Color invariant chroma keying and color spill neutralization for dynamic scenes and cameras

In this article we show how temporal backdrops that alternately change their color rapidly at recording rate can aid chroma keying by transforming color spill into a neutral background illumination. Since the chosen colors sum up to white, the chromatic (color) spill component is neutralized when integrating over both backdrop states. The ability to separate both states additionally allows to compute high-quality alpha mattes. Besides the neutralization of color spill, our method is invariant to foreground colors and supports applications with real-time demands. In this article, we explain different realizations of temporal backdrops and describe how keying and color spill neutralization are carried out, how artifacts resulting from rapid motion can be reduced, and how our approach can be implemented to be compatible with common real-time post-production pipelines.     

Adaptive popularity-driven replica placement in hierarchical data grids

Data grids support access to widely distributed storage for large numbers of users accessing potentially many large files. Efficient access is hindered by the high latency of the Internet. To improve access time, replication at nearby sites may be used. Replication also provides high availability, decreased bandwidth use, enhanced fault tolerance, and improved scalability. Resource availability, network latency, and user requests in a grid environment may vary with time. Any replica placement strategy must be able to adapt to such dynamic behavior. In this paper, we describe a new dynamic replica placement algorithm, Popularity Based Replica Placement (PBRP), for hierarchical data grids which is guided by file “popularity”. Our goal is to place replicas close to clients to reduce data access time while still using network and storage resources efficiently. The effectiveness of PBRP depends on the selection of a threshold value related to file popularity. We also present Adaptive-PBRP (APBRP) that determines this threshold dynamically based on data request arrival rates. We evaluate both algorithms using simulation. Results for a range of data access patterns show that our algorithms can shorten job execution time significantly and reduce bandwidth consumption compared to other dynamic replication methods.     

Anonymizing bipartite graph data using safe groupings

Private data often come in the form of associations between entities, such as customers and products bought from a pharmacy, which are naturally represented in the form of a large, sparse bipartite graph. As with tabular data, it is desirable to be able to publish anonymized versions of such data, to allow others to perform ad hoc analysis of aggregate graph properties. However, existing tabular anonymization techniques do not give useful or meaningful results when applied to graphs: small changes or masking of the edge structure can radically change aggregate graph properties. We introduce a new family of anonymizations for bipartite graph data, called (k, ℓ)-groupings. These groupings preserve the underlying graph structure perfectly, and instead anonymize the mapping from entities to nodes of the graph. We identify a class of “safe” (k, ℓ)-groupings that have provable guarantees to resist a variety of attacks, and show how to find such safe groupings. We perform experiments on real bipartite graph data to study the utility of the anonymized version, and the impact of publishing alternate groupings of the same graph data. Our experiments demonstrate that (k, ℓ)-groupings offer strong tradeoffs between privacy and utility.     

An improved constraint satisfaction adaptive neural network for job-shop scheduling

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.     

The Effect of Subfilter-Scale Physics on Regularization Models

The subfilter-scale (SFS) physics of regularization models are investigated to understand the regularizations’ performance as SFS models. Suppression of spectrally local SFS interactions and conservation of small-scale circulation in the Lagrangian-averaged Navier-Stokes α-model (LANS-α) is found to lead to the formation of rigid bodies. These contaminate the superfilter-scale energy spectrum with a scaling that approaches k ⁺¹ as the SFS spectra is resolved. The Clark-α and Leray-α models, truncations of LANS-α, do not conserve small-scale circulation and do not develop rigid bodies. LANS-α, however, is closest to Navier-Stokes in intermittency properties. All three models are found to be stable at high Reynolds number. Differences between L ² and H ¹ norm models are clarified. For magnetohydrodynamics (MHD), the presence of the Lorentz force as a source (or sink) for circulation and as a facilitator of both spectrally nonlocal large to small scale interactions as well as local SFS interactions prevents the formation of rigid bodies in Lagrangian-averaged MHD (LAMHD-α). LAMHD-α performs well as a predictor of superfilter-scale energy spectra and of intermittent current sheets at high Reynolds numbers. It may prove generally applicable as a MHD-LES.     

Structure and Recognition of Graphs with No 6-wheel Subdivision

The subgraph homeomorphism problem has been shown by Robertson and Seymour to be polynomial-time solvable for any fixed pattern graph H. The result, however, is not practical, involving constants that are worse than exponential in |H|. Practical algorithms have only been developed for a few specific pattern graphs, the most recent of these being the wheels with four and five spokes. This paper looks at the subgraph homeomorphism problem where the pattern graph is a wheel with six spokes. The main result is a theorem characterizing graphs that do not contain subdivisions of W ₆. We give an efficient algorithm for solving the subgraph homeomorphism problem for W ₆. We also give a strengthening of the previous W ₅ result.     

An alternative Kalman-Yakubovich-Popov lemma and some extensions

This paper introduces an alternative formulation of the Kalman-Yakubovich-Popov (KYP) Lemma, relating an infinite dimensional Frequency Domain Inequality (FDI) to a pair of finite dimensional Linear Matrix Inequalities (LMI). It is shown that this new formulation encompasses previous generalizations of the KYP Lemma which hold in the case the coefficient matrix of the FDI does not depend on frequency. In addition, it allows the coefficient matrix of the frequency domain inequality to vary affinely with the frequency parameter. One application of this results is illustrated in an example of computing upper bounds to the structured singular value with frequency-dependent scalings.     

Instructional animations can be superior to statics when learning human motor skills

Based on the assumption of a working memory processor devoted to human movement, cognitive load theory is used to explore some conditions under which animated instructions are hypothesised to be more effective for learning than equivalent static graphics. Using paper-folding tasks dealing with human movement, results from three experiments confirmed our hypothesis, indicating a superiority of animation over static graphics. These results are discussed in terms of a working memory processor that may be facilitated by our mirror-neuron system and may explain why animated instructional animations are superior to static graphics for cognitively based tasks that involve human movement.     

Kinetic masks: a new approach and device for dispersing biologically relevant fluids

The controlled dispersion of fluids, particularly biologically relevant solutions in micro-volumes, is of high practical interest in biotechnology and medicine. Pharmaceutical test assays, for example, need a method for the fast and defined deposition of fluid samples. Most current micro-dispensing methods, i.e. contact-based pin printing, have problems such as time delays, limited dosing velocity, minimum volume or high interference that limit biological applications. Spraying techniques suffer from a lack of reproducibility; a defined deposition of samples on targets is not possible. Here, we introduce a new method for the parallel and spatially defined dispersion of many micro-volumes that overcomes disadvantages of common micro-dispensers. The overall approach is that a fluid drop, produced by a droplet generator, falls on a free trajectory with a defined kinetic energy, and is split by a masking unit placed perpendicular to the flight direction into at least two smaller droplets (Zimmermann et al. in Method and device for dosing fluid media, WO/2002/102515, Germany, 2002). On the target, the resulting droplets form reproducible patterns, which are enlarged and scalable images of the grid pattern. Possible applications for this method are non-contact cell patterning, cell encapsulation, cryopreservation and fast mixing processes in micro-volumes. Here, we use this method for the direct and defined parallel positioning of cell suspensions on specific substrates, which can be useful for test assays, tissue engineering and cryopreservation.     

Culture & biometrics: regional differences in the perception of biometric authentication technologies

Previous research has identified user concerns about biometric authentication technology, but most of this research has been conducted in European contexts. There is a lack of research that has investigated attitudes towards biometric technology in other cultures. To address this issue, data from India, South Africa and the United Kingdom were collected and compared. Cross-cultural attitudinal differences were seen, with Indian respondents viewing biometrics most positively while respondents from the United Kingdom were the least likely to have a positive opinion about biometrics. Multiple barriers to the acceptance of biometric technology were identified with data security and health and safety fears having the greatest overall impact on respondents’ attitudes towards biometrics. The results of this investigation are discussed with reference to Hofstede’s cultural dimensions and theories of technology acceptance. It is argued that contextual issues specific to each country provide a better explanation of the results than existing theories based on Hofstede’s model. We conclude that cultural differences have an impact on the way biometric systems will be used and argue that these factors should be taken into account during the design and implementation of biometric systems.