Digitalization enables the automation required to operate modern cyber-physical energy systems (CPESs), leading to a shift from hierarchical to organic systems. However, digitalization increases the number of factors affecting the state of a CPES (e.g., software bugs and cyber threats). In addition to established factors like functional correctness, others like security become relevant but are yet to be integrated into an operational viewpoint, i.e. a holistic perspective on the system state. Trust in organic computing is an approach to gain a holistic view of the state of systems. It consists of several facets (e.g., functional correctness, security, and reliability), which can be used to assess the state of CPES. Therefore, a trust assessment on all levels can contribute to a coherent state assessment. This paper focuses on the trust in ICT-enabled grid services in a CPES. These are essential for operating the CPES, and their performance relies on various data aspects like availability, timeliness, and correctness. This paper proposes to assess the trust in involved components and data to estimate data correctness, which is crucial for grid services. The assessment is presented considering two exemplary grid services, namely state estimation and coordinated voltage control. Furthermore, the interpretation of different trust facets is also discussed.

We describe a Big Data-practical, SQL-implementable algorithm for efficiently determining connected components for graph data stored in a Massively Parallel Processing (MPP) relational database. The algorithm described is a linear-space, randomised algorithm, always terminating with the correct answer but subject to a stochastic running time, such that for any ε > 0 and any input graph G = 〈V,E〉 the algorithm terminates after O(log|V |) SQL queries with probability of at least 1 - ε, which we show empirically to translate to a quasi-linear runtime in practice.

BACKGROUND: Network motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes. Network motifs may also become pivotal in the rational design and engineering of complex biological systems underpinning the field of synthetic biology. Distinguishing true motifs from arbitrary substructures, however, remains a challenge. RESULTS: Here we demonstrate both theoretically and empirically that implicit assumptions present in mainstream methods for motif identification do not necessarily hold, with the ramification that motif studies using these mainstream methods are less able to effectively differentiate between spurious results and events of true statistical significance than is often presented. We show that these difficulties cannot be overcome without revising the methods of statistical analysis used to identify motifs. CONCLUSIONS: Present-day methods for the discovery of network motifs, and, indeed, even the methods for defining what they are, are critically reliant on a set of incorrect assumptions, casting a doubt on the scientific validity of motif-driven discoveries. The implications of these findings are therefore far-reaching across diverse areas of biology.

Strict Minimum Message Length (SMML) is an information-theoretic statistical inference method widely cited (but only with informal arguments) as providing estimations that are consistent for general estimation problems. It is, however, almost invariably intractable to compute, for which reason only approximations of it (known as MML algorithms) are ever used in practice. Using novel techniques that allow for the first time direct, non-approximated analysis of SMML solutions, we investigate the Neyman-Scott estimation problem, an oft-cited showcase for the consistency of MML, and show that even with a natural choice of prior neither SMML nor its popular approximations are consistent for it, thereby providing a counterexample to the general claim. This is the first known explicit construction of an SMML solution for a natural, high-dimensional problem.

We introduce a problem set-up we call the Iterated Matching Pennies (IMP) game and show that it is a powerful framework for the study of three problems: adversarial learnability, conventional (i.e. non-adversarial) learnability and approximability. Using it, we are able to derive the following theorems. (i) It is possible to learn by example all of |$\Sigma^0_1 \cup \Pi^0_1$| as well as some supersets; (ii) in adversarial learning (which we describe as a pursuit-evasion game), the pursuer has a winning strategy (in other words, |$\Sigma^0_1$| can be learned adversarially, but |$\Pi^0_1$| not); (iii) some languages in |$\Pi^0_1$| cannot be approximated by any language in |$\Sigma^0_1$|⁠. In particular, we show that some languages that are not computable are nevertheless learnable by example. We show corresponding results also for |$\Sigma^0_i$| and |$\Pi^0_i$| for arbitrary |$i$|⁠.

Over the past three decades we have steadily increased our knowledge on the genetic basis of many severe disorders. Nevertheless, there are still great challenges in applying this knowledge routinely in the clinic, mainly due to the relatively tedious and expensive process of genotyping. Since the genetic variations that underlie the disorders are relatively rare in the population, they can be thought of as a sparse signal. Using methods and ideas from compressed sensing and group testing, we have developed a cost-effective genotyping protocol to detect carriers for severe genetic disorders. In particular, we have adapted our scheme to a recently developed class of high throughput DNA sequencing technologies. The mathematical framework presented here has some important distinctions from the 'traditional' compressed sensing and group testing frameworks in order to address biological and technical constraints of our setting.