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Within only a few years after the launch of video sharing platforms, viral videos have become a pervasive Internet phenomenon. Yet, notwithstanding growing scholarly interest, the suitability of the viral metaphor seems not to have been studied so far. In this paper, we therefore investigate the attention dynamics of viral videos from the point of view of mathematical epidemiology. We introduce a novel probabilistic model of the progression of infective diseases and use it to analyze time series of YouTube view counts and Google searches. Our results on a data set of almost 800 videos show that their attention dynamics are indeed well accounted for by our epidemic model. In particular, we find that the vast majority of videos considered in this study show very high infection rates.

The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of mining large numbers of redundant subgroups, subgroup set discovery (SSD) has been proposed. State-of-the-art SSD methods have their limitations though, as they typically heavily rely on heuristics and/or user-chosen hyperparameters.We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists. We argue that the best subgroup list is the one that best summarizes the data given the overall distribution of the target. We restrict our focus to a single numeric target variable and show that our formalization coincides with an existing quality measure when finding a single subgroup, but that—in addition—it allows to trade off subgroup quality with the complexity of the subgroup. We next propose SSD++, a heuristic algorithm for which we empirically demonstrate that it returns outstanding subgroup lists: non-redundant sets of compact subgroups that stand out by having strongly deviating means and small spread.

Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines. Recent ``deep-learning-style'' implementations of PCs strive for a better scalability, but are still difficult to train on real-world data, due to their sparsely connected computational graphs. In this paper, we propose Einsum Networks (EiNets), a novel implementation design for PCs, improving prior art in several regards. At their core, EiNets combine a large number of arithmetic operations in a single monolithic einsum-operation, leading to speedups and memory savings of up to two orders of magnitude, in comparison to previous implementations. As an algorithmic contribution, we show that the implementation of Expectation-Maximization (EM) can be simplified for PCs, by leveraging automatic differentiation. Furthermore, we demonstrate that EiNets scale well to datasets which were previously out of reach, such as SVHN and CelebA, and that they can be used as faithful generative image models.

Sum-product networks (SPNs) are expressive probabilistic models with a rich set of exact and efficient inference routines. However, in order to guarantee exact inference, they require specific structural constraints, which complicate learning SPNs from data. Thereby, most SPN structure learners proposed so far are tedious to tune, do not scale easily, and are not easily integrated with deep learning frameworks. In this paper, we follow a simple “deep learning” approach, by generating unspecialized random structures, scalable to millions of parameters, and subsequently applying GPU-based optimization. Somewhat surprisingly, our models often perform on par with state-of-the-art SPN structure learners and deep neural networks on a diverse range of generative and discriminative scenarios. At the same time, our models yield well-calibrated uncertainties, and stand out among most deep generative and discriminative models in being robust to missing features and being able to detect anomalies.

Sum-Product Networks (SPNs) are a deep probabilistic architecture that up to now has been successfully employed for tractable inference. Here, we extend their scope towards unsupervised representation learning: we encode samples into continuous and categorical embeddings and show that they can also be decoded back into the original input space by leveraging MPE inference. We characterize when this Sum-Product Autoencoding (SPAE) leads to equivalent reconstructions and extend it towards dealing with missing embedding information. Our experimental results on several multi-label classification problems demonstrate that SPAE is competitive with state-of-the-art autoencoder architectures, even if the SPNs were never trained to reconstruct their inputs.

While all kinds of mixed data---from personal data, over panel and scientific data, to public and commercial data---are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult. Users spend significant amounts of time in identifying the parametric form of the random variables (Gaussian, Poisson, Logit, etc.) involved and learning the mixed models. To make this difficult task easier, we propose the first trainable probabilistic deep architecture for hybrid domains that features tractable queries. It is based on Sum-Product Networks (SPNs) with piecewise polynomial leaf distributions together with novel nonparametric decomposition and conditioning steps using the Hirschfeld-Gebelein-Renyi Maximum Correlation Coefficient. This relieves the user from deciding a-priori the parametric form of the random variables but is still expressive enough to effectively approximate any distribution and permits efficient learning and inference.Our experiments show that the architecture, called Mixed SPNs, can indeed capture complex distributions across a wide range of hybrid domains.

Complex systems can be modelled at various levels of detail. Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by introducing exact transformations between SEMs. This provides a general language to consider, for instance, the different levels of description in the following three scenarios: (a) models with large numbers of variables versus models in which the `irrelevant' or unobservable variables have been marginalised out; (b) micro-level models versus macro-level models in which the macro-variables are aggregate features of the micro-variables; (c) dynamical time series models versus models of their stationary behaviour. Our analysis stresses the importance of well specified interventions in the causal modelling process and sheds light on the interpretation of cyclic SEMs.

Despite their popularity, many questions about the algebraic constraints imposed by linear structural equation models remain open problems. For causal discovery, two of these problems are especially important: the enumeration of the constraints imposed by a model, and deciding whether two graphs define the same statistical model. We show how the half-trek criterion can be used to make progress in both of these problems. We apply our theoretical results to a small-scale model selection problem, and find that taking the additional algebraic constraints into account may lead to significant improvements in model selection accuracy.

Automated tracking is key to many computer vision applications. However, many tracking systems struggle to perform in real-time due to the high computational cost of detecting people, especially in ultra high resolution images. We propose a new algorithm called PartiMax that greatly reduces this cost by applying the person detector only to the relevant parts of the image. PartiMax exploits information in the particle filter to select k of the n candidate pixel boxes in the image. We prove that PartiMax is guaranteed to make a near-optimal selection with error bounds that are independent of the problem size. Furthermore, empirical results on a real-life dataset show that our system runs in real-time by processing only 10% of the pixel boxes in the image while still retaining 80\% of the original tracking performance achieved when processing all pixel boxes.

The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the form of a data modality that is not available at test time. Here, we show that privileged information can naturally be treated as noise in the latent function of a Gaussian Process classifier (GPC). That is, in contrast to the standard GPC setting, the latent function is not just a nuisance but a feature: it becomes a natural measure of confidence about the training data by modulating the slope of the GPC sigmoid likelihood function. Extensive experiments on public datasets show that the proposed GPC method using privileged noise, called GPC+, improves over a standard GPC without privileged knowledge, and also over the current state-of-the-art SVM-based method, SVM+. Moreover, we show that advanced neural networks and deep learning methods can be compressed as privileged information., 14 pages with figures

Color refinement is a basic algorithmic routine for graph isomorphismtesting and has recently been used for computing graph kernels as well as for lifting belief propagation and linear programming. So far, color refinement has been treated as a combinatorial problem. Instead, we treat it as a nonlinear continuous optimization problem and prove thatit implements a conditional gradient optimizer that can be turned into graph clustering approaches using hashing and truncated power iterations. This shows that color refinement is easy to understand in terms of random walks, easy to implement (matrix-matrix/vector multiplications) and readily parallelizable. We support our theoretical results with experiments on real-world graphs with millions of edges.

Mosselzaadbanken in de Waddenzee worden bevist. In 2006 is een onderzoek gestart naar de effecten van mosselzaadvisserij op sublitorale natuurwaarden, het PRODUS onderzoek. Omdat de biodiversiteit in de Waddenzee nauw samenhangt met de driedimensionale structuur van de zeebodem wordt binnen PRODUS het effect van visserij op deze structuur bestudeerd aan de hand van sidescan sonar opnames die voor en na visserij worden uitgevoerd

In this paper we introduce an approximation of the Weisfeiler-Lehman graph kernel algorithm aimed at improving the computation time of the kernel when applied to Resource Description Framework (RDF) data. Typically, applying graph kernels to RDF is done by extracting subgraphs from a large RDF graph and computing the kernel on this set of subgraphs. In contrast, our algorithm computes the Weisfeiler-Lehman kernel directly on the large RDF graph, but still retains the subgraph information. We show that this algorithm is faster than the regular Weisfeiler-Lehman kernel for RDF data and has at least the same performance. Furthermore, we show that our method has similar or better performance, and is faster, than other recently introduced graph kernels for RDF.

Existing reinforcement learning approaches are often hampered by learning tabula rasa. Transfer for reinforcement learning tackles this problem by enabling the reuse of previously learned results, but may require an inter-task mapping to encode how the previously learned task and the new task are related. This paper presents an autonomous framework for learning inter-task mappings based on an adaptation of restricted boltzmann machines. Both a full model and a computationally efficient factored model are introduced and shown to be effective in multiple transfer learning scenarios.keywordstransfer learningreinforcement learninginter-task mappingboltzmann machinesleast squares policy iteration.

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Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches., Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)

Het doel van het hier gepresenteerde onderzoek is om door middel van meerjarige monitoring in voor- en najaar inzicht te krijgen in de ontwikkeling van ongestoorde sublitorale mosselbanken en de daarmee geassocieerde biodiversiteit. Voor elk van de gesloten gebieden gelden de volgende vragen: 1. Hoe is de ontwikkeling van de mosselbank wanneer er geen bodemberoerende activiteiten plaatsvinden? Kijk hiervoor naar parameters als areaal, biomassa ontwikkeling, leeftijdsklassen van mosselen en aanwas als gevolg van nieuwe broedval 2. Hoe is de ontwikkeling van de geassocieerde biodiversiteit (flora en fauna). 3. Ontwikkelt zich na sluiting voor mosselzaad- en garnalenvisserij ook andere benthos dan tot dusver op mosselvoorkomens in de Waddenzee wordt aangetroffen.

Dit rapport beschrijft de zeevogelgemeenschap die zich in de loop van het jaar bevindt in een zeegebied voor de Noord-Hollandse kust, waar in de nabije toekomst twee windmolenparken gebouwd zullen worden, de zogenaamde Nearshore Windfarm (NSW) en het windmolenpark Q7. Om deze ¿T-nul¿ situatie te kunnen vastleggen zijn de zeevogels gedurende acht weken, verspreid door het jaar, geïnventariseerd vanaf een speciaal daartoe ingezet schip. Er werd hierbij steeds een gebied geïnventariseerd dat ruimschoots groter was dan de beide toekomstige windmolenparken samen. Hierdoor konden de vogelaantallen binnen en buiten de contour van de windmolenparken worden vergeleken, rekening houdend met de ruimtelijke patronen die zeevogelverspreidingen in het gebied laten zien. In de nul-situatie, dus nog voordat er windturbines in gebied staan, is niet gebleken dat de aangewezen gebieden voor de turbines qua vogelbevolking afwijken van de omgeving. T-één studies (na de bouw) zullen moeten uitwijzen of dat in de toekomst zo zal blijven. De aantallen vogels, aanwezig in een relatief klein gebied zoals nu bestudeerd, kunnen van dag tot dag, week tot week en jaar tot jaar sterk variëren, zoals in deze studie werd vastgesteld aan de hand van langer lopende dataseries. Dit maakt dat een directe vergelijking van aantallen nu en straks minder zinvol is, dan een vergelijking van de aantallen binnen en buiten een windpark, op hetzelfde moment gemeten.

The response of rocky shore ecosystems to increased nutrient availability was examined in eight land-based mesocosms designed for hard-bottom littoral communities built at Marine Research Station Solbergstrand (Norway). The average seawater volume in each basin was 9 m3 with an average water residence time of about 2 h. A tidal regime resembling that in the fjord was maintained in the basins, and waves were generated regularly. NH4NO3 and H3PO4, at a constant molar NP ratio of 16:1, was added into 6 basins at concentrations 1, 2, 4, 8, 16, 32 ŠM DIN above the background DIN concentration during 1z years. Two mesocosms were kept as control treatment. Marine communities were introduced into the basins two years prior to the start of nutrient dosage. The effects of nutrient enrichment were few and only marginal during the first year of nutrient addition, while some effects became more obvious during the second year. The growth rate of the periphyton and fast-growing macroalgae communities was stimulated by nutrient enrichment, while the response was less evident among the perennial fucoids. The structure of the macroalgal communities, however, did not change during 16 months' measurements. In contrast, growth on artificial rock substrates during the same period of time revealed intensive growth of the fast-growing Ulva lactuca in high-dosed basins compared with low-dosed and control basins, which were dominated by the fucoid Fucus serratus. The fauna communities exhibited only a minor response to nutrient treatment. The common periwinkle Littorina littorea, however, appeared with increased abundance in the high-dosed basins. The total system metabolism tended to increase slightly, but not significantly, with increased nutrient loading.

Recommendations for investigation of a problem herd are discussed. Our findings suggest that the historical increases in milk production associated with the dairy industry's increases in ration density, as well as overt contamination of haylage, may be creating a new "niche" opportunity for an organism previously considered non-pathogenic., American Association of Bovine Practitioners Proceedings of the Annual Conference, 2001