Data mining: a tool for detecting cyclical disturbances in supply networks.

Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means clustering and the RULES-6 classification rule-learning algorithms, developed by the present authors’ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause

Emotion Recognition using Wireless Signals

This paper demonstrates a new technology that can infer a person's emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a person's body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition accuracy is on par with state-of-the-art emotion recognition systems that require a person to be hooked to an ECG monitor. Keywords: Wireless Signals; Wireless Sensing; Emotion Recognition; Affective Computing; Heart Rate VariabilityNational Science Foundation (U.S.)United States. Air Forc

Digital image modeling using Pickard random fields

This paper outlines a modeling technique for digital images which relies on Markov random fields proposed by Pickard for the purpose of representing fuzzy contextual concepts such as "the uniformity of a region" or "the continuity of a contour" . We develop a maximum likelihood estimation technique which is a straightforward generalization of an approach which is used quite extensively in speech recognition circles . Next, we outline two nonsupervised parameter estimation techniques which enable us to infer the model parameters front actual imagery data. We offer a number of practical examples providing evidence that our approach is well suited to handle problems of image restauration and/or segmentation .Dans cet article, nous développons un modèle d'image qui fait appel aux champs aléatoires markoviens de Pickard dans le but de modéliser des notions contextuelles aussi vagues et imprécises que « l'uniformité d'une région » ou « la continuité du bord d'un objet ». Nous décrivons une méthode d'estimation par maximum de vraisemblance a posteriori obtenue par une généralisation simple d'une méthode largement utilisée dans le contexte unidimensionel de la reconnaissance de la parole . Nous développons deux méthodes d'estimation non supervisée des paramètres du modèle et nous montrons au moyen de plusieurs exemples que notre technique permet de traiter avec succès des problèmes de restauration et de segmentation d'images digitales à niveaux de gris .Dans cet article, nous développons un modèle d'image qui fait appel aux champs aléatoires markoviens de Pickard dans le but de modéliser des notions contextuelles aussi vagues et imprécises que « l'uniformité d'une région » ou « la continuité du bord d'un objet ». Nous décrivons une méthode d'estimation par maximum de vraisemblance a posteriori obtenue par une généralisation simple d'une méthode largement utilisée dans le contexte unidimensionel de la reconnaissance de la parole . Nous développons deux méthodes d'estimation non supervisée des paramètres du modèle et nous montrons au moyen de plusieurs exemples que notre technique permet de traiter avec succès des problèmes de restauration et de segmentation d'images digitales à niveaux de gris

MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications

Copyright © Springer 2013. The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-013-9399-zHidden Markov models (HMMs) are flexible, well established models useful in a diverse range of applications. However, one potential limitation of such models lies in their inability to explicitly structure the holding times of each hidden state. Hidden semi-Markov models (HSMMs) are more useful in the latter respect as they incorporate additional temporal structure by explicit modelling of the holding times. However, HSMMs have generally received less attention in the literature, mainly due to their intensive computational requirements. Here a Bayesian implementation of HSMMs is presented. Recursive algorithms are proposed in conjunction with Metropolis-Hastings in such a way as to avoid sampling from the distribution of the hidden state sequence in the MCMC sampler. This provides a computationally tractable estimation framework for HSMMs avoiding the limitations associated with the conventional EM algorithm regarding model flexibility. Performance of the proposed implementation is demonstrated through simulation experiments as well as an illustrative application relating to recurrent failures in a network of underground water pipes where random effects are also included into the HSMM to allow for pipe heterogeneity

ROC curves and the chi2 test

In this paper we review the Receiver Operating Characteristic (ROC) curve, and the chi(2) test statistic, in relation to the analysis of a confusion matrix. We then show how these two methods are related, and propose an extension to the ROC curve so that it shows contours of chi(2) values. These contours can be used to provide further insight into the appropriate setting of the decision threshold for a particular application

