Outlier based literature exploration for cross-domain linking of Alzheimer’s disease and gut microbiota

In knowledge discovery, experts frequently need to combine knowledge from different domains to get new insights and derive new conclusions. Intelligent systems should support the experts in the search for relationships between concepts from different domains, where huge amounts of possible combinations require the systems to be efficient but also sufficiently general, open and interactive to enable the experts to creatively guide the discovery process. The paper proposes a cross-domain literature mining methodology that achieves this functionality by combining the functionality of two complementary text mining tools: clustering and topic ontology creation tool OntoGen and cross-domain bridging terms exploration tool CrossBee. Focusing on outlier documents identified by OntoGen contributes to the efficiency, while CrossBee allows for flexible and user-friendly bridging concepts exploration and identification. The proposed approach, which is domain independent and can support cross-domain knowledge discovery in any field of science, is illustrated on a biomedical case study dealing with Alzheimer’s disease, one of the most threatening age-related diseases, deteriorating lives of numerous individuals and challenging the ageing society as a whole. By applying the proposed methodology to Alzheimer’s disease and gut microbiota PubMed articles, we have identified Nitric oxide synthase (NOS) as a potentially valuable link between these two domains. The results support the hypothesis of neuroinflammatory nature of Alzheimer’s disease, and is indicative for the quest for identifying strategies to control nitric oxide-associated pathways in the periphery and in the brain. By addressing common mediators of inflammation using literature-based discovery, we have succeeded to uncover previously unidentified molecular links between Alzheimer’s disease and gut microbiota with a multi-target therapeutic potential.     

Perceived similarity in face measurement

Automatic face recognition is a widely used biometric identification method, since it is based on standard video surveillance systems and does not require the collaboration of the person to be recognised. The recognition process involves decisions based on the face measurement results. The reliability and robustness of the recognition procedure are critical factors, strongly affected by influence quantities, such as face-camera relative orientation and position, illumination and expression. To improve performance it makes sense to imitate the recognition process in humans, who succeed in recognising faces even from poor quality images. It is therefore interesting to investigate the perception of similarity between faces, with the aim of gaining insight into the process of human face recognition. We report on an investigation that has been carried out by original methods developed in our laboratory for the direct measurement of perceptual quantities. Such methods have been implemented for two complementary recognition procedures, based either on the global appearance of a face or on the detection of specific repere points.     

Phasor measurement based on IEC 61850-9-2 and Kalman–Filtering

This paper proposes a new technique for estimating phasors during the transient phenomena in the electrical power systems. Unlike traditional Phasor Measurement Units (PMUs) that estimates phasors at process level, the proposed technique estimates phasors at bay level. The estimation technique relies on a robust recursive Kalman–Filtering, which is employed for computing the phasors based on the time-critical IEC 61850-9-2 Sampled Measured Values casted by the substation Merging Units (MUs). Many evolution tests of the proposed technique have been carried out for both stationary and transient phenomena in power systems to determine the accuracy of the measurements obtained at various conditions. The proposed estimator can produce phasors at bay level with reporting rate of 100 frames per second, which is encouraged in IEEE Std C37.118 and can satisfy higher accuracies than provided by the traditional Phasor Measurement Units, which are 0.01 degree and 0.01% for phase and magnitude, respectively.     

Numerical simulation of particulate processes for control and estimation by spectral methods

Results of application of pseudospectral methods, also known as spectral collocation methods, to practical particulate processes including growth, nucleation, aggregation, and breakage are presented. For growth‐dominated processes, a considerable reduction in model dimension can be achieved; for pure aggregation and breakage they form a viable option. To handle problems that include aggregation, breakage, and growth phenomena simultaneously, we introduce a hybrid algorithm combining the advantages of spectral methods and cell average or fixed pivot methods for aggregation and breakage. Results are shown for analytical examples as well as real processes taken from the fields of granulation and crystallization. © 2011 American Institute of Chemical Engineers AIChE J, 58: 2309–2319, 2012     

Swarm intelligence based approach for sinkhole attack detection in wireless sensor networks

