Payoff-monotonic game dynamics and the maximum clique problem

Evolutionary game-theoretic models and, in particular, the so-called replicator equations have recently proven to be remarkably effective at approximately solving the maximum clique and related problems. The approach is centered around a classic result from graph theory that formulates the maximum clique problem as a standard (continuous) quadratic program and exploits the dynamical properties of these models, which, under a certain symmetry assumption, possess a Lyapunov function. In this letter, we generalize previous work along these lines in several respects. We introduce a wide family of game-dynamic equations known as payoff-monotonic dynamics, of which replicator dynamics are a special instance, and show that they enjoy precisely the same dynamical properties as standard replicator equations. These properties make any member of this family a potential heuristic for solving standard quadratic programs and, in particular, the maximum clique problem. Extensive simulations, performed on random as well as DIMACS benchmark graphs, show that this class contains dynamics that are considerably faster than and at least as accurate as replicator equations. One problem associated with these models, however, relates to their inability to escape from poor local solutions. To overcome this drawback, we focus on a particular subclass of payoff-monotonic dynamics used to model the evolution of behavior via imitation processes and study the stability of their equilibria when a regularization parameter is allowed to take on negative values. A detailed analysis of these properties suggests a whole class of annealed imitation heuristics for the maximum clique problem, which are based on the idea of varying the parameter during the imitation optimization process in a principled way, so as to avoid unwanted inefficient solutions. Experiments show that the proposed annealing procedure does help to avoid poor local optima by initially driving the dynamics toward promising regions in state space. Furthermore, the models outperform state-of-the-art neural network algorithms for maximum clique, such as mean field annealing, and compare well with powerful continuous-based heuristics.     

Efficient additive kernels via explicit feature maps

Large scale nonlinear support vector machines (SVMs) can be approximated by linear ones using a suitable feature map. The linear SVMs are in general much faster to learn and evaluate (test) than the original nonlinear SVMs. This work introduces explicit feature maps for the additive class of kernels, such as the intersection, Hellinger's, and χ2 kernels, commonly used in computer vision, and enables their use in large scale problems. In particular, we: 1) provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels; 2) derive corresponding approximate finite-dimensional feature maps based on a spectral analysis; and 3) quantify the error of the approximation, showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as χ2. We demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train/test times of SVMs. We also compare with two other approximation methods: Nystrom's approximation of Perronnin et al., which is data dependent, and the explicit map of Maji and Berg for the intersection kernel, which, as in the case of our approximations, is data independent. The approximations are evaluated on a number of standard data sets, including Caltech-101, Daimler-Chrysler pedestrians, and INRIA pedestrians.     

Multistage particle windows for fast and accurate object detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.     

Ontological query answering under expressive Entity-Relationship schemata

The Entity-Relationship (ER) model is a fundamental tool for database design, recently extended and employed in knowledge representation and reasoning due to its expressiveness and comprehensibility. We address the problem of answering conjunctive queries under constraints representing schemata expressed in an extended version of the Entity-Relationship model. This extended model, called ER⁺, comprises is-a constraints among entities and relationships, plus functional and mandatory participation constraints. In particular, it allows for arbitrary permutations of the roles in is-a among relationships. A key notion that ensures high tractability in ER⁺ schemata is separability, i.e., the absence of interaction between the functional participation constraints and the other constructs of ER⁺. We provide a precise syntactic characterization of separable ER⁺ schemata by means of a necessary and sufficient condition. We present a complete complexity analysis of the conjunctive query answering problem under separable ER⁺ schemata, and also under several sublanguages of ER⁺. We show that the addition of so-called negative constraints does not increase the complexity of query answering. With such constraints, our model properly generalizes the most widely adopted tractable ontology languages, including those in the DL-Lite family.     

Counting clicks and beeps: Exploring numerosity based haptic and audio PIN entry

Haptic and audio cues now appear commonly in computer interfaces, partially due to inherent advantages such as their support for eyes-free interaction. Their invisible, unobservable nature also makes them ideal candidates for security interfaces in which users have to enter secret information such as passwords. In particular, researchers have explored this idea through the design of PIN entry authentication systems based on multi-modal combinations of visual and non-visual content or on the recognition of small sets of unimodal haptic or audio stimuli. This paper highlights the benefits and performance limitations of these approaches and introduces an alternative based on unimodal audio or haptic temporal numerosity – the ability to accurately and rapidly determine the number of cues presented in rapid temporal succession. In essence, in a numerosity interface, rather than recognizing distinct cues, users must count the number of times that a single cue occurs. In an iterative process of design and evaluation, three prototypes implementing this concept are presented and studies of their use reported. The results show the fastest PIN entry times and lowest error rates to be 8 s and 2%, figures that improve substantially on previous research. These results are attained while maintaining low levels of workload and substantial resistance to observation attack (as determined via camera attack security studies). In sum, this paper argues that unimodal audio and haptic numerosity is a valuable and relatively unexplored metaphor for non-visual input and demonstrates the validity of this claim in the demanding task of unobservable authentication systems.     

Self-Organising Maps: A new way to screen the level of satisfaction of dialysis patients

Evaluation of patient satisfaction has become an important indicator for assessing health care quality. Fresenius Medical Care (FME) as a global provider of dialysis services through its NephroCare network has a strong interest in monitoring patient satisfaction.The aim of the paper is to test and validate a methodology for detecting a residual area of low satisfaction in dialysis patients.The FME Patient Satisfaction Programme questionnaire was distributed to haemodialysis (HD) patients treated in 335 centers of its network. It contained 79 questions covering various satisfaction aspects regarding Dialysis Unit, Dialysis Arrangement, Nurses, Doctors, etc.To analyse the data provided by the questionnaire, the Self-Organising Map (SOM) method was used. SOM is a neural network model for clustering and projecting high-dimensional data into a low-dimensional space, preserving topological relationships of original high-dimensional data spaces.10,632 HD patients completed the questionnaire. Mean age was 63.05 ± 14.93 years with 56.69% males. Response rate was 66%. Overall level of satisfaction was 1.99 (range from ?3 to +3). On average patients were very satisfied with all issues. Nevertheless, a group of patients, around 60 years old, balanced gender ratio, whose level of satisfaction was lower than 1, were highlighted.In the NephroCare clinics patient satisfaction with service is rather high. While traditional analysis usually stops here, the SOM method allows identification of areas of potential improvement for specific patient groups.     

Discrete time sliding mode control of robotic manipulators: Development and experimental validation

This paper presents a robust discrete-time sliding mode control coupled with an uncertainty estimator designed for planar robotic manipulators. Experimental evidence shows satisfactory trajectory tracking performances and noticeable robustness in the presence of model inaccuracies, disturbances and payload perturbations. Ultimate boundedness of the tracking errors is proved, as well as boundedness of the estimation error with arbitrary precision.     

Ultimate behavior of axially loaded RC wall-like columns confined with GFRP

The assessment of the effectiveness of the fiber reinforced polymer (FRP) confinement on rectangular reinforced concrete (RC) columns with high aspect ratio (wall-like) still represents an unresolved issue. The present paper aims at providing more experimental evidence about the behavior of such members confined with both uni-directional and quadri-directional glass FRP laminates. Particular attention is devoted to issues related to the premature failure of confining fibers experimentally observed in wall-like columns. Test results on nine axially loaded columns are herein presented; emphasis is also given to the analysis of FRP strain profiles along the sides of the cross-section. The analysis of test results highlights that glass FRP (GFRP) confinement could determine significant strength and ductility increases; the discussion of failure modes points out that the failure of GFRP confined wall-like columns is controlled by the shape of the cross-section and occurs at transverse strains in the jacket much lower than those ultimate of the fibers. Theoretical–experimental comparisons are performed using some available models for strength prediction of such members.     

The effect of ionising radiation on photosynthetic oxygenic microorganisms for survival in space flight revealed by automatic photosystem II-based biosensors

Photosynthetic microorganisms are expected to be useful to maintain an oxygenic atmosphere and to provide biomass for astronauts in the International Space Station as well as in future long-term space flights. However, fluxes of complex ionizing radiation of various intensities and energies make space an extreme environment for the microorganisms, affecting their photosynthetic efficiency. To automatically monitor the photosynthetic Photosystem II (PSII) activity of microorganisms under space conditions an optical biosensor, which utilizes chlorophyll fluorescence as biological transduction system, was built; the PSII activity was monitored by the biosensor during balloon flights at stratospheric altitudes of about 40 km. The effect of space stress on quantum yield of PSII varied among the tested species depending on the growth light conditions at which they were exposed during the flights.