Lifted rule injection for relation embeddings

Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks. Yet, a major challenge is how to efficiently incorporate commonsense knowledge into such models. A recent approach regularizes relation and entity representations by propositionalization of first-order logic rules. However, propositionalization does not scale beyond domains with only few entities and rules. In this paper we present a highly efficient method for incorporating implication rules into distributed representations for automated knowledge base construction. We map entity-tuple embeddings into an approximately Boolean space and encourage a partial ordering over relation embeddings based on implication rules mined from WordNet. Surprisingly, we find that the strong restriction of the entity-tuple embedding space does not hurt the expressiveness of the model and even acts as a regularizer that improves generalization. By incorporating few commonsense rules, we achieve an increase of 2 percentage points mean average precision over a matrix factorization baseline, while observing a negligible increase in runtime

Isolate Specific Cold Response of Yersinia enterocolitica in Transcriptional, Proteomic, and Membrane Physiological Changes

Yersinia enterocolitica, a zoonotic foodborne pathogen, is able to withstand low temperatures. This psychrotrophic ability allows it to multiply in food stored in refrigerators. However, little is known about the Y. enterocolitica cold response. In this study, isolate-specific behavior at 4°C was demonstrated and the cold response was investigated by examining changes in phenotype, gene expression, and the proteome. Altered expression of cold-responsive genes showed that the ability to survive at low temperature depends on the capacity to acclimate and adapt to cold stress. This cold acclimation at the transcriptional level involves the transient induction and effective repression of cold-shock protein (Csp) genes. Moreover, the resumption of expression of genes encoding other non-Csp is essential during prolonged adaptation. Based on proteomic analyses, the predominant functional categories of cold-responsive proteins are associated with protein synthesis, cell membrane structure, and cell motility. In addition, changes in membrane fluidity and motility were shown to be important in the cold response of Y. enterocolitica. Isolate-specific differences in the transcription of membrane fluidity- and motility-related genes provided evidence to classify strains within a spectrum of cold response. The combination of different approaches has permitted the systematic description of the Y. enterocolitica cold response and gives a better understanding of the physiological processes underlying this phenomenon

Adversarial Sets for Regularising Neural Link Predictors

In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances. However, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances. For example, a link predictor for the is-a relation needs to be consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3) hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for deriving an inconsistency loss, measuring the degree to which the model violates the assumptions on an adversarially-generated set of examples. The training objective is defined as a minimax problem, where an adversary finds the most offending adversarial examples by maximising the inconsistency loss, and the model is trained by jointly minimising a supervised loss and the inconsistency loss on the adversarial examples. This yields the first method that can use function-free Horn clauses (as in Datalog) to regularise any neural link predictor, with complexity independent of the domain size. We show that for several link prediction models, the optimisation problem faced by the adversary has efficient closed-form solutions. Experiments on link prediction benchmarks indicate that given suitable prior knowledge, our method can significantly improve neural link predictors on all relevant metrics.Comment: Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 201

Stuttgart – a Livable City: The global Agenda 2030 at a local level Baseline study depicting the Sustainable Development Goals (SDGs)

The United Nations adopted the Agenda 2030 in 2015. This was a basis for the transition to a world in which economic efficiency, ecological compatibility and social justice can be in accord with one another. The Agenda 2030 addresses all states (“every country is a developing country”) at an international, national and, just as much, at a regional and local level. An essential component of Agenda 2030 are the 17 goals for a sustainable development (Sustainable Development Goals, SDGs). To achieve the goals of Agenda 2030 the focus is on partnerships between various actors from administration, politics, business and civil society. In the further development of the Sustainable Development Strategy for Germany in 2017 the Federal Government oriented itself systematically towards the Agenda 2030 with the 17 SDGs. Many other German states also developed strategies geared towards the SDGs. In Baden-Württemberg the Advisory Council of the State Government prepared a proposal as to how the SDGs could be integrated into the state-specific guidelines for sustainable development. The municipalities, having a close relationship with the residents, play a particular role when it comes to implementing Agenda 2030. To map out the status of sustainable development on a quantitative basis of SDGs and at a local level, seven organisations started a nationwide project in 2017 “SDG indicators for municipalities” – proposals for SDGs at a local level: Association of German Cities, German County Association, German Association of Towns and Municipalities, German Institute for Urban Studies (Difu), Federal Institute for Building, Urban Affairs and Spatial Research, Service Agency Communities in One World of Engagement Global and the Bertelsmann Foundation. As one of the first municipalities in Germany the State Capital Stuttgart took on the challenging task of pilot-testing the “SDG indicators for municipalities” from June to October 2018. A second phase between July and September 2019 saw the update of the data. The baseline study was carried out in cooperation with the Bertelsmann Foundation and Difu. The SDG baseline study for the State Capital Stuttgart has two main objectives: first, to analyse the current status of the city on the basis of data in place as regards social, ecological and economic sustainability and to improve the possibilities of a target-oriented, strategic development of the city’s measures; second, with this SDG baseline study to make a methodological contribution to a target-oriented strategic, further development of SDG indicators for an appropriate and effective design for the SDG baseline-studies in municipalities. The different starting conditions make a comparison of cities neither possible nor envisaged – however, the municipalities will receive a toolbox so they can gauge their own development. A qualitative depiction of selected programmes and measures of the State Capital Stuttgart complements the quantitative baseline study. These descriptions give an impression of the spectrum of the measures which can be taken with a view to sustainability. This should also address the issue in other cities and communities. Stuttgart sees itself here as an impulse-giver, but also as a learner, in a national and international network of local actors. The SDGs offer a comprehensive target system for sustainability and, at the same time, they point out possible conflicts of interests. The implementation of strategic objectives requires continuous monitoring. The participative, cross-divisional process of the baseline study shows that the tried and tested SDG indicators for municipalities are a suitable instrument to be quantitatively supportive in realising the existing objectives and approaches of the State Capital Stuttgart for social, ecological and economic sustainability. It was constructive to discuss the SDG indicators methodologically on a cross-sectoral basis, and to select and expand on issues to do justice to the distinctiveness of a municipality. This way, the cross-divisional knowledge management and the understanding of the correlations between the individual sustainability measures could be strengthened. All divisions and departments of the City of Stuttgart worked with enormous commitment on this report. Based on SDG indicators, this baseline study has for the first time developed a cross-sectoral instrument for a regular, all-embracing monitoring of correlations of social, economic and ecological sustainability. This forms an important basis for future recommendations and an effective action on the part of politics, administration and urban society which will help to serve the further development of municipal objectives and measures of implementing the SDGs in the State Capital Stuttgart. The present executive summary provides an overview of the methodological approach, a compilation of the selected indicators as well as the main results with regards to the process and further development of SDG- indicators

Medizinische Leitlinien: Ein Qualitätsinstrument wird erwachsen

Medizinische Leitlinien: Ein Qualitätsinstrument wird erwachsen: Die Leitlinienarbeit hat international sowohl qualitativ wie quantitativ große Fortschritte gemacht. Qualitätsgesicherte Leitlinienentwicklung muss nun vermehrt auch eingesetzt werden. Internationale Kooperation ist in Hinblick auf den nicht unbeträchtlichen Aufwand der Leitlinienentwicklung ein Muss. Methoden zur optimalen Implementierung sind zum Teil noch zu erarbeiten und erfordern die Mitwirkung aller Interessengruppen. In praktisch allen Ländern, die beim Thema Leitlinien führend sind, existieren Organisationen auf nationaler Ebene, die die Leitlinienarbeit fördern, methodisch unterstützen oder koordinieren

Requirements for, and Cytoplasmic Concentrations of, Sulphate and Chloride, and Cytoplasmic Volume Spaces in the Halophilic Bacterium Ectothiorhodospira mobilis

Ectothiorhodospira mobilis is a halophilic phototrophic bacterium that has been isolated from soda lakes containing high concentrations of sulphate, chloride and carbonates. It utilizes reduced sulphur compounds as photosynthetic electron donors and oxidizes them to sulphate, but can also grow photoheterotrophically with sulphate as sole sulphur source. The requirements for, and the cytoplasmic concentrations of, sulphate and chloride have been determined. High concentrations of sulphate are neither required for nor inhibit growth. Although chloride is by far the dominant anion of the environment, growth of E. mobilis occurs in the absence of added chloride. Sodium chloride can be replaced by sodium sulphate and sodium carbonate. Chloride is excluded from the cytoplasm with decreasing ratios of cytoplasmic/external chloride at increasing external chloride concentrations (under iso-osmotic conditions)

Regularization in Relevance Learning Vector Quantization Using l one Norms

International audienceWe propose in this contribution a method for l one regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance profiles in hyperspectral data analysis fade down those spectral bands which are not necessary for classification. In particular, we consider the sparsity in the relevance profile enforced by LASSO optimization. The latter one is obtained by a gradient learning scheme using a differentiable parametrized approximation of the $l_{1}$-norm, which has an upper error bound. We extend this regularization idea also to the matrix learning variant of LVQ as the natural generalization of relevance learning