Electrical characterization of individual metal‐coated polymer spheres used in isotropic conductive adhesives

Isotropic conductive adhesives (ICAs) filled with metal‐coated polymer spheres (MPS) have been proposed to improve the mechanical reliability compared to conventional ICAs filled with silver flakes. The electrical properties of MPS play an important role in the electrical performance of macroscopic MPS‐based ICAs. This article deals with the electrical characterization of individual MPS using a nanoindentation‐based flat punch method, in which the resistance and the deformation of single MPS were monitored simultaneously. Four groups of silver‐coated polymer spheres (AgPS) with identical polymer cores but different silver coating thicknesses were tested. The resistance of AgPS decreases gradually with increasing deformation degree of particles, and increases when the deformation of particles is reduced. In addition, the resistance of individual AgPS is dependent on the physical properties of the silver coating, such as thickness, uniformity, and porosity. The thicker the silver coating is, the lower and more stable the resistance of AgPS is. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43764.     

Latent Classification Models

One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. These models rely on two assumptions: (i) All the attributes used to describe an instance are conditionally independent given the class of that instance, and (ii) all attributes follow a specific parametric family of distributions. In this paper we propose a new set of models for classification in continuous domains, termed latent classification models. The latent classification model can roughly be seen as combining the Naïve Bayes model with a mixture of factor analyzers, thereby relaxing the assumptions of the Naïve Bayes classifier. In the proposed model the continuous attributes are described by a mixture of multivariate Gaussians, where the conditional dependencies among the attributes are encoded using latent variables. We present algorithms for learning both the parameters and the structure of a latent classification model, and we demonstrate empirically that the accuracy of the proposed model is significantly higher than the accuracy of other probabilistic classifiers.Editors: Pedro Larrañaga, Jose A. Lozano, Jose M. Peña and Iñaki Inza     

Effects of socio-emotional stressors on ventilation rate and subjective workload during simulated CPR by lay rescuers

Several studies have documented the occurrence of high ventilation rates during cardiopulmonary resuscitation, but to date, there have been no scientific investigation of the causes of hyperventilation. The objective of the current study was to test the effects of socio-emotional stressors on lay rescuers' ventilation rate in a simulated resuscitation setting using a manikin model. A within-subjects experiment with randomized order of conditions tested lay rescuers' ventilation rate on an intubated manikin during exposure to socio-emotional stressors and during a control condition where no external stressors were present. Ventilation rates and subjective workload were significantly higher during exposure to socio-emotional stressors than during the control condition. All but one of the nine participants ventilated at a higher ventilation rate in the experimental condition. All nine participants rated the subjective workload to be higher during exposure to socio-emotional stressors. Hence, exposure to socio-emotional stressors is associated with increased ventilation rates performed by lay rescuers during simulated cardiac arrest using a manikin model. These findings might have implications for the understanding of the type of situations which hyperventilation may occur. Awareness of these situations may have implications for training of lay rescues.     

Interactive visual analysis of contrast-enhanced ultrasound data based on small neighborhood statistics

Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization in cancer diagnosis. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper we present a pipeline that enables interactive visual exploration and semi-automatic segmentation and classification of CEUS data.For the visual analysis of this challenging data, with characteristic noise patterns and residual movements, we propose a robust method to derive expressive enhancement measures from small spatio-temporal neighborhoods. We use this information in a staged visual analysis pipeline that leads from a more local investigation to global results such as the delineation of anatomic regions according to their perfusion properties. To make the visual exploration interactive, we have developed an accelerated framework based on the OpenCL library, that exploits modern many-cores hardware. Using our application, we were able to analyze datasets from CEUS liver examinations, being able to identify several focal liver lesions, segment and analyze them quickly and precisely, and eventually characterize them.     

Latent classification models for binary data

One of the simplest, and yet most consistently well-performing set of classifiers is the naïve Bayes models (a special class of Bayesian network models). However, these models rely on the (naïve) assumption that all the attributes used to describe an instance are conditionally independent given the class of that instance. To relax this independence assumption, we have in previous work proposed a family of models, called latent classification models (LCMs). LCMs are defined for continuous domains and generalize the naïve Bayes model by using latent variables to model class-conditional dependencies between the attributes. In addition to providing good classification accuracy, the LCM has several appealing properties, including a relatively small parameter space making it less susceptible to over-fitting. In this paper we take a first step towards generalizing LCMs to hybrid domains, by proposing an LCM for domains with binary attributes. We present algorithms for learning the proposed model, and we describe a variational approximation-based inference procedure. Finally, we empirically compare the accuracy of the proposed model to the accuracy of other classifiers for a number of different domains, including the problem of recognizing symbols in black and white images.     

Verification and enforcement of access control policies

Access control mechanisms protect critical resources of systems from unauthorized access. In a policy-based management approach, administrators define user privileges as rules that determine the conditions and the extent of users’ access rights. As rules become more complex, analytical skills are required to identify conflicts and interactions within the rules that comprise a system policy—especially when rules are stateful and depend on event histories. Without adequate tool support such an analysis is error-prone and expensive. In consequence, many policy specifications are inconsistent or conflicting that render the system insecure. The security of the system, however, does not only depend on the correct specification of the security policy, but in a large part also on the correct interpretation of those rules by the system’s enforcement mechanism. In this paper, we show how policy rules can be formalized in Fusion Logic, a temporal logic for the specification of behavior of systems. A symbolic decision procedure for Fusion Logic based on Binary Decision Diagrams (BDDs) is provided and we introduce a novel technique for the construction of enforcement mechanisms of access control policy rules that uses a BDD encoded enforcement automaton based on input traces which reflect state changes in the system. We provide examples of verification of policy rules, such as absence of conflicts, and dynamic separation of duty and of the enforcement of policies using our prototype implementation (FLCheck) for which we detail the underlying theory.     

Multicriteria dimensioning of hard-finishing operations regarding cross-process interdependencies

The production of high performance metal parts requires a well balanced sequence of operations and can usually technologically not be achieved in a single process. This issue leads to complex process chains on the shop floor and the requirement to split single processes into multistage processes in hard-finishing operations. In both cases, the objective for the process planning is to achieve not only a local optimum for the individual processes but rather an overall optimum for the complete sequence. In order to reach the overall optimum, the process planner needs to integrate the effects between the processes into consideration. These effects which are referred to as cross-process interdependencies lead to balanced settings of process parameters and results in a much more effective operation of the sequence. In this paper, a novel approach dealing with the integration of cross-process interdependencies in the hard-finishing process of multistage grinding is introduced. It is based on an a-priori multicriteria dimensioning procedure which allows the integration of cross-process interdependencies and is implemented in software programmed in JAVA. A multistage plunge grinding process for crankshaft journals is used for evaluation of the results predicted by a developed software prototype.     

State‐saving transformations for efficient bottom‐up evaluation of logic programs and rule‐based systems

Redundant computations in bottom‐up evaluation of logic programs and rule‐based systems can be avoided by caching intermediate joins – i.e., partial rule instantiations – for later use. Joins can be cached either at representation level by program transformation techniques introducing supplementary predicates or at implementation level by specialized implementation techniques using internal memory cells. The efficiency of these state‐saving techniques depends on the selection of premises for storing the joins. In this paper we first present a general program transformation technique for saving joins which heuristically reorders subgoals and performs predicate splitting optimization, an extension of unfolding. This state‐saving transformation can be applied for any goal‐directed and model‐generation evaluation. It does not require any information about the query. We show that the program transformation approach is equivalent to the specialized state‐saving implementations known from production systems and model‐generation theorem provers. To use the efficient and complete bottom‐up evaluation also for query answering, we improve the supplementary extensions of Generalized Magic Sets and Magic Templates rewriting strategies by the presented state‐saving transformation. This revised version was published online in June 2006 with corrections to the Cover Date.     

Efficient Vector Quantization Using the WTA-Rule with Activity Equalization

We propose a new algorithm for vector quantization, the Activity Equalization Vector quantization (AEV). It is based on the winner takes all rule with an additional supervision of the average node activities over a training interval and a subsequent re-positioning of those nodes with low average activities. The re-positioning is aimed to both an exploration of the data space and a better approximation of already discovered data clusters by an equalization of the node activities. We introduce a learning scheme for AEV which requires as previous knowledge about the data only their bounding box. Using an example of Martinetz et al. [1], AEV is compared with the Neural Gas, Frequency Sensitive Competitive Learning (FSCL) and other standard algorithms. It turns out to converge much faster and requires less computational effort.