Sparse neural networks with large learning diversity

Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages, much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory

MobileAppScrutinator: A Simple yet Efficient Dynamic Analysis Approach for Detecting Privacy Leaks across Mobile OSs

Smartphones, the devices we carry everywhere with us, are being heavily tracked and have undoubtedly become a major threat to our privacy. As "tracking the trackers" has become a necessity, various static and dynamic analysis tools have been developed in the past. However, today, we still lack suitable tools to detect, measure and compare the ongoing tracking across mobile OSs. To this end, we propose MobileAppScrutinator, based on a simple yet efficient dynamic analysis approach, that works on both Android and iOS (the two most popular OSs today). To demonstrate the current trend in tracking, we select 140 most representative Apps available on both Android and iOS AppStores and test them with MobileAppScrutinator. In fact, choosing the same set of apps on both Android and iOS also enables us to compare the ongoing tracking on these two OSs. Finally, we also discuss the effectiveness of privacy safeguards available on Android and iOS. We show that neither Android nor iOS privacy safeguards in their present state are completely satisfying

Fast Computation of Minimal Interpolation Bases in Popov Form for Arbitrary Shifts

We compute minimal bases of solutions for a general interpolation problem, which encompasses Hermite-Pad\'e approximation and constrained multivariate interpolation, and has applications in coding theory and security. This problem asks to find univariate polynomial relations between $m$ vectors of size $\sigma$; these relations should have small degree with respect to an input degree shift. For an arbitrary shift, we propose an algorithm for the computation of an interpolation basis in shifted Popov normal form with a cost of $\mathcal{O}\tilde{~}(m^{\omega-1} \sigma)$ field operations, where $\omega$ is the exponent of matrix multiplication and the notation $\mathcal{O}\tilde{~}(\cdot)$ indicates that logarithmic terms are omitted. Earlier works, in the case of Hermite-Pad\'e approximation and in the general interpolation case, compute non-normalized bases. Since for arbitrary shifts such bases may have size $\Theta(m^2 \sigma)$, the cost bound $\mathcal{O}\tilde{~}(m^{\omega-1} \sigma)$ was feasible only with restrictive assumptions on the shift that ensure small output sizes. The question of handling arbitrary shifts with the same complexity bound was left open. To obtain the target cost for any shift, we strengthen the properties of the output bases, and of those obtained during the course of the algorithm: all the bases are computed in shifted Popov form, whose size is always $\mathcal{O}(m \sigma)$. Then, we design a divide-and-conquer scheme. We recursively reduce the initial interpolation problem to sub-problems with more convenient shifts by first computing information on the degrees of the intermediate bases.Comment: 8 pages, sig-alternate class, 4 figures (problems and algorithms

Synthetic aperture radar demonstration kit for signal processing education

A Synthetic Aperture Radar scale model has been developed to improve signal processing teaching. Based on low frequency ultrasound transmission, it is a low cost demonstration kit. The overall software is directly running on Matlab® and allows easy and realtime modifications. This educational tool can be used to illuminate a scene using different waveforms, and then see the effects on the formed image. It can also be used in a more advanced way to test different signal processing in order to improve image focusing or to reduce computation burden

The Role Of Cell Proliferation In Hepatic Encephalopathy

Chronic alcohol misuse or alcohol use disorder is a common problem worldwide. Excessive alcohol consumption can affect almost every organ in the body but neurological complications can occur due to acute intoxicating effects as well as longer term damage known as alcohol-related brain damage. The cognitive impairments associated with chronic alcohol misuse can be compounded by associated liver damage leading to an increase in circulating neurotoxic substances such as ammonia giving rise to a condition known as hepatic encephalopathy. The pathogenesis of hepatic encephalopathy is currently unknown however animal models of alcohol misuse suggest that aberrant cell proliferation attributed to neurogenesis may play a role. Neurogenesis occurs in the adult mammalian brain in two neurogenic niches the subventricular zone lining the lateral ventricles and the subgranular zone of the hippocampus and involves the proliferation of a neural stem cell and eventual integration of an immature neuron into the existing circuitry. Although this process has been widely proven in animals its existence in humans remains controversial. So, prior to addressing a role for neurogenesis in disease its existence needs to be proven in normal individuals. Here, cell proliferation and neurogenesis were simultaneously examined in the subventricular zone and subgranular zone of 23 individuals aged 0.2-59 years, using immunohistochemistry and immunofluorescence in combination with unbiased stereology. This demonstrated a marked decline in proliferating cells in both the subventricular zone and subgranular zone in early infancy such that the levels of proliferation were similar to the adjacent parenchyma by four and one years, respectively. Furthermore, the phenotype of proliferating cells changed with age such that in the adult subventricular zone and subgranular zone, and adjacent parenchyma, all proliferating cells co-localised with the microglial marker, Iba1. Taken together this suggests that adult neurogenesis is a residual process and that any potential disease-related alterations in proliferation in the adult brain are likely associated with microglia. Indeed, widespread proliferating cells that co-localised with the microglial marker Iba1 were found in a subset of chronic alcoholics with a pathological diagnosis of HE. In contrast cases without microglial proliferation displayed microglial dystrophy and associated neuronal loss in the prefrontal cortex. There were no obvious differences between these subsets from the clinical and pathological data available. To determine the cause and pathogenic significance of this microglial proliferation, a pilot study was conducted to develop an animal model of chronic hepatic encephalopathy using the hepatotoxin, thioacetamide and combinations of known risk factors for hepatic encephalopathy; chronic alcohol use and a high-fat diet. Animals receiving thioacetamide had macroscopic evidence of liver injury, elevations of transaminases and associated anxiety-like behaviour measured in an open-field test. There were however no associated microglial or astrocytic changes in these animals and combinations of alcohol and high-fat diet had no additional effects. In conclusion, this work has shown that the majority of the rare proliferative events in the adult human brain are microglia. Chronic alcoholism with a pathological diagnosis of hepatic encephalopathy results in shifts in microglial phenotype with one subset of patients demonstrating proliferation and another dystrophy. Future work is required to develop an animal model of chronic hepatic encephalopathy, where the role of microglial dysfunction in hepatic encephalopathy pathogenesis can be further elucidated

Fractal Dimension of Particle Showers Measured in a Highly Granular Calorimeter

We explore the fractal nature of particle showers using Monte-Carlo simulation. We define the fractal dimension of showers measured in a high granularity calorimeter designed for a future lepton collider. The shower fractal dimension reveals detailed information of the spatial configuration of the shower. %the information hidden in the details of shower spatial configuration, It is found to be characteristic of the type of interaction and highly sensitive to the nature of the incident particle. Using the shower fractal dimension, we demonstrate a particle identification algorithm that can efficiently separate electromagnetic showers, hadronic showers and non-showering tracks. We also find a logarithmic dependence of the shower fractal dimension on the particle energy.Comment: 4 pages, 5 figure