SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of labelled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA is able to model the messages and accurately discriminate between the two classes without the need for a pre-labelled training set. The results are compared against the state-of-the-art spam detection algorithms with our proposed approach achieving over 97 % accuracy which compares favourably to the best reported algorithms presented in the literature

Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning

Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.Large margin nearest neighbor classification (LMNN) is a popular technique to learn a metric that improves the accuracy of a simple k-nearest neighbor classifier via a convex optimization scheme. However, the optimization problem is convex only under the assumption that the nearest neighbors within classes remain constant. In this contribution we show that an iterated LMNN scheme (multi-pass LMNN) is a valid optimization technique for the original LMNN cost function without this assumption. We further provide an empirical evaluation of multi-pass LMNN, demonstrating that multi-pass LMNN can lead to notable improvements in classification accuracy for some datasets and does not necessarily show strong overfitting tendencies as reported before

Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning

Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.Large margin nearest neighbor classification (LMNN) is a popular technique to learn a metric that improves the accuracy of a simple k-nearest neighbor classifier via a convex optimization scheme. However, the optimization problem is convex only under the assumption that the nearest neighbors within classes remain constant. In this contribution we show that an iterated LMNN scheme (multi-pass LMNN) is a valid optimization technique for the original LMNN cost function without this assumption. We further provide an empirical evaluation of multi-pass LMNN, demonstrating that multi-pass LMNN can lead to notable improvements in classification accuracy for some datasets and does not necessarily show strong overfitting tendencies as reported before

An ERP study reveals how training with Dual N-Back task affects risky decision making in a gambling task in ADHD patients

Impaired decision making and Working Memory (WM) are among the characteristic symptoms of patients affected by Attention Deficit/Hyperactivity Disorder (ADHD). In order to investigate whether a WM training can affect the attitude towards risky decision making, we designed a study where participants had to perform a Probabilistic Gambling Task. Our study has demonstrated that WM training affects in a different way controls and ADHD patients, who showed an increased tendency towards a risk-taking attitude in case of the adaptive variant of the memory task. In ADHD patients, the frontal sites appeared the most affected, whereas global brain activity was likely to be affected in controls. This study shows also the benefits of cognitive training in ADHD patients, but in healthy subjects too

Non-Negative Kernel Sparse Coding for the Analysis of Motion Data

Hosseini B, Hülsmann F, Botsch M, Hammer B. Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In: E.P. Villa A, Masulli P, Javier Pons Rivero A, eds. Artificial Neural Networks and Machine Learning – ICANN 2016. Lecture Notes in Computer Science. Vol 9887. Cham: Springer; 2016: 506-514.We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC as follows: an efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear representations of signals and base vectors in order to guarantee a meaningful dictionary. Empirical evaluations on motion capture benchmarks show the effectiveness of our framework regarding interpretation and discrimination concerns

Visual thalamocortical circuits in parvalbumin-deficient mice

The dorsal lateral geniculate nucleus (dLGN) is considered as the visual gateway to the visual cortex (VC) and sends collaterals to the thalamic reticular nucleus (RTN) that in turn receives collaterals of the corticofugal feedback projections. At all levels of this thalamocortical circuit there are GABAergic neurons expressing the calcium-buffer parvalbumin (PV). The present study reports for the first time the analysis of in vivo extracellular electrophysiological recordings performed simultaneously in dLGN, RTN and VC of anesthetized wild-type (WT) and parvalbumin-deficient (PVKO) mice. The firing rates of VC and RTN cells were increased in PVKO during spontaneous activity as well as in the presence of a photic stimulation (strobe flash at 2.5 Hz). Interestingly, dLGN cells in PVKO did not show significant changes in the rate of firing in comparison to WT. dLGN responses to the light flashes were characterized by ripples of inhibition and phasic excitation/rebound. We have analyzed the pattern of functional interactions between pairs of neighboring cells in VC, dLGN and RTN and across these areas in simultaneously recorded thalamocortical triplets, with one neuron from each area. We found that in PVKO the strength of the interactions tended to decrease locally, between neighboring cells, but tended to increase across the areas. The combination of these analyses provides new evidence on the important role played by PV-expression in regulating information processing in the central visual pathway suggesting that the ability to process information along parallel channels is decreased in the thalamocortical pathway of PV-deficient mice

Dopamine deficiency increases synchronized activity in the rat subthalamic nucleus

Abnormal neuronal activity in the subthalamic nucleus (STN) plays a crucial role in the pathophysiology of Parkinson's disease (PD). In this study we investigated changes in rat STN neuronal activity after 28 days following the injection of 6-OHDA in the substantia nigra pars compacta (SNc). This drug provoked a lesion of SNc that induced a dopamine (DA) depletion assessed by changes in rotating capacity in response to apomorphine injection and by histological analysis. By means of extracellular recordings and waveshape spike sorting it was possible to analyze simultaneous spike trains and compute the crosscorrelations. Based on the analysis of the autocorrelograms we classified four types of firing patterns: regular (Poissonian-like), oscillatory (in the range 4–12 Hz), bursty and cells characterized by a long refractoriness. The distribution of unit types in the control (n = 61) and lesioned (n = 83) groups was similar, as well as the firing rate. In 6-OHDA treated rats we observed a significant increase (from 26% to 48%) in the number of pairs with synchronous firing. These data suggest that the synchronous activity of STN cells, provoked by loss of DA cells in SNc, is likely to be among the most significant dysfunctions in the basal ganglia of Parkinsonian patients. We raise the hypothesis that in normal conditions, DA maintains a balance between funneling information via the hyperdirect cortico-subthalamic pathway and parallel processing through the parallel cortico-basal ganglia-subthalamic pathways, both of which are necessary for selected motor behaviors

Assessing Connections in Networks of Biological Neurons

In this work spike trains of firing times of neurons recorded from various locations in the cat's auditory thalamus are studied. A goal is making inferences concerning connections amongst different regions of the thalamus in both the presence and the absence of a stimulus. Both second-order moment (frequency domain) and full likelihood analyses (a threshold crossing model), are carried through. 1 Introduction The sequence of spikes of a neuron, referred to as a "spike train", may carry important information processed by the brain and thus may underlie cognitive functions and sensory perception [1]. The data studied are recorded stretches of point processes corresponding to the firing times of Statistics Department, University of California, Berkeley y Institute of Physiology, University of Lausanne, Switzerland Pars dorsalis (D) Pars lateralis (PL) Pars magnocellularis (M) Auditory Cortex RE Input Figure 1: A block diagram of the auditory regions of the cat's brain. neurons mea..