An efficient hardware architecture for a neural network activation function generator

This paper proposes an efficient hardware architecture for a function generator suitable for an artificial neural network (ANN). A spline-based approximation function is designed that provides a good trade-off between accuracy and silicon area, whilst also being inherently scalable and adaptable for numerous activation functions. This has been achieved by using a minimax polynomial and through optimal placement of the approximating polynomials based on the results of a genetic algorithm. The approximation error of the proposed method compares favourably to all related research in this field. Efficient hardware multiplication circuitry is used in the implementation, which reduces the area overhead and increases the throughput

Estimation of uncertainty in flood forecasts - a comparison of methods

The scientific literature has many methods for estimating uncertainty, however, there is a lack of information about the characteristics, merits and limitations of the individual methods, particularly for making decisions in practice. This paper provides an overview of the different uncertainty methods for flood forecasting that are reported in literature, concentrating on two established approaches defined as the ensemble and the statistical approach. Owing to the variety of flood forecasting and warning systems in operation, the question ‘which uncertainty method is most suitable for which application’ is difficult to answer readily. The paper aims to assist practitioners in understanding how to match an uncertainty quantification method to their particular application using two flood forecasting system case studies in Belgium and Canada. These two specific applications of uncertainty estimation from the literature are compared, illustrating statistical and ensemble methods, and indicating the information and output that these two types of methods offer. The advantages, disadvantages and application of the two different types of method are identified. Although there is no one ‘best’ uncertainty method to fit all forecasting systems, this review helps to explain the current commonly used methods from the available literature for the non-specialist

Security Evaluation of Support Vector Machines in Adversarial Environments

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector Machine Applications

Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers

Echocardiographic deformation curves provide detailed information on myocardial function. Deep neural networks (DNNs) may enable automated detection of disease features in deformation curves, and improve the clinical assessment of these curves. We aimed to investigate whether an explainable DNN-based pipeline can be used to detect and visualize disease features in echocardiographic deformation curves of phospholamban (PLN) p.Arg14del variant carriers. A DNN was trained to discriminate PLN variant carriers (n = 278) from control subjects (n = 621) using raw deformation curves obtained by 2D-speckle tracking in the longitudinal axis. A visualization technique was used to identify the parts of these curves that were used by the DNN for classification. The PLN variant carriers were clustered according to the output of the visualization technique. The DNN showed excellent discriminatory performance (C-statistic 0.93 [95% CI 0.87–0.97]). We identified four clusters with PLN-associated disease features in the deformation curves. Two clusters showed previously described features: apical post-systolic shortening and reduced systolic strain. The two other clusters revealed novel features, both reflecting delayed relaxation. Additionally, a fifth cluster was identified containing variant carriers without disease features in the deformation curves, who were classified as controls by the DNN. This latter cluster had a very benign disease course regarding development of ventricular arrhythmias. Applying an explainable DNN-based pipeline to myocardial deformation curves enables automated detection and visualization of disease features. In PLN variant carriers, we discovered novel disease features which may improve individual risk stratification. Applying this approach to other diseases will further expand our knowledge on disease-specific deformation patterns. Graphical abstract: [Figure not available: see fulltext.] Overview of the deep neural network-based pipeline for feature detection in myocardial deformation curves. Firstly, phospholamban (PLN) p.Arg14del variant carriers and controls were selected and a deep neural network (DNN) was trained to detect the PLN variant carriers. Subsequently, a clustering-based approach was performed on the attention maps of the DNN, which revealed 4 distinct phenotypes of PLN variant carriers with different prognoses. Moreover, a cluster without features and a benign prognosis was detected

Non-Invasive Molecular Imaging of Fibrosis Using a Collagen-Targeted Peptidomimetic of the Platelet Collagen Receptor Glycoprotein VI

Background: Fibrosis, which is characterized by the pathological accumulation of collagen, is recognized as an important feature of many chronic diseases, and as such, constitutes an enormous health burden. We need non-invasive specific methods for the early diagnosis and follow-up of fibrosis in various disorders. Collagen targeting molecules are therefore of interest for potential in vivo imaging of fibrosis. In this study, we developed a collagen-specific probe using a new approach that takes advantage of the inherent specificity of Glycoprotein VI (GPVI), the main platelet receptor for collagens I and III. Methodology/Principal: Findings An anti-GPVI antibody that neutralizes collagen-binding was used to screen a bacterial random peptide library. A cyclic motif was identified, and the corresponding peptide (designated collagelin) was synthesized. Solid-phase binding assays and histochemical analysis showed that collagelin specifically bound to collagen (Kd 10−7 M) in vitro, and labelled collagen fibers ex vivo on sections of rat aorta and rat tail. Collagelin is therefore a new specific probe for collagen. The suitability of collagelin as an in vivo probe was tested in a rat model of healed myocardial infarctions (MI). Injecting Tc-99m-labelled collagelin and scintigraphic imaging showed that uptake of the probe occurred in the cardiac area of rats with MI, but not in controls. Post mortem autoradiography and histological analysis of heart sections showed that the labeled areas coincided with fibrosis. Scintigraphic molecular imaging with collagelin provides high resolution, and good contrast between the fibrotic scars and healthy tissues. The capacity of collagelin to image fibrosis in vivo was confirmed in a mouse model of lung fibrosis. Conclusion/Significance: Collagelin is a new collagen-targeting agent which may be useful for non-invasive detection of fibrosis in a broad spectrum of diseases.Psycholog

Platelet clearance via shear-induced unfolding of a membrane mechanoreceptor

Mechanisms by which blood cells sense shear stress are poorly characterized. In platelets, glycoprotein (GP)Ib-IX receptor complex has been long suggested to be a shear sensor and receptor. Recently, a relatively unstable and mechanosensitive domain in the GPIba subunit of GPIb-IX was identified. Here we show that binding of its ligand, von Willebrand factor, under physiological shear stress induces unfolding of this mechanosensory domain (MSD) on the platelet surface. The unfolded MSD, particularly the juxtamembrane € Trigger' sequence therein, leads to intracellular signalling and rapid platelet clearance. These results illustrate the initial molecular event underlying platelet shear sensing and provide a mechanism linking GPIb-IX to platelet clearance. Our results have implications on the mechanism of platelet activation, and on the pathophysiology of von Willebrand disease and related thrombocytopenic disorders. The mechanosensation via receptor unfolding may be applicable for many other cell adhesion receptors

The P2X1 receptor and platelet function

Extracellular nucleotides are ubiquitous signalling molecules, acting via the P2 class of surface receptors. Platelets express three P2 receptor subtypes, ADP-dependent P2Y1 and P2Y12 G-protein-coupled receptors and the ATP-gated P2X1 non-selective cation channel. Platelet P2X1 receptors can generate significant increases in intracellular Ca2+, leading to shape change, movement of secretory granules and low levels of αIIbβ3 integrin activation. P2X1 can also synergise with several other receptors to amplify signalling and functional events in the platelet. In particular, activation of P2X1 receptors by ATP released from dense granules amplifies the aggregation responses to low levels of the major agonists, collagen and thrombin. In vivo studies using transgenic murine models show that P2X1 receptors amplify localised thrombosis following damage of small arteries and arterioles and also contribute to thromboembolism induced by intravenous co-injection of collagen and adrenaline. In vitro, under flow conditions, P2X1 receptors contribute more to aggregate formation on collagen-coated surfaces as the shear rate is increased, which may explain their greater contribution to localised thrombosis in arterioles compared to venules within in vivo models. Since shear increases substantially near sites of stenosis, anti-P2X1 therapy represents a potential means of reducing thrombotic events at atherosclerotic plaques