Swarm intelligence, a nature inspired computing applies an algorithm situated within the context of agent based models that mimics the behavior of ants to detect sinkhole attacks in wireless sensor networks. An Ant Colony Optimization Attack Detection (ACO-AD) algorithm is proposed to identify the sinkhole attacks based on the nodeids defined in the ruleset. The nodes generating an alert on identifying a sinkhole attack are grouped together. A voting method is proposed to identify the intruder. An Ant Colony Optimization Boolean Expression Evolver Sign Generation (ABXES) algorithm is proposed to distribute the keys to the alerted nodes in the group for signing the suspect list to agree on the intruder. It is shown that the proposed method identifies the anomalous connections without generating false positives and minimizes the storage in the sensor nodes in comparison to LIDeA architecture for sinkhole attack detection. Experimental results demonstrating the Ant Colony Optimization approach of detecting a sinkhole attack are presented.     

MultiCL: Enabling automatic scheduling for task-parallel workloads in OpenCL

The OpenCL specification tightly binds a command queue to a specific device. For best performance, the user has to find the ideal queue-device mapping at command queue creation time, an effort that requires a thorough understanding of the underlying device architectures and kernels in the program. In this paper, we propose to add scheduling attributes to the OpenCL context and command queue objects that can be leveraged by an intelligent runtime scheduler to automatically perform ideal queuedevice mapping. Our proposed extensions enable the average OpenCL programmer to focus on the algorithm design rather than scheduling and to automatically gain performance without sacrificing programmability. As an example, we design and implement an OpenCL runtime for task-parallel workloads, called MultiCL, which efficiently schedules command queues across devices.Our case studies include the SNU benchmark suite and a real-world seismology simulation. To benefit from our runtime optimizations, users have to apply our proposed scheduler extensions to only four source lines of code, on average, in existing OpenCL applications. We evaluate both single-node and multinode experiments and also compare with SOCL, our closest related work. We show that MultiCL maps command queues to the optimal device set in most cases with negligible runtime overhead.     

Modified relative arrival time technique for sizing inclined cracks

In this paper, a modified relative time technique is proposed for estimating the length and the orientation of inclined cracks using ultrasonic B-scan signals. The proposed technique utilizes the relative position of the tip diffraction echo to the corner reflection echo in an ultrasonic B-scan image to estimate the crack length and the crack inclination angle. Both finite element simulated signals and experimental signals, which are obtained from a specimen with a 3 mm crack with different inclination angles, are used to evaluate the estimation error of the proposed technique. The results show that the proposed technique overcomes the shortcoming of the conventional relative arrival time technique in sizing inclined cracks.     

On Jonckheere's k-Sample Test Against Ordered Alternatives

In this paper a k-sample non-parametric test for trend is considered. Given a sample of size n_i , i = 1, …, k respectively from each of k populations, the test rejects the hypothesis that the k populations are identical if S = Σ ^k _i=2 S_i ≥ S_i . Here Si is the Mann-Whitney statistic computed when each observation in the i-th sample is compared with the combined observations from the first (i – 1) populations. A recurrence formula is derived for computing the exact distribution of S. Tables of exact probabilities and critical values are given for nominal values of α = 0.5, 0.2, 0.1, 0.05, 0.025, 0.01, and 0.005 for k = 3 and all possible sample sizes from 2 to 8, and for equal sample sizes for values of n = 2(1)6, k = 4(1)6.     

Physarum Learner: A bio-inspired way of learning structure from data

A novel Score-based Physarum Learner algorithm for learning Bayesian Network structure from data is introduced and shown to outperform common score based structure learning algorithms for some benchmark data sets. The Score-based Physarum Learner first initializes a fully connected Physarum-Maze with random conductances. In each Physarum Solver iteration, the source and sink nodes are changed randomly, and the conductances are updated. Connections exceeding a predefined conductance threshold are considered as Bayesian Network edges, and the score of the connected nodes are examined in both directions. A positive or negative feedback is given to the edge conductance based on the calculated scores. Due to randomness in selecting connections for evaluation, an ensemble of Score-based Physarum Learner is used to build the final Bayesian Network structure.