GSK3beta, a centre-staged kinase in neuropsychiatric disorders, modulates long term memory by inhibitory phosphorylation at Serine-9

peer reviewedAccumulating evidence implicates deregulation of GSK3beta as a converging pathological event in Alzheimer's disease and in neuropsychiatric disorders, including bipolar disorder and schizophrenia. Although these neurological disorders share cognitive dysfunction as a hallmark, the role of GSK3beta in learning and memory remains to be explored in depth. We here report increased phosphorylation of GSK3beta at Serine-9 following cognitive training in two different hippocampus dependent cognitive tasks, i.e. inhibitory avoidance and novel object recognition task. Conversely, transgenic mice expressing the phosphorylation defective mutant GSK3beta[S9A] show impaired memory in these tasks. Furthermore, GSK3beta[S9A] mice displayed impaired hippocampal L-LTP and facilitated LTD. Application of actinomycin, but not anisomycin, mimicked GSK3beta[S9A] induced defects in L-LTP, suggesting that transcriptional activation is affected. This was further supported by decreased expression of the immediate early gene c-Fos, a target gene of CREB. The combined data demonstrate a role for GSK3beta in long term memory formation, by inhibitory phosphorylation at Serine-9. The findings are fundamentally important and relevant in the search for therapeutic strategies in neurological disorders associated with cognitive impairment and deregulated GSK3beta signaling, including AD, bipolar disorder and schizophrenia

Modulation of synaptic plasticity and Tau phosphorylation by wild-type and mutant presenilin1

peer reviewedThe function of presenilin1 (PS1) in intra-membrane proteolysis is undisputed, as is its role in neurodegeneration in FAD, in contrast to its exact function in normal conditions. In this study, we analyzed synaptic plasticity and its underlying mechanisms biochemically in brain of mice with a neuron-specific deficiency in PS1 (PS1(n-/-)) and compared them to mice that expressed human mutant PS1[A246E] or wild-type PS1. PS1(n-/-) mice displayed a subtle impairment in Schaffer collateral hippocampal long-term potentiation (LTP) as opposed to normal LTP in wild-type PS1 mice, and a facilitated LTP in mutant PS1[A246E] mice. This finding correlated with, respectively, increased and reduced NMDA receptor responses in PS1[A246E] mice and PS1(n-/-) mice in hippocampal slices. Postsynaptically, levels of NR1/NR2B NMDA-receptor subunits and activated alpha-CaMKII were reduced in PS1(n-/-) mice, while increased in PS1[A246E] mice. In addition, PS1(n-/-) mice, displayed reduced paired pulse facilitation, increased synaptic fatigue and lower number of total and docked synaptic vesicles, implying a presynaptic function for wild-type presenilin1, unaffected by the mutation in PS1[A246E] mice. In contrast to the deficiency in PS1, mutant PS1 activated GSK-3beta by decreasing phosphorylation on Ser-9, which correlated with increased phosphorylation of protein tau at Ser-396-Ser-404 (PHF1/AD2 epitope). The synaptic functions of PS1, exerted on presynaptic vesicles and on postsynaptic NMDA-receptor activity, were concluded to be independent of alterations in GSK-3beta activity and phosphorylation of protein tau

Single Channel Music Sound Separation Based on Spectrogram Decomposition and Note Classification

Separating multiple music sources from a single channel mixture is a challenging problem. We present a new approach to this problem based on non-negative matrix factorization (NMF) and note classification, assuming that the instruments used to play the sound signals are known a priori. The spectrogram of the mixture signal is first decomposed into building components (musical notes) using an NMF algorithm. The Mel frequency cepstrum coefficients (MFCCs) of both the decomposed components and the signals in the training dataset are extracted. The mean squared errors (MSEs) between the MFCC feature space of the decomposed music component and those of the training signals are used as the similarity measures for the decomposed music notes. The notes are then labelled to the corresponding type of instruments by the K nearest neighbors (K-NN) classification algorithm based on the MSEs. Finally, the source signals are reconstructed from the classified notes and the weighting matrices obtained from the NMF algorithm. Simulations are provided to show the performance of the proposed system. © 2011 Springer-Verlag Berlin Heidelberg