constant Price of Anarchy for any network creation game.Comment: To appear at SAGT'1

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201

Greedy Selfish Network Creation

We introduce and analyze greedy equilibria (GE) for the well-known model of selfish network creation by Fabrikant et al.[PODC'03]. GE are interesting for two reasons: (1) they model outcomes found by agents which prefer smooth adaptations over radical strategy-changes, (2) GE are outcomes found by agents which do not have enough computational resources to play optimally. In the model of Fabrikant et al. agents correspond to Internet Service Providers which buy network links to improve their quality of network usage. It is known that computing a best response in this model is NP-hard. Hence, poly-time agents are likely not to play optimally. But how good are networks created by such agents? We answer this question for very simple agents. Quite surprisingly, naive greedy play suffices to create remarkably stable networks. Specifically, we show that in the SUM version, where agents attempt to minimize their average distance to all other agents, GE capture Nash equilibria (NE) on trees and that any GE is in 3-approximate NE on general networks. For the latter we also provide a lower bound of 3/2 on the approximation ratio. For the MAX version, where agents attempt to minimize their maximum distance, we show that any GE-star is in 2-approximate NE and any GE-tree having larger diameter is in 6/5-approximate NE. Both bounds are tight. We contrast these positive results by providing a linear lower bound on the approximation ratio for the MAX version on general networks in GE. This result implies a locality gap of $\Omega(n)$ for the metric min-max facility location problem, where n is the number of clients.Comment: 28 pages, 8 figures. An extended abstract of this work was accepted at WINE'1

Using eddy covariance to measure the dependence of air–sea CO2 exchange rate on friction velocity

Parameterisation of the air–sea gas transfer velocity of CO2 and other trace gases under open-ocean conditions has been a focus of air–sea interaction research and is required for accurately determining ocean carbon uptake. Ships are the most widely used platform for air–sea flux measurements but the quality of the data can be compromised by airflow distortion and sensor cross-sensitivity effects. Recent improvements in the understanding of these effects have led to enhanced corrections to the shipboard eddy covariance (EC) measurements. Here, we present a revised analysis of eddy covariance measurements of air–sea CO2 and momentum fluxes from the Southern Ocean Surface Ocean Aerosol Production (SOAP) study. We show that it is possible to significantly reduce the scatter in the EC data and achieve consistency between measurements taken on station and with the ship underway. The gas transfer velocities from the EC measurements correlate better with the EC friction velocity (u*) than with mean wind speeds derived from shipboard measurements corrected with an airflow distortion model. For the observed range of wind speeds (u10 N = 3–23 m s−1), the transfer velocities can be parameterised with a linear fit to u*. The SOAP data are compared to previous gas transfer parameterisations using u10 N computed from the EC friction velocity with the drag coefficient from the Coupled Ocean–Atmosphere Response Experiment (COARE) model version 3.5. The SOAP results are consistent with previous gas transfer studies, but at high wind speeds they do not support the sharp increase in gas transfer associated with bubble-mediated transfer predicted by physically based models

Enzymatic production of defined chitosan oligomers with a specific pattern of acetylation using a combination of chitin oligosaccharide deacetylases

Chitin and chitosan oligomers have diverse biological activities with potentially valuable applications in fields like medicine, cosmetics, or agriculture. These properties may depend not only on the degrees of polymerization and acetylation, but also on a specific pattern of acetylation (PA) that cannot be controlled when the oligomers are produced by chemical hydrolysis. To determine the influence of the PA on the biological activities, defined chitosan oligomers in sufficient amounts are needed. Chitosan oligomers with specific PA can be produced by enzymatic deacetylation of chitin oligomers, but the diversity is limited by the low number of chitin deacetylases available. We have produced specific chitosan oligomers which are deacetylated at the first two units starting from the non-reducing end by the combined use of two different chitin deacetylases, namely NodB from Rhizobium sp. GRH2 that deacetylates the first unit and COD from Vibrio cholerae that deacetylates the second unit starting from the non-reducing end. Both chitin deacetylases accept the product of each other resulting in production of chitosan oligomers with a novel and defined PA. When extended to further chitin deacetylases, this approach has the potential to yield a large range of novel chitosan oligomers with a fully defined architecture

Prostate Brachytherapy Seed Migration to the Heart Seen on Cardiovascular Computed Tomographic Angiography

Brachytherapy consists of placing radioactive sources into or adjacent to tumors, to deliver conformal radiation treatment. The technique is used for treatment of primary malignancies and for salvage in recurrent disease. Permanent prostate brachytherapy seeds are small metal implants containing radioactive sources of I-125, Pd-103, or Cs-131 encased in a titanium shell. They can embolize through the venous system to the lungs or heart and subsequently be detected by cardiovascular computed tomography. Cardiovascular imagers should be aware of the appearance of migrated seeds, as their presence in the chest is generally benign, so that unnecessary worry and testing are avoided. We report a case of a patient who underwent brachytherapy for prostate cancer and developed a therapeutic seeds embolus to the right ventricle

Promoted Thermal Reduction of Copper Oxide Surfaces by N-Heterocyclic Carbenes

The influence of metallic and oxide phases coexisting on surfaces is of fundamental importance in heterogeneous catalysis. Many reactions lead to the reduction of the oxidized areas, but the elucidation of the mechanisms driving these processes is often challenging. In addition, intermediate species or designed organic ligands increase the complexity of the surface. In the present study, we address the thermal reduction of a copper oxide overlayer grown on Cu(111) in the presence of N-heterocyclic carbene (NHC) ligands by means of scanning tunneling microscopy (STM) and density functional theory (DFT). We show that the NHC ligands actively participate in the copper oxide reduction, promoting its removal at temperatures as low as 470 K. The reduction of the oxide was tracked by employing scanning tunneling spectroscopy (STS), providing a chemical identification of metallic and oxide areas at the nanometric scale

Identifying new potential biomarkers in adrenocortical tumors based on mrna expression data using machine learning

Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several “multiple-omics” studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC