1,047 research outputs found

    Avian Haemosporidian blood parasite infections at a migration hotspot in Eilat, Israel

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    Haemosporidian blood parasites are frequent amongst passerines. Though they often do not cause detectable  consequences to host health, however, their presence or absence and also their prevalence across host  populations may potentially carry meaningful information about the health, stress, body condition and viability of bird individuals or populations. The study of migratory birds captured in Eilat, Israel, allowed us to evaluate the prevalence of blood parasite infections in a wide range of both migrant and resident species in spring (N = 1,950) and autumn (N = 538) of 2004 and 2005. According to blood film microscopy, Haemoproteus spp. and Leucocytozoon spp. were more prevalent in the spring than in the autumn (0.289, 0.082 vs. 0.132, 0.033, respectively), whilst Plasmodium spp. exhibited a slight opposite trend (0.034, 0.056). All other parasites (such as trypanosomes, microfilaria and haemococcidians) were rare. During the spring seasons, prevalences were significantly higher in migrant than in resident species, whilst this difference was only marginally significant in the autumn. Given that Eilat is a migration hotspot for several Palearctic passerine species, the present descriptive study may hopefully serve to set the baseline values for future long-term epidemiological monitoring

    Distributed NEGF Algorithms for the Simulation of Nanoelectronic Devices with Scattering

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    Through the Non-Equilibrium Green's Function (NEGF) formalism, quantum-scale device simulation can be performed with the inclusion of electron-phonon scattering. However, the simulation of realistically sized devices under the NEGF formalism typically requires prohibitive amounts of memory and computation time. Two of the most demanding computational problems for NEGF simulation involve mathematical operations with structured matrices called semiseparable matrices. In this work, we present parallel approaches for these computational problems which allow for efficient distribution of both memory and computation based upon the underlying device structure. This is critical when simulating realistically sized devices due to the aforementioned computational burdens. First, we consider determining a distributed compact representation for the retarded Green's function matrix GRG^{R}. This compact representation is exact and allows for any entry in the matrix to be generated through the inherent semiseparable structure. The second parallel operation allows for the computation of electron density and current characteristics for the device. Specifically, matrix products between the distributed representation for the semiseparable matrix GRG^{R} and the self-energy scattering terms in Σ<\Sigma^{<} produce the less-than Green's function G<G^{<}. As an illustration of the computational efficiency of our approach, we stably generate the mobility for nanowires with cross-sectional sizes of up to 4.5nm, assuming an atomistic model with scattering

    Are Accuracy and Robustness Correlated?

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    Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In this paper, we perform experiments on various adversarial example generation approaches with multiple deep convolutional neural networks including Residual Networks, the best performing models on ImageNet Large-Scale Visual Recognition Challenge 2015. We compare the adversarial example generation techniques with respect to the quality of the produced images, and measure the robustness of the tested machine learning models to adversarial examples. Finally, we conduct large-scale experiments on cross-model adversarial portability. We find that adversarial examples are mostly transferable across similar network topologies, and we demonstrate that better machine learning models are less vulnerable to adversarial examples.Comment: Accepted for publication at ICMLA 201

    Adversarial Diversity and Hard Positive Generation

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    State-of-the-art deep neural networks suffer from a fundamental problem - they misclassify adversarial examples formed by applying small perturbations to inputs. In this paper, we present a new psychometric perceptual adversarial similarity score (PASS) measure for quantifying adversarial images, introduce the notion of hard positive generation, and use a diverse set of adversarial perturbations - not just the closest ones - for data augmentation. We introduce a novel hot/cold approach for adversarial example generation, which provides multiple possible adversarial perturbations for every single image. The perturbations generated by our novel approach often correspond to semantically meaningful image structures, and allow greater flexibility to scale perturbation-amplitudes, which yields an increased diversity of adversarial images. We present adversarial images on several network topologies and datasets, including LeNet on the MNIST dataset, and GoogLeNet and ResidualNet on the ImageNet dataset. Finally, we demonstrate on LeNet and GoogLeNet that fine-tuning with a diverse set of hard positives improves the robustness of these networks compared to training with prior methods of generating adversarial images.Comment: Accepted to CVPR 2016 DeepVision Worksho

    Adversarial Robustness: Softmax versus Openmax

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    Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning models, including the best performing DNNs, suffer from a fundamental problem: they can unexpectedly and confidently misclassify examples formed by slightly perturbing otherwise correctly recognized inputs. Various approaches have been developed for efficiently generating these so-called adversarial examples, but those mostly rely on ascending the gradient of loss. In this paper, we introduce the novel logits optimized targeting system (LOTS) to directly manipulate deep features captured at the penultimate layer. Using LOTS, we analyze and compare the adversarial robustness of DNNs using the traditional Softmax layer with Openmax, which was designed to provide open set recognition by defining classes derived from deep representations, and is claimed to be more robust to adversarial perturbations. We demonstrate that Openmax provides less vulnerable systems than Softmax to traditional attacks, however, we show that it can be equally susceptible to more sophisticated adversarial generation techniques that directly work on deep representations.Comment: Accepted to British Machine Vision Conference (BMVC) 201

    Henri Temianka Correspondence; (rozsa)

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    This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4160/thumbnail.jp

    Henri Temianka Correspondence; (rozsa)

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    This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4159/thumbnail.jp

    Henri Temianka Correspondence; (rozsa)

    Get PDF
    This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4154/thumbnail.jp

    Henri Temianka Correspondence; (rozsa)

    Get PDF
    This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4152/thumbnail.jp

    Henri Temianka Correspondence; (rozsa)

    Get PDF
    This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4153/thumbnail.jp
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