Recognition of architectural and electrical symbols by COSFIRE filters with inhibition

The automatic recognition of symbols can be used to automatically convert scanned drawings into digital representations compatible with computer aided design software. We propose a novel approach to automatically recognize architectural and electrical symbols. The proposed method extends the existing trainable COSFIRE approach by adding an inhibition mechanism that is inspired by shape-selective TEO neurons in visual cortex. A COSFIRE filter with inhibition takes as input excitatory and inhibitory responses from line and edge detectors. The type (excitatory or inhibitory) and the spatial arrangement of low level features are determined in an automatic configuration step that analyzes two types of prototype pattern called positive and negative. Excitatory features are extracted from a positive pattern and inhibitory features are extracted from one or more negative patterns. In our experiments we use four subsets of images with different noise levels from the Graphics Recognition data set (GREC 2011) and demonstrate that the inhibition mechanism that we introduce improves the effectiveness of recognition substantially

Detection of curved lines with B-COSFIRE filters: A case study on crack delineation

The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure=0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201

Microscopic Foundation of Nonextensive Statistics

Combination of the Liouville equation with the q-averaged energy $U_q = _q$ leads to a microscopic framework for nonextensive q-thermodynamics. The resulting von Neumann equation is nonlinear: $i\dot\rho=[H,\rho^q]$. In spite of its nonlinearity the dynamics is consistent with linear quantum mechanics of pure states. The free energy $F_q=U_q-TS_q$ is a stability function for the dynamics. This implies that q-equilibrium states are dynamically stable. The (microscopic) evolution of $\rho$ is reversible for any q, but for $q\neq 1$ the corresponding macroscopic dynamics is irreversible.Comment: revte

Nanoantennas for visible and infrared radiation

Nanoantennas for visible and infrared radiation can strongly enhance the interaction of light with nanoscale matter by their ability to efficiently link propagating and spatially localized optical fields. This ability unlocks an enormous potential for applications ranging from nanoscale optical microscopy and spectroscopy over solar energy conversion, integrated optical nanocircuitry, opto-electronics and density-ofstates engineering to ultra-sensing as well as enhancement of optical nonlinearities. Here we review the current understanding of optical antennas based on the background of both well-developed radiowave antenna engineering and the emerging field of plasmonics. In particular, we address the plasmonic behavior that emerges due to the very high optical frequencies involved and the limitations in the choice of antenna materials and geometrical parameters imposed by nanofabrication. Finally, we give a brief account of the current status of the field and the major established and emerging lines of investigation in this vivid area of research.Comment: Review article with 76 pages, 21 figure

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers, our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.Comment: To appear at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD '20), November 2-5, 2020, Virtual Event, US

Dietary protected fat and conjugated linoleic acid improves ewe milk fatty acid composition

Publication history: Accepted - 4 february 2023; Published - 15 May 2023.The effects of protected fats (Optima 100) and conjugated linoleic acid (Endulac®-CLA) supplementation on sheep milk saturated and unsaturated fatty acid composition were investigated. Sheep were divided into four experimental groups (15 ewes/group) including: i) a control group - basal diet without any nutritional supplements; ii) experimental group 1 - basal diet + 12g/sheep/day of the protected source of fats in the feed; iii) group 2 - 12 g of CLA in the feed; iv) group 3 - 12 g of protected fats and CLA in feed. Sixty milk fatty acids were different in milk from treated fat and CLA-treated sheep compared to the control group. The most biologically important fatty acid constituents of milk were identified as butyric, caproic, caprylic, lauric, myristic, palmitic, stearic, arachidonic, behenic, oleic, and linoleic acid (C4 to C18). Ewes that received protected fat or CLA, or both, displayed an increased concentration of oleic acid compared to the control. Both treatments modified milk lipid quality parameters and increased the polyunsaturated/saturated fatty acids ratio (PUFA/SFA), the polyunsaturation index (PI), and the thrombogenic index (TI). Group 3 had similar milk lipid quality parameters as untreated animals. Compared to the CLA and control groups, milk production in the protected fat treatment was higher in Turcana dairy ewes. The inclusion of protected fats and CLA as dietary supplements in lactating ewes modified the milk fatty acid profile, with a concomitant impact on suckling lamb performance and consumer health.This work was funded by a research grant awarded to L.S. by Banats University of Agricultural and Veterinary Medicine, King Michel First from Timisoara, Romani

Impact of FTO genotypes on BMI and weight in polycystic ovary syndrome : a systematic review and meta-analysis

Aims/hypothesis FTO gene single nucleotide polymorphisms (SNPs) have been shown to be associated with obesity-related traits and type 2 diabetes. Several small studies have suggested a greater than expected effect of the FTO rs9939609 SNP on weight in polycystic ovary syndrome (PCOS). We therefore aimed to examine the impact of FTO genotype on BMI and weight in PCOS. Methods A systematic search of medical databases (PubMed, EMBASE and Cochrane CENTRAL) was conducted up to the end of April 2011. Seven studies describing eight distinct PCOS cohorts were retrieved; seven were genotyped for SNP rs9939609 and one for SNP rs1421085. The per allele effect on BMI and body weight increase was calculated and subjected to meta-analysis. Results A total of 2,548 women with PCOS were included in the study; 762 were TT homozygotes, 1,253 had an AT/CT genotype, and 533 were AA/CC homozygotes. Each additional copy of the effect allele (A/C) increased the BMI by a mean of 0.19 z score units (95% CI 0.13, 0.24; p = 2.26 × 10−11) and body weight by a mean of 0.20 z score units (95% CI 0.14, 0.26; p = 1.02 × 10−10). This translated into an approximately 3.3 kg/m2 increase in BMI and an approximately 9.6 kg gain in body weight between TT and AA/CC homozygotes. The association between FTO genotypes and BMI was stronger in the cohorts with PCOS than in the general female populations from large genome-wide association studies. Deviation from an additive genetic model was observed in heavier populations. Conclusions/interpretation The effect of FTO SNPs on obesity-related traits in PCOS seems to be more than two times greater than the effect found in large population-based studies. This suggests an interaction between FTO and the metabolic context or polygenic background of PCOS

An NLO QCD analysis of inclusive cross-section and jet-production data from the ZEUS experiment

The ZEUS inclusive differential cross-section data from HERA, for charged and neutral current processes taken with e+ and e- beams, together with differential cross-section data on inclusive jet production in e+ p scattering and dijet production in \gamma p scattering, have been used in a new NLO QCD analysis to extract the parton distribution functions of the proton. The input of jet data constrains the gluon and allows an accurate extraction of \alpha_s(M_Z) at NLO; \alpha_s(M_Z) = 0.1183 \pm 0.0028(exp.) \pm 0.0008(model) An additional uncertainty from the choice of scales is estimated as \pm 0.005. This is the first extraction of \alpha_s(M_Z) from HERA data alone.Comment: 37 pages, 14 figures, to be submitted to EPJC. PDFs available at http://durpdg.dur.ac.uk/hepdata in LHAPDFv

High-E_T dijet photoproduction at HERA

The cross section for high-E_T dijet production in photoproduction has been measured with the ZEUS detector at HERA using an integrated luminosity of 81.8 pb-1. The events were required to have a virtuality of the incoming photon, Q^2, of less than 1 GeV^2 and a photon-proton centre-of-mass energy in the range 142 < W < 293 GeV. Events were selected if at least two jets satisfied the transverse-energy requirements of E_T(jet1) > 20 GeV and E_T(jet2) > 15 GeV and pseudorapidity requirements of -1 < eta(jet1,2) < 3, with at least one of the jets satisfying -1 < eta(jet) < 2.5. The measurements show sensitivity to the parton distributions in the photon and proton and effects beyond next-to-leading order in QCD. Hence these data can be used to constrain further the parton densities in the proton and photon.Comment: 36 pages, 13 figures, 20 tables, including minor revisions from referees. Accepted by Phys. Rev.