A user-study examining visualization of lifelogs

With continuous advances in the pervasive sensing and lifelogging technologies for the quantified self, users now can record their daily life activities automatically and seamlessly. In the existing lifelogging research, visualization techniques for presenting the lifelogs and evaluating the effectiveness of such techniques from a lifelogger's perspective has not been adequately studied. In this paper, we investigate the effectiveness of four distinct visualization techniques for exploring the lifelogs, which were collected by 22 lifeloggers who volunteered to use a wearable camera and a GPS device simultaneously, for a period of 3 days. Based on a user study with these 22 lifeloggers, which required them to browse through their personal lifelogs, we seek to identify the most effective visualization technique. Our results suggest various ways to augment and improve the visualization of personal lifelogs to enrich the quality of user experience and making lifelogging tools more engaging. We also propose a new visualization feature-drill-down approach with details-on-demand, to make the lifelogging visualization process more meaningful and informative to the lifeloggers

Combining Symbolic Representations for Solving Timed Games

Abstract. We present a general approach to combine symbolic state space representations for the discrete and continuous parts in the synthesis of winning strategies for timed reachability games. The combination is based on abstraction refinement where discrete symbolic techniques are used to produce a sequence of abstract timed game automata. After each refinement step, the resulting abstraction is used for computing an under- and an over-approximation of the timed winning states. The key idea is to identify large relevant and irrelevant parts of the precise weakest winning strategy already on coarse, and therefore simple, abstractions. If neither the existence nor nonexistence of a winning strategy can be established in the approximations, we use them to guide the refinement process. Based on a prototype that combines binary decision diagrams [7,9] and difference bound matrices [5], we experimentally evaluate the technique on standard benchmarks from timed controller synthesis. The results clearly demonstrate the potential of the new approach concerning running time and memory consumption compared to the classical on-the-fly algorithm implemented in Uppaal-Tiga [10,4].

DDDLIB: A library for solving quantified difference inequalities

DDDLIB is a library for manipulating formulae in a first-order logic over Boolean variables and inequalities of the form x1 x2 d, where x1; x2 are real variables and d is an integer constant. Formulae are represented in a semi-canonical data structure called difference decision diagrams (DDDs) which provide efficient algorithms for constructing formulae with the standard Boolean operators (conjunction, disjunction, negation, etc.), eliminating quantifiers, and deciding functional properties (satisability, validity and equivalence). The library is written in C and has interfaces for C++, Standard ML and Objective Caml

The octahedron abstract domain

Abstract. An interesting area in static analysis is the study of numeric properties. Complex properties can be analyzed using abstract interpretation, provided that an adequate abstract domain is defined. Each domain can represent and manipulate a family of properties, providing a different trade-off between the precision and complexity of the analysis. The contribution of this paper is a new numeric abstract domain called octahedron that represents constraints of the form (±xj ±... ± xk ≥ c), where xi are numerical variables such that xi ≥ 0. The implementation of octahedra is based on a new kind of decision diagrams called Octahedron Decision Diagrams (OhDD).

A structural systems biology approach for quantifying the systemic consequences of missense mutations in proteins.

Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell's behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation

Can Decision Diagrams Overcome State Space Explosion in Real-Time Verification?

In this paper we analyze the efficiency of binary decision diagrams (BDDs) and clock difference diagrams (CDDs) in the verification of timed automata. Therefore we present analytical and empirical complexity results for three communication protocols. The contributions of the analyses are: Firstly, they show that BDDs and CDDs of polynomial size exist for the reachability sets of the three protocols. This is the first evidence that CDDs can grow only polynomially for models with non-trivial state space explosion. Secondly, they show that CDD-based tools, which currently use at least exponential space for two of the protocols, will only find polynomial-size CDDs if they use better variable orders, as the BDD-based tool Rabbit does. Finally, they give insight into the dependency of the BDD and CDD size on properties of the model, in particular the number of automata and the magnitude of the clock values