Compact Partial Order Index (CPOI)
Large amounts of RDF/S data are produced and published lately, and several modern applications require the provision of versioning and archiving services over such datasets. In this paper we propose a novel storage index for archiving versions of such datasets, called CPOI (compact partial order index), that exploits the fact that an RDF Knowledge Base (KB), is a graph (or equivalently a set of triples), and thus it has not a unique serialization (as it happens with text). If we want to keep stored several versions we actually want to store multiple sets of triples. CPOI is a data structure for storing such sets aiming at reducing the storage space since this is important not only for reducing storage costs, but also for reducing the various communication costs and enabling hosting in main memory (and thus processing efficiently) large quantities of data. CPOI is based on a partial order structure over sets of triple identifiers, where the triple identifiers are represented in a gapped form using variable length encoding schemes. For this index we evaluate analytically and experimentally various identifier assignment techniques and their space savings. The results show significant storage savings, specifically, the storage space of the compressed sets in large and realistic synthetic datasets is about the 8% of the size of the uncompressed sets. …

ENCI
Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying distributions. In ENCI, we transform the complicated relation of a cause-effect pair into a linear model of variables of which observations correspond to the kernel embeddings of the cause-and-effect distributions in different domains. In this way, we are able to estimate the causal direction by exploiting the causal asymmetry of the transformed linear model. Furthermore, we extend ENCI to causal graph discovery for multiple variables by transforming the relations among them into a linear nongaussian acyclic model. We show that by exploiting the nonstationarity of distributions, both cause-effect pairs and two kinds of causal graphs are identifiable under mild conditions. Experiments on synthetic and real-world data are conducted to justify the efficacy of ENCI over major existing methods. …

Semi-Automated Spectral Relevance Analysis
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly ‘intelligent’ behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner. …

Micro-Browsing Model
Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been considerable work on user browsing models, to model and analyze both the examination and the relevance components of CTR. In this paper, we propose a novel formulation: a micro-browsing model for how users read result snippets. The snippet text of a result often plays a critical role in the perceived relevance of the result. We study how particular words within a line of snippet can influence user behavior. We validate this new micro-browsing user model by considering the problem of predicting which snippet will yield higher CTR, and show that classification accuracy is dramatically higher with our micro-browsing user model. The key insight in this paper is that varying relatively few words within a snippet, and even their location within a snippet, can have a significant influence on the clickthrough of a snippet